Optimisation of Renewable Energy Microgrid Systems for Developing Countries

Abstract

This research proposes a strategy for reducing the running costs of hybrid microgrids which include both renewable and conventional power generation. The developed system uses metaheuristic methods for microgrid optimisation enabling the planning, maintenance, and effective cost management of the system. The target application for the research is a typical Nigerian remote rural community, not connected to any form of centralised power supply with the dwellers of the community practising peasant farming.

The location for application of the proposed microgrid is first examined to determine renewable resources available, current power supply source, their behaviour and electricity consumption patterns, future plans for consumption, and willingness to purchase electricity if provided. In the absence of smart metering, energy use data are gathered through questionnaires and the bottom-up approach adopted for hourly time-step load demand profiles development. Using both end-use and econometric indices, a ten year load forecast is done with the fifth year forecast employed in design analysis. These forecast based on real world questionnaire will provide good resource in real world application.

The Hybrid Microgrid (HMG) system is designed using HOMER to cope with variability from both weather and unexpected changes in the load, and has photovoltaic panels, wind turbines, battery storage systems, and a diesel generator in its configuration. The research compares the effectiveness of three optimization strategies, the Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), and Simulated Annealing (SA) by tuning algorithm parameters to improve the speed and quality of solutions.

This is the first time its being used for developing country microgrids. The HMG optimisation objective is to minimise its operating costs by reducing the generator running hours. The optimisation is constrained by the requirement to meet the variable load demand at all times. The results showed PSO had the lowest diesel generator run hours, a 65.2% reduction in the diesel running hours is, achieved compared to HOMER simulations of the HMG. The adaptability of the system means that the operator can choose the optimisation strategy based on the required output.