Bi-level Optimal Scheduling of Multi-source Combined System Considering Wind Power Output Uncertainty
For a multi-source integrated system incorporating wind,fire,and storage,the wind power output exhibits uncertainty.There is a discrepancy between the predicted and actual power of the wind turbine during specific time periods.When the actual output of the wind turbine fails to meet the scheduled power in the dispatch plan,it leads to a significant reduction in the economic efficiency of the system.To address this issue,this paper proposes a two-layer optimization strategy that considers wind power prediction errors and demand-side response.The upper-level model aims to minimize the overall operating cost of wind power,thermal power,and dispatchable loads,utilizing an Improved Particle Swarm Algorithm(IPSO)to formulate optimal scheduling strategies for thermal power and dispatchable loads.Subsequently,the Gibbs method is employed to sample the probability density function of the maximum output prediction error of the wind turbine,obtaining a certain amount of samples and determining the power deficit for each sample in the upper-level power sources.The lower-level model aims to minimize the overall operating cost of energy storage and interruptible loads.It employs linear programming to offset the power deficits from the upper-level sources,thereby formulating the lower-level model's power dispatch strategy.With a large number of sampled scenarios,the proposed two-layer optimization strategy's economic and effective nature is validated by comparing the expected value and variance of the total cost function values for each sample.
wind power prediction errordemand responseipsocollaborative optimizationgibbs sampling
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