Abstract
Energy Storage Systems (ESS) and Distributed Generation (DG) are topics in a large number of recent research works. Moreover, given the increasing adoption of EVs, high capacity EV batteries can be used as ESS, as most vehicles remain idle for long periods during work or home parking. However, the high EV penetration introduces some issues related to the charging power requirements, thereby increasing the peak demand for microgrids where EV chargers are installed. In addition, photovoltaic distributed generation is becoming another issue to deal with in EV charging microgrids. Therefore, this new scenario requires an Energy Management System (EMS) able to deal with charging demand, as well as with generation intermittency. This paper presents an EMS strategy for Microgrids that contain an EV parking lot (EVM), Photovoltaic (PV) arrays, and dynamic loads connected to the grid considering a Point of Common Coupling (PCC). The EVM-EMS utilizes the projections of future PV generation and future demand to accomplish a dynamic programming technique that optimizes the EVs’ charging (G2V) or discharging (V2G) profiles. This algorithm attends to user preferences while reducing the demand grid dependences and improves the microgrid efficiency.
Highlights
A microgrid can be seen as a small energy network composed of loads, Distributed Generators (DG), and in some cases, Energy Storage Systems (ESS) to support supply demands [1]
The gain γ defines a weight for the final state; ∆State Of Charge (SOC) f,EVE,V2G represents the error between the desired SOC at final time (SOC f,EVE,V2G ) and the calculated one (SOCEVE,V2G (t f )); and Ebat,EVE,V2G is the maximum capacity of each vehicle in ECO and V2G modes
In the case of the demand limitation related to vehicles in ULTRA and FAST modes and if the SOC of the EV is over 40%, the index of discharging power follows the same expression of ECO mode (see (24))
Summary
A microgrid can be seen as a small energy network composed of loads, Distributed Generators (DG), and in some cases, Energy Storage Systems (ESS) to support supply demands [1]. If electric vehicles are properly considered in the management system, it may be possible to reduce demand peaks and integrate the elements of micro-grids This will contribute to economic benefits, while using energy from renewable sources more efficiently and reliably. Most of the management systems apply optimization methods and generation and demand forecasting to improve the results of microgrid operation. In [13], irradiance and wind speed predictions were used for PV and wind renewable power sources and a heuristic based algorithm was built in the Python language This strategy considers energy balance, charging limits, State Of Charge (SOC), and power generation. The proposed scheme takes the PV generation forecast and the modes priority into account and applies dynamic programming for the optimization process, considering multiple factors in the cost functions.
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