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As a critical infrastructure to support the development of digital economy, the scale of data center (DC) power consumption has been growing continuously in recent years. The plug-in of a large number of DCs to the distribution network (DN) presents both opportunities and challenges for the secure, stable, and sustainable operation of the system. In order to address the planning issues arising from the integration of DCs into the DN, this paper proposes a two-stage (planning stage and operation stage) collaborative planning method for DCs and the DN while considering the source-demand uncertainty. Firstly, spatial-temporal demand response characteristic of DCs is explicitly elaborated, and based on the energy consumption model, a two-stage stochastic planning model considering conditional value at risk (CVaR) for DCs and the DN is constructed using probabilistic stochastic simulation, which obeys low-carbon and security, and satisfies the source-network-load constraints of the system. The siting and sizing for DCs and renewable generation, feeder expansion for the DN are determined to minimize the investment cost during the planning stage, while the power output for the devices considering uncertainties from load and renewable energy are optimized to minimize the operation cost during the operation stage. Secondly, the L-shaped decomposition algorithm is proposed to solve the two-stage stochastic optimization problem by reconstructing the problem into a master problem and subproblems, where the dual multiplies are calculated to generate feasibility cuts and optimality cuts to achieve iterative solving. Finally, numerical case studies on a 33-bus distribution system demonstrate that the participation of DCs in demand response can achieve a significant reduction in the investment and operation cost of the DN, and further promote the consumption of distributed renewable energy sources. • The spatial-temporal characteristics of DC demand response is explicitly elaborated and the mathematical formulation is proposed. • A two-stage stochastic collaborative planning approach of DC and DN based on CVaR is proposed. • Guidance for investment decisions for DSO with large numbers of DCs plug-in under uncertainties. • Demand response of DCs can effectively support renewable energy consumption and reduce the cost of DN. [ABSTRACT FROM AUTHOR]

In the age of digitalization, data centers are critical facilities that help people and businesses in many sectors. Data centers, like other assets requiring high investment, face several risks during their operation. Also, data centers seek sustainable sources to boost their operations when selecting the most appropriate location. Therefore, the location of a data center is chosen based on sustainability and risk assessment. To the best of our knowledge, no study has been conducted for data center risk evaluation with environmental and sustainability assessment. This paper evaluates the eight main criteria and the forty-five sub-criteria for selecting the most appropriate location for the data center when considering the fifty different economies in different business regions. For this particular purpose, the continuous intuitionistic fuzzy sets (CIFS)-based analytical hierarchy process (AHP) is presented to define the weights of each criterion and sub-criterion. Then, the CIFS-based technique for order of preference by similarity to the ideal solution (TOPSIS) is developed to rank the fifty economies integrated with the weights from the CIFS-based AHP. Moreover, eight different scenarios were conducted, and comparison studies were presented. Finally, Denmark, Switzerland, and The Netherlands are the top three selections based on the criteria. [Display omitted] • Fuzzy multi-criteria methods are developed to evaluate data center locations. • Natural, energy, and infrastructure risks are the top three pillars. • Economies are ranked based on the eight scenarios with sustainability and risks. • Denmark tops the charts with the highest score associated with fifty economies. • Based on each income level, Denmark, Brazil, and Tunisia are suitable locations. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

Tobacco drying is a highly energy-consuming process that requires a large amount of thermal energy. In this study, the waste heat from a data center is utilized to reduce fossil energy consumption in the tobacco drying process. A heat pump system is designed to address the issue of the waste heat temperature from the data center not meeting the required temperature for tobacco drying. Multi-objective optimization is performed with the goal of maximizing the specific moisture extraction rate and minimizing the specific investment cost, and seven hydrofluoroolefins are selected as the circulating working fluids. Coal-fired tobacco drying and Air Source Heat Pump Tobacco Drying (ASHPTD) are selected as comparison benchmarks to evaluate the energy efficiency and environmental benefits of the Data Center Waste Heat Tobacco Drying (DCWHTD) method in this study. The results indicate that R1234ze(Z) is the best working fluid. The specific moisture extraction rate of DCWHTD is improved by 36.69% and 20.21% compared to the coal-fired tobacco drying and ASHPTD, respectively, demonstrating enhanced energy efficiency. Although the specific investment cost of DCWHTD is higher compared with the coal-fired tobacco drying, it is 21.00% lower than that of ASHPTD. DCWHTD produces fewer emissions, the life-cycle carbon emissions are 26.93% and 16.80% lower than that of the coal-fired tobacco drying and ASHPTD, respectively. Furthermore, waste heat recovery improves the energy reuse efficiency of the data center by 64.92%. The tobacco drying method proposed in this study has better comprehensive performance and greater application potential. • Waste heat of data center is recovered by heat pump and used for tobacco drying. • Optimal design of system is obtained by multi-objective optimization method. • R1234ze(Z) is determined to be the optimal working fluid among the selected fluids. • Economic and environmental advantages over common drying methods are revealed. • Energy efficiency improvement of data center by waste heat recovery is evaluated. [ABSTRACT FROM AUTHOR]

A more comprehensive understanding of the impact of artificial intelligence (AI) on energy transition and carbon emissions could help to use AI to achieve carbon neutrality. To this end, the STIRPAT approach, the mediation effect technique and the panel threshold technique are developed using the panel data in 69 countries from 1993 to 2019. The results show that: (i) AI promote energy transition and carbon emission reduction, and trade openness (indicated by imports, exports and total trade volume) has the mediating effect. (ii) There is a single-threshold of trade openness in the impact of AI on carbon emissions. When trade openness is below the threshold, AI has an insignificant impact on carbon emissions; when trade openness crosses the threshold, AI has a significant negative impact on carbon emissions. There is a double-threshold of trade openness in the impact of AI on energy transition. When trade openness is lower than the first threshold, the impact of AI on energy transition is not significant; When trade openness is higher than the second threshold, the positive impact of AI on energy transition is increased. (iii) When considering the heterogeneity of income levels and AI levels, the trade threshold for achieving carbon emission reductions in the high-income group is lower than that of the global group, and the trade threshold for achieving carbon emission reductions in the low-AI level group is higher than that of the global group. While this study unequivocally delineates the affirmative role of artificial intelligence in carbon emission reduction and energy transformation, particularly in the context of trade openness, we concurrently acknowledge that this viewpoint is not devoid of contention. Amidst the rapid advancement of technology and the landscape of open trade, we discern the presence of counterarguments. The efficacy of artificial intelligence is susceptible to the influence of multifaceted factors. It is imperative to consider associated factors, such as the significant energy consumption required for storing and cooling data centers and servers. The study's conclusions aid policymakers in devising nuanced emission reduction policies tailored to specific needs. [Display omitted] • Investigate the effect of artificial intelligence on carbon emissions and the energy transition. • Trade openness is incorporated in the research framework of artificial intelligence, carbon emissions, and energy transition. • Artificial intelligence can promote carbon emission reduction and energy transition. • Trade openness has mediating effects and threshold effects. • Heterogeneity across income levels and artificial intelligence development levels is investigated. [ABSTRACT FROM AUTHOR]

The waste heat recovery technology of data center has attracted the attentions for energy conservation and emission reduction in recent years. This paper presents a new scheme which uses heat recovery technology to link the data center and the agricultural sector. The detailed system design, technical validation, and environmental and economic analysis show that the project can fully absorb a large amount of waste heat in the data center, and use the air source heat pump (ASHP) to transport heat to an ecological farm, reducing the consumption of electric energy and promoting the development of agriculture and animal husbandry. In a case study of China Brain Green Data Center, the dynamic payback period is about 3.4 years, and the cumulative net present value will reach $666 k. In terms of environmental impact, the proposed ASHP-based complementary utilization scheme can save 676 MWh of electricity for the data center and 88 MWh of electricity for the ecological farm in each year, which is equivalent to reducing coal by 230 tons and reducing CO 2 emissions by 168 tons. Therefore, from the perspective of economic and environmental benefits, this new complementary waste heat utilization technology realizes a win-win development. [ABSTRACT FROM AUTHOR]

The service life, safety, and capacity of lithium-ion power battery packs relies heavily on the consistency among battery cells. Grouping is an effective procedure to improve consistency by screening cells with similar performance and assembling them into an identical group. Battery grouping can be achieved via clustering techniques based on characteristics like static capacity, internal resistance etc. The dynamic characteristics-based method considers the battery performance during the entire charging-discharging process and has become one of the most promising grouping method. However, it suffers from high computational complexity. Nowadays, facing stricter quality standards and the increasing demand for battery products, existing dynamic characteristics-based grouping scheme cannot meet the performance and time requirement of the modern high-quality, large-scale battery manufacturing. In this paper, a novel grouping scheme based on distributed time-series clustering is proposed to match the need of both efficiency and consistency improvement. The proposed scheme designs an effective "cloud-edge" mode and utilizes an innovative two-stage trick to achieve parallel processing, which split the original centralized clustering approach into local clustering and global merging. The host computers for data acquisition and battery control are regarded as distributed edge computing resources to implement local clustering on the acquired battery discharging voltage sequence set. Cluster contours are extracted via a denoising contour extraction algorithm considering the irregularity of the discharging voltage sequences. The results of the above preliminary processing are uploaded to a cloud data center. A pragmatic merging scheme based on an integrated merging indicator is established to solve the decision-making problem of global merging on the cloud data center. The final global cluster set is transmitted back to host computers to instruct cell unloading operation. Experimental results based on real battery discharging voltage sequence data suggest that the proposed scheme can reduce the inconsistency rate by 43.56% and the time cost by 92.87%. The computing efficiency and resource utilization rate of the distributed scheme is much higher than the centralized scheme. Meanwhile, compared with three existing advanced grouping approaches, our scheme perform the best in reducing inconsistency rate. [ABSTRACT FROM AUTHOR]

Data centers are estimated to have the fastest growing carbon footprint from across the whole information and communication technology (ICT) sector. Evaluating the performance of data centers in terms of energy efficiency and sustainability is becoming an increasingly important matter for organizations and governments (e.g., for regulation or reputation purposes). It nonetheless remains difficult to achieve such evaluation, as data centers imply to take into consideration a wide range of dimensions and stakeholders. Even though a wide range of sustainability performance indicators exist in the literature, there is still a lack of frameworks to help data center stakeholders (spanning from data center owners, governmental regulators to engineers/field operators) to evaluate and understand how a data center performs in terms of sustainable development/behavior. Our research work proposes such a framework, whose originality lies in the combination of state-of-the-art sustainability metrics with the biomimicry commandments of eco-mature system, which enables holistic sustainability assessment of data centres. From a theoretical perspective, the proposed model is designed based on a benefit-cost analysis using the Analytic Hierarchy Process (AHP) technique. This approach allows data center stakeholders for specifying their own preferences and/or expertise in the comparison process, whose practicability is demonstrated in this paper considering three data center candidates, which are respectively located in France, Germany and Sweden. • A new model for comparing data center sustainability performance is proposed. • The model makes use of state-of-the-art sustainability metrics together with biomimicry lessons. • The model is designed based on a benefit-cost approach using the AHP technique. • A comparison study is carried out with three distinct data center candidates. [ABSTRACT FROM AUTHOR]

Abstract Currently, the vast amount of heat generated by electronic equipment in the data canters is removed using high energy consumption mechanical systems. Hence, there is a good potential to use waste heat recovery systems for space heating purposes in order to reduce the energy consumption, energy cost as well as pollutant emissions. In present study, the feasibility of using the air source heat pump (ASHP), as a waste heat recovery system operating with different refrigerants, in a 54-racks data center in Mashhad city of Iran has been investigated to save the electrical energy consumption of the mechanical cooling as well as the natural gas fuel consumption of the adjoining office building with area of 416 m2. The energy-environmental-economic analysis results indicated that around 35000 m3/year natural gas saving, 20.8 MW h/year electrical energy saving, 121 tons/year CO 2 emission reduction and 25000$/year cost saving can be achieved through using the proposed waste heat recovery system. Furthermore, the proposed system, with a payback period of 2.5 years, can improve the power usage effectiveness (PUE), energy reuse factor (ERF) and energy reuse effectiveness (ERE) of the data center. Graphical abstract Image 103 Highlights • The feasibility of the waste heat recovery of a data center was examined using the air source heat pump (ASHP). • The effects of the type of the refrigerant and the return air temperature from the data center were explored. • The proposed system was evaluated both economically and environmentally. • The effect of using the ASHP on the indices ERE, ERF and PUE was investigated. [ABSTRACT FROM AUTHOR]

As the number of data centers is increasing worldwide, there are concerns about their growing electricity demand on power grids and its impact on the environment. These concerns can be mitigated at the urban scale by utilizing the waste heat generated by data centers and multi-energy hub integration. Using Combined Cooling, Heating, and Power (CCHP) and Photovoltaic (PV) systems within a data center, in combination with district energy network connections, can create energy interactions with residential customers in a multi-energy hub. However, connecting large energy consumers and distributed energy systems to the electrical distribution grid presents challenges due to the conventional design of the power grid with unidirectional electricity flow. There is a lack of understanding about the grid impact of data centers with multi-energy systems regarding electrical performance parameters, energy demand, and emissions. To address these open issues, we developed an hourly energy interaction model that includes a CCHP-PV-based data center and residential customers. In addition, the CCHP-PV-based data center was operated with a cost-optimized energy scheduling. We conducted six case studies to distinguish the specific effects of CCHP, PV, and district energy network connections. The model takes into account hourly outdoor temperatures, energy prices, and varying energy demands to capture the seasonal impact on electrical performance parameters and energy results. From an energy and environmental perspective, CCHP-PV systems can eliminate the data center's electricity demand from the grid, reduce residential energy demand, and decrease carbon dioxide equivalent emissions. This cleaner production and energy exchange further support the United Nations Sustainable Development goals. The experimental results showed a 43% reduction in power loss in the distribution grid and helped the data center's main electrical bus and residential bus against the voltage drop phenomenon. The CCHP-PV-based data center with district heating and cooling connection reduced the peak hour electricity demand on the grid by 39% and annual electricity consumption from the grid by 66%. Furthermore, Greenhouse Gas Protocols (GHG) Scope-2 emissions associated with the data center's grid electricity consumption and only residential electricity consumption were reduced by 77% and 36%, respectively. [Display omitted] • CCHP, PV, free cooling, and residual heat recovery for sustainable data centers. • A comprehensive model for the hourly energy flows of multi-energy systems. • Distribution grid electrical performance and emissions were evaluated. • Electrical parameters were analyzed with different cases. • Energy consumption and cost reductions were achieved with the optimized operation. [ABSTRACT FROM AUTHOR]

The volume of landfill gas (LFG) produced during municipal solid waste (MSW) decomposition in local landfills has increased rapidly in recent years. To promote circular economy (CE) applications through resource recovery, this article investigates the feasibility of utilizing LFG as a sustainable energy to power data centers (DCs) in Xiamen (China) and evaluates the implications of waste heat reuse on the energy consumption of DC. The objective of this study was to investigate the environmental impacts and economic benefits of this scheme based on a SFC model. With respect to its novelty, LFG is utilized as an energy supply to DCs, while the waste heat generated in the energy production is recovered. The socio-economic benefits generated in this process are also elaborated. It was found that a large number of MSW was completely recycled to generate energy. The LFG enterprise could benefit from energy sale, while the energy satisfied the annual demand for DC power consumption. Utilizing this energy output resulted in the reduction of primary energy and less CO₂ emissions. By reusing the waste heat for domestic heating, the energy efficiency could be improved, while primary energy could be further saved, avoiding more CO₂ emissions. This implies that utilizing LFG output and waste heat to power DCs not only facilitate resource recovery from unused resources to improve energy efficiency and obtain socio-economic benefits, but also promote the digitalization of DCs and sustainable development of urban cities in the framework of CE. [Display omitted] • LFG can be used to power data centers (DCs) in order to promote CE. • The recovery of waste heat from LFG generation reduced CO 2 emission of 8.46 × 108kg. • Within 15 years, 4.97 × 10 10 kg of MSW can generate 4.65 × 109 kW ⋅ h of electricity. • The recovery of waste heat benefited landfill operators and DC firms financially. [ABSTRACT FROM AUTHOR]

Nowadays, energy system planners are looking for effective solutions to take advantage of the opportunities inherent in integrated energy systems. Data centers and electric vehicles are appealing candidates for implementing integrated energy management strategies due to their high energy capacity and high flexibility potential. Hence, this paper presents a multi-objective optimization framework for optimal energy management of a virtual energy hub (VEH) plant to manage and meet the required demands of data centers, adjacent buildings, and plug-in electric vehicles (PEVs). To this end, a mixed robust-stochastic strategy is established to solve the unit commitment problem in the context of the VEH plant and implement an integrated demand response program (DRP) to maximize the VEH plant's profit, minimize carbon emission, and mitigate the risk of uncertainties. The considered VEH plant can serve the demands using the unique features of energy conversion equipment, i.e., electrical chiller, electrical boiler, combined heat and power, and renewable energy sources. The Pareto optimal solution is employed to handle the multi-objective problem with the ε -constrained method's means as mixed-integer linear programming. The presented structure is solved with a powerful commercial optimization tool, namely GAMS software. The simulation results confirm the effectiveness of the proposed strategy in the presence of the integrated DRP and up-to-date energy storage systems by increasing the profit of the VEH plant by up to 7.14% and reducing carbon emissions by up to 1.02% compared to the base case study in meeting the demands of the data center and PEVs. • Proposing an optimal energy dispatching in VEH under the multi-objective framework. • Extending interaction capability between VEH plant and electricity, gas markets bilaterally. • Proposing mixed robust-stochastic strategy within the multi-objective framework. • Developing the integrated DR in the VEH operation for multiple sectors. [ABSTRACT FROM AUTHOR]

Due to high demand for power and cooling in the data centers, employing combined cooling, heating and power (CCHP) systems could be an effective solution. The use of an operationally optimized CCHP system plays an important role in improvement of data center's performance. In this study, at first, based on energy, exergy and exergoeconomic analysis, an ICE-driven CCHP system following electrical load (FEL) is proposed to meet the power and cooling energy requirements of a data center as a case study. Then, the performance of CCHP system under two different operating scenarios are compared to introduce the optimal energy supply design. Finally, the optimal energy supply design is employed to optimize the performance of the selected CCHP system through a parametric study. In the first scenario, the water flow is applied for cooling of the ORC condenser while in the second scenario, the refrigerant of absorption chiller is considered as cooling source for the ORC condenser. Results of energetic and exergetic analysis show that using the second scenario, the cooling capacity, energy efficiency and exergy efficiency of the CCHP system are higher than the first scenario by 30.66%, 14.18% and 13.48%, respectively. In addition, exergoeconomic analysis results show that the total cost rate associated with fuel and product in the CCHP system under second operating scenario are lower than first one by 38.9% and 31.58%, respectively. • An ICE-driven CCHP system coupled with ORC and AC system is applied in a sample data center. • Performance of the proposed system is investigated under two operating scenarios. • The effects of different parameters based on energy-exergy-exergoeconomic viewpoints is evaluated. • The optimal operational points are obtained using Pareto front curve. [ABSTRACT FROM AUTHOR]

The growing concerns about environmental impacts, electricity costs, and energy required of data centers are mounting. To overcome these challenges, the data center needs to integrate high-efficiency and environmentally friendly technologies to cover these issues. The combined cooling, heating, and power (CCHP) system refers to the synchronous generation of electricity and valuable heating and cooling energy through multi-energy conversion, which can address many of the data center's challenges. Hence, this paper focuses on the optimal energy management of the CCHP-based data center integrated with renewable energy, multi-energy storage, and multi-energy conversion to minimize daily electricity fees. The presence of multiple electrical and thermal consumptions in the data center creates an ideal opportunity to implement integrated demand response (IDR) for both types of demands. Besides the local workload in the data center, the operator seeks to optimize the resource scheduling to serve the electrical, heating, and cooling demands in nearby buildings and participate in the gas and power markets. Hence, the robust framework is extended in the proposed energy management to control the market price uncertainty, which denotes the decision-maker's conservatism level. The proposed model is examined in the sample data center structure. Simulation and results are carried out in the GAMS software environment and solved via CPLEX solver, which is discussed for several cases studies. Under the coordinated robust energy management with the incentive-based IDR and multi-energy conservation technologies, a 13.2% cost-saving can be achieved for the data center. • Proposing the optimal energy management strategy for the CCHP-based data center. • Providing the IDR for both electrical and heat demands in the data center to enhance the system's flexibility. • Introducing robust optimization in data center's energy management model to capture price uncertainty. • Investigating multi-energy technologies to serve data center's workloads, besides consumptions of nearby buildings. [ABSTRACT FROM AUTHOR]