The increasing penetrations of renewable energy and electric vehicles bring more uncertainties and challenges to the existing power grid. The coordinated networked microgrids (MGs) contain renewable distributed generations (DGs) and nonrenewable DGs, which will be an important component in the future. We formulate an optimization problem based on a transactive energy (TE) framework for the energy schedule of upstream network and networked MGs to minimize the operation cost. The energy management between MGs and upstream network is operated by the distribution system operator (DSO), which is different from the direct control signal and fixed pricing mechanism in the traditional power system. We develop a distributionally robust optimization algorithm with ambiguity set based on Wasserstein distance (DROW) to solve the optimization problem with the uncertainties from real-time electricity price, renewable energy, loads, and electric vehicles. We carry out case studies about the energy schedule of the modified IEEE 33-bus and IEEE 118-bus power system with networked MGs. Numerical results indicate that the TE framework is conducive to schedule the energy of upstream network and networked MGs efficiently with the dynamic pricing scheme and the proposed DROW algorithm can seek a robust energy schedule of DSO and networked MGs with uncertainties.

With the advances in the Internet-of-Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This article investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor–critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor–critic learning charging (CALC) algorithm by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs’ expected cost by 24.03%, 21.49%, 13.80%, compared with the eagerly charging algorithm, online charging algorithm, reinforcement learning (RL)-based adaptive energy management algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.

With the increasing use of electric vehicles and the development of emerging transportation network services, autonomous electric vehicles (AEVs) may play an important role in the future of transportation. AEVs can automatically plan their route, park in the charging station and support the vehicle-togrid (V2G) services. However, V2G services may influence user dissatisfaction due to the task delays. There is a trade-off between the optimization of electricity cost and user dissatisfaction. In this paper, we formulate the problem to minimize the electricity cost of AEVs and the degradation cost of the charging station batteries with the constraint of V2G services and user dissatisfaction, which is a nonconvex problem and is difficult to solve. To solve the nonconvex optimization problem, we design a suboptimal charging algorithm with some constraints (SCAC) based on the Lyapunov optimization technique to find a trade-off between the total cost and user dissatisfaction. This algorithm can not find the optimal solution but can give a selection criterion. Furthermore, in order to get a global charging schedule, we use the criterion from SCAC algorithm as a priori knowledge to design charging scheduling reinforcement-learning-based (CSRL) algorithm, which is more efficient than reinforcement learning (RL) method without any particular criterion. We do simulations by using day-ahead price and practical profiles of AEVs to evaluate the proposed algorithms. The numerical results show that CSRL algorithm has a better performance 5.12% SCAC algorithm and both algorithms are 12.66% and 17.14% better than the benchmark algorithm which is the shortest path (SP)-based algorithm. CSRL algorithm has more efficiency (1-Pr((t)=0)) than SCAC algorithm, where Pr((t)=0) is a selection criterion calculated from the SCAC algorithm.

This paper studies an energy management problem for a typical grid-connected microgrid system that consists of renewable energy sources, Combined Heat and Power (CHP) co-generation, and energy storages to satisfy electricity and heat demand simultaneously. We formulate this problem into a stochastic non-convex optimization programming to achieve the minimum microgrid’s operating cost, which is difficult to solve due to its non-convexity and coupling feature of constraints. Existing approaches such as dynamic programming (DP) assume that all the system dynamics are known, which results in a high computational complexity and thus are not feasible in practice. The focus of this paper is on the design of a real-time energy management strategy for the optimal operation of microgrids with low computational complexity. Specifically, derived from a modified Lyapunov optimization technique, an online algorithm with random inputs (e.g., the charging/discharging of energy storage devices, power from the CHP system, the electricity from external power grid, and the renewables generation, etc.), which requires no statistic system information, is proposed. We provide an implementation of the proposed energy management algorithm and prove its optimality theoretically. Based on real-world data traces, extensive empirical evaluations are presented to verify the performance of our algorithm.

As the electric vehicles (EVs) become prevalent, the demand for smart charging rises. The disordered charging problem of EVs, the high cost, and the location problem of charging stations bring a great challenge to the power grids and transport networks. The Internet of Things (IoT) technology enables the IoT-based EV (IoEV) to plan the route and process the information with smart wireless charging. However, how to schedule the optimal routing and wireless charging is challenging. In this article, we consider a joint routing and wireless charging scheduling problem with a microwave power transfer system to minimize the travel distance, the charging cost, and battery degradation cost when IoEVs provide shuttle services. To solve this mixed linear programming problem for the joint routing and charging schedule of IoEVs with the integer routing variables and continuous charging variables, we propose a routing and charging customized benders decomposition (RCBD) algorithm. To increase the time efficiency of the RCBD algorithm, we propose an improved RCBD (IRCBD) algorithm with the trajectory similarity measurement method. Extensive simulation results show the effectiveness and correctness of the proposed scheduling algorithms. We compare the IRCBD algorithm with the actor–critic algorithm and the RCBD algorithm. The charging cost of the IRCBD algorithm with the threshold 0.9 of trajectory similarity is 5.56% more than that of the RCBD algorithm. The running time of the IRCBD algorithm is 50.01% less than that of the RCBD algorithm when there are 300 pickups and deliveries. The running time of the IRCBD algorithm is less than that of the RCBD algorithm and the actor–critic algorithm.

The availability of conventional mobile-edge computing (MEC) for vehicles is often hindered by signal interference and attenuation, limiting its efficiency in supporting computationally intensive and latency-sensitive applications. To address these challenges, we propose a novel blockchain-enabled vehicular MEC (VMEC) system that enhances resource sharing and energy efficiency in electric vehicle (EV)-centric services. The system employs an improved RAFT-based consensus mechanism (mRAFT), which dynamically evaluates the reputation of access point (AP) nodes based on their available resources, ensuring fair leader election and enhancing consensus reliability and efficiency. Furthermore, a probabilistic model is introduced to describe AP behaviors, improving the security of the consensus process. To minimize overall energy consumption, we develop a decentralized optimization framework using the alternating direction method of multipliers (ADMM). This framework jointly optimizes AP clustering, computation resource allocation, and bandwidth scheduling to achieve energy-efficient task offloading and consensus. Simulation results demonstrate that the proposed VMEC system reduces latency by 29.53% and energy consumption by 43.43% compared to benchmark schemes, showcasing its effectiveness in delivering low-latency, energy-efficient services for advanced vehicular applications.