A Game Theory‐Based Coordination and Optimization Control Methodology for a Wind Power‐Generation Hybrid Energy Storage System

Abstract

The installation of an energy storage system to smooth the fluctuations of wind power output at a certain wind farm can improve the electric quality of wind power connected to the grid. In order to reduce the capacity of the energy storage system and the loss of the battery and make full use of the advantages of the super-capacitor, a game theory-based coordination and optimization control methodology for a wind power-generation and storage system (WPGSS) is presented in this paper. Aiming to maximize the WPGSS's overall profit, the methodology, taking the smoothing effect of the active power, the cost of the hybrid energy storage system (HESS), and the earnings of wind power connected to grid into consideration, builds a coordination and optimization control model based on the ensemble empirical mode decomposition (EEMD) algorithm combined with game theory. In the model, the low-pass filtering signal obtained by the EEMD is used to smooth the fluctuations of wind power output, and the band-pass filtering signal and high-pass filtering signal obtained by the EEMD are used to achieve energy distribution among the HESS. Cooperative game theory is introduced to determine the filter order of the EEMD according to the state of charge (SOC) of the HESS and to achieve the coordination and optimization control of the WPGSS taking the maximization of the WPGSS's overall profit as the game's goal constraint conditions. The genetic algorithm (GA) and particle swarm optimization (PSO) are adopted to solve the model's optimal solution, and the simulation tests were realized to verify the effectiveness of the proposed method, which can provide a theoretical basis for the coordination and optimization control of the WPGSS.

Dual-Regulating Feedback Optimization Control of Distributed Energy Storage System in Power Smoothing Scenariox Based on KF-MPC

“…The difference between the measured power and predicted power at a wind farm was decomposed by the Hilbert-Huang transform to realize the energy distribution of the HESS. In [7], the output of the wind power was smoothed and the energy of the HESS was distributed by adjusting the filtering orders of the empirical mode decomposition.…”

Managing a hybrid energy smart grid with a renewable energy source

“…Unfortunately, due to its nonlinear nature, this is not the case in the problem of managing an MG. In [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], the nonlinear optimization problem of MG management is solved by quadratic programming. Although these approaches are simple in their application, the accuracy and optimality of the solution are often questioned [24].…”

“…Some research on active/reactive power flow planning has been carried out in intelligent distribution systems with renewable sources . In these studies, the optimization is performed in two sequential stages: the first establishes the optimal distribution (minimizing the cost function) of the active power by assuming that the requested reactive power is null.…”

Robust Wind Speed Estimation and Control of Variable Speed Wind Turbines

“…Wind energy for electric power generation is an area of research interest, and nowadays, the emphasis is given to the cost-effective utilization of this energy aiming at quality and reliability in the electricity delivery [1,2]. Variable speed wind turbines (VSWT) are continuously increasing their market share, since it is possible to track the changes in wind speed by adapting shaft speed and thus maintaining optimal power generation [3][4][5].…”