The construction of large energy bases is a major strategic layout to promote the transformation and upgrading of China's energy industry. Exploring the optimal scheduling scheme is particularly important for the high-quality development of bases. Capacity tariff and deep peak-load regulation compensation are introduced to a large energy base in western Mongolia， and a multi-objective optimal dispatch model of the base considering its revenue is constructed with step climbing rate constraints and fuzzy processing methods. The optimal schedule schemes are carried out according to the characteristics of local new energy generation. The simulation results show that the scheduling scheme aiming for the smallest net load variance has the smallest regulation pressure， which can ensure the security of the power grid； the scheme aiming for accommodating the most new energy can keep the new energy abandonment rate below 2.26% ， which is conducive to the development of new energy； the scheme aiming for the lowest total cost can reduce the operating cost of the base by decreasing the amount of grid-connected wind and solar power； and the scheme aiming for the highest revenue has a daily revenue 0.52%-24.12% higher than that of the rest of the schemes and has a second-highest new energy accommodating rate among all the schemes. The last scheme well balances the overall economy and new energy consumption level of the energy base. The optimal scheduling model can effectively realize multi-objective optimal scheduling according to the economic conditions of energy bases. The study provides reference for the optimal scheduling for similar energy bases.

With the growth of the economic level， the proportion of electricity consumption in buildings continues to increase. Optimizing building energy management strategies can effectively enhance energy conservation and reduce emissions. In commercial building loads， air conditioning loads and electric vehicles loads account for a large proportion of energy consumption. Therefore， a smart commercial building energy management model considering building thermal inertia and incorporating electric vehicle integration was proposed， with the behavioral characteristics of electric vehicles modeled via Monte Carlo simulation. Based on the thermal capacitance-resistance model， a building thermal dynamic balance equation was established. Scenario-based stochastic programming was adopted to establish a smart commercial building energy management model， and Conditional Value at Risk （CVaR） was introduced to improve robustness. The results of case studies showed that considering building thermal inertia could reduce the operating costs of air conditioning loads by 9.7% and increase revenue by 1.4%. Although CVaR reduces the operational risks of buildings， it also lowers their comprehensive expected returns.

Due to the spatial distribution characteristics of wind turbines and the randomness of wind speed， wind farm power output exhibits significant intermittency and fluctuation， which undermines the grid-friendly operation capability of wind farms. To address this issue， an optimization method for suppressing wind farm power fluctuations based on model predictive control （MPC） coupled with wake effect was proposed. A power output prediction model under varying wind speeds and directions was established using a coordinate transformation method， and wind speed forecasting was performed using a least squares support vector machine （LS-SVM）. Within the MPC framework， a multi-dimensional coupled optimization objective was formulated by integrating the wake effect， wind direction deviation， and turbine constraints. An optimization problem considering the variance of active power output was then solved. The case study showed that compared with the proportional distribution method， the proposed approach reduced the average relative deviation and root mean square deviation of active power output by 93% and 97%， respectively， verifying the effectiveness of the multi-dimensional coupling model in fluctuation suppression.

The optimal scheduling of integrated energy systems （IES） is affected by fluctuations in uncertain factors such as renewable energy and load. Failure to accurately describe and process these uncertain parameters will constrain system reliability， and the lack of refined modeling and optimization methods makes uncertainty analysis more complex. To comprehensively and systematically analyze uncertainty modeling and optimization methods， the structure of IES， sources of uncertainty， and modeling approaches are reviewed. Monte Carlo simulation， information gap decision theory， interval methods， robust optimization， and data-driven methods are summarized， along with their applications and studies in uncertainty optimization. Research findings indicate that there is no single best optimization method. The complementarity of multiple methods can maximize the economic and environmental benefits of IES. Based on current research challenges and hotspots， future directions for uncertainty optimization are outlined.

To ensure the economic operation of an integrated energy system（IES）， a coordinated optimization scheduling method for IES based on energy routers is proposed to address the issues such as difficulty in solving optimization scheduling models， slow convergence， and unsatisfactory performance in traditional model-driven scheduling methods. The IES was divided into three regions using three electrical， thermal， and cooling energy routers. Energy devices were modelled， and a Markov cooperative game model for IES optimization scheduling was established， forming a framework of centralized training and distributed execution. A Multi-Agent Deep Deterministic Policy Gradient （MADDPG） algorithm based on an improved two-layer Actor-Critic network was used， where the two-layer Critic network evaluated action values to avoid their overestimation. Additionally， a prioritized experience replay mechanism was incorporated to improve data utilization efficiency in the experience replay pool without compromising the diversity of data. The simulation results showed that the proposed algorithm was 10.13 s faster in calculation speed and reduced daily scheduling costs by 1 638.13 yuan compared to the unimproved method， achieving coordinated optimization scheduling of IES while ensuring system economic efficiency.

With the accelerated construction of the new power system， virtual power plants（VPPs） are increasingly participating in market-driven interactions with the grid， becoming an important tool for enhancing the regulation capacity of the new power system. To address the optimization problem of VPP operation under continuous market-based regulation， this paper proposes an optimization method for VPP operation considering the active recovery of remaining available regulating capacity. Based on the regulation and rebound characteristics of VPPs， the effect of active rebound on the recovery of remaining available regulating capacity was analyzed， and an equivalent energy storage regulation model for VPPs considering the active recovery of remaining available regulating capacity was constructed. Based on the equivalent energy storage regulation model， a market-based operation mode and joint optimization model for VPP participation in spot markets， peak-shaving， and frequency regulation were established. Simulation results of the case study indicated that the proposed method helped ensure continuous and stable VPP market operation while improving economic efficiency. However， market pricing should fully consider the regulatory capacity boundaries of the VPP.

To support the achievement of the "carbon peak and carbon neutrality" goals， the optimization of green hydrogen production systems has become an important path to achieving a low-carbon economy. This paper designs a green hydrogen system equipped with compressed air energy storage and proposes an optimization model for capacity configuration between wind-solar fluctuations and energy storage and hydrogen production systems. This paper uses real wind-solar data for 8 760 h to simulate wind-solar fluctuations under different seasons and climate conditions， overcoming the limitations of traditional models that rely on simplified assumptions and idealized data. The results show that after optimization， the system's abandoned electricity is only 2%， and the levelized cost of hydrogen is 19.30 yuan/kg. A typical week's hourly verification is selected to demonstrate the effectiveness and feasibility of the system scheme in actual operation.The research provides theoretical support for the capacity allocation and operation scheduling of this green hydrogen system and offers guidance for related projects.

Driven by the "dual-carbon" goals， developing efficient and low-carbon integrated energy systems has become a critical priority. To address the issue of high carbon emissions， high operating costs， and low energy utilization efficiency of integrated energy systems， an optimization model for low-carbon economic operation of integrated energy systems was proposed based on multi-level utilization of hydrogen production from electricity. By integrating two-stage power-to-gas technology with carbon capture and storage technologies， carbon recycling within the system was achieved. Based on this， a multi-level utilization system for hydrogen production from electricity was established， which included methane production， hydrogen blending in natural gas， and hydrogen storage， thus improving hydrogen utilization efficiency. By incorporating the tiered carbon trading mechanism， an optimization model with the goal of minimizing total operating costs was developed. Case analysis showed that the optimization model improved hydrogen utilization efficiency by 8.2%， promoting the multi-level utilization of hydrogen. The total operating cost and carbon emissions were reduced by 7.58% and 10.1%， respectively， effectively promoting the low-carbon economic operation of the integrated energy system.

Integrated energy system is an important approach for green and low-carbon transformation of the energy structure. It can achieve multi-energy complementarity and high-ratio renewable energy consumption through generalized energy storage （i.e. a combination of flexible loads and traditional energy storage）. However， there is a lack of quantitative analysis on the economic performance of using generalized energy storage in integrated energy systems， particularly in load-shifting capability and cost-effectiveness. An integrated energy system for a building prosumer community， employing generalized energy storage， was developed in this study， and evaluation metrics for load-shifting capability and economic performance of generalized energy storage were proposed. A day-ahead economic dispatch optimization of the integrated energy system was formulated to maximise operational economic benefits. Four optimization scenarios were designed to compare the contributions of traditional energy storage， flexible loads， and both generalized energy storage approaches to the load-shifting capability and economic benefits of the proposed system. The optimization scenarios were resolved using the Yalmip toolbox and Gurobi solver in Matlab. The optimization results showed that generalized energy storage reduced the daily operational cost of the integrated energy system by 24.0%. Both the load-shifting capability and economic performance of the integrated energy system with the generalized energy storage outperformed traditional energy storage， while the flexible load management provided superior economic performance compared to traditional energy storage. However， in the case with high compensation price， the economic performance of flexible load management might be less favorable than that of traditional energy storage.