Evolutionary algorithms for power generation planning with uncertain renewable energy. (1st October 2016) - Zaman, Forhad; Elsayed, Saber M.; Ray, Tapabrata; Sarker, Ruhul A.
Abstract: To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability. Highlights: A scenario-generation scheme is proposed for the uncertain wind speed and demand. Two solution approaches for the periodic wind-thermal DED problems are developed. A heuristic technique is proposed to handle the uncertainty of DED problem. Both deterministic and stochastic wind-thermal DED problems are solved. The performances of the proposed approaches are found superior than existing ones.