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| Carbon Emissions Constraint Distributed Low-Carbon Economic Dispatch of Power System |
| Li Junhui1, Shao Yan1, Zhu Xingxu1, Guo Qi2, Qi Jun3 |
1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China;
2. Branch of Power Dispatching Control, Inner Mongolia Power (Group) Co. Ltd Hohhot 010020 China;
3. Inner Mongolia Power Group Co. Ltd Alxa Electirc Power Supply Company Alxa 750306 China |
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Abstract In a multi-area interconnected power system, to meet the needs of emission reduction policies, carbon emission constraints can be added to the economic dispatch model to regulate the output of the unit and achieve low-carbon economic dispatch of the system. This method is the most common in centralized economic dispatch. Because centralized optimal dispatch requires great communication bandwidth and large computing and information storage requirements, distributed economic dispatch has become a research hotspot. In recent years, scholars have made some achievements in the research of distributed economic dispatch, but the methods involved can only solve the economic dispatch problem with a global equality constraint and several local constraints. For the economic dispatch problem with global inequality constraints, these methods are not applicable. Therefore, this paper proposes a new distributed low-carbon economic scheduling method. The proposed method can solve the economic scheduling problem with global equality constraints and global inequality constraints, which is more universal than the existing distributed economic scheduling methods.
First, a low-carbon economic dispatch model is constructed to determine the objective function and the corresponding equality constraints and inequality constraints. Then, based on the duality principle, the original problem is transformed into a corresponding dual problem. Then, the dual problem model is decomposed by the variable decomposition method, so that the objective function and constraints are transformed into optimization sub-problems related to a single region. Finally, based on ADMM, an iterative solution framework for the collaborative optimization of each region is designed.
To improve the convergence of the algorithm and avoid the problem of penalty factor parameter selection, a dynamic multiplier update strategy is adopted. The emission reduction factor is set at 15 %. According to the method proposed in this paper, the IEEE 6 node test system and the 72-node test system are optimized for distributed low-carbon economy scheduling. In the example of the IEEE 6 node system, the convergence condition is reached when iterating 247 times. The relative error of distributed economic dispatch is 0.88 % compared with centralized operation cost, and the calculation result can be regarded as consistent. The carbon emission of the system is reduced from 224 t to 190.06 t, which is reduced by 15.15% and meets the carbon emission constraint. Then, the 72-node test system is analyzed, and the distributed solution effects of different network topology types and partition methods are compared. The universality of the method to different network topologies and the applicability under different partition conditions of the same test system is verified. Finally, the communication fault in the interconnected area verifies that the method meets the needs of “plug and play” in the optimized area.
Through the example analysis, it is verified that the method can effectively reduce the amount of information transmission between regions, fully guarantee the requirements of information privacy of each regional unit, meet the demand of “plug and play” in the optimization region, and realize the distributed low-carbon economic dispatch of the multi-area interconnected system. The next work will improve the algorithm to accelerate its convergence speed, and further enrich and improve the model of controllable units within each region.
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Received: 14 September 2022
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