An integrated distributed generation optimization model for distribution system planning

El-Khattam, Walid; Hegazy, Yasser G.; Salama, M.M.A.

doi:10.1109/pes.2005.1489232

Cited by 81 publications

(122 citation statements)

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“…This effect is even known as "non-wire solution" [20], to meet the load growth. One method for exact calculation of this deferral is integrated planning models [12] in which network reinforcement and DG planning are performed simultaneously. The other methods use simplifying assumptions by assuming that each MVA of installed DG reduces the need for reinforcing substation and feeders [1], [18].…”

Section: ) Total Incentive For Network Reinforcement Deferralmentioning

confidence: 99%

“…Different technical constraints can limit the connection of DG units to the distribution network. These constraints are namely, voltage profile [5], [9], [10], conductor capacity limits [5], [9]- [11] and penetration level of DG units [12] and also stability of the network [13].…”

Section: Introductionmentioning

confidence: 99%

“…This makes a chance for DNO to identify the technical characteristics of the network and provide economic signals for investors to know how much and where they should invest. Most of the reported models and methods of the literature deal with the DG sizing and placement problem in a DG-owned DNO environment, like [3], [9], [12], [15], [16]. In [3], a fuzzy goal programming is proposed to solve a multi-objective model which minimizes the active losses and improves voltage profile.…”

Section: Introductionmentioning

confidence: 99%

“…Although it might be interesting to get away from the edge of technical limits of bus voltage and capacity of feeders, however, the planners have a clearly defined upper and lower limit on voltage and also upper limit for feeders and they are happy to plan by making full use of the voltage and feeder bandwidth of the given system. In [12], an integrated model for determination of total capacity of installed DG units and their appropriate connection point is proposed in which the sum of network reinforcement costs, operating and investment costs of DG units and finally the total loss costs are minimized. This model is solved using an analytical method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Soroudi

Ehsan

2010

Int Trans Elec Energy Syst

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This paper proposes a hybrid heuristic optimization method based on genetic algorithm and immune systems to maximize the benefits of distribution network operators (DNOs) accrued due to sizing and placement of distributed generation (DG) units in distribution networks. The effects of DG units in reducing the reinforcement costs and active power losses of distribution network have been investigated. In the presented method, the integration of DG units in distribution network is done considering both technical and economical aspects. The strength of the proposed method is evaluated by applying it on a small and a realistic large scale distribution network and the results are compared with analytical classic and other heuristic methods and discussed.

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Section: ) Total Incentive For Network Reinforcement Deferralmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Soroudi

Ehsan

2010

Int Trans Elec Energy Syst

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“…The authors used Monte Carlo approach to model the operating histories of the installed distributed generators. Walid El-Khattam et al [2,3] proposed a method of solving distributed generation planning problem (location and size) in different utility scenarios as an optimization problem. The objective function was based on supply-demand chain which aimed to minimize the investment and operating costs of local candidate DGs, payments towards purchasing the required extra power by the DISCO, payments toward loss compensation services, as well as the investment cost of other chosen new facilities for different market scenarios.…”

Section: Introductionmentioning

confidence: 99%

Distributed Generation Planning Strategy with Load Models in Radial Distribution System

Singh¹,

Singh²,

Verma³

2009

IJCEE

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Abstract-This paper proposes a new approach for distributed generation (DG) planning from the perspective of a distribution company (disco). Optimal sizing and siting decisions for distributed generator is obtained through different approaches: Power loss minimization, System MVA minimization, System cost minimization and System Energy loss minimization. The methodology adopted permits the planner to decide optimal location and size of DG with compromise between Power loss, System MVA, System cost and System energy loss. Normally a constant power (real and reactive) load model is assumed in most of the studies. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. A comparative study of optimal location and size of distributed generator provided by installing DG resources with different type of loads models has been performed.

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A solution to multi‐objective optimal accommodation of distributed generation problem of power distribution networks: An analytical approach

Babu

Sydulu

2019

Int Trans Electr Energ Syst

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Summary Distributed generation (DG) technologies are considered an integral part of power distribution networks. The desired performance of power distribution networks can be obtained through the optimal accommodation of DG units. This paper proposes an analytical method called Branch Loss Bus Injection Index (BLBII) to solve the multi‐objective optimal accommodation of distributed generation (MO‐OADG) problem of power distribution networks. Four objectives were considered in the optimisation model: energy loss minimisation, overall bus voltage deviation minimisation, overall voltage stability margin maximisation, and energy not served (ENS) minimisation. A multiple criteria decision‐making method such as the analytical hierarchy process is employed to optimise the weight coefficients of the individual objectives as the optimisation problem is solved based on the weighted‐sum method. The proposed method is demonstrated on benchmark radial power distribution networks of IEEE 33‐bus and INDIAN 85‐bus. The applicability of the proposed BLBII has been validated, and results are presented.

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An integrated distributed generation optimization model for distribution system planning

Cited by 81 publications

References 0 publications

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Distributed Generation Planning Strategy with Load Models in Radial Distribution System

A solution to multi‐objective optimal accommodation of distributed generation problem of power distribution networks: An analytical approach

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