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| Multi-Time-Scale Optimal Scheduling of AC-DC Hybrid Multi-Energy Microgrid Considering Multiple Differences |
| Cai Yao1, Lu Zhigang1, Pan Yao1, He Liangce1, Zhou Changlei2 |
1. Key Laboratory of Power Electronics for Energy Conservation and Motor Drive of Hebei Province Yanshan University Qinhuangdao 066004 China;
2. Jining Qufu Airport Company Limited Jining 272000 China |
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Abstract In recent years, technologies such as renewable energy generation, combined heat and power, energy storage, and power electronic transformer (PET) have developed rapidly, which is conducive to construct a multi-energy complementary, clean, and high-efficiency AC-DC hybrid multi-energy microgrid (MEMG) system. However, immature PET models and complex uncertainties bring challenges to its optimal scheduling. Some multi-time-scale optimal scheduling methods are presented to achieve the safe and economic operation of the MEMG under uncertainties. But the differences of the demand response resources and the generation and load uncertainties in different time scales, and the energy response characteristic difference are not fully considered in these scheduling models. Furthermore, when the reserve allocation method is used to deal with the uncertainties, the reserve models of energy storage equipment and electrothermal coupling equipment are unsatisfactory. Therefore, this paper aims to propose a multi-time-scale optimal scheduling model considering multiple differences for the AC-DC hybrid multi-energy microgrid with the employment of PET, and improve the reserve models of its internal equipment.
Firstly, an AC-DC hybrid MEMG structure with a three-stage PET is constructed, and the models of the devices including PET and the uncertainties are established. Secondly, the multi-time-scale optimal scheduling strategy of the MEMG is proposed, which considers the multiple differences, including the differences of demand response resources, system scheduling objectives and prediction accuracy of generation and loads in different time scales, and the difference of energy response characteristic. The coordination mechanism of the day-ahead, intraday and real-time scheduling is presented by the proposed strategy. Then, the corresponding multi-time-scale optimal scheduling model is established, which includes day-ahead robust chance-constrained optimization model, intraday stochastic optimization model and real-time hierarchical rolling modification model. In the day-ahead scheduling, the reserve models of traditional energy storage and electrothermal coupling equipment are improved, and the reserve sharing mechanism of AC and DC systems based on the PET is designed, and the scheduling model is transformed into a mixed-integer linear programming model. In the intraday scheduling, the mixed time resolution scheme is adopted to reduce real-time adjustment pressure. In addition, the detailed steps of solving the multi-time-scale optimal scheduling model with CPLEX solver are given. Finally, the feasibility and economy of the proposed scheduling strategy, as well as the reliability and flexibility of the designed day-ahead reserve scheme, are verified by a case study.
The following conclusions can be drawn from the simulation analysis: (1) The implement of the day-ahead, intraday and real-time scheduling in sequence can obtain an economical and reasonable scheduling scheme. The demand response strategies under different time scales can further promote the balance between supply and demand, and reduce scheduling costs. (2) The proposed reserve allocation method can ensure energy supply reliability under the extreme scenario, and achieve the flexible choice of the risk preference according to the decision maker’s demand. (3) The influence of generation and load uncertainties cannot be ignored in the day-ahead or intraday scheduling. Both the day-ahead reserve allocation and intraday stochastic optimization strategies can reduce the real-time modification cost caused by the fluctuations of renewable energy generation and load demands.
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Received: 08 May 2023
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2024, Vol. 39 
