Optimized Dispatching Method for Flexibility Improvement of AC-MTDC Distribution Systems Considering Aggregated Electric Vehicles

With the increasing use of renewable resources and electric vehicles(EVs), the variability and uncertainty in their nature put forward a high requirement for flexibility in AC distribution system incorporating voltage source converter(VSC) based multi-terminal direct current(MTDC) grids. In order to improve the capability of distribution systems to cope with uncertainty, the flexibility enhancement of AC-MTDC distribution systems considering aggregated EVs is studied. Firstly, the charging and discharging model of one EV is proposed considering the users' demand difference and traveling needs. Based on this, a vehicle-to-grid(V2G) control strategy for aggregated EVs to participate in the flexibility promotion of distribution systems is provided. After that, an optimal flexible dispatching method is proposed to improve the flexibility of power systems through cooperation of VSCs, controllable distributed generations(CDGs), aggregated EVs, and energy storage systems(ESSs). Finally, a case study of an AC-MTDC distribution system is carried out. Simulation results show that the proposed dispatching method is capable of effectively enhancing the system flexibility, reducing renewable power curtailment, decreasing load abandonment, and cutting down system cost.

A novel forecasting approach to schedule aggregated electric vehicle charging

To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.

Impact of Electric Vehicle Aggregator with Communication Time Delay on Stability Regions and Stability Delay Margins in Load Frequency Control System

This paper investigates the impact of electric vehicle(EV)aggregator with communication time delay on stability regions and stability delay margins of a single-area load frequency control(LFC)system.Primarily,a graphical method characterizing stability boundary locus is implemented.For a given time delay,the method computes all the stabilizing proportional-integral(PI)controller gains,which constitutes a stability region in the parameter space of PI controller.Secondly,in order to complement the stability regions,a frequency-domain exact method is used to calculate stability delay margins for various values of PI controller gains.The qualitative impact of EV aggregator on both stability regions and stability delay margins is thoroughly analyzed and the results are authenticated by time-domain simulations and quasi-polynomial mapping-based root finder(QPmR)algorithm.