An Optimization Framework for Collaborative Control of Power Loss and Voltage in Distribution Systems With DGs and EVs Using Stochastic Fuzzy Chance Constrained Programming

Abstract

A stochastic fuzzy chance-constrained programming model with multi-objective optimization for coordinated control of power loss and voltage in distribution systems with renewable energy is presented by taking power output of DGs and charging-discharging power of EVs as random fuzzy variables and load power as random variables. Considering the fuzziness and randomness of active power output of distributed generation systems with wind and solar energy and charging power of electric vehicles, the key parameters of probability density function are determined by fitting incomplete data of uncertainties such as wind speed, sunlight intensity and charging power of electric vehicles. According to the principle of random fuzzy compatibility, the probability density function of the uncertainties is transformed into the probability distribution function of the uncertainties. The NDC(Normal Distribution Crossover)-based non-dominated sorting genetic algorithm is used to solve the optimization problem, and the Pareto solution set of the multiobjective optimization problem is obtained. The feasibility and applicability of the proposed model and algorithm are verified by simulating IEEE-33 and IEEE-118 distribution system. INDEX TERMS Distribution system with renewable energy, fuzzy collaborative control of power loss and voltage, non-dominated sorting genetic algorithm with normal distribution crossover, distributed generation, controllable load, electric vehicle.

A Review of Metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks

“…Huiling et al [83] formed a stochastic fuzzy chanceconstrained model for coordinated charging and discharging of EVs and the integration of RES-based DG units in a multi-objective space. A modified NSGA-II algorithm was developed for minimizing power loss and voltage deviation.…”

“…The mutation clock's addition enhances the computational time because unlike in [71], where the number of extraction is based on the number of genes, it allows for only one extraction of genes per chromosome. In [83], a Normal Distribution Crossover (NDC) was used to produce new chromosomes to optimize power loss and voltage deviation. It was reported that the NDC-based GA converges faster than the PSO and PSO-GA algorithms.…”

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

“…Furthermore, there is a substantial body of literature on the simultaneous installation of EVCS with DGs and DSTAT-COMs/capacitors. Table-1 [2], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36] provides a complete review of current research on EVCS planning using Distribution Generator (DG), Capacitor, Network Reconfiguration (NR), Battery Energy Storage System (BESS), and DSTATCOM for various types of distribution systems, optimization methodologies, and objective functions.…”

Distributed Optimization for Distribution Grids With Stochastic DER Using Multi-Agent Deep Reinforcement Learning

“…The uncertainty of load can substantially affect the system loss computations and the DER prices in this stochastic problem [13]. Recently, there have been several probabilistic approaches proposed to deal with this issue.…”