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Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel density estimation model

Sujan Ghimire, Ravinesh C. Deo, S. Ali Pourmousavi, David Casillas-Pérez, Sancho Salcedo-Sanz, U. Rajendra Achary
Journal PaperEngineering Applications of Artificial Intelligence, 135:108702, May 2024


Electricity demand prediction is crucial to ensure the operational safety and cost-efficient operation of the power system. Electricity demand has predominantly been predicted deterministically, while uncertainty analysis has been usually overlooked. To address this research gap, an integrated Neural Facebook Prophet (NFBP) model and Gaussian Kernel Density Estimation (KDE) model is proposed in this paper, as a way to obtain point and interval predictions of electricity demand, quantifying this way the uncertainty in the predictions. First, historical lagged data, created by utilising the Partial Auto-correlation Function and Mutual Information Test, is applied to train a prediction model based on NFBP, Deep Learning (DL) as well as Statistical Models. Second, the model Prediction Errors (PE) are derived from the difference between actual and predicted values. A splitting strategy based on the mean and standard deviation of PE is proposed. Finally, electricity demand prediction intervals are obtained by applying Gaussian KDE on split PE. To verify the effectiveness of the proposed model, simulation studies are carried out for three prediction horizons on freely available datasets for the Bulimba sub-station in Southeast Queensland, Australia. Compared with DL models (Long-Short Term Memory Network and Deep Neural Network), the Root Mean Square Error of the NFBP model was reduced by 6.1% and 11.3% for 0.5-hr ahead, 22.7% and 26.3% for 6-hr ahead, and 31.8% and 29.9% for daily prediction. In addition, the Prediction Interval normalized Interval width is smaller in magnitude for the proposed NFBP-KDE model compared to other DL and Statistical models.

Battery scheduling optimisation in energy and ancillary services markets: quantifying unrealised revenue in the Australian NEM

Sahand Karimi-Arpanahi, S. Ali Pourmousavi, Nariman Mahdavi
Conference PaperACM e-Energy, Singapore, June 4-7, 2024


Despite the high flexibility of Battery Energy Storage Systems (BESS), existing operation strategies often fail to fully utilise these assets. Additionally, the current literature on optimal BESS scheduling often relies on simplistic assumptions regarding their power efficiency and ignores the intricacies of simultaneous participation in energy and ancillary services markets. This makes these models inadequate for estimating the maximum potential revenue of existing BESSs. Thus, this paper aims to quantify their unrealised revenue in the Australian National Electricity Market (NEM). We first introduce a new methodology that systematically identifies the operational characteristics of BESSs using public data. Then, we propose a mathematical model to optimise BESS scheduling across NEM energy and ancillary services markets. By applying this model to six BESSs in the NEM, we uncover their unrealised potential revenue and show that nearly half of potential energy arbitrage revenue is forfeited due to suboptimal dispatch decisions or inaccuracies in price forecasting.

A new zero-dimensional dynamic model to study the capacity loss mechanism of vanadium redox flow batteries

Hao Wang, S. Ali Pourmousavi, Yifeng Li, Wen L. Soong, Xinan Zhang, Bingyu Xiong
Journal PaperJournal of Power Sources, 603:234428, May 2024


The study of the capacity loss mechanisms of vanadium redox flow batteries (VRFBs) is important for optimising battery design and performance. To facilitate this, a new zero-dimensional (0-D) dynamic model is proposed in this study that considers different electrolyte transfer (osmosis and electro-osmosis) and vanadium species crossover (convection, electro-migration and diffusion) mechanisms based on the configuration of a 5 kW/3 kWh VRFB system with cation membranes (Nafion 115). The proposed model is validated under three constant current regimes and achieves a mean absolute error (MAE) of less than 2 %. Furthermore, its accuracy in estimating capacity over 100 cycles is evaluated using the experimental results of a single-cell VRFB system, which achieves a low MAE of 1.9 %. Most importantly, an in-depth analysis of the capacity loss mechanism, including the electrolyte volume transfer, electrolyte imbalance, and electrolyte flow rate, is conducted under different constant current and flow rate regimes. The influence of all electrolyte transfer and crossover mechanisms mentioned above are carefully examined and discussed. This work offers practical recommendations to mitigate capacity loss. Furthermore, the proposed model facilitates the development of electrolyte re-balancing techniques and advanced optimisation methods for optimal battery operation with low computational requirements for battery management systems (BMS).

Modelling irrational behaviour of residential end users using non-stationary Gaussian processes

Nam Trong Dinh, Sahand Karimi-Arpanahi, Rui Yuan, S. Ali Pourmousavi, Mingyu Guo, Jon A. R. Liisberg, and Julian Lemos-Vinasco
Journal PaperAccepted for publication in the IEEE Transactions on Smart Grid, March, 2024


Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal use of network assets. Yet, existing DR models often overlook a crucial element, the irrational behaviour of electricity end users. In this work, we propose a price-responsive model that incorporates key aspects of end-user irrationality, specifically loss aversion, time inconsistency, and bounded rationality. To this end, we first develop a framework that uses Multiple Seasonal-Trend decomposition using Loess (MSTL) and non-stationary Gaussian processes to model the randomness in the electricity consumption by residential consumers. The impact of this model is then evaluated through a community battery storage (CBS) business model. Additionally, we apply a chance-constrained optimisation model for CBS operation that deals with the unpredictability of the end-user irrationality. Our simulations using real-world data show that the proposed DR model provides a more realistic estimate of end-user price-responsive behaviour when considering irrationality. Compared to a deterministic model that cannot fully take into account the irrational behaviour of end users, the chance-constrained CBS operation model yields an additional 19% revenue. Lastly, the business model reduces the electricity costs of solar end users by 11%.

Industrial internet of things in mine electrification: Necessity or luxury?

Mohsen Maadani, Hossein Ranjbar, S. Ali Pourmousavi
Journal PaperIEEE Electrification Magazine, 12(1), 45-55, March, 2024


Mining electrification, defined as the process of using electricity to power mine sites instead of fossil fuels, is one of the most significant and transformative trends in the mining industry of our time. It has direct impacts on the environment, social, and governance, license to operate, global competitiveness, and the ability to raise capital from investors and financiers. Recent industry efforts reflect the recognition of the importance of this trend by the mining industry. However, successful electrification implementation cannot be achieved in isolation. It requires successful implementation of mining digitalization in both the design and operation stages of an electrified mine. This is possible using the industrial Internet of Things (IIoT), a developing paradigm of interconnected “industrial things” equipped with embedded networking, sensors, and actuators. IIoT has the potential to revolutionize the mining industry by improving safety and efficiency and reducing costs through advanced sensors, data analytics, and predictive maintenance (PdM) techniques. Additionally, the integration of IIoT in the electrification and digitalization of mining operations is a crucial step in achieving compliance with international and national regulations on reducing carbon emissions, such as those issued by the International Council on Mining and Metals and the Minerals Council of Australia (MCA). Digital transformation of mines, explicitly focusing on implementing unmanned technologies, remote process control, and smart robots, cannot be achieved without IIoT devices. These technologies enable the mining industry to reduce labor costs, increase production, and improve safety by removing workers from potentially hazardous environments. Mining digitalization has its challenges that are summarized in Figure 1. As illustrated in this figure, the main challenges are associated with implementation cost, technical complexity, regulatory compliance, workforce training, and data management. Despite these challenges, the long-term benefits of increased productivity, reduced costs, and improved safety can outweigh these challenges. This article discusses the necessity of mine electrification and how the deployment of IIoT can help achieve this goal. It defines IIoT, highlights key differences with commonly known IoT applications, provides an verview of industry initiatives, and presents use cases and further developments necessary to transition to mining electrification and digitalization.

Toward underground mobile fleet electrification: Three essential steps to make a real change

Hirad Assimi, Sayed Nasrollah Hashemian Ataabadi, Shah Mohammad Mominul Islam, Wen Liang Soong, S. Ali Pourmousavi
Journal PaperIEEE Electrification Magazine, 12(1), 16-26, March, 2024


Electrifying haul trucks and other vehicles in underground mines will have a significant impact on reducing greenhouse gas (GHG) emissions and operational costs as well as improving worker health and safety. The Australian mining industry is the sector with the second largest contribution to carbon emissions [about 100 million tons of carbon dioxide-equivalent (CO2-e) per annum] in the country and has the largest growth in emissions over the past 30 years. It consumes approximately 14% of the total Australian energy use. Figure 1 shows the Australian mining industry energy usage for the period between 2015 and 2020 and indicates an annual growth of approximately 2%. The sector obtains roughly 50% of its energy from diesel, 22% from natural gas, and 25% from grid electricity, with the rest provided by a combination of renewables, biofuels, and other fossil fuels. A significant amount of GHG emissions is associated with underground mining operations. These generally use diesel-powered vehicles and, thus, require high-power ventilation systems to provide acceptable air quality levels for underground workers. Using a battery electric vehicle (BEV) fleet will eliminate diesel emissions and, hence, reduce the exposure of workers to fumes and will also substantially reduce the underground ventilation power requirements. To achieve a net-zero-emission mining operation, electrifying the vehicles and shifting to renewables are essential. However, there are significant challenges in electrifying mining fleets:

  • lower energy density of the batteries versus diesel fuel
  • slower recharging time versus diesel refueling
  • limited commercial availability of electric mining vehicles and higher up-front cost of mining BEVs
  • increased electric energy demand for use in vehicle battery charging
  • the need to retrain the workforce in EV skills.
These could impact electrification costs and productivity. Therefore, it is essential that the mining industry has the right tools and strategies for informed decision making toward net-zero-emissions operation. Drawing from our work with the Mine Operational Vehicle Electrification (MOVE) project, funded by the Future Battery Industries Cooperative Research Center in partnership with BHP Nickel West, IGO, and various other mining companies and institutions, we outline the three essential steps for successful electrification. In conclusion, we provide recommendations for the future of mining electrification.

Unleashing the benefits of smart grids by overcoming the challenges associated with low-resolution data

Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Andrew J. Black, Jon A.R. Liisberg, Julian Lemos-Vinasco
Journal PaperCell Reports Physical Science, 5(2), 101830, Feb. 21, 2024


Smart meters have been widely deployed worldwide, but there is an often-overlooked problem that remains unresolved: the data collected from these meters is of relatively low time resolution, hindering the realization of smart grid benefits. This perspective unfolds the roadblocks to achieving high-resolution data from a smart metering infrastructure. We highlight the loss of critical information, essential for many smart grid applications, due to low-resolution readings of residential consumer data. We then outline the main reasons behind the lack of high-resolution data, the tetralemma on balancing data collection, transmission, warehousing, and privacy concerns. Finally, we hypothesize a framework for data collection, maintenance, communication, and storage of smart utility meters data to obtain high-resolution records by tackling the challenges using a dictionary-based compression method and separately maintaining the compressed products at the user ends and data center.

Probabilistic-based electricity demand forecasting with hybrid convolutional neural network-extreme learning machine model

Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez, Sancho Salcedo-Sanz, S. Ali Pourmousavi, U. Rajendra Achary
Journal PaperEngineering Applications of Artificial Intelligence, 132, June 2024


Implementing key engineering solutions to optimise the operation of energy industries requires daily electricity demand forecasting and including uncertainty, to promote markets insight analysis as part of their strategic planning, regulating and supplying electricity to consumers. This paper proposes hybrid artificial intelligence models combining convolutional neural networks (CNN) as a feature extraction algorithm with extreme learning machines (ELM) as a framework to predict electricity demand with confidence intervals generated by Kernel Density Estimation (KDE) approaches. In order to develop CELM-KDE model, time-lagged series of daily electricity demand with local climate variables based on the air temperature, atmospheric vapour pressure, evaporation, solar radiation, humidity and sea level pressures are used to train the proposed CELM-KDE hybrid model. In order to fully evaluate the newly developed model from a point-based, as well as a probabilistic prediction strategy, the observed and predicted electricity demand as well as the probability distribution of errors are analysed using KDE method that operates without prior data distribution assumptions. Based on observed and predicted electricity demand and the relevant probabilistic confidence intervals generated by the CELM-KDE model, the final results show that the proposed method attains significantly better probability interval predictions than traditionally-used point-based models. The proposed CELM-KDE model is demonstrated to be highly effective in providing a comprehensive coverage of predicted errors, as well as providing greater insights into the average bandwidth and detailed predicted electricity demand in the testing stage. The results also indicate that the proposed hybrid model is a reliable decision support tool to develop engineering solutions in area of energy modelling, monitoring and forecasting, which could potentially be useful to the industry policymakers. We show that the point-and probabilistic-based electricity demand predictive models can be employed as an effective tool to improve accuracy of forecasting and provision of insights for national electricity markets and key energy industry stakeholder application tools.

A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market

Cameron Cornell, Nam Trong Dinh, S. Ali Pourmousavi
Journal PaperInternational Journal of Forecasting, Accepted for publication, Dec. 2023


The South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps. We propose using quantile regression as an ensemble tool for probabilistic forecasting, with our combined forecasts achieving superior results compared to all constituent models. Within our ensemble framework, we demonstrate that averaging models with varying training length periods leads to a more adaptive model and increased prediction accuracy. The applicability of the final model is evaluated by comparing our median forecasts with the point forecasts available from the Australian NEM operator, with our model outperforming these NEM forecasts by a significant margin

Cost-effective community battery sizing and operation within a local market framework

Nam Trong Dinh, Sahand Karimi-Arpanahi, S. Ali Pourmousavi, Mingyu Guo, and Jon A. R. Liisberg
Journal PaperIEEE Transactions on Energy Markets, Policy and Regulation, 1:4, Dec. 2023


Extreme peak power demand is a major factor behind high electricity prices, despite only occurring for a few hours annually. This peak demand drives the need for costly upgrades to the network asset, which is ultimately passed on to the end-users through higher electricity network tariffs. To alleviate this issue, we propose a solution for cost-effective peak demand reduction in a local neighbourhood by utilising prosumer-centric flexibility and community battery storage (CBS). Accordingly, we present a CBS sizing framework for peak demand reduction considering receding horizon operation and a bilevel program in which a profit-making entity (leader) operates the CBS and dynamically sets mark-up prices. Through the dynamic mark-up and real-time wholesale market prices, the CBS operator can harness the demand-side flexibility provided by the load-shifting behaviour of the local prosumers (followers). To this end, we develop a realistic price-responsive model that adjusts prosumers’ behaviour with respect to fluctuations of dynamic prices while considering prosumers’ discomfort caused by load shifting. The simulation results based on real-world data show that adopting the proposed framework and the price-responsive model not only increases the CBS owner’s profit but also reduces peak demand and prosumers’ electricity bills by 38% and 24%, respectively

A synthetic dataset of Danish residential electricity prosumers

Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Andrew J. Black, Jon A. R. Liisberg, Julian Lemos-Vinasco
Journal PaperScientific Data, 10:371, June 2023


Conventional residential electricity consumers are becoming prosumers who not only consume electricity but also produce it. This shift is expected to occur over the next few decades at a large scale, and it presents numerous uncertainties and risks for the operation, planning, investment, and viable business models of the electricity grid. To prepare for this shift, researchers, utilities, policymakers, and emerging businesses require a comprehensive understanding of future prosumers’ electricity consumption. Unfortunately, there is a limited amount of data available due to privacy concerns and the slow adoption of new technologies such as battery electric vehicles and home automation. To address this issue, this paper introduces a synthetic dataset containing five types of residential prosumers’ imported and exported electricity data. The dataset was developed using real traditional consumers’ data from Denmark, PV generation data from the global solar energy estimator (GSEE) model, electric vehicle (EV) charging data generated using emobpy package, a residential energy storage system (ESS) operator and a generative adversarial network (GAN) based model to produce synthetic data. The quality of the dataset was assessed and validated through qualitative inspection and three methods: empirical statistics, metrics based on information theory, and evaluation metrics based on machine learning techniques.

Model-based nonlinear dynamic optimisation for the optimal flow rate of vanadium redox flow batteries

Hao Wang, S. Ali Pourmousavi, Wen L. Soong, Xinan Zhang, Nesimi Ertugrul, Bingyu Xiong
Journal PaperJournal of Energy Storage, 68:107741, September 2023


The control of the electrolyte flow rate is crucial to ensure the efficient operation of a vanadium redox flow battery (VRFB) system. In this paper, a model-based nonlinear dynamic optimisation (MNDO) method is proposed and implemented in MATLAB/Simulink to study the optimal flow rate under constant current (CC) and constant current-constant voltage (CC-CV) charging methods for a VRFB. A 5kW/3kWh VRFB system is considered to investigate the feasibility and accuracy of the established model. System efficiency is the optimisation objective in this study, where all case studies are carried out within 15% to 85% state of charge (SOC). The simulation results show that the proposed method enhanced battery performance by improving the overall VRFB system efficiency under different charging methods. Furthermore, the simulation results show that the CC-CV method is more energy-efficient than the CC method but requires more charging time. An in-depth analysis is carried out to discuss the underlying merits of the proposed method in balancing the losses caused by the concentration overpotential and pump energy consumption in a varying power environment. Moreover, further analyses are carried out to highlight the merits of the CC-CV charging method in saving energy while charging and reducing system imbalance. Finally, a 2D lookup table is designed based on the results from the proposed MNDO method that offers a practical controller of the electrolyte flow rate without requiring excessive computational resources. The performance of the 2D lookup table has been evaluated within 100 charging/discharging cycles. It achieves a system efficiency gain of up to 1.48& under CC-CV charging operation compared with the CC charging method and optimal conventional flow rate control method.

Thermal dynamics assessment of vanadium redox flow batteries and thermal management by active temperature control

Hao Wang, Wen L. Soong, S. Ali Pourmousavi, Xinan Zhang, Nesimi Ertugrul, Bingyu Xiong
Journal PaperJournal of Power Sources, 570:233027, June 2023


Understanding the thermal dynamics of vanadium redox flow batteries (VRFB) is critical in preventing the thermal precipitation of vanadium species that result in capacity fading and unsafe operation. This paper presents a comprehensive thermal model of a 5kW/60kWh VRFB system by considering the impact of current, ambient temperature and electrolyte flow rate to investigate the dynamic and steady-state thermal conditions of VRFB systems. To analyse the feasibility of using air conditioners for effective thermal management, a room temperature model is proposed to simulate the room temperature variations with air flow cooling. Finally, based on the proposed VRFB thermal model and the room temperature model, two case studies with different temperature profiles are presented to evaluate the performance of the proposed model. Most importantly, an improved cooling strategy is proposed and validated for the two case studies considering the different thermal behaviours of VRFBs during charging and discharging. The simulation results show that the proposed strategy can save up to 48% on air conditioner consumption. Also, the modelling work presented in this paper is useful for studying the thermal dynamics of a VRFB system after many operational cycles and providing guidance for the thermal management of VRFBs in real-world applications

Quantifying the predictability of renewable energy data for improving power systems decision-making

Sahand Karimi-Arpanahi, S. Ali Pourmousavi, Nariman Mahdavi
Journal PaperPatterns, 100708, March 2023


Decision-making in the power systems domain often relies on predictions of renewable generation. While sophisticated forecasting methods have been developed to improve the accuracy of such predictions, their accuracy is limited by the inherent predictability of the data used. However, the predictability of time series data cannot be measured by existing prediction techniques. This important measure has been overlooked by researchers and practitioners in the power systems domain. In this paper, we systematically assess the suitability of various predictability measures for renewable generation time series data, revealing the best method and providing instructions for tuning it. Then, using real-world examples, we illustrate how predictability could save end users and investors millions of dollars in the electricity sector.

Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy

Sahand Karimi-Arpanahi, S. Ali Pourmousavi
Conference Paper   Best Paper     32nd Australasian Universities Power Engineering Conference (AUPEC), Adelaide, Australia, Sep. 26-28, 2022


The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.

Optimal activity and battery scheduling algorithm using load and solar generation forecasts

Yogesh Pipada Sunil Kumar, Rui Yuan, Nam Trong Dinh, S. Ali Pourmousavi
Conference Paper32nd Australasian Universities Power Engineering Conference (AUPEC), Adelaide, Australia, Sep. 26-28, 2022


Energy usage optimal scheduling has attracted great attention in the power system community, where various methodologies have been proposed. However, in real-world applications, the optimal scheduling problems require reliable energy forecasting, which is scarcely discussed as a joint solution to the scheduling problem. The 5 th IEEE Computational Intelligence Society (IEEE-CIS) competition raised a practical problem of decreasing the electricity bill by scheduling building activities, where forecasting the solar energy generation and building consumption is a necessity. To solve this problem, we propose a technical sequence for tackling the solar PV and demand forecast and optimal scheduling problems, where solar generation prediction methods and an optimal university lectures scheduling algorithm are proposed.

Investigation of short-term intermittency in solar irradiance and its impacts on PV converter systems

Yuan Yao, Nesimi Ertugrul, S. Ali Pourmousavi
Conference Paper   Best Paper     32nd Australasian Universities Power Engineering Conference (AUPEC), Adelaide, Australia, Sep. 26-28, 2022


Increased installation of solar photovoltaic (PV) systems has drawn concerns of managing and mitigating the fast and unpredictable variations in solar power generation. This paper presents a detailed analysis of short-term (seconds level, transients) solar intermittency and its implications on solar PV array and DC/DC power converters. The impacts of the short term intermittency to the output power variations have also been studied considering the filtering components of the converter. In addition, the paper addresses the challenges about the identification of the short-term solar intermittency by utilising a moving window method. Furthermore, simulation and data analysis results provide insights of overcoming short-term solar uncertainty for PV system design and technical requirements of suitable power converters.

Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression

Ravinesh C. Deo, A. A. Masrur Ahmed, David Casillas-Perez, S. Ali Pourmousavi, Gary Segal, Yanshan Yu, Sancho Salcedo-Sanz
Journal PaperRenewable Energy, 203:113-130, February 2023


Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models, such as Global Forecast System (GFS), can aid renewable energy engineers in monitoring and forecasting solar photovoltaic power generation. A major challenge is the systematic bias in TCDC simulations induced by the errors in the numerical model parameterization stages. Correction of GFS-derived cloud forecasts at multiple time steps can improve energy forecasts in electricity grids to bring better grid stability or certainty in the supply of solar energy. We propose a new kernel ridge regression (KRR) model to reduce bias in TCDC simulations for medium-term prediction at the inter-daily, e.g., 2–8 day-ahead predicted TCDC values. The proposed KRR model is evaluated against multivariate recursive nesting bias correction (MRNBC), a conventional approach and eight machine learning (ML) methods. In terms of the mean absolute error (MAE), the proposed KRR model outperforms MRNBC and ML models at 2-8 day ahead forecasts, with MAE ≈ 20–27%. A notable reduction in the simulated cloud cover mean bias error of 20–50% is achieved against the MRNBC and reference accuracy values generated using proxy-observed and non-corrected GFS-predicted TCDC in the model’s testing phase. The study ascertains that the proposed KRR model can be explored further to operationalize its capabilities, reduce uncertainties in weather simulation models, and its possible consideration for practical use in improving solar monitoring and forecasting systems that utilize cloud cover simulations from numerical weather predictions.

Battery and energy management system for Vanadium Redox Flow Battery: A critical review and recommendations

Hao Wang, S. Ali Pourmousavi, Wen L. Soong, Xinan Zhang, Nesimi Ertugrul
Journal PaperJournal of Energy Storage, 58:106384, February 2023


As one of the most promising large-scale energy storage technologies, vanadium redox flow battery (VRFB) has been installed globally and integrated with microgrids (MGs), renewable power plants and residential applications. To ensure the safety and durability of VRFBs and the economic operation of energy systems, a battery management system (BMS) and an energy management system (EMS) are inevitable parts of a VRFB-based power system. In particular, BMSs are essential to conducting efficient monitoring, control and diagnosis/prognosis functions with the help of a feasible and comprehensive battery model. Considering the application of a VRFB is normally integrated within a grid-level system, an EMS is required to operate the entire system in coordination with the BMS optimally. Several papers have reviewed the design and modelling of VRFB recently. However, the BMS and EMS in VRFB applications have received limited attention in the literature. This review article introduces the principles, applications, and merits of VRFBs and presents a critical review of the state-of-art VRFB modelling techniques related to BMS and EMS operation. More importantly, the state-of-the-art BMS for VRFBs is reviewed by taking the unique design of the VRFB systems into account, and recommendations are given for future development. Finally, several VRFB EMSs are discussed to illustrate their importance in improving the stability and reliability of grid-level power systems.

A three-layer joint distributionally robust chance-constrained framework for optimal day-ahead scheduling of e-mobility ecosystem

Mahsa Bagheri Tookanlou, S. Ali Pourmousavi, M. Marzband
Journal PaperApplied Energy, 331:120402, February 2023


A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation to overcome range anxiety and create a viable business model for charging stations (CSs). The framework must account for the stochastic nature of all stakeholders’ operations, including EV drivers, CSs, and retailers and their mutual interactions. In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations for e-mobility ecosystems. The proposed three-layer joint DRCC framework formulates the interactions of the stochastic behaviour of the stakeholders in an uncertain environment with unknown probability distributions. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. An iterative process is proposed to solve the problem using joint DRCC formulation. To achieve computational tractability, the second-order cone-programming reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of the temporal correlation of uncertain PV generation on CSs operation is considered in the formulation. A simulation study is carried out for an ecosystem of three retailers, nine CSs, and 600 EVs based on real data from San Francisco, USA. The simulation results show the necessity and applicability of such a scheduling framework for the e-mobility ecosystem in an uncertain environment, e.g., by reducing the number of unique EVs that failed to reach their destination from 272 to 61. In addition, the choice of confidence level significantly affects the cost and revenue of the stakeholders as well as the accuracy of the schedules in real-time operation, e.g., for a low-risk case study, the total net cost of EVs increased by 247.3% compared to a high-risk case study. Also, the total net revenue of CSs and retailers decreased by 26.6% and 10.6%, respectively.

IRMAC: Interpretable refined motifs in binary classification for smart grid applications

Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Giang Nguyen, Jon A. R. Liisberg
Journal PaperEngineering Applications of Artificial Intelligence, 117:105588, Part A, January 2023


Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users’ demand is of utmost value to the retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users’ types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users. The performance of the IRMAC is studied and compared with various state-of-the-art methods. The proposed method reached an accuracy of 96% in identifying rooftop PV users and 94.4% in identifying electric heating users, which is comparable to the best solution based on deep learning, while the speed of the inference model is a thousand times faster. Last but not least, the proposed method is a transparent algorithm, which can tackle the concerns regarding the agnostic decision-making process when policies prohibit some machine learning methods.

A stochastic methodology to exploit maximum flexibility of swimming pool heating systems

Mohsen Banaei, Francesco D’Ettorre, Razgar Ebrahimy, S. Ali Pourmousavi, Emma MV Blomgren, Henrik Madsen
Journal PaperInternational Journal of Electrical Power and Energy Systems, 145:108643, Feb 2023


Swimming pool heating systems are known as one of the best flexible resources in buildings. However, they can be flexible only for a certain number of hours throughout a day due to the comfort constraints of the users. In this study, a new approach is proposed to determine a group of contract hour sets to procure maximum flexibility of swimming pool heating systems supplied by heat pumps for trading in the regulation market while respecting the comfort of users. The main advantage of the contract hour sets is the certainty in response to flexibility requests. The proposed approach consists of three main steps. First, a stochastic mixed-integer linear program is proposed to find the optimal operation of a swimming pool heating system that has agreed to provide flexibility in a contract hours set. Then, a metric is proposed to evaluate the effectiveness of contract hour sets using the results obtained in the first step. Finally, an algorithm is proposed to identify a group of the most efficient contract hour sets using the calculated metric. The proposed approach is validated through comprehensive simulation studies for a summerhouse with an indoor pool heated by a heat pump. Also, a cost-benefit analysis is performed to examine the feasibility of these contract hour sets from financial viewpoint. Simulation results show that the maximum contract hours can vary from 2 to 12 hours depending on the building occupation pattern and the minimum payment to owners is between 0.03 to 0.06 (Euro/kW).

Optimal offering strategy for an aggregator across multiple products of European day-ahead market

Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Markus Wagner, J. Liisberg
Conference Paper Innovative Smart Grid Technologies (ISGT) Europe, Novi Sad, Serbia, Oct. 10-12, 2022


Most literature surrounding optimal bidding strategies for aggregators in European day-ahead market (DAM) considers only hourly orders. While other order types (e.g., block orders) may better represent the temporal characteristics of certain sources of flexibility (e.g., behind-the-meter flexibility), the increased combinations from these orders make it hard to develop a tractable optimization formulation. Thus, our aim in this paper is to develop a tractable optimal offering strategy for flexibility aggregators in the European DAM (a.k.a. Elspot) considering these orders. Towards this, we employ a price-based mechanism of procuring flexibility and place hourly and regular block orders in the market. We develop two mixed-integer bi-linear programs: 1) a brute force formulation for validation and 2) a novel formulation based on logical constraints. To evaluate the performance of these formulations, we proposed a generic flexibility model for an aggregated cluster of prosumers that considers the prosumers’ responsiveness, inter-temporal dependencies, and seasonal anddiurnal variations. The simulation results show that the proposed model significantly outperforms the brute force model in terms of computation speed. Also, we observed that using block orders has potential for profitability of an aggregator.

Leveraging the flexibility of electric vehicle parking lots in distribution networks with high renewable penetration

Sahand Karimi-Arpanahi, Mohammad Jooshaki, S. Ali Pourmousavi, and Matti Lehtonen
Journal PaperInternational Journal of Electrical Power and Energy Systems, 142:Part B, 2022


The ongoing rapid increase in the integration of variable and uncertain renewable energy sources calls for enhancing the ways of providing flexibility to power grids. To this end, we propose an optimal approach for utilizing electric vehicle parking lots to provide flexibility at the distribution level. Accordingly, we present a day-ahead scheduling model for distribution system operators, where they can offer discounts on the network tariff to electric vehicle parking lot operators. This way, they will be encouraged to exploit the potential flexibility of electric vehicle batteries to assist in alleviating the steep ramps of system net-load. To determine the optimal discounts, the distribution system operator minimizes the network operating costs considering the network operational constraints, while the electric vehicle parking lot operators try to maximize their profits. Due to the contradictory objectives and decision hierarchy, the problem is an instance of Stackelberg games and can be formulated as a bi-level program, which is linearized and converted to a single-level mixed-integer linear program using strong-duality theorem and Karush-Kuhn-Tucker conditions. To validate the proposed model, comprehensive simulation studies are performed on a test distribution network. The simulation results show that implementing the model can reduce the peak-off-peak difference and peak-to-average ratio of the network net-load by up to 15% and 24%, respectively.

Optimal sizing and scheduling of community battery storage within a local market

Nam Dinh, S. Ali Pourmousavi, S. Karimi-Arpanahi, Y. Kumar, M. Guo, D. Abbott, J. Liisberg
Conference Paper ACM e-Energy conference, Oldenburg, Germany, June 28 - July 1, 2022


The ever-increasing uptake of distributed energy resources necessitates the introduction of local electricity markets at the residential level. Electric retailers, who are adversely affected by these changes, can make a profit by operating local trading platforms and offering services through community-level battery storage. In this work, we propose a Stackelberg game-based approach for sizing the centralized battery unit together with the operation of the multi-interval local market. The optimization is formulated as a bilevel program, where the leader is the market aggregator responsible for determining the local prices and battery charging/discharging schedules. Also, the followers in the bilevel program are prosumers, who can vary electricity consumption with respect to their comfort and cost of electricity. Upon obtaining the optimal capacity of the community storage, we modify the algorithm to efficiently operate the battery on a daily basis. The applicability of the proposed model is evaluated using real-world data of residential prosumers with rooftop photovoltaic systems for two different pricing schemes, which represents the profit trade-off between the aggregator and prosumers. The results show the profitability of the proposed model for community storage installation, where a relatively short payback period can be achieved on either pricing schemes.

Exploiting demand-side flexibility: state-of-the-art, open issues and social perspective

F. D’Ettorre, M. Banaei, R. Ebrahimy, S. Ali Pourmousavi, E. M. V. Blomgren, J. Kowalski, Z. Bohdanowicz, Beata Lopaciuk-Gonczaryk, Cezary Biele, H. Madsen
Journal Paper Renewable & Sustainable Energy Reviews , 165:112605, 2022


Demand-side flexibility will play a key role in reaching high levels of renewable generation and making the transition to a more sustainable energy system. Indeed, end users can actively contribute to grid balancing and management, if equipped with energy management systems and communication infrastructure. Demand response programmes encompass a broad range of load management measures, such as direct or indirect load control, aimed at adapting end users' consumption to grid needs. However, the exibility potential of the demand side has not yet been fully exploited. The demand response programmes have not been fully realised in practice and different barriers are yet to be addressed properly. Among others, these include a fragmented regulatory framework, the lack of market products suitable for small end users, and the lack of common measurement and quantification methodologies. The present article provides an overview on the state-of-the-art of demand response programmes and their current implementation. Measurement and verification methodologies are also presented with a special focus on baseline estimation methodologies for quantifying the flexibility provided by the demand side through demand response programmes. Alongside technical and regulatory aspects, the social perspective on demand response is investigated through a quantitative survey carried out in four different European countries: Denmark, France, Italy and Spain. Finally, open issues and research gaps are identified and analysed to provide recommendations for future research activities.

Power sharing and voltage regulation in islanded DC microgrids with centralized double-layer hierarchical control

Yuan Yao, Nesimi Ertugrul, and S. Ali Pourmousavi
Conference Paper Australasian Universities Power Engineering Conference (AUPEC), Perth, Australia, Sep. 26-30, 2021


As the conventional AC power grid is evolving with the integration of renewable energy resources (PV and wind) and power electronic converters, we discover more technical and economic values for DC microgrids (MGs), particularly with electric vehicles (EVs) and battery energy storage systems (BESSs) changing the load landscape. This paper presents a double-layer centralized hierarchical control framework to achieve the DC bus voltage regulation and power coordination of a PV-BESSs based DC MG, which operates in fully islanding condition. The proposed control strategy consists of two levels: 1) The primary control can achieve the DC bus voltage regulation by modifying the commonly-used inner and outer PI control loops; 2) The secondary control considers the characteristics of different types of BESSs. The latter is also responsible for load sharing by generating power/current reference signals among different DGs and DC converters. The advantage of the proposed control strategy is to eliminate the droop control as well as the conventional secondary control, which is designed to restore the voltage deviation caused by droop equations in a three-level hierarchical control system. The simulation results show that the proposed control strategy is effective to stabilize the DC bus voltage and it can share the dynamic power variation within DC MG.

Behind-the-meter energy flexibility modelling for aggregator operation with a focus on uncertainty

Emma M. V. Blomgren, Razgar Ebrahimy, S. Ali Pourmousavi, Jan Kloppenborg Møller, Francesco D’Ettorre, Mohsen Banaei, and Henrik Madsen
Conference Paper Innovative Smart Grid Technologies (ISGT) Europe, Espoo, Finland, Oct. 18-21, 2021


Aggregators are expected to become an inevitable entity in future power system operation, playing a key role in unlocking flexibility at the edge of the grid. One of the main barriers to aggregators entering the market is the lack of appropriate models for the price elasticity of flexible demand, which can properly address time dependent uncertainty as well as non-linear and stochastic behavior of end-users in response to time varying prices. In this paper, we develop a probabilistic price elasticity model utilizing quantile regression and B-splines with penalties. The proposed model is tested using data from residential and industrial customers by assuming automation through energy management systems. Additionally, we show an application of the proposed method in quantifying the number of consumers needed to achieve a certain amount of flexibility through a set of simulation studies.

An optimal day-ahead scheduling framework for e-mobility ecosystem operation with drivers’ preferences

Mahsa Bagheri Tookanlou, S. Ali Pourmousavi, and M. Marzband
Journal PaperIEEE Transactions on Power Systems, 36(6):5245-5257, 2021


The future e-mobility ecosystem will be a complex1structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ references are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers preferences on the optimal solutions and cost/revenue of the stakeholders.

A comprehensive day-ahead scheduling strategy for electric vehicles operation

Mahsa Bagheri Tookanlou, S. Ali Pourmousavi, and M. Marzband
Journal PaperInternational Journal of Electrical Power and Energy Systems, 131, 106912, 2021


Distribution networks are envisaged to host significant number of electric vehicles and potentially many charging stations in the future to provide charging as well as vehicle-2-grid services to the electric vehicle owners. The main goal of this study is to develop a comprehensive day-ahead scheduling framework to achieve an economically rewarding operation for the ecosystem of electric vehicles, charging stations and retailers using a comprehensive optimal charging/discharging strategy that accounts for the network constraints. To do so, an equilibrium problem is solved using a three-layer iterative optimisation problem for all stakeholders in the ecosystem. EV routing problem is solved based on a cost-benefit analysis rather than choosing the shortest route. The proposed method can be implemented as a cloud scheduling system that is operated by a non-profit entity, e.g., distribution system operators or distribution network service providers, whose role is to collect required information from all agents, perform the day-ahead scheduling, and ultimately communicate the results to relevant stakeholders. To evaluate the effectiveness of the proposed framework, a simulation study, including three retailers, one aggregator, nine charging stations and 600 electric vehicles, is designed based on real data from San Francisco, the USA. The simulation results show that the total cost of electric vehicles decreased by 17.6%, and the total revenue of charging stations and retailers increased by 21.1% and 22.6%, respectively, in comparison with a base case strategy.

Robust flexible unit commitment in network-constrained multi-carrier energy systems

M. Amin Mirzaei, M. Nazari-Heris, B. Mohammadi-Ivatloo, K. Zare, M. Marzband, and S. Ali Pourmousavi
Journal PaperIEEE Systems Journal, 15 (4):5267-5276, 2021


The coordinated operation of different energy systems, such as electrical, gas, and heating, can improve the efficiency of the whole energy system and facilitate the larger penetration of renewable energy resources in the electricity generation portfolio. However, appropriate models considering various technical constraints of the energy carriers (e.g., gas system pressure limit and heat losses in the district heating networks) are needed to effectively assess the true impact of integrated energy system (IES) operation on the overall system’s performance. This paper proposes a flexible unit commitment (UC) problem for coordinated operation of electricity, natural gas, and district heating networks, called multi-carrier network-constrained unit commitment (MNUC), to minimize the operation cost of the IES. Besides, an integrated demand response (IDR) program is considered as a promising solution to improve consumers’ electrical, gas, and heat consumption patterns and increase the power dispatch of combined heat and power units. Multi-energy storage systems are also included in the proposed model to decrease the impact of multi-energy network constraints on the overall system’s performance. To model the uncertainties involved in the operation of the three networks, a combined robust/stochastic approach is preferred in the MNUC problem considering multi-carrier energy storage systems and the IDR program. Numerical results show that the whole operation cost of the IES has decreased by %2.58 considering the IDR program and multi-energy storage systems.

A data-driven approach to estimate battery cell temperature using a nonlinear autoregressive exogenous neural network model

Md Mehedi Hasan, S. Ali Pourmousavi, Ali Jahanbani Ardakani, and Tapan K. Saha
Journal PaperJournal of Energy Storage, 32, 101879, 2020


Battery cell temperature is a key parameter in battery life degradation, safety, and dynamic performance. Intense charging-discharging operations and high-ambient temperatures escalate battery cell temperature, which in turn accelerates its degradation. Therefore, accurate battery cell temperature estimation can play a significant role in ensuring the optimal operation of a battery energy storage system (BESS). In order to estimate battery cell temperature as accurate as possible, use of non-linear models is imperative due to the non-linear nature of the battery operation. This paper proposes a data-driven model based on a Non-linear Autoregressive Exogenous (NARX) neural network to estimate battery cell temperatures in a utility-scale BESS, considering strongly-correlated independent variables, e.g., charging-discharging current and ambient temperature. Due to different temperature and weather characteristics in each season, seasonal NARX models have also been derived and compared with the universal one. The proposed models’ performance has been verified using the field data collected from a grid-connected BESS within a PV plant. The simulation results show high accuracy of the proposed model compared to the measured data for both seasonal and universal models without considering the complexity of the large-scale battery and container thermal dynamics. Inparticular, in more than 95% of the time, the estimated values yield root mean squared errors (RMSE) below 1C in different conditions, which confirms the validity and accuracy of the proposed model. Moreover, seasonal models show better performance with 18% to 50% less RMSE on average (for 1 hour to 24 hours forward estimation) compared to the universal model.

Sizing HESS as inertial and primary frequency reserve in low inertia power system

Umer Akram, N. Mithulananthan, Rakibuzzaman Shah, and S. Ali Pourmousavi.
Journal PaperIET Renewable Power Generation, 15(1), 2021


Energy storage systems are recognised as the potential solution to alleviate the impacts of reduced inertia and intermittency in power systems due to the integration of renewable energy sources. Several energy storage technologies are available in the market with diverse power and energy characteristics, operational limitations, and costs. Besides, frequency regulations in power systems have different requirements, for example, inertial response requires high power for a short period while primary frequency regulation requires steady power for a longer time. Thus, it is crucial to find out the optimum sizes and types of storage technologies for these services. In this paper, a methodology for sizing fast responsive energy storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is developed. The sizing of storage systems for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is done separately. The sizing of storage for inertial response is done in two steps. A region reduction iterative algorithm is proposed to estimate the storage size for inertial response. The sizing of the storage system for primary frequency regulation is done analytically. The sizing methodology incorporates the frequency dynamics of storage, converters, and other associated controls that affect the frequency response. Moreover, an economic analysis is carried out to find the optimum combination of storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation services. The accuracy of the proposed sizing method has been compared with the metaheuristic algorithm based technique. The effectiveness of the proposed method is also compared with those in the literature. Simulation results show that the proposed method outperforms the existing methods in the literature. Finally, the non‐linear simulations revealed the validity of the optimal solutions.

Status of mine electrification and future potentials

Nesimi Ertugrul, S. Ali Pourmousavi, Miles Davies, Daniel Sbarbaro, and Luis Moran
Conference Paper International Conference on Smart Grids and Energy Systems (SGES), Perth, Australia, Nov. 23-26, 2020


The electrification of mining operations is rapidly emerging as a central issue for the resources sector and its efforts to improve reliability and health/safety, and to reduce cost. The reliance on fossil fuel and gas generated electricity is a significant proportion of current mining operational costs and the prevalence of diesel fuel usage is a significant health and safety concern. The use of electric vehicles and machinery combined with partial or stand-alone renewable energy powered microgrids provides a pathway to more efficient, sustainable and safer mining operations, both for underground mining and open-pit mining. Electrification also presents an opportunity to integrate Internet of Things (IoT) technologies such as autonomous vehicles, communications networks and data analysis for safety and higher efficiency. Digitalization and automation is also the solution to reduce operational costs in areas related with concentration and transport. The transition to an electric mining future is complex and will require substantial investment in infrastructure, technologies, and hardware as well as collaboration between the mine operators and service industries, research organisations and regional, State and Federal governments, and newly skilled workforce in Australia. This paper provides an overview of the status of mine electrification and highlights the potential research directions, which aimed to help shape the resource industries transition to an electric and renewables mining future.

Feasibility study of a P2P energy trading algorithm in a grid-tied power network

M Imran Azim, S. Ali Pourmousavi, Wayes Tushar, and Tapan K. Saha
Conference PaperAccepted for presentation in the IEEE PES General Meeting, Atlanta, USA, Aug. 4-8, 2019


This paper studies the feasibility of peer-to-peer(P2P) energy trading in a grid-tied network. The main objectives are to understand the impact of a P2P energy trading model onthe network operation, and thus demonstrate the importance of taking various issues related to power network into account while designing a P2P trading scheme. To do so, firstly a simple mechanism is developed for energy trading among prosumers without considering any network constraints, as done by many existing studies. Once the trading outcome is finalised, the developed scheme is tested on the low-voltage (LV) network model to check its feasibility in real world. It is shown that while the considered trading scheme is economically beneficial to the prosumers compared to the current incentive mechanisms (such as feed-in-tariff), 1) it could be unfit for real deployment due to violating voltage limits in some scenarios, and 2) the grid may experience financial losses for compensating the path losses during P2P trading at a given interval.

Analysis of rebound effect modelling for flexible electrical consumers

Giulia De Zotti, Daniela Guericke, S. Ali Pourmousavi, Juan Miguel Morales, Henrik Madsen, and Niels K. Poulsen.
Conference PaperIFAC Workshop on Control of Smart Grid and Renewable Energy Systems, Jeju, Republic of Korea, June 10-12, 2019


Demand response (DR) will be an inevitable part of the future power system operation to compensate for stochastic variations of the ever-increasing renewable generation. A solution to achieve DR is to broadcast dynamic prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential exibility of different types of consumers for day-ahead and real-time ancillary services provision, while accounting for the rebound effect (RE). In this study, two RE models are presented and compared to investigate the behaviour of flexible electrical consumers and quantify the aggregate exibility provided. The stochastic nature of consumers' price response is also considered in this study using chance constrained (CC) programming.

Battery cell temperature estimation model and cost analysis of a grid-connected PV-BESS plant

Md Mehedi Hasan, S. Ali Pourmousavi, and Tapan K. Saha.
Conference PaperIEEE PES ISGT Asia, Chengdu, China, May 21-24, 2019


Battery cell temperature is an important factor in battery capacity degradation, performance, and safety. Elevated battery cell temperature, due to intense battery operation with high charging-discharging current and ambient temperature, accelerates battery capacity degradation as well as causing extra cooling cost. Therefore, it is indispensable to estimate battery cell temperature accurately for optimal BESS operation considering capacity degradation and its associated costs. The main objectives of this paper are to propose a linear model to estimate battery cell temperature using Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) considering strongly correlated independent variables and propose a cost function of PV plant with and without battery operation. The simulation results using field data show high accuracy of the proposed temperature estimation model without considering the complex thermal dynamics of the entire system. In addition, comprehensive cost functions are developed to show the benefit of integrating battery storage into a PV plant and determine influential factors to consider in any optimal battery operation systems.

Consumers' flexibility estimation at the TSO level for balancing services

Giulia De Zotti, S. Ali Pourmousavi, Juan M. Morales, Henrik Madsen, and Niels K. Poulsen
Journal PaperIEEE Transactions on Power Systems, 34(3):1918-1930, 2019


  • what is the problem?
    Using consumers' flexibility by the TSO is a way to provide extra AS required to accommodate higher level of renewable generation. This can be done by a time-varying prices submitted to flexible loads. However, effective models are needed for the TSO to generate price signal.
  • why is it interesting?
    Larger renewable generation in the power system is not going to have without providing enough flexibility for safe operation of the system. Demand flexibility resources are free of charge, available at all times, and will be achievable with minimum upfront cost of infrastructure. An efficient and detail model to estimate consumers' reaction to price signals is necessary to achieve this.
  • what is the approach?
    We formulated consumers' flexibility as an optimisation problem for rational end-users. Consumers willingness to respond to price signals are modelled stochastically and actual aggregated load data are used for 29 categories of load demand. The deterministic optimisation model was converted to a chance-constrained program to account for the stochasticities.
  • what is new?
    The proposed formulation was new in essence and including different load categories to solve the problem was unique. Converting the deterministic problem into a stochastic one was offered to estimate load flexibility considering the confidence level.
  • How was it tested?
    Optimisation model was implemented in GAMS and the problem was solved in MATLAB by calling GAMS model using CPLEX solver. The normality assumption of the stochastic terms are tested and proved, and the problem was solved for two confidence levels.

A control-based method to meet TSO and DSO ancillary services needs by flexible end-users

Giulia De Zotti, S. Ali Pourmousavi, Juan M. Morales, Henrik Madsen, and Niels K. Poulsen
Journal PaperIEEE Transactions on Power Systems, 35(3), 1868-1880, 2020


This paper presents a new methodology to exploit consumers’ flexibility for the provision of ancillary services (AS). The proposed framework offers a control-based approach that adopts price signals as the economic driver to modulate consumers’ response. In this framework, various system operators broadcast price signals independently to fulfil their AS requirements. Appropriate flexibility estimators are developed from the transmission system operator (TSO) and distribution system operator (DSO) perspectives for price generation. An artificial neural network (ANN) controller is used for the TSO to infer the price-consumption reaction from pools of consumers in its territory. A PI controller is preferred to represent the consumers’ price-response and generate time-varying electricity prices at the DSO level for voltage management. A multi-timescale simulation model is built in MATLAB to assess the proposed methodology in different operational conditions. Numerical analyses show the applicability of the proposed method for the provision of AS from consumers at different levels of the grid and the interaction between TSO and DSOs through the proposed framework.

Variability, Scalability, and Stability of Microgrids - Chapter 2: Microgrid Control Overview

S. Ali Pourmousavi, F. Shahnia, M Imran Azim, Md. Asaduzzaman Shoeb, and G.M. Shafiullah.
Book Chapter edited by S.M. Muyeen, Syed Mofizul Islam, Frede Blaabjerg, The Institution of Engineering and Technology (IET) Press, 624 pages, Oct. 7, 2019


To be included later ...

Improving predictability of renewable generation through optimal battery sizing

S. Ali Pourmousavi, P. Wild, and Tapan K. Saha
Journal PaperIEEE Transactions on Sustainable Energy, 11(1):37-47, 2020


  • what is the problem?
    Renewable energies such as PV and wind are unpredictable by nature to a large extent. Despite all the new forecasting methods, they still conservatively bid in the market and pay penalty to the ancillary services market based on causer pay.
  • why is it interesting?
    If there is a way that the predictability of renewable generation can be improved, it helps plant operators as well as bring larger economic benefits to the plant owners. It would be more interesting if the proposed approach can be prediction-method-agnostic.
  • what is the approach?
    An optimal battery sizing methodology is proposed to improve renewable generation predictability using “Seasonal-Trend decomposition based on LOESS1 (STL)” decomposition technique, self-similarity estimation, and enhancing it through filtering.
  • what is new?
    The idea of improving predictability and using the self-similarity concept in this context was offered for the first time. In addition, the STL decomposition technique application in this study was new in the power system engineering community. Moreover, the battery degradation over its useful lifetime was added to the optimisation problem.
  • How was it tested?
    The optimisation problem is solved using Gurobi in MATLAB. The battery sizes are examined in terms of power magnitude and battery SOC limits using the test dataset. Then, predictability improvement was tested by developing four prediction methods for three different horizons. Finally, the cost-effectiveness of the battery application was examined by standard LCOE calculation against average AS market prices.

Learning from a 3.275 MW utility-scale PV plant project: Update and new remarks

S. Ali Pourmousavi, P. Wild, F. Bai, R. Yan, Tapan K. Saha, and D. Eghbal
Conference Paper CIGRE Conference, Paris, August 26-31, 2018


  • what is the problem?
    Batteries are inevitable in the future power system. At the same time, different battery technologies are under development and lots of unknown exist to the research and engineering communities about their operation. In addition, the benefit and technical limits of operating batteries next to a PV plant is relatively unknown in the research and industry communities.
  • why is it interesting?
    It is interesting to see a comprehensive analyses of the battery operation within a relatively large PV plant in terms of operation and technical constraints. It provides insights into the hybrid system operation and offers a wide range of learnings to create better systems of the same type in the future.
  • what is the approach?
    In this paper, statistical analyses are carried out based on two years of 600 kW/760 kWh Li-Polymer battery operation within the UQ Gatton Solar Research Facility with 3.275 MWp of PV. In addition, the performance of different PV tracking technologies are evaluated based on field data to reveal the overall yield of the plant.
  • what is new?
    While there are numerous simulation studies and small-scale field data analysis for performance evaluation of storage and PV systems, there is no such a study based on real data for a utility-scale PV and battery plant. It provides valuable insights into the system operation and performance over the years.
  • How was it tested?
    The entire study is done using actual data from the UQ Gatton Solar Research Facility. Different statistical tools are used to draw insights from the real operation.

Utilizing flexibility resources in the future power system operation: Alternative approaches

Giulia De Zotti, S. Ali Pourmousavi, Henrik Madsen, and Niels K. Poulsen
Conference Paper ENERGYCON Conference, Limassol, Cyprus, June 3-7, 2018


  • what is the problem?
    The future power system should accommodate large amount of variable renewable generation in order to achieve renewable targets. Also, using conventional generators to provide ancillary services in the system should be limited to special circumstances as a way to decrease emissions from power plants by increasing conventional generators efficiencies.
  • why is it interesting?
    The most cost-effective way to provide such solutions is to use the resources that exist. Storage of different kinds are still very expensive. Conventional generators contribute to emission and should be prohinited. Therefore, an alternative solutions are needed to solve the problem.
  • what is the approach?
    A control-based ancillary services method is proposed that can exploit the load demand flexibility in order to provide ancillary services for power system operation. It allows multiple system operators to fulfil their requirements simultaneously and seamlessly at all times.
  • what is new?
    Different alternative approaches including P2P and Transactive Energy (TE) are introduced and compared in this paper along with an introduction to the AS4.0 approach.
  • How was it tested?
    It is a conceptual paper that hypothesized alternative approaches. There is no simulation study in the paper.

Optimal coordinated bidding of a profit-maximizing EV aggregator under uncertainty

Yelena Vardanyan, Frederik Banis, S. Ali Pourmousavi, and Henrik Madsen
Conference Paper ENERGYCON Conference, Limassol, Cyprus, June 3-7, 2018


  • what is the problem?
    Application of EVs in providing services to the grid through aggregator depends on the economic benefits of different stakeholders. Therefore, developing appropriate aggregation algorithms that deal with uncertainties involved in the process becomes very important.
  • why is it interesting?
    From power system operation point of view, EVs are mobile storage that can be used for the benefit of power system operation. Thousands of EVs can provide substantial amount of storage without upfront costs to the system operator.
  • what is the approach?
    An optimisation formulation is developed considering undertainty in the market prices and EVs availability considering battery degradation cost. Then, the problem is solved as a two-stage stochastic programming to find the best bids for the aggregator's participation in the market.
  • what is new?
    The bidding strategy has not been reported in the literature for EV aggregator. Also, considering the battery degradation cost in the formulation was new, and solving the problem as a two-stage stochastic programming offered a new perspective to solve the problem.
  • How was it tested?
    The optimisation problem was implemented in GAMS with day-ahead and real-time market mechanisms.

Learning from an operational utility-scale Li-Polymer battery system in a PV plant

S. Ali Pourmousavi, and Tapan K. Saha
Conference Presentation EECON Conference, April 05-06, 2018, Brisbane QLD, Australia


Utility-scale energy storage is becoming the most viable and widespread solution to safely accommodate large-scale renewable generation without harming the power system operation. Different storage technologies have been considered for such applications, where various Li-based battery technologies gained the largest share of the market, after pump hydro storage. While numerous research studies addressed optimal operation of battery storage using mathematical modelling, there are few examples of battery performance analysis in such applications. The University of Queensland owns and operates one of the largest PV/battery research facility in the world at the Gatton campus. It includes 3.275 MWp solar PV modules with three different solar tracking technologies, and 600kW/760 kWh Li-Polymer battery system. The plant is primarily responsible for meeting the campus load demand, and the excess energy, if available, can be pushed back to the local network. This presentation will provide valuable insights into the utility-scale battery operation in connection with a PV plant and local network based on 1.5 years of actual data. The focus will be on evaluating the operation and performance of the battery system from different perspectives. I will start with briefly introducing the plant, battery system configuration, cooling mechanism, and inverter. Then, I will briefly review battery operation rules in the plant which is managed by a central supervisory controller. Battery operation and performance for reactive power support will be reviewed next. Afterwards, voltage agreement with the local network operator will be reviewed, and the impact of the battery will be quantified in that regard. Finally, the performance of the battery for demand charge management (or peak shaving), excess PV storage, PV ramp-rate control, and additional services to the local network will also be examined based on operational data. Data analysis will show that the battery operation had positive impacts on the plant operation.

Evaluation of battery operation in ramp-rate control mode within a PV plant: A case study

S. Ali Pourmousavi, and Tapan K. Saha
Journal PaperSolar Energy, 166:242–254, 2018
  • what is the problem?
    Batteries are used (and will be used) in renewable generation plants to compensate quick variations in the output of these resources, among other things. It could be very useful for researchers and engineers to know the kind of regime that battery will experience in such applications in real-world condition beyond simulation studies.
  • why is it interesting?
    Having insights in to the different ways that battery is stressed during ramp-rate control mode can help to select suitable battery for the application, design better battery kWh and kW sizes more realistically, and develop energy management systems for battery operation which account for the real-wrold conditions.
  • what is the approach?
    In this paper, one year of operational data of a 600 kW/760 kWh Li-Polymer battery during ramp-rate control mode of a 3.275 MWp PV plant at the UQ Gatton campus are analysed. Maximum, minimum, average, standard deviation, skewness, and kurtosis are calculated for different parameters while statistical models are derived based on the given data.
  • what is new?
    The insights offered to a utility-scale battery experience operating in a PV plant are unique in this paper. Battery energy, power, SOC, cell temperature, and time difference between two consecutive events are among the parameters that have been evaluated. In addition, technical and operational values of a super-capacitor in such a system is hypothesized. Moreover, the impact of PV inverter operation on the ramping events on the DC and AC sides are analysed which resulted in very important observations.
  • How was it tested?
    This paper is developed based on actual data of a battery system in ramp-rate control mode. So, there is no test required in this kind of study.

Ancillary services 4.0: A top-to-bottom control-based approach for solving ancillary service problems in smart grids

Giulia De Zotti, S. Ali Pourmousavi, Henrik Madsen, and Niels K. Poulsen
Journal Paper IEEE Access, 6:11694-11706, 2018
  • what is the problem?
    Existing Ancillary Services (AS) market and its alternative approaches are not suitable for the future power systems with large amount of renewable energy. They are slow, linear, and limited to a specific power system functionality.
  • why is it interesting?
    This is an important problem for wider use of renewable generation. They are variable and unpredictable in nature, which makes it difficult to deal with in real-time operation. Larger penetration of power systems with the existing AS mechanism is not possible.
  • what is the approach?
    The AS 4.0 is an alternative to the existing market-based approaches, which utilises real-time pricing mechnism along with control principles to provide services to the grid from any possible flexibility resources. The proposed method redefines AS problem into space and time for a comprehensive solution.
  • what is new?
    It is a holistic change in the existing AS provision mechanism. The proposed method can accommodate AS provision at the different time and space. It also utilise any flexibility potential within the network. It also can be tied up with other energy carriers easily. Moreover, the proposed approach respect users' privacy to an unprecedented level.
  • How was it tested?
    The AS 4.0 is still an idea. This paper lays out a general framework to define the solution. It also provides a comprehensive review of the existing AS mechanisms in comparison to the proposed method.

An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory

Mousa Marzband, Masoumeh Javadi, S. Ali Pourmousavi, and Gordon Lightbody
Journal Paper Electric Power Systems Research, 157:187–199, 2018
  • what is the problem?
    Active distribution networks are the future of power system with prosumers. Currently, there is no market mechanism to encourage energy exchange among prosumers to increase competition with the utility companies.
  • why is it interesting?
    The prosumers can trade energy and ancillary services among each other, which reduce stress on the main power grid, losses, and cost of operation by facilitating local generation and consumption. It also helps retailers to defer upgrade in the system.
  • what is the approach?
    A game-theory market structure with multiple retailers, prosumers, and devices are developed. Load flexibility, storage, and dispatchabe and non-dispatchable resources are considered to participate in the market looking after their benefits. The proposed market structure encourage local generation and demand.
  • what is new?
    Considering three types of players and load flexibility is new in this study. Additionally, the Nikaido-Isoda Relaxation Algorithm (NIRA) is used to obtain global optimal solutions among all different players. Uncertain parameters are appropriately modelled with statistical techniques and taguchi0s orthogonal array testing (TOAT) is utilised to reduce number of scenarios for faster simulation.
  • How was it tested?
    A small active distribution system consisting of three home microgrids and two retailers are simulated. Three test cases are defined and simulated for comparison purposes. The proposed market structure outperformed the other scenarios significantly in different aspects.

The impact of temperature on battery degradation for large-scale BESS in PV plant

Md Mehedi Hasan, S. Ali Pourmousavi, Feifei Bai, and Tapan Kumar Saha
Conference Paper In Proc. of the AUPEC, Melbourne, Australia, November 19-22, 2017
  • what is the problem?
    Large-scale batteries generate heat during charging and discharging events. So, we are interested to know the impact of charge/discharge regime on the generated heat. Also, it is quite important to quantify the impact of excessive heat in terms of battery degradation.
  • why is it interesting?
    Since batteries in large-scale application sit in an enclosed container, the generated heat should be removed as fast as possible. This will improve battery operation in terms of available capacity, available charge, and round-trip efficiency. To do so, active and passive cooling mechanisms are employed in such applications. However, excessive heat leads to over-consumption of the active cooling system. This will, in turn, reduce the overall efficiency of the plant.
  • what is the approach?
    In this study, we utilised operational data of a 600kW/760kWh battery within a 3.2MWp PV plant at the University of Queensland campus in Gatton, QLD, Australia. Through the data analyses, we show that both charging and discharging events increase battery temperature substantially. We also found a strong linear relationship between current and temperature of the battery during discharge events. Such as strong correlation has not been identified during charging incidents. We also found out that the extra battery degradation caused by the excessive heat is substantial.
  • what is new?
    This is the first study of its kind to show such impact for large-scale battery systems using real-world operational data. The linear relation between the temperature and current during discharging events is a remark with huge consequences which is made in this paper. The difference identified between charging and discharging regimes on battery temperature is yet another significant insight presented in this study.
  • How was it tested?
    First, we selected charge and discharge events where the ambient temperature had nothing to do with the temperature rise. Then, we developed several measures (such as Temperature Rising Slope, Peak Temperature, Absolute Temperature Change, Peak Current, Total Charge, and Temperature Rising Delay) to conduct analyses. After that, we tried to find a linear or piecewise linear relationship between the measures and battery temperature during the event. Finally, we used Zhurkov model quantify the extra degradation occurred due to excessive temperature.

Optimal battery sizing for behind-the-meter applications considering participation in demand response programs and demand charge reduction

Ali Hooshmand, S. Ali Pourmousavi Kani, Ratnesh K. Sharma, Shankar Mohan
Patent September 7 2017. US Patent 10,497,072 B2


A system and method are provided. The system includes a processor. The processor is configured to receive power related data relating to power usage of power consuming devices at a customer site from a plurality of sources. The processor is further configured to generate object function inputs from the power related data. The processor is additionally configured to apply the generated object function inputs to an objective function to determine an optimal capacity for a battery storage system powering the power consuming devices at the customer site while minimizing a daily operational power cost for the power consuming devices at the customer site. The processor is also configured to initiate an act to control use of one or more batteries of the battery storage system in accordance with the optimal capacity for the battery storage system.

Resilient battery charging strategies to reduce battery degradation and self-discharging

S. Ali Pourmousavi Kani, Babak Asghari, Ratnesh K. Sharma
Patent August 10 2017. US Patent 10,298,042 B2


Computer-implemented methods and, a system are provided. A method includes constructing by an Energy Management System (EMS), one or more optimization-based techniques for resilient battery charging based on an optimization problem having an EMS cost-based objective function. The one or more optimization-based techniques are constructed to include a battery degradation metric in the optimization problem. The method further includes charging, by the EMS, one or more batteries in a power system in accordance with the one or more optimization-based techniques.

Innovative framework combining cycling and calendar aging models

S. Ali Pourmousavi Kani, Babak Asghari, Ratnesh K. Sharma
Patent Submitted on April 27 2017, Granted on Sep. 24, 2019. US Patent 10,422,835


Aspects of the present disclosure describe a single battery degradation model and methods that considers both CYCLING and CALENDAR aging and useful in both energy management and battery management systems that may employ any of a variety of known battery technologies.

Method for Real-Time Power Management of a Grid-Tied Microgrid to Extend Storage Lifetime and Reduce Cost of Energy

Babak Asghari, Ratnesh K. Sharma, S. Ali Pourmousavi
Patent U.S. Patent 9,020,649 - 2015


A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy

Electrical Circuits I&II: Solution Manual

S. Ali Pourmousavi
Book 320 pages, Payam Daneshgahi press 2009 | Isfahan, Iran | ISBN:978-864-8622-43-0. (In Farsi)
first page of my book


This book is a comprehensive solution manual and briefing courses for Electrical Circuits of first and second-order. Every chapter starts with a short course and notes about a specific topic in Electrical Circuits which is followed by solving numerous questions. Every question is explained in details and the equations are dervied step-by-step. This is a valuable source to learn this difficult toipc in Electrical Engineering in depth.

Assessing the potential of plug-in electric vehicles in active distribution networks

Reza Ahmadi Kordkheili, S. Ali Pourmousavi, Mehdi Savaghebi, Josep M Guerrero, and Mohammad Hashem Nehrir
Journal Paper Energies, 9(1):34, 2016


A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs) in distribution networks by intelligent management of plug-in electric vehicle (PEV) storage. The proposed algorithm is defined to manage the reverse power flow (PF) from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner’s quality of service (QoS). The method uses genetic algorithm (GA) to increase photovoltaic (PV) penetration without jeopardizing PEV owners’ (QoS) and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV) grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up to 50%, depending on the PEV penetration level and the freedom of the system operator in managing the available PEV storage.

Multi-timescale power management for islanded microgrids including storage and demand response

S. Ali Pourmousavi, M. Hashem Nehrir, and Ratnesh K. Sharma
Journal Paper IEEE Transactions on Smart Grid, 6(3):1185–1195, 2015


Power management is an essential tool for microgrid (MG) safe and economic operation, particularly in the islanded operation mode. In this paper, a multi-timescale costeffective power management algorithm (PMA) is proposed for islanded MG operation targeting generation, storage, and demand management. Comprehensive modeling, cost, and emission calculations of the MG components are developed in this paper to facilitate high accuracy management. While the MGs overall power management and operation is carried out every several minutes to hours, depending on the availability of the required data, simulation for highly dynamic devices, such as batteries and electric water heaters (EWHs) used for demand response (DR), are performed every minute. This structure allows accurate, scalable, and practical power management taking into consideration the intrainterval dynamics of battery and EWHs. Two different on/off strategies for EWH control are also proposed for DR application. Then, the PMA is implemented using the two different DR strategies and the results are compared with the no-DR case. Actual solar irradiation, ambient temperature, nonEWH load demand, and hot water consumption data are employed in the simulation studies. The simulation results for the MG studied show the effectiveness of the proposed algorithm to reduce both MGs cost and emission.

Real-time demand response through aggregate electric water heaters for load shifting and balancing wind generation

S. Ali Pourmousavi, Stasha N. Patrick, and M. Hashem Nehrir
Journal Paper IEEE Transactions on Smart Grid, 5(2):769–778, 2014


Demand response (DR) has shown to be a promising tool for balancing generation and demand in the future power grid, specifically with high penetration of variable renewable generation, such as wind. This paper evaluates thermostat setpoint control of aggregate electric water heaters (EWHs) for load shifting, and providing desired balancing reserve for the utility. It also assesses the economic benefits of DR for the customers through time-of-use pricing. Simulation results reveal the achievement of the economic benefits to the customers while maintaining their comfort level and providing a large percentage of desired balancing reserve at the presence of wind generation.

Introducing dynamic demand response in the LFC model

S. Ali Pourmousavi and M. Hashem Nehrir
Journal Paper IEEE Transactions on Power Systems, 29(4):1562–1572, 2014


Demand response (DR) has proved to be an inevitable part of the future grid. Much research works have been reported in the literature on the benefits and implementation of Dr However, little works have been reported on the impacts of DR on dynamic performance of power systems, specifically on the load frequency control (LFC) problem. This paper makes an attempt to fill this gap by introducing a DR control loop in the traditional LFC model (called LFC-DR) for a single-area power system. The model has the feature of optimal operation through optimal power sharing between DR and supplementary control. The effect of DR communication delay in the controller design is also considered. It is shown that the addition of the DR control loop increases the stability margin of the system and DR effectively improves the system dynamic performance. Simulation studies are carried out for single-area power systems to verify the effectiveness of the proposed method.

Real-time central demand response for primary frequency regulation in microgrids

S. Ali Pourmousavi and M. Hashem Nehrir
Journal Paper IEEE Transactions on Smart Grid, 3(4):1988–1996, 2012


Providing ancillary services for future smart microgrid can be a challenging task because of lack of conventional automatic generation control (AGC) and spinning reserves, and expensive storage devices. In addition, strong motivation to increase the penetration of renewable energy in power systems, particularly at the distribution level, introduces new challenges for frequency and voltage regulation. Thus, increased attention has been focused on demand response (DR), especially in the smart grid environment, where two-way communication and customer participation are part of. This paper presents a comprehensive central DR algorithm for frequency regulation, while minimizing the amount of manipulated load, in a smart microgrid. Simulation studies have been carried out on an IEEE 13-bus standard distribution system operating as a microgrid with and without variable wind generation. Simulation results show that the proposed comprehensive DR control strategy provides frequency (and consequently voltage) regulation as well as minimizing the amount of manipulated responsive loads in the absence/presence of wind power generation.

Very short-term wind speed prediction: a new artificial neural network–markov chain model

S. Ali Pourmousavi and M. M. Ardehali
Journal Paper Energy Conversion and Management, 52(1):738–745, 2011


As the objective of this study, artificial neural network (ANN) and Markov chain (MC) are used to develop a new ANN–MC model for forecasting wind speed in very short-term time scale. For prediction of very short-term wind speed in a few seconds in the future, data patterns for short-term (about an hour) and very short-term (about minutes or seconds) recorded prior to current time are considered. In this study, the short-term patterns in wind speed data are captured by ANN and the long-term patterns are considered utilizing MC approach and four neighborhood indices. The results are validated and the effectiveness of the new ANN–MC model is demonstrated. It is found that the prediction errors can be decreased, while the uncertainty of the predictions and calculation time are reduced.

An innovative hybrid algorithm for very short-term wind speed prediction using linear prediction and markov chain approach

S. Ali Pourmousavi, G. H. Riahy, and D. Mazhari
Journal Paper International Journal of Green Energy, 8(2):147–162, 2011


A new hybrid algorithm using linear prediction and Markov chain is proposed in order to facilitate very short-term wind speed prediction. First, the Markov chain transition probability matrix is calculated. Then, linear prediction method is applied to predict very short-term values. Finally, the results are modified according to the long-term pattern by a nonlinear filter. The results from proposed method are compared by linear prediction method, persistent method and actual values. It is shown that the prediction-modification processes improves very short-term predictions, by reducing the maximum percentage error and mean absolute percentage error, while it retains simplicity and low CPU time and improvement in uncertainty of prediction.

Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization

S. Ali Pourmousavi, M. Hashem Nehrir, Christopher M. Colson, and Caisheng Wang
Journal Paper IEEE Transactions on Sustainable Energy, 1(3):193–201, 2010


Energy sustainability of hybrid energy systems is essentially a multiobjective, multiconstraint problem, where the energy system requires the capability to make rapid and robust decisions regarding the dispatch of electrical power produced by generation assets. This process of control for energy system components is known as energy management. In this paper, the application of particle swarm optimization (PSO), which is a biologically inspired direct search method, to find real-time optimal energy management solutions for a stand-alone hybrid wind-microturbine (MT) energy system, is presented. Results demonstrate that the proposed PSO-based energy management algorithm can solve an extensive solution space while incorporating many objectives such as: minimizing the cost of generated electricity, maximizing MT operational efficiency, and reducing environmental emissions. Actual wind and end-use load data were used for simulati on studies and the well-established sequential quadratic programming optimization technique was used to validate the results obtained from PSO. Promising simulation results indicate the suitability of PSO for real-time energy management of hybrid energy systems.

Ownership cost calculations for distributed energy resources using uncertainty and risk analysis

S. Ali Pourmousavi, Mahdi Behrang-Rad, Ali Jahanbani Ardakani, and M. Hashem Nehrir
Conference Paper Publicaly available on ArXiv, September 2017


Ownership cost calculation plays an important role in optimal operation of distributed energy resources (DERs) and microgrids (MGs) in the future power system, known as smart grid. In this paper, a general framework for ownership cost calculation is proposed using uncertainty and risk analyses. Four ownership cost calculation approaches are introduced and compared based on their associated risk values. Finally, the best method is chosen based on a series of simulation results, performed for a typical diesel generator (DiG). Although simulation results are given for a DiG (as commonly used in MGs), the proposed approaches can be applied to other MG components, such as batteries, with slight modifications, as presented in this paper. The analyses and proposed approaches can be useful in MG optimal design, optimal power flow, and market-based operation of the smart grid for accurate operational cost calculations.

A Two-Layer Incentive-Based Controller for Aggregating BTM Energy Storage Devices

M. Parandehgheibi, S. Ali Pourmousavi, Kiyoshi Nakayama, and Ratnesh K. Sharma
Conference Paper In Proc. of the IEEE PES General meeting, Chicago, USA, July 16-20, 2017


In this paper, a two-layer controller is proposed to aggregate a fleet of behind-the-meter (BTM) energy storage devices based on the Transactive Energy (TE) concept. In the proposed model, aggregator offers an incentive to consumers to purchase power from and/or sell the excess power back to the grid. To do so, controller at the aggregator’s side determines optimal incentive which has to be offered to consumers by maximizing its own profit. Then, local controller at the consumer’s location optimizes battery operation by calculating purchased/sold power from/to the grid based on the local demand, PV generation, retail time-of-use (ToU) prices and demand charge, and the incentive offered by the aggregator to maximize its own profit. Different optimization problems are formulated in the two layers, and the profit of aggregator and consumers in the day-ahead energy market under perfect and imperfect prediction scenarios are compared.

BSS sizing and economic benefit analysis in grid-scale application

Shankar Mohan, Ali Hooshmand, S. Ali Pourmousavi, and Ratnesh K. Sharma
Conference Paper In Proc. of the IEEE ISGT–North America Conference, Minneapolis, USA, 2016


Grid-scale energy storage systems are attracting more attention because of increased public-awareness and declining prices. However, there is still one question which needs to be answered: when utilization of Battery Storage System (BSS) is economical? To address this question, problems of simultaneously sizing BSSs and optimal power sharing –with an objective of decreasing daily operational cost– is investigated to assess the economic viability of BSSs. The assessment is carried out by specializing the problem formulation to mid-sized C&I customers associated with PG&E and by simulating scenarios that differ in the size of load, PV installation, cost of BSS and participation in Demand Response (DR). Simulation results indicate that, using price projections from DOE and Navigant, BSSs can be used to shift loads economically (savings of 10%) around the year 2019. Furthermore, the effective daily savings, when participating in DR programs, is noted to be independent of the load, and that participating in DR does not require a significantly up-sized BSS.

A novel algorithm to integrate battery cyclic and calendar agings within a single framework

S. Ali Pourmousavi, Babak Asghari, and Ratnesh Sharma
Conference Paper In Proc. of the IEEE ISGT–North America Conference, Minneapolis, USA, 2016


Cyclic and Calendar agings are the two primary sources of degradation in a battery. An accurate battery degradation model can only be achieved when both processes are considered. In this paper, a novel framework is proposed to integrate Cyclic and Calendar aging processes. The proposed framework is able to accommodate different individual Cyclic and Calendar aging models only with slight modifications. It also can work conveniently as a universal degradation framework in different applications, such as large-scale battery storage systems in microgrids (MGs) and electric vehicles (EVs).

LFC model for multi-area power systems considering dynamic demand response

S. Ali Pourmousavi, Mahdi Behrangrad, M. Hashem Nehrir, and Ali Jahanbani Ardakani
Conference Paper In Proc. of the IEEE Transmission and Distribution Conference, Dallas, USA, 2016


Dynamic demand response (DR) is an integral part of ancillary services markets. The integration of dynamic DR control loop into the conventional load frequency control (LFC) model is presented by the authors in [1] . Extensive analytical analyses were carried out on single-area power system in previous study. In this paper, the idea is expanded to a general multi-area interconnected power system. Then, impacts of the proposed LFC-DR on the dynamic performance of the multi-area power systems in different conditions are simulated. Simulation results show a superior performance of the LFC-DR model for different conditions and power system models.

Optimal sizing and allocation of residential photovoltaic panels in a distribution network for ancillary services application

R. Ahmadi Kordkheili, S. Ali Pourmousavi, J.R. Pillai, H.M. Hasanien, B. Bak-Jensen, and M. Hashem Nehrir
Conference Paper In Proc. of the International Conference on Optimization of Electrical and Electronic Equipment (OPTIM 2014), Romania, Feb. 2014


Tremendous penetration of renewable energy in electric networks, despite its valuable opportunities, such as balancing reserve and ancillary service, has raised concerns for network operators. Such concern stems from grid operating conditions. Such huge penetration can lead to violation in the grid requirements, such as voltage and current limits. This paper proposes an optimization method for determining the number of photovoltaic (PV) panels together with their arrangement in the grid in order to maximize ancillary service, without violating grid operation limits. The proposed optimization method is based on genetic algorithm. To do so, single-objective and multi-objective optimization have been considered. The proposed method is implemented on a model of a part of a Danish distribution grid to verify its effectiveness. The simulation results prove the viability of the method, while keeping the grid requirements within standard operating limits.

The application of demand response for frequency regulation in an islanded microgrid with high penetration of renewable generation

K. Marchese, S. Ali Pourmousavi, and M. Hashem Nehrir
Conference Paper In Proc. of the 2013 North American Power Symposium, Kansas city, USA, 2013, pp. 1-6


Increasing the penetration of variable generation sources, such as wind and solar, potentially threatens the stability of the power systems. Past studies have shown that 15-20% renewable penetration is the largest amount the power systems can handle using conventional control. To overcome the challenge that variable renewable generation presents, this paper proposes that real-time demand response (DR) be used for ancillary services (AS). The impact of varying the amount of DR on the performance of a microgrid-configured distribution feeder is evaluated in this study. Simulation results have shown that a proper amount of DR resources can help to achieve higher penetrations of renewable generation while maintaining the desired system frequency.

Impact of high penetration of PV generation on frequency and voltage in a distribution feeder

S. Ali Pourmousavi, A.S. Cifala, and M.H. Nehrir
Conference Paper In Proc. of the 2012 North American Power Symposium,Urbana-Champaign, USA, 2012, pp. 1-8


This paper presents an evaluation of the impact of various levels of photovoltaic (PV) power penetration in a distribution feeder connected to a simplified grid model (SGM). PV generation is implemented in second-by-second iterations with power output based on actual solar radiation and air temperature data. High penetration levels of intermittent PV generation (15% and 30%) are employed in a feeder-configured microgrid to evaluate grid frequency and voltage characteristics. In this study, only governor droop control is included in the proposed SGM without the secondary control action (known as load frequency control). Two different grid models (fast and slow grid), PV generation configurations (concentrated and distributed), and PV penetration levels (15% and 30%) are considered in the simulation studies. Simulation results indicate the impact of the aforementioned parameters on the system frequency and voltage. Results also reveal that distributed PVs in a wide geographical area with different weather regime have less impact on the frequency and voltage.

Technology selection and unit sizing for a combined heat and power microgrid: comparison of DER-CAM and HOMER application programs

A.J. Litchy, C. Young, S. Ali Pourmousavi, and M.H. Nehrir
Conference Paper In Proc. of the 2012 North American Power Symposium,Urbana-Champaign, USA, 2012, pp. 1-8


The purpose of this paper is to design an optimal CHP islanded microgrid, through technology selection and unit sizing software, to be used for further research on real-time energy management. Two software packages, HOMER and WebOpt, originally developed at the National Renewable Energy Laboratory (NREL) and Lawrence Berkley Laboratory (LBL), respectively, are utilized. Using these programs, different cases are created and compared to justify the selected technologies and their respective prices. The final microgrid design contains renewable and alternative energy generation, hydrogen as an energy carrier, and electric storage.

Real-time optimal demand response for frequency regulation in smart microgrid environment

S. Ali Pourmousavi and M.H. Nehrir
Conference Paper In Proc. of the 11th IASTED PES (EuroPES2012), Naples, Italy, 2012


Real-time demand response (DR) in smart μgrid has been shown to be an effective tool for frequency regulation with increased penetration of renewable energy resources into the grid. Since DR is recognized as an incentive or direct payment to the participants, it is consequently desired to minimize the cost of DR for the utility. This paper presents an optimal DR strategy for minimizing the cost of DR for the utility in smart grid era. The economic model developed by Pennsylvania/New Jersey/Maryland (PJM) utility in the USA is used on an IEEE 13-bus standard system. Simulation results verify the effectiveness of the proposed approach to minimize the cost of DR for the utility. It is also shown that the DR, with or without optimization, decreases the overall cost of frequency regulation for the utility compared to the conventional spinning reserve, without sacrificing system stability.

A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy

S. Ali Pourmousavi, Ratnesh K. Sharma, and Babak Asghari
Conference Paper In Proc. of the 3rd IEEE PES Conference on Innovative Smart Grid Technologies (ISGT 2012), Washington D.C., 2012, pp. 1-8


Because of different technical and economical concerns, battery is happened to be an inevitable part of a microgrid as well as the most expensive component. This fact brings up the necessity of a real-time power management to guarantee the maximum possible battery lifetime based on the final cost of energy. In this way, this study attempts to present a real-time management framework for a grid-tied microgrid based on battery life and cost estimation. In order to verify the effectiveness of the proposed framework, a grid-tied commercial microgrid, which is equipped with wind turbine, PV solar panels and Li-Ion battery package, is optimally sized by HOMER and dynamic models of different components have been developed in MATLAB/Simulink. Then, simulation study has been carried out for a year on the system. All data such as load demand, wind, temperature, solar radiation, and time-based electricity tariff are grasped from different places for a year. Results show that the proposed framework effectively extends the battery lifetime while slightly decreases the cost of energy for customer.

Providing ancillary services through demand response with minimum load manipulation

S. Ali Pourmousavi, M.H. Nehrir, and C. Sastry
Conference Paper In Proc. of the IEEE 43rd North American Power Symposium (NAPS 2011), Boston, MA, 2011, pp. 1-6


This paper presents a demand response (DR) algorithm for regulating system frequency using responsive customer loads, while minimizing the amount of manipulated loads. The dynamic model for a small islanded microgrid and an improved hill climbing controller are developed in MATLAB/Simulink to show the proof of concept. Simulation results show that the improved DR control strategy provides frequency and voltage regulation while minimizing the amount of manipulated responsive loads. As a result, customer quality-of-service (QoS) is not compromised, while a higher percentage of responsive loads (more non-spinning reserve) would be available for additional control for responding to unexpected disturbances.

Demand response for smart microgrid: initial results

S. Ali Pourmousavi and M.H. Nehrir
Conference Paper In Proc. of the IEEE PES Conference on Innovative Smart Grid Technologies (ISGT 2011), Anaheim, CA, 2011, pp. 1-6


This study is an attempt to address the frequency and voltage regulation inside of an islanded microgrid. Central demand response along with an adaptive hill climbing methodology is applied to a small islanded microgrid powered by a diesel generator. All dynamic models are developed in MATLAB/Simulink. Simulation results show that the proposed method has the potential to suppress the frequency variations and stabilize the voltage of the microgrid.

Towards real-time microgrid power management using computational intelligence methods

C.M. Colson, M.H. Nehrir, and S. Ali Pourmousavi
Conference Paper In Proc. of the IEEE PES General Meeting, Minneapolis, MN, 2010, pp. 1-8


Microgrids are an emerging technology which promises to achieve many simultaneous goals for power system stakeholders, from generator to consumer. The microgrid framework offers a means to capitalize on diverse energy sources in a decentralized way, while reducing the burden on the utility grid by generating power close to the consumer. As a critical component to enabling power system diversity and flexibility, microgrids encompass distributed generators and load centers with the capability of operating islanded from or interconnected to the macrogrid. To make microgrids viable, new and innovative techniques are required for managing microgrid operations given its multi-objective, multi-constraint decision environment. In this article, two example computational intelligence methods, particle swarm optimization (PSO) and ant colony optimization (ACO), for application to the microgrid power management problem are introduced. A mathematical framework for multi-objective optimization is presented, as well as a discussion of the advantages of intelligent methods over traditional computational techniques for optimization. Finally, a three-generator microgrid with an ACO-based power management algorithm is demonstrated and results are shown.

A new ANN-based methodology for very short term wind speed prediction using Markov chain approach

S. Ali Pourmousavi and G.H. Riahy
Conference Paper In Proc. of the Electrical Power & Energy Conference 2008 (EPEC 2008), October 6-7, Vancouver, BC, Canada, 2008


Since 2000, the increase of the installed wind energy capacity all over the world (mainly in Europe and United States) attracted the attention of electricity companies, wind farm promoters and researchers towards the short term prediction, mainly motivated by the necessity of integration into the grid of an increasing 'unknown' (fluctuating) amount of wind power. Besides, in a deregulated system, the ability to trade efficiently, make the best use of transmission line capability and address concerns with system frequency, accurate very short-term forecasts are motivated more than ever. In this study, very short term wind speed forecasting is developed utilizing Artificial Neural Networks (ANN) in conjunction with Markov chain approach. Artificial neural networks predict short term values and the results are modified according to the long term patterns due to applying Markov chain. For verification purposes, the integrated proposed method is compared with ANN. The results show the effectiveness of the integrated method.

Very short-term wind speed prediction using linear regression among ANN and Markov chain

S. Ali Pourmousavi, S.M. Mousavi, A. Kashefi Kaviani, and G.H. Riahy
Conference Paper In Proc. of the International Conference on Power System Analysis, Control and Optimization (PSACO-2008), March 13-15, India, 2008


The growing revolution in wind energy encourages for more accurate models for wind speed forecasting. In this study, a new integrated approach, which contains ANN, Markov chains and linear regression, is used due to very short-term prediction of wind speed. In this method, First ANN is used for primary prediction of wind speed. Then, second-order Markov chain is applied to calculate transition probability matrix for predicted wind speed in the first step. Finally, a linear regression among ANN primary prediction and calculated probability with Markov chain is used for the final prediction .The results of proposed method in comparison with the ANN results shows lower error of wind speed prediction particularly in the case of higher prediction horizons .The results are based on real wind speed data in an area of Denmark with 2.5 second resolution.

A new integrated approach for very short-term wind speed prediction using wavelet networks and PSO

E. Safavieh, A. Jahanbani Ardakani, A. Kashefi Kaviani,S. Ali Pourmousavi, S. H. Hosseinian, and M. Abedi
Conference Paper In Proc. of the International Conference on Power System (ICPS2007), Dec. 12-14, India 2007


Very short term wind speed forecasting is necessary for wind turbine control system. In this study, a new integrated approach using Wavelet-based networks and PSO is proposed for very short term wind speed forecasting. PSO algorithm is used for training a Wavelet networks and the whole integrated approach is applied for wind speed prediction. As a case study, the wind speed data from a site in Denmark with 2.5 s measured resolution is used for training and test of the network. Proposed approach is compared to multi layer perceptron networks with Back Propagation training algorithm. Results show that the new approach improve Mean Absolute Percentage Error (MAPE) and Maximum error of prediction.

Siting and sizing of distributed generation for loss reduction

A. Jahanbani Ardakani, A. Kashefi Kaviani,S. Ali Pourmousavi, S. H. Hosseinian, and M. Abedi
Conference Paper In Proc. of the International Conference on Power System (ICPS2007), Dec. 12-14, India 2007


The introduction of distributed generation (DG) onto distribution networks has a significant effect on losses. This effect cannot be characterized as a detrimental or beneficial but is dependent on the allocation of DG on each distribution network. This paper proposes a new method to calculate the optimal size and to identify the corresponding optimal location for DG placement (allocation) for minimizing the total power losses in distribution networks. The proposed (presented) algorithm is an evolutionary algorithm named Particle Swarm Optimization (PSO). The method is implemented and tested on a sample distribution network. The results show the importance of placement of DGs for minimizing losses.

Short-term wind speed prediction using MLP Neural Networks trained by PSO algorithm in wind turbine applications

A. Kashefi Kaviani,S. Ali Pourmousavi, A. Jahanbani Ardakani, and G. H. Riahy
Conference Paper In Proc. of the 22nd International Power System Conference (PSC’07), Nov. 19-21, Tehran, Iran 2007 (IN FARSI)


In this paper a new method for wind prediction in wind turbine application is proposed. Considering increase in share of electric power generation from wind across the world and random variations in wind, it is so vital to predict wind speed in different ranges. In this study, a multilayer perceptron artificial neural network trained by Particle Swarm Optimization (PSO) is used for wind speed prediction. Aperiodic and stochastic structure of wind causes those conventional training methods which use gradient tools could not train the network properly. In the other hand, the objective of training a neural network is finding weights and biases so that minimizes the training error. Hence, we can approach problem of training neural networks as an optimization problem. Since wind prediction using ANN is a sophisticated and nonlinear function, use of optimization methods is considered. The proposed method is applied on filtered and real wind data. Filtering ignores improper frequencies in wind frequency spectrum that will be eliminated in wind turbine blades.

Annual electricity demand prediction for Iranian agriculture sector using ANN and PSO

S. Ali Pourmousavi and Nima Farrokhzad Ershad
Conference Paper In Proc. of the IEEE Congress on Renewable and Alternative Energy Resources (EPC’07), Oct. 25-26, Montreal, Canada 2007


In this study, we used PSO algorithm and ANN to predict annual electricity consumption in Iranian agriculture sector. The economic indicators used in this paper are price, value added, number of customers and consumption in the previous periods. To predict the future values, a linearlogarithmic model of electrical energy demand is considered. The PSO algorithm applied in this study has been tuned for all its parameters and the best coefficients with minimum error are identified, while all parameter values are tested concurrently. Consumption in the previous periods has been used for testing estimated model. The estimation errors of PSO algorithm are less than that of estimated by genetic algorithm and regression method. In addition, ANN is used to forecast each independent variable and then electricity consumption is forecasted up to year 2010. Electricity consumption in Iranian agriculture sector from 1981 to 2005 is considered as the case for this study.

Multi-layer Artificial Neural Networks’ training using PSO algorithm

A. Kashefi Kaviani, S. Ali Pourmousavi and A. Jahanbani Ardakani
Conference PaperIn Proc. of the 1st Joint Congress on fuzzy and Intelligent Systems (FIS’07), Aug. 30-31, Mashhad, Iran 2007 (IN FARSI)


The main object of training of artificial neural networks is founding weights and biases so that minimize training error. Hence we can approach problem of ANN training as an optimization problem. Conventional methods for ANN training uses back propagation algorithm and other gradient algorithms. When the target function is severely nonlinear and sophisticated, the conventional methods has a lot of weak points. Using PSO algorithm in ANN training and comparing with back propagation algorithm shows that in sophisticated problems, the new algorithm has higher performance. At the end, new algorithm is applied on two problems and results of comparison with back propagation algorithm is presented.

Dynamic modeling and simulation of a PEMFC for DG Applications

Nima Farrokhzad Ershad and S. Ali Pourmousavi
Conference PaperIn Proc. of the 6th Iranian Energy Symposium, Jul. 23-25, Tehran, Iran 2007 (IN FARSI)


This paper describes dynamic modeling and simulation results of a Hybrid Wind-PEM fuel cell System. The system consists of a proton exchange membrane fuel cell (PEMFC),a Wind turbine, ultracapacitors, an electrolyzer, and a power converter. The output fluctuation of the load voltage due to load variation is reduced using a fuel cell stack. At mid-night, when the amount of demand is low, excess energy form Wind Turbine is converted to hydrogen using an electrolyzer for later use in the fuel cell. Ultracapacitors and a power converter unit are proposed to minimize voltage fluctuations in the system and generate AC voltage. Dynamic modeling of various components of this small isolated system is presented. Dynamic aspects of temperature variation and double layer capacitance of the fuel cell are also included. PID type controllers are used to control the fuel cell system. MATLAB/SIMULINK is used for the simulation of this highly nonlinear energy system. System dynamics are studied to determine the voltage variation throughout the system. Transient responses of the system to step changes in the load current are presented. Analysis of simulation results and limitations of the fuel cell energy system are discussed. The voltage variation at the output was found to be within the acceptable range. The proposed system does not need conventional battery storage. It may be used for off-grid power generation in remote communities.