Carbon-Aware Energy Cost Minimization for Distributed Internet Data Centers in Smart Microgrids

In Internet data center operations, the operators are faced with high energy cost and carbon emission. Moreover, for socially responsible Internet data center operators, they are expected to minimize energy cost and carbon emission simultaneously. Since smart microgrids have many advantages in supporting the operations of Internet data centers (e.g., low electricity distribution loss, high utilization ratio in renewable energy), we consider the problem of minimizing the long-term weighted summation of energy cost and carbon emission for Internet data center operators in smart microgrids. To achieve the above aim, we propose an efficient operation algorithm considering the uncertainties in renewable generation output, electricity price, workload, and carbon emission rate.

In this paper, we investigate the problem of minimizing energy cost for distributed Internet data centers (IDCs) in smart microgrids while taking system dynamics into consideration. Specifically, IDC operators expect to minimize the long-term energy cost with the uncertainties in electricity price, workload, renewable energy generation, and power outage state. At first, we formulate the problem as a stochastic program that captures service request distribution, server provisioning, energy storage management, generator scheduling, power transactions between smart microgrids, and main grids. Second, we use the Lyapunov optimization technique to design an operation algorithm, which enables an explicit tradeoff between energy cost saving and battery investment cost. Finally, the effectiveness of the proposed algorithm is evaluated with practical data.

Energy generation and consumption are the main aspects of social life due to the fact that modern people’s necessity for energy is a crucial ingredient for existence. Therefore, energy efficiency is regarded as the best economical approach to provide safer and affordable energy for both utilities and consumers, through the enhancement of energy security and reduction of energy emissions. One of the problems of cloud computing service providers is the high rise in the cost of energy, efficiency together with carbon emission with regards to the running of their internet data centres (IDCs). In order to mitigate these issues, smart micro-grid was found to be suitable in increasing the energy efficiency, sustainability together with the reliability of electrical services for the IDCs. Therefore, this paper presents idea on how smart micro-grids can bring down the disturbing cost of energy, carbon emission by the IDCs with some level of energy efficiency all in an effort to attain green cloud computing services from the service providers. In specific term, we aim at achieving green information and communication technology (ICT) in the field of cloud computing in relations to energy efficiency, cost-effectiveness and carbon emission reduction from cloud data center’s perspective.

With the adoption of smart grid, some cyber-related vulnerabilities may also be introduced. When cyber attacks are launched, the power grid may become unstable and ultimately power outages may occur. In this situation, backup generators would be scheduled to support the operation of Internet data centers. Since the generation cost of backup generators are far higher than the average price of electricity in main grids, higher energy cost for Internet data center operators would be incurred. When power outages caused by cyber attacks occur frequently, the increased energy cost of Internet data center operators would be very large. Thus, it is necessary to consider the operation of Internet data centers in power outage environment. Since Internet data center operators can reduce energy cost by fully utilizing the spatial and temporal diversities of renewable energy in smart microgrids when there are power outages, we consider a scenario that running Internet data centers in smart microgrids and propose an efficient algorithm to minimize the long-term energy cost.

As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emission-efficient Application placement method (DECA) for ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with a Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the ability of DECA in providing a tradeoff and optimizing energy cost and carbon emission at the same time.

Green and sustainable development of Internet data centers (IDCs) has attracted more attention in both academia and industry. Full utilization of renewable energy sources is widely known as the most effective way to supply electrical and thermal energy while reducing carbon emission. However, the integration of renewable energy into IDCs is still challenging due to the mismatch between uncertain renewable supply and time-varying demand requirements, and high requirement of operation reliability against IDC failures. Therefore, in this paper a hydrogen-water-based energy (HWBE) system is developed and its integrated planning-and-operation problem is formulated as a mixed-integer linear programming problem to determine the optimal capacity of energy facilities in the HWBE system with considering IDC operation reliability. A hybrid physics-based and data-driven method is developed to accurately capture the electrical and thermal energy consumption characteristics and their coupling which are the basis for the optimal planning of the HWBE system. Furthermore, a Benders decomposition-based reliability improvement algorithm is developed to enhance the operation reliability, which decomposes the problem into the planning problem with normal operation as the master problem and the operation problem with IDC failure as the subproblem. The reliability can be enhanced using the solution obtained by the master problem with the feasibility cut obtained from the subproblem. Numerical results show that the developed HWBE system is energy-efficient with low carbon emission, since the power usage efficiency of IDCs could be as low as 1.09 and the carbon emission could be reduced by 74.9% as compared by the electricity-driven IDC energy system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper focuses on the integrated planning-and-operation optimization of an HWBE system for the application in IDCs. We improve the energy consumption model of IDCs based on a hybrid physics-based and data-driven method, which can describe the interaction between the dynamic thermal process and electricity consumption of IDCs. In this way, both the high accuracy of the physics-based model and the lower computational effort of the data-driven method could be simultaneously achieved in the energy consumption model. Furthermore, in practice, the optimal planning problem of IDCs is necessary to take into account the operation reliability against data center failures, since the capital expenditure of the backup energy devices is generally significant. This means that a trade-off between the solution accuracy of the planning problem and the computational complexity caused by the operation problem should be considered. Therefore, we develop a Benders decomposition-based reliability improvement algorithm to address the trade-off mentioned above. This technique can integrate the feasibility cut obtained from the operation problem with IDC failure into the planning problem, in order to improve the operation reliability against the supply-demand mismatching and IDC failures while reducing the capital cost, as compared to the system designed by the conventional redundancy standard. Numerical results show the effectiveness of the developed method which can make full use of renewable energy sources and support the green and sustainable development of IDCs.

Cloud computing is powered by an engine known as Internet data center (IDC). As cloud computing flourishes, the energy consumption and cost for IDCs are soaring. The energy cost minimization problem for IDCs in deregulated electricity markets has generated growing interest. In this paper we study how to leverage both the geographic and temporal variation of energy price to minimize energy cost for distributed IDCs. We propose a novel architecture and two algorithms for unified spatial and temporal load balancing. Rigorous analysis shows that our algorithms have a low computational complexity, require a relaxed accuracy in electricity price estimation, and guarantee a service completion time for user requests. Using real-life electricity price and workload traces, extensive evaluations demonstrate that compared to the schemes using either spatial load balancing or temporal load balancing alone, the proposed spatio-temporal load balancing method significantly reduces energy cost for distributed IDCs.

This paper aims to present renewable air-conditioning as a sustainable system for varied climatic conditions with the feasibility of optimization to reduce the level of energy consumption and the rate of carbon emissions. Extreme use of air-conditioning has caused substantial growth in the level of energy consumption and carbon emissions. This fact clarifies the requirement for considering improvement applications of renewable energy sources for air-conditioning systems. The components of solar air-conditioning are studied and employed as the basis for system optimization. The approach this paper presented implements a key component-based modelling analysis of renewables and modelling concepts that the geometry of this air-conditioning is founded on. The optimized model is performed using Polysun program, a renewable system analysis tool. As an exercise in the system modelling, the principle component analysis also accounts for the renewables of air-conditioning in relation to the context of the application and with respect to their integration into the climatic conditions of London, Toulouse and Rome. This, in turn, allows for the interpretation of the findings on the significance of renewables in energy consumption and carbon emissions. It also allows for the generation of a sustainability-based system that can reduce the level of energy consumption and the rate of carbon emissions. In this way, this paper uncovers the significance of renewables as a source of clean energy and sustainable practice in air-conditioning. It also reveals the particular contribution they make to the levels of energy consumption and carbon emissions that not only tackles global warming but also combats climate change.

Transport profoundly affects energy use and carbon dioxide emissions in the tourism sector. The Wulingyuan Scenic Area (WSA), a natural heritage destination in China, is chosen for the case study. The energy consumption and carbon emission of 10 types of tourism transportation modes at the destination are measured and analyzed using a bottom‐up approach for the period of 1979 to 2010. Scenarios were created to project the effects of single and multiple factors on energy consumption and carbon emission by tourism transportation during 2011‐2020. The results showed the following: (a) there is a large difference in energy consumption and carbon emission per capita and per kilometer per capita among the 10 vehicle modes; (b) the monthly energy consumption and carbon emission of tourism transportation differed significantly, the month with the highest (October) are respectively 6.8 and 4 times that of the lowest month (January); (c) the highest annual growth rate of energy consumption and carbon emission are respectively as 32.16% and 27.98% during 1979‐2010; and (d) the amount of energy consumption and carbon emission in the multiple factor scenarios are lower than that in the reference and single factor scenarios during 2011‐2020.

Ultra-high voltage network induced energy cost and carbon emissions

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R2 ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization.

Large commercial buildings may display demand flexibility, which reduces electric energy expenses for the building owner and carbon emissions from grid operations, provides distributed energy resources, and increases the penetration of renewable energy sources. Demand-controlled ventilation (DCV) and building thermal mass control can individually and jointly provide such flexibility. The performance and financial payback of these technology options can be dramatically improved if based on hourly electric prices and carbon emissions rates. In this study, a modeled but actual large office building, simulated using New York City hourly electric prices, hourly CO2e emissions rates, and weather data for the summer 2019 cooling season is based on these dynamic driving parameters. A joint optimization of a building’s thermal mass and indoor CO2 content is presented. Superior energy savings and carbon emissions reductions are found for the joint optimization scenario when compared to both the baseline operation and individual optimization of building thermal mass and indoor CO2 content. These findings motivate the development of a real-time joint control system that utilizes closed-loop model predictive control (MPC) to optimally harness both sources of demand flexibility, a system that would require the future development of forecasting algorithms for external and control-oriented system models.

Cloud computing is supported by an infrastructure known as Internet data center (IDC). As cloud computing thrives, the energy consumption and cost for IDCs are exploding. There is growing interest in energy cost minimization for IDCs in deregulated electricity markets. In this paper we study how to leverage both geographic and temporal variation of energy price to minimize energy cost for distributed IDCs. To this end, we propose a novel spatio-temporal load balancing approach. Using reallife electricity price and workload traces, extensive evaluations demonstrate that the proposed spatio-temporal load balancing approach significantly reduces energy cost for distributed IDCs.

In data centers, traffic demand varies in both large and small time scales. A data center with dynamic traffic often needs to over-provision active servers to meet the peak demand, which incurs significant energy cost. In this paper, our goal is to reduce energy cost of a set of distributed Internet data centers (IDCs) while maintaining the quality of service of the dynamic traffic. In particular, we consider the outage probability as the QoS metric, where outage is defined as service demand exceeding the capacity. We require the outage probability at each IDC to be smaller than a predefined threshold. Our goal is thus to minimize total energy cost over all IDCs, subject to the outage probability constraint. We achieve the goal by dynamically adjusting server capacity and performing load shifting in different time scales. We propose three different load-shifting and joint capacity allocation schemes with different complexity and performance. Our schemes leverage both stochastic multiplexing gain and electricity-price diversity. Thus, improving over prior work, our schemes reduce energy consumption/cost even when all IDCs have the same electricity price. We use both simulated load traces and real traffic traces to evaluate the performance of the proposed schemes. Results show that our proposed schemes are efficient in reducing energy cost, and robust in QoS provisioning.

Cloud computing services are becoming integral part of people's daily life. These services are supported by infrastructure known as Internet data center (IDC). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. Both academia and industry have paid great attention to energy management of IDCs. This paper studies an important energy management problem-how to minimize energy cost for IDCs in deregulated electricity markets. We propose a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit the temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed using real-life electricity price and workload traces at an enterprise production data center. The evaluation results demonstrate that the proposed approach significantly reduces energy cost for IDCs, guarantees a service delay bound, and alleviates workload drop if the service delay bound is sufficiently large.