Electric power distribution system design and planning in a deregulated environment

Wong, Steven; Bhattacharya, Kankar; Fuller, J. David

doi:10.1049/iet-gtd.2008.0553

Cited by 98 publications

(50 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section is devoted to explore the efficiency of the proposed day-ahead optimal scheduling framework through the modified IEEE 33-bus distribution system [21,22]. Figure 1 shows the single line diagram of the 33-bus radial distribution system.…”

Section: Case Studiesmentioning

confidence: 99%

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Yuan

Deng

et al. 2016

Energies

View full text Add to dashboard Cite

Abstract:In the traditional paradigm, large power plants provide active and reactive power required for the transmission system and the distribution network purchases grid power from it. However, with more and more distributed energy resources (DERs) connected at distribution levels, it is necessary to schedule DERs to meet their demand and participate in the electricity markets at the distribution level in the near future. This paper proposes a comprehensive operational scheduling model to be used in the distribution management system (DMS). The model aims to determine optimal decisions on active elements of the network, distributed generations (DGs), and responsive loads (RLs), seeking to minimize the day-ahead composite economic cost of the distribution network. For more detailed simulation, the composite cost includes the aspects of the operation cost, emission cost, and transmission loss cost of the network. Additionally, the DMS effectively utilizes the reactive power support capabilities of wind and solar power integrated in the distribution, which is usually neglected in previous works. The optimization procedure is formulated as a nonlinear combinatorial problem and solved with a modified differential evolution algorithm. A modified 33-bus distribution network is employed to validate the satisfactory performance of the proposed methodology.

show abstract

Section: Case Studiesmentioning

confidence: 99%

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Yuan

Deng

et al. 2016

Energies

View full text Add to dashboard Cite

show abstract

“…In [20], a Monte-Carlo simulation based method was applied to the nonlinear three-phase load flow equations of distribution networks including wind farms. A Latin Hypercube sampling (LHS) combined with Cholesky decomposition method (LHS-CD) was proposed in [21] or state space pruning [22] to reduce the computational burden of MCS. In [23], Cornish-Fisher expansion series were used to obtain the cumulative distribution function (CDF) of the output variables.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The results obtained by the S2PEM and US2PEM are compared with different methods namely, Monte Carlo, Latin Hypercube sampling (LHS) [21] and Gram Charlier method [17]. The technical characteristics of wind turbines and PV modules are described in Table.I and II, respectively [2].…”

Section: Application Studymentioning

confidence: 99%

A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

Soroudi

Aien

Ehsan

2012

IEEE Systems Journal

217

View full text Add to dashboard Cite

“…Reference [9] put forward Key issues and the future major research orientation in the field of ADN planning from five aspects including load forecasting, resources features, integrated design modes, optimized methods and cost-benefit analysis. References [10]- [12] put forward dynamic planning model of ADN, and many aspects of indicators were integrated in the objective function. But these literatures involve less on operation features of ADN, random volatility of renewable distributed generation (RDG) and flexibility of network topology changing is flexible.…”

mentioning

confidence: 99%

Multi-scenario Active Distribution Network Planning Considering of Demand Response and Emission Reduction

2017

JOCET

View full text Add to dashboard Cite

Abstract-Active distribution network promotes the power systems rely less on large-scale power plants and transmission networks, which brings new challenges to the distribution network planning. This paper sets up an active distribution network planning model based on integration of operation and planning, studies a new active distribution network operation mode containing demand side response, and puts forward temporary additional price incentives to promote renewable distributed generation (RDG) absorption. In view of the uncertainty of RDG, Multi-scenario probability model is used to simulate the actual output of RDG. This paper sets up an active distribution network planning model with the optimization target of minimum of construction operation cost, renewable energy output cutting quantity and carbon emission, and the constraints contain users' satisfaction about electricity supply and reduction of loads. Simulation of IEEE33 nodes distribution system show that the model can effectively solve high permeability of DG, give full play to the advantage of the demand response for distribution network, and achieve rational economic and environmental planning solutions. The adopted algorithm can effectively deal with different objective functions with different dimension.

show abstract

Electric power distribution system design and planning in a deregulated environment

Cited by 98 publications

References 11 publications

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

Multi-scenario Active Distribution Network Planning Considering of Demand Response and Emission Reduction

Contact Info

Product

Resources

About