This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity market in an energy management system. The price decision algorithm proposed in this paper derive...This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity market in an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity price while considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supply balance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method.展开更多
基金supported by the Core Research for Evolutional Science and Technology,Japan Science and Technology Agency(JST-CREST)
文摘This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity market in an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity price while considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supply balance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method.
