Optimal Charging Policy Research Articles

Optimizing Battery Charging Using Neural Networks in the Presence of Unknown States and Parameters.

This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control methodology that not only ensures safety and optimizes the charging current, but also substantially reduces the computational complexity with respect to traditional model-based approaches. In addition to their high computational costs, model-based approaches are also hindered by their need to accurately know the model parameters and the internal states of the battery, which are typically unmeasurable in a realistic scenario. In this regard, the deep learning-based methodology described in this work was been applied for the first time to the best of the authors' knowledge, to scenarios where the battery's internal states cannot be measured and an estimate of the battery's parameters is unavailable. The reported results from the statistical validation of such a methodology underline the efficacy of this approach in approximating the optimal charging policy.

A constrained DRL-based bi-level coordinated method for large-scale EVs charging

With the vigorous development of battery electric vehicles (BEVs), BEVs’ charging scheduling is essential for better economy and safety. In this paper, we aim to minimize the electricity purchasing cost considering a large number of BEVs and distributed energy. This problem is challenging to get the optimal charging policy due to a large number of uncertainties and dimension disasters caused by a large scale of BEVs and renewable energy. To meet these challenges, we propose an improved bi-level schedule framework, which decomposes the primal problem into two sub-problems to reduce the computational complexity and designs a communication mechanism to ensure the consistency of optimality between different levels. Then the problem is modeled as constrained multi-level Markov decision processes (CMMDP). In the upper level, a constrained deep reinforcement learning method (CDRL) is proposed to get the total charging or discharging energy of BEV groups. An action constraint module is constructed to ensure the feasibility of chosen actions and a novel reward shaping function is designed to optimize action selection. In the lower level, an optimal descending order charging policy (DOCP) is taken to fast decide the charging or discharging behavior for each BEV based on the upper level’s decision. Numerical experiments show that our method has obvious superiority in training efficiency and solution accuracy compared with state of art DRL methods, and reduces the cost by 12% to 28% compared with an experience charging policy.

On optimal charging scheduling for electric vehicles with wind power generation

We consider the scheduling of battery charging of electric vehicles (EVs) integrated with renewable power generation. The increasing adoption of EVs and the development of renewable energies contribute importance to this research. The optimization of charging scheduling is challenging because of the large action space, the multi-stage decision making, and the high uncertainty. To solve this problem is time-consuming when the scale of the system is large. It is urgent to develop a practical and efficient method to properly schedule the charging of EVs. The contribution of this work is threefold. First, we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed generation. An algorithm is proposed to obtain the optimal charging policy when the sufficient condition holds. Second, the scenario when the supply of the renewable power generation is deficient is investigated. We prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first (mLLLP) rule. Third, we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general situations. We test the proposed algorithm by numerical experiments. The results show that it performs better than the other existing rule-based methods.

Definition and Evaluation of Model-Free Coordination of Electrical Vehicle Charging With Reinforcement Learning

Demand response (DR) becomes critical to manage the charging load of a growing electric vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but models are largely uncertain for the EV scenario (e.g., customer behavior). Model-free approaches, based on reinforcement learning (RL), are an attractive alternative. We propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of charging stations. State-of-the-art algorithms either focus on a single EV, or control an aggregate of EVs in multiple steps (e.g., 1) make aggregate load decisions and 2) translate the aggregate decision to individual EVs). In contrast, our RL approach jointly controls the whole set of EVs at once. We contribute a new MDP formulation with a scalable state representation independent of the number of charging stations. Using a batch RL algorithm, fitted $Q$ -iteration, we learn an optimal charging policy. With simulations using real-world data, we: 1) differentiate settings in training the RL policy (e.g., the time span covered by training data); 2) compare its performance to an oracle all-knowing benchmark (providing an upper performance bound); 3) analyze performance fluctuations throughout a full year; and 4) demonstrate generalization capacity to larger sets of charging stations.

Optimal battery purchasing and charging strategy at electric vehicle battery swap stations

A battery swap station (BSS) is a facility where electric vehicle owners can quickly exchange their depleted battery for a fully-charged one. In order for battery swap to be economically sound, the BSS operator must make a long-term decision on the number of charging bays in the facility, a medium-term decision on the number of batteries in the system, and short-term decisions on when and how many batteries to recharge. In this paper, we introduce a periodic fluid model to describe charging operations at a BSS facing time-varying demand for battery swap and time-varying prices for charging empty batteries, with the objective of finding an optimal battery purchasing and charging policy that best trades off battery investment cost and operating cost including charging cost and cost of customer waiting. We consider a two-stage optimization problem: An optimal amount of battery fluid is identified in the first stage. In the second stage, an optimal charging rule is determined by solving a continuous-time optimal control problem. We characterize the optimal charging policy via Pontryagin’s maximum principle and derive an explicit upper bound for the optimal amount of battery fluid which allows us to quantify the joint effect of demand patterns and electricity prices on battery investment decisions. In particular, fewer batteries are needed when the peaks and the troughs of these periodic functions occur at different times.

Optimization based AIMD saturated algorithms for public charging of electric vehicles

The Additive Increase Multiplicative Decrease (AIMD) algorithm is an interesting approach in congestion control of communication networks, as it maintains the good features of a distributed strategy, without sacrificing the network stability and robustness. Recent applications of these algorithms also concern other industrial fields such as Electric Vehicles (EVs) based transportation systems, for which the introduction of an optimal charging policy is an important challenge for power systems operation. Moreover, saturation constraints on the resource allocated to each vehicle need to be taken into account in order to avoid peak power requirements and grid overloads. Optimization based AIMD algorithms with saturation constraints are proposed in this paper for public charging of EVs. Specifically, a new AIMD approach is presented in order to capture the main advantages of optimal algorithms which minimize either the sum of charging times or the operation time of each vehicle, giving rise to a mixed AIMD strategy. Simulation results illustrate the performance of the proposal, even in comparison to the corresponding centralized optimal solutions.

Optimal Charging Operation of Battery Swapping and Charging Stations With QoS Guarantee

Motivated by the urgent demand for the electric vehicle (EV) fast refueling technologies, battery swapping and charging stations (BSCSs) are envisioned as a promising solution to provide timely EV refueling services. However, inappropriate battery charging operation in BSCSs cannot only incur unnecessary high charging cost but also threaten the reliability of the power grid. In this paper, we aim at obtaining an optimal charging operation policy for a single BSCS to minimize its charging cost while ensuring its quality-of-service. Leveraging the novel queueing network model, we propose to formulate the charging operation problem as a constrained Markov decision process and derive the optimal policy by the standard Lagrangian method and dynamic programming. To avoid the curse of dimensionality in practical large-scale systems, we further analyze the structure of the optimal policy and transform the dynamic programming procedure into an equivalent threshold optimization problem with a discrete separable convex objective function. Numerical results validate our theoretical analysis and the computational efficiency of our proposed algorithms. This paper also shows the impact of the system parameters (e.g., numbers of batteries and chargers) on the average cost under the optimal charging policy, which gives rich insights into the infrastructure planning of future BSCS networks.

Optimal Charging Schemes for Electric Vehicles in Smart Grid: A Contract Theoretic Approach

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.

Information-Theoretic Privacy for Smart Metering Systems with a Rechargeable Battery

Smart-metering systems report electricity usage of a user to the utility provider on almost real-time basis. This could leak private information about the user to the utility provider. In this paper, we investigate the use of a rechargeable battery in order to provide privacy to the user. We assume that the user load sequence is a first-order Markov process, the battery satisfies ideal charge conservation, and that privacy is measured using normalized mutual information (leakage rate) between the user load and the battery output. We study the optimal battery charging policy that minimizes the leakage rate among the class of battery policies that satisfy causality and charge conservation. We propose a series reduction on the original problem and ultimately recast it as a Markov Decision Process (MDP) that can be solved using a dynamic program. In the special case of i.i.d. demand, we explicitly characterize the optimal policy and show that the associated leakage rate can be expressed as a single-letter mutual information expression. In this case, we show that the optimal charging policy admits an intuitive interpretation of preserving a certain invariance property of the state. Interestingly an alternative proof of optimality can be provided that does not rely on the MDP approach, but is based on purely information theoretic reductions.

Optimal Charging Control for Plug-in Electric Vehicles in Smart Microgrids Fueled by Renewable Energy Sources

There is a growing interest in plug-in electric vehicles (EVs). Charging EVs from smart microgrids fueled by renewable energy resources is becoming a popular green approach. Although some works have been done on renewable energy sources and EVs in smart microgrids, the stochastic characteristics and the dynamic interplay between these two important green solutions should be carefully considered. Furthermore, one of the important performance metrics, service availability for EVs, is largely ignored in the existing works. In this article, we study the charging policies in smart microgrids with EVs and renewable energy sources. We analyze service availability under different charging policies. Based on the renewable energy sources states, battery states, and the number of charging EVs, an optimal charging policy is obtained to maximize the energy utilization with service availability constraints. We formulate the optimal charging problem as a stochastic decision process. Maximizing the energy utilization with service availability constraints is the objective in our model. Extensive simulation results are presented. It is illustrated that the proposed scheme can significantly improve the service availability for EVs in microgrids fueled by renewable energy sources.