a b s t r a c tElectric vehicles (EVs) and distributed generation are expected to play a major role in modern power systems. Although many studies have introduced novel models to integrate distributed generation into high levels of EV-adoption scenarios, none has considered EV-embedded battery performance degradation and its economic effect on system planning. Based on well-established models and data to emulate the capacity fading of lithium-ion batteries, the current work presents a mixed-integer linear programming optimization framework with decision variables to size renewable energy resources (RESs) in modern microgrids. The objective function aims to minimize the total cost of the system while guaranteeing a profitable operation level of vehicle-to-grid (V2G) application, narrowing the gap between design stage and real-life daily operation patterns. Stochastic modeling is used to incorporate the effect of different uncertainties involved in the issue. A case study on a residential system in Okinawa, Japan, is introduced to quantitatively illustrate how a profitable V2G operation can affect RES sizing. The results reveal that accounting for the economic operation of EVs leads to the integration of significantly higher capacities of RESs compared with a sizing model that excessively relies on V2G and does not recognize battery-fading economics.
This paper considers the contribution of independent owners (IOs) operating within microgrids (MGs) toward green power generation in deregulated energy markets. An optimization scheme is introduced for sizing distributed renewable generation (DRG) and a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for demand response (DR), DESS dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that schedules resources in order to minimize the objective function, to deal with the complicated nonlinear nature of the problem and to enable efficient long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used for testing the algorithm and analyzing the potential for IO/MG investments and their strategies.
Sizing the physical components, choosing proper brands, setting the optimal operation parameters, and the computation time are crucial issues when designing a dieselhybrid renewable energy system. In this paper, a variant of one of the conventional methods used for designing and configuring these systems is proposed. This variant employs a hybrid genetic algorithm resulting in a time-efficient searching method. This method was applied in the design of an autonomous system that supplies two of the different kinds of loads typically encountered in Japan. The predicted costs were feasible and encouraging for more investment in this field of power.
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