RESEARCH ON THE STRATEGY OF ELECTRIC VEHICLE INTEGRATION TO GRID PARTICIPATING IN ELECTRICITY MARKET

The establishment of power markets holds promise for fostering the uptake of renewable energy sources, enhancing energy efficiency, and bolstering the stability and flexibility of local power systems. With the proliferation of electric vehicles (EVs) and the development of smart grids, the grid integration of EV participation in China's electricity markets looms as an inevitable trend. This paper aims to comprehensively examine the market dynamics, key players, and trading strategies pertaining to EV-grid integration, with the objective of constructing a robust framework for EV participation in the market. The principal research components as follows:

(1) A novel management framework is introduced, designating the distribution system operator (DSO) as a key player within the electricity markets of China. Comparative analyses between EV aggregators (EVAs) and DSOs from a market perspective are conducted, thereby illuminating the superior advantages and delineating the distinctive role of DSOs in the electricity markets. The correlation between wholesale and retail electricity prices in a competitive market, along with the application of second-order cone programming relaxation conditions within the bidding model is established. To explore the varied benefits sought by EV users, analyses comparing two distinct contractual models—discount price contracts (DPC) and revenue sharing contracts (RSC)—are performed. Case studies within a 31-node university distribution system are utilized to demonstrate the profound impact of EV distribution on DSO profitability, revealing DSOs' advantage in revenue generation and reduction of market volatility risk as compared to EVAs. The purpose of this analysis is to substantiate the differential efficacy of EVA and DSO amidst market uncertainties.

(2) A novel day-ahead bidding strategy has been developed, incorporating management of conventional loads, participation of EVs, and demand response mechanisms tailored for DSOs. A data-driven methodology that merges Particle Swarm Optimization with Long Short-Term Memory neural networks has been utilized to create a scenario-based polyhedral uncertainty set, capturing a wide spectrum of potential outcomes. Through the implementation of a two-stage robust optimization strategy, which encompasses considerations for V2G operations, a significant enhancement in DSO revenue compared to traditional methodologies within a real-world scenario has been achieved, highlighting its effectiveness and practical applicability. Notably, a revenue increase of 17.9% and 25.3% for the DSO, compared to stochastic programming and robust optimization approaches respectively, has been exhibited by the results.

(3) Recognizing the limitations of data-driven power load forecasting for preemptive load deviation management, a pseudo-local reserve market is proposed between the DSO and EVs, enabling the integration of EV flexibility into the market framework and compensating for load deviations across the wholesale electricity market. A two-stage framework is also developed to support EV participation in electricity markets. In the initial stage, EV reserve capacity is incorporated into the decision-making process for day-ahead bidding, considering uncertainties related to load and electricity. Subsequently, day-ahead results are coordinated to facilitate the efficient deployment of EVs for real-time market engagement. The paper explores the economic implications of load deviation constraints on EV reserves, demonstrating a reduction in penalties amounting to 3,100 yuan for reserving 1,000 EVs daily and an increase in net profit of 1,300 yuan for DSOs leveraging reserves.

(4) A novel methodology for the grid integration of EVs, predicated on the Shenzhen Carbon Inclusive Management Measures is proposed. This methodology is capable of optimizing EV charging schedules based on carbon emission pricing, thereby enhancing the economic viability of transitions to low-carbon through the utilization of EVs in China. The Black-Scholes model is incorporated for the quantification of carbon emission option pricing, taking into account the spatiotemporal uncertainties associated with EVs and electricity markets. Numerical simulations, utilizing real-world data from Shenzhen's carbon market and the Guangdong electricity market, are employed to ascertain the efficacy of the proposed methodology. It is revealed through the results that the appropriate scheduling of charging/discharging behaviors for 100 EVs can yield a revenue of 2379.4 yuan for the distribution system operator, while the revenue from carbon emission reduction stands at merely 9.35 yuan.

(5) A blockchain-enabled cyber-physical system is conceptualized, drawing upon the fundamental principles of the internet of smart charging points (ISCP) within a distributed power system. The ISCP framework leverages the cyber-physical capabilities of smart charging points (SCPs), transforming them into multifunctional nodes that not only facilitate energy metering but also play integral roles in computation, communication, and storage within the blockchain network. Utilizing smart contracts as transaction agents, the system aims to automate and enhance transactional efficiency. Moreover, a non-cooperative game model is employed for the strategic energy dispatching of EVs, where each participant seeks to maximize individual revenue through optimal strategy selection. The paper establishes the existence and uniqueness of the Nash Equilibrium through variation inequality theory and introduces a fast consensus mechanism to expedite transaction validation. An ISCP-based optimal algorithm is developed to efficiently resolve the Nash Equilibrium, ensuring convergence to a unique solution and maximizing the revenue for each EV owner within this decentralized trading ecosystem.

电力市场的建立，可以促进新能源的消纳、提高能源利用效率以及提升本地电力系统的稳定性和灵活调节能力。随着电动汽车的快速普及以及智能电网的建设，未来车网融合参与中国电力市场化交易成为必然的趋势。本文旨在深入探讨电动汽车并网参与电力市场的市场主体、交易策略及市场机制，以构建一个全面的车—网—市场框架。本研究的主要内容如下：

(1) 通过将配电系统运营商（DSO）界定为参与中国电力市场的核心主体，对比了电动汽车聚合商（EVA）与DSO在市场中的特点，进一步证明了在竞争市场下，批发电价与零售电价之间关联性的模型，并采用二阶锥松弛技术优化竞价模型。此外，通过对折扣价格合同与收入分成合同的比较，探讨了电动汽车用户收益风险下的不同选择。通过在一个31个节点的大学配电系统案例研究，展示了电动汽车的分布对DSO盈利能力的影响，并突显了DSO在应对市场波动风险方面相对于EVA的优势。

(2) 为应对在日前市场与实时市场中出现的负荷预测偏差，提出了一种新的融合了传统负荷、电动汽车和需求响应机制的日前竞价策略。采用数据驱动方法，结合粒子群优化与长短期记忆神经网络，创建基于场景的多面体不确定性集，以分析潜在的结果。通过实施两阶段鲁棒优化策略，相比传统方法在现实场景中实现了DSO收入的显著提升。此外，与随机规划及鲁棒优化方法相比，DSO收入分别增加17.9%和25.3%。

(3) 针对数据驱动在日前电力负荷预测精确度不足，难以较好解决电力市场的负荷偏差问题，构建了一个伪本地储备市场，发挥电动汽车的潜在备用能力，对偏差进行补偿。采用两阶段框架支持电动汽车参与电力市场，第一阶段考虑负荷和电力等不确定性因素，将电动汽车备用容量纳入日前竞价决策。第二阶段结合日前结果，促进电动汽车充放电管理，实现实时市场参与。探讨了广东省电力市场负荷偏差下对电动汽车备用的影响，发现每日备用1000辆电动汽车可减少3100元罚款，而DSO通过备用可增加1300元净利润。

(4) 基于《深圳碳普惠方法学》，提出了一种新的电动汽车并网减碳计算方法，旨在通过电动汽车参与电力市场和碳市场优化充放电计划，提升中国向低碳转型的经济可行性。采用Black-Scholes模型对碳排放权进行定价，同时考虑电动汽车与电力市场的时空不确定性，建立最优竞价策略。利用深圳碳市场和广东电力市场的实际数据进行数值模拟。结果显示，合理规划100辆电动汽车的充放电行为可为DSO带来2379.4元的收益，额外增加碳减排收益9.35元。

(5) 利用在DSO内的智能充电桩互联网（ISCP），提出了区块链网络物理信息系统的框架。ISCP框架通过智能充电桩（SCP）的网络物理功能转化为多功能节点，促进电力计量并在区块链网络的计算、通信和存储中发挥核心作用。通过智能合约实现交易自动化，提高交易效率。针对电动汽车电力调度，采用非合作博弈模型，通过最优策略选择实现个体最大收益。应用变分不等式证明了纳什均衡的存在唯一性，并通过快速共识机制加速交易验证。开发基于ISCP的优化算法解决了纳什均衡问题，并在分散交易系统中最大化每个电动汽车所有者的收入。

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