Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources

Abstract

This paper proposes a stochastic multiobjective framework for daily volt/var control (VVC), including hydroturbine, fuel cell, wind turbine, and photovoltaic powerplants. The multiple objectives of the VVC problem to be minimized are the electrical energy losses, voltage deviations, total electrical energy costs, and total emissions of renewable energy sources and grid. For this purpose, the uncertainty related to hourly load, wind power, and solar irradiance forecasts are modeled in a scenario-based stochastic framework. A roulette wheel mechanism based on the probability distribution functions of these random variables is considered to generate the scenarios. Consequently, the stochastic multiobjective VVC (SMVVC) problem is converted to a series of equivalent deterministic scenarios. Furthermore, an Evolutionary Algorithm using the Modified Teaching-Learning-Algorithm (MTLA) is proposed to solve the SMVVC in the form of a mixed-integer nonlinear programming problem. In the proposed algorithm, a new mutation method is taken into account in order to enhance the global searching ability and mitigate the premature convergence to local minima. Finally, two distribution test feeders are considered as case studies to demonstrate the effectiveness of the proposed SMVVC.

Comparative study of reactive power control methods for photovoltaic inverters in low‐voltage grids

“…The solution to the mentioned problem can be obtained by using several non-linear optimisation techniques including those described in Section 1 [24][25][26][27][28][29]. However, in this paper, the Nelder-Mead non-linear minimisation method is employed due to its easy application [37].…”

“…For example, in [21][22][23], the local data is sent to all the PV units with the aim of equalising the reactive power injection. In addition, non-linear control techniques have been used to optimise the operation of these systems, such as fuzzy logic in [24], neural network in [25], swarm optimisation in [26], and genetic and evolutionary algorithms in [27][28][29]. Control algorithms with better performance are expected using these techniques as discussed in [29].…”

“…In addition, non-linear control techniques have been used to optimise the operation of these systems, such as fuzzy logic in [24], neural network in [25], swarm optimisation in [26], and genetic and evolutionary algorithms in [27][28][29]. Control algorithms with better performance are expected using these techniques as discussed in [29]. Given that the control is normally distributed among the PV units, the non-linear techniques also require using the low-bandwidth communication infrastructure.…”

Research of Short-Term Load Forecasting Using DWT and LSSVM Optimized by QDE

“…Most of these methods, including vector autoregressive (VAR) model [6] and ARMA model [7][8] are based on time series analysis. Pappas.…”

Development and Field Test of Voltage VAR Optimization in the Korean Smart Distribution Management System

“…Decentralized equipment controls the voltage at the connection point or the reactive power output, using only local measurement. The loading and generating values, in general, determine the reference values of equipment in the planning phase [5,6]. In a centralized VVO method, the DMS calculates the voltage and reference values for reactive power control equipment based on real-time data from the distribution system, and the voltage and reference values are transmitted to the control equipment.…”