Multi-objective optimal allocation of distributed generations under uncertainty based on D-S evidence theory and affine arithmetic

Zhao, Qida; Wang, Shouxiang; Wang, Kai; Huang, Bin

doi:10.1016/j.ijepes.2019.04.044

Cited by 44 publications

(19 citation statements)

References 39 publications

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“…The influence of stochastic uncertainties has been handled by using affine arithmetic model by Zhao et al. [28]. Zio et al.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of distributed energy resource penetration and placement using probabilistic framework

Paliwal

2021

IET Renewable Power Gen

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Integration of distributed energy resources (DERs) affect system performance in multitude of ways such as reliability, losses and voltage profile. Thus, increasing distributed energy resource penetration necessitates impact assessment particularly when DERs comprise of stochastic RES based distributed generators (DGs) and storage system. This paper presents a formulation for the analysis of DER penetration and placement on system losses and voltage profile in probabilistic framework. Penetration level has been defined, giving due consideration to stochastic behaviour of RES based distributed generators. The work presented incorporates analysis and consideration of two important factors in placement problem, that is, intermittent nature of RES based distributed generators and dual nature of storage units. In order to account for uncertainties inherently present in RES, probabilistic load flow has been used in this work. Placement problem is solved for three different penetration levels, that is, 20%, 40% and 60% using utterfly particle swarm optimisation. The impact of seasonal effect on system losses and voltage profile in correlation with penetration levels has also been investigated. Results provide useful insights for identification of optimum penetration level.

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“…The influence of stochastic uncertainties has been handled by using affine arithmetic model by Zhao et al. [28]. Zio et al.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of distributed energy resource penetration and placement using probabilistic framework

Paliwal

2021

IET Renewable Power Gen

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“…A multi-objective technique based on a genetic algorithm was studied by (Ochoa & Harrison, 2011) to determine optimal power flow to accommodate renewable energy sources by minimizing total energy losses. In (Q. Qianyu Zhao et al, 2019), the objective function is to investigate the uncertainty of DGs and loads, power generation cost and environmental cost of different renewable DG integration. A multi-objective optimization problem applied based on a double trade-off procedureε À constrained approach for simultaneous minimization of DG integration by considering, cost energy, cost of energy not served and cost of grid energy purchased.…”

Section: Introductionmentioning

confidence: 99%

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

2020

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Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution

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“…The genetic algorithm (GA) is one of these algorithms and is formulated for optimal DERs siting and sizing with the best compromise between various costs [10,11]. GA has been utilized in combined with evidence theory (ET) to solve this problem as a multi-objective function where GA has been used to generate different solutions and ET has been employed to assess the candidate allocations [12]. In [13], nondominated sorting particle swarm optimization (NSPSO) method is proposed to find the optimal locations and sizes of DERs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Distributed Energy Resources Allocation for Enriching Reliability and Economic Benefits Using Sine-Cosine Algorithm

Abdelsalam

2020

Technol Econ Smart Grids Sustain Energy

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This paper presents an optimization algorithm called sine-cosine algorithm (SCA) for optimal distributed energy resources (DERs) allocation in various configurations of radial distribution networks. This study is demonstrated in two statuses to find the optimal locations and sizes of DERs to be installed on the distribution system. First status; the most candidate locations for connecting DERs are suggested by using the loss sensitivity factor theory and the proposed SCA is applied to select the optimal capacities of DERs. Second status; the SCA is used to determine both the optimal locations and sizes of DERs. The positive impact of DERs on distribution systems reliability has been investigated in addition to the system power losses and bus voltages. The fitness function is to maximize the savings produced by not only power losses reduction but also reliability enhancement. The proposed algorithm is applied to IEEE 33 and 69-bus radial distribution networks with installing different number of DERs. The simulation results using Matlab programming environment show that the proposed methodology is viable, supporting reliability as well as fulfilling the conventional objectives such as cost minimization, voltage profile improvement, system losses reduction. A comparison between SCA and other methods is introduced to verify the superiority of SCA where SCA fulfils the maximum saving and maximum reduction of power losses equals to 61.3% and 69.2% for IEEE 33-bus and 69-bus networks, respectively.

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Multi-objective optimal allocation of distributed generations under uncertainty based on D-S evidence theory and affine arithmetic

Cited by 44 publications

References 39 publications

Comprehensive analysis of distributed energy resource penetration and placement using probabilistic framework

Comprehensive analysis of distributed energy resource penetration and placement using probabilistic framework

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

Optimal Distributed Energy Resources Allocation for Enriching Reliability and Economic Benefits Using Sine-Cosine Algorithm

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