Assessment for Voltage Violations considering Reactive Power Compensation Provided by Smart Inverters in Distribution Network

Abstract

Due to the high proportion of renewable energies, traditional voltage regulation methods such as on-load tap changers (OLTCs) and switching capacitors (SCs) are currently facing the challenge of providing fast, step-less, and low-cost reactive power to reduce the increasing risks of voltage violations in distribution networks (DNs). To meet such increasing demand for voltage regulation, smart inverters, including photovoltaics (PVs) and electric vehicle (EV) chargers, stand out as a feasible approach for reactive power compensation. This paper aims to assess the voltage violation risks in DNs considering the reactive power response of smart inverters. Firstly, reactive power compensation models of PVs and EV chargers are investigated and voltage deviation indexes of the regulation results are proposed. Moreover, kernel density estimation (KDE) and slice sampling are adopted to provide the PV output and EV charging demand samples. Then, the risk assessment is carried out with a voltage regulation model utilizing OLTCs, SCs, and available smart inverters. Numerical studies demonstrate that the reactive power support from smart inverters can significantly mitigate the voltage violation risks and reduce the switching and cost of OLTCs and capacitors in DNs.

Optimizing Voltage Stability in Distribution Networks via Metaheuristic Algorithm-Driven Reactive Power Compensation from MDHD EVs

“…Conversely, in the absence of reactive power compensation, the voltage magnitude falls below 0.9, negatively impacting the stability of the distribution network. In discussing these voltage stability results, we also want to return to the weights of the performance index: the selection of the best performing metaheuristic algorithm is based on the performance index I in Equation (22). In this study, we emphasize the significance of computational time (w3 = 0.2) over the other two factors-voltage deviation (w1 = 0.5) and power loss (w2 = 0.3), given the potential need for the timely execution of In discussing these voltage stability results, we also want to return to the weights of the performance index: the selection of the best performing metaheuristic algorithm is based on the performance index I in Equation (22).…”

“…In discussing these voltage stability results, we also want to return to the weights of the performance index: the selection of the best performing metaheuristic algorithm is based on the performance index I in Equation (22). In this study, we emphasize the significance of computational time (w3 = 0.2) over the other two factors-voltage deviation (w1 = 0.5) and power loss (w2 = 0.3), given the potential need for the timely execution of In discussing these voltage stability results, we also want to return to the weights of the performance index: the selection of the best performing metaheuristic algorithm is based on the performance index I in Equation (22). In this study, we emphasize the significance of computational time (w 3 = 0.2) over the other two factors-voltage deviation (w 1 = 0.5) and power loss (w 2 = 0.3), given the potential need for the timely execution of charging scenarios for MDHD EVs in the future.…”

“…Reactive Power Provider Optimization Algorithm [14] Battery storage Single metaheuristic algorithm (PSO) [15] DERs Lagrange multipliers [11] Shunt capacitors, wind Deterministic method [16] PVs Closed-loop control [20][21][22] Regular EVs Closed-loop control, MPC control, N/A Our paper MDHD EVs Multiple metaheuristic algorithms Our proposed methodology explores and compares the capabilities of multiple metaheuristic algorithms. We have carefully evaluated an assortment of metaheuristics to determine which may be the most effective in stabilizing distribution network voltage.…”

“…In [21], the authors utilize a model predictive control method to enable EVs to act as reactive power compensation devices and mitigate voltage instability. In [22], the authors assess the voltage violation risks in distribution networks considering the reactive power response of smart inverters including both EV and PV chargers. However, the locations of the smart chargers are randomly assigned and there is no voltage profile provided after the reactive power compensation.…”

Research on the multi-timescale optimal voltage control method for distribution network based on a DQN-DDPG algorithm

“…For a DN connected to DGs with strong coupling between active and reactive power, it is obviously not possible for a regulation system to only consider reactive power (Hu et al, 2021;Wang et al, 2021). To ensure safe and stable operation of the DN, both active and reactive power should be taken into account in the control link.…”