Abstract
In this paper, an optimization model is developed for co-ordinately scheduling electricity and heat production within a microgrid. The model achieves the minimization of total operating cost including electricity and fuel consumption with various operational constraints considered. Comparative research between the mathematical method of mixed-integer programming (MIP) and meta-heuristic technique of improved particle swarm optimization (IPSO) for solving this model is implemented. Simulation results for the scheduling problem with different sizes and different operational constraints show that the solution precision achieved by IPSO and MIP is very similar, IPSO is much less time-consuming than MIP for the large-scale scheduling problem when the non-linear constraints of power flow within the microgrid are considered and the situation is the opposite when power flow constraints are not considered.
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