With the increasing integration of new energy sources into the power system, their inherent volatility and intermittency have exacerbated the challenges of energy consumption. This study examines source-grid-load-storage systems that incorporate adjustable loads and decentralized energy storage, including distributed new energy, power grids, air conditioners, and electric vehicles. A multi-time-scale collaborative optimization strategy is proposed to enhance the capacity for new energy consumption. The article investigates the response characteristics and consumption potential of flexible resources across different time scales, namely, monthly, day-ahead, and intraday, and develops a multi-objective optimization model aimed at maximizing new energy consumption while minimizing system operating costs. Corresponding collaborative consumption strategies are formulated for each time scale. Specifically, a multi-time-scale source-grid-load-storage collaborative framework that accounts for the flexibility of demand-side management is initially established. Subsequently, a rolling adjustment method based on multi-objective optimization is proposed for monthly, day-ahead, and intraday operations. Finally, the detailed modeling and collaborative utilization of adjustable loads and decentralized energy storage are achieved. Simulation results demonstrate that the proposed strategy reduces the system’s wind and solar curtailment rate to below 3.5%, decreases operating costs by 12.7%, and significantly improves the system’s economic performance and new energy utilization efficiency.

As the global energy structure undergoes transformation, large-scale access to renewable energy presents the power system with unprecedented dynamic balance challenges. Traditional centralized power supply architecture is challenging to adapt to complex scenarios where high proportions of new energy, high-density power electronic devices, and diversified load demands are intertwined. There is an urgent need to build a new balancing mechanism for collaborative interaction between source, grid, load and storage. Aiming at the scientific problem of deep integration of cluster security constraints and multi-level interaction, this study proposes an integrated optimization strategy. By quantitatively characterizing the CIA (Confidentiality, Integrity, Availability) triple security criterion, it establishes three types of constraint models, including extreme weather equipment current carrying capacity correction coefficient, node health index and adjustment instruction convergence time threshold. Experimental verification demonstrates that this strategy effectively controls the system frequency deviation within 0.010 Hz and stabilizes the voltage deviation to below 1.50% during the 16-period scheduling cycle. At the same time, it improves energy utilization efficiency to 92%, with clean energy accounting for 61%. Carbon emissions were reduced to 10,200 tons, and pollutant emissions were reduced to 5,100 tons. The direct trust of the high-precision recommendation module in the system is positively correlated with its precision trust value, and the source-grid-load-storage (SGLS) samples exhibit significant differences in aggregation characteristics under different feature representation methods. In addition, the short-circuit current capacity of the system is stable at 31 kA, and the transient stability index reaches 0.94, which verifies the robustness of the strategy under extreme working conditions. By analyzing the dynamic influence of the ψ parameter on the detection results of each cluster, the effectiveness of the security constraint embedding method is further confirmed.

The integrated energy system in the park faces challenges in producing and consuming renewable energy on a large scale as well as in achieving equilibrium between supply and demand for energy, making it a novel form in the study of integrated energy systems. The study takes the integrated energy system of the park as an example, and constructs a real-time rolling regulation model of two-layer optimal dispatch with multiple time scales. The model includes an upper-layer rolling economic optimization scheduling model and a lower-layer dynamic performance optimization control model, which takes economy and real-time as the objectives and realizes dynamic rolling optimization through model predictive control theory. The electric chillers are producing power to give cold energy during the whole dispatching cycle, while the absorption chillers produce power to supply cold energy only during the peak cold load period. The cold storage tank lowers the system’s operational costs by storing cold energy during low hours and releasing it during portions of the system’s high cold load hours. For the park's integrated energy system's primary energy exchange nodes 1 and 2, the micro gas turbine, and the gas boiler. The dynamic response process of the output power of the equipment takes a long time in model 2, with a value of about 10 min, while the time for the output value to reach the desired value is greatly reduced in model 1, with a value of about 4 min, and at the same time, it can foresee the change of the output power in advance, and make adjustments accordingly. The model constructed in the study has a more rapid calculation process and higher calculation accuracy in a short period of time, which has obvious advantages in online real-time prediction operation.

Most of the renewable energy sources have unstable supply and high volatility. With the growing share of renewable energy in the integrated energy system, it is more and more difficult to execute the energy pre-dispatch regulation decision of the integrated energy system. To address the problem of increased volatility of the system, the study proposes to optimize the pre-dispatch decision-making of the system’s control center by analyzing the difference between the day-ahead market clearing price and the declared price of electricity supply. The results show that the node declared power is much higher than the ground's actual clearing power during the period from 1:00 am to 4:00 am. During this period the declared power of the nodes is at 2500kW and the actual clearing power of the nodes is around 1500kW. The outgoing power of the integrated energy system electrical load can be reduced in advance during the period from 10 am to 3 pm. The proposed pre-dispatch decision of the integrated energy system on the basis of the difference between the day-ahead clearing price and the node declared price can ensure the stability of the system operation while reducing the operating cost of the integrated energy system.