Microgrid energy management system based on artificial intelligence

Abstract

Microgrids provide the opportunity to combine various distributed energy resources to supply a local load independently from, or in parallel with the national grid. This allows the load to utilise renewable energy as much as possible while having the option to utilise energy storage or fossil fuel generators during times when renewable energy is not available. This provides robustness and quality of supply for the load. Due to the abundance of solar irradiation in Africa, the microgrid is seen as a solution to provide reliable, clean energy at affordable rates. The aim of this study is to develop an energy management strategy for an industrial, low-voltage microgrid in Johannesburg, South Africa. The main objective for the energy management strategy is to reduce the electricity costs of the facility. Several objectives are identified as performance measures through which the savings can be achieved. From literature, promising energy management strategies are identified. A simulation framework is developed in Simulink in order to simulate the operation of the energy management strategy. It is verified to be an accurate representation of how the actual system would work. An iterative design process is followed in order to develop, model and verify a truth-table based logic controller, as well as a fuzzy logic controller that both achieve the objectives set-out. An artificial neural network short term load forecasting approach is also investigated, which proved to be promising. The truth-table based logic controller is converted to PLC-code and implemented on the physical microgrid controller. Field data is collected which serves to validate that the objectives defined, did in fact result in the expected savings. Furthermore, the simulation framework in Simulink is utilised in conjunction with the fuzzy logic controller to investigate the effect that various sizing options of the distributed energy resources in the system might have on the cost savings. A more ideal configuration is also investigated, where the energy management system receives an additional input from the solar-PV power production, which is not available in the system studied. This study emphasizes the importance of simulating microgrid energy management algorithms and illustrates how the expected performance can be achieved through effective planning, design and simulation of an energy management algorithm. The study also illustrates the versatility of MATLAB and Simulink for microgrid-related work. This project shows that artificial intelligence techniques have promising potential when applied to microgrid energy management.