Development of a Load Scheduling Algorithm to aid in demand side management for an Industrial use case
Matende, Powell Abraham
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This report suggests novel Binary Particle swarm optimization algorithm to be used in Scheduling Algorithm to Aid in Demand Side Management for Industrial Consumers Information on the general background behind the concepts of Optimization and Demand Side Management and a comprehensive review of the literature on load scheduling algorithms. The problem of failure of load scheduling algorithms to find true optima and their long convergence times in doing so is discussed. In addition, the reasons why this project is important are also elaborately discussed. The literature concerning the project is then discussed in the next, previous work on the area the project is addressed to are analyzed as we try to identify the gaps in this work and broaden the understanding of our project. methodology of the report follows starting with information on the case study relevant to the optimization problem. We highlight the specific objectives and the techniques and physical and mental tools used to achieve each objective i.e., to collect data through an energy audit to feed into the algorithm, to develop the load scheduling algorithm to aid in Demand Side Management (DSM). With the aid of Python programming, we were able to simulate a Binary Particle Swarm Optimization (BPSO) for our case study and evaluation of performance of the load scheduling algorithm was by adopting a pre-built Genetic Algorithm (GA) in Excel Solver (ES) and running both algorithms and comparing results. Results were obtained from the project through a series of tests done on both the algorithms, these results were visualized and discussed in detail, During the discussion of results we noted that the BPSO algorithm had 21.256% reduction in cost, 28% reduction in peak demand and registered energy losses of about 24%, as compared to the GA’s 8.13% reduction in cost, 4.13% reduction in peak demand. From the results obtained it is concluded that the BPSO algorithm is superior in cost reduction, peak reduction and shorter convergence time. Some of the code used in the simulation for specific relevant functions is provided in the appendix.