A Joint Optimization Approach on Monthly Balancing Mechanism and Unit Maintenance Scheduling

Abstract

In the Chinese electricity market, the deviation between monthly actual electricity demand and generation plans has become particularly prominent amidst deepening power system reform. This study focuses on the uncertainties of wind power and photovoltaic power generation in the medium to long term electricity market. It also examines the impact of maintenance scheduling for conventional generation units on the implementation of medium to long-term generation scheduling plans. The article proposes a model for optimizing the joint generation and maintenance scheduling on a monthly basis. The model is based on deviation handling and employs upward and downward price incentive mechanisms. The aim is to minimize the cost of adjusting the deviation of monthly transaction contract quantities. To deal with the challenges posed by source-load uncertainties, the model utilizes fuzzy chance-constrained programming. The research findings confirm that the proposed model can significantly reduce system operating costs, curtailment of wind power and photovoltaic power, and enhance the market integration of renewable energy. Additionally, it can achieve uniformity in the completion rate of monthly contract quantities for conventional thermal power units, ensuring a balanced power system in terms of economy and security. The study presents an optimization method for executing contract quantities in the electricity market, contributing to improved efficiency in power resource allocation and reduced energy losses. The research enriches the theoretical foundation of power system scheduling and provides practical operational guidelines for power dispatch, making it significant for promoting sustainable development in the power industry and ensuring energy security.

Keywords

• Fuzzy chance-constrained programming
• balancing mechanism
• long-term transactions
• maintenance scheduling
• contract power