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Fast charging of electric vehicles (EVs) significantly impacts both power distribution networks (PDN) and transportation networks (TN). However, developing a systematic EV fast charging strategy faces challenges in modeling the coupled power-transportation networks (PTN), devising charging price mechanisms, and optimizing path planning. This paper addresses the EV fast charging navigation problem using a coupled network weighted pricing approach. The charging navigation problem for each EV is formulated as an optimization problem that aims to minimize comprehensive costs, including time consumption cost and charging cost. Specifically, a novel PTN model based on the multilayer network theory is first proposed, which captures the dynamic characteristics of TN and PDN using extended graphic models and also considers the influence of coupled nodes on the PTN through a coupled weighting matrix. Then, a coupled network weighted charging pricing is developed, considering the dynamic characteristics and interdependence of PTN. To handle the large-scale dynamic complexity of coupled networks, a distributed biased min-consensus algorithm is employed to solve the optimization problem. Finally, simulation results verify the effectiveness and practicality of the proposed navigation strategy, facilitating the optimal operation of “Vehicle-Station-Networks”.

The battery swapping and charging system (BSCS) is an emerging and promising infrastructure to provide the energy refueling service for EVs. However, each operator (i.e., battery swapping module (BSM) operator and battery charging module (BCM) operator) in BSCS has privacy-preserving requirement and individual interest, which brings challenge to the operation management of BSCS. To this end, this paper investigates the privacy-preserving operation management problem (OMP) of BSCS from a social perspective, in which a comprehensive battery-swapping-charging process is modeled. Specifically, we first model the OMP as a constrained mixed-integer programming (MIP), where the evolutionary of the dynamic battery inventory and the SoC of charging batteries are considered simultaneously. Due to the privacy-preserving requirements of different operators and the strong coupling between continuous and binary decision variables, a novel dual-based Benders decomposition (DBD) algorithm is then developed, which has the following two merits: 1) Each BCM and BSM operator can independently solve its own charging and swapping scheduling problem, thus preserving the privacy of individual operator; 2) The combination of dual decomposition and Benders decomposition techniques facilitates a parallel implementation, thus improving the scheduling efficiency and saving the computational time. Finally, simulation results are provided to validate the effectiveness and scalability of the proposed algorithm.

It is of great significance to develop a coordinated battery swapping station (BSS) recommendation method to reduce the cost of electric vehicles (EVs) and optimize the operation of BSS system. In this paper, the BSS recommendation problem is studied by comprehensively considering the battery swapping cost, diversity of BSS capacities, and differentiated demands of EVs, so as to be as close to the actual situation as possible. To describe the interactions among EVs, we propose a game theory-based approach to recommend appropriate BSSs for EVs to minimize the total cost (namely the sum of travel cost and battery swapping cost) of each EV. Under the game framework, a price function is designed to regulate the swapping price of each BSS, which acts as a coordination signal to induce EVs to join the game and also to alleviate congestion of BSSs. Then, an iterative algorithm is devised to seek the Nash equilibrium, through which a suitable BSS is determined for each EV. Compared to the shortest distance approach, the case studies indicate that the proposed approach can effectively reduce the average cost of EVs, improve the success rate of battery swapping, and balance the utilization ratio of BSSs.

This paper studies the charging/discharging scheduling problem of plug-in electric vehicles (PEVs) in smart grid, considering the users' satisfaction with state of charge (SoC) and the degradation cost of batteries. The objective is to collectively determine the energy usage patterns of all participating PEVs so as to minimize the energy cost of all PEVs while ensuring the charging needs of PEV owners. The challenges herein are mainly in three folds: 1) the randomness of electricity price and PEVs' commuting behavior; 2) the unknown dynamics model of SoC; and 3) a large solution space, which make it challenging to directly develop a model-based optimization algorithm. To this end, we first reformulate the above energy cost minimization problem as a Markov game with unknown transition probabilities. Then a multi-agent deep reinforcement learning (DRL)-based data-driven approach is developed to solve the Markov game. Specifically, the proposed approach consists of two networks: an extreme learning machine (ELM)-based feedforward neural network (NN) for uncertainty prediction of electricity price and PEVs' commuting behavior and a Q network for optimal action-value function approximation. Finally, the comparison results with three benchmark solutions show that our proposed algorithm can not only adaptively decide the optimal charging/discharging policy by on-line learning process, but also yield a lower energy cost within an unknown market environment. [ABSTRACT FROM AUTHOR]

This paper studies price-based residential demand response management (PB-RDRM) in smart grids, in which non-dispatchable and dispatchable loads (including general loads and plug-in electric vehicles (PEVs)) are both involved. The PB-RDRM is composed of a bi-level optimization problem, in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company (UC) by selecting optimal retail prices (RPs), while the lower-level demand response (DR) problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior. The challenges here are mainly two-fold: 1) the uncertainty of energy consumption and RPs; 2) the flexible PEVs' temporally coupled constraints, which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM. To address these challenges, we first model the dynamic retail pricing problem as a Markovian decision process (MDP), and then employ a model-free reinforcement learning (RL) algorithm to learn the optimal dynamic RPs of UC according to the loads' responses. Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches (i.e., distributed dual decomposition-based (DDB) method and distributed primal-dual interior (PDI)-based method), which require exact load and electricity price models. The comparison results show that, compared with the benchmark solutions, our proposed algorithm can not only adaptively decide the RPs through on-line learning processes, but also achieve larger social welfare within an unknown electricity market environment.

In many areas such as sensor networks and smart grid, synchronized monitoring and control are usually required to enable the functionalities of the overall system. Clock synchronization is treated as a precondition to satisfy the synchronized monitoring and control. However, clock synchronization alone is not sufficient to meet these requirements, especially for the situations where low cost distributed embedded systems are involved. Latency and hardware resources are typical impact factors to the synchronization. This paper focuses on proposing a task alignment framework for low cost distributed systems relying only on a minimal set of hardware components. The system architecture and hardware components are presented first. Then the paper presents a task alignment algorithm under the proposed framework. The issues such as inevitable communication delays and execution delays are also addressed. Finally, simulation results verify the effectiveness of the proposed framework.