Plug-in Electric Vehicles (PEVs) are a rapidly developing technology that can reduce greenhouse gas emissions and change the way vehicles obtain power. PEV charging stations will most likely be available at home and at work, and occasionally be publicly available, offering flexible charging options. Ideally, each vehicle will charge during periods when electricity prices are relatively low, to minimize the cost to the consumer and maximize societ al benefits. A Demand Response (DR) service for a fleet of PEVs could yield such charging schedules by regulating consumer electricity use during certain time periods, in order to meet an obligation to the market. We construct an automated DR mechanism for a fleet of PEVs that facilitates vehicle charging to ensure the demands of the vehicles and the market are met. Our dynamic algorithm depends only on the knowledge of a few hundred driving behaviors from a previous similar day, and uses a simple adjusted pricing scheme to instantly assign feasible and satisfactory charging schedules to thousands of vehicles in a fleet as they plug-in. The charging schedules generated using our adjusted pricing scheme can ensure that a new demand peak is not created and can reduce the consumer cost by over 30 when compared to standard charging, which may also increase peak demand by 3.5 . In this paper, we present our formulation, algorithm and results.
in @cite_7 use dynamic programming to assign charging schedules that provide frequency regulation. Similarly, in the work by @cite_18 an algorithm is constructed that makes dynamic decisions for lowest-cost charging schedules of PEVs. Neither of these works considers an obligation to the market that needs to be met, and the demand of each vehicle is considered individually (i.e., not as part of a fleet); both suggested algorithms would result in an increased peak demand. In @cite_5 , establish a decentralized algorithm that determines an equilibrium price so that the total amount of charging done in the fleet fills the overnight demand valley.' This algorithm takes into account all vehicles in the fleet, but is not dynamic (i.e., it is solves an offline problem) and assumes only a few types of driving behaviors exist. A dynamic (or online) algorithm does not require that all vehicles are connected to the grid at the same time to exactly report their future driving schedules, and makes decisions for each vehicle without solving a problem that depends on knowing the demands of the entire fleet.
@cite_5 @cite_18 @cite_7
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We propose a decentralized algorithm to optimally schedule electric vehicle (EV) charging. The algorithm exploits the elasticity of electric vehicle loads to fill the valleys in electric load profiles. We first formulate the EV charging scheduling problem as an optimal control problem, whose objective is to impose a generalized notion of valley-filling, and study properties of optimal charging profiles. We then give a decentralized algorithm to iteratively solve the optimal control problem. In each iteration, EVs update their charging profiles according to the control signal broadcast by the utility company, and the utility company alters the control signal to guide their updates. The algorithm converges to optimal charging profiles (that are as “flat” as they can possibly be) irrespective of the specifications (e.g., maximum charging rate and deadline) of EVs, even if EVs do not necessarily update their charging profiles in every iteration, and use potentially outdated control signal when they update. Moreover, the algorithm only requires each EV solving its local problem, hence its implementation requires low computation capability. We also extend the algorithm to track a given load profile and to real-time implementation. We address the optimal bidding problem of an aggregator that manages the charging of a group of plug-in electric vehicles. The aggregator purchases energy in the day-ahead market, and offers capacity in the frequency regulation market. The charging flexibility of the fleet is expressed as a set of time-varying power and energy constraints. The impact of the aggregator's demand on day-ahead market prices is endogenously modelled by formulating the problem as a bilevel problem: The upper level represents the aggregator's cost minimisation, and the lower level represents the market clearing. We explore several settings of the regulation market, analysing the impact of restrictions that may apply on the capacity offers (e.g., symmetric bids). Moreover, we compare the options of providing regulation with unidirectional charging, and with vehicle-to-grid (V2G). We test the proposed method in a case study for Switzerland, with historical day-ahead and regulation market data, and realistic driving patterns. This paper proposes an operating framework for aggregators of plug-in electric vehicles (PEVs). First, a minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands. The same algorithm is applicable for negotiating bilateral contracts. Second, a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day. Simulation results are used to evaluate the proposed algorithms, and to demonstrate the potential impact of an aggregated PEV fleet on the power system. Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2 sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management coordination unit, gaining robustness against single-link node failures, and being scalable in terms of single-node computations. Plug-in electric vehicles (PEVs) can be considered flexible loads, as the time when they are charged can be shifted to a certain extent without impacting the drivers' mobility. An aggregator coordinating these flexible resources aims to minimize the costs of charging, subject to individual PEV constraints, imposed by battery and charging infrastructure characteristics, as well as driving patterns. Since driving behavior cannot be perfectly forecasted, this problem is stochastic. In this paper, we propose a decentralized control algorithm to coordinate charging, based on the Alternating Direction Method of Multipliers (ADMM). In this setup, the aggregator and PEVs find the global solution by individually solving local optimization problems. The solution is found iteratively, whereby information between the PEVs and the aggregator is exchanged at each iteration. When the objective function of the charging optimization problem and the PEV constraints are convex, the algorithm converges to the global optimum. To take driving behavior uncertainty into account, the scheme considers several scenarios of driving patterns for each vehicle. A receding time horizon optimization is used, whereby at each new stage the representation of the fleet is updated with new scenarios consistent with the current observations. The local optimization problems, which can be solved very fast, could be solved in parallel for each PEV and scenario using decentralized computing, making the approach suitable for large-scale problems. A numerical example shows how this method can be applied to flatten the system load. The paper develops and illustrates a novel decentralized charging control algorithm for large populations of plug-in electric vehicles (PEVs). The proposed algorithm is an application of the so-called Nash certainty equivalence principle (or mean-field games.) The control scheme seeks to achieve social optimality by establishing a PEV charging schedule that fills the overnight demand valley. The paper discusses implementation issues and computational complexity, and illustrates concepts with various numerical examples. This paper proposes a novel cooperative charging strategy for a smart charging station in the dynamic electricity pricing environment, which helps electric vehicles (EVs) to economically accomplish the charging task by the given deadlines. This strategy allows EVs to share their battery-stored energy with each other under the coordination of an aggregator, so that more flexibility is given to the aggregator for better scheduling. Mathematically, the scheduling problem is formulated as a constrained mixed-integer linear program (MILP) to capture the discrete nature of the battery states, i.e., charging, idle and discharging. Then, an efficient algorithm is proposed to solve the MILP by means of dual decomposition and Benders decomposition. At last, the algorithm can be implemented in a distributed fashion, which makes it scalable and thus suitable for large-scale scheduling problems. Numerical results validate our theoretical analysis. Research predicts that in 2030, around 30 of all vehicles in Belgium will be plug-in hybrid electric vehicles (PHEVs). Because most PHEVs are charged after working hours, the existing peak load in the evening will increase significantly. Large peak loads cause more expensive production and can even damage the electricity infrastructure. In a Smart Grid, the charging of PHEVs can be controlled to reduce peak load, denoted as demand side management (DSM). The goal of our research is to compare several solutions for DSM of PHEVs. This paper takes a first step by benchmarking a multi-agent solution against an optimal quadratic programming (QP) scheduler solution. Simulations show that a QP scheduler is able to optimally flatten peak loads while sufficiently charging the PHEV batteries. However, this solution is unfeasible in practice because it scales poorly and requires complete information on when and how much PHEVs need to charge beforehand, which is not available. The MAS solution proves to be scalable and adaptable to incomplete and unpredictable information while peaks are still reduced with an efficiency up to 95 compared to the QP scheduler. This paper develops a strategy to coordinate the charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand “valley”. The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized control is not possible, but where optimal or near-optimal charging patterns are essential to system operation. The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme. There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with an increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid-level storage. Independent system operators such as the New York Independent System Operator NYISO require that battery storage operators place bids into an hour-ahead market although settlements may occur in increments as small as five minutes, which is considered near "real-time". The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour and simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program. We describe and employ a convergent approximate dynamic programming ADP algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy; using optimal benchmarks, we empirically show the computational benefits of the algorithm. Furthermore, we propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process and makes no assumptions regarding a specific real-time price model. We demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective. A large penetration of electric and plug-in hybrid electric vehicles would likely result in increased system peaks and overloading of power system assets if the charging of vehicles is left uncontrolled. In this paper we propose both a centralized and a decentralized smart-charging scheme which seek to minimize system-wide generation costs while respecting grid constraints. Under the centralized scheme, vehicles' batteries are aggregated to virtual storage resources at each network node, which are optimally dispatched with a multiperiod Optimal Power Flow. On the other hand, under the decentralized scheme, price profiles broadcasted to vehicles day-ahead are determined so that the optimal response of individual vehicles to this tariff achieves the goal of cost minimization. Two alternative tariffs are explored, one where the same price profile applies system-wide, and another where different prices can be defined at different nodes. Results show that compared with uncontrolled charging, these smart-charging schemes successfully avoid asset overloading, displace most charging to valley hours and reduce generation costs. Moreover they are robust in the face of forecast errors in vehicle behavior. Optimizing shared vehicle systems (bike-sharing car-sharing ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of complex network externalities. In particular, changes in the demand supply at any location (via dynamic pricing, rebalancing of empty vehicles, etc.) affect future supply throughout the system within short timescales. Such externalities are well captured by steady-state Markovian models, which are therefore widely used to analyze and design shared vehicle systems. However, using such models to design pricing control policies is computationally difficult since the resulting optimization problems are high-dimensional and non-convex. To this end, we develop a general approximation framework for designing pricing policies in shared vehicle systems, based on a novel convex relaxation which we term elevated flow relaxation. Our approach provides the first efficient algorithms with rigorous approximation guarantees for a wide range of objective functions (throughput, revenue, welfare). For any shared vehicle system with @math stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1) m. This guarantee is particularly meaningful when m n, the average number of vehicles per station is large, as is often the case in practice. Further, the simplicity of our approach allows us to extend it to more complex settings. Apart from pricing, shared vehicle systems enable other control levers for modulating demand and supply, e.g. rebalancing empty vehicles, redirecting riders to nearby vehicles, etc. Our approach yields efficient algorithms with the same approximation guarantees for all these problems, and in the process, obtains as special cases several existing heuristics and asymptotic guarantees. We also extend our approach to obtain bi-criterion guarantees in multi-objective settings; we illustrate this with the example of Ramsey pricing. From a technical perspective, our work develops a new approach for obtaining control policies with approximation guarantees in steady-state Markovian models. Our approach can be distilled into the following three-step program: (i) construct an upper bound via a relaxation to the original problem that encodes essential conservation laws of the system, (ii) identify a family of control policies inducing known steady-state distributions that achieve the value of the relaxed solution in an appropriate scaling limit (in our case, state-independent policies in the limit m++), and (iii) characterize the performance loss between the finite system (i.e. fixed m) and the scaling limit. This technique may be of independent interest for other settings. An investigation of the single-vehicle, many-to-many, immediate-request dial-a-ride problem is developed in two parts I and II. Part I focuses on the “static” case of the problem. In this case, intermediate requests that may appear during the execution of the route are not considered. A generalized objective function is examined, the minimization of a weighted combination of the time to service all customers and of the total degree of “dissatisfaction” experienced by them while waiting for service. This dissatisfaction is assumed to be a linear function of the waiting and riding times of each customer. Vehicle capacity constraints and special priority rules are part of the problem. A Dynamic Programming approach is developed. The algorithm exhibits a computational effort which, although an exponential function of the size of the problem, is asymptotically lower than the corresponding effort of the classical Dynamic Programming algorithm applied to a Traveling Salesman Problem of the same size. Part II extends this approach to solving the equivalent “dynamic” case. In this case, new customer requests are automatically eligible for consideration at the time they occur. The procedure is an open-ended sequence of updates, each following every new customer request. The algorithm optimizes only over known inputs and does not anticipate future customer requests. Indefinite deferment of a customer's request is prevented by the priority rules introduced in Part I. Examples in both “static” and “dynamic” cases are presented. In this paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grids. The uncertainty of the arrival of the EV, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the required charging energy for each EV is random. The goal is to minimize the mean waiting time for EVs under the long-term constraint on the cost. We propose queue mapping to convert the EV queue to the charging demand queue, and we prove the equivalence between the minimization of the two queues' average length. Then, we focus on the minimization for the average length of the charging demand queue under the long-term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this constrained stochastic optimization problem. The system state includes the charging demand queue length, the charging demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally, the number of charging demands and the allocated energy from the storage battery compose the 2-D policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the 2-D policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally, we investigate the proposed policies numerically. Smart grids (SG) energy management system and electric vehicle (EV) have gained considerable reputation in recent years. This has been enabled by the high growth of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand and to manage building energy during the day, it is important to perform an intelligent scheduling for EVs charging and discharging service and buildings areas by including different metrics, such as real-time price and demand–supply curve. In this paper, we propose a real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Our proposed approach uses a decentralized cloud computing architecture based on software define networking (SDN) technology and network function virtualization (NFV). We aim to schedule user's requests in a real-time way and to supervise communications between microgrids controllers, SG and user entities (i.e., EVs, electric vehicles public supply stations, advance metering infrastructure, smart meters, etc.). We formulate the problem as a linear optimization problem for EV and a global optimization problem for all microgrids. We solve the problems by using different decentralized decision algorithms. To the best of our knowledge, this is the first paper that proposes a pricing model based on decentralized Cloud-SDN architecture in order to solve all the aforementioned issues. The extensive simulations and comparisons with related works proved that our proposed pricing model optimizes the energy load during peak hours, maximizes EVs utility, and maintains the microgrid stability. The simulation is based on real electric load of the city of Toronto. We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off points in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with a battery swap facility that helps cope with the EVs' limited range. The goal of the system is to maximise the number of customers that are serviced. Thus, we first model and solve this problem optimally using Mixed-Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop a greedy heuristic algorithm that is shown to generate near-optimal solutions and can scale to thousands of consumer requests and EVs. Both algorithms are evaluated in a setting using data of real locations of shared vehicle pick-up and drop-off stations and the greedy algorithm is shown to be on average 90 of the optimal in terms of average task completion. Plug-in electric vehicles (PEVs) are emerging as an eco-friendly and cost-effective alternative to conventional vehicles driven by internal combustion engines. However, uncoordinated charging of a large number of PEVs may cause grid failure. Therefore, charge scheduling of PEVs is an important problem. However, the charge scheduling by a single aggregator does not scale well as the PEV population grows. We propose a distributed framework for efficient PEV charging with multiple aggregators in a city where a PEV raises its charging request to a specific aggregator, and each aggregator has partial information about others. The aggregators collaborate among themselves for scheduling PEVs for charging in different charging stations owned by it or others. In this paper, we formulate a bi-objective charge scheduling optimization problem that attempts to maximize the total profit of the aggregators while maximizing the total number of PEVs charged. We first prove that the problem is NP-complete. We then propose distributed offline and online algorithms to solve the problem, and present simulation results for some realistic traffic scenarios. In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging and complex problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked (and hence not served) by the closest charging station to their location. Overall, the paper addresses key issues related to the efficient operation, both from the perspective of the power grid and the drivers satisfaction, of a network of charging stations. Urban traffic gridlock is a familiar scene. At the same time, the mean occupancy rate of personal vehicle trips in the United States is only 1.6 persons per vehicle mile. Ridesharing has the potential to solve many environmental, congestion, pollution, and energy problems. In this paper, we introduce the problem of large scale real-time ridesharing with service guarantee on road networks. Trip requests are dynamically matched to vehicles while trip waiting and service time constraints are satisfied. We first propose two scheduling algorithms: a branch-and-bound algorithm and an integer programing algorithm. However, these algorithms do not adapt well to the dynamic nature of the ridesharing problem. Thus, we propose kinetic tree algorithms which are better suited to efficient scheduling of dynamic requests and adjust routes on-the-fly. We perform experiments on a large Shanghai taxi dataset. Results show that the kinetic tree algorithms outperform other algorithms significantly.