A Multi-Time Scale Reactive Power Optimization Model Considering Network Reconfiguration

High penetration of distributed renewables in the distribution grid has adverse effects on nodal voltage, network loss, and system stable operation. Essentially, the cause of these phenomena lies in the difficulty of the distribution network's reactive power to dynamically regulate and adapt to the varying power flow. Therefore, a multi-timescale reactive power optimization model for distribution networks considering network reconfiguration and the demand response (DR) of air conditioning (AC) systems is proposed in this paper. In the day-ahead reactive power optimization timescale, the On-Load Tap Changers (OLTCs) and capacitor banks (CBs) are modeled with a limited number of daily switching times, afterward are optimized with the network reconfiguration to alleviate power grid risk and minimize network loss, voltage deviation and operation cost of the distribution grid. Based on the day-ahead optimized operation statuses of OLTCs and CBs, in the intraday reactive power optimization timescale, the active and reactive power output of SVCs, SVGs, and photovoltaic (PV) inverters are flexibly controlled to achieve the minimum network loss and voltage deviations. To solve the nonlinear multi-timescale optimization model quickly and accurately, the adaptive immune particle swarm optimization (AIPSO) hybrid with embedded K-means strategy is designed to obtain the optimal tap positions of OLTCs and CBs as well as the optimized PV inverters' power output. Case studies based on a modified IEEE33-bus and standard IEEE118-bus distribution network validate the effectiveness of the proposed multi-timescale reactive power optimization strategy.