Investigations into microgrid sizing and energy management strategies

Abstract

The evolution of microgrids represents a significant step towards the transition to more sustainable power systems. Recent trends in microgrids include the integration of renewable energy resources (RERs), alternative energy resources (AERs) and energy storage systems (ESSs). However, the integration of these systems creates new challenges on microgrid operation because of their stochastic and intermittent nature. To mitigate these challenges, determining the appropriate size together with the best energy management strategy (EMS) systems are essential to ensure economic and optimal performance. This thesis presents an investigation into sizing and energy management of microgrids. In the first part of the thesis, an analytical and economic sizing (AES) approach is developed to find the optimal size of a grid-connected photovoltaicbattery energy storage system (PV-BESS). The proposed approach determines the optimal size based on the minimum levelised cost of energy (LCOE). Fundamental to this approach obtains an improved formula of LCOE which includes new parameters for reflecting the impact of surplus PV energy and the energy purchased from the grid. In the second part of this thesis, an integrated framework is proposed for finding the best size-EMS combination of a stand-alone hybrid energy system (HES). The HES consists of PV, BESS, diesel generator, fuel cell, electrolyser, and hydrogen tank. The proposed framework includes three consecutive steps; first, performing the AES to obtain the initial size of the HES, second, implementing the initial EMS using finite automata (FA) and instantiating multiple EMSs; and third, developing an evaluation model to assess the instantiated EMSs and extract the featured conditions to produce an improved EMS. Then the AES approach is re-exercised using the improved EMS to obtain the best size-EMS combination. The core of this framework is utilising FA to implement various EMSs and capturing the impact of selecting the best EMS on the sizing of the HES. Furthermore, a sensitivity analysis is performed to address the uncertainty in demand and solar radiation data showing their effect on the HES performance. The analysis is carried out by assuming variations in solar radiation and demand annual data. Several scenarios are generated from the sensitivity analysis, and a number of performance indices are computed for each scenario. Following that, a vii fuzzy logic controller is designed using the performance indices as fuzzy input sets. The objective of this controller is to modify the EMS obtained from the integrated framework. This can be accomplished by detecting any changes in the demand and solar radiation and accordingly modify the operating conditions of the diesel generator, fuel cell, and electrolyser. The performance of the proposed approaches is validated using real datasets for both demand and solar radiation. The results show the optimal size and EMS for both grid-connected and stand-alone microgrids. Moreover, the designed fuzzy logic controller enables the microgrid to mitigate the uncertainty in the demand and generation data. The proposed approaches can be used with various scales of microgrids to extract manifold benefits where reliability, environmental and cost requirements can not be tolerated.