Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units

Abstract

The energy management system (EMS) of an electrical distribution system (EDS), with the integration of distributed generation (DG) and distribution static compensator (DSTATCOM), provides numerous benefits and significantly differs from the existing EDSs. This paper presents an optimal integration of DG based on photovoltaic (PV) solar panels and DSTATCOM in EDS. A single objective function, based on maximizing the active power loss level (APLL) in EDS, is deployed to find the optimal size and location of photovoltaic DG and DSTATCOM simultaneously in different study cases using various particle swarm optimization (PSO) algorithms. These PSO algorithms are the basic PSO, adaptive acceleration coefficients PSO (AAC-PSO), autonomous particles groups for PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). These algorithms are applied to the standard IEEE 33- and 69-bus EDSs, which are used as test systems to verify the effectiveness of the proposed algorithms. Simulation results prove that the TVA-PSO algorithm exhibits higher capability and efficiency in finding optimum solutions. Comparing the simulation results attained for different study cases leads to the conclusion that DG and DSTATCOM were optimally-allocated simultaneously, which resulted in a significant reduction of power losses and an enhancement of the voltage profile.

A hybrid algorithm for voltage stability enhancement of distribution systems

“…The optimal location and sizing of DG and D-STATCOM are obtained by the proposed optimization algorithm HFPSO. To demonstrate the effectiveness of the proposed algorithm, the overall findings/results are compared with other optimization techniques such as; bacterial foraging optimization algorithm (BFOA) [18] and the PSO [10], [33], [34] for four different cases as follows: i) case 1 the system without DG and D-STATCOM, ii) case 2 the system with only D-STATCOM, iii) case 3 the system with only DG and iv) case 4 the system with multi DG and D-STATCOM.…”

“…DGs are contributed to the centralized power grids that are managed at the distribution level [9]. The integration of renewable energy sources in the conventional distribution system is becoming valuable and more attractive due to their economic and technical impacts [10]. Many researcher have adopted various types of renewable sources (fuel cell, PV, battery, biomass, wind) [11]- [14].…”

Simultaneous allocation of photovoltaic DG and DSTATCOM for techno‐economic and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms

“…Figure 5B represented the second test system, which is standard IEEE 69‐bus EDS, which is composed of 69 buses and 68 lines and branches. The data of these standard test systems are available in References 30–35.…”

“…In addition, in 2020, applied Discrete Rooted Tree Optimization (DRTO) with uncertainty modeling of DG and load, 30 Analytical method‐based technical and economic centric integrated with different multi‐criteria decision‐making methodologies 31 . Applied Dynamic Adaptive PSO (DAPSO) algorithm for cooperative voltage control based reactive power scheduling, 32 Time‐Varying Acceleration PSO (TVA‐PSO) algorithm to maximizing the APL level in EDS, 33 Krill Herd Algorithm (KHA) as a powerful optimization solution is applied to solve the installation cost based on eco‐reliability stochastic modeling, 34 Improved Gray Wolf Algorithm (IGWA) for reducing the total investment costs and applied in Egyptian distribution networks 35 . Multi‐Objective SSA algorithm to minimize APL, and improving the VSI, 36 Multi‐Objective DE algorithm for minimization of APL, voltage drop and the annual cost, 37 and multi‐objective hybrid DE‐GWO algorithm to reduced APL, TVV, and total allocation cost 38 .…”

Optimal integration of photovoltaic distributed generation in electrical distribution network using hybrid modified PSO algorithms

“…In 2019, applied spider monkey optimization (SMO) algorithm for reduced of voltage deviation problem [13], wind driven optimization (WDO) algorithm consider maximizing the VSI [14], modified crow search algorithm (MCSA) algorithm for minimizing APL and overall voltage deviation [15], moth flame optimization (MFO) algorithm, is implemented to optimal allocation of the PV-DG to minimize the APL of the distribution system [16], and also used the genetic algorithm (GA) with the aim of APL and voltage regulation [17], and application of adaptive dissipative PSO (ADPSO) algorithm with an objective of minimizing the APL [18]. In 2020, used virus colony search (VCS) algorithm for reduced the not supplied energy (NSE) [19], comprehensive learning PSO (CLPSO) algorithm with an objective of minimizing the APL [20], applied various adaptive acceleration coefficients PSO algorithms on maximizing the APL level [21], various adaptive PSO algorithms for minimizing the three technical parameters [22], and hybrid chaotic maps and adaptive acceleration coefficients PSO algorithm to multi-objective functions [23]. Recently, applied finetuned particle swarm optimization (FPSO) algorithm for APL with EDN reconfiguration [24], chaotic grey wolf optimizer (CGWO) to minimize a multi-objective function considering overcurrent relays indices [25], and adaptive quantum inspired evolutionary algorithm (AQiEA) to minimization of APL in addition to voltage dependent load models [26].…”