Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty

A load shifting algorithm based on economic linear programming with model predictive control was developed to minimize the operating cost of a biomass combined heat and power based microgrid system. The model simultaneously manages supply and demand of both electrical and thermal energy as decision variables. An algorithm was developed to optimize the shifting of loads based on the renewable energy generation and time-of-use tariff. As an illustrative example, a case study was examined for a conceptual utility grid-connected microgrid application in Davis, California. For the assumptions used, the proposed load shifting algorithm improved the performance of the microgrid by changing the load pattern and reduced the operating cost by 6.06% and increased the renewable energy fraction by 6.34% compared with the conventional no-load shift case. Monte Carlo simulation was used to evaluate uncertainties among the renewable energy, demand side, and economic assumptions, generating a probability density function for the cost of energy.