A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers

Shojaabadi, Saeed; Talavat, Vahid; Galvani, Sadjad

doi:10.1016/j.renene.2022.04.163

Cited by 16 publications

(7 citation statements)

References 37 publications

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“…Total cost, rate of energy loss, and CO 2 emissions MGSO-ACL algorithm and IOR [6] EVs and grid Total cost Linear programming algorithm [7] EVs and grid Power failure time of the load Linear programming algorithm [8] EVs The charging cost of the EVs Deep reinforcement learning [9] EVs and grid The charging cost of the EVs. Linear programming algorithm [10] PV, BESS, and grid Total cost Multi-agent PSO [11] Wind, BESS, and grid Wind curtailment rate and profit NSGA-II and VIKOR [12] Wind, EVs, and thermal power units Rate of change of load, the total cost of generating electricity, and the wind curtailment rate Modified PSO algorithm [13] PV, EVs, and grid Total revenue Stackelberg and GA [14] EVs and wind Total revenue A game theory [15] EVs and wind Total revenue Linear programming algorithm [16] PV, EVs and grid Total cost Robust chaotic optimization algorithm [17] BESS and grid Total cost of the BESS PSO-based frequency control [18] PV, EVs, BESS, and grid Total revenue Linear programming algorithm [19] PV, EVs, and grid Electricity purchasing cost Linear programming algorithm [20] PV, BESS, EVs, and grid Total cost Deep learning algorithm [21] EVs, and grid The charging cost of the EVs and the busbar voltage deviation NSGA-II Ours PV, BESS, EVs, and grid…”

Section: Componentsmentioning

confidence: 99%

“…The authors of [13] developed a Stackelberg game model for the interaction between the EV charging station, photovoltaic (PV) generation, and the microgrid. Meanwhile, the authors of [14] proposed a game-theory-based energy exchange method between the wind farms and the EV charging stations, which reduced the risk of wind energy and EV imbalances in the energy market using an optimal bidding strategy to maximize profits. The authors of [15] established an optimized scheduling model for off-grid photovoltaics operators, EV charging stations, and energy storage systems, with the goal of maximizing profit.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies

et al. 2023

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Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.

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Section: Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies

et al. 2023

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“…EV aggregators are able to provide energy balancing services. For example, in [20], EV aggregators' competition is modelled as a non-cooperative game for providing flexibility services through the energy exchange between EV and wind power producers.…”

Section: Electric Vehicles As Flexible Loadsmentioning

confidence: 99%

Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers

et al. 2022

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The efficient use of energy resources is profoundly changing power grid regulation and policy. New forms of power generation coupled with storage and the presence of new, increasingly flexible loads such as electric vehicles enable the development of multi-agent planning systems based on new forms of interaction. For instance, consumers can take advantage of flexibility by interacting directly with the grid or through aggregators that bridge the gap between these end-users and traditional centralised markets. This paper aims to provide insight into the benefits for aggregators and end-users from a financial perspective by proposing a methodology that can be applied to different scenarios. End-users may provide flexibility services related to private vehicle charging stations or battery storage systems. The paper will analyse different remuneration levels for end-users by highlighting the most beneficial scenarios for aggregators and end-users and providing evidence on potential conflict of interests. The numerical results show that some consumers may benefit more from aggregation. This is because if taken individually, consumption habits do not allow the same flexibility when considering clusters of consumers with different behaviour. It is also shown that there are cases in which consumers do not seem to benefit from the presence of intermediate parties. We provide extensive numerical results to gain insight for better decision making.

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“…A day-ahead and intraday charging scheduling model is proposed in [18] for multiple EVAs, where the cooperative charging scheduling is established based on the generalized Nash bargaining. Similarly, a Nash bargaining based price bidding strategy is proposed in [19] for the competition among EVAs, aiming at compensating the supply and demand imbalance. However, the charging scheduling models in [18] and [19] are solved in a centralized manner without considering the privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, a Nash bargaining based price bidding strategy is proposed in [19] for the competition among EVAs, aiming at compensating the supply and demand imbalance. However, the charging scheduling models in [18] and [19] are solved in a centralized manner without considering the privacy concerns. To preserve the privacy of EV users, [20] proposes a neurodynamic-based approach to solve the charging scheduling model of EVAs in a distributed manner.…”

Section: Introductionmentioning

confidence: 99%

Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators

Xu,

Qiu,

Zhang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source−load−storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.

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A game theory-based price bidding strategy for electric vehicle aggregators in the presence of wind power producers

Cited by 16 publications

References 37 publications

Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies

Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies

Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers

Distributed Source-Load-Storage Cooperative Low-carbon Scheduling Strategy Considering Vehicle-to-grid Aggregators

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