Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems

Abu-Mouti, F. S.; El‐Hawary, Mohamed E.

doi:10.1049/iet-gtd.2009.0739

Cited by 100 publications

(43 citation statements)

References 13 publications

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“…Similar results have been specified using approaches A2 and A3 as shown in Figure 4.8 and Table 4.4. It is noted that optimal sizes and locations obtained for power loss minimization using the above analytical expressions are in close agreement with the results reported in [120] and [32] using heuristic and ABC algorithms, respectively.…”

Section: Numerical Resultssupporting

confidence: 83%

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Smart integration of distributed renewable generation and battery energy storage

Hung¹

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Renewable energy (i.e., biomass, wind and solar) and Battery Energy Storage (BES) are emerging as sustainable solutions for electricity generation. In the last decade, the smart grid has been introduced to accommodate high penetration of such renewable resources and make the power grid more efficient, reliable and resilient. The smart grid is formulated as a combination of power systems, telecommunication communication and information technology. As an integral part of the smart grid, a smart integration approach is presented in this thesis. The main idea behind the smart integration is locating, sizing and operating renewable-based Distributed Generation (DG) resources and associated BES units in distribution networks strategically by considering various technical, economical and environmental issues. Hence, the aim of the thesis is to develop methodologies for strategic planning and operations of high renewable DG penetration along with an efficient usage of BES units.The first contribution of the thesis is to present three alternative analytical expressions to identify the location, size and power factor of a single DG unit with a goal of minimising power losses. These expressions are easily adapted to accommodate different types of renewable DG units for minimizing energy losses by considering the time-varying demand and different operating conditions of DG units. Both dispatchable and nondispatchable renewable DG units are investigated in the study. Secondly, a methodology is also introduced in the thesis for the integration of multiple dispatchable biomass and nondispatchable wind units. The concept behind this methodology is that each nondispatchable wind unit is converted into a dispatchable source by adding a biomass unit with sufficient capacity to retain the energy loss at a minimum level. Thirdly, the thesis studies the determination of nondispatchable photovoltaic (PV) penetration into distribution systems while considering time-varying voltage-dependent load models and probabilistic generation. The system loads are classified as an industrial, commercial or residential type or a mix of them with different normalised daily patterns. The Beta probability density function model is used to describe the probabilistic nature of solar irradiance. An analytical expression is proposed to size a PV unit. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of iii three indices, namely active power loss, reactive power loss and voltage deviation. The IMO is minimised in determining the optimal size and power factor of a PV unit. Fourthly, the thesis discusses the integration of PV and BES units considering optimal power dispatch. In this work, each nondispatchable PV unit is converted into a dispatchable source by adding a BES unit with sufficient capacity. An analytical expression is proposed to determine the optimal size and power factor of PV and BES units for reducing energy losses and enhancing voltage stability. A self-correction algorith...

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Section: Numerical Resultssupporting

confidence: 83%

“…It is worth noting that the optimal size and location of DG units obtained for power loss minimization using the proposed method are in close agreement with the results of recently published methods such as heuristic [120], PSO [30,122], SA [28], ABC [32], MTLBO [33], HSA [34]. …”

Section: Voltage Profilessupporting

confidence: 84%

Smart integration of distributed renewable generation and battery energy storage

Hung¹

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“…In view of the fact that the configuration of the distribution network is in radial connection, it causes the power to flow in only a single direction, and the total power loss will increase with respect to the distance between the load and the main power supply. However, the existence of Distribution Generation (DG) can reduce the total power loss problem in the distribution network [1][2][3]. Furthermore, the presence of DG units will also increase the reliability of supply and provide numerous benefits, such as relieving transmission and distribution bottlenecks, adding new generation capacity, and can be used as a support to the power system maintenance and restoring operations [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal multiple distributed generation output through rank evolutionary particle swarm optimization

2015

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a b s t r a c tThe total power losses in a distribution network are usually minimized through the adjustment of the output of a distributed generator (DG). In line with this objective, most researchers concentrate on the optimization technique in order to regulate the DG 0 s output and compute its optimal size. In this article, a novel Rank Evolutionary Particle Swarm Optimization (REPSO) method is introduced. By hybridizing the Evolutionary Programming (EP) in Particle Swarm Optimization (PSO) algorithm, it will allow the entire particles to move toward the optimal value faster than usual and reach the convergence value. Moreover, the local best (P best ) and global best (G best ) values are obtained in simplify manner in the REPSO algorithm. The performance of this new algorithm will be compared to 3 well-known PSO methods, which are Conventional Particle Swarm Optimization (CPSO), Inertia Weight Particle Swarm Optimization (IWPSO), and Iteration Particle Swarm Optimization (IPSO) on 10 mathematical benchmark functions, and solving the optimal DG output problem. From the results, the IWPSO, IPSO and REPSO methods gave the similar "best" value in all functions after being tested 50 times, except for Function 6. However, the REPSO algorithm provided the lowest SD value in all problems. In the power system analysis, the performance of REPSO is similar to IWPSO and IPSO, and better than CPSO, but the REPSO algorithm requires less numbers of iteration and computing time. It can be concluded that the REPSO is a superior method in solving low dimension analysis, either in numerical optimization problems, or DG sizing problems.

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“…The DG units may bring different benefits for Distribution Network Operators (DNOs) such as: shorter construction period [1], network investment deferral [2], active loss reduction [3]- [5], environmental emission reduction [6] and reliability improvement [7], [8].…”

Section: B Literature Reviewmentioning

confidence: 99%

Imperialist competition algorithm for distributed generation connections

Soroudi

Ehsan

2012

IET Gener. Transm. Distrib.

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This paper proposes an Imperialist Competition Algorithm (ICA) to maximize the benefits of distribution network operators (DNOs) accrued due to presence of distributed generation (DG) units in distribution networks.The sum of active loss reduction and network investment deferral incentives has been considered as objective function to be maximized in this study. The optimal location and size of DG units in the network are found considering various techno-economical issues. The application of the proposed methodology in the UK under current Ofgem financial incentives for DNOs is investigated. The strength of the proposed approach is validated by comparing the obtained results with other methods of the literature.

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Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems

Cited by 100 publications

References 13 publications

Smart integration of distributed renewable generation and battery energy storage

Smart integration of distributed renewable generation and battery energy storage

Optimal multiple distributed generation output through rank evolutionary particle swarm optimization

Imperialist competition algorithm for distributed generation connections

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