Intelligent energy management and demand-response control for a photovoltaic-battery system

The work shown in this thesis is to develop an intelligent energy management and demand-response controller (IEMDC) for a photovoltaic-battery (PVB) system with a programmable load. Specifically, the research work related to this issue focuses on the modeling and real-time simulation of a microgrid with an emphasis on photovoltaic, battery and programmable load and the development of an energy management controller for optimally dispatching the power from the PVB system to the loads. The IEMDC is used for daytime grid-independent operation. The energy management controller is based on fuzzy logic controller (FLC) whose membership functions are optimized using mean-variance optimization (MVO). The controller is implemented in real-time on a digital signal processor (DSP) interfaced to a real-time digital simulator (RTDS). The objective of the intelligent energy management and demand-response controller is threefold- first, to supply energy to meet the needs of the priority loads; secondly, to maintain a sufficient battery state of charge to meet the priority load demand in hours of short or no PV outputs; and thirdly, with the first and second objectives met, maximize supply to demand-responsive loads. Results are presented for different types of days (sunny day, moderate day and rainy day) during daytime. The perfom1ance of the IEMDC is compared against traditional PV-priority controller and shown to deliver better performance. More priority load is satisfied even in worse weather condition: a higher state of charge of battery is maintained and the duty cycle for charge and discharge is reduced; controllable load participated in demand-response can be maximally satisfied --Abstract, page iii