Abstract

In recent years, the share of renewable energy sources (RES) in the electricity generation mix has been expanding rapidly. However, limited predictability of the RES poses challenges for traditional scheduling and dispatching mechanisms based on unit commitment (UC) and economic dispatch (ED). This paper presents an advanced UC-ED model to incorporate wind generators as RES-based units alongside conventional centralized generators. In the proposed UC-ED model, an imbalance cost is introduced reflecting the wind generation uncertainty along with the marginal generation cost. The proposed UC-ED model aims to utilize the flexibility of fleets of plug-in electric vehicles (PEVs) to optimally compensate for the wind generation uncertainty. A case study with 15 conventional units and 3 wind farms along with a fixed-sized PEV fleet demonstrates that shifting of PEV fleets charging at times of high wind availability realizes generation cost savings. Nevertheless, the operational cost saving incurred by controlled charging appears to diminish when dispatched wind energy becomes considerably larger than the charging energy of PEV fleets. Further analysis of the results reveals that the effectiveness of PEV control strategy in terms of CO2 emission reduction is strongly coupled with generation mix and the proposed control strategy is favored in cases where less pollutant-based plants like nuclear and hydro power are profoundly dominant.

Highlights

  • Electrical power systems are facing fundamental changes for integrating significant amounts of renewable energy sources (RES) as well as new forms of energy consumption like Plug-in Electric Vehicles (PEVs)

  • Further analysis of the results reveals that the effectiveness of PEV control strategy in terms of CO2 emission reduction is strongly coupled with generation mix and the proposed control strategy is favored in cases where less pollutant-based plants like nuclear and hydro power are profoundly dominant

  • An approach is presented in [5] to evaluate the potential of electric vehicles (EVs) to reduce the amount of non-wind generation considering the intermittent nature of wind power

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Summary

Introduction

Electrical power systems are facing fundamental changes for integrating significant amounts of renewable energy sources (RES) as well as new forms of energy consumption like Plug-in Electric Vehicles (PEVs). A multi-period framework of controlled EV charging is proposed in [17] incorporating a day-ahead dispatch with a real-time control of PEV fleet charging for peak load reduction in the distribution networks. The ramping capabilities need to be within the physical constraints of the non-wind generation units Under these circumstances, the large penetration of PEV fleets in the transport sector can play a crucial role with its flexibility in the charging process. The recent introduction of the Universal Smart Energy Framework (USEF) enables a BRP to optimize its portfolio more efficiently by procuring the flexibility from the small-scale prosumers like PEV owners [25] In this regard, the electric mobility is identified as a notable source of flexibility in the network, since the success of the EVs depends on the availability of the public charging facilities.

Background
Constituents of the UC-ED model
Wind power forecasting
PEV charging and discharging
Clustering of PEV fleets for optimization
Problem formulation for the proposed UC-ED model
Nk min
PEV cluster charge balance constraints
Constraints
Generator limits constraints
Simulation setup
Test scenario
Overview of assumptions
Simulation results and analysis
PEV charging strategies
Effect on CO2 emissions
Effect on peak load
Season-wise cost savings
Conclusions
Full Text
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