Distributed Consensus Hierarchical Optimization and Control Method for Integrated Energy System Based on Event-Triggered Mechanism

Question

1. What is the role of energy hubs (EHs) in multi-energy system scheduling and optimization operation?

2. What is the hierarchical control framework in IES?

3. What is the incremental cost economic optimization strategy for electric and thermal power outputs in IES?

4. What is the purpose of dynamic event-triggered communication mechanism?

Accepted Answer

The energy hub (EH) is a key technology for optimizing the integrated operation of electricity and heat in multi-energy systems. EHs actively manage the power outputs of different energy sources according to energy supply and demand, ensuring proper sharing of energy resources. They play a crucial role in multi-energy system scheduling and optimization operation, as they help in coordinating the energy flow between different energy stations and external networks. EHs have been reviewed in various application scenarios, such as residential buildings, commercial buildings, industrial parks, and agricultural applications. They contribute to the efficient and effective management of energy resources, leading to improved energy efficiency, reduced energy losses, and enhanced system reliability. However, the dynamic characteristics of electrical and thermal equipment are not always considered in EH models, and there is a need for further research to address this aspect for more precise resolution in optimization operations.

Accepted Answer

The hierarchical control framework in IES is proposed to minimize generation cost and ensure stable operation of EHs. It includes two objectives: calculating desired trajectories of electric and thermal power outputs in the second layer, and tracking these trajectories in the primary layer. Optimal solutions of cost functions in the second layer are adopted as desired trajectories and fed into an event-triggered consensus controller for global stable operation. A mathematical model with total operation cost as optimization objective is developed in the second layer, considering thermal and electric output cost coefficients of each EH.

Accepted Answer

The incremental cost economic optimization strategy for electric and thermal power outputs in IES involves treating the incremental cost of each EH as a consensus control variable integrated in the strategy. The supply-demand mismatch is fed back to the incremental cost function to achieve supply-demand balance. Communication links among intelligent agents of EHs guarantee the convergence of incremental costs to the desired equilibrium point. Optimal power outputs are calculated and conveyed to power controllers, enabling accurate power sharing under economic constraints. The Lagrange function is constructed to minimize the total power cost, and the equal increment principle is executed to minimize operation cost and ensure economic operation. The strategy includes establishing cost functions, determining communication topology, calculating desired power output trajectories, and achieving consensus on incremental costs for economical operation.

Accepted Answer

The purpose of dynamic event-triggered communication mechanism is to achieve consensus control goals while saving bandwidth and computational resources. It overcomes the effect of redundant communications and heavy computation burden caused by frequent communications in traditional consensus control. The mechanism is designed based on power deviations and event triggering coefficients to optimize communication between electrical and thermal networks. It also includes frequency control and pressure control objectives to regulate current values and pressure deviations at rated values. Overall, the dynamic event-triggered communication mechanism improves efficiency and effectiveness in sharing power outputs of EHs.

Accepted Answer

The proposed method for achieving accurate power sharing and coordinated operation of EHs in IES is a distributed consensus control based on an event-triggered mechanism. This method involves each EH broadcasting its state information to neighboring EHs at the event-triggered time through a communication network. For thermal power control, the deviation of thermal power outputs between the latest event time and the current time is used, and a thermal power consensus control law based on the event-triggered mechanism is described. The PI controller is designed to eliminate thermal power tracking error. Similarly, for electrical power control, the deviation of electrical power outputs is used, and an electrical power consensus control law based on the event-triggered mechanism is designed. The PI controller is adopted to decrease electrical power error. The proposed method also includes frequency regulation of the electrical branch and a distributed consensus controller for pressures. The PI controller is designed to track the rated frequency and adjust the pressure error. Overall, the proposed method enables accurate power sharing and coordinated operation of EHs in IES through a distributed consensus control based on an event-triggered mechanism.

Accepted Answer

The IES model was realized in MATLAB/Simulink. The hardware codes of the IES model could be automatically downloaded from MATLAB/Simulink to Control Desk of dSPACE by real-time workshop (RTW) and real-time interface (RTI). This allowed for the implementation of the distributed consensus hierarchical optimization and control method based on an event-triggered mechanism in the hardware-in-the-loop (HIL) experiment conducted using dSPACE.

Accepted Answer

In Case 1, the proposed consensus control method demonstrates quick response to simultaneous decreases and increases in electrical and thermal loads, without the need for global IES information. The method accurately shares electrical and thermal power outputs of the EHs, maintaining small tracking errors of the desired trajectories. Frequency adjusts with electrical power output mutation, and pressure adjusts with thermal power output mutation. The electrical power outputs and frequency response waveforms show that the method achieves stable recovery in electrical branches within 0.05 seconds. However, the thermal power outputs and pressure response waveforms indicate a recovery time of 75 seconds in thermal branches, highlighting the necessity of a dynamic event-triggered communication mechanism. The proposed method ensures safe and stable operation of IES, with proper event-triggered conditions and reduced redundant communications. The economic optimization strategy based on the equal increment principle effectively minimizes power output costs, enhancing the overall economic operation performance of IES. This is evidenced by the comparison of power outputs and economic cost changes in Table 2, which align with the demands of electrical and thermal loads.

Accepted Answer

In Case 2, when the electrical and thermal demands increase or decrease in opposite trends, the good dynamic response performance of the electrical and thermal networks is maintained. The EHs quickly track the desired trajectories, and accurate power sharing between sources and loads is realized. The electrical branch achieves smooth recovery in 0.05 seconds, while the thermal branch takes slightly longer, about 80 seconds. The proposed dynamic event-triggered communication mechanism effectively obtains the control objectives in a fast manner, ensuring the stable operation of IES. The power outputs and economic costs of EHs in Case 2 are accurately adjusted, demonstrating the effectiveness of the proposed economic optimization strategy based on the equal increment principle.

Accepted Answer

The proposed method based on the dynamic event-triggering mechanism shows better dynamic response performance compared to the compared method. It achieves quicker adjustment of electrical and thermal power outputs, resulting in smaller overshoot and shorter adjustment time. The proposed method also ensures frequency of EH2 at the rated value of 50 Hz faster than the compared method. Additionally, it effectively suppresses frequency deviation caused by electrical load changes. The proposed method also controls pressure in the thermal branch quickly within the allowable range, ensuring stable operation of IES. Overall, the proposed method has better dynamic response performance, with a reduced time required to recover in the electrical and thermal branches compared to the compared method. It also achieves economic cost savings, with a reduction of CNY 58.3 (1.78% reduction) when there is an increase in electrical and thermal demands and CNY 100.1 (1.89% reduction) when there is a decrease in electrical and thermal demands. The proposed economic optimization strategy based on the equal increment principle also shows better performance in minimizing power output costs of IES.

Accepted Answer

The proposed economic optimization strategy is based on the incremental cost consensus algorithm. It ensures that IES economically operates and the power outputs of the EHs converge to the optimal working points while the supply-demand balance target is satisfied. This strategy promotes efficient operation and cost reduction in the IES system.

Distributed Consensus Hierarchical Optimization and Control Method for Integrated Energy System Based on Event-Triggered Mechanism

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Most frequently asked questions

1. What is the role of energy hubs (EHs) in multi-energy system scheduling and optimization operation?

2. What is the hierarchical control framework in IES?

3. What is the incremental cost economic optimization strategy for electric and thermal power outputs in IES?

4. What is the purpose of dynamic event-triggered communication mechanism?

5. What is the proposed method for achieving accurate power sharing and coordinated operation of EHs in IES?

6. How was the IES model realized?

7. How does the proposed consensus control method perform in Case 1?

8. How does the proposed distributed consensus control method perform in Case 2 with opposite trends in electrical and thermal demands?

9. How does the proposed method compare to the compared method in dynamic response performance?

10. What is the proposed economic optimization strategy?

References

A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks

Distributed Multi-Energy Operation of Coupled Electricity, Heating, and Natural Gas Networks

Optimal management of energy hubs and smart energy hubs – A review

Short-Term Self-Scheduling of Virtual Energy Hub Plant Within Thermal Energy Market

Optimal Planning of Islanded Integrated Energy System With Solar-Biogas Energy Supply

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