Modern power grids are getting harder to manage. The growth of distributed energy resources such as rooftop solar, batteries, and electric vehicles makes the system more variable and interconnected. This paper reviews whether quantum computing — a new way to compute that uses quantum bits, or qubits — can help meet that rising computational challenge.

The author performed a transparent, keyword-based literature search and organized the results. Studies are grouped across monitoring and estimation, planning, operation and control, security, reliability and resilience, stability assessment, data-driven intelligence, and digital twins. The review also explains basic quantum ideas in plain language. For example, qubits can hold combinations of states at once (superposition) and can be linked in ways that let them share information in unusual ways (entanglement). The paper highlights quantum algorithms that seem relevant to power systems, such as the Harrow–Hassidim–Lloyd (HHL) algorithm for solving large systems of linear equations and the Quantum Approximate Optimization Algorithm (QAOA) for hard scheduling and configuration tasks.

The review covers the current quantum ecosystem as well. It summarizes available hardware types, including superconducting circuits, trapped ions, photonic devices, and neutral-atom systems. It mentions major vendors and cloud services and notes that software frameworks and simulators such as Qiskit, Cirq, and PennyLane make it easier to test ideas without owning hardware. The paper also describes the current noisy intermediate-scale quantum (NISQ) era, where small, imperfect quantum processors can run hybrid quantum–classical methods such as variational algorithms.

Why this matters: many core power-system problems are large, nonlinear, or combinatorial and become costly as systems grow. The review argues that quantum methods could complement classical tools for tasks like power flow analysis, state estimation, contingency analysis, unit commitment, network reconfiguration, resource placement, and demand-response scheduling. It also points to newer applications where fast, large-scale computation is useful, such as peer-to-peer energy trading and digital twins of power networks.