Electricity systems are key to transforming today’s society into a carbon-free economy.Long-term electricity market mechanisms,including auctions,support schemes,and other policy instruments,are critical in shaping t...Electricity systems are key to transforming today’s society into a carbon-free economy.Long-term electricity market mechanisms,including auctions,support schemes,and other policy instruments,are critical in shaping the electricity generation mix.In light of the need for more advanced tools to support policymakers and other stakeholders in designing,testing,and evaluating long-term markets,this work presents a multi-agent reinforcement learning model capable of capturing the key features of decarbonizing energy systems.Profitmaximizing generation companies make investment decisions in the wholesale electricity market,responding to system needs,competitive dynamics,and policy signals.The model employs independent proximal policy optimization,which was selected for suitability to the decentralized and competitive environment.Nevertheless,given the inherent challenges of independent learning in multi-agent settings,an extensive hyperparameter search ensures that decentralized training yields market outcomes consistent with competitive behavior.The model is applied to a stylized version of the Italian electricity system and tested under varying levels of competition,market designs,and policy scenarios.Results highlight the critical role of market design for decarbonizing the electricity sector and avoiding price volatility.The proposed framework allows assessing long-term electricity markets in which multiple policy and market mechanisms interact simultaneously,with market participants responding and adapting to decarbonization pathways.展开更多
基金the European Research Council,ERC grant agreement number 101044703(EUNICE)CUP D87G22000340006.European Union PNRR-Missione 4-Componente 2-Avviso 341 del 15/03/2022-Next Generation EU,in the framework of the project GRINS-Grow-ing Resilient,INclusive and Sustainable project(GRINS PE00000018-CUP C83C22000890001)European Union’s Horizon Europe programme under the Marie Skłodowska-Curie grant agreement number 101148367.
文摘Electricity systems are key to transforming today’s society into a carbon-free economy.Long-term electricity market mechanisms,including auctions,support schemes,and other policy instruments,are critical in shaping the electricity generation mix.In light of the need for more advanced tools to support policymakers and other stakeholders in designing,testing,and evaluating long-term markets,this work presents a multi-agent reinforcement learning model capable of capturing the key features of decarbonizing energy systems.Profitmaximizing generation companies make investment decisions in the wholesale electricity market,responding to system needs,competitive dynamics,and policy signals.The model employs independent proximal policy optimization,which was selected for suitability to the decentralized and competitive environment.Nevertheless,given the inherent challenges of independent learning in multi-agent settings,an extensive hyperparameter search ensures that decentralized training yields market outcomes consistent with competitive behavior.The model is applied to a stylized version of the Italian electricity system and tested under varying levels of competition,market designs,and policy scenarios.Results highlight the critical role of market design for decarbonizing the electricity sector and avoiding price volatility.The proposed framework allows assessing long-term electricity markets in which multiple policy and market mechanisms interact simultaneously,with market participants responding and adapting to decarbonization pathways.
