Abstract
In recent years, residential rate consumptions have increased due to modern appliances which require a high level of electricity demands. Although mentioned appliances can improve the quality of consumers’ lives to a certain extent, they suffer from various shortcomings including raising the electricity bill as well as serious technical issues such as lack of balance between electricity generation and load disturbances. This imbalance can generally lead to the frequency excursion which is a significant concern, especially for low-inertia microgrids with unpredictable parameters. This research proposes an intelligent combination of two approaches in order to alleviate challenges related to the frequency control mechanism. Firstly, a learning-based fractional-order proportional-integral-derivative (FOPID) controller is trained by recurrent adaptive neuro-fuzzy inference (RANFIS) in the generation side during various operational conditions and climatic changes. In the following, a decentralized demand response (DR) programming in the load side is introduced to minimize consumption rate through controllable appliances and energy storage systems (ESSs). Furthermore, parameters uncertainties and time delay, which are generally known as two main concerns of isolated microgrids, are regarded in the frequency plan of a low-inertia microgrid including renewable energy sources (RESs), and energy storage systems (ESSs). Simulation results are illustrated in three different case studies in order to compare the performance of the proposed two methods during various operational conditions. It is obvious that the frequency deviation of microgrid can be improved by taking advantage of intelligent combination of both DR program and modern control mechanism.
Highlights
Over the last decades, renewable energy sources (RESs) have played a significant role in the generation of electricity and meeting consumers’ requirement due to recent environmental concerns as well as excessive changes in weather conditions
One of the detrimental effects of lack of balance between generation and load changes is frequency deviation which can be addressed by taking advantage of traditional and modern control mechanisms in an effective way [5,6]
Several studies have been published in the field of load frequency control during various operational conditions as well as structured uncertainties, so that these methods have mostly focused on the frequency control in the generation side of the power systems [7]
Summary
Renewable energy sources (RESs) have played a significant role in the generation of electricity and meeting consumers’ requirement due to recent environmental concerns as well as excessive changes in weather conditions. One of the detrimental effects of lack of balance between generation and load changes is frequency deviation which can be addressed by taking advantage of traditional and modern control mechanisms in an effective way [5,6]. In reference [8], authors introduced a model predictive control (MPC) during frequency regulation of a power system in Nordic with some certain parameters. This presented control approach has been compared to the conventional automation generation control (AGC) scheme which includes proportional-integral (PI)
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