Intelligent energy resource management considering vehicle-to-grid: A Simulated Annealing approach

Sousa, Tiago; Morais, H.; Vale, Zita; Faria, Pedro; Soares, João

doi:10.1109/pesgm.2012.6344637

Cited by 140 publications

(84 citation statements)

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“…Although the proposed methodology cannot ensure that the optimal solution is obtained, it has been applied in realistic scenarios with good results [6,22]. The results obtained with the proposed method have consistently been better than the results obtained with alternative, metaheuristic based approaches [18,23].…”

Section: Mathematical Formulationmentioning

confidence: 88%

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

et al. 2012

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A B S T R A C TEnergy resource scheduling becomes increasingly important, as the use of distributed resources is inten-sified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network con-straints.The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

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Section: Mathematical Formulationmentioning

confidence: 88%

“…This mathematical formulation has been implemented in Generic Algebraic Modeling System (GAMS) software, with an AC power flow algorithm that allows considering network constraints [18]. GAMS DIscrete and Continuous OPTimizer (DICOPT) has been used to solve the envisaged MINLP problem.…”

Section: Mathematical Formulationmentioning

confidence: 99%

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

et al. 2012

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“…The model presented in this paper aims at extending the already obtained results [11,12,13,14,15,16,17], contributing to overcome present demand response limitations. The main objective is to provide the involved players with decision-support concerning DR programs and contracts use and adoption.…”

Section: Introductionmentioning

confidence: 92%

Decision Support Concerning Demand Response Programs Design and Use - A Conceptual Framework and Simulation Tool

Faria¹,

Vale²

2014

Appl. Math. Inf. Sci.

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Abstract:Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

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“…The technology of using the energy stored in the gridable Electric Vehicles (EVs) batteries to supply power to the electric grid is commonly referred to as Vehicle-to-Grid (V2G). Continuous improvements of EVs envisage their massive use, meaning that large quantities of EVs must be considered by future power systems, regarding the required supply to ensure their users' daily travels [3]. In future scenarios of intensive EVs penetration, the typical load diagram can be significantly changed from the present one without EVs [3], [4].…”

mentioning

confidence: 99%

“…Continuous improvements of EVs envisage their massive use, meaning that large quantities of EVs must be considered by future power systems, regarding the required supply to ensure their users' daily travels [3]. In future scenarios of intensive EVs penetration, the typical load diagram can be significantly changed from the present one without EVs [3], [4]. On the other hand, smart grids can use V2Gs intelligently as distributed energy resources when the vehicles are parked.…”

mentioning

confidence: 99%

Day-ahead resource scheduling including demand response for electric vehicles

Soares

Morais

Sousa

et al. 2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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Abstract-The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.Index Terms-Demand response, electric vehicle, energy re-source management, particle swarm optimization. NOMENCLATURE:Period duration (e.g., 15 min., 30 min., 1 hour) Grid-to-Vehicle efficiency when the vehicle is in charge mode Vehicle-to-Grid efficiency when the vehicle is in discharge mode Voltage angle at bus (rad) Maximum voltage angle at bus (rad) Minimum voltage angle at bus (rad) Voltage angle at bus (rad) Manuscript

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Intelligent energy resource management considering vehicle-to-grid: A Simulated Annealing approach

Cited by 140 publications

References 0 publications

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

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

Decision Support Concerning Demand Response Programs Design and Use - A Conceptual Framework and Simulation Tool

Day-ahead resource scheduling including demand response for electric vehicles

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