Economic-environmental scheduling of microgrid considering V2G-enabled electric vehicles integration

As an important part of the Energy Internet, microgrid (MG) scheduling problem has always attracted great attention, especially under the background that large-scale penetration of electric vehicles (EVs) into buildings poses both opportunity and challenge to the MG energy management. This research presents a two-stage optimization strategy, for improving the economic and environment -friendly operation of MG considering EVs integration. In Stage 1, model of EVs under coordinate charging/discharging stimulated by time-of-use incentive mechanism is established, and the peak- valley hours are dynamically divided to obtain a load curve with minimized peak-to-valley difference (PVD). In Stage 2, aiming for the best tradeoff between the generation cost and pollutant emission, daily optimal scheduling of the MG generators is efficiently calculated according to the varying power demand. For enhancing the convergence, an advanced genetic algorithm with elite preservation strategy is employed. Two cases from a residential MG system is exploited to validate the proposed method, and the results show that firstly the power supply pressure could be obviously relieved owing to the load shifting effect of the coordinated vehicle-to-grid (V2G) service reflected by the decreased PVD (Case 1: from 76.16 to 54.35 kW, Case 2: from 76.25 to 46.19 kW); simultaneously, via applicable power planning of the MG components, cost saving and emission reduction can be both achieved (Case 1: yen 190.12 under V2G management compared to yen 281.66 under basic load, Case 2: yen 177.28 compared to yen 350.24), ensuring the feasibility of the control strategy, which promotes the economic-environmental and reliable operation of the MG system. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This research presents a two-stage optimization strategy for the microgrid (MG) scheduling problem considering the integration of electric vehicles (EVs) into buildings. The first stage establishes a model for coordinated charging/discharging of EVs, and dynamically divides the peak and valley hours to minimize the peak-to-valley difference (PVD) of the load curve. In the second stage, the daily optimal scheduling of MG generators is calculated efficiently considering the tradeoff between generation cost and pollutant emission. The proposed method is validated using two cases from a residential MG system, showing the relief of power supply pressure and cost saving achieved through coordinated vehicle-to-grid (V2G) service and power planning of MG components.