With the adjustment of the energy structure and the rapid development of commercial complex buildings,building energy systems(BES)are playing an increasingly important role.To fully utilize smart building management t...With the adjustment of the energy structure and the rapid development of commercial complex buildings,building energy systems(BES)are playing an increasingly important role.To fully utilize smart building management techniques for coordinating and optimizing energy systems while limiting carbon emissions,this study proposes a smart building energy scheduling method based on distributionally robust optimization(DRO).First,a framework for day-ahead market interaction between the distribution grid(DG),buildings,and electric vehicles(EVs)is established.Based on the the price elasticity matrix principle,demand side management(DSM)technology is used to model the price-based demand response(PBDR)of building electricity load.Meanwhile,the thermal inertia and thermal load flexibility of the building heating system are utilized to leverage the energy storage capabilities of the heating system.Second,a Wasserstein DRO Stackelberg game model is constructed with the objective of maximizing the benefits for both buildings and EVs.This Wasserstein distributionally robust model is then transformed into a mixed-integer model by combining the Karush–Kuhn–Tucker(KKT)conditions and duality theory.Finally,the optimization effect of temperature load storage characteristics on BES flexible scheduling and the coordination of DRO indicators on the optimization results were verified through simulations.The strategy proposed in this article can reduce the total operating cost of BES by 26.37%,significantly enhancing economic efficiency and achieving electricity and heat substitution,resulting in a smoother load curve.This study provides a theoretical foundation and assurance for optimal daily energy scheduling of BES.展开更多
Price-based and incentive-based demand response(DR)are both recognized as promising solutions to address the increasing uncertainties of renewable energy sources(RES)in microgrids.However,since the temporally optimiza...Price-based and incentive-based demand response(DR)are both recognized as promising solutions to address the increasing uncertainties of renewable energy sources(RES)in microgrids.However,since the temporally optimization horizons of price-based and incentive-based DR are different,few existing methods consider their coordination.In this paper,a multi-agent deep reinforcement learning(MA-DRL)approach is proposed for the temporally coordinated DR in microgrids.The proposed method enhances micrigrid operation revenue by coordinating day-ahead price-based demand response(PBDR)and hourly direct load control(DLC).The operation at different time scales is decided by different DRL agents,and optimized by a multiagent deep deterministic policy gradient(MA-DDPG)using a shared critic to guide agents to attain a global objective.The effectiveness of the proposed approach is validated on a modified IEEE 33-bus distribution system and a modified heavily loaded 69-bus distribution system.展开更多
This paper reviews the state of the art of research and industry practice on demand response and the new methodology of transactive energy. Demand response programs incentivize consumers to align their demand with pow...This paper reviews the state of the art of research and industry practice on demand response and the new methodology of transactive energy. Demand response programs incentivize consumers to align their demand with power supply conditions, enhancing power system reliability and economic operation. The design of demand response programs, performance of pilot projects and programs, consumer behaviors, and barriers are discussed.Transactive energy is a variant and a generalized form of demand response in that it manages both the supply and demand sides. It is intended for a changing environment with an increasing number of distributed resources and intelligent devices. It utilizes the flexibility of various generation/load resources to maintain a dynamic balance of supply and demand. These distributed resources are controlled by their owners. However, the design of transaction mechanisms should align the individual behaviors with the interests of the entire system. Transactive energy features real-time, autonomous, and decentralized decision making.The transition from demand response to transactive energy is also discussed.展开更多
Since 2002,the People's Bank of China has frequently used both quantity-based direct monetary instruments and price-based indirect monetary instruments to promote economic growth and stabilize price level.Specific...Since 2002,the People's Bank of China has frequently used both quantity-based direct monetary instruments and price-based indirect monetary instruments to promote economic growth and stabilize price level.Specifically,this study estimates 13 three-variable factor-augmented vector autoregression (FAVAR) models to explore how two types of monetary instruments affect China's economy and price level.Overall,we find that monetary policy has positive effects on China's economy and price level.Second,this study clearly states that the effectiveness of China's monetary policy on the economy has depended on China's quantity-based direct monetary instruments since 2002.Third,the effectiveness of quantity-based direct monetary instruments on China's economy and price level is dependent on the significant and positive effects of quantity-based direct monetary instruments after the 2008 financial crisis.Fourth,the significant and positive effects of price-based indirect monetary instruments on China's economy and price level before 2008 cannot fundamentally change their current insignificant effects on China's economy and price level.展开更多
基金funded by the National Natural Science Foundation of China(52008014)the Fundamental Research Funds for the Central Universities(JKF-20240037)supported by the“111 Center”and Beihang World TOP University Cooperation Program.
文摘With the adjustment of the energy structure and the rapid development of commercial complex buildings,building energy systems(BES)are playing an increasingly important role.To fully utilize smart building management techniques for coordinating and optimizing energy systems while limiting carbon emissions,this study proposes a smart building energy scheduling method based on distributionally robust optimization(DRO).First,a framework for day-ahead market interaction between the distribution grid(DG),buildings,and electric vehicles(EVs)is established.Based on the the price elasticity matrix principle,demand side management(DSM)technology is used to model the price-based demand response(PBDR)of building electricity load.Meanwhile,the thermal inertia and thermal load flexibility of the building heating system are utilized to leverage the energy storage capabilities of the heating system.Second,a Wasserstein DRO Stackelberg game model is constructed with the objective of maximizing the benefits for both buildings and EVs.This Wasserstein distributionally robust model is then transformed into a mixed-integer model by combining the Karush–Kuhn–Tucker(KKT)conditions and duality theory.Finally,the optimization effect of temperature load storage characteristics on BES flexible scheduling and the coordination of DRO indicators on the optimization results were verified through simulations.The strategy proposed in this article can reduce the total operating cost of BES by 26.37%,significantly enhancing economic efficiency and achieving electricity and heat substitution,resulting in a smoother load curve.This study provides a theoretical foundation and assurance for optimal daily energy scheduling of BES.
基金supported in part by the Guangdong Provincial Key R&D Program under Grant no.2019B111109002。
文摘Price-based and incentive-based demand response(DR)are both recognized as promising solutions to address the increasing uncertainties of renewable energy sources(RES)in microgrids.However,since the temporally optimization horizons of price-based and incentive-based DR are different,few existing methods consider their coordination.In this paper,a multi-agent deep reinforcement learning(MA-DRL)approach is proposed for the temporally coordinated DR in microgrids.The proposed method enhances micrigrid operation revenue by coordinating day-ahead price-based demand response(PBDR)and hourly direct load control(DLC).The operation at different time scales is decided by different DRL agents,and optimized by a multiagent deep deterministic policy gradient(MA-DDPG)using a shared critic to guide agents to attain a global objective.The effectiveness of the proposed approach is validated on a modified IEEE 33-bus distribution system and a modified heavily loaded 69-bus distribution system.
基金This work is sponsored by Department of Commerce,State of Washington,and US Department of Energy,USA,through the Transactive Campus Energy Systems project,in collaboration with Pacific Northwest National Lab and University of Washington.
文摘This paper reviews the state of the art of research and industry practice on demand response and the new methodology of transactive energy. Demand response programs incentivize consumers to align their demand with power supply conditions, enhancing power system reliability and economic operation. The design of demand response programs, performance of pilot projects and programs, consumer behaviors, and barriers are discussed.Transactive energy is a variant and a generalized form of demand response in that it manages both the supply and demand sides. It is intended for a changing environment with an increasing number of distributed resources and intelligent devices. It utilizes the flexibility of various generation/load resources to maintain a dynamic balance of supply and demand. These distributed resources are controlled by their owners. However, the design of transaction mechanisms should align the individual behaviors with the interests of the entire system. Transactive energy features real-time, autonomous, and decentralized decision making.The transition from demand response to transactive energy is also discussed.
基金The anthors thank the support from Tianjin Philosophy and Social Science Planning Project(No.TJYYQN19-004)Project of National and Regional Research Center,Ministry of Education(No.ZX20170183)National Natural Science Foundation Youth Project(No.71803089).
文摘Since 2002,the People's Bank of China has frequently used both quantity-based direct monetary instruments and price-based indirect monetary instruments to promote economic growth and stabilize price level.Specifically,this study estimates 13 three-variable factor-augmented vector autoregression (FAVAR) models to explore how two types of monetary instruments affect China's economy and price level.Overall,we find that monetary policy has positive effects on China's economy and price level.Second,this study clearly states that the effectiveness of China's monetary policy on the economy has depended on China's quantity-based direct monetary instruments since 2002.Third,the effectiveness of quantity-based direct monetary instruments on China's economy and price level is dependent on the significant and positive effects of quantity-based direct monetary instruments after the 2008 financial crisis.Fourth,the significant and positive effects of price-based indirect monetary instruments on China's economy and price level before 2008 cannot fundamentally change their current insignificant effects on China's economy and price level.
