Research And Implementation Of A Power Market Simulation System Supporting Multi-objective Optimization

With the continuous development of smart power network technology in China,the traditional grid has undergone major changes in several aspects in the field of grid research,with changes mainly focusing on network structure,operational planning and schedule control.The traditional optimization model,which is mainly characterised by single-objective decision-making,is gradually becoming divided when it comes to solving the conflicts between the various stakeholders involved in balancing the grid.Therefore,game theory for multi-objective optimisation is increasingly used to solve problems in new types of grids.In this paper,we take the time-of-use tariff strategy as the basis,consider the customer demand response and satisfaction factors,and build a model for the overall revenue of all parties in the electricity market and the electricity sales side,seeking to balance the electricity sales side and maximise revenue.The responding to the national call for green energy saving to "cut peaks and fill valleys" to a feasible extent.The paper used a local optimisation particle swarm algorithm based on an adaptive variational strategy to solve a multi-objective optimisation problem that takes into account both revenue and resources.This paper aims to study the multi-objective optimisation problem in the electricity market as follows.1.Research and modelling of the multi-party game relationship in the electricity market.At present,the domestic electricity market subjects mainly include the electricity supply side(divided into power generation enterprises,power sales enterprises and grid enterprises)and the demand side.The generation side sells electricity to the sales side,and the price of electricity is negotiated independently.As the main intermediary in the electricity market,the electricity sales side sells electricity as its main business,by purchasing electricity from the generation side using little money while selling it to customers at a higher price.The electricity market is a major intermediary in the sale of electricity,purchasing electricity from the generation side at a low price and then selling it to customers at a higher price,earning a price difference.Grid companies mainly undertake the guaranteed power supply service for the area under their jurisdiction to ensure the basic power supply capacity,in the process,grid companies earn the corresponding transmission and distribution fees according to the total number of transmission points.The selling side,the generation side and the grid enterprises together constitute the offering side as defined in this paper.Taking into account the demand response and satisfaction of customers and other constraints,we seek a tariff strategy with optimal overall benefits for the offering side and construct a multi-party game model for the electricity market.2.Using the adaptive variation strategy-based local optimization particle swarm algorithm to solve the electricity market tariff strategy.The objective is to optimise the profitability of the electricity on sale and to maximise the utilisation of electricity resources in the electricity market,so as to regulate the pricing of electricity and to lead customers to waste electricity at the wrong time.In the process of solving the model,a particle swarm optimisation algorithm is implemented to find the optimal solution,using the unit tariff pricing as the horizontal and vertical coordinates of the solution,and the weighted sum of the overall revenue on the offer side and the difference between peak and valley hours as the fitness function,and solving for the global optimal solution that satisfies the needs of the model after several iterations.The 3D coordinates show the trajectory of the particles in space and the 2D coordinates show the solution process of the fitness function,which clearly and intuitively shows the degree of adaptation of the particle swarm algorithm to the application scenario.At the same time,through the adaptive variation strategy based on the local optimization particle swarm algorithm proposed in this paper to achieve the optimal solution of the revenue and resource balance tariff strategy,to a certain extent,to improve the particle swarm algorithm itself is easy to stuck in a local optimum state,convergence speed is slow resulting in poor performance of the algorithm and other problems,after the experiment found that the convergence effect has been greatly improved,the experimental effect is good.3.A cooperative game based on multi-party equilibrium electricity market trading tariff formulation.In this model,the upper layer is based on the non-cooperative game in the electricity market for the overall external optimisation of the electricity market.The lower layer of the model is based on a cooperative game model with complete information between the parties on the supply side for internal optimisation of the electricity supply side,using the kernel-based stable Shapley value method to allocate the benefits of cooperation between these three parties.The hierarchical model allows for a reasonable distribution of internal benefits within the supply side through the kernel-based stable Shapley value method,while balancing the supply side and the utilisation of power resources and satisfying the larger benefits on the offer side.