Optimized operation of energy storage systems of wind power based on demand response and cost model

Abstract: In recent years, the global energy shortage and environmental pollution become more and more serious. There is growing concern for the development of the power grid is facing more severe challenge. Therefore, it is important to research economic optimal operation problem of wind power/energy storage systems, and make the comprehensive economic benefits of power grid achieve the top level. At present, the trend in the global energy sector has turned to a new type of clean power source that is environmentally friendly. In China, while the scale of renewable energy development continues to expand, it has caused the problem of the mismatch between growth in power installation capacity and growth in demand-side electricity consumption. In addition, the renewable energy consumption market is in an immature stage of development, the export channel is not smooth, and there exist other factors causing that the renewable energy output is limited and the abandonment of wind/photovoltaic energy resource is serious. With the development and application of new power sources in the actual power grid, this also puts forward new requirements for the planning and operation of traditional power grids. This paper analyzes the structure of wind power/energy storage systems, and summarizes the key techniques of optimal operation which are demand-side response technology and energy storage technology. There are 2 steps in the establishment of models, and the details are as follow: First, a model of demand-side response is built, which depends on load reduction, load reduction climb rate and total reduction. The second model is the total principal balance model of wind power. In addition, the third one is the energy storage cost model for the life of energy storage equipment. It just ends up with a model which combines the second one and the third one-optimized operation model of electric distribution network for wind power/energy storage. It is constrained by power balance, transmission power between systems, and operation behavior for energy storage systems. In order to solve the established model, this paper mentions a differential evolution algorithm (DEA). After introducing the basic DEA, some steps are described in detail. The steps include the data initialization, DEA mutation operation, cross operation and selection operation. In this paper, an improved differential evolution algorithm (IDEA) is put forward. This greatly improves the economic benefit and the life of energy storage. The problem of early maturity may be solved by solving group transformation. The IDEA was used to solve the model and then the optimal scheduling strategy was developed. Finally, the electric distribution network for wind power/energy storage is simulated. In this study, IDEA was programmed by MATLAB and its results were compared with the basic DEA results. It is proved that IDEA is more beneficial than DEA to maintain population diversity and avoid convergence to local optimum effectively. To illustrate the advantage of IDEA, genetic algorithm (GA) was selected for comparison. The scheduling cost of IDEA scheme is less than that of GA scheme, so the IDEA scheme is better.