Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method

Liu, Xinrui; Hou, Min; Sun, Siluo; Wang, Jiawei; Sun, Qiuye; Dong, Chaoyu

doi:10.1016/j.energy.2022.124311

Cited by 19 publications

(6 citation statements)

References 27 publications

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“…The diagram of the strategy is shown in Figure 2. In the initial scenario, the time-scale is set to 4 h [28,29], that is, rolling once every 4 h to optimize the dispatching for the next 4 h. When the system available reserve is not enough to compensate for the wind power forecasting error, 15 min is taken as one step to shorten the dispatching time-scales. Accordingly, the wind power forecasting steps are shortened as well.…”

Section: The Time-scale Adaptive Forecasting and Dispatching Integrat...mentioning

confidence: 99%

A time‐scale adaptive a forecasting and dispatching integration strategy of the combined heat and power system considering thermal inertia

Wang

Han

et al. 2023

IET Renewable Power Gen

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With the integration of wind, electricity and heat, the combined heat and power system has the characteristic of multi‐time scales, in which the wind power has high uncertainty, the thermal system is difficult to synchronize with the power system for the large inertia and delay. Therefore, the fixed time‐scale dispatching is unable to guarantee the reliability of the system. Given the above problems, this paper proposes a time‐scale adaptive dispatching strategy of the combined heat and power system considering thermal inertia. This strategy analyzes the time characteristics of wind power forecasting error and the thermal inertia, while later establishing time‐scale adaptive dispatching sub‐main model of combined heat and power system. Specifically, in the time‐scale adaptive method, the system available reserve model is improved due to the power support of the thermal inertia. Based on the existing fixed time‐scale dispatching, this method dynamically adjusts the steps of forecasting and the time‐scales of dispatching when the system available reserve is inadequate to compensate for the wind power forecasting error. Taking the 4‐h fixed time‐scale dispatching as an example, the simulation results suggest that the proposed strategy achieves the dynamic dispatching of the combined heat and power system, while improving its reliability and stability.

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Section: The Time-scale Adaptive Forecasting and Dispatching Integrat...mentioning

confidence: 99%

A time‐scale adaptive a forecasting and dispatching integration strategy of the combined heat and power system considering thermal inertia

Wang

Han

et al. 2023

IET Renewable Power Gen

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“…Cheng et al 17 proposed a multitime scale coordinated optimization method for energy hubs with multiple energy flows and types of energy storage systems. Liu et al 18 combined the enhanced interval optimization method to propose a multitime scale optimization scheduling model for improving the grid connection of wind power during the heating season. Pan et al 19 proposed an improved multitime scale coordinated control strategy for integrated energy systems with hybrid energy storage systems.…”

Section: Introductionmentioning

confidence: 99%

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

Zheng,

Hou,

et al. 2024

Energy Science & Engineering

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In the context of the dual carbon goal strategy, the proportion of new energy generation has increased annually, large‐scale renewable energy integration has been achieved, and the intermittent and uncertain operating characteristics pose an enormous challenge to the complete and stable operation of an integrated energy system (IES), promoting the complexity of IES optimization models. To increase the stability and accuracy of the system and improve the operation efficiency of the system, a Gaussian mixture model is used to fit the probability distribution of wind power and the prediction errors of the photovoltaic output. In addition, the expected maximization method is used to solve a model with hidden variables, the results show that the expectation maximization algorithm can improve the fitting accuracy, reduce the error caused by the subjective setting of the initial value, and make the fitting accuracy of the wind‐solar power prediction error greater. Then, to solve the problem in which the low resolution of the day‐ahead scheduling time leads to large errors, day‐ahead, day‐to‐day multitime scale operation optimization model takes into account the comprehensive demand response according to the differences in wind and solar output and load forecasting accuracy. Finally, simulation validation is conducted in multiple scenarios, and a comparative analysis is performed for single and multitime scale comprehensive demand response scenarios. The simulation results show that compared with the optimization results of no demand response day, the optimization results of this model reduce the total cost by 23.21%, carbon emissions by 13.98%, purchased electricity by 13.87%, and purchased gas by 19.31%, effectively improving the use of gas turbines in the system. The multitime scale scheduling strategy increases the overall operating cost of the IES, modifies the scheduling results, and agrees with real operating scenarios.

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“…Ref. [11] incorporated renewable energy into electric boilers and introduced a multi-timescale optimal scheduling method for electric power and district heating systems. This method emphasizes the thermal comfort of users, enhancing the integration of the energy system while ensuring heating demands are met.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Multi-Timescale Dispatch Strategy for a City-Integrated Energy System with Carbon Capture Power Plants

Liu,

Lu,

Dou

et al. 2024

Energies

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In city-integrated energy systems containing electric–thermal multi-energy sources, the uncertainty of renewable energy sources and the fluctuation of loads challenge the safe, efficient, economic and stable operation of the integrated energy systems. This paper introduces a novel approach for the operation of a carbon capture plant/CHP with PV accommodation within a city-integrated energy system. The proposed strategy aims to maximize the utilization of photovoltaic (PV) power generation and carbon capture equipment, addressing issues related to small-scale CHP climbing constraints and short-term output regulation. Additionally, this paper presents a multi-timescale optimal scheduling strategy, which effectively addresses deviations caused by PV fluctuations and load changes. This was achieved through a detailed analysis of the CHP climbing constraints, carbon capture equipment operation and real-time characteristics of PV power generation. This paper introduces a fully distributed neural dynamics-based optimization algorithm designed to address multi-timescale optimization challenges. Utilizing rolling cycles, this algorithm computes both day-ahead and real-time scheduling outcomes for urban integrated energy systems. Theoretical analyses and numerical simulations were conducted to validate the precision and efficacy of the proposed model and algorithm. These analyses quantitatively evaluate the scheduling performance of PV power generation and carbon capture CHP systems across various timescales.

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Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method

Cited by 19 publications

References 27 publications

A time‐scale adaptive a forecasting and dispatching integration strategy of the combined heat and power system considering thermal inertia

A time‐scale adaptive a forecasting and dispatching integration strategy of the combined heat and power system considering thermal inertia

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

A Distributed Multi-Timescale Dispatch Strategy for a City-Integrated Energy System with Carbon Capture Power Plants

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