Optimal Unit Commitment with Renewable Energy in Regulated and Deregulated Systems

In this paper, the impact of the wind power generation system on the total cost and profit of the system is studied by using the proposed procedure of binary Sine Cosine (BSC) optimization algorithm with optimal priority list (OPL) algorithm. As well, investigate the advantages of system transformation from a regulated system to a deregulated system and the difference in the objective functions of the two systems. The suggested procedure is carried out in two parallel algorithms;The goal of the first algorithm is to reduce the space of searching by using OPL, while the second algorithm adjusts BSC to get the optimal economic dispatch with minimum operation cost of the unit commitment (UCP) problem in the regulated system. But, in the deregulated system, the second algorithm adopts the BSC technique to find the optimal solution to the profit-based unit commitment problem (PBUCP), through the fast of researching the BSC technique. The proposed procedure is applied to IEEE 10-unit test system integrated with the wind generator system. While the second is an actual system in the Egyptian site at Hurghada. The results of this algorithm are compared with previous literature to illustrate the efficiency and capability of this algorithm. Based on the results obtained in the regulated system, the suggested procedure gives better results than the algorithm in previous literature, saves computational efforts, and increases the efficiency of the output power of each unit in the system and lowers the price of kWh. Besides, in the deregulated system the profit is high and the system is more reliable.

Binary glowworm swarm optimization for unit commitment

This paper proposes a new algorithm—binary glowworm swarm optimization(BGSO)to solve the unit commitment(UC)problem.After a certain quantity of initial feasible solutions is obtained by using the priority list and the decommitment of redundant unit,BGSO is applied to optimize the on/off state of the unit,and the Lambda-iteration method is adopted to solve the economic dispatch problem.In the iterative process,the solutions that do not satisfy all the constraints are adjusted by the correction method.Furthermore,different adjustment techniques such as conversion from cold start to hot start,decommitment of redundant unit,are adopted to avoid falling into local optimal solution and to keep the diversity of the feasible solutions.The proposed BGSO is tested on the power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantuminspired evolutionary algorithm(QEA),improved binary particle swarm optimization(IBPSO)and mixed integer programming(MIP).Simulated results distinctly show that BGSO is very competent in solving the UC problem in comparison to the previously reported algorithms.