New research discusses the use of quantum computing for energy optimization in prosumer communities. The authors propose a novel approach to the prosumer problem, which involves scheduling household loads based on user needs, electricity prices, and local renewable energy availability.
The approach uses a hybrid quantum algorithm, the Quantum Approximate Optimization Algorithm (QAOA), and its variant, the Recursive QAOA. The study reports encouraging results from extensive experiments on simulators and real quantum hardware, with the Recursive QAOA providing optimal solutions for problems involving up to 10 qubits. The computation time was found to be nearly independent of the system size.
Quantum Computing for Energy Optimization in Prosumer Communities
The efficient management of energy communities relies on the solution of the prosumer problem, which involves scheduling household loads based on user needs, electricity prices, and the availability of local renewable energy. The goal is to reduce costs and energy waste. Quantum computers can offer a significant breakthrough in treating this problem due to the intrinsic parallel nature of quantum operations. The most promising approach is to devise variational hybrid algorithms, where 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 compared to classical approaches.
The Prosumer Problem
The prosumer problem is commonly expressed as a Mixed Integer Linear Programming (MILP) problem, where the objective function is a linear combination of decision variables, some of which are discrete. The constraints given by the energy balance and by the prosumers’ requirements are linear as well. Classical exact optimization algorithms can become infeasible for large instances due to the NP-hard nature of the MILP problem. Metaheuristic algorithms and machine-learning methods can be used for large communities when approximate solutions are acceptable.
Quantum Computing for Energy Optimization
In recent years, research and industrial efforts are showing that quantum computing can offer the opportunity to approach energy optimization problems with a completely different paradigm. The main potential advantage is the computational speedup that can be achieved by exploiting the quantum parallelism stemming from the superposition principle. Noise and decoherence issues of available quantum hardware hinder the use of pure quantum algorithms, but in the current noisy intermediatescale quantum (NISQ) era, a valid alternative is to devise variational quantum algorithms (VQAs) in which the computation is hybrid.
Quantum Approximate Optimization Algorithm (QAOA)
The Quantum Approximate Optimization Algorithm (QAOA) is one of the most renowned hybrid quantum-classical algorithms. The prosumer problem is first formulated as an Integer Linear Programming (ILP) where the objective is to minimize the energy cost while satisfying a number of constraints related to the maximum available energy and user requirements. The ILP problem is then transformed into an Ising problem, where binary variables are changed into discrete variables taking the values -1,1. The Ising expression defines a Hamiltonian operator which is used to define and later measure the energy of a set of qubits, each corresponding to one discrete variable of the Ising problem.
Recursive QAOA
The Recursive QAOA algorithm is often exploited to improve the quality of the solution. The objective of this variant is to reduce the size of the problem by identifying through quantum computation the couples of discrete variables that show maximum correlation. This approach is able to provide optimal and admissible solutions with good probabilities while the computation time is nearly independent of the system size.
Conclusion
Quantum computing offers a promising approach to solving the prosumer problem in energy communities. The use of variational hybrid algorithms, such as QAOA and Recursive QAOA, can provide significant speedups and more efficient solutions compared to classical approaches. However, further research and development are needed to fully realize the potential of these quantum algorithms in practical applications.
The article titled “Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community” was published in the IEEE Transactions on Smart Grid on January 1, 2024. The authors of the article are Carlo Mastroianni, Francesco Plastina, Luigi Scarcello, Jacopo Settino, and Andrea Vinci. The article can be accessed through the DOI reference https://doi.org/10.1109/tsg.2023.3286106.
