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Many power plants have been transformed and operated using low‐pressure cylinder near‐zero output (LP‐ZO) technology in China to make way for or provide necessary guarantees for renewable energy power generation. This research focuses on utilizing numerical simulation tools to improve the operational economy of units with LP‐ZO. The LP‐ZO thermodynamic and energy‐consumption model of a reference plant is established, and the momentum particle swarm optimization method is used to optimize the unit operations. Moreover, a model to determine the economic operational range of the combined heat and power plant with LP‐ZO is proposed. The results show that the LP‐ZO mode operations of the unit can reduce the minimum peak shaving rate and improve the flexibility of operations. The energy efficiency of the LP‐ZO mode is increased due to the recovery of a significant portion of the cold source losses. However, the irreversible exergy loss caused by large temperature difference heat exchange reduces the exergy efficiency of the unit. After operational optimization, the reference plant can save approximately 68.15 t d−1 of coal and reduce carbon dioxide emissions by about 27 t tce−1 d−1. This study provides an operating approach to the CHP plant with LP‐ZO unit in enhancing flexibility and economy. [ABSTRACT FROM AUTHOR]

• Based on the particle swarm algorithm, chaos is introduced to combine the speed advantage of the particle swarm algorithm with the ergodic advantage of the chaos idea, so that the chaotic particle swarm algorithm can find the optimal configuration of the coupled energy system quickly and accurately. • The impact of the introduction of energy storage devices on the investment cost of microgrid systems is explored. • The introduction of hydrogen energy and energy storage equipment completes the integrated energy systems. • It provides a new algorithm to solve the problem of real-time load distribution of microgrids. Integrated energy system (IES) is difficult to achieve the optimal scheduling of the system due to the coupling of multiple energy devices and the high volatility of renewable energy loads. To address the above problems, a chaos-based particle swarm algorithm is proposed, which uses a chaotic mapping of optimal particles by a Logistic algorithm to increase the anisotropy of particles and improve the search accuracy in the case of wide area multi-polarity. The example analysis shows that the chaotic particle swarm algorithm can improve the economic cost by 3.868% compared with the conventional particle swarm after increasing the number of iterations. Finally, based on this algorithm, the economic impact of the energy storage device on the integrated energy system is analyzed. The results show that the IES system based on the composite energy storage can reduce the total economic cost significantly, while avoiding the excessive use of other energy supply devices and ensuring the stability of the overall IES system operation. [ABSTRACT FROM AUTHOR]

Two of the most critical issues encountered in the day-ahead scheduling of regional integrated energy systems are the uncertainty of renewable energy resources and complexity of load demand. Furthermore, varying operating conditions also pose challenges for economical day-ahead scheduling. This paper proposes a scenario-based day-ahead scheduling approach for regional integrated energy systems to minimize operating costs by mining historical data. A hybrid dynamic energy hub model with variable efficiency that integrates an extreme gradient boosting (XGBoost) algorithm and analytical formulation is proposed. We developed a scenario-based scheduling optimization model in which climate data and load data are predicted using XGBoost and the probability distributions of their predicted errors are estimated using a Gaussian mixture model. Monte Carlo simulation and K-means clustering were used to generate and reduce scenarios and a success-history-based adaptive differential evolution algorithm was adopted to search for optimal solutions for day-ahead scheduling. Furthermore, a weighted average electricity purchasing strategy was adopted to address uncertainty and further improve operating economy by adjusting the output of gas turbines and electricity purchasing for actual scheduling. Case studies were conducted to verify that the proposed approach can reduce daily operating costs and enhance the operating economy of regional integrated energy systems. • A hybrid DEH model integrating operating data and an analytical formulation characterizes flexible performance accurately. • Considering the uncertainty by scenario-based stochastic optimization method increases the operating cost. • A weighted average electricity purchasing strategy can address the uncertainty and further improve operating economy. [ABSTRACT FROM AUTHOR]