The sharing-economy-based business model has recently seen success in the transportation and accommodation sectors with companies like Uber and Airbnb. There is growing interest in applying this model to energy systems, with modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). In this work, we exploit the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits spatial crowdsourcing, reinforcement learning, and reverse auction theory. Specifically, the platform uses reinforcement learning to understand the drivers' preferences towards different ride-sharing and energy-sharing tasks. Based on these preferences, a personalized list is recommended to each driver through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid on their preferred tasks in their list in a reverse auction fashion. Then e-Uber solves the task assignment optimization problem that minimizes cost and guarantees V2G energy requirement. We prove that this problem is NP-hard and introduce a bipartite matching-inspired heuristic, Bipartite Matching-based Winner selection (BMW), that has polynomial time complexity. Results from experiments using real data from NYC taxi trips and energy consumption show that e-Uber performs close to the optimum and finds better solutions compared to a state-of-the-art approach

翻译：以共享经济为基础的商业模式近期在交通和住宿领域取得了成功，典型代表如优步（Uber）和爱彼迎（Airbnb）。人们日益关注将该模式应用于能源系统，具体形式包括点对点（P2P）能源交易、基于电动汽车（EV）的车网互动（V2G）、车家互动（V2H）、车车互动（V2V）以及电池更换技术（BST）。在本研究中，我们利用电动汽车日益普及的趋势，构建了一个名为e-Uber的众包平台，该平台通过V2G和BST技术联合实现共享出行与能量共享。e-Uber综合运用了空间众包、强化学习和逆向拍卖理论。具体而言，该平台利用强化学习来理解驾驶员对不同共享出行和能量共享任务的偏好。基于这些偏好，平台通过基于CMAB的任务推荐系统算法（CARS）为每位驾驶员推荐个性化任务列表。驾驶员以逆向拍卖方式对其列表中的偏好任务进行投标。随后，e-Uber求解任务分配优化问题，以最小化成本并保障V2G能量需求。我们证明该问题为NP难度问题，并引入一种基于二分图匹配的启发式算法——基于二分图匹配的获胜者选择算法（BMW），该算法具有多项式时间复杂度。基于纽约市出租车行程和能耗真实数据的实验结果表明，e-Uber的性能接近最优解，并且与现有先进方法相比能够找到更优的解决方案。