Sensibility Analysis with Genetic Algorithm to Allocate Distributed Generation and Capacitor Banks in Unbalanced Distribution Systems

Martins, Antônio Sobrinho Campolina; Araujo, Leandro Ramos de; Penido, Débora Rosana Ribeiro

doi:10.1016/j.epsr.2022.107962

Cited by 18 publications

(4 citation statements)

References 47 publications

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“…In addition, the test results of IBSOA on benchmark functions F 1 –F 10 have also been compared with that of GA [45], BSOA [46], and bird swarm algorithm (BSA) [47], with the threshold for difference between the convergence results and the expected values being set at

1\times 10^{-4}

. The statistical test results of GA, BSOA, BSA, and IBSOA on benchmark functions F 1 –F 10 after 50 independent runs are presented in Table 1.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

Tang,

Huang,

Zhang

et al. 2023

IET Generation Trans & Dist

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A novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.

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1\times 10^{-4}

. The statistical test results of GA, BSOA, BSA, and IBSOA on benchmark functions F 1 –F 10 after 50 independent runs are presented in Table 1.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

Tang,

Huang,

Zhang

et al. 2023

IET Generation Trans & Dist

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show abstract

“…• The work presented by Campolina et al [121] proposes a strategy to determine the best location of DG devices and capacitor banks (CBs) in EDS. To this effect, the authors use a GA entrusted with improving operating conditions and minimizing energy losses in unbalanced three-phase distribution systems.…”

Section: B Strategies That Address the Simultaneous Integration And M...mentioning

confidence: 99%

On Integrating and Operating Distributed Energy Resources in Distribution Networks: A Review of Current Solution Methods, Challenges, and Opportunities

Guzmán-Henao,

Bolaños,

Montoya

et al. 2024

IEEE Access

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The growing demand for electric power and the need for an energy transition that contributes to the reduction of global greenhouse gas emissions have driven the development of various energy generation, storage, and offset technologies. These technologies are known as distributed energy resources. Their integration into distribution power systems not only contributes to improving operating aspects, but also allows supplying electricity to areas that do not have access to large-scale power systems. Therefore, the integration and management of these resources has become a topic of interest, and several studies seek to optimize their impact on technical, economic, and environmental aspects. However, this optimization poses specific challenges related to the type and number of variables related to the operation of a distribution power system. This review article aims to describe the main challenges posed by three-phase AC threephase distribution power systems under scenarios involving the integration of distributed energy resources. In addition, it presents some approaches proposed by different authors to improve the technical, economic, and environmental aspects of power grids. It can be stated that the strategies presented in the literature fail to consider scenarios that simultaneously integrate different types of technologies and optimize them while following a multi-objective approach and considering three-phase systems in a context of variable generation and demand. Therefore, future work in this field should address these aspects in a holistic manner, taking into account the computation efforts and processing times required by intelligent algorithms.INDEX TERMS Electrical mathematical model, DERs, DG, Energy costs, CO 2 emissions, Energy losses, BESS.

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“…Te chance constraints have been considered while optimally minimizing the expected power losses. GA [40] methodology has been carried out with the integer framework in the distribution network. Te optimization problem has been dealt with probabilistic structure considering variabilities of electrical load and PV output powers.…”

Section: Planning Frameworkmentioning

confidence: 99%

Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current

Barutcu

2023

International Transactions on Electrical Energy Systems

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Changes are emerging that will significantly alter structure and operation of this century’s distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation.

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Sensibility Analysis with Genetic Algorithm to Allocate Distributed Generation and Capacitor Banks in Unbalanced Distribution Systems

Cited by 18 publications

References 47 publications

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

On Integrating and Operating Distributed Energy Resources in Distribution Networks: A Review of Current Solution Methods, Challenges, and Opportunities

Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current

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