To address the issue of low utilization rates, constrained operational modes, and the underutilization of flexible energy storage resources at the end-user level, this research paper introduces a collaborative operational approach for shared energy storage operators in a multiple microgrids (ESO-MGs) system. This approach takes into account the relation of electricity generated by MGs and the integration of diverse energy storage resources managed by ESO. A hybrid game-theoretic energy trading strategy is employed to address the challenges associated with energy trading and revenue distribution in this joint operational mode. Firstly, a multi-objective master–slave game optimization model is developed with the objective of maximizing the revenue earned by shared energy storage operators while simultaneously minimizing the operational costs of multiple microgrids. Secondly, acknowledging the peer-to-peer (P2P) energy sharing dynamics inherent in the multiple microgrid system, a non-co-operative game model is formulated. This model seeks to establish a multi-microgrid Nash equilibrium and equitable income allocation. Finally, leveraging the Karush–Kuhn–Tucker (KKT) conditions and drawing upon the principles of strong duality theory, precise dimensionality reduction is executed on the master–slave game model. The non-co-operative income is iteratively determined using the alternating direction multiplier algorithm. The empirical findings of this study indicate that the integration of electric vehicle clusters contributes to flexible storage resources for shared energy storage operators. Moreover, the proposed hybrid game optimization strategy enhances the overall benefits for shared energy storage operators and multiple microgrids, thereby affirming the economic viability and reliability of this innovative strategy.

Due to the high cost of hydrogen utilization and the uncertainties in renewable energy generation and load demand, significant challenges are posed for the operation optimization of hydrogen-containing integrated energy systems (IESs). In this study, a robust operational model for an electric–heat–gas IES (EHG-IES) is proposed, considering the hydrogen energy system heat recovery (HESHR) and multiple uncertainties. Firstly, a heat recovery model for the hydrogen system is established based on thermodynamic equations and reaction principles; secondly, through the constructed adjustable robust optimization (ARO) model, the optimal solution of the system under the worst-case scenario is obtained; lastly, the original problem is decomposed based on the column and constraint generation method and strong duality theory, resulting in the formulation of a master problem and subproblem with mixed-integer linear characteristics. These problems are solved through alternating iterations, ultimately obtaining the corresponding optimal scheduling scheme. The simulation results demonstrate that our model and method can effectively reduce the operation and maintenance costs of HESHR-EHG-IES while being resilient to uncertainties on both the supply and demand sides. In summary, this study provides a novel approach for the diversified utilization and flexible operation of energy in HESHR-EHG-IES, contributing to the safe, controllable, and economically efficient development of the energy market. It holds significant value for engineering practice.

Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal power units engaging in deep load cycling and rapid frequency adjustment, conventional proportional-integral-derivative (PID) control struggles to meet the demands of effective control. This study introduces a control strategy that incorporates a “state observer + Linear Quadratic Regulator (LQR) state feedback + Improved Quantum Genetic Algorithm (IQGA) optimized PID”. Initially, local linear mathematical models of an SCR denitrification system at 340 MW, 450 MW, and 540 MW loads were used to design state observer and LQR state feedback control parameters for each operational condition. At a single load point, the IQGA was employed to optimize the outer loop PID parameters, followed by simulation experiments of load increases and decreases between 340 MW and 540 MW. The results demonstrated that, compared to two other strategies, the proposed approach reduced the overshoot by a minimum of 1.5% and shortened the adjustment time by 31.7% under conditions of step disturbances and internal perturbations. Throughout variable operational conditions, the strategy consistently exhibited minimal output fluctuations, rapid adjustment capabilities, strong disturbance rejection, and robust stability. This algorithm proves to be an effective method for controlling NOx concentrations, offering insights for precise ammonia injection control in future applications.

Against the backdrop of high renewable energy integration, operational uncertainties in energy systems have significantly increased, posing challenges such as coordination difficulties, unfair revenue distribution, and insufficient risk resilience for multi-integrated energy service providers (IESPs) during collaborative operations. To address these issues, this study proposes a distributed robust scheduling method for IESP alliances considering uncertainties in hydrogen energy systems and renewable energy sources. The research establishes a master-slave game model with distribution system operators (DSOs) as upper-level leaders and IESP alliances as lower-level followers, complemented by cooperative game models within alliances focused on collaborative optimization and equitable revenue sharing. The lower-level scheduling incorporates hydrogen energy systems while integrating demand response mechanisms and carbon trading frameworks. Based on Nash negotiation theory, the cooperative game is decomposed into two sub-problems: alliance cost minimization and payment benefit maximization. The solution is derived using a dichotomy approach combined with Column and Constraint Generation (C&CG) algorithms and Alternating Directional Multiplier Method (ADMM). Simulation results demonstrate that the hybrid game model significantly reduces joint operation costs for IESP alliances compared to traditional master-slave models. The hydrogen energy system effectively enhances system flexibility and reduces carbon emissions, while the membership-based revenue distribution mechanism ensures equitable cooperation benefits and boosts participation incentives in energy transactions. This study provides valuable insights for designing collaborative operation frameworks and incentive mechanisms for multi-integrated energy service providers.

1. The ongoing global climate crisis, characterized by increasingly frequent extreme weather events and mounting ecological pressure, is accelerating the transition of power systems toward low-carb...