Comprehensive multi‐year distribution system planning using back‐propagation approach

Humayd, Abdullah S. Bin; Bhattacharya, Kankar

doi:10.1049/iet-gtd.2012.0706

Cited by 45 publications

(34 citation statements)

References 20 publications

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“…Economic objectives ·Minimization of investment and operation costs [23], [24], [25], [26], [30], [31], [38], [42], [46]... ·Minimization of network losses [34], [40], [41], [56], [63], [73], [84], [91], [102]... ·Maximization of net profit value [33], [44], [72], [81], [84], [92], [97], [103]...…”

Section: Objectives Of Ads Planning Modelmentioning

confidence: 99%

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

Wang

Chen³

et al. 2017

Energies

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Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.

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Section: Objectives Of Ads Planning Modelmentioning

confidence: 99%

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

Wang

Chen³

et al. 2017

Energies

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“…To achieve this, the DSPLAN model presented in [1] is employed and significantly modified to replace the linear demand-supply balance by ac power flow equations so as to deal with the planning of DG and capacitors collectively. Besides, the proposed model also considers demand response (DR) as an option for distribution planning to mitigate the potential increase in demand incurred by PEV charging loads.…”

Section: Introductionmentioning

confidence: 99%

An adequacy-constrained integrated planning method for effective accommodation of DG and electric vehicles in smart distribution systems

Tan

Xie

Zhao

et al. 2018

AIP Conference Proceedings

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Abstract. This paper presents a new integrated planning framework for effective accommodating electric vehicles in smart distribution systems (SDS). The proposed method incorporates various investment options available for the utility collectively, including distributed generation (DG), capacitors and network reinforcement. Using a back-propagation algorithm combined with cost-benefit analysis, the optimal network upgrade plan, allocation and sizing of the selected components are determined, with the purpose of minimizing the total system capital and operating costs of DG and EV accommodation. Furthermore, a new iterative reliability test method is proposed. It can check the optimization results by subsequently simulating the reliability level of the planning scheme, and modify the generation reserve margin to guarantee acceptable adequacy levels for each year of the planning horizon. Numerical results based on a 32-bus distribution system verify the effectiveness of the proposed method.

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“…Heuristics approaches such as tabu search [3], particle swarm optimization (PSO) [4][5][6][7][8], and genetic algorithm (GA) [9][10][11] have been used to handle large problem sizes. While, decomposition techniques such as forward filling [12], backward pull-out [13], and recursive forward-backward approach [4], [5], [11], [14] have been used for handling time dynamics of MSDEP problems. The uncertainties of load and renewable generation have been modeled either using probability distribution functions (PDF) based on the available T historical probabilistic data [7] or by generating possible scenarios based on experience and knowledge [11], [15][16][17][18][19][20][21] when the probabilistic data is not available or using a hybrid possibilistic-probabilistic model [22].…”

Section: Introductionmentioning

confidence: 99%

“…The inclusion of DGs in MSDEP along with the possible change to lines such as addition, removing, combining, and replacing of lines is investigated in [12]. In addition, a backward approach is proposed to find location and size of DGs, along with substations and feeders [13]. Nara et al propose a recursive forward-backward approach for MSDEP using the branch and bound method.…”

Section: Introductionmentioning

confidence: 99%

A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

Arefi

Abeygunawardana

Ledwich

2016

IEEE Trans. Sustain. Energy

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Abstract-The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary nonnetwork solutions (NNSs) such as demand response (DR) and temporary energy storage system (ESS)/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the "optimal level of demand" for each year at which the network should be upgraded using network solutions (NSs) while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward-backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach.Index Terms-Demand response, Energy storage, Multi-stage electric network distribution planning, Risk-managed cost, Uncertainty, Probability of exceedance.

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Comprehensive multi‐year distribution system planning using back‐propagation approach

Cited by 45 publications

References 20 publications

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

An adequacy-constrained integrated planning method for effective accommodation of DG and electric vehicles in smart distribution systems

A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

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