Distributed optimization for EVs integrated power system considering flexible ramping requirement

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.

The rapid expansion of electric vehicles (EVs) presents a transformative opportunity to integrate sustainable transportation with the energy sector. EV aggregators, which consolidate the charging demands of multiple EVs, play a pivotal role in balancing electricity markets by scheduling EV charging in response to grid conditions. This study comprehensively reviews the strategies and mechanisms employed by EV aggregators in scheduling EV fleets to optimize their participation in electricity markets. The EVA pools the power output of numerous EVs and puts out bids for the combined power output in power markets. EVA acts as a go-between for SO & EV owners, doing optimisation and aggregation while also pragmatically overseeing and managing the EV asset portfolio. G2V technology allows for the efficient integration of electric vehicles with the power grid.

The increasing deployments of renewable generation methods, such as wind, affects the flexibility of electric power system operating due to their inherent variability and uncertainty. To mitigate this, power systems need flexible resources. This paper investigates the potential for wind power to provide flexible ramping products in the real-time market, an additional value stream to the energy it provides. The proposed model for wind power’s strategic offering is formulated as a bi-level optimization problem with wind profit maximization at the upper level and the independent system operator’s economic dispatch—considering both the energy balance and the flexible ramping requirement to counter uncertainty—at the lower level. This bi-level model is converted to a mathematical program with equilibrium constraints (MPEC) by recasting the lower level problem with its Karush–Kuhn–Tucker optimality conditions. Then, through strong duality theory and the big-M method, the MPEC model is converted to a mixed-integer linear programming model. The opportunity cost and the price for wind power-providing ramping products are analyzed. Numerical examples based on a 5-bus network are presented to verify the proposed model and concept.

Chapter 11 - Optimal participation of electric vehicles aggregator in energy and flexible ramping markets

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.

With the rapid increase of electric vehicle (EV) ownership, the impact of EV charging load on the power grid is becoming more and more prominent. To reasonably guide EV charging/discharging to participate in Demand Response (DR) and help the power grid achieve peak cutting and valley filling, the charge‐discharge compensation pricing strategy of EV Aggregator (EVA) considering user response willingness from the perspective of Stackelberg game is proposed. Firstly, EVA, as the leader, provides charge‐discharge compensation price, to maximise its income within a day, taking into account user satisfaction constraints. Secondly, a user response willingness model is established. User engagement is used to describe the change in the number of EV responses with the change of the charge‐discharge compensation price by EVA and select the random EV set that accepts EVA charge‐discharge guidance. Finally, EV, as a follower, conducts charging/discharging behaviour to minimise the charging cost. By using the Karush–Kuhn–Tucker (KKT) condition, strong duality theory and iterative method, the strategy equilibrium solution is solved. The results show that considering the user response willingness can effectively reduce the decision risk when EVA participates in bidding. Although EVA income slightly decreases considering the response willingness, the average user satisfaction increases by 0.1.

Flexible ramping products (FRPs) have been implemented by several independent system operators (ISOs) to procure adequate flexible resources. Currently, system operators estimate the system level ramping requirements ignoring the spatiotemporal correlations among various uncertainty sources. This leads to overestimates/underestimates of ramping requirements. In addition, the explicit FRPs model considers only the generation ramping limitation. Other security constraints, such as the transmission limits, are not considered, which leads to deliverability issues of FRPs. To deal with these shortcomings of current FRPs models, this paper proposes a deliverable FRPs based on a distributionally-robust chance constrained multi-interval optimal power flow (DRCC-MIOPF) considering the spatiotemporal correlation of wind power and demand uncertainties endogenously. Furthermore, an asymmetrical affine policy (AAP) is proposed to leverage generation flexibility and mitigate the uncertainty in different directions. The pricing mechanism of the FRPs is proposed using a novel uncertainty-contained locational marginal price (U_LMP) which is derived from the proposed AAP-DRCC-MIOPF model. New components representing the price of FRPs are added into the traditional LMP formulation. Finally, the PJM 5-bus, IEEE 39-bus and IEEE 118-bus systems case studies validate the proposed FRPs approach. The payment of uncertain demand and wind power on FRPs are analyzed.

This paper is devoted to analyze market equilibrium solutions when new market agents, such as renew- able/intermittent producers, elastic demands and electric vehicles, are exposed to time-varying Locational Marginal Prices in the context of a competitive electricity market. Two economic equilibrium models are studied in detail. First, we analyze the perfect competition solution driven by a benevolent planner in which real and reactive power dispatches as well as the battery charge-discharge schedule aims to maximize the global social welfare. Secondly, we also address the monopoly solution when the total profit of electric vehicle (EV) aggregators and renewable generators are maximized considering that both producers belong to the same firm. The perfect competition and monopoly system models were applied to an illustrative 3-node test system. Solution shows that under perfect competition, the battery dispatch is smooth in order to get maximum social welfare and therefore minimal grid losses. Conversely, when battery and renewable power injections are managed by only one firm capable to alter the locational prices, the maximum firm profit is get by producing a non-smooth battery dispatch and high grid losses.

As the Japanese Government has set a goal that all new cars for sale will be limited to motor-drive car or electric vehicle (EV) by the mid-2030s, EV aggregators are expected to appear soon even in Japan. On the other hand, EVs are expected nowadays to play a role in providing flexible source due to the intermittent characteristics of rapidly increasing renewable energy. This paper aims to evaluate the EV aggregator's potential in the role of providing the flexible source in Japan. Upward/downward regulation of tertiary regulation markets which will be established as well as day-ahead market are dealt with in this paper to assess the EV aggregator's potential revenue using stochastic programming approach. The simulation results show that entering the upward/downward market as well as the day-ahead market increases the revenue of EV aggregator more than only entering the day-ahead market. This paper also presents how the upward/downward regulation capacity the EV aggregator can provide is changed by the EV penetration rate.

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

The growing prominence of electric vehicle (EV) aggregators in the modern power system is drawing more attention towards modeling their behavior in the short-term electricity markets. The demand-side flexibility offered by the EVs can be leveraged to reduce their charging costs. In this paper, the participation of an EV aggregator in the intraday and balancing market is modeled as a multistage stochastic programming problem. The computational complexity introduced by the peculiarities of the intraday market is solved by a progressive hedging algorithm (PHA), a scenario-based decomposition technique. A randomized scenario sampling approach is implemented to accelerate the PHA which is further improved with a parallel randomized PHA. Finally, an asynchronous version of the parallel randomized PHA is leveraged to speed up the multistage model of EV aggregator trading. We compare the computation time of the modified versions of the PHA algorithm with the conventional PHA for the proposed EV aggregator model. Furthermore, we also show the value of EV aggregator trading in the intraday and balancing markets by comparing its cost to baseline models.

Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties

Electric Vehicles (EVs) are emerging as promising participants in electricity markets with the advancement of Vehicle-to-Grid (V2G) technologies. However, the diverse temporal-spatial characteristics of EVs presents challenges in fully leveraging their potential. This paper proposes a hierarchical coordination framework for the EV Aggregator (EVA) to manage EV fleets for energy arbitrage and ancillary services in Singapore electricity market. In the upper level, the EVA maximizes its profits by optimizing bidding decisions for energy arbitrage and ancillary service, leveraging the Flexibility Region (FR) of aggregated EVs that quantifies the fleets' response power region and the required energy demand. In the lower level, a power allocation model is developed to ensure EV user satisfaction while balancing economic and convenience requirements. Numerical case studies designed based on real data from the National Electricity Market of Singapore (NEMS) have validated the effectiveness of the proposed methods.

In this paper an innovative, hierarchical, four-level optimization framework is proposed to model the optimal participation of Electric Vehicle Aggregators (EVAs) in day-ahead and real-time energy and regulation markets in conjunction with the optimal real-time charging management of the EV fleet. The scope of the framework is to incorporate in an integrated approach all the conjugated optimization tasks that an EVA should follow for cost-efficient and reliable market participation as well as for successful electric vehicles smart charging in real-time that will satisfy the EV owners. The PJM market design is modeled and a case study with 1000 PHEVs, represented by an EVA, is presented for one operating day. In the base case the EVA participates in the regulation market by following the dynamic regulation control signal. The base case is compared with one where the EVA follows the traditional regulation control signal and one where the EV fleet behaves in real-time differently than initially expected. The presentation of the main functionalities of the framework highlight its ability to model and evaluate the performance of the EV charging process in second-by-second time resolution in a market environment.

This article presents a spatial-temporal demand management scheme and cooperative benefit allocation method to optimize the operation of geodistributed electric vehicle (EV) charging station and EV aggregators. Considering the spatial-temporal characteristics of the EV charging demand and energy cost distribution, we first propose a collaborative demand management model to optimize the dispatch of the charging demand and maximize the social welfare. After that, a two-stage cooperative benefit allocation mechanism based on the Nash bargaining and Shapley value theory is proposed to allocate the incremental social welfare while avoiding the conflict between individual interests and collective interests. A case study with multiple testing scenarios is provided to evaluate the performance of the proposed demand management model and benefit allocation mechanism. The numerical results demonstrate the effectiveness of the proposed model, which not only improves the operation efficiency of charging stations and EV aggregators but also achieves a benefit allocation solution that guarantees individual and collective rationalities.