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The problem that the early capacity planning of microgrid in data center is unreasonable and cannot adapt to the operation characteristics of data center has not been solved. At the same time, in order to improve the power supply economy and renewable energy consumption capacity of the microgrid in data center, a bi-level optimal configuration model of the microgrid capacity in data center taking into account the load characteristics is proposed in this paper. Scenario reduction-based selection method is used to deal with the uncertainty of renewable energy and the computational complexity caused by a large number of scenarios. The corresponding load scheduling strategies are proposed according to the characteristics of different loads together with their flexibility. The problem of capacity planning for different microgrid resources, including energy storage system, photovoltaic and wind turbine, is solved by using non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) and Gurobi. Finally, the conpromise optimal soulation is obtained by the fuzzy multi-attribute decision-making approach. The simulation results show that the proposed approach can reduce the construction costs, operation costs and carbon emissions of data center microgrids. Besides, the satisfaction of data center operators is improved.

In response to the exist problems such as the lack of accuracy in load forecasting leading to large errors in contracted power purchases, and the ambiguous market boundaries leading to duplication in assessment and settlement when load aggregators organize demand-side resources to participate in the peaking market and the electricity energy market, a load aggregator potential calculation and peaking strategy based on the longitudinal modified autoregressive integrated moving average (ARIMA) model is proposed. Firstly, the longitudinal modified ARIMA forecasting algorithm is applied to forecast the load baseline for obtaining the model of peak regulation potential of load aggregators, thus exploring the adjustable capacity of demand-sideresources to provide a database for power market trading. Secondly, the load deviation assessment method of electric energy market and peak shaving market is formulated, and the load aggregator peak shaving model considering the deviation assessment is constructed with the goal of maximizing the monthly rolling time domain comprehensive settlement income. Finally, the proposed method is analyzed based on the monthly historical load data of load aggregators in a typical region. The results show that the proposed load aggregator peak shaving strategy considering load deviation assessment can improve the peak shaving revenue of aggregators by about 23.7% and reduce the load aggregator peak-to-valley difference of about 10%, which verifies the rationality and effectiveness of the method.

Energy Internet technology can realize the information fusion of demand side adjustable power load resources, transmission and distribution resources, power generation resources, and play the value benefits of demand side adjustable power load in peak load and frequency regulationof power grid. In this paper, by integrating the advantages and disadvantages of different control modes for demand side resources, a framework and method of decentralized collaborative control within demand side resource cluster and regional grid coordinated control is proposed, which can effectively improve the flexibility and rapidity of adjustable resource response grid frequency regulation.

With the rapid development of renewable energy and the urgent need for global carbon emission reduction, virtual power plants have become a high-profile energy management model that can integrate multiple energy resources. How to effectively integrate renewable energy to reduce carbon emissions, how to optimize the use of different energy resources, and how to fairly distribute economic benefits within virtual power plant clusters while encouraging the reduction of carbon emissions are issues that need to be addressed in research. The study first established a virtual power plant model and conducted in-depth optimization for its economic and environmental indicators. Subsequently, the study constructed a game model within the virtual power plant cluster, aiming to solve the problem of income distribution in this diversified energy system. The research results found that commercial users have the highest carbon emissions, followed by industrial users, while residential users have the lowest carbon emissions. In terms of optimized user electricity consumption behavior, the peak-to-valley difference rate of industrial users has been reduced by 17%, and the daily load rate has increased by 6%; the peak-to-valley difference rate of commercial users has been reduced by 12%, and the daily load rate has increased by 6%; The peak-to-trough difference rate for residential users decreased by 8%, and the daily load rate increased by 4%. In addition, the research also proposes a method of internal revenue distribution of virtual power plant clusters based on a carbon reward and punishment mechanism, which provides a new way for the synergy effects and economic benefit distribution of virtual power plants. Research is of positive significance in solving pressing issues in the field of energy management and provides strong support for the development of future sustainable energy systems. [ABSTRACT FROM AUTHOR]

With the large-scale grid integration of new energy sources, the power relationship between source and demand side is becoming more and more complex, the flexibility requirements of the distribution network are increasing, and new requirements for system safety and reliability are put forward, and it can't meet the peak regulating requirements of grid by relying solely on source side to cope with variable loads. In view of this, to make the grid operation flexibility improved by using an adjustable load capacity, the study first constructs a load classifying method on the foundation of fuzzy style K-plane clustering method to understand the interaction information of the load devices. Then the adjustable value of the load is analyzed from the demand response and standby perspectives, respectively, and an improved dynamic time-bending-based source-load similarity inscription method is proposed, which aims to unify the multiple load information obtained by clustering. The proposed clustering algorithm takes the highest value of 0.096 for the Davies-Bouldin index index, which is 0.012 and 0.014 higher than the K-plane clustering algorithm and the K-means algorithm, respectively. In addition, the load demand response regulation with the improved dynamic time bending method has a higher capacity for new energy consumption than the variance method, with a difference of 2.0 kW. This indicates that using load regulation to consume new energy to exploit the load curve will stimulate the active participation of customer-side load in maintaining power balance between the electricity consumption side and the demand side, and form an interest community between power supplying and demanding sides. [ABSTRACT FROM AUTHOR]

Studying the grid integration of renewable energy power generation is crucial for achieving the goal of carbon neutrality since it may have a significant influence on the secure and reliable functioning of the power system. In order to solve the problem of deviation impact caused by renewable energy fluctuations and the optimal scheduling of VPP (Virtual Power Plant), the study divides the internal aggregation unit of the virtual power plant into two parts to model. One part is the source equipment, including wind power generation equipment, gas turbine, gas boiler and waste heat boiler. And the other part is the generalized Energy storage, including electric vehicles, air conditioners and alternative response loads. Ultimately, a generalized energy storage-based virtual power plant operation optimization model is developed under multi-market coordination of electricity-gas-heat-carbon. According to the study's findings, adding more power-to-gas technology boosts revenue in the carbon trading market by 25.24 percent. The energy market's revenue is equal to that in the absence of a carbon trading market, and the income of the natural gas market increases by $ 32.96. The income of the carbon trading market is $ 181.51, and the final operating cost is reduced by $ 180.80, a drop of 7.81%. To sum up, the suggested approach may more effectively achieve the best distribution of different energy sources, increase the dependability of VPP operation, and make it more low-carbon. [ABSTRACT FROM AUTHOR]