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Associates, Primer on demand side management. Factoring the Elasticity of Demand in Electricity Prices.
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Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Vehicle-to-grid power: battery, hybrid and fuel cell vehicles as resources for distributed electric power in California, Davis, CA. Kempton, W., Tomic, J., Letendre, S., Brooks, A. A genetic algorithm solution to the unit commitment problem. Strategic Plan for the IEA Demand-Side Management Program 2004-2009. Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches. A Novel Straightforward Unit Commitment method for Large-Scale Power Systems. Reliability Enhancement and Nodal Price Volatility Reduction of Restructured Power Systems with Stochastic Demand Side Load Shift. Reliability Enhancement of a Deregulated Power System Considering Demand Response. On integration of plug-in hybrid electric vehicles into existing power system structures/ Energy Policy 38: 6736–6745. Assessment of Demand Response and Advanced Metering. International Journal of Electrical Power & Energy Systems 24(2): 149-158.įERC. Unit commitment by annealing-genetic algorithm. Multi-Stage Robust Unit Commitment Considering Wind and Demand Response Uncertainties. A Computationally Efficient Mixed-Integer Linear Formulation for the Thermal Unit Commitment Problem. Renewable and Sustainable Energy Reviews. Design, demonstrations and sustainability impact assessments for plug-in hybrid electric vehicles. The Demand Elasticity Impacts on the Strategic Bidding Behavior of the Electricity Producers. Journal of Cleaner Production 17: 911–918.īompard, E., Ma, Y., Napoli, R. Development of SuperSmart Grids for a more efficient utilisation of electricity from renewable sources. Analysis of Corporate Average Fuel Economy Regulation Compliance Scenarios Inclusive of Plug in Hybrid Vehicles, Applied Energy, 113: 1323-1337.īattaglini, A., Lilliestam, J., Haas, A. Review of Hybrid and Electric Vehicle Market Modeling Studies, Renewable and Sustainable Energy Reviews, 21: 190-203.Īl-Alawi, B. Total cost of ownership, payback, and consumer preference modeling of plug-in hybrid electric vehicles, Applied Energy, 103: 488–506.Īl-Alawi, B. Unit commitment by parallel simulated annealing. Modeling and Prioritizing Demand Response Programs in Power Markets. Demand Response Modeling Considering Interruptible/Curtailable Loads and Capacity Market Programs. Demand Response Model Considering EDRP and TOU Programs. A MADM-based Support System for DR Programs. Electrical Engineering (Springer) 92(6): 215-225.Īalami, H., Yousefi, G. An implementation of harmony search algorithm to unit commitment problem. Moreover the benefits of implementing demand response resources and gridable vehicle in electricity markets are demonstrated.Īfkousi-Paqaleh, M., Rashidi-Nejad, M.
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The proposed method is conducted on the conventional 10-unit test system to illustrate the impacts of smart grid environment on the unit commitment problem. The objective function of the unit commitment problem has been modified to incorporate demand response resources and gridable vehicles. This paper formulates a mixed-integer programming approach to solve the unit commitment problem with demand response resources and gridable vehicles.
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#Gridable images portable
On the other hand, a gridable vehicle can be used as a small portable power plant to improve the reliability as well as security of the power system. An economic model of incentive responsive loads is modelled based on price elasticity of demand and customers’ benefit function. Demand response resources can be used as a demand side virtual power plant (resource) to enhance the security and reliability of utility and have the potential to offer substantial benefits in the form of improved economic efficiency in wholesale electricity markets. Demand response resources and gridable vehicle are two interesting programs which can be utilized in the smart grid environment. The future of power systems known as smart grids is expected to involve an increasing level of intelligence and incorporation of new information and communication technologies in every aspect of the power grid.
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