Distribution system planning to accommodate distributed energy resources and PEVs

Humayd, Abdullah S. Bin; Bhattacharya, Kankar

doi:10.1016/j.epsr.2016.12.016

Cited by 42 publications

(23 citation statements)

References 36 publications

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“…The planning results suggest that optimal allocations of ESSs and capacitors are beneficial to improving penetration and utilization level of RDGs and achieving the upgrade deferral. Similar with [75], the benefits brought by optimal allocation of voltage control devices on accommodation of RDGs are also verified in [41,68,79,88,96]. …”

Section: Planning Objectivesmentioning

confidence: 57%

“…Correspondingly, the planning horizons of them are 16-30 years [35,41,52,64], 6-15 years [34,42,59,61,71,96,97], and 1-5 years [31,38,43,67,84,86], respectively. In most of the articles, capital recovery factor is adopted to calculate the net present value of equal annual cash flows and economic objectives.…”

Section: Methods To Deal With Multiple Time Scalesmentioning

confidence: 99%

“…What is noteworthy is that the smart metering devices are taken into consideration in the ADS planning methodology. In [81,84,89,96], DR serves as an AM scheme integrated into ADS planning model. Among them, authors in [81] adopt the flexible load as a kind of virtual energy storage unit with bi-directional power output to reduce the operation costs.…”

Section: Integration Of Esss and Drmentioning

confidence: 99%

“…Different from [55], the mixed-integer nonlinear programming problem proposed in [36] is solved by DP. In general, these classical numerical methods and the software tools based on these numerical methods (CONOPT [51,63,69], IPOPT [65,98], SNOPT [96], GUROBI [57,58,71,89], etc.) have been widely used to solve the ADS planning problem.…”

Section: Solving Algorithmsmentioning

confidence: 99%

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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: Planning Objectivesmentioning

confidence: 57%

Section: Methods To Deal With Multiple Time Scalesmentioning

confidence: 99%

Section: Integration Of Esss and Drmentioning

confidence: 99%

Section: Solving Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

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

Wang

Chen³

et al. 2017

Energies

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“…Constraints (2), (3) represent electricity and natural gas power balance in each EH. Constraints (4.a)-(4.c) set the bounds on the output power of EHs' components to their capacity and (4.d) is to limit penetration of CHP units within the electricity distribution network [21][22][23][24]. Capacity of EHs' components in each time stage is determined based on their capacity in previous time stage and newly added capacity using (5.a)-(5.c).…”

Section: Expansion Planning Of Energy Hubsmentioning

confidence: 99%

Multistage Expansion Co-Planning of Integrated Natural Gas and Electricity Distribution Systems

et al. 2019

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This paper focuses on expansion co-planning studies of natural gas and electricity distribution systems. The aim is to develop a mixed-integer linear programming (MILP) model for such problems to guarantee the finite convergence to optimality. To this end, at first the interconnection of electricity and natural gas networks at demand nodes is modelled by the concept of energy hub (EH). Then, mathematical model of expansion studies associated with the natural gas, electricity and EHs are extracted. The optimization models of these three expansion studies incorporate investment and operation costs. Based on these separate planning problems, which are all in the form of mixed-integer nonlinear programming (MINLP), joint expansion model of multi-carrier energy distribution system is attained and linearized to form a MILP optimization formulation. The presented optimization framework is illustratively applied to an energy distribution network and the results are discussed.

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Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

Abo‐Elyousr

Sharaf²,

Darwish

et al. 2022

Energy Science & Engineering

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Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility-owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost-effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day-ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self-adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K-means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE-24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near-optimal day-ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.

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Distribution system planning to accommodate distributed energy resources and PEVs

Cited by 42 publications

References 36 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

Multistage Expansion Co-Planning of Integrated Natural Gas and Electricity Distribution Systems

Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

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