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.
J. Salehi; F.S. Gazijahani; A. Safari
Abstract
Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal ...
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Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal location and capacity of ILs for a given incentive rate is of great interest to distribution companies. To do so, in this paper simultaneous allocation and sizing of ILs, wind turbines (WT), photovoltaic (PV) and capacitors have been done in the radial distribution network for different demand levels and subsequently the optimal value of compensation price for the ILs has been determined. Given the probabilistic nature of load, wind and solar generation as well as the price of energy at the pool, we have also proposed a stochastic model based on fuzzy decision making for modelling the technical constraints of the problem under uncertainty. The objective functions are technical constraint dissatisfaction, the total operating costs of the Distribution Company and CO2 emissions which are minimized by NSGA2. To model the uncertainties, a scenario-based method is used and then by using a scenario reduction method the number of scenarios is reduced to a certain number. The performance of the proposed method is assessed on the IEEE 33-node test feeder to verify the applicability and effectiveness of the method.
