(587b) Economic Model Predictive Control of Integrated Energy Systems: A Multi-Time-Scale Framework

Due to rising concerns about climate change and energy shortage, clean energy development, which can play an important role in reducing greenhouse gas emissions and securing energy has attracted growing attention. In particular, there has been an increase in the use of renewable energy and natural gas in electric power supply networks worldwide [1]. Accordingly, there have been significant demands for integrated energy systems (IESs) because of their capabilities of absorbing renewable energy and improving overall fuel efficiency in distributed energy systems. IESs are usually composed of various components that are coupled through the working substance and energy flows to provide electricity as well as cooling and/or heating to customers, which leads to diverse dynamics and time-scale multiplicity of IESs. Therefore, the design of a proper control strategy for IESs faces new challenges.

Most of the existing results on control of the IESs focus on the long-term operation optimization [2, 3, 4, 5]. However, less effort was made to design short-term coordinated control schemes for IESs, but that is of critical importance to the operation of IESs. For the design of short-term control schemes for IESs, the conventional centralized controller is an intuitive idea. But the issue of this scheme is that it will lead to unmanageable computation complexity and cannot deal with the time scale multiplicity in IESs' dynamics, which will cause a loss of systems' performance and may lead to an ill-conditioning optimization or the loss of closed-loop stability.

These considerations motivate us to propose a novel composite economic MPC based on the multi-time-scale decomposition, inspired by [6, 7, 8], for IESs in this work. The time scale multiplicity exhibited in IESs, which poses challenges for designing a centralized control system, is taken into account and addressed using multi-time-scale decomposition. Based on the multi-time-scale decomposition, the IES is decomposed into three subsystems: slow, medium, and fast subsystem. Subsequently, the CEMPC, which consists of a slow economic model predictive control (EMPC), a medium EMPC and a fast EMPC, is developed according to the slow, medium and fast subsystem to match IESs multi-time-scale based dynamics. The EMPCs communicate with each other to ensure consistent optimization results. In the slow EMPC, all of the control objectives are optimized, and the manipulated inputs affecting the slow dynamics are implemented. The medium EMPC optimizes the control objectives correlated with the medium dynamics and applies corresponding inputs to the IES, while the fast EMPC optimizes the fast dynamics and makes a decision on the manipulated inputs connected to the fast subsystem. Meanwhile, thermal comfort is integrated into the CEMPC in the form of the zone tracking of building temperature for improving operating performance and reducing operating costs of the IES. Moreover, a long-term EMPC is adopted to ensure the operation of the IES accommodates long-term forecasts of external conditions. Finally, the effectiveness of the proposed method is verified via simulations, and its superiority in matching systems dynamics, lightening computational burden and improving operating performance is demonstrated via a comparison with a hierarchical real-time optimization.

References

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