Encyclopedia
Please note this is a comparison between Version 1 by Mohammed H. Alsharif and Version 2 by Vicky Zhou.
Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI).
- available renewable energy potential (AREP)
- capacitor banks (CBs)
- distributed generations (DGs)
- enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO)
- power loss
- voltage deviation index (VDI)
- voltage stability index (VSI)
1. Introduction
Distributed generations (DGs) have become an attractive option for integrating power distribution systems due to their economic, technical, and environmental advantages [1][2][1,2]. Although DGs can offer several benefits to the system, their installation is subject to their primary energy source’s availability and geographical location [3]. On the other hand, DGs can cause undesired effects in the system, such as fluctuations in the voltage profile, increased fault current, and inversion in the power flow direction, etc. [4][5][4,5]. These effects become more evident when DGs use renewable energy resources (RER). RER play an energetic role in resolving environmental and security issues. They have a probabilistic nature, such as wind speed and solar irradiation [6]. Therefore, technical studies should be conducted to properly install DGs in passive systems, avoiding the degradation of reliability, system operation, and supply quality [3].
In a radial distribution system (RDS), the reactive power flow is considered the main reason for power quality issues [6]. The compensation of reactive power plays the leading part in power system planning. The capacitor banks (CBs) are treated as the familiar reactive power resources that offer loss reduction, voltage regulation, stability improvement, and a financial return for distribution companies when optimally installed in distribution systems [3][7][8][3,7,8].
To reduce the overall production costs and improve system reliability, both the DGs and CBs are commissioned as real and reactive power injection sources [7]. The installation of DG and the capacitor in the distribution network has various technical and economic benefits [9]. These multiple benefits cannot be achieved without the appropriate allocation of the DGs and CBs in power system networks. Further, the optimal allocation of both DG and CB can be carried out using optimization techniques using several methodologies [10]. The complete analysis of the existing works relating to the optimization is described in the following section.
The techniques proposed for the placement of DG and CB in distribution networks can be divided into four types: numerical, analytical, metaheuristic, and hybrid optimization [10][11][10,11]. The analytical methods have fast convergence, but their computational time and complexity become high when the type and number of DGs and CBs increases [12]. This is particularly true for multi-objective formulations with a large number of equality and inequality constraints. Analytical approaches require more robust algorithms to solve differential and nonlinear equations. In this respect, metaheuristic techniques are helpful in solving the distribution system problems which do not involve differential equations. However, the algorithms need to be appropriately tuned to reach the global solution for DG sizing and placement [12][13][12,13].
Among various metaheuristic optimization techniques, particle swarm optimization (PSO) is the most widely used for the siting and sizing DGs. PSO has significantly better computation efficiency, i.e., its functional evaluation. A vital issue with PSO is the trapping of particles into local optima that could consume a large amount of time to converge to an optimal solution. Additionally, there is no guarantee that the optimal solution will be global optima. As a result, several research works highlighted the hybridization of the standard PSO with analytical approaches or other optimization techniques for achieving better results [14]. By using a hybrid optimization technique, better optimization results can be produced by merging two optimization algorithms [9]. Instead of searching in the whole area, the search space is limited by loss sensitivity factor (LSF), increasing the possibility of finding a good solution. After selecting the candidate buses by LSF, an optimizer finds the optimum size of DGs and CBs on the buses. Consequently, a fast convergence is achieved without compromising the siting and sizing aspects [10][14][10,14].
2. Optimal Installation of DGs and CBs
A complete survey of numerous literary works associated with the optimal installation of DGs and CBs is elaborated in Table 1 [15][16][15,16].Table 1. Survey of prevailing research works with respect to optimal installation of DGs and CBs.
| Ref. No. | Year | Optimization Algorithm | Objectives | Constraints | Allocation | Inferences | Limitations | |
|---|---|---|---|---|---|---|---|---|
| DG | CB | |||||||
| [17] | 2018 | Improved grey wolf optimizer (IGWO) | Minimizing generation cost, power loss, and voltage deviation | Equality, generator, transformer, bus voltage, line loading, and installed reactive power resource constraints | √ | Improved rate of convergence with quality solution | Voltage stability and power factor constraints are neglected | |
| [10] | 2018 | Modified power loss index + Crow search (MPLI + CS) | Minimize active power loss and cost | Bus voltage, reactive power injected, complex power, capacitor size and power factor | √ | Reduced search space, accurate and quick convergence | Voltage stability is not considered | |
| [18] | 2019 | Voltage stability index + Genetic algorithm (VSI + GA) | Minimize feeder current, voltage deviation and power losses | Voltage and branch current carrying capacity | √ | √ | Hourly variation of load demand is modelled | Relaxed network constraints and single test system |
| [7] | 2020 | Enhance grey wolf algorithm (EGWA) | Minimize total investment costs, maximize voltage profile, loading capacity, and benefits from the reduction of losses and purchased power | Equality constraints, DG penetration level, power factor limit, CB size, node voltage, and branch current limits | √ | √ | Improved performance, highly stable and superior capabilities | Voltage stability and emission perspectives are ignored |
| [3] | 2016 | Tabu search + Chu–Beasley genetic algorithm (TS + CBGA) | Minimize investment and operation costs | Technical and operational constraints | √ | √ | Very efficient and used for planning the system | Single test system and stability constraint is ignored |
| [19] | 2017 | Grasshopper optimization algorithm + Cuckoo search algorithm (GOA + CSA) | Minimize voltage deviation, line losses, and cost | Equality, load bus voltage and DG capacity | √ | Less complexity with reduced computational time | Limited type of DGs, voltage stability, and emission analysis are ignored | |
| [11] | 2017 | Hybrid grey wolf optimizer (HGWO) | Minimizing total real power losses | Equality, Bus voltage, DG unit size, and power factor | √ | Algorithm performance is enhanced without tuning | Demand uncertainties and reliability are not considered | |
| [20] | 2017 | Harmony search algorithm + Particle artificial bee colony (HSA + PABC) | Minimize real power loss, line loading, and voltage deviation | Bus voltage, thermal limit of the lines, maximum power injection from DGs and CBs | √ | √ | Enhanced performance with fast convergence | Economic and voltage stability constraints are ignored |
| [21] | 2018 | HGWO-PSO | Minimizing power losses | Equality, bus voltage, line current, total generated power, and DG size | √ | Optimal solution with less iteration. | Power factor and voltage stability constraints are ignored | |
| [13] | 2019 | Multi-objective hybrid teaching learning-based optimization-grey wolf optimizer (MOHTLBOGWO) | Minimizing power losses and improving reliability | Equilibrium, bus voltage, DG size, and line capacity | √ | Improved speed of convergence and no local trapping | Solar PV and wind resources are only considered | |
| [12] | 2019 | Hybrid teaching-learning based optimization | Minimize power losses, voltage deviation and maximize voltage stability index | Equality, active and reactive power balance, voltage and thermal limits, and DG penetration | √ | Avoidance of local minima/maxima trappings and improved convergence | Tuning of algorithm parameters are required; limited type of DGs | |
| [22] | 2019 | Hybrid Whale optimization algorithm—Salp swarm algorithm (WOA-SSA) | Minimize power losses and voltage deviation | Bus voltage magnitude, DG number, and capacity | √ | More effective and better execution time | Convergence is ignored, and limited types of DGs | |
| [23] | 2019 | Hybrid weight improved particle swarm optimization + gravitational search algorithm (WIPSO + GSA) | Maximize total cost benefit | DG and capacitor power limits, voltage limits of bus | √ | √ | Feeder’s failure rate is evaluated through compensation coefficients, greater convergence speed | DGs with reactive power capabilities and stability are ignored |
| [1] | 2020 | Hybrid GA + PSO | Minimize active, reactive power losses and voltage deviation | Active and reactive power balance, voltage, line, and DG power limits | √ | More realistic, accurate, improved performance, and easy to apply | Cost analysis, stability, and environmental factors are ignored | |
| [14] | 2020 | Analytical hybrid PSO (AHPSO) | Minimize total cost | Real power of DG, angle deviation limit, and line current flow | √ | Modified 2/3rd rule is used, faster convergence | No power factor and voltage stability assessment | |
| [24] | 2020 | Hybrid Parameter improved PSO—Sequential quadratic programming (PIPSO-SQP) | Minimize real power loss | Net power flow, DG limit and node voltage | √ | Highly stable, rapid convergence and less computation time | No power factor, cost analysis, and voltage stability assessment | |
| [25] | 2020 | Hybrid Phasor PSO and GSA (PPSOGSA) | Minimize active power losses | Equality, bus voltage, THD of voltage, branch flow, DG and capacitor capacity, and positions | √ | √ | Different constraints are used, solutions are effective, robust with high-quality and less no. of iterations | Limited type of DGs, power factor constraint, stability, and economic issues are ignored |
| [26] | 2020 | Hybrid CBGA—Vortex search algorithm (CBGA- VSA) | Minimize power loss | Complex power and network voltage | √ | Successive approximation power flow is used. More efficient and better solution with low computational times | Limited type of DGs, emission, and stability investigations are ignored | |
| [27] | 2021 | Hybrid empirical discrete metaheuristic—Steepest descent method (EDM-SDM) | Minimize power losses | Active and reactive power balance, DG status and limits, and voltage | √ | High-quality and straightforward solutions with low tuning parameters | Stability and economic evaluations are ignored | |
The existing works demonstrate the optimal allocation of DGs without considering the potential of the renewable resources. Further, optimal DG allocation combined with the potential assessment of renewable energy sources (RESs) could save time, effort, and planning for current and future DG unit installations [28] for real-time systems. Herein examines the available renewable energy potentials (AREPs) at all the locations of IEEE 33- and 69-bus RDSs.
3. Conclusions
An AREP-based hybrid EGWO-PSO technique was proposed as a multi-criterion-multi-objective framework for the optimal re-allocation and re-sizing of DGs in distribution systems. It was observed that the AREP-EGWO-PSO technique could effectively re-allocate the DGs and re-size the capacity optimally. Notably, real power loss of the system was condensed significantly by up to 92.35% and 93.94% for 33 and 69 test systems, respectively, using AREP constraints. Further, the VSI of the system was greatly enhanced from its base value. Moreover, an excellent emission reduction had taken place by up to 69%, with a significant cost reduction of up to 10%. All these observed outcomes show superior performance compared with other existing optimization techniques. Notably, the AREP-based re-allocation and re-sizing of DGs offer closer performance with EGWO-PSO in all criteria (technical, economic, and environmental). Therefore, the AREP-based re-allocation and re-sizing of DGs using the EGWO-PSO algorithm can be employed to solve complex multi-objective problems for real-time systems.
The future developments accredited to dynamic load variations can be analyzed from the perspective of optimal power system operation. This research work can be extended to include reliability metrices with a reconfiguration of the distribution system. Moreover, a real-time potential assessment of an existing power system can be performed along with the reallocation of DGs based on AREP to validate the effectiveness of the proposed EGWO-PSO algorithm.
