Energy Management
Sh. Shadi; J. Salehi; A. Safari
Abstract
Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual ...
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Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual role of electric vehicles (EVs) acting as a power source and load, they presented both challenges and opportunities in EM. The complexity of EM increases as DERs become more prevalent in SDN. Moreover, the uncertainties of renewable resources, price, and load besides the uncertainties related to the place, amount, and time of EV’s charging makes EM a more intricate field. This supports the necessity of extensive tools and approaches to manage EM in SDNs. In this respect, this paper proposes an optimum scenario-based stochastic energy management scheme for intelligent distribution networks. The proposed approach is modeled as a MINLP problem and solved in GAMS software under the DICOPT solver. The test is conducted on a 33-bus SDN with and without factoring in uncertainties.
V. Bagheri; A. F. Ehyaei; M. Haeri
Abstract
Increasing requirements of electric vehicles with different capacities of batteries and increasing number of small-sized renewable energy sources lead to complexity of calculations, voltage drop, power quality loss, and unevenness in the load curve. This paper proposes a modified version of the mean-field ...
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Increasing requirements of electric vehicles with different capacities of batteries and increasing number of small-sized renewable energy sources lead to complexity of calculations, voltage drop, power quality loss, and unevenness in the load curve. This paper proposes a modified version of the mean-field decentralized method to smooth the load curve, maximize vehicle owners' profit, and meet vehicle owners’ demands. Different capacity of batteries is a challenging problem in the charging and discharging control of electric vehicles; so to solve this problem, a weighted average method is used, which determines the design weighting parameters based on the capacity of batteries. Finally, a comparison has been made between five different centralized and decentralized strategies with weighted and weightless average methods.
