This paper investigates the effective management of distributed Renewable Energy (RE) sources in multi-user Park-level Integrated Energy Systems (PIESs), aiming to coordinate power supply, cooling, and other forms of energy through a Virtual Power Plant (VPP) while enhancing environmental and economic benefits in regulation markets. Beyond the uncertain influence of solar PhotoVoltaic (PV) power on VPP operation, the challenge exists in establishing carbon trading and Integrated Demand Response (IDR) mechanisms to enable internal regulation of VPP. To address the above problem, a data-driven two-stage Distributed Robust Optimization (2-DRO) model is proposed. The overall goal is to minimize the operating costs of VPP by maintaining a supply-demand balance in PIESs along with protecting the electricity for production tasks. A case study of an actual PIES in eastern China reveals that our model is more practical and cost-effective. It integrates the deployment of both electricity and cooling energy under the mechanism of IDR and carbon emissions, realising flexible scheduling for multi-scenario operation of PIES.

The rapid evolution of distributed energy resources, particularly photovoltaic systems, poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing costs. The integrated nature of distributed markets, blending centralized and decentralized elements, holds the promise of maximizing social welfare and significantly reducing overall costs, including computational and communication expenses. However, achieving this balance requires careful consideration of various hyperparameter sets, encompassing factors such as the number of communities, community detection methods, and trading mechanisms employed among nodes. To address this challenge, we introduce a groundbreaking neural network-based framework, the Energy Trading-based Artificial Neural Network (ET-ANN), which excels in performance compared to existing algorithms. Our experiments underscore the superiority of ET-ANN in minimizing total energy transaction costs while maximizing social welfare within the realm of photovoltaic networks.

In this paper, we investigate the stable matching problem with multiple preferences in bipartite graphs, where each agent has various preference lists for all available partners with respect to different criteria. The problem requires that each matched agent must have exactly one partner and the obtained matching should be stable for all criteria. As our main contribution, we present an integer linear programming (ILP) model for determining whether there exists a globally stable matching in bipartite graphs, which has been proved to be NP-hard. Since the time consumed for solving ILPs might dramatically increase as the size of instances grows, we develop a preprocessing technique that helps to eliminate pairs that will never be a member of any globally stable matching and thus accelerates the computing process. We perform experiments on randomly generated preference lists and observe a significant speedup when we preprocess the instance before solving the ILPs. As there does not need to exist a perfect matching that is stable for all given criteria, we extend our ILP to the optimized version of the aforementioned problem, which asks to find a matching with maximum cardinality that is stable among all matched agents.