Energy Reports (Dec 2023)
Demand response for frequency regulation with neural network load controller under high intermittency photovoltaic systems
Abstract
The rate of change of frequency (ROCOF) has become a key parameter to be monitored under high penetration of renewable energy. Any significant ROCOF should be mitigated immediately to avoid any shutdown of power plants and hence power interruptions to the customers. However, ROCOF is found to be significant and often in a region near to equatorial line because the power output of PV is highly intermittent due to a large number of passing clouds. Demand response (DR) is one of the most cost-effective means for mitigating any high ROCOF through controlling heating elements, refrigerators, and air-conditioners. However, most of the DR controllers use pre-determined conditions, also known as the condition-based approach, to switch the loads for reducing frequency changes. This approach may not be effective enough to respond to a new load condition. Furthermore, many DR controllers, including those accompanied by machine learning, are mainly developed to mitigate frequency changes with little focus on the mitigation of high ROCOF. Hence, an artificial neural network (ANN)-based controller is proposed in this article for frequency regulation with auto corrective efforts on high ROCOF under high intermittency of PV systems. The proposed controller is designed to manage several controllable loads on a grid emulator with PV systems at Universiti Tunku Abdul Rahman, Malaysia. It is shown that the proposed controller can reduce the frequency deviation by 23% and improve ROCOF by 19.7% as compared to that without any controller.
