Integrated Stochastic Energy Management for Data Center Microgrid Considering Waste Heat Recovery

Ding, Zhaohao; Cao, Yongqiang; Xie, Liye; Ying, Lü; Wang, Peng

doi:10.1109/tia.2018.2890789

Cited by 73 publications

(33 citation statements)

References 42 publications

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“…(6) In the lower-level transaction, EUs are more sensitive to MEG's electricity price, and can adapt to the TOU price with dynamic load balancing. Taking the industrial EU as an example, in the valley price periods (1)(2)(3)(4)(5)(6)(7)(8)(19)(20)(21)(22)(23)(24), the industrial EU shifts a certain loads from peak periods to these periods, which results in the lower dissatisfaction. In the peak price periods (9-13, 14-18), the electrical demands are large, so the industrial EU cuts the non-essential loads as possible as it can, for reducing the expenditure.…”

Section: B Numerical Results Analysismentioning

confidence: 99%

“…The MEG responses to the TOU gas prices and reschedules its gas consumption to a certain degree. In the off-peak periods (1)(2)(3)(4)(5)(6)(7)(8)(19)(20)(21)(22)(23)(24), the gas consumption has a certain growth, except periods 21 and 22. This may be due to the lower gas prices in these periods.…”

Section: B Numerical Results Analysismentioning

confidence: 99%

“…Li et al [7] established a scenario-based optimal operation model for the integrated energy system to handle uncertainties in energy demand and renewable generation. In [8], an integrated energy management scheme was proposed to optimize the operation cost for a data center microgrid. An optimal scheduling model with unit commitment constraints was established to coordinate the complementary operation of different devices in the integrated energy system [9].…”

Section: Introductionmentioning

confidence: 99%

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Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Lin

Liu

2020

IEEE Access

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The application of time-of-use (TOU) pricing in the electricity market has significant value, which can improve economic efficiency and promote effective management of resources. However, this pricing mechanism has not been applied and promoted in the gas market in China. In this paper, we propose a bi-level transaction model considering TOU gas pricing for analyzing the economic efficiency of micro energy grid (MEG) operation based on Game Theory. The multiple energies trading is conducted between the natural gas company (NGC), the MEG and energy users (EUs), which forms a hierarchical Stackelberg game model. We first analyze utilities and strategies of aforementioned participants, and derive the Stackelberg equilibrium (SE) analytically as a balanced solution that captures the equilibrium strategies of participants. Then, a mixed integer nonlinear programming is formulated to determine the optimal TOU gas prices delivering maximum NGC profit, the optimal energy sales prices corresponding to the MEG' SE utility, and the optimal load pattern for EUs. Finally, numerical experiments are conducted in two scenarios, which reveal that the TOU pricing can well balance the gas supply and demand, and has significant potential for improving the economic efficiency of the MEG. INDEX TERMS Micro energy grid, Stackelberg game, economic efficiency, time-of-use pricing, equilibrium.

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Section: B Numerical Results Analysismentioning

confidence: 99%

Section: B Numerical Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Lin

Liu

2020

IEEE Access

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“…The research findings can be applied to designing the location of cooling devices and servers within DC rooms. Exploring an integrated approach to reusing the heat generated from servers as a renewable energy source is another main stream of the Green DC literature (Ding et al, 2019). Scholars in building engineering also investigate the impact of geographical location of DCs to cooling energy saving (Kim et al, 2017).…”

Section: Green Dc: Just An Engineering Issue?mentioning

confidence: 99%

COVID19 Led Virtualization: Green Data Center for Information Systems Research

Lee

Choi

Nguyen

et al. 2020

Information Systems Management

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Recent lockdowns over the world due to COVID19 pandemic accelerated virtualization of our social and work life. While virtualization contributes to reducing greenhouse gas (GhG) through reduced mobility, it also dramatically increases energy consumption by data centers (DCs) which host large number of servers for realizing virtualization. This paper argues that current engineering-oriented studies on Green DCs need to be complemented by IS scholars to address human and organizational issues.

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“…3 In a current energy scheme, micro-grid (MG) has been implemented to decrease complexities. 4 A MG is considered a successful platform for elevated renewable energy penetration. The significant benefits are the loss of transmission power, reliable power supply, clients' decreased energy costs, etc.…”

Section: Introductionmentioning

confidence: 99%

A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

Kumari

2020

Int J Numerical Modelling

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This dissertation shows energy scheduling in grid-connected micro grid (MG) using an amazing hybrid method for optimal energy management. The hybrid method is the mixture of Radial Basis Neural Function Network (RBFNN) and Bumble Bees Mating Optimization (BBMO) and is therefore referred to as RBFNN-BBMO technique. The MG system consists of photovoltaic, wind turbine, battery storage and micro turbine systems. Here, batteries are used to maintain a constant and stable output power. The necessary load demand of the grid-connected MG scheme is continually monitored using the RBFNN method. BBMO optimizes the perfect combination of MG taking into account the expected load demand. In addition to decrease the impact of renewable energy prediction mistakes, a two strategy for MG energy management is adopted. The first approach is to program various renewable energy sources (RESs) during the operation of MG in order to minimize the electricity costs. The second approach is power flow balancing and forecasting errors, decreasing from the scheduled energy reference based on the summarized rule. Access of RESs, power supply and charging modules are the constraints. Two existing methods such as the Genetic Algorithm and the Cuttlefish Algorithm compare the supremacy of the proposed method.

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Integrated Stochastic Energy Management for Data Center Microgrid Considering Waste Heat Recovery

Cited by 73 publications

References 42 publications

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

COVID19 Led Virtualization: Green Data Center for Information Systems Research

A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

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