A Multi-Objective Approach for Voltage Stability Enhancement and Loss Reduction Under PQV and P Buses Through Reconfiguration and Distributed Generation Allocation

Abstract

This paper proposes a novel approach of voltage stability enhancement and power loss minimization in addition to maintenance of good voltage profile in radial distribution networks through optimally placed distributed generation, network reconfiguration and voltage control of PQV bus through variable reactive power source at P bus. A multi-objective function has been proposed that considers maximum system loadability enhancement and network loss minimization. Optimization of proposed multi-objective function, under distributed generation and network reconfiguration with presence of PQV and P buses in the system have been done using grey wolf optimization technique. Case studies performed on IEEE 33-bus radial distribution system shows that presence of PQV and P buses in the system yields significant enhancement in voltage stability margin under optimal placement of distributed generations and network reconfiguration.

Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

“…The system stability index statement is as follows 40,41 : $$${{VSI}}_{k}={|{V}_{k}|}^{4}-4{({P}_{k}{X}_{{km}}-{Q}_{k}{R}_{{km}})}^{2}-4({P}_{k}{X}_{{km}}+{Q}_{k}{R}_{{km}}){|{V}_{k}|}^{2},$$$ $$$\sum {VSI}=\sum _{h=1}^{24}\sum _{k=1}^{{NB}}{{VSI}}_{k},$$$where $${{VSI}}_{k}$$ is the voltage stability index, $${R}_{{km}}$$ represent the resistance of the transmission lines while the $${X}_{{km}}$$ is its reactance. $${P}_{k}$$ and $${Q}_{k}$$ define the real and reactive power at bus, respectively.…”

“…P i j ( , ) is another variable used in the algorithm, which represents the proportional difference between of the jth position of the best-obtained solution and the jth position of the current solution. This calculation is performed using Equation (41).…”

Pareto Front-Based Multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

“…In the table, bold numbers are the objective functions, and highlighted gray cell shows the best values of parameters. In case 1, objective functions are P L and VD, the best sites for the DG's are located at [14], [25], and [28] buses of different ratings for the PQ, CI, COM and MIX load models. Minimum power loss of 69.6 kW was obtained in IND load model and the smallest VD of 0.113p.u is computed in CZ load model.…”

“…By changing the objective functions DG site and hence size are changed, therefore, weighted sum Multiobjective approaches were implemented considering various simultaneous technical, economic and environmental objective functions to find the optimal site and size of DG and SC. That includes: ant lion optimizer (ALO) [24], Jellyfish Search Algorithm [1], Constriction Factor PSO (CFPSO) [2], Grey Wolf Optimization (GWO) [25], Differential Evolutions (DE) [26], hybrid enhanced GWO and PSO (EGWO-PSO) [27], battery energy storage system (BESS) and reconfiguration along with DG and SC allocation using PSO in [28], water cycle algorithm (WCA) [29], Manta Ray foraging optimization (MRFO) were implemented in [30], whale optimization algorithms (WOA) [31], hybrid ant colony optimization (ACO) and artificial bee colony (ACO-ABC) [32]. In weighted sum approach, selection of weights for the particular objective functions is highly complex and difficult.…”

Novel Real Valued Improved Coral-Reef Optimization Algorithm for Optimal Integration of Classified Distributed Generators

“…Contemporarily most power system optimization problems are being catered through metaheuristic algorithms. Numerous applications of metaheuristic optimization approaches in power system include optimal DG integration [9], network reconfiguration [10], optimal phasor measurement unit (PMU) placement [11], optimal scheduling of generators [12], [13], real time economic dispatch with DGs [14], [15] and VAR compensation [16].…”