Quantum computing in power systems

Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today’s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids

Computationally Distributed and Asynchronous Operational Optimization of Droop-Controlled Networked Microgrids

Networked microgrids (MGs) with inverter-based and droop-controlled distributed energy resources (DERs) require operational optimization with guaranteed stability performance to ensure the stable energy supply with minimum cost, yet it remains an open challenge. Additionally, the discrete nature of MGs leads to convergence issues to existing optimization methods thereby leading to difficulties obtaining feasible solutions for large-scale networks. This article develops a paradigm for discrete droop control to improve microgrids’ controllability in managing voltage and frequency fluctuations. With the emergence of Internet of Things, the computational tasks are distributed among local resources. The utilized Distributed and Asynchronous Surrogate Lagrangian Relaxation (DA-SLR) method distributes the optimization tasks among the MGs and efficiently coordinates the distributed subsystems. A small-signal model of the operational optimization is then developed to verify the system’s stability. Numerous case studies have proven the DA-SLR’s efficacy in comparison to various variations of the alternating direction of multipliers method (ADMM)

Economical and environmental operation of smart networked microgrids under uncertainties using NSGA-II

The future smart grid is expected to be an interconnected network of small-scale and self-contained micro-grids (MGs), in which renewable energy sources (RESs) play significant role in generation level as well as attract special attention to the aim at a friendly environmental society. In this paper, optimal operation of distributed generations (DGs) are analyzed probabilistically due to uncertainties of loads and RESs. In addition, probability distribution function (PDF) is used to describe the fluctuation model of input data. This paper establishes smart networked Microgrids (MGs) based on NSGA-II algorithm, including the lowest operating cost and the least pollutants emission. In order to make a comparison, the problem is converted to a single-objective function and then, solved by two heuristic algorithms, namely particle swarm optimization (PSO) and Imperialist competitive algorithm (ICA). Simulation results support the capability of the proposed algorithm to minimize jointly the operating power and pollution emission as compared to the results obtained by using current heuristics