Optimization models for the management of the one-way station-based electric car-sharing system integrated with Vehicle-to-Grid technology

Electric Vehicle (EV)-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits. Car-Sharing Systems (CSSs) are becoming increasingly popular in urban areas replacing car ownership. The most attractive CSSs give users the opportunity to make one-way trips. This behavior creates an unbalanced status between stations. Hence, some users could leave the system because they may not find a car/parking place available near their origin/destination. In recent years, CSSs are employing Electric Vehicles (EVs). A recent technology applied to EVs, called Vehicle-to-Grid (V2G), has allowed selling energy by transferring it from EV batteries to an electric grid. The Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for the energy supply support system. A management system combining Car-Sharing Systems (CSSs) and V2G technology is a recent challenge for academia and industry. In this thesis, two optimization models are proposed to find the optimal EVs management of the one-way station-based CSS integrated with V2G technology. The proposed models allow finding the start-of-day EVs distribution, maximizing revenues from system users, and V2G profits through daily EVs charging/discharging schedules. These schedules are based on EVs daily users’ requests and on electric energy prices. The CSS considered is the one-way station-based. Models’ outputs suggest EVs distribution among stations at the beginning of each day, making the most of V2G technology and satisfying CSSs customers’ requests simultaneously. These distributions represent the final configurations that should be obtained through overnight vehicle relocation. The proposed models have been formulated as a simulation-based model and a Mixed Integer Linear Programming (MILP) model, respectively. The simulation-based optimization models include a smart charge/discharge algorithm for the management of the one-way station-based CSS with EVs in a V2G framework. Furthermore, two different day-ahead energy markets (i.e., Italian and Dutch day-ahead energy markets) were used for V2G profits evaluation. Additionally, two cases referred to the non-optimized and the optimized EVs distribution among stations at the beginning of the day have been evaluated. In order to assess the effectiveness of the proposed CSS optimization management, two simulation-based models and a charge/discharge process have been applied to a real-size test case (i.e., the city centre of Bari, Italy). The numerical application has been carried out changing some key parameters of the problem, namely electricity market trend and the demand level of the simulated CSS. Similarly, the proposed MILP model aims to maximize the CSS revenues and the V2G profits simultaneously, and to provide the optimal start-of-day EVs distribution. As an additional output, it is possible to evaluate the daily amount of energy transferred from/to the smart grid trough the energy sale/purchase phase from/to EV batteries. In order to validate the MILP model, a small-size and a large-size test (i.e., the city of Delft, The Netherlands) were conducted achieving promising results in terms of V2G profitability and energy supply network support. Furthermore, a sensitivity analysis has been carried out through parameters tuning and six different scenarios were analyzed. The main goal of the proposed thesis is to provide a first-step analysis on a real-size network and to evaluate the applicability and profitability of the proposed smart charging management system combining electric CSSs with V2G technology.