An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. In this paper, we propose a new formulation of the joint scheduling of EV and Unit Commitment (UC), called EVUC. Our formulation considers the characteristics of EVs while optimizing the system total running cost. We employ Chemical Reaction Optimization (CRO), a general-purpose optimization algorithm to solve this problem and the simulation results on a widely used set of instances indicate that CRO can effectively optimize this problem.
Existing work of V2G operation scheduling of EVs can be divided into two classes: charging-only scheduling and bi-directional scheduling. In charging-only scheduling, the algorithms try to optimize the electricity flow from the power grid to the batteries of EVs. For example, Shrestha optimized the EV charging cycles to off-peak periods to flatten the demand curve, in order to reduce the charging cost @cite_3 . Mets presented a smart energy control strategy to charge residential plug-in hybrid EVs (PHEVs) to smooth the system load profile @cite_23 . However, with the development of V2G technology, bi-directional charging, i.e., V2G and G2V, is possible, and bi-directional scheduling algorithm has attracted much research recently. The role of EVs in the power system may change during the day from loads to sources, and vice versa. Binary particle swarm optimization was employed to tackle the V2G scheduling problem to minimize the total running cost and reduce green house gas emission in @cite_4 and @cite_25 . Han proposed an aggregator for V2G frequency regulation in @cite_18 , aiming to maximize the revenue.
@cite_18 @cite_4 @cite_3 @cite_23 @cite_25
2051431619 2542505360 1968987283 2529606212 2225115145 2346452219 2138114791 2094270660 2750105184 2016038157 2115594466 1997915897 2024974439 2061926759 2119418624 2131091718 2058606879 2762734442 2015555034 2161715775
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. This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle-to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end. Vehicle-to-grid (V2G) has the potential of reducing the cost of owning and operating electric vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it can be implemented on standard J1772 chargers and it does not degrade EV batteries from cycling. In this work an optimal combined bidding formulation for regulation and spinning reserves is developed to be used by aggregators. This formulation takes into account unplanned departures by EV owners during contract periods and compensates accordingly. Optional load level and price constraints are also developed. These algorithms maximize profits to the aggregator while increasing the benefits the customers and utility. Simulations over a three month period on the ERCOT system show that implementation of these algorithms can provide significant benefits to customers, utilities, and aggregators. Comparisons with bidirectional V2G show that while the benefits of unidirectional V2G are significantly lower, so are the risks. 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. 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 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. 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. Vehicle-to-grid (V2G) control has the potential to provide frequency regulation service for power system operation from electric vehicles (EVs). In this paper, a decentralized V2G control (DVC) method is proposed for EVs to participate in primary frequency control considering charging demands from EV customers. When an EV customer wants to maintain the residual state of charge (SOC) of the EV battery, a V2G control strategy, called battery SOC holder (BSH), is performed to maintain the battery energy around the residual SOC along with adaptive frequency droop control. If the residual battery energy is not enough for next trip, the customer needs to charge the EV to higher SOC level. Then, a smart charging method, called charging with frequency regulation (CFR), is developed to achieve scheduled charging and provide frequency regulation at the same time. Simulations on a two-area interconnected power system with wind power integration have shown the effectiveness of the proposed method. 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. 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. 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. Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands. For vehicle-to-grid (V2G) frequency regulation services, we propose an aggregator that makes efficient use of the distributed power of electric vehicles to produce the desired grid-scale power. The cost arising from the battery charging and the revenue obtained by providing the regulation are investigated and represented mathematically. Some design considerations of the aggregator are also discussed together with practical constraints such as the energy restriction of the batteries. The cost function with constraints enables us to construct an optimization problem. Based on the developed optimization problem, we apply the dynamic programming algorithm to compute the optimal charging control for each vehicle. Finally, simulations are provided to illustrate the optimality of the proposed charging control strategy with variations of parameters. 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. The electrification of the vehicle fleet will result in an additional load on the power grid. Adequately dealing with such pluggable (hybrid) electrical vehicles (PHEV) forms part of the challenges and opportunities in the evolution towards Smart Grids. In this paper, we investigate the potential benefits of using control mechanisms, that could be offered by a Home Energy control box, in optimizing energy consumption stemming from PHEV charging in a residential use case. We present smart energy control strategies based on quadratic programming for charging PHEVs, aiming to minimize the peak load and flatten the overall load profile. We compare two strategies, and benchmark them against a business-as-usual scenario assuming full charging starting upon plugging in the PHEV. The first, local strategy only uses information at the home where the PHEV is charged: as a result the charging is optimized for local loads. The local strategy is compared to a global iterative strategy which controls the charging of multiple vehicles based on global load information over a residential area. Both strategies control the duration and rate of charging and result in charging schedules for each vehicle. We present quantitative simulation results over a set of 150 homes, and discuss the strategies in terms of complexity and performance (esp. resulting energy consumption), as well as their requirements concerning infrastructure and communication. An automatic Vehicle-to-Grid (V2G) technology can contribute to the utility grid. V2G technology has drawn great interest in the recent years. Success of the sophisticated automatic V2G research depends on efficient scheduling of gridable vehicles in constrained parking lots. Parking lots have constraints of space and current limits for V2G. However, V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO optimizes the on off states of power generating units easily. Vehicles are presented by signed integer number instead of 0 1 to reduce the dimension of the problem. Typical discrete version of PSO has less balance between local and global searching abilities to optimize the number of charging discharging gridable vehicles in the constrained system. In the same model, balanced PSO is proposed to optimize the V2G part in the constrained parking lots. Finally, results show a considerable amount of profit for using proper scheduling of gridable vehicles in constrained parking lots. Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging recharging frequency and strategies (uncoordinated coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future. Dynamic energy pricing policy introduces real-time power-consumption-reflective pricing in the smart grid in order to incentivise energy consumers to schedule electricity-consuming applications (tasks) more prudently to minimise electric bills. This has become a particularly interesting problem with the availability of photovoltaic (PV) power generation facilities and controllable energy storage systems. This study addresses the problem of concurrent task scheduling and storage management for residential energy consumers with PV and storage systems, in order to minimise the electric bill. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and power dependent. Tasks are allowed to support suspend-now and resume-later operations. A negotiation-based iterative approach has been proposed. In each iteration, all tasks are ripped-up and rescheduled under a fixed storage charging discharging scheme, and then the storage control scheme is derived based on the latest task scheduling. The concept of congestion is introduced to gradually adjust the schedule of each task, whereas dynamic programming is used to find the optimal schedule. A near-optimal storage control algorithm is effectively implemented. Experimental results demonstrate that the proposed algorithm can achieve up to 60.95 in the total energy cost reduction compared with various baseline methods. While electric vehicles (EVs) are expected to provide environmental and economical benefit, judicious coordination of EV charging is necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, this paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for optimal price adjustment. The dependencies on price responsiveness among consumers are captured by a conditional random field (CRF) model. To account for temporal dynamics potentially in a strategic setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for tracking the CRF parameters. The proposed model is then used as an input to a stochastic profit maximization module for real-time price setting. Numerical tests using simulated and semi-real data verify the effectiveness of the proposed approach. Due to green initiatives adopted in many countries, renewable energy will be massively incorporated into the future smart grid. However, the intermittency of the renewables may result in power imbalance, thus adversely affecting the stability of a power system. Voltage regulation may be used to maintain the power balance at all times. As electric vehicles (EVs) become popular, they may be connected to the grid to form a vehicle-to-grid (V2G) system. An aggregation of EVs can be coordinated to provide voltage regulation services. However, V2G is a dynamic system where EVs are connected to the grid according to the owners' habits. In this paper, we model an aggregation of EVs with a queueing network, whose structure allows us to estimate the capacities for regulation up and regulation down, separately. The estimated capacities from the V2G system can be used for establishing a regulation contract between an aggregator and the grid operator, and facilitate a new business model for V2G.