The charge‐discharge compensation pricing strategy of electric vehicle aggregator considering users response willingness from the perspective of Stackelberg game

This study proposes an electric vehicle (EV) aggregator operation mechanism in a residential community. The EV charging and discharging operation behaviours are scheduled to maximise the EV aggregator revenue, while EV aggregator provides reserve service for the grid. This study not only considers the energy and information interactions between three stakeholders: EV aggregator, EV owners, and power grids, but also the economic interests of aggregator and owners are considered. The aggregator‐owner economic inconsistency issue (EV owners get higher charging cost in aggregator scheduling than self‐scheduling) is presented. In order to mediate this issue, a rebate factor is proposed. In the first stage, the objective is to minimise the day‐ahead (DA) charging cost of EV owners. Then the second stage is to maximise DA aggregator revenue with different rebate values. Finally, in the third stage, a real‐time scheduling strategy is proposed to maximise aggregator revenue using the optimal rebate value. In addition, the battery degradation in influencing scheduling is formulated. Scheduling results show the effectiveness of the proposed strategy, e.g. economic inconsistency of different parties can be mediated. Significant reduction of EV owners’ cost from self‐scheduling can be achieved while the revenue of EV aggregator is maximised under the proposed strategy.

This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners’ viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty―including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators―are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush–Kuhn–Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment.

With the popularization of charging piles and the development of V2G (vehicle-to-grid) technology, electric vehicles (EVs) will have more and more opportunities to participate in the operation and scheduling of electric power system. As an agent between the power grid and EV customers, electric vehicle aggregator (EVA) need to comprehend the available EVs' response capacity (RC) when trading with the system operator. This paper proposes a model aiming to evaluate the multitimescale RC of EVA. The RC evaluation of EVA takes into account the response willingness of EV customers, which can make the evaluation of RC boundary more accurate. Firstly, a temporal RC evaluation model of EV monomer considering chargedischarge state and state of charge (SOC) is established. Secondly, based on the consumer psychology model which reflects the relationship between customers' responsivity and incentive price, a multi-timescale RC evaluation model of EVA considering customers' willingness is built. The day-ahead RC of EVA is evaluated by the state prediction data of EVs. According to the control strategy which considers response time (RT) and SOC indicators comprehensively, the RC evaluation results of EVA is revised in intra-day. Finally, using the statistical data from the EU MERGE project, the effectiveness of the proposed evaluation model is verified, and the impact of incentive price and scheduling time scale on the RC of EVA are analyzed. The results indicate that the proposed model can effectively track the scheduling goals of the system and realize the dynamic update of the RC of EVA.

A bi-level risk-constrained offering strategy of a wind power producer considering demand side resources

Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty

A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem

This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximization. In the Day‐Ahead Market (DAM), the EV aggregator submits a bidding plan to the Independent Systems Operator (ISO) including base‐load and reserve up/down capacities plans. In the Real‐Time Market (RTM), the EV aggregator should deploy reserve based on the ISO's requirements, and the EV aggregator could receive income by deploying reserve or penalty for reserve shortage. The stochastic programming method is applied to address the uncertain reserve deployment requirements in RTM. In addition, Energy Storage Systems (ESS) are utilized by the EV aggregator to enhance the ability in providing reserve service. The aggregator–owner contract is designed to guarantee EV owners' economic benefits. Case studies show the expected profits of the EV aggregator are maximized and the risk of the reserve shortage is well managed, i.e., penalty is minimized. With the utilization of ESS, the performance of the EV aggregator in making response to the ISO's requirements is improved. That is, the required reserve percentage increases from 5.68% to 7.85%, and the deployed reserve percentage increases from 69.71% to 88.47%.

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system’s supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA’s participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO’s revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO’s revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA’s reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets.

In this paper, an Electric Vehicle (EV) aggregator scheduling strategy is proposed for revenue maximization both in Day-Ahead (DA) and Real-Time (RT) markets. EV aggregator participates in energy and reserve markets to maximize revenue by coordinating charging and discharging operations of EVs based on real-time price and reserve prices. Firstly, the economic benefits of each EV owner are considered in aggregator scheduling, i.e. to take owners economic benefits into account. Secondly, the reserve call-up service is regarded as an interrupt for aggregator scheduling in RT market. Case study shows the impact of call-for-reserve from power grids on aggregator revenue. The potential reserve call-up service issue (revenue loss due to stochastic reserve call-up) is presented and two business models for aggregator in reserve market are proposed to solve the reserve call-up service issue. It is found that the reserve callup service information availability in the real-time market plays an important role for EV aggregator and the common one-price- one-penalty model may not suffice for the practical scheduling of EV aggregators due to the revenue loss caused by the stochastic call-up service.

Electric vehicle (EV) aggregators participating in frequency regulation services with a large number of EVs may have a delayed response problem due to their underlying communication infrastructure and scheduling. In this paper, we investigate the effect of delays in EV aggregators’ domain on their frequency regulation payments, especially mileage payment. Since the delays affect frequency regulation performance, in order to consider the delay effect on regulation performance, we enhance the conventional load frequency control model by including multiple EV aggregators with varying delays. Under this model, we evaluate the effect of delays in EV aggregators’ domain on their profits under two ISOs’ mileage payments such as PJM and NYISO. Through simulations, it is shown that the profit of EV aggregators increases as the delays increases. The conventional mileage payments did not motivate EV aggregators to reduce the delay in order to improve regulation performance. To solve the problem, we propose a new mileage payment to reduce the delay. We finally show that the proposed payment can provide an incentive to reduce the delay.

Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets

Electric vehicle (EV) aggregator, as an agent between the electricity market and EV owners, participates in the future and pool market to supply EVs’ requirement. Because of the uncertain nature of pool prices and EVs’ behaviour, this paper proposed a two-stage scenario-based model to obtain optimal decision making of an EV aggregator. To deal with mentioned uncertainties, the aggregator’s risk aversion is applied using conditional value at risk (CVaR) method in the proposed model. The proposed two-stage risk-constrained decision-making problem is applied to maximize EV aggregator’s expected profit in an uncertain environment. The aggregator can participate in the future and pool market to buy the required energy of EVs and offer optimal charge/discharge prices to the EV owners. In this model, in order to assess the effects of EVs owners’ reaction to the aggregator’s offered prices on the purchases from electricity markets, a sensitivity analysis over risk factor is performed. The numerical results demonstrate that with the application of the proposed model, the aggregator can supply EVs with lower purchases from markets.

With the electrification in transportation systems, Electric Vehicles (EVs) have developed rapidly in recent years. At the same time, with large-scale EV integration to power grids, the charging behaviours of EVs bring both challenges and opportunities to power grids operation. This thesis focuses on the EV energy management in smart grids, and the EV energy management problem is studied considering three stakeholders' interests, i.e. EV owner, aggregator and grid, respectively. First, the economic relationship between EV owners and the aggregator is studied (EV owners' and aggregator's interest). Two multi-objective optimisation methods are applied to investigate the economic relationship between these two stakeholders and the aggregator{owner economic inconsistency issue is presented. To mediate this issue, a rebate factor is proposed in the model. The results show that a signi cant reduction in the EV owners' charging fee from self-scheduling can be achieved while the aggregator pro t is maximised. Second, the EV aggregator bidding strategy in the electricity market is studied (aggregator's interest). By jointly considering the reserve capacity in the day-ahead market and the uncertainty of reserve deployment requirements in the real-time market, a scenario-based stochastic programming method is used to maximise the expected aggregator pro t. The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. In addition, an owner{aggregator contract is designed to mitigate the economic inconsistency issue between EV owners and the aggregator. The results show that the expected aggregator pro t is guaranteed by maximising reserve deployment payments and mitigating the penalties and thus the uncertainty of the reserve market is well managed. Third, the EV integration in a transmission system is studied (grid's interest) to achieve the coordination between generators and EVs. To tackle the challenge of large-scale EV integration problem, a bi-level scheduling strategy is proposed. The bi-level strategy clearly de nes the responsibility of transmission system operator and the aggregator. An EV information grouping method is designed, which could e ciently tackle the optimisation complexity problem. In addition, a detailed EV battery charging model is built. The results show that the total cost of the systems is minimised and EVs could shave the peak and ll the valley loads. This thesis discusses the EV energy management problem considering three stakeholders' interests, respectively. The proposed strategies in this thesis clearly evaluate and de ne the economic relationships and responsibility among EV owners, aggregator and the grid in managing EV charging and discharging behaviours. Based on three case studies conducted in this thesis, EV energy management could bene t the stakeholders as follows: (1) the EV owner charging fee is minimised while their driving requirements are satis ed; (2) the aggregator pro t is maximised by participation in the electricity market; (3) the cost of the system is minimised by achieving the coordination between EVs and generators.

This paper presents a stochastic method for an electric vehicle (EV) aggregator to coordinate EV charging schedule considering uncertainty in EV departures. The EV aggregator is responsible for managing the charging schedule of EVs while participating in the electricity markets. The managed EV charging can provide additional revenues to the aggregator from regulation market participation and charging cost reductions to EV owners. The aggregator needs to coordinate the charging schedule considering various uncertain factors such as electricity market prices and the stochastic characteristics of EVs. In this paper, the EV charging scheduling problem incorporating uncertainty in EV departures is formulated as a stochastic optimization problem. A stochastic optimization method is used to solve the EV charging scheduling problem. Latin hypercube sampling (LHS) and a scenario-reduction method are also applied to reduce the computational efforts of the proposed method. The results of a numerical example are presented to show the effectiveness of the proposed stochastic EV charging coordination method.

A key motivation to fasten roll-out of electric vehicles (EVs) to the market is to implement Vehicle-to-Grid (V2G) functionalities. With V2G in place, EV owners can have extra freedom to interact their battery energy with power grids, namely by selling their energy to the grid when their EVs are not in use. On the other hand, EV aggregators and utility companies can leverage the flexibility of the collected energy to implement various ancillary services to the grids, which may significantly reduce costs of, for instance, running spinning reserve of traditional power plants on the grid side. However, this extra freedom also poses practical challenges in terms of how to devise a discharging strategy for a group of EVs that is fair and in some sense optimal. In this paper, we present a new design of EV discharging strategy in a typical V2G energy trading framework whilst leveraging the whale optimization algorithm in a decentralized manner, a metaheuristic algorithm that has been shown effective in solving large-scale centralized optimization problems. We demonstrate that by using simple ideas of data shuffling and aggregation, one can design an EV discharging strategy in a fair, optimal and privacy-aware manner, where the privacy refers to the fact that no critical information of EVs should be exchanged with the EV aggregator, and vice versa. The fairness implies that a common discharge rate needs to be sought for all EVs so that no one gets better benefits than others in the same V2G programme. Simulation results are presented to illustrate the efficacy of our proposed system.