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| Low-Carbon Optimal Dispatch of Electric-Hydrogen-Heat System in Park Based on Alternating Direction Method of Multipliers |
| Kong Lingguo1, Shi Lihao1, Shi Zhenyu2, Wang Shibo1, Cai Guowei1 |
1. China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China;
2. Engineering and Training Teaching Center Northeast Electric Power University Jilin 132012 China |
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Abstract With the transition to low-carbon energy and the promotion of the "double carbon" goal, the full use of clean energy has become the consensus of social development. Hydrogen energy, which has no carbon emissions and can realize combined heat and power supply, is an important way to achieve the goal of "double carbon". In order to improve the flexibility and coordination of users' energy use, the renewable energy-based electric-hydrogen multi-energy complementary park energy system has become the focus of attention of experts and scholars, so how to achieve economic and optimal operation of the park electric-hydrogen-thermal decarbonized energy system. To solve this problem, this paper proposes a double-layer energy optimal scheduling method of electric-hydrogen-thermal energy in park based on the alternating direction method of multipliers (ADMM). By combining distributed (rooftop photovoltaic, park building electricity, thermal load) and centralized (battery storage, hydrogen storage, electric heat storage) energy, it realizes precise and optimal interaction of point-to-point energy of multiple subjects.
First, the mathematical model of each building is established based on the power interaction cost between buildings and grid, equipment operation and maintenance cost, power interaction cost between buildings and carbon benefit, and the final economic function of the upper level optimization is obtained by decoupling the interaction power between buildings through ADMM. Second, as each building solves its own optimization problem synchronously, its interactive power values need to be coordinated by a centralized energy optimization scheduling center to achieve a balance between supply and demand. Third, the lower layer optimization uses the building interaction energy and the residual power of each building obtained from the upper layer to solve the exact interaction power among buildings and the optimal output of each centralized energy equipment in the park based on mixed integer programming with the objective of minimizing the park operation cost.
Simulation results for the electric-hydrogen-thermal system of the park show that each building, as an independent entity, can provide certain electric power support to other buildings with insufficient PV power generation when its own PV power generation is sufficient, or it can be absorbed by the electrolyzer and converted into hydrogen energy or stored by battery storage. When there is a power shortage in the park's energy system, fuel cells and battery storage act as power providers to meet the park's load in conjunction with the grid. In addition, while hydrogen production by electrolysis of water and waste heat of fuel cell power generation are matched with electric boilers to meet the heat load of the park, the remaining heat is stored by the heat storage tank, which fully improves the utilization rate of clean energy. The park has a certain support capacity externally, and the energy system of the park can share a certain load for the grid when the external load peaks. The park calculates carbon emission reductions and carbon benefits based on the output of each equipment.
The following conclusions can be drawn from the simulation analysis: (1) Each building in the park can solve its own optimization problem in parallel, solve the desired interactive power and upload it to the centralized energy optimization scheduling center, while taking into account the privacy of user data. (2) Realize the precise interaction of energy from multiple buildings and multiple energy sources (electricity, hydrogen, heat) in the park. solving for the interactive power values available for energy sharing in each building with the support of the ADMM algorithm. The lower layer is based on a mixed integer planning model to accurately solve for the interactive power between buildings and the output of each controllable energy source in the park according to the system operation economy. (3) The adjustable equipment of the park energy system has the ability to support external flexibility while retaining the corresponding margin, and the system can calculate the carbon emission reduction and carbon revenue of the park according to the output of each equipment, so as to realize the low-carbon optimal scheduling of the park electricity-hydrogen-heat system.
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Received: 10 May 2022
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2023, Vol. 38 
