A virtual battery(VB)provides a succinct interface for aggregating distributed storage-like resources(SLR)to interact with a utility-level system.To overcome the drawbacks of existing VB models,including conservatism ...A virtual battery(VB)provides a succinct interface for aggregating distributed storage-like resources(SLR)to interact with a utility-level system.To overcome the drawbacks of existing VB models,including conservatism and neglecting network constraints,this paper optimizes the power and energy parameters of VB to enlarge its flexibility region.An optimal VB is identified by a robust optimization problem with decision-dependent uncertainty.An algorithm based on the Benders decomposition is developed to solve this problem.The proposed method yields the largest VB satisfying constraints of both network and SLRs.Case studies verify the superiority of the optimal VB in terms of security guarantee and less conservatism.展开更多
Demand-side flexibility is crucial to balancing supply and demand,as renewable energy sources are increasingly integrated into the energy mix,and heating and transport systems are becoming more and more electrified.Hi...Demand-side flexibility is crucial to balancing supply and demand,as renewable energy sources are increasingly integrated into the energy mix,and heating and transport systems are becoming more and more electrified.Historically,this balancing has been managed from the supply side.However,the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants,increasing the importance of demand response(DR)techniques to achieve the required flexibility.Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer,a task complicated by numerous influencing variables.Based on a top-down approach,this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads.We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads,subject to flexibility,which is simulated by a virtual battery model.The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions.The results demonstrate the model’s ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption.The model achieves a mean absolute percentage error(MAPE)lower than 17.0%,comparable to the accuracy without flexibility.The results obtained are compared with a direct data-driven approach,demonstrating the validity and effectiveness of our model.展开更多
To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging...To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.展开更多
基金supported by the Science and Technology Institute of China Three Gorges Corporation under Grant 202103386.
文摘A virtual battery(VB)provides a succinct interface for aggregating distributed storage-like resources(SLR)to interact with a utility-level system.To overcome the drawbacks of existing VB models,including conservatism and neglecting network constraints,this paper optimizes the power and energy parameters of VB to enlarge its flexibility region.An optimal VB is identified by a robust optimization problem with decision-dependent uncertainty.An algorithm based on the Benders decomposition is developed to solve this problem.The proposed method yields the largest VB satisfying constraints of both network and SLRs.Case studies verify the superiority of the optimal VB in terms of security guarantee and less conservatism.
基金the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No.957755,project SENDER:Sustainable consumer engagement and demand response.
文摘Demand-side flexibility is crucial to balancing supply and demand,as renewable energy sources are increasingly integrated into the energy mix,and heating and transport systems are becoming more and more electrified.Historically,this balancing has been managed from the supply side.However,the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants,increasing the importance of demand response(DR)techniques to achieve the required flexibility.Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer,a task complicated by numerous influencing variables.Based on a top-down approach,this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads.We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads,subject to flexibility,which is simulated by a virtual battery model.The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions.The results demonstrate the model’s ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption.The model achieves a mean absolute percentage error(MAPE)lower than 17.0%,comparable to the accuracy without flexibility.The results obtained are compared with a direct data-driven approach,demonstrating the validity and effectiveness of our model.
文摘To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.
