Application of Quantum Computing in Power Systems

1. Introduction

Due to continuous expansion, the current interconnected power system is the largest and most complex man-made dynamic system on the planet. These bulk systems are highly nonlinear, exhibiting multi-scale behavior in space and time. Moreover, the randomness and uncertainty of power systems are becoming stronger due to the integration of renewable energy resources. The increasing complexity makes it more and more difficult to analyze a series of related problems in power systems. Here, we provide some typical examples. Transient stability assessment (TSA) is a key technology for securing today’s bulk power networks, and the high degree of nonlinearity makes the transient stability analysis of power systems increasingly difficult. Optimal power flow (OPF) is an important optimization problem for the energy industry, and it is used for system planning, to determine the price of the day-ahead market, and to effectively allocate power generation capacity during the day. The power flow equation constraints make OPF problems nonconvex and difficult to solve. Unit commitment (UC) is a very significant optimization problem in power system dispatching, and it can be modelled as NP-hard mixed-integer nonlinear programming. There are also other problems related to power system analysis, such as economic dispatch, static stability, etc. Generally speaking, all of the abovementioned problems are becoming progressively more difficult for the traditional computing paradigm due to the increasing scale and complexity of power systems. Researchers are trying to find other and more effective computing paradigms to solve these multifaceted problems.

With the development of quantum hardware, quantum computation has started to attract more and more attention as a promising computing paradigm. Algorithms such as HHL, Shor’s factorization, and Grover search can be implemented on quantum hardware to make use of quantum characteristics (i.e., superposition and entanglement) to achieve quantum advantages. Large-scale error correction quantum computers can solve problems that even the largest classical supercomputer cannot. However, in the noisy intermediate-scale quantum (NISQ) era, due to the limitations of qubit resources (including but not limited to the number of qubits and the depth of quantum circuits), it was difficult for the quantum algorithms implemented on quantum hardware to be applied in practical industry in a short time. Therefore, two other interesting types of algorithms have been proposed. One is hybrid quantum–classical algorithms, which combine quantum computing with classical computing to reduce the qubit resource requirements. The other is quantum-inspired algorithms, which run on a classical computer and introduce quantum concepts into classical algorithms. These two types of algorithms can also introduce potential performance improvements. The development and application of the abovementioned three kinds of quantum-related algorithms have attracted great attention and have found application in many scenarios, including in power systems.

This Special Issue aims to explore novel quantum-related approaches to power system problems such as economic dispatch, optimal power flow, unit commitment, transient stability, and static stability. The approaches are based on the application of quantum computing (QC) techniques (i.e., by employing quantum algorithms, quantum-inspired algorithms, quantum reinforcement learning, or quantum neural networks). By exploring quantum-related strategies in power system problems, this Special Issue tries to show what and how power systems benefit from QC. Since this Special Issue only recently starting accepting submissions, no papers have been accepted as of the present moment, but relevant studies have previously been published in this journal, and some of these papers will be briefly described in the next section.

2. A Short Review of the Relevant Studies

As for the research on the application of quantum algorithms in power systems, Ahn et al. [1] claim that quantum computing is a game-changing technology that affects modern distributed energy resource (DER) systems and propose potential quantum attack defense strategies such as post-quantum cryptography (PQC) and quantum key distribution (QKD), which can be applied to DER networks.

As for research on the application of hybrid quantum–classical algorithms in power systems, Correa-Jullia et al. [2] explore the potential advantages of quantum support vector machines (Q-SVMs) over traditional machine learning approaches. Q-SVMs are quantum kernel methods and are suitable for classification tasks. The results show that the performance of Q-SVMs is comparable to that of conventional machine learning models and better than random forest (RF) and k-nearest neighbors (k-NN).

As for the research on the application of quantum-inspired algorithms in power systems, the following two studies fall within this scope:

Fan et al. [3] combine the quantum evolutionary algorithm and the genetic algorithm to solve regional integrated energy system planning problems under different load structure, multi-cycle, and multi-scenario operation modes. Energy system planning is essentially a complex multi-objective optimization problem. The concepts of qubits and quantum superposition are borrowed to encode chromosomes in the quantum evolution algorithm.

Wang et al. [4] propose a short-term multi-load forecasting model based on quantum weighted GRU (QWGRU) and multi-task learning frameworks. Compared to LSTM, GRU, and single-task learning QWGRU models, the multi-task learning QWGRU model is more effective in the multi-load forecasting of regional integrated energy systems. QWGRU introduces quantum-weighted neurons into the classical GRU, making QWGRU have the superposition property of quantum states and thus simulate the information processing mechanism more sufficiently.

3. Conclusions

Due to the increasing scale and complexity of power systems, algorithms under the traditional computing paradigm gradually suffer from insufficient performance. Under the quantum computing paradigm, quantum computing techniques, including quantum algorithms, hybrid quantum–classical algorithms, and quantum-inspired algorithms, are increasingly being used for a range of highly nonlinear power system problems, and some studies have shown quantum superiority over classical algorithms. Of course, there is still room for improvement in the algorithms developed under the quantum computing paradigm. It is difficult for quantum algorithms that require huge quantum resources to be used in practical applications nowadays due to the limitations of the quantum resources that are available in the NISQ era. As for hybrid quantum–classical algorithms, the mapping between classical data and quantum data is usually the key step to be studied and optimized. In addition, data-driven quantum machine learning and quantum neural networks are also faced with the same problem of insufficient interpretability found in traditional machine learning and traditional neural networks. Despite these problems, the quantum computing paradigm will become an important complement to the classical computing paradigm as research continues to deepen. Presumably, the application of quantum computing in the power system will significantly enhance or improve the security, stability, and efficiency of the power system.