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.
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 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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
To be included later ...
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.
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.
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.
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.
A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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).
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  . 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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.