Energy communities are collectives that manage their own production, distribution, and consumption of energy, usually from renewable sources. To increase efficiency and sustainability, the scheduling of the loads on the basis of the user needs, the electricity prices, the availability of local renewable energy and the battery storage capacity must be optimized. Quantum computing, leveraging principles like superposition and entanglement, offers new computational power for complex optimization tasks. This paper lays the basis for exploring the potential of quantum computing in optimizing the operation of energy communities by modeling the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem.
Energy communities are collectives that manage their own production, distribution, and consumption of energy, usually from renewable sources. To increase efficiency and sustainability, the scheduling of the loads on the basis of the user needs, the electricity prices, the availability of local renewable energy and the battery storage capacity must be optimized. Quantum computing, leveraging principles like superposition and entanglement, offers new computational power for complex optimization tasks. This paper lays the basis for exploring the potential of quantum computing in optimizing the operation of energy communities by modeling the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem.
The efficient management of energy communities relies on the solution of the “prosumer problem”, i.e., the problem of scheduling the household loads on the basis of the user needs, the electricity prices, and the availability of local renewable energy, with the aim of reducing costs and energy waste. Quantum computers can offer a significant breakthrough in treating this problem thanks to the intrinsic parallel nature of quantum operations. The most promising approach is to devise variational hybrid algorithms, in which quantum computation is driven by parameters that are optimized classically, in a cycle that aims at finding the best solution with a significant speed-up with respect to classical approaches. This paper provides a reformulation of the prosumer problem, allowing to address it with a hybrid quantum algorithm, namely, Quantum Approximate Optimization Algorithm (QAOA), and with a recent variant, the Recursive QAOA. We report on an extensive set of experiments, on simulators and real quantum hardware, for different problem sizes. Results are encouraging in that Recursive QAOA is able, for problems involving up to 10 qubits, to provide optimal and admissible solutions with good probabilities, while the computation time is nearly independent of the system size.
This work explores the use of variational quantum algorithms to optimize energy distribution among users in energy communities, using real data from a community lab. This requires integrating various energy sources, storage solutions, and the ability to respond to variations in demand within energy systems, while maintaining the capacity to adapt to the variability of renewable energy sources. Given the increasing complexity as the problem size grows and the limitations of classical computing methods in addressing it, we study an approach that models the problem using QUBO and solves it applying quantum optimization algorithms. Our analysis includes a comparative study of two variational techniques, QAOA and VQE, and the implementation of the former on Qmio, an actual superconducting quantum processor. Although deploying QAOA on Qmio required specific adaptations to the hardware, we were able to achieve a shift of the probability distribution towards lower-energy solutions without error mitigation techniques, which highlights both QAOA’s potential and the intrinsic challenges of quantum optimization.
Optimization is a crucial challenge across various domains, including finance, resource allocation, and mobility. Quantum computing has the potential to redefine the way we handle complex problems by reducing computational complexity and enhancing solution quality. Optimization, particularly of objective functions, stands to benefit significantly from quantum solvers, which leverage principles of quantum mechanics like superposition, entanglement, and tunneling. The Ising and Quadratic Unconstrained Binary Optimization (QUBO) models are the most suitable formulations for these solvers, involving binary variables and constraints treated as penalties within the overall objective function. To harness quantum approaches for optimization, two primary strategies are employed: exploiting quantum annealers—special-purpose optimization devices—and designing algorithms based on quantum circuits. This review provides a comprehensive overview of quantum optimization methods, examining their advantages, challenges, and limitations. It demonstrates their application to real-world scenarios and outlines the steps to convert generic optimization problems into quantum-compliant models. Lastly, it discusses available tools and frameworks that facilitate the exploration of quantum solutions for optimization tasks.
We present a method to formulate the unit commitment problem in energy production as quadratic unconstrained binary optimization (QUBO) problem, which can be solved by classical algorithms and quantum computers. We suggest a first approach to consider uncertainties in the renewable energy supply, power demand and machine failures. We show how to find cost-saving solutions of the UCP under these uncertainties on quantum computers. We also conduct a study with different problem sizes and we compare results of simulated annealing with results from quantum annealing machines.
This bachelor’s thesis examines the applicability of quantum computing to the complex optimization problems inherent in energy systems. The transition toward decentralized production and renewable energy sources has increased computational complexity to a degree that challenges the performance of traditional classical optimization methods. In particular, combinatorial challenges—such as unit commitment and power grid management—have been identified as areas where traditional algorithms reach their limits as the number of variables increases exponentially. The thesis introduces the physical and computational foundations of quantum computing, including superposition, entanglement, and quantum parallelism, and analyzes the theoretical advantage they offer relative to classical bit-based computing. The analytical focus is on quantum optimization methods, such as Quadratic Unconstrained Binary Optimization (QUBO), quantum annealing, and variational hybrid algorithms (QAOA). Additionally, the role of quantum simulations in material physics, such as the development of battery materials and catalysts, is examined. Based on the literature review, quantum computing offers promising opportunities to support energy efficiency and the green transition. However, the technological limitations of current hardware (the NISQ era), such as the low number of qubits and sensitivity to noise, still necessitate the extensive use of quantum simulators and classical-quantum hybrid models. This work synthesizes current research in the field and identifies critical research gaps that must be addressed before the full extent of quantum advantage can be realized in practical energy engineering solutions.
Quantum Green Computing represents a transformative paradigm that merges the power of quantum computational techniques with the principles of sustainability to drive innovation in renewable resource management.This paper investigates the interplay between quantum algorithms, energy-efficient system architectures, and advanced renewable energy technologies-including solar photovoltaics, wind energy systems, and bioenergy solutions.By leveraging the unique capabilities of quantum computing, such as superior optimization, high-dimensional modeling, and rapid problem-solving, the study explores novel strategies for addressing the inherent complexities of renewable energy integration.The research emphasizes three core dimensions: (1) the application of quantum optimization algorithms to enhance grid stability, load balancing, and real-time decision-making; (2) the development of energy-efficient quantum-inspired hardware architectures that align with green computing objectives; and (3) the formulation of predictive and adaptive models for renewable resource allocation using quantum-inspired approaches.Furthermore, the paper highlights case studies that demonstrate how quantum techniques can significantly improve grid management, predictive maintenance of renewable infrastructures, and long-term forecasting of resource availability under dynamic environmental conditions.By critically evaluating these emerging pathways, the study outlines a vision where quantum-enabled sustainability not only reduces carbon emissions but also maximizes energy utilization efficiency.This integration holds the potential to revolutionize the global renewable energy landscape, enabling resilient, lowcarbon ecosystems capable of supporting the growing demands of a sustainable future.