Zhang, D; (2014) Optimal Design and Planning of Energy Microgrids. Doctoral thesis , UCL (University College London).

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Abstract

Microgrids are local energy providers which reduce energy expense and gas emissions by utilising distributed energy resources (DERs) and are considered to be promising alternatives to existing centralised systems. However, currently, problems exist concerning their design and utilisation. This thesis investigates the optimal design and planning of microgrids using mathematical programming methods. First, a fair economic settlement scheme is considered for the participants of a microgrid. A mathematical programming formulation is proposed involving the fair electricity transfer price and unit capacity selection based on the Game-theory Nash bargaining approach. The problem is first formulated as a mixed integer non-linear programming (MINLP) model, and is then reformulated as a mixed integer linear programming (MILP) model. Second, an MILP model is formulated for the optimal scheduling of energy consumption of smart homes. DER operation and electricity consumption tasks are scheduled based on real-time electricity pricing, electricity task time windows and forecasted renewable energy output. A peak charge scheme is also adopted to reduce the peak demand from the grid. Next, an MILP model is proposed to optimise the respective costs among multiple customers in a smart building. It is based on the minimisation/maximisation optimisation approach for the lexicographic minimax/maximin method, which guarantees a Pareto-optimal solution. Consequently each customer will pay a fair energy cost based on their respective energy consumption. Finally, optimum electric vehicle (EV) battery operation scheduling and its related degradation are addressed within smart homes. EV batteries can be used as electricity storage for domestic appliances and provide vehicle to grid (V2G) services. However, they increase the battery degradation and decrease the battery performance. Therefore the objective is to minimise the total electricity cost and degradation cost while maintaining the demand under the agreed threshold by scheduling the operation of EV batteries.

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