Control of energy storage utilisation for a building integrated microgrid using multi-objective metaheuristic optimisation methods
Date of Award
Doctor of Philosophy
Process, Energy & Transport Engineering
Dr. Michael D. Murphy
Dr. Ted Scully
The aim of this thesis was to develop and analyse optimisation strategies for commercial buildings with integrated microgrids, in order to find optimal trade-offs between maximising profitability and facilitating renewable energy from the national power grid. The continued proliferation of microgrids, as well as the increase in electricity produced by renewable energy sources on the Irish national grid has necessitated the requirement for these strategies. Models to simulate the performance of a photovoltaic system, wind turbine and battery bank were developed and validated. The most suitable optimisation algorithm to generate an optimal charge/discharge rate schedule for a battery bank was selected and developed in order to minimise operating costs for building with an integrated photovoltaic system, wind turbine and battery bank. Furthermore, a comprehensive analysis was carried out using multi-objective optimization to investigate trade-offs between optimising the building operating costs while simultaneously facilitating high levels of wind generation the national power grid to reduce curtailment. The results showed that battery charge/discharge scheduling using multiple charge/discharge rates produced superior results (24% reduction in building operating costs) in comparison to a standard controller using a single charge/discharge rate. A Genetic Algorithm was chosen as the most suitable optimisation algorithm due to its superior optimization performance in comparison to other algorithms tested. The results demonstrated that the building operating costs decreased as the number of available charge and discharge rates was increased, with the most suitable number of potential charge/discharge rates being 12. Multi-objective XXII optimisation was then implemented with a priority weighting factor (α) being applied to the objectives of minimising electricity costs (building operating cost) whilst also maximizing the facilitation of wind generation on the grid. The trade-offs between the two objectives were then assessed for varying conditions. Upon evaluating 96 scenarios with varying weather conditions, building electricity demand, electricity pricing, microgrid output and wind penetration on the national grid. It was observed, that when α was 20% or higher (whereby the objective function was gradually weighted away from minimising costs and towards wind generation facilitation), the amount of extra wind energy facilitated from the grid was negligible while building operating costs continued to increase. Moreover, the results indicated that large gains in wind energy facilitation could be achieved for very small increases in building operating costs (€0.06 per 1% increase in wind energy facilitation), demonstrating the efficacy of the optimization strategy under all 96 scenarios. The analyses carried out in this thesis produced interesting and pertinent, results which could be used as a comprehensive means of optimising battery utilisation in microgrids to help facilitate increased wind penetration. The outputs of the thesis may be used to provide information to end users, electricity suppliers and government bodies to aid in cost saving and wind energy facilitation for commercial buildings.
Phan, Quang An, "Control of energy storage utilisation for a building integrated microgrid using multi-objective metaheuristic optimisation methods" (2020). Theses [online].
Available at: https://doi.org/10.34719/n9q1-av04
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