Distributionally robust planning for data center park considering operational economy and reliability

Wang, Zhiying; Wang, Yang; Ji, Haoran; Hasanien, Hany M.; Zhao, Jinli; Yu, Lei; He, Jiafeng; Yu, Hao; Li, Peng

doi:10.1016/j.energy.2023.130185

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…Formula (33) represents the power constraint of power interaction between the IES and the power grid. In order to solve the operation uncertainty of the WT and PV power, the robust optimization method was introduced into the deterministic model established in Section 2.2.1 [30,31]. The uncertainty of the PV and WT output is expressed as a boxtype uncertain U set with equal scaling of the upper and lower bounds:…”

Section: Low-carbon-oriented Capacity Optimization Model 221 Objectiv...mentioning

confidence: 99%

“…Among the IES investigated in this study, when the PV and WT output reaches the interval boundary, the operating cost of the IES is higher, which is more in line with the definition of the "worst scenario". Therefore, Formula (35) can be rewritten as follows [30]:…”

Section: Low-carbon-oriented Capacity Optimization Model 221 Objectiv...mentioning

confidence: 99%

“…where f 1 (x) is the objective function of the capacity allocation model of the first stage, which can be expressed by Formula (15); h(x) ≤ 0 is the first stage constraint, corresponding to the installed capacity constraint of Formula (21); f 2 (y, x, u) is the objective function of the second stage, which can be expressed by Formula ( 16); x defines the decision variable of the first stage, that is, the configuration capacity of each energy equipment; u defines the uncertain variable, including the PV and WT output; y defines the decision variable of the second stage, that is, the output power of each energy equipment; s defines the decision variable of the second stage, which is the 0/1 state variable related to the power of the ES and power grid; g(y, x, u) is the inequality constraint of the second stage, which can be expressed by Formulas ( 24)-( 28), (31), and (32); and l(y, s, u) is the equality constraint of second stage, which can be expressed by Formulas ( 22), ( 23), ( 29), (30), and (32).…”

Section: Low-carbon-oriented Capacity Optimization Model 221 Objectiv...mentioning

confidence: 99%

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Low-Carbon-Oriented Capacity Optimization Method for Electric–Thermal Integrated Energy System Considering Construction Time Sequence and Uncertainty

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Zhao,

Huang

2024

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The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric–thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of the proposed capacity optimization method in terms of economy and environmental friendliness through a comparative analysis of different planning cases. Simulation results show that, compared with the scenario of completing planning at the beginning of the life cycle at one time, the proposed low-carbon-oriented capacity optimization method that considers construction time sequence and uncertainty can not only reduce the cost of the integrated energy system, but also help to enhance renewable energy utilization and reduce the system’s carbon emissions; the total cost of phased planning is reduced by 11.91% compared to the total cost of one-time planning at the beginning of the year.

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Section: Low-carbon-oriented Capacity Optimization Model 221 Objectiv...mentioning

confidence: 99%

Section: Low-carbon-oriented Capacity Optimization Model 221 Objectiv...mentioning

confidence: 99%

Section: Low-carbon-oriented Capacity Optimization Model 221 Objectiv...mentioning

confidence: 99%

See 1 more Smart Citation

Low-Carbon-Oriented Capacity Optimization Method for Electric–Thermal Integrated Energy System Considering Construction Time Sequence and Uncertainty

Wang,

Zhao,

Huang

2024

Processes

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Operation optimization considering multiple uncertainties for the multi-energy system of data center parks based on information gap decision theory

Wang,

Lin,

Zhang

et al. 2024

Front. Energy Res.

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With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods.

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Distributionally robust planning for data center park considering operational economy and reliability

Cited by 2 publications

References 30 publications

Low-Carbon-Oriented Capacity Optimization Method for Electric–Thermal Integrated Energy System Considering Construction Time Sequence and Uncertainty

Low-Carbon-Oriented Capacity Optimization Method for Electric–Thermal Integrated Energy System Considering Construction Time Sequence and Uncertainty

Operation optimization considering multiple uncertainties for the multi-energy system of data center parks based on information gap decision theory

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