Optimal dispatch of low-carbon integrated energy system considering nuclear heating and carbon trading

Abstract

The development of miniaturized nuclear power (NP) units and the improvement of the carbon trading market provide a new way to realize the low-carbon operation of integrated energy systems (IES). In this study, NP units and carbon trading mechanisms are introduced into the IES to build a new low-carbon scheduling model. In view of the decrease in system operation flexibility caused by the introduction of NP unit, on the one hand, the heating renovation of the NP unit is carried out to make it a cogeneration unit, to expand its operating range and improve its operating flexibility; on the other hand, auxiliary equipment such as electricity storage system, heat storage system and power to gas unit, which can carry out energy time translation or energy form conversion, are introduced into IES to jointly improve the flexibility of system operation. In the model-solving stage, the chance-constrained programming (CCP) model considering the uncertainty of the renewable energy (RE) output is converted into an equivalent mixed-integer linear programming (MILP) model using discretized step transformation. The test system built based on real data of an IES in North China shows that the proposed method has good economic and low-carbon environmental protection benefits.

arXiv commentAcceptted by Journal of Cleaner Production

References in corpus (9)

• Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties
• Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach
• Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings
• Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: a bi-level programming approach via real-time pricing
• Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting
• Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach
• Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game
• Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments
• The cost of a future low-carbon electricity system without nuclear power for Sweden

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