AUTHOR=Chen Liyuan , Ye Qingquan , Wu Xuguang , Chen Yundi , Wang Wanhuan 

TITLE=Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1348823

DOI=10.3389/fenrg.2024.1348823

ISSN=2296-598X

ABSTRACT=In order to improve the operating benefits of the distribution network (DN) and reduce the energy consumption costs of small-micro industrial parks (SMIP), a two-layer optimal electricity trading method for DN with SMIP is proposed. Firstly, based on the Stackelberg game, a multi-objective two-layer optimal trading model for DN and SMIP is established. In the upper layer, the DN agent is regarded as the leader, and a trading model is established with the goal of maximizing the profits of agents. In the lower layer, an energy optimization model is proposed for the SMIP operators, which are regarded as the followers, with the goal of minimizing the operating costs. According to the buying and selling electricity prices at the upper and lower layers, a dynamic pricing strategy is formulated. The karush kuhn tucker condition (KKT) is introduced to transform the two-layer model into a single-layer model, and based on linear transformations, the model is further converted into a mixed-integer linear programming model. The transformations aim to address the nonlinear issues arising from multivariable coupling between the upper and lower-layers trading models. The simulation results show that the trading strategy proposed in this paper can effectively increase the profit of DN, while reducing the operating costs of SMIP, and can provide reference for decisionmaking in the electricity market (EM) with the participation of SMIP.Distribution network ES Energy storage SMIP Small-micro industrial park DEG Diesel generator KKT Karush kuhn tucker condition PV Photovoltaic power EM Electricity market WT Wind power Formulas c sell t Electricity selling price of DN agent at time t c av Average electricity price for DN agent c buy t Electricity selling price of SMIP operators at time t T Time period of electricity sold by DN agent Δt Optimization time period K Time period of electricity bought by DN agent c tra t Trading price among DN agents at c DEG the cost coefficient for DEG Dual variable for the opportunity constraint at time t c sell,min Lower limits of the electricity selling price setting by DN agent