Abstract

The design of renewable-powered mining microgrids is an essential study for the shift towards electric and carbon-neutral operations within the mining sector. This paper presents a multi-objective two-stage optimisation framework for the planning of microgrids in remote mine sites. The first-stage (strategic) problem aims to minimise the total net present cost (NPC), total greenhouse gas (GHG) emissions, renewable energy curtailment, and improve system reliability. In the second-stage (scheduling) problem, the energy scheduling of the microgrid’s energy sources is determined using a rule-based approach. The proposed framework provides optimal sizing for renewable energy sources, energy storage systems, and fossil-fuel backup generators to meet the microgrid’s electricity demand, demonstrating the feasibility and benefits of renewable-based microgrid deployment in mining operations. To solve this complex problem, several state-of-the-art multi-objective evolutionary algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II), the NSGA-III, the unified NSGA-III (U-NSGA-III), S-metric selection evolutionary multi-objective algorithm (SMSEMOA), and the adaptive geometry estimation multi-objective evolutionary algorithm (AGE-MOEA) and its newer version AGE-MOEA-II, are applied to efficiently explore the performance of these techniques in finding the best trade-off between competing objectives. The effectiveness of these algorithms is evaluated and compared in the context of a real-world mining case study. The simulation results demonstrate that SMS-EMOA and AGE-MOEA outperform other algorithms in terms of convergence and diversity, particularly for complex microgrid configurations. The study highlights the potential of renewable-based microgrids to significantly reduce emissions, with trade-offs in cost, depending on the inclusion of energy storage solutions.

Abstract

This paper presents a new frequency-constrained microgrid (MG) planning methodology for mining industry with a high penetration of renewable energy sources (RES). The proposed model is formulated as a multiobjective bi-level optimisation problem in which the upper-level (UL) problem aims to minimise the total net present cost (NPC) of the MG, mitigate the greenhouse gas (GHG) emissions, and improve system reliability. In the lower-level (LL) problem, the operation of the MG is simulated considering unit commitment, battery operation, and frequency stability constraints. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve the UL multi-objective optimisation problem, generating candidate solutions that define the MG’s energy source capacities. Each candidate solution is evaluated by solving the LL problem, formulated as a mixed-integer linear programming (MILP) problem and solved using Gurobi solver. This iterative process ensures accurate evaluation of operational costs and emissions while exploring trade-offs between objectives. The fuzzy decision-making method is then applied to the Pareto optimal solutions to obtain the final optimal plan. Through this model, optimal capacities of RES units, battery energy storage systems (BESSs), and back-up fossil fuel generators are obtained to meet the MG demand. Moreover, the proposed approach ensures that the frequency stability requirements, including rate of change of frequency (RoCoF) minimum/maximum frequency and steady-state frequency remain within acceptable thresholds after considering any imbalance events. Finally, the simulation study is conducted to validate the effectiveness of the proposed model using a real-world MG in a remote underground mine in Australia utilising historical load, expected RES generation, and commodity/capital price data. The results demonstrate that the proposed model effectively ensures compliance with frequency stability requirements—always maintaining frequency above 49.5 Hz and RoCoF below 0.5 Hz/s—while achieving a balanced trade-off between cost and emissions. Compared to the conventional approach, the proposed solution results in a slightly higher NPC (2.8%) and GHG emissions (4.5%), but significantly reduces RES curtailment (13%) and eliminates severe frequency deviations, which in the conventional case occurred 60% of the time with drops as low as 48.9 Hz and RoCoF as high as 0.875 Hz/s. This highlights the critical role of BESS in enhancing both economic performance and frequency stability in off-grid mining MGs.

Abstract

Existing battery sizing methods tend to oversimplify battery operation within their sizing frameworks by ignoring several practical aspects of operation. Such assumptions may lead to suboptimal battery capacity, resulting in significant financial losses in battery projects. In this study, we compared the most common existing battery sizing methods in the literature with a battery sizing model that incorporates more realistic battery operation, specifically using receding horizon operation, also known as model predictive control. This approach continuously updates battery decisions based on new data and forecasts, ensuring realistic operation over the sizing period. In our comprehensive simulation studies, we quantified the financial losses caused by the suboptimal capacities obtained by these models for a realistic case study related to community battery storage (CBS). We developed a case study by constructing a mathematical framework for CBS and local end users. Our analysis indicates that conventional sizing strategies can cause financial losses as much as 22% in a simulation study with 84-day out-of-sample data including 120 end users in real wholesale market scenarios in New South Wales, Australia.

Abstract

The practical and effective design of the battery management system (BMS) is crucial to achieving high performance, long service life, and safe operation of all battery types, including vanadium redox flow batteries (VRFBs). However, without having a comprehensive and practical battery management scheme design as the foundation to develop an industrial or commercial-scale BMS for VRFBs, various underlying factors that promote the deployment of VRFBs in many projects that incorporate renewable energy sources (RESs) for decarbonisation resulting in economic benefits cannot be accomplished. In this paper, an advanced VRFB-BMS scheme is proposed that achieves high performance in state of charge (SOC) estimation, hydraulic control and thermal management without requiring excessive computational resources. Rigorous validations of this proposed VRFB-BMS scheme are carried out based on a state-of-the-art zero-dimensional (0-D) model to demonstrate the performance of the proposed BMS scheme design including case studies that showed: (1) a 8.1 % increase in round-trip efficiency; (2) automatic capacity rebalancing and highly accurate half-system SOC estimation method with a mean absolute percentage error (MAPE) less than 1% when the system is severely imbalanced; and (3) the ability to conduct effective thermal management with the use of a heating, ventilation and air-conditioning system (HVAC). The studies also demonstrated the capability of integrating the BMS with the energy management system (EMS) to achieve specified objectives for the users. This can bring numerous economic and environmental benefits to satisfy the objectives of the decision-makers or the investors.

Abstract

Inaccurate modelling of battery energy storage systems (BESSs) leads to significant financial and technical challenges, undermining investment confidence in large-scale BESS projects and other applications and hindering global carbon reduction efforts. This paper underscores the critical need for precise battery modelling using a thorough evaluation of experimental data to illustrate the limitations of inaccurate battery models in remaining energy estimation. In addition, advanced simulation studies are conducted using actual residential data to demonstrate the negative consequences of power mismatch and economic returns using these inaccurate models. Key discoveries highlight how accurate battery models can improve the accuracy of techno-economic evaluation and mitigate investment risks. This is demonstrated using a novel and computationally tractable energy management system (EMS) architecture. Future research should focus on developing standardised modelling protocols and fostering collaboration among manufacturers, researchers, and operators to bridge existing knowledge gaps. By increasing public awareness about the significance of accurate battery modelling and promoting interdisciplinary cooperation, this work aims to drive improved decision-making and accelerate the adoption of reliable, efficient BESS operations in the global transition to sustainable energy systems.

Abstract

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.

Abstract

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).

Abstract

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%.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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

Abstract

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

Abstract

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.

Abstract

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.

Abstract

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

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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).

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

• 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.

Abstract

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.

Abstract

• 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.

• 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.

• 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.

• 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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.