Reserve Market Operation with the Integration of Distributed Energy Resources

Integrating renewable energy resources (RERs) into power systems has been developing very fast recently. The uncertainty and intermittence of RERs can easily result in the mismatch between generation and load, which may cause power systems to collapse. Operation reserve is necessary for the stable and reliable operation of power systems. Traditionally, the reserve capacity is provided by generators. With the development of demand-side management technology and battery technology, distributed energy resources, such as electric vehicles (EVs), prosumers, and so on, present a new opportunity to provide reserve to the power systems. For example, EVs can inject power back to the grid in vehicle-to-grid mode when necessary. This thesis focuses on the reserve market operation with the integration of distributed energy resources.

First, we study the reserve management problem of the EV aggregator. A bilevel optimization model is developed to describe the interaction between the aggregator and the EV owners. Unlike existing literature, binary variables are employed to model the exclusive right constraint for accessing EV battery in the lower-level model. An exact and finite algorithm is proposed to solve the proposed model.

Second, based on the above work, we consider the uncertain EV connectivity to the grid due to the inevitable transportation randomness. We propose a trilevel profit maximization model for EV aggregators participating in the dayahead reserve market. Firstly, total unimodularity property, primal-dual, and value-function methods convert this problem into a single-level mixed-integer nonlinear program (MINLP). Then, a sample-based algorithm is developed to solve the single-level MINLP, and the convergence is proved. In addition, an acceleration strategy is proposed to facilitate the computation.

Third, we investigate the day-ahead economic and secure management problem of the prosumer in the energy and reserve market, considering the uncertainties of renewable energy, market prices, and reserve activation rate. Due to the distributions of these uncertain parameters are ambiguous, Wasserstein distance-based distributionally robust optimization (DRO) approach is employed to hedge against these uncertainties. The proposed optimization model includes DRO objective function and chance constraints. We employ inner approximation to convert the developed DRO model into a conveniently computable form. Different from existing literature, we analyze the optimality gap in objective function approximation. Finally, we study the joint energy and reserve sharing problem between massive prosumers considering renewable generation uncertainty and limited network resources. Firstly, a data-driven distributionally robust energy and reserve sharing model between different agents in electricity markets is proposed. Then, considering the agents in the internet of things exchange information by a resource-limited network, we develop a communication-censored consensus alternating direction method of multipliers to save the limited network resources and solve the sharing problem fully decentralized. We also analyze the convergence of the proposed algorithm. In addition, an adaptive penalty parameter method is proposed to speed up the convergence. 

可再生能源融合到电力系统中最近发展很快。可再生能源的不确定性和间歇性很容易导致发电和负荷之间的不匹配，这可能导致电力系统崩溃。稳定的运行储备是必要的为了维持电力系统的可靠运行。传统上，备用容量是由发电机提供。随着需求侧管理的发展技术和电池技术，分布式能源，如电动汽车、产消者等提供了一个新的机会来提供备用给电力系统。例如，电动汽车可以将电力注入电网在V2G模态时。本论文主要研究分布式能源在电力市场化的运行。

首先，我们研究了 EV 聚合器的备用管理问题。提出了双层优化模型来描述聚合器和 EV 所有者的交流。与现有文献不同的是，我们采用了二元变量描述了 EV 电池的专有权利约束。提出了一种精确且有限步收敛的算法来求解提出的双层混合整数模型。

其次，基于上述工作，我们考虑了不确定的 EV 连通性由于不可避免的交通随机性。我们提出一个参与日前备用市场的电动汽车聚合商的三层利润最大化模型。首先，全单模性，对偶法，和值函数方法将此问题转换为单层混合整数非线性规划）。然后，提出了采样算法来求解提出的单层模型并证明了收敛性。此外，为了加速计算，提出了一个加速策略。

第三、我们研究了产销者的经济和安全管理问题考虑可再生能源、市场价格和储备激活率的不确定性。由于这些不确定参数的分布是不明确，基于距离的分布式鲁棒优化方法用来解决这些不确定性。提出的优化模型包括分布鲁棒目标函数和机会约束。我们利用内近似将提出的模型转换为方便的可计算形式。

最后，我们研究了联合能源和备用共享问题。考虑可再生能源的不确定性和有限的网络资源。首先，提出了数据驱动的分布式鲁棒共享模型。然后，考虑物联网中的智能体通过资源有限的网络交换信息，我们设计了通信审查共识算法节省有限网络资源。我们也分析所提出算法的收敛性。此外，提出了自适应惩罚变量法来加快收敛速度​。

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