Due to the increasing penetration of Distributed Energy Resources (DERs), power system operators face significant challenges of ensuring the effective integration of DERs. The Virtual Power Plant (VPP) enables DERs to provide their valuable services by aggregating them and participating in the wholesale market as a single entity. However, the available capacity of VPP depends on its DER outputs, which is time-varying and not exactly known when the independent system operator (ISO) runs the day-ahead unit commitment engine. In this study, we develop a model to evaluate the physical characteristics of the VPP, i.e., its maximum capacity and ramping capabilities, given the uncertainty in wind power output and load consumption. The proposed model is based on a distributionally robust optimization approach that utilizes moment information (e.g., mean and covariance) of the historical net load data. We reformulate the model as a binary second-order conic program and develop a separation framework to address it. We first solve a two-stage problem and then benchmark it with a multi-stage case. Case studies are conducted to show the performance of the proposed approach.

As the penetration of intermittent renewable energy increases in bulk power systems, flexible generation resources, such as quick-start gas units, become important tools for system operators to address the power imbalance problem. To better capture their flexibility, we proposed a two-stage distributionally robust unit commitment framework with both regular and flexible generation resources, in which the unit commitment decisions for flexible generation resources can be adjusted in the second stage to accommodate the renewable energy intermittency. In order to tackle this challenging two-stage distributionally robust mixed-binary model, to which traditional separation algorithms won’t apply, we designed a revised integer L-shaped algorithm with lift-and-project cutting plane techniques. In comparison to the traditional distributionally robust unit commitment, the proposed approach can reduce the system cost through an improved flexible resource quantification in the modeling.

Motivated by the Federal Energy Regulatory Commission’s (FERC) recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs. This paper summarizes the business drivers, challenges, guidance, and best practices for cloud adoption in power systems from the Task Force’s perspective, after extensive review and deliberation by its members, including grid operators, utility companies, software vendors, and cloud providers. The paper begins by enumerating various business drivers for cloud adoption in the power industry. It follows with the discussion of the challenges and risks of migrating power grid utility workloads to the cloud. Next, for each corresponding challenge or risk, the paper provides appropriate guidance. Notably, the guidance is directed toward power industry professionals who are considering cloud solutions and are yet hesitant about the practical execution. Finally, to tie all the sections together, the paper documents various real-world use cases of cloud technology in the power system domain, which both the power industry practitioners and software vendors can look toward to design and select their own future cloud solutions. We hope that the information in this paper will serve as helpful guidance for the development of NERC guidelines and standards relevant to cloud adoption in the industry.

The Benders’ decomposition algorithm is a technique in mathematical programming for complex mixed-integer linear programming (MILP) problems with a particular block structure. The strategy of Benders’ decomposition can be described as a strategy of divide and conquer. The Benders’ decomposition algorithm has been employed in a variety of applications such as communication, networking, and machine learning. However, the master problem in Benders’ decomposition is still NP-hard, which motivates us to employ quantum computing. In the paper, we propose a hybrid quantum-classical Benders’ decomposition algorithm. We transfer the Benders’ decomposition’s master problem into the quadratic unconstrained binary optimization (QUBO) model and solve it by the state-of-the-art quantum annealer. Then, we analyze the computational results and discuss the feasibility of the proposed algorithm. Due to our reformulation in the master problem in Benders’ decomposition, our hybrid algorithm, which takes advantage of both classical and quantum computers, can guarantee the solution quality for solving MILP problems.

This paper develops a novel co-optimized distribution system management algorithm for the distribution system operator to optimally manage the ever-increasing penetration of networked microgrids that are participating in the transactive distribution market as prosumers. For the first time, we evaluate how the incorporation of joint bidirectional energy and ancillary service exchanges could quantitatively increase the operation economy, flexibility, and reliability for both the microgrids and the distribution grid. We formulate the joint distribution system management problem using a bilevel programming approach, in which the upper level is a distribution system optimal management problem for the distribution system operator and the lower level is a microgrid optimal scheduling problem for each participating microgrid operator. Uncertainties from renewable energy generation and responsive loads within the distribution system are also incorporated in the problem formulation. The bilevel stochastic programming model is then reformulated as a single mixed-integer model to solve. The simulation-based case study supports that the proposed management scheme is capable of enhancing energy independency, system-wide efficiency, operational reliability, and economy of the distribution system in comparison with conventional transactive market management schemes.

The flourishing of the data era has led to a higher demand for hyper-scale data centers, resulting in a great energy gap. It is necessary to comprehensively analyze the energy management in data centers to minimize the operation cost. In this paper, we first propose a new optimal energy management model for data centers by considering energy consumers such as heating, ventilation, air conditioning (HVAC), server, solar, and battery. Then, inspired by the great computation power of quantum computing techniques, we propose a new hybrid quantum-classical multi-cuts Benders' decomposition algorithm, which utilizes quantum advantages in parallel computing for generating multi-cuts in a single iteration. Finally, experiments are conducted to verify the effectiveness and efficiency of the novel model and algorithms.