Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community

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 sizeComment: 14 pages, 13 figures. IEEE Transactions on Smart Grid (2023

On the Complexity of Core, Kernel, and Bargaining Set

Coalitional games are mathematical models suited to analyze scenarios where players can collaborate by forming coalitions in order to obtain higher worths than by acting in isolation. A fundamental problem for coalitional games is to single out the most desirable outcomes in terms of appropriate notions of worth distributions, which are usually called solution concepts. Motivated by the fact that decisions taken by realistic players cannot involve unbounded resources, recent computer science literature reconsidered the definition of such concepts by advocating the relevance of assessing the amount of resources needed for their computation in terms of their computational complexity. By following this avenue of research, the paper provides a complete picture of the complexity issues arising with three prominent solution concepts for coalitional games with transferable utility, namely, the core, the kernel, and the bargaining set, whenever the game worth-function is represented in some reasonable compact form (otherwise, if the worths of all coalitions are explicitly listed, the input sizes are so large that complexity problems are---artificially---trivial). The starting investigation point is the setting of graph games, about which various open questions were stated in the literature. The paper gives an answer to these questions, and in addition provides new insights on the setting, by characterizing the computational complexity of the three concepts in some relevant generalizations and specializations.Comment: 30 pages, 6 figure

Optimization Model for IoT-Aware Energy Exchange in Energy Communities for Residential Users

In recent years, the distribution of local and renewable generation plants has introduced significant challenges in the management of electrical energy. In order to increase the usage of renewable energy, the prosumers, i.e., the residential users that can act both as producers and consumers, can benefit from joining together and forming energy communities. The deployment of an energy community is based both on technological advancements and on a deep understanding of human decision-making, which in turn requires knowledge about the factors that influence the behavior of residential users. This new scenario calls for great research investigations aimed to improve the management of energy exchanges inside energy communities. An important role in this context is played by the Internet of Things (IoT) technology, as smart IoT objects are used both as a source of real-time information regarding the energy production and the users’ requirements, and as actuators that can help to regulate the distribution and use of energy. In this paper, an IoT-aware optimization model for the energy management in energy communities is presented. The main novelty consists in modeling the entire energy community as a whole, rather than each prosumer separately, with the goal of optimizing the energy sharing and balance at the community level. Experimental results, performed in an university campus, show the advantages of the approach and its capability of reducing the energy costs and increasing the community’s energy autonomy

On the Computational Complexity of solution concepts in compact coalitional games

Dottorato di Ricerca in Ingegneria dei Sistemi ed Informatica, XXII Ciclo, 2009Università della Calabri

Edge Computing Parallel Approach for Efficient Energy Sharing in a Prosumer Community

The transition towards more sustainable energy management can be supported by the diffusion of energy communities, i.e., coalitions of prosumers that are willing to exchange the energy produced locally. The optimization of energy management requires the solution of a prosumer problem that can become impractical when the number of users increases. This paper presents a parallel approach, based on an edge computing architecture, which is suitable for large communities. The users are partitioned into groups whose proportions, in terms of producers and consumers, mirror the composition of the whole community. The prosumer problems for the different groups are first solved separately and in parallel by local edge nodes. Then, the solutions are combined by a central entity to redistribute the energy among the groups and minimize the exchange of energy with the external grid. A set of experiments show that the parallel approach, when compared with an approach that solves the optimization problem in a single stage, leads to a notable reduction of computing resources, and becomes feasible in large communities for which the single-stage approach is impossible. Moreover, the achieved solution is close to the optimal solution in terms of energy costs

Edge Computing Parallel Approach for Efficient Energy Sharing in a Prosumer Community

The transition towards more sustainable energy management can be supported by the diffusion of energy communities, i.e., coalitions of prosumers that are willing to exchange the energy produced locally. The optimization of energy management requires the solution of a prosumer problem that can become impractical when the number of users increases. This paper presents a parallel approach, based on an edge computing architecture, which is suitable for large communities. The users are partitioned into groups whose proportions, in terms of producers and consumers, mirror the composition of the whole community. The prosumer problems for the different groups are first solved separately and in parallel by local edge nodes. Then, the solutions are combined by a central entity to redistribute the energy among the groups and minimize the exchange of energy with the external grid. A set of experiments show that the parallel approach, when compared with an approach that solves the optimization problem in a single stage, leads to a notable reduction of computing resources, and becomes feasible in large communities for which the single-stage approach is impossible. Moreover, the achieved solution is close to the optimal solution in terms of energy costs