Optimal allocation of photovoltaic/wind energy system in distribution network using meta-heuristic algorithm

Arasteh, Armin; Alemi, Payam; Beiraghi, Mojtaba

doi:10.1016/j.asoc.2021.107594

Cited by 40 publications

(29 citation statements)

References 44 publications

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“…The maximum index needs to be transformed into the minimum index by the method of finding the reciprocal. If there are (20) n objects to be evaluated and m indices to be evaluated, an evaluation matrix can be obtained, as:…”

Section: Application For Improved Topsis In Comparing Optimization Re...mentioning

confidence: 99%

“…An efficient optimization method named hybrid multi-objective moth-flame optimization is proposed to solve the established MOP in [19]. To seek the optimal solutions for minimizing voltage deviation and network power losses, a meta-heuristic algorithm is proposed in [20]. However, there is still a lack of optimization on reactive power regulation when EVs are connected to distribution network.…”

Section: Introductionmentioning

confidence: 99%

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Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

Zhang

et al. 2022

Prot Control Mod Power Syst

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As high amounts of new energy and electric vehicle (EV) charging stations are connected to the distribution network, the voltage deviations are likely to occur, which will further affect the power quality. It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode. If the reactive power regulation potentials of new energy and EVs can be tapped, it will greatly reduce the reactive power optimization pressure on the network. Keeping this in mind, our reasearch first adds EVs to the traditional distribution network model with new forms of energy, and then a multi-objective optimization model, with achieving the lowest line loss, voltage deviation, and the highest static voltage stability margin as its objectives, is constructed. Meanwihile, the corresponding model parameters are set under different climate and equipment conditions. Ultimately, the optimization model under specific scenarios is obtained. Furthermore, considering the supply and demand relationship of the network, an improved technique for order preference by similarity to an ideal solution decision method is proposed, which aims to judge the adaptability of different algorithms to the optimized model, so as to select a most suitable algorithm for the problem. Finally, a comparison is made between the constructed model and a model without new energy. The results reveal that the constructed model can provide a high quality reactive power regulation strategy.

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Section: Application For Improved Topsis In Comparing Optimization Re...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

Zhang

et al. 2022

Prot Control Mod Power Syst

View full text Add to dashboard Cite

show abstract

“…T final and N brahch are the total optimization time and the number of DN branches, respectively. λ DN ,

\lambda _{{\Xi}}

, and λ rate satisfy [27]

\begin{equation*} \lambda _{\mathrm{DN}}+\lambda _{{\Xi}}+\lambda _{\mathrm{rate}}=1 \end{equation*}

…”

Section: Optimal Allocation Framework Of Photovoltaic Generations In ...mentioning

confidence: 99%

“…The active power loss is calculated as [27]

\begin{equation} P_{_\mathrm{loss}} =\frac{1}{T_{\mathrm{final}}\times 4} \sum _{t=1}^{T_{\mathrm{final}} \times 4} P_{\text{loss}}^t \end{equation}

…”

Section: Optimal Allocation Framework Of Photovoltaic Generations In ...mentioning

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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“…e study [27] supposed four different data of solar radiation in four seasons of a year. e IEEE 69 bus has been employed for the assumption, and a multiobjective function with loss, voltage, and cost has been optimized.…”

Section: Introductionmentioning

confidence: 99%

Coot Optimization Algorithm for Optimal Placement of Photovoltaic Generators in Distribution Systems Considering Variation of Load and Solar Radiation

Kien

Nga

Phan

et al. 2022

Mathematical Problems in Engineering

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In this paper, photovoltaic generators (PVGs) are placed in radial distribution networks (RDNs) for reducing active power loss of one operation day by using three recently published metaheuristic algorithms including coot optimization algorithm (COOA), transient search algorithm (TSA), and crystal structure algorithm (CRSA). In one operation day, the variation of loads is considered, and the change of solar radiation over daytime is also taken. The study has two main contributions regarding the effectiveness of COOA: energy loss reduction and voltage improvement. COOA can reach high energy loss reduction, better solutions, and faster search speed than TSA and CRSA. In fact, COOA finds better energy loss than the algorithms by 1% and 1.77% for the IEEE 69-node system and 0.75% and 1.4% for the IEEE 85-node system. Furthermore, COOA is at least three times faster than CRSA and two times faster than TSA. As compared to a base system without PVGs, COOA can find better energy loss up to 60.96% and improve voltage up to 4.5%. Thus, COOA is a highly effective optimization tool with the optimal solution, high stability, and fast computation process for placing PVGs in RDNs.

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Optimal allocation of photovoltaic/wind energy system in distribution network using meta-heuristic algorithm

Cited by 40 publications

References 44 publications

Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

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

Coot Optimization Algorithm for Optimal Placement of Photovoltaic Generators in Distribution Systems Considering Variation of Load and Solar Radiation

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