Quantum mechanics (QM) can be understood as a set of rules that forms the basis for
developing all quantum theories. One of these theories is quantum computation (QC), ie …

Electric power systems provide the backbone of modern industrial societies. Enabling
scalable grid analytics is the keystone to successfully operating large transmission and …

This paper proposes the use of annealing-based quantum computing for solving
combinatorial optimal power flow problems. Quantum annealers provide a physical …

As a large number of renewable energy resources are connected to power systems, the
operation, planning, and optimization of power systems have been becoming more and …

The advent of quantum computing can potentially revolutionize how complex problems are
solved. This article proposes a two-loop quantum-classical solution algorithm for generation …

In this paper, the use of quantum computing is explored to solve a crucial optimization
problem in the formation of microgrids (MGs), which can enhance the resilience of …

A combinatorial optimization problem (COP) is the problem of finding the optimal solution in
a finite set. When the size of the feasible solution set is large, the complexity of the problem …

The unit commitment problem (UC) is crucial for the operation and market mechanism of
power systems. With the development of modern electricity, the scale of power systems is …

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 …

A challenging engineering optimization problem in electrical power generation is the unit
commitment problem (UCP). Determining the scheduling for the economic consumption of …