Genetic Algorithm-Based Optimization of Regional Power Scheduling Problem and Provincial Electricity Market Bidding Mechanism

Electricity marketization is the inevitable trend of the power industry. This paper proposes a regional grid energy-saving generation scheduling model that takes into account the bidding in the power market, and designs a regional grid generation scheduling algorithm that adopts firstly provincial sorting and then inter-provincial summary replacement. The objective function is first defined as reducing the total power purchase cost of the system, and the constraints are determined. The bidding and trading process is designed, and the optimal offer parameters are obtained by using an improved genetic algorithm for optimization. Finally, a comparative analysis of energy savings, power purchase costs, and inter-provincial trading among different models is carried out using example calculations. Taking Central China Power Grid as an example, the whole region can save 4,305,175,000 tons of coal for the whole year under the energy-saving dispatch mode, followed by the concurrent mode, which can save 3,976,675,000 tons of coal. In the simulation experiment of hydropower short-term optimization example, the optimization solution can achieve higher expected returns and lower risk values by addressing the terminal reservoir capacity constraint simultaneously. The experiment fully proves that the energy-saving power generation scheduling model of regional power grid designed in this paper, taking into account the market bidding, has the functions of energy saving, reducing the cost of power purchase and optimizing the allocation of resources.