Xiaoou Liu

Under the carbon neutrality goals, this paper addresses the critical challenge of enhancing operational flexibility and low‐carbon performance in northern China’s heating regions, where the power generation determined by heat for combined heat and power (CHP) unit restricts renewable energy consumption. To tackle this, a novel virtual power plant (VPP) framework is proposed by incorporating electric heat pump (EHP), carbon capture system, and shared energy storage. A leader‐follower game model is also built between the VPP and multiple industrial users. The upper‐level model optimizes VPP profit by designing differential electricity pricing strategies considering the synergistic effect of green certificate trading (GCT) and carbon emission trading (CET), solved using a self‐adaptive immune genetic algorithm (SIGA). The lower‐level model minimizes user costs through production process‐based load management, solved via a dual ascent and alternating direction multiplier method. Example analysis from Tianjin, China, demonstrates that the proposed strategy increases VPP profit by over 110% compared to a baseline scenario, reduces carbon emissions by 13.9%, enhances the capacity of renewable energy utilization, and lowers total user costs by 4.56%. These results verify that the synergistic GCT‐CET mechanism, combined with flexible equipment configuration and differentiated pricing, effectively coordinates low‐carbon transition, economic efficiency, and renewable energy consumption. It can provide a viable pathway for VPP operation in northern heating regions.

Current agricultural park integrated energy system (IES) face challenges such as oversimplified modeling and difficulties in reconciling environmental satisfaction with economic performance. To enhance both operational efficiency and crop growth environmental quality assurance, this study develops refined electrical and thermal load models based on crop physiological ecology. A bi‐level optimal scheduling method of economy and environment is proposed. The upper‐level model minimizes system operating costs by coordinating photovoltaics (PVs), wind turbine (WT), electrical energy storage (EES) device, and power exchange with power grid. The lower‐level model ensures crop growth environmental satisfaction by synergistically optimizing temperature, light, humidity, and ventilation parameters throughout the growth cycle. Example analysis on a cucumber‐growing park in Xinjiang demonstrates that the proposed method reduces system operating costs by 10.56%, 7.21%, and 14.38% during the germination, growth, and maturity stages, respectively, while increasing environmental satisfaction to 95.9%. The local renewable energy consumption rate is improved by over 6%. Moreover, under uncertain renewable energy power generation, the method maintains growing degree day (GDD) and daily light integral (DLI) within crop physiological safety thresholds. The results confirm that the refined load models and bi‐level optimization mechanism effectively coordinate economic operation with crop growth environmental quality assurance, providing theoretical and technical support for optimizing agricultural park IES with high permeability renewable energy power generation.

With the development of green energy, steel parks have great potential for energy saving and emission reduction, but their enthusiasm for participating in market demand response is insufficient. With the advancement of the construction of the electricity and carbon market, the steel park urgently needs to arrange own operating mode based on market orientation, in order to achieve carbon reduction and efficiency improvement. Therefore, this paper proposes a trading method for steel parks to participate in the electricity–carbon–green certificate market, guiding the mode optimization of steel parks and achieving low‐carbon economic operation. First, an integrated energy system framework has been established for the production process of the steel park. Second, based on the production process, energy supply structure, and carbon emission characteristics of the steel park, a production system model and an energy supply system model were constructed. Then, based on the electricity price model and carbon trading price model, a low‐carbon economic operating mode of steel parks to participate in the electricity–carbon–green certificate market was proposed. Aiming at the problem of difficulty in solving the game equilibrium with a large number of participants in the market, a method for solving the equilibrium point based on mean field theory is proposed. Mann‐type iterative method is adopted to accelerate the solving process of equilibrium point. Finally, the effectiveness of proposed method was verified by the example analysis of Yunnan Steel Park in China, which helps to achieve energy conservation and emission reduction, and low‐carbon economic operation of steel park.

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP.