Considering cost and reliability in electrical and thermal distribution networks reinforcement planning

Abbasi, Ali Reza; Seifi, Ali Reza

doi:10.1016/j.energy.2015.01.113

Cited by 36 publications

(7 citation statements)

References 36 publications

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“…Artificial intelligence is an appropriate candidate in solving many of the real-world electrical problems [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. In this part, we have solved a problem of combined heat and power economic dispatch [6,71] using our proposed method and different optimization algorithms.…”

Section: Experimental Results: a Real-world Problemmentioning

confidence: 99%

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

et al. 2020

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Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

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Section: Experimental Results: a Real-world Problemmentioning

confidence: 99%

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

et al. 2020

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“…Jangdoost et al in [27] claimed that the optimal system reconfiguration involving plug-in hybrid electric vehicles improved reliability indices and minimized the operation costs of EDNs. In [28], the authors used the teaching-learning optimization algorithm to find the optimal system reconfiguration to improve the system reliability-based indices such as system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), and average energy not supplied (AENS).…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-objective framework for distributed energy resources planning and storage management

et al. 2022

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“…From a network reliability improvement point of view, distribution network planning also plays an important role. When feeders are newly expanded in a distribution system, allocation of new feeders and new routes must be determined to minimize installation costs of new facilities in Asakura et al This paper defines the problem of distribution network expansion planning in terms of mixed‐integer linear programming and pseudodynamic planning approach is utilized for the same, whereas in Abbasi and Seifi, problems are formulated to improve the reliability‐based indices system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and average energy not supplied (AENS). Further, sometimes, it is really difficult to find space for substation and feeder expansion, especially in the urban areas.…”

Section: Introductionmentioning

confidence: 99%

TLBO‐based approach to optimally place and sizing of energy storage system for reliability enhancement of radial distribution system

Lata

Vadhera

2020

Int Trans Electr Energ Syst

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Summary This paper presents an algorithm for optimal placement and sizing of energy storage systems (ESSs) to enhance the reliability of a radial distribution system employing teacher learning‐based optimization (TLBO) method. The location and size of ESSs have great impact on system reliability. However, the number of placed ESSs increases the overall cost of the system. Hence, the algorithm is designed to minimize the objective function of problem, which includes cost of energy not supplied (CENS), an additional cost such as investment cost and operational cost of ESSs (life cycle cost), and power loss due to line flows in distribution system. The proposed algorithm utilizes the TLBO method to minimize the objective function. To assess the performance of the algorithm, IEEE standard 30‐bus radial distribution system has been considered. The optimal location and size of ESSs have been obtained by considering various case studies. Subsequently, comparative performance analysis is done on results obtained by using TLBO, particle swarm optimization (PSO), differential evolution (DE), and genetic algorithm (GA). Furthermore, rigorous analysis is performed by imposing limit on number of applied ESSs and by varying the maximum capacity of participating ESSs. Lastly, the application of proposed method for placement of ESSs in IEEE 69‐bus distribution system provides 17% reduction in total cost of operation.

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Considering cost and reliability in electrical and thermal distribution networks reinforcement planning

Cited by 36 publications

References 36 publications

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

A multi-objective framework for distributed energy resources planning and storage management

TLBO‐based approach to optimally place and sizing of energy storage system for reliability enhancement of radial distribution system

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