当前位置:
X-MOL 学术
›
IEEE Trans. Autom. Sci. Eng.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Decentralized Hierarchical Planning of PEVs Based on Mean-Field Reverse Stackelberg Game
IEEE Transactions on Automation Science and Engineering ( IF 5.6 ) Pub Date : 2020-04-27 , DOI: 10.1109/tase.2020.2986374 Mohammad Amin Tajeddini , Hamed Kebriaei , Luigi Glielmo
IEEE Transactions on Automation Science and Engineering ( IF 5.6 ) Pub Date : 2020-04-27 , DOI: 10.1109/tase.2020.2986374 Mohammad Amin Tajeddini , Hamed Kebriaei , Luigi Glielmo
In the reverse Stackelberg mechanism, by considering a decision function for the leader rather than a decision value in the conventional Stackelberg game, the leader can explore a wider decision space. This flexibility can result in realizing the globally optimal solution of the leader’s objective function, while controlling the reaction function of the followers, simultaneously. We consider an aggregator who purchases energy from the wholesale energy market. The aggregator acts as the leader for a group of plugged in electric vehicles (PEVs) and determines the price of energy versus consumption at each hour a day as its decision function. In the followers level, since the optimal charging strategies of the PEVs are coupled through the electricity price, the PEVs in a group are considered to cooperate in finding their Nash–Pareto-optimal charging strategy, by minimizing a social cost function. For a large number of PEVs, the cooperative cost minimization of PEVs can be modeled as a cooperative mean-field (MF) game. We propose a decentralized MF optimal control algorithm and prove that the algorithm converges to leader–follower MF $\varepsilon _{N}$
-Nash equilibrium point of the game. Furthermore, a decentralized reverse Stackelberg algorithm is implemented to achieve the optimal linear price function of the leader. Simulation results and comparison with benchmark methods are performed to demonstrate the advantages of the proposed method. Note to Practitioners
—The effect of a large population of PEVs on the power grid such as overload and voltage drop is inevitable. Motivated by this, there are many research articles which propose different centralized and decentralized charging coordination solutions to address this problem. The core idea in the most of literature is: “How to IMPLEMENT a demand response (DR) program appropriately for charging coordination problem to avoid high peak load?” However, none of them propose a practical algorithm on “How to DESIGN a DR optimally by having limited information from the clients?” We propose a bilevel optimization algorithm to both design a DR program (i.e., price function) and also implement DR program to flatten the demand curve, accordingly. Another advantage of the proposed method is that the information structure of the problem is close to reality. There is no information exchange among the clients and also the utility company does not need to know the private information of the clients. The Utility Company only knows the charging profile of the PEVs in each day and broadcasts the price signal to the clients for the next day. The method is illustrated in an IEEE 5-bus system that supports our claims.
中文翻译:
基于均场反向Stackelberg博弈的PEV分散式分层规划
在反向Stackelberg机制中,通过考虑领导者的决策功能而不是传统Stackelberg游戏中的决策值,领导者可以探索更广阔的决策空间。这种灵活性可以导致实现领导者目标功能的全局最优解,同时控制跟随者的反应功能。我们考虑一个从批发能源市场购买能源的聚合商。聚合器充当一组插电式电动汽车(PEV)的负责人,并将其每天的每小时能源价格与消耗量确定为其决策功能。在追随者层面,由于电动汽车的最佳充电策略是通过电价耦合的,通过最小化社会成本函数,一个群体中的私家车被认为可以合作找到他们的纳什-帕累托最优收费策略。对于大量的PEV,可以将PEV的协作成本最小化建模为协作平均场(MF)博弈。我们提出了一种分散的中频最优控制算法,并证明了该算法收敛于前导跟随中频 $ \ varepsilon _ {N} $
-游戏的纳什均衡点。此外,实现了分散的反向Stackelberg算法以实现领导者的最佳线性价格函数。仿真结果和与基准方法的比较证明了该方法的优点。执业者注意
—不可避免会有大量PEV对电网的影响,例如过载和压降。因此,有很多研究文章提出了不同的集中式和分散式充电协调解决方案来解决这个问题。大部分文献中的核心思想是:“如何适当地实施需求响应(DR)程序以解决充电协调问题以避免高峰值负载?” 但是,他们都没有提出关于“如何通过限制来自客户端的信息来最佳设计DR的实用算法”。我们提出了一种双层优化算法,既可以设计DR程序(即价格函数),又可以实施DR程序来平化需求曲线。提出的方法的另一个优点是问题的信息结构接近实际。客户之间没有信息交换,公用事业公司也不需要知道客户的私人信息。公用事业公司每天仅知道PEV的充电配置文件,并在第二天向客户广播价格信号。该方法在支持我们的主张的IEEE 5总线系统中进行了说明。
更新日期:2020-04-27
中文翻译:
基于均场反向Stackelberg博弈的PEV分散式分层规划
在反向Stackelberg机制中,通过考虑领导者的决策功能而不是传统Stackelberg游戏中的决策值,领导者可以探索更广阔的决策空间。这种灵活性可以导致实现领导者目标功能的全局最优解,同时控制跟随者的反应功能。我们考虑一个从批发能源市场购买能源的聚合商。聚合器充当一组插电式电动汽车(PEV)的负责人,并将其每天的每小时能源价格与消耗量确定为其决策功能。在追随者层面,由于电动汽车的最佳充电策略是通过电价耦合的,通过最小化社会成本函数,一个群体中的私家车被认为可以合作找到他们的纳什-帕累托最优收费策略。对于大量的PEV,可以将PEV的协作成本最小化建模为协作平均场(MF)博弈。我们提出了一种分散的中频最优控制算法,并证明了该算法收敛于前导跟随中频