This paper addresses two issues that concern the electricity market participants under the European day-ahead market(DAM)framework,namely the feasibility of the attained schedules and the non-confiscation of cleared v...This paper addresses two issues that concern the electricity market participants under the European day-ahead market(DAM)framework,namely the feasibility of the attained schedules and the non-confiscation of cleared volumes.To address the first issue,new resource-specific orders,i.e.,thermal orders for thermal generating units,demand response orders for load responsive resources,and energy limited orders for storage resources,are proposed and incorporated in the existing European DAM clearing problem.To address the second issue,two approaches which lead to a non-confiscatory market are analyzed:①discriminatory pricing with side-payments(U.S.paradigm);and②non-discriminatory pricing excluding out-ofmoney orders(European paradigm).A comparison is performed between the two approaches to investigate the most appropriate pricing rule in terms of social welfare,derived revenues for the sellers,and efficiency of the attained results.The proposed model with new resource-specific products is evaluated in a European test system,achieving robust solutions.The feasibility of the attained schedules is demonstrated when using resource-specific orders compared with block orders.Finally,the results indicate the supremacy of discriminatory pricing with side-payments compared with the current European pricing rule.展开更多
Being capable of aggregating multiple energy resources, the energy service company(ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitating the consumption of renewabl...Being capable of aggregating multiple energy resources, the energy service company(ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitating the consumption of renewable resources in the electricity market. Considering the uncertain variables in day-ahead(DA) market trading, an ESCO can hardly determine their accurate probability distribution functions. Traditional interval optimization methods are used to process these uncertain variables without specific probability distribution functions.However, the lower and upper bounds of the intervals may change due to extreme weather conditions and other emergent events. Hence, a dual interval optimization based trading strategy(DIOTS) for ESCO in a DA market with bilateral contracts(BCs) is proposed. First, we transfer the dual interval optimization model into a simple model consisting of several interval optimization models. Then, a pessimistic preference ordering method is applied to solve the derived model. Case studies illustrating an actual test system corroborate the validity and the robustness of the proposed model, and also reveal that ECSO is critical in improving power system flexibility and facilitating the ability of absorbing renewable resources.展开更多
In this paper,the short-,medium-,and long-term effects of the COVID-19 pandemic on the Italian power system,particularly electricity consumption behavior and electricity market prices,are investigated by defining vari...In this paper,the short-,medium-,and long-term effects of the COVID-19 pandemic on the Italian power system,particularly electricity consumption behavior and electricity market prices,are investigated by defining various metrics.The investigation reveals that COVID-19 lockdown caused a drop in load consumption and,consequently,a decrement in day-ahead market prices and an increase in ancillary service prices.展开更多
In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each...In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.展开更多
文摘This paper addresses two issues that concern the electricity market participants under the European day-ahead market(DAM)framework,namely the feasibility of the attained schedules and the non-confiscation of cleared volumes.To address the first issue,new resource-specific orders,i.e.,thermal orders for thermal generating units,demand response orders for load responsive resources,and energy limited orders for storage resources,are proposed and incorporated in the existing European DAM clearing problem.To address the second issue,two approaches which lead to a non-confiscatory market are analyzed:①discriminatory pricing with side-payments(U.S.paradigm);and②non-discriminatory pricing excluding out-ofmoney orders(European paradigm).A comparison is performed between the two approaches to investigate the most appropriate pricing rule in terms of social welfare,derived revenues for the sellers,and efficiency of the attained results.The proposed model with new resource-specific products is evaluated in a European test system,achieving robust solutions.The feasibility of the attained schedules is demonstrated when using resource-specific orders compared with block orders.Finally,the results indicate the supremacy of discriminatory pricing with side-payments compared with the current European pricing rule.
基金jointly supported by the National Key R&D Program of China(No.2018YFB0905200)State Grid Henan Economic Research Institute(No.52170018000S)。
文摘Being capable of aggregating multiple energy resources, the energy service company(ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitating the consumption of renewable resources in the electricity market. Considering the uncertain variables in day-ahead(DA) market trading, an ESCO can hardly determine their accurate probability distribution functions. Traditional interval optimization methods are used to process these uncertain variables without specific probability distribution functions.However, the lower and upper bounds of the intervals may change due to extreme weather conditions and other emergent events. Hence, a dual interval optimization based trading strategy(DIOTS) for ESCO in a DA market with bilateral contracts(BCs) is proposed. First, we transfer the dual interval optimization model into a simple model consisting of several interval optimization models. Then, a pessimistic preference ordering method is applied to solve the derived model. Case studies illustrating an actual test system corroborate the validity and the robustness of the proposed model, and also reveal that ECSO is critical in improving power system flexibility and facilitating the ability of absorbing renewable resources.
文摘In this paper,the short-,medium-,and long-term effects of the COVID-19 pandemic on the Italian power system,particularly electricity consumption behavior and electricity market prices,are investigated by defining various metrics.The investigation reveals that COVID-19 lockdown caused a drop in load consumption and,consequently,a decrement in day-ahead market prices and an increase in ancillary service prices.
基金This work was supported in part by the US Department of Energy(DOE),Office of Electricity and Office of Energy Efficiency and Renewable Energy under contract DE-AC05-00OR22725in part by CURENT,an Engineering Research Center funded by US National Science Foundation(NSF)and DOE under NSF award EEC-1041877in part by NSF award ECCS-1809458.
文摘In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.
