In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price diffe...In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage value.However,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage systems.As such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare.To incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices.Then we present an analytical SDP algorithm that does not require state discretization.Apart from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value.Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage.The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.Index Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.展开更多
为探索虚拟电厂(virtual power plant,VPP)兼顾经济性与低碳性的竞价策略,从VPP作为价格制定者的角度提出一种计及碳交易与风险的VPP参与电能量市场和备用市场的主从博弈竞价模型。以含风电、光伏的VPP为研究对象,首先,采用基准线法为VP...为探索虚拟电厂(virtual power plant,VPP)兼顾经济性与低碳性的竞价策略,从VPP作为价格制定者的角度提出一种计及碳交易与风险的VPP参与电能量市场和备用市场的主从博弈竞价模型。以含风电、光伏的VPP为研究对象,首先,采用基准线法为VPP无偿分配碳排放配额,建立VPP的碳交易模型;之后建立了基于主从博弈理论的双层竞价模型,上层领导者为参与碳、电、备用市场的VPP运营商,下层跟随者为电力市场运营商;同时,运用条件风险值(conditional value at risk,CVaR)将上层问题转化为计及风险的多目标优化问题;最后采用遗传算法和求解器联合求解。算例表明该模型可以在多市场的环境下提供经济、低碳的竞价策略及不同市场的出力计划,并分析了不同市场类型、碳交易的加入、不同风险厌恶系数对VPP竞价结果的影响,为提高VPP运营商收益提供了新思路。展开更多
基金supported in part by the Joint Funds of the National Natural Science Foundation of China(U2066214)in part by Shanghai Sailing Program(22YF1414500)in part by the Project(SKLD22KM19)funded by State Key Laboratory of Power System Operation and Control.
文摘In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage value.However,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage systems.As such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare.To incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices.Then we present an analytical SDP algorithm that does not require state discretization.Apart from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value.Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage.The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.Index Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.
文摘为探索虚拟电厂(virtual power plant,VPP)兼顾经济性与低碳性的竞价策略,从VPP作为价格制定者的角度提出一种计及碳交易与风险的VPP参与电能量市场和备用市场的主从博弈竞价模型。以含风电、光伏的VPP为研究对象,首先,采用基准线法为VPP无偿分配碳排放配额,建立VPP的碳交易模型;之后建立了基于主从博弈理论的双层竞价模型,上层领导者为参与碳、电、备用市场的VPP运营商,下层跟随者为电力市场运营商;同时,运用条件风险值(conditional value at risk,CVaR)将上层问题转化为计及风险的多目标优化问题;最后采用遗传算法和求解器联合求解。算例表明该模型可以在多市场的环境下提供经济、低碳的竞价策略及不同市场的出力计划,并分析了不同市场类型、碳交易的加入、不同风险厌恶系数对VPP竞价结果的影响,为提高VPP运营商收益提供了新思路。
