Short-term Dispatch Model Including Wind Park And Economic Risk Management

Comparing the traditional power generation, wind power has the advantage of no coal consumption and free pollution. However, because of intermittent and uncertainties of wind speed, power system will exist potential security and stability problems when large-capacity wind power integrating it. This article mainly studies the short-term diapatch model including wind park and economic risk management which quantifies the uncertainty of wind speed as the cost of wind power into a short-term scheduling model, and combines the intelligent optimization algorithms to find the optimal solution. According to the different factors considered, this article includes two different models as follows:(1) This article integrates the Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) theory into the dispatch model to establish the short-term dispatch model including wind park based on CVaR. The genetic optimization, adaptive algorithm and manual immunity theory are conbined to create Immune Adaptive Genetic Algorithms(IAGA) to find the optimal solution of the model.(2) Considering the impact of insufficiency and surplus of the wind power to diapatch strategy and its advantages of non-polluting, the reserve-penalty function, surplus-penalty function and pollution assessment of penalty function have been joined in the dispatch model to establish the short-term dispatch model considering efficiency of power plant. The chaos theory, manual immunity theory, particle swarm optimization and adaptive algorithm theory are integrated to create the Immune Chaotic Particle Swarm Optimization (ICPSO) to resolve the problem.Through the simulation system of ten generators and twenty-five generators in Shanghai, it can be seen that the advantage of no coal consumption and free pollution has been more and more obvious when the size of wind farm and simulation system increases. But the uncertainty impact of wind speed will also become larger. At the same time, the simulation also can be seen that the convergence speed and convergence accuracy of IAGA and ICPSO is superior to the traditional optimization method.