PSO based placement of multiple wind DGs and capacitors utilizing probabilistic load flow model

Jain, Naveen; Singh, S. N.; Srivastava, Suresh C.

doi:10.1016/j.swevo.2014.08.001

Cited by 55 publications

(31 citation statements)

References 41 publications

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“…For this application the PPBIL is assigned in the master role, locating the generators, and the PSO algorithm [50] is the slave responsible for sizing the generators and evaluating the objective function of the individuals in the population. Such a PSO function was selected due to the satisfactory results reported in literature concerning the sizing of DGs [8,20,22,36]. Both stages use a vector of (|Ω N | − 1) elements for their codification, except for the Slack node, which is always occupied by the main generator of the grid.…”

Section: Sizing and Location Of Dgs Using Ppbil-psomentioning

confidence: 99%

“…It is worth noting that, in recent years, most researchers have focused their efforts on providing solutions to the DGs sizing problem and, in many cases, have addressed the location problem using sensitivity indicators [21,25]. Evolutionary algorithms have been also used to face this problem [22,23] due to the satisfactory results given by this type of optimization techniques in non-convex mixed-integer nonlinear problems [26], which is the type of problem describing the location of DGs in DS [27]. However, such an approach becomes ineffective as the distribution systems grow because, as the solution space expands and the complexity of the problem increases, the computation time becomes longer and the possibility of falling into a local optima increases, which, in most cases, fails to provide a good solution for the system.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the PSO has been extensively used in literature [20,22,36] to address the DGs sizing problem. The mathematical model, adopted in this paper, to represent the sizing problem is based on a weighted single-objective function designed to minimize both active power losses and voltage square error, however such a model also includes a penalty to control the penetration level of the DGs into the DS.…”

Section: Introductionmentioning

confidence: 99%

“…The adoption of a heuristic technique for the generator location ensured a reduced computation burden in comparison with previous solutions, however, the solution space is not optimally explored, and hence the method could be trapped into a local optima. Another application of the PSO algorithm in a DGs location process was reported in [22]. In that work the authors propose a modified PSO algorithm to locate photovoltaic generators and capacitors in a grid, applying an additional Monte Carlo algorithm to define the generators size, hence, its main objective is to reduce the power losses and to improve the voltage profiles.Other optimization techniques have been used to design DGs systems.…”

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confidence: 99%

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Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

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The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.Energies 2018, 11, 1018 2 of 27 generation [11,12]: reduction of power losses (due to transmission), improvement of voltage profiles and stability index, power factor enhancement, reduction of the harmonic distortion and increased line loadability, among others [13]. However, incorrect location or sizing procedures may result in voltage profiles out of conventional ranges, voltage fluctuations, line capacity violation, increased failure levels due to intermittent generation and higher costs associated to the DGs [14].Different methods have been developed to optimize the location and sizing of DGs. Those methods are aimed at reducing the computation time required and to improve the technical criteria of the grid, such as power losses, voltage profiles and power factor, among others [15,16]. For example, the work reported in [17] presents a hybrid method between the genetic algorithm proposed by Chu and Beasley (GACB) [18] and a heuristic approach to locate, select and size the feeders in a distributed generation environment. Such a strategy enables the reduction of the costs associated with the feeders (DGs) and the power losses, which is achieved by reducing the peak load using active power injection based on diesel-based DGs only. On the other hand, the work reported in [19] describes a multi-target approach based on the Particle Swarm Optimization (PSO) technique for locating the DGs, and an optimal flow analysis for sizing the DGs. That work considers several generators and different load models, thus enabling high penetration levels of distributed generation.A similar approach was presented in [20], which is a hybrid solution based on both the GACB and PSO. The main drawbacks of such a solution were the high level of power injection requested to the DGs and the lack of analysis of the computation time required by the proposed method. Another solution, based on the Loss ...

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Section: Sizing and Location Of Dgs Using Ppbil-psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

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“…Mokryani and Siano use a genetic algorithm to select wind site locations and turbine size then solve the optimal power flow problem to determine how many wind turbines to build at each site. Jain et al combine particle swarm optimization and Monte Carlo simulation to select locations and sizes for distributed wind generators and capacitors for a small area across which they assume wind potential is uniform. Phillips and Middleton develop a mixed integer program to simultaneously select wind sites, transmission lines, and load centers for Texas but only consider a single time period, which does not capture variability.…”

Section: Introductionmentioning

confidence: 99%

Optimizing wind farm siting to reduce power system impacts of wind variability

et al. 2019

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We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics-forward and backward greedy algorithms-to find approximate solutions. The results are compared with the exact optimization results for a well-selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. We find an approximate efficient frontier to compare trade-offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm is more effective at reducing the wind power variability than the forward greedy algorithm.Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset. KEYWORDS multiobjective optimization, siting, variability reduction INTRODUCTIONWind is widely recognized as an important resource for a sustainable energy future. In the United States, wind power accounted for 4.7% of electricity generation in 2015 and was the second most used renewable energy resource after hydropower. 1 As many countries seek to decrease their reliance on fossil fuels, wind power will become increasingly important.A well-known challenge to integrating wind power into power systems is the uncontrollability and variability of wind. Increased reliance on wind power makes power balance more difficult. To ensure system reliability, generators that can ramp up and down quickly are required to respond to fluctuations in wind power. When wind variability is high, some of these systems may be left on to meet reserve requirements, potentially negating the benefit of wind power. In this research, we compare strategies for selecting wind farm locations during the capacity planning stage of power system planning so as to minimize the burden of wind variability on the rest of the power generation system.We use residual demand to indirectly measure the impact from wind power on the rest of the power generation system. We seek to minimize both residual demand and the variability in residual demand, analyzing two variants of the variability objective in order to consider both average variability over time and sudden fluctuations in wind power. We first solve the ...

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Estimating the impact of uncertainty on optimum capacitor placement in wind‐integrated radial distribution system

Das

Malakar

2020

Int Trans Electr Energ Syst

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The unpredictable load variations and raising presence of wind farms have caused modern-day distribution system operation and planning extremely complex. This paper puts an earliest effort toward the optimal allocation of the shunt capacitors in radial distribution system (RDS) under uncertain load and wind power generation. The authors aim to minimize the annual operating cost (AOC) for RDS by deciding the optimum placements of shunt capacitors. Here, three-point estimate method is applied to estimate the uncertainties in the load demand and power output from the wind generator. The stochastic behavior of wind generators and load demands are modeled by using Weibull and normal distribution functions, respectively. An opposition-based competitive swarm optimizer (OCSO) algorithm is applied to minimize the AOC, and the optimization problem is solved for both probabilistic and deterministic approach. The Cornish-Fisher expansion is used in this work to provide more accurate probability distribution functions of AOC. Furthermore, the impact of VAr injection on AOC values and power losses are also studied in this work. Moreover, the results obtained from the OCSO algorithm are compared with the original competitive swarm optimizer results. The proposed problem is tested on 69 bus RDS with different loading conditions, and results are analyzed for each scenario.competitive swarm optimizer, load uncertainty, optimum capacitor allocation, point estimate method, wind generation uncertainty | INTRODUCTIONShunt capacitors (SC) are being used in power distribution system (DS) conventionally to reduce power loss, improve the system voltage, and power factor. The power loss in DS is higher than that of the transmission system because of its List of Symbols and Abbreviations: V W , wind speed; P WT , power output of WGS; Pr, rated power output of WGS; Vr, rated wind speed; Vci, cut-in wind speed; Vco, cut-out wind speed;V w , mean of the wind speed; σ w , SD of the wind speed; P Li , real power demand at the bus "i"; Q Li , reactive power demand at the bus "i"; μ PLi , mean of real power demand; μ QLi , mean of reactive power demand; σ PLi , SD of real power demand; σ QLi , SD of reactive power demand; P μ loss , mean active power loss; Q μ ci, , mean VAr supply cost; nl, number of branches; cb, number of candidate bus; nb, number of load bus; P ss , real power of the substation bus; Q ss , reactive power of the substation bus; P μ w , mean power output from WGS; Q μ ci , mean VAr injection at bus "i"; P μ di , mean demands for real power at bus "i"; Q μ di , mean demands for reactive power at bus "i"; P μ lossj , mean real power loss at branch "j"; Q μ lossj , mean reactive power loss at branch "j"; Q cn , VAr injection at bus "n"; Q cn min , minimum values of Q cn ; Q cn max , maximum values of Q cn ; S li , complex power flow through line "i"; S li max , maximum line limit through line "i".

show abstract

PSO based placement of multiple wind DGs and capacitors utilizing probabilistic load flow model

Cited by 55 publications

References 41 publications

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Optimizing wind farm siting to reduce power system impacts of wind variability

Estimating the impact of uncertainty on optimum capacitor placement in wind‐integrated radial distribution system

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