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

Hassan, Abdurrahman Shuaibu; Sun, Yanxia; Wang, Zenghui

doi:10.1080/23311916.2020.1766394

Cited by 44 publications

(25 citation statements)

References 141 publications

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“…(3) DISTRIBUTED GENERATION DGs are also called; dispersed generation, on-site generation, decentralized generation, embedded generation (EG), situ generation, decentralized energy or distributed energy, generates electricity from many small energy sources [55]. In literatures there are several definitions attributed to DG [56]. DG is defined as an electric power source connected directly to the distribution network or on the customer site of the meter [55], [57], [58].…”

Section: Figurementioning

confidence: 99%

Potential of Renewable Energy Sources for Distributed Generations: An Overview

Anaza¹,

Haruna²,

L.³

et al. 2023

International Journal of Scientific Advances

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The universal energy predicament and the exhaustion of fossil fuel reserves necessitate the requirement for green and dependable source of energy. Renewable energies are produced from the natural processes that are replenished such as sunlight, wind, rain, tides, and geothermal heat. Furthermore, transmission of electricity which is tantamount to high cost and energy losses resulted to the integration technology of distributed generator (DG) during the past decade to supplements centralized generation. DGs are sustainable solution to provide electricity to customers at reliable and affordable price. This paper is an overview of renewable energy sources as used in distributed generation by presenting an updated data of the status of renewable energy resources. It also discusses impact of DG in distribution system, benefits of DG and its classification. Renewable DG technologies and factor hindering establishment of Distributed Generation were also highlighted.

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Section: Figurementioning

confidence: 99%

Potential of Renewable Energy Sources for Distributed Generations: An Overview

Anaza¹,

Haruna²,

L.³

et al. 2023

International Journal of Scientific Advances

View full text Add to dashboard Cite

show abstract

“…Wind energy (WE) is assumed to have the most commercial technics and economic prospects among the various renewable resources. Integrating the RES represented as a Distributed Generation (DG) into the grid has received wide attention and scope in power systems [6,7]. In addition, DG can help utilize distributed energy sources with relatively small resources and reduce the use of electrical power transmission capacity [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Integration of Series and Shunt FACTS with Wind Energy for Active Power Loss Reduction

Wartana¹,

Agustini²

2023

Renewable Energy - Recent Advances

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The integration of wind energy (WE) with flexible AC transmission system (FACTS) devices into the grid to improve grid performance is one of the latest advances in renewable energy (RE) technology. This work proposes the optimal placement and size of a WE, a doubly-fed induction generator (DFIG) with two of FACTS controller, viz. thyristor-controlled series compensator (TCSC) static var compensator (SVC). The goal is to maximize system bus load (Max. LBS) and minimize active power loss (Min. Ploss) by satisfying various safety and stability constraints. Newton Raphson\'s power flow study involving TCSC, SVC, and DFIG is a bi-objective that meets multiple constraints: lines, generation, voltage limits, and small signal stability. A variant of the genetic algorithm, non-dominated sorting GA II (NSGA-II), was applied to solve the contradictory bi-objective optimization problem. A modified standard and practical test system, the IEEE 14-bus and the Indonesia Java Bali 24-bus, integrated with DFIG, TCSC, and SVC, were simulated to investigate the efficacy of the suggested technique. The simulation shows that the optimal placement and size of DFIG with both FACTS can improve system performance with all system loading conditions and meet all system constraints.

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“…Recent research shows that there is a direct relationship between a country's level of development and its energy consumption. Advanced optimization techniques on the placement and sizing of distributed generation units based on renewable resources can have stabilizing and compensatory effects on environmental, economic, technological, and regulatory factors [1]. In [2], the paper plans to expand a novel compound approach referred to as backtrapping assisted elephant herding optimization (BAEHO), to address the RPD issues in the power system under distortion situations.…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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One of the most important aims of the sizing and allocation of distributed generators (DGs) in power systems is to achieve the highest feasible efficiency and performance by using the least number of DGs. Considering the use of two DGs in comparison to a single DG significantly increases the degree of freedom in designing the power system. In this paper, the optimal placement and sizing of two DGs in the standard IEEE 33-bus network have been investigated with three objective functions which are the reduction of network losses, the improvement of voltage profiles, and cost reduction. In this way, by using the backward-forward load distribution, the load distribution is performed on the 33-bus network with the power summation method to obtain the total system losses and the average bus voltage. Then, using the iterative search algorithm and considering problem constraints, placement and sizing are done for two DGs to obtain all the possible answers and next, among these answers three answers are extracted as the best answers through three methods of fuzzy logic, the weighted sum, and the shortest distance from the origin. Also, using the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) and setting the algorithm parameters, thirty-six Pareto fronts are obtained and from each Pareto front, with the help of three methods of fuzzy logic, weighted sum, and the shortest distance from the origin, three answers are extracted as the best answers. Finally, the answer which shows the least difference among the responses of the iterative search algorithm is selected as the best answer. The simulation results verify the performance and efficiency of the proposed method.

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

Cited by 44 publications

References 141 publications

Potential of Renewable Energy Sources for Distributed Generations: An Overview

Potential of Renewable Energy Sources for Distributed Generations: An Overview

Optimal Integration of Series and Shunt FACTS with Wind Energy for Active Power Loss Reduction

Archives of Electrical Engineering

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