Abstract
In order to reduce the impacts caused by large-scale renewable energy resources accessing the utility grid, the micro-energy grid system, as a natural extension of the microgrid in the energy internet era, is proposed and developed to provide a new solution for the optimal utilization of renewable energy resources. In this paper, a multi-energy integrated micro-energy system is proposed which contains wind, PV, bedrock energy storage, magnetic levitation electric refrigeration, solid oxide fuel cell, solar thermal collector, energy storage, and V2G technologies, and detailed models of the energy generation/conversion/storage devices are formulated. Besides this, the uncertainties of renewable energy resources and cold/heat/electricity loads are considered, and the optimal dispatch problem of the micro-energy system is established from day-ahead and real-time time scales based on a model predictive control method. The day-ahead optimal scheduling aims at economic optimization and guides real-time scheduling, and real-time scheduling utilizes rolling optimization and a feedback correction mechanism to effectively correct the deviation of renewable energy generations and loads at a real-time horizon, which improves the optimization control accuracy, follows the day-ahead dispatch plan, and ensures the economics of real-time scheduling at the same time.
Highlights
In the context of the carbon peak and carbon neutrality goal, vigorously developing clean energy and renewable energy and reducing the proportion of fossil energy is the only way to eradicate smog and reduce greenhouse gas emissions
Rolling optimization of limited time horizons may not be able to get the global optimal results, but it can constantly consider the impact of uncertainty and make timely correction, which is more adaptable to the actual process and has stronger robustness compared with the one-time optimization only based on the model
Even if the uncertainty reaches 10%, the scheduling results continue to follow the plan of DA, which shows that the simple feedback correction in Model predictive control (MPC) improves robustness and has good dynamic control effect for the uncertainty in micro-energy grid (MEG)
Summary
In the context of the carbon peak and carbon neutrality goal, vigorously developing clean energy and renewable energy and reducing the proportion of fossil energy is the only way to eradicate smog and reduce greenhouse gas emissions. The access of large-scale new energy sources to the power grid will have a huge impact on the power grid and bring severe challenges to the safe and stable operation of the power system (Li et al, 2021; Xu et al, 2021) In this context, the research and construction of a new energy system represented by the micro-energy grid (MEG) provides a new solution for the optimization and utilization of renewable energy. The above research based on the MPC method are mostly aimed at microgrids or relatively simple MEGs. The energy forms involved and the various links of energy generation, conversion, transmission, storage, and utilization in MEGs are not considered in enough detail. 2) A two-stage optimal scheduling method based on model predictive control is proposed for the energy management of the actual MEG system to improve the optimization control accuracy
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