Optimized management of reactive power reserves of transmission grid-connected photovoltaic plants driven by an IoT solution

Article at: International Journal of Electrical Power & Energy Systems

Title: Optimized management of reactive power reserves of transmission grid-connected photovoltaic plants driven by an IoT solution

Language: English

Authors: Mustapha Habib, Annika Gram, Abdelghani Harrag, Qian Wang

Abstract:

This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic (PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltage profiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes into account the varying reactive power capacity in PV inverters, which depends on weather conditions.

To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions are required. In this context, the grid status and weather data need to be transmitted continuously, via wireless communication technology, to an edge computer. Based on the transmitted data, and using the system mathematical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpoint for each plant in real time.

The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWER IEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the grid with a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordination of PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In this context, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. An improvement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved. Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent.