Artificial Intelligence-Based Control and Coordination of Multiple PV Inverters for Reactive Power/Voltage Control of Power Distribution Networks

Abstract

The integration of Renewable Energy Resources (RERs) into Power Distribution Networks (PDN) has great significance in addressing power deficiency, economics and environmental concerns. Photovoltaic (PV) technology is one of the most popular RERs, because it is simple to install and has a lot of potential. Moreover, the realization of net metering concepts further attracted consumers to benefit from PVs; however, due to ineffective coordination and control of multiple PV systems, power distribution networks face large voltage deviation. To highlight real-time control, decentralized and distributed control schemes are exploited. In the decentralized scheme, each zone (having multiple PVs) is considered an agent. These agents have zonal control and inter-zonal coordination among them. For the distributed scheme, each PV inverter is viewed as an agent. Each agent coordinates individually with other agents to control the reactive power of the system. Multi-agent actor-critic (MAAC) based framework is used for real-time coordination and control between agents. In the MAAC, an action is created by the actor network, and its value is evaluated by the critic network. The proposed scheme minimizes power losses while controlling the reactive power of PVs. The proposed scheme also maintains the voltage in a certain range of ±5%. MAAC framework is applied to the PV integrated IEEE-33 test bus system. Results are examined in light of seasonal variation in PV output and time-changing loads. The results clearly indicate that a controllable voltage ratio of 0.6850 and 0.6508 is achieved for the decentralized and distributed control schemes, respectively. As a result, voltage out of control ratio is reduced to 0.0275 for the decentralized scheme and 0.0523 for the distributed control scheme.

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