Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Sousa, Tiago; Morais, Hugo; Soares, João; Vale, Zita

doi:10.1016/j.apenergy.2012.01.053

Cited by 116 publications

(59 citation statements)

References 18 publications

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“…The smart charge and discharge consists in the network operator controlling the time and the power of EVs charge/discharge. The network operator will decide on when the most suitable periods for charging/discharging occur and on the amount of power [27]. Different goals can be considered in the charge/discharge scheduling, namely the operation costs minimization, the greenhouse gas emissions minimization [28] or the load variations minimization [29].…”

Section: Impact Of Electric Vehiclesmentioning

confidence: 99%

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

Morais

Sousa

Vale

et al. 2014

Energy Conversion and Management

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128

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a b s t r a c tSmart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

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Section: Impact Of Electric Vehiclesmentioning

confidence: 99%

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

Morais

Sousa

Vale

et al. 2014

Energy Conversion and Management

Self Cite

128

View full text Add to dashboard Cite

show abstract

“…As the study confirms, taking advantage of the rapidly growing number of electric vehicles, there is no need for preliminary investment in storage systems. A novel technique for backing up the scheduling of a VPP is suggested in [25]. The purpose of this technique is to reduce the operation costs by considering the extensive integration of Vehicle-to-Grid (V2G).…”

Section: B Storage Unitsmentioning

confidence: 99%

A review on the virtual power plant: Components and operation systems

Ghavidel

Aghaei

et al. 2016

2016 IEEE International Conference on Power System Technology (POWERCON)

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Abstract--Due to the high penetration of Distributed Generations (DGs) in the network and the presently involving competition in all electrical energy markets, Virtual Power Plant (VPP) as a new concept has come into view, with the intention of dealing with the increasing number of DGs in the system and handling effectively the competition in the electricity markets. This paper reviews the VPP in terms of components and operation systems. VPP fundamentally is composed of a number of DGs including conventional dispatchable power plants and intermittent generating units along with possible flexible loads and storage units. In this paper, these components are described in an all-inclusive manner, and some of the most important ones are pointed out. In addition, the most important anticipated outcomes of the two types of VPP, Commercial VPP (CVPP) and Technical VPP (TVPP), are presented in detail. Furthermore, the important literature associated with Combined Heat and Power (CHP) based VPP, VPP components and modeling, VPP with Demand Response (DR), VPP bidding strategy, and participation of VPP in electricity markets are briefly classified and discussed in this paper.

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“…The energy resource scheduling problem is getting more atten-tion, as the use of DER is intensified and massive V2G use is envisaged [15][16][17][18]. Previous works were developed by the authors regarding scheduling considering V2G [2,3,[17][18][19].…”

Section: State Of the Art: V2g In Energy Resources Managementmentioning

confidence: 99%

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

Soares

Sousa

Morais

et al. 2013

Applied Soft Computing

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This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem.The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intel-ligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence charac-teristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered.The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Program-ming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

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Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Cited by 116 publications

References 18 publications

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment

A review on the virtual power plant: Components and operation systems

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

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