分布式车网能量交互调度策略研究

随着全球能源危机的不断加剧和民众环保意识的持续增强，电动汽车的发展普及进入了快车道。然而，如何协调大规模电动汽车随机接入电网充电引起了国内外学者的广泛关注。据统计，私家车每天大约有4%的时间用于交通，这使得电动私家车有可能在剩余96%的空闲时间作为其他用途。车网能量交互技术可以通过优化空闲的电动汽车在电网负荷谷时段充电，并在负荷峰时段作为分布式电源向电网馈电，在电网和电动汽车之间架起一座双向电能量流动的桥梁，其优化决策变量是电动汽车在每个时间间隔的充放电功率。然而，随着电动汽车数量的持续增加，车网能量交互模型在优化调度过程中的计算复杂度变高，使问题难以求解。此外，随着人民群众对个人隐私保护意识的不断增强，信息安全问题在各方面都受到了高度关注。因此，高效率、高安全、高稳定的系统架构和调度策略成为车网能量交互领域极具创新性和挑战性的研究方向之一。本文以车网能量交互中高计算性能、高信息安全的技术需求为契机，深入研究了分布式车网能量交互模型架构、调度策略、多维验证等若干关键问题，获得了一些创新性研究成果。

（1）为实现高效率、高安全、高稳定的车网能量交互，对现存的各种调度模型及策略进行了分析，并在此基础上提出了去中心化智能充电桩互联网创新架构。首先，从计算性能、通信性能及信息安全性能等多个角度对现存车网能量交互进行全面梳理总结，能够为关注大规模电动汽车接入电网问题的同行提供有益的技术参考。然后，基于以上性能分析，提出了去中心化分布式车网能量架构：智能充电桩互联网架构。该架构的核心是融合了低成本计算模块的电动汽车充电桩，即智能充电桩，其具有本地计算、本地信息处理的功能。此外，智能充电桩之间通过分布式调度互相协调，为降低模型计算复杂度，避免用户隐私泄露，以及优化系统能流创造了有利条件。

（2）为兼顾车网能量交互调度策略的调度效果和计算效率，基于所提出的去中心化智能充电桩互联网架构，设计形成了具有高计算性能的日前负荷削峰填谷和实时负荷削峰及光伏本地消纳两种需求响应策略。具体来讲，将车网能量交互划分为六种典型场景，形成了与分布式边缘调度相匹配的“单车单桩单问题”计算模式，可以高效地协调大规模电动汽车的随机接入以及无缝消纳可再生能源。基于南方科技大学校园配电网的仿真结果验证了所提两种需求响应策略的正确性和有效性，以及其适应分布式调度“可拓展”、“高计算性能”等功能需求的能力。

（3）为满足车网能量交互过程中各参与方对信息安全的强烈需求，提出了基于去中心化智能充电桩互联网架构的全链条数字资产安全型交互方案。一方面，生成了一个面向商业中心的包含常规负荷数据、光伏出力数据和电动汽车充电数据的车网能量交互数据集。另一方面，根据真实数据集，分别提出了基于长短期记忆网络的用户隐私保护和数据支撑赋能方法以及基于深度学习和联邦学习的数字资产保障方法。具体来讲，在充电桩侧利用分布式边缘计算进行车网能量交互调度预测，保护用户隐私；在充电站侧使用脱敏数据通过深度学习进行模型训练，降低对未来预测数据的依赖；在云端服务器通过联邦学习进行模型整合，避免数据孤岛，保护充电站数据资产。仿真结果验证了所提两种数据驱动方法的正确性和有效性，以及其适应信息安全“算后即焚”、“脱敏训练”等功能需求的能力。

（4）为深入分析分布式车网能量交互各关键要素之间的耦合机理，创造性地建立了基于去中心化智能充电桩互联网架构的多维度模型方案。一方面，开发了“信息-物理-能量”联合仿真模型，能量优化方面采用基于并行边缘计算的车网能量交互双层调度策略，可以保证调度效果并且提高计算效率；网络通信方面采用小世界网络，可以保证通信效率并且降低布线成本。另一方面，提出了“两级协调、情境耦合、逐级细化”的多时间尺度车网能量交互调度实物平台，日前24小时能量调度采用传统优化方法，以获取实时调度中的安全边界；实时滚动能量调度借助空间解耦交替方向乘子法，将各充电桩之间的联系解耦，以提高调度效能，并且可以做到全局收敛。仿真平台和实物平台结果验证了所提方案在能量调度、隐私保护和网络通信方面的正确性和有效性，以及其适应分布式物理信息能量系统“网络鲁棒”、“计算解耦”等功能需求的能力。

The ever-increasing global energy crisis and the increasing public awareness of environmental protection have promoted the rapid development of electric vehicles (EVs). As a new type of power load, how to coordinate large-scale EVs to the power grid has attracted extensive attention. Statistically, private vehicles spend only 4% of their day in traffic, making it possible for electric private vehicles to be used for other purposes during the remaining 96% of their free time. The vehicle-to-grid (V2G) technology can optimize the idle EVs to charge during the grid load valley period and feeding the grid as a distributed power source during the load peak period. This bidirectional energy flow technology builds a bridge between the power grid and EVs, and its optimization decision variable are the charging and discharging power of the EV at each time slot. However, with the increase of the scale of EVs, the variables that need to be optimized during the unified dispatching process of the traditional centralized V2G become larger, and the computational complexity becomes higher, making the problem difficult to solve. Additionally, this also raises privacy anxiety among EV users, as EV aggregator need collect charging information from individual EVs. Therefore, high-efficiency, high-safety, and high-stability scheduling strategies have become one of the most innovative and challenging research directions in the field of V2G technology. Based on the technical requirements of high computing performance and high information security in V2G, this paper systematically studies several key issues such as the architecture, scheduling strategy, and multi-dimensional verification of distributed V2G, and obtains some innovative research results. (1) In order to achieve the V2G of high-efficiency, high-safety, and high-stability, various existing scheduling models and strategies are compared and analyzed, and on this basis, an innovative distributed internet of smart charging points (ISCP) architecture is proposed. First, a comprehensive review of the V2G from multiple perspectives such as computing performance, communication performance and information security performance was conducted, which can provide beneficial technical reference for peers concerned about large-scale EVs access to the power grid. Second, based on the above performance analysis, a distributed V2G scheduling architecture is proposed, i.e., ISCP. The core of the architecture is the EV charging point that integrates low-cost computing module,namely, intelligent charging point, which has the function of local computing and local information processing. In addition, intelligent charging points coordinate with each other through distributed scheduling, which creates favorable conditions to reduce the computational complexity of the model, avoid user privacy disclosure, and optimize the power energy flow. (2) In order to take into account the scheduling effect and computing efficiency of the V2G scheduling strategy, based on the proposed ISCP architecture, two demand response strategies with high computational performance, namely day-ahead load peak-shaving and valley-filling, real-time load flatting and photovoltaic absorption, were designed and formed. Specifically, the V2G is divided into six typical scenarios to form the calculation model of “single vehicle, single point, single problem”, which can efficiently coordinate the random access of large-scale EVs and seamless absorption of renewable energy. The simulation results based on the campus distribution grid of Southern University of Science and Technology verifies the correctness and effectiveness of the two proposed demand response strategies, as well as their ability to adapt to the requirements of distributed scheduling such as “scalability” and “high computing performance”. (3) In order to meet the strong demand for information security of all participants in the V2G, a secure interaction scheme for the whole chain digital assets based on ISCP architecture is proposed. On the one hand, a V2G dataset is generated for commercial centers, including conventional load data, photovoltaic output data and EV charging data. On the other hand, based on real datasets, two methods of user privacy protection and data support empowerment based on long and short term memory network and digital asset protection based on deep learning and federated learning are proposed respectively. Specifically, distributed edge computing is used for scheduling prediction at charging point to protect user privacy; desensitized data is used for model training through deep learning at charging stations, which can avoid the challenge of difficult future data acquisition; model integration is performed at cloud servers through federated learning to protect the digital assets of charging stations. The simulation results verify the correctness and effectiveness of the two proposed data-driven methods, as well as their ability to adapt to the requirements of information security such as “burning after scheduling” and “desensitization training”. (4) In order to deeply analyze the coupling mechanism among key elements of V2G, a multi-dimensional model scheme based on decentralized internet of smart charging points architecture was creatively established. On the one hand, a cyber-physical-energy joint model has been developed. In terms of energy optimization, a distributed multi-layer parallel V2G model is used, which can ensure the scheduling effect and improve computing efficiency; in terms of network communication, a small world network can be used to ensure communication efficiency and reduce wiring cost. On the other hand, a multi-time scale V2G framework of “two-level coordination, situational coupling, and detailed step by step” is proposed. The traditional optimization method is used for day-ahead energy scheduling to obtain the safety boundary in real-time scheduling; the real-time rolling energy dispatching uses the spatial decoupling alternating direction method of multipliers to decouple the connection between charging points to improve dispatching efficiency and achieve global convergence. The simulation platform and physical platform results verify the correctness and effectiveness of the proposed framework in energy scheduling, privacy protection, and network communication, as well as their ability to adapt to the requirements of distributed cyber-physical-energy system such as “network robustness” and “computation decoupling”.

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