Multi‐objective and multi‐period distribution expansion planning considering reliability, distributed generation and self‐healing

Pinto, Rafael S.; Unsihuay-Vila, Clodomiro; Fernandes, Thelma Solange Piazza

doi:10.1049/iet-gtd.2018.5037

Cited by 28 publications

(31 citation statements)

References 16 publications

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“…Although scenario generation drastically considers the inherent nature of uncertain parameters, computational efficiency is reduced by the increasing number of scenarios. Researchers proposed scenario-reduction techniques to enhance tractability; to name a few, backward and forward scenario reduction, clustering methods, interval programming, Taguchi's orthogonal testing array, and in the context of DEP we can mention the works in [123], [124], [118], [167]- [173]. In [123], a framework is proposed to solve a stochastic DEP problem, and in [167], a coordinated stochastic DEP and renewable expansion planning are presented considering demand response and storage systems.…”

Section: Stochastic Programmingmentioning

confidence: 99%

Overview of Electric Energy Distribution Networks Expansion Planning

et al. 2020

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Planning of the electric distribution networks is complex and about upgrading the system to satisfy the demand and constraints with the best economic plan. The planning alternatives include the expansion of substations, installing new distributed generation (DG) facilities, upgrading distribution feeders, etc. In the modern networks, distribution planners must gain the confidence of the reversibility of the investment where renewable energy resources (RERs) inject clean and cost-effective electrical power to respond to the rising demand and satisfy environmental standards. This paper is an exhaustive review on the distribution network expansion planning (DEP) including the modelling of DEP (possible objective functions, problem constraints, different horizon time, and problem variables), optimization model (single/multi-objective), the expansion of distributed energy resources (DERs), problem uncertainties, etc. We discuss the requirements of integrated energy district master planning to avoid conflicts between the goal of independence of district planning on energy, e.g. heat and electricity, and that of dependencies on the local electric utilities regarding instant power balance and stability services. Finally, we describe the primary future R&D trends in the field of distribution network planning. INDEX TERMS Distribution expansion planning, distributed energy resources, multi-objective optimization, decomposition optimization, uncertainty handling.

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Section: Stochastic Programmingmentioning

confidence: 99%

Overview of Electric Energy Distribution Networks Expansion Planning

et al. 2020

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“…In addition to the variety of GEP optimization problem-solving techniques, the objective functions and the network-imposed constraints widely vary among different design cases. For example, objective functions can be profit maximization of a generation company in the restructured power market [9], maximizing reliability [10], [11], minimizing operating costs [12], [13], and minimizing environmental pollution [14], [15]. In addition, constraints such as network security constraints [16], [17], [18], [19] , [20] , [21], investment costs [22], [23], [24], reliability [25], [26], [27], [28], [29], and environmental pollution [30] could be part of the constraints that are required to be considered in the GEP problem.…”

Section: Introductionmentioning

confidence: 99%

Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model

et al. 2020

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One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on a genetic algorithm (GA) for GEP in the presence of wind power plants. Since it is desirable to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow the maximum reasonable amount of wind penetration in the network to be obtained. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be minimized.

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“…Also, the problem has been optimized by the multiobjective hybrid gray wolf optimization method where the objectives were loss reduction and reliability improvement based on energy not‐supplied index. In Pinto et al, a methodology to solve the multiperiod distribution expansion planning problem considering DGs, capacitor, and switch placement has been presented. The developed computational model has been formulated as a mixed‐integer nonlinear optimization problem and solved through the combination of metaheuristics and stochastic simulation methods.…”

Section: Introductionmentioning

confidence: 99%

Olympic ranking–based allocation of distributed generation units in distribution networks

Gholami

Zakariazadeh

2019

Int Trans Electr Energ Syst

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Summary This paper presents a new analytical approach for optimum placement of distributed generations (DGs) in distribution systems. The proposed algorithm is inspired by the Olympic Games ranking process, which is able to simultaneously consider a wide range of goals such as voltage profile, losses, security, loading, and short‐circuit level without worrying about increasing in complexity or computational burden of the problem. This approach takes advantages of nonsensitivity to initial optimization control parameters, a proper normalization of objective values, less computation time, and providing a unique response in different runs. In viewpoint of distribution system operators and planners, the proposed method can provide a simple and comprehensive tool for analytical and validation purposes due to its transparent decision‐making process. The proposed method is implemented on the IEEE 37‐Bus test system, and the results are compared with the ones obtained by two commonly used methods. The simulation results show the ability, effectiveness, and flexibility of the proposed DG placement method.

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Multi‐objective and multi‐period distribution expansion planning considering reliability, distributed generation and self‐healing

Cited by 28 publications

References 16 publications

Overview of Electric Energy Distribution Networks Expansion Planning

Overview of Electric Energy Distribution Networks Expansion Planning

Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model

Olympic ranking–based allocation of distributed generation units in distribution networks

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