Energy and Information Management of Electric Vehicular Network: A Survey

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

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.

Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for the power grid, stimulates new ideas in current smart grid research. Since EVs are selfish individuals owned by different parties, how to motivate them to provide ancillary services becomes an issue. In this paper, game theoretic approaches using non-cooperative and cooperative game are proposed to motivate EVs to provide frequency regulation services for the power grid. In a non-cooperative V2G system, the interaction between the EV aggregator and EVs is formulated as a non-cooperative Stackelberg game. The EV aggregator as the leader decides the electricity trading price, and EVs as the followers determine their charging/discharging strategies. In a cooperative V2G system, a potential game is formulated to achieve the optimal social welfare of the V2G system. The existence and uniqueness of the Nash equilibrium of these two games are validated. Our simulation results show that the proposed game theoretic approaches can motivate EVs to smooth out the power fluctuations from the grid while EVs schedule their charging/discharging activities to maximize their utilities. This demonstrates the effectiveness of the use of the V2G game in providing regulation services to the grid. Through cooperation and extra information exchange, the social welfare of EVs and the EV aggregator can be improved to the global optimum and the V2G regulation services can also achieve near-optimal performance.

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.

Grid integration of Electric Vehicles (EVs) and renewable generation are major operational challenges for System Operator (SO) due to their respective mobility behavior dynamics and intermittent behavior. To deal with these challenges, an EV Aggregator (EVA) can employ Vehicle-to-Grid (V2G) technology to synergize grid integration of renewable energy resources (RESs) and EVs. EVA provides smart coordination between SO and EV owners providing grid support services through V2G charge/discharge scheduling of EVs. However, energy market prices uncertainties involved in market operation would significantly affect profit and behavior of EVA. Proposed work models an integrated DR and risk-aversive V2G scheduling of EVA for its expected profit maximization and effective utilization of photovoltaic (PV) generation from rooftop solar charging park incentivizing EV owners and flexibility enhancement to SO. Revenue of EVA is due to regulation and charging services to SO and EV owners respectively. The operational cost of EVA considers procurement cost of charging energy from wholesale electricity market and cost of battery degradation while ensuring EV owners’ driving requirements. The CVaR index is utilized for measuring EVA's risk. Results validate efficacy of proposed model and impact analysis of DR integration on electricity market operations of EVA through performance metrices.

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%.

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

Along with the diversification of electricity market, the voltage regulation (VR) service is opened up to various qualified providers to meet the enormous demand, among which, the electric vehicle (EV) aggregators (AGGs) can integrate the scattered EVs and play the role of VR sources with a high response speed and low cost. In order to coordinate the VR service providers to satisfy the total VR requirement, auction mechanisms are widely used to select VR sources to achieve better performance. Yet, it is challenging to optimize the strategy of each EV AGG in the auction process due to the stochastic EV mobility, various distribution network topology, and the competition mechanism. To address these challenges, we proposed a discounted stochastic multiplayer game (DSMG) approach to analyze the competition among EV AGGs. Due to the constraint of distribution network topology, the efficiency of the VR sources at different locations can be different. Thus, the impact of distribution network topology on the VR efficiency is investigated by DSO when evaluating the capacity of AGGs. The randomness of EV numbers is considered when predicting the AGGs’ available VR capacity so that the tendency for the AGGs to follow the optimal strategies can be modeled accurately. Accordingly, a linear power flow analysis approach and a battery pool model are developed to address the distribution network topology and EV mobility, respectively. Then, the DSMG approach is used in the VR auction process to optimize the AGGs’ strategies. The existence proof of the stationary Markov perfect equilibrium is presented, and the corresponding algorithms to obtain the equilibrium is proposed. The performance of the proposed DSMG approach is evaluated and compared with other approaches based on the IEEE 33-bus test feeder, IEEE 123-bus test feeder, and the real-world generation and load data from PVWatts Calculator and Market Analysis and Information System, respectively.

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.

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.

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.

This paper proposes a stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy. The forecast errors of EV fleet characteristics, hourly loads, and wind energy as well as random outages of generating units and transmission lines are considered as potential uncertainties, which are represented by scenarios in the Monte Carlo Simulation (MCS). The conditional value at risk (CVaR) index is utilized for measuring EV aggregators' risks caused by the uncertainties. The EV aggregator's optimal bidding strategy is formulated as a mathematical programming with equilibrium constraints (MPEC), in which the upper level problem is the aggregators' CVaR maximization while the lower level problem corresponds to the system operation cost minimization. The bi-level problem is transformed into a single-level mixed integer linear programming (MILP) problem using the prime-dual formulation with linearized constraints. The progressive hedging algorithm (PHA) is utilized to solve the resulting single-level MILP problem. A game theoretic approach is developed for analyzing the competition among the EV aggregators. Numerical cases are studied for a modified 6-bus system and the IEEE 118-bus system. The results show the validity of the proposed approach and the impact of the aggregator's bidding strategies on the stochastic electricity market operation.

This paper proposes a day-ahead market framework for congestion management in smart distribution networks. The presented scheme provides a platform for collaboration between distribution-level market operator (DMO) and data traffic operator (DTO) to alleviate congested feeders such that data transmission traffic between market participants is effectively managed in a smart grid. In addition, a decentralized mechanism is developed for collaboration of electric vehicle (EV) aggregators with common clients to take advantage of EVs not only as flexible loads but also as mobile distributed storage (MDS) for congestion management. Moreover, the proposed framework outlines an administrative action for distribution system operator (DSO) to support the market when the decentralized competitions among distributed generation (DG) aggregators and EV aggregators do not fully relieve a serious congestion. The proposed day-ahead congestion management scheme is validated on an unbalanced 136-bus distribution system massively integrated with wind turbine DGs (WTDGs), photovoltaic DGs (PVDGs), diesel-engine DGs (DEDGs), and EVs.

Distributed state-of-charge and power balance estimation for aggregated battery energy storage systems with EV aggregators

In the presence of the increasing penetration of electric vehicles (EVs) and conflict of independent optimization objectives among each electric vehicle aggregator (EVA), real-time optimal scheduling (RTOS) of large-scale EVs based on dynamic non-cooperative game approach is proposed for optimal decision makings in a dynamic pricing market. First, real-time optimal scheduling framework is designed to describe the flow of energy and information. Then, equivalent model of large-scale EVs is formulated to address “curse of dimensionality” caused by a large number of decision variables. Then, the potential game theory is used to study the existence and uniqueness of the Nash equilibrium (NE) solution. Finally, a distributed approach based on alternating direction method of multipliers (ADMM) is designed to achieve the equilibrium. Case studies demonstrate that the proposed approach achieves peak load shifting and reduces cost of EVAs significantly. Furthermore, the proposed method obtains higher-quality solution compared with other methods and is more applicable for real-time optimal scheduling of large-scale EVs due to its high computation efficiency and privacy protection.