S. Cheshme-Khavar; A. Abdolahi; F.S. Gazijahani; N.T. Kalantari; J.M. Guerrero
Abstract
With the exponential penetration of renewable energy sources (RES), the need for compatible scheduling of these has increased from economic and environmental points of view. Due to the high-efficiency and fast-response features of combined heat and power (CHP) generation units, these units can immunize ...
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With the exponential penetration of renewable energy sources (RES), the need for compatible scheduling of these has increased from economic and environmental points of view. Due to the high-efficiency and fast-response features of combined heat and power (CHP) generation units, these units can immunize the system against RES fluctuations. To address the operational challenges associated with RES, this paper aims to schedule the arbitrage of cryogenic energy storage (CES) not only to maximize its owner but also to minimize RES variability. On the other hand, plug-in electric vehicles (PEV) are applied in the proposed model as responsible loads to smooth the system's load profile by changing the consumers' consumption patterns. The proposed problem is modeled as second-order cone programming and solved by the dominated group search optimization algorithm. To verify the applicability and effectiveness of the proposed approach, four different case studies have been executed.
Smart Grid
H. Rashidizadeh-Kermani; H. R. Najafi; A. Anvari-Moghaddam; J. M. Guerrero
Abstract
Electric vehicle (EV) aggregator, as an agent between the electricity market and EV owners, participates in the future and pool market to supply EVs’ requirement. Because of the uncertain nature of pool prices and EVs’ behaviour, this paper proposed a two-stage scenario-based model to obtain ...
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Electric vehicle (EV) aggregator, as an agent between the electricity market and EV owners, participates in the future and pool market to supply EVs’ requirement. Because of the uncertain nature of pool prices and EVs’ behaviour, this paper proposed a two-stage scenario-based model to obtain optimal decision making of an EV aggregator. To deal with mentioned uncertainties, the aggregator’s risk aversion is applied using conditional value at risk (CVaR) method in the proposed model. The proposed two-stage risk-constrained decision-making problem is applied to maximize EV aggregator’s expected profit in an uncertain environment. The aggregator can participate in the future and pool market to buy the required energy of EVs and offer optimal charge/discharge prices to the EV owners. In this model, in order to assess the effects of EVs owners’ reaction to the aggregator’s offered prices on the purchases from electricity markets, a sensitivity analysis over risk factor is performed. The numerical results demonstrate that with the application of the proposed model, the aggregator can supply EVs with lower purchases from markets.
