A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation

Abstract

Energy loss minimization, voltage profile improvement, and increasing reliability of the power system are the prominent advantages of distributed generation (DG) unit's integration in distribution systems. Therefore, optimal placement and sizing of DG become a critical issue. This article proposes a recently developed adaptive particle swarm optimization (PSO) algorithm known as phasor particle swarm optimization (PPSO) to solve the problem of optimal placement and sizing of DG units in radial distribution networks. The PPSO algorithm is based on modeling the particle control parameters with a phase angle (θ) transforming standard PSO into a self‐adaptive and parametric independent metaheuristic optimization algorithm. The main objective is to minimize the energy loss in the network considering various technical constraints and load variations. In particular, the proposed approach is implemented on a practical radial distribution feeder located at the Haripur area in Pakistan taking into account the hourly actual load values. Comparison of the devised PPSO algorithm with other techniques in the literature was done via an IEEE 33 bus test system. The results obtained by the proposed PPSO algorithm show that it enables a significant reduction in power loss for all the analyzed cases, and outperforms many of other solution techniques applied for the same problem. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

“…For more clarity on the decision variables, taking the example of the optimal power flow (OPF) [11,28]. Control variables control the power flow, while the state variables describe the power system state, such as: where PNG is the generated power at all generation buses except the slack bus, VNG is the voltage at generation buses, TSNT is the transformers tap-setting, and QNC is the shunt VAR compensators.…”

Analysis of the voltage profile and power losses in distribution network with renewable energy sources using CONOPT solver

“…U literaturi se za rješavanje problema optimalne alokacije i snage obnovljivih izvora najčešće koriste brojni metaheuristički algoritmi: Particle Swarm Optimization (PSO) [3], Phasor Particle Swarm Optimization (PPSO) [4], Backtracking Search Optimization Algorithm (BSOA) [5], Augmented Lagrangian Genetic Algorithm (ALGA) [6], Ant Lion Optimization Algorithm (ALOA) [7], Particle Swarm Optimization with Constriction Factor Approach (PSOCFA) [8]. Neki autori predlažu primjenu kombinovanih, odnosno hibridnih metoda: hybrid PIPSO-SQP [9], kombinacija Genetic algorithm (GA) i Particle Swarm Optimization (PSO) [10], Binary Particle Swarm Optimization and Shuffled Frog Leap (BPSO-SLFA) [11], kombinacija Harmony Search Algorithm (HSA) i Particle Artificial Bee Colony Algorithm (PABCA) [12], Shuffle Bat Algorithm (ShBAT) [13].…”

“…Pored baznog slučaja proračun optimalnih tokova snaga je izveden za još 4 scenarija u kojima su priključivani: 1, 2, 3 i 6 obnovljivih izvora. Mjesta priključivanja i snage obnovljivih izvora preuzete su iz [4][5], [8][9] i [12]. Slika 3 -Naponi u čvorovima u slučaju sa i bez obnovljivih izvora Na slici 3 prikazani su naponi u čvorovima, dok na slici 4 padovi napona po granama, za slučaj sa i bez obnovljivih izvora.…”

“…obnovljivih izvora Mjesta priključivanja i snage obnovljivih izvora preuzete su iz [4], gdje je proračun izveden primjenom PPSO algoritma. Posmatrajući navedene slike zaključuje se da priključivanje obnovljivih izvora dovodi do povećavanja napona u priključnim čvorovima, i smanjivanja padova napona po granama.…”

Optimal Placement and Size of Distributed Generators Based on Autoadd and PSO to Improve Voltage Profile and Minimize Power Losses

“…Some literature concerns the optimal layout of distributed generators as follows. Ullah et al have suggested an analytical approach determine the optimum location and size of distributed generators using (PSO) and (PPSO) method to reduce losses and improve the voltage profile [7]. Montoya et al have proposed a general algebraic modeling system (GAMS) with a BONMIN solver in which the problem was diagnosed as a mixed-integer nonlinear programming (MINLP) to define the optimal location and size of DGs [3].…”