A three-dimensional intelligent engineering management and control system for the construction of a long-span valve hall project based on a microservice architecture.

Three-dimensional intelligent engineering management and control systems (EMCS) based on the browser/server (B/S) model are an important part of intelligent engineering development. These systems are used for solving the difficulties encountered in engineering management with frequent cross-specialties and are vital tools for data exchange and service sharing among multiple departments. Currently, most engineering management and control systems are based on service-oriented architectures (SOAs). The integration mechanism and high coupling of SOAs leads to the reduction in system expansibility, service quality and service safety of the engineering system, making it difficult for these architectures to serve the construction of long-span valve hall engineering. To address these concerns, the management and application technology of the multidisciplinary data of valve hall engineering based on a microservice architecture (MSA) is proposed to improve the management efficiency of engineering data. A 3D integration modeling method for valve hall engineering structures and geological environments is proposed to establish the topological association between engineering structures and geological environments, without increasing the amount of model data required. A 3D intelligent engineering management and control technology for the entire process of the construction of long-span valve hall engineering is proposed, which realizes the entire process simulation and control of engineering construction based on WebGL technology. Accordingly, a three-dimensional intelligent engineering management and control system for the entire construction process of a long-span valve hall project in Southeast China is established, which can effectively manage and apply the data, display and analyze the three-dimensional model, and control and make decisions regarding the construction schedule. This study provides support for the construction of "smart engineering", promotes information communication and transmission between different project units, and speeds up the transformation from traditional construction management relying on drawings to three-dimensional intelligent construction management based on cloud services.

Self-powered bifunctional sensor based on tribotronic planar graphene transistors.

With the development of material science, micro-nano-fabrication and microelectronics, the higher level requirements are posed on the electronic skins (E-skin). The lower energy consumption and multiple functions are the imperative requirements to spurred scientists and mechanists to make joint efforts to meet. To achieve lower energy consumption, a promising energy-harvesting style of triboelectric nanogenerators (TENG) is incorporated into the field effect transistors (FETs) to play the important role for sensor. For bifunctional sensor, to harness the difficult for reflecting the magnitude of frequency, we resorted to synaptic transistors to achieve more intelligentization. Furthermore, with regards to the configuration of FET, we continued previous work: using the electrolyte gate dielectrics of FET-ion gel as the electrification layer to achieve high efficient, compact and extensively adoption for mechanosensation. The working principle of the GFET was based on the coupling effects of the FET and the TENG. This newly emerged self-powered sensor would offer a new platform for lower power consumption sensor for human-machine interface and intelligent robot.

Multi-energy conversion based on game theory in the industrial interconnection.

The multi-energy conversion system (MCS) plays an important role in improving the utilization of energy resources and realizing the energy transition. With the application of the new generation of information technologies, the new MCS can realize real-time information interaction, multi-energy collaboration, and real-time demand response, in which energy suppliers can intelligently motivate consumers' energy use behavior. In this paper, an MCS coupled with a cloud platform is proposed to address information explosion and data security issues. Due to the development of Internet technology, the increasing energy data, and the serious energy coupling, it is difficult for traditional optimization methods to deal with the interaction between participants of the MCS. Therefore, the non-cooperative game is used to formulate the interactions between participants with the aim of maximizing the energy suppliers' profit and minimizing the customers' cost. It is proved that the game model is an ordinary game with one Nash equilibrium. The simulation was performed with a gradient projection algorithm and the results show that the proposed MCS improves energy utilization efficiency through energy conversion while ensuring consumer satisfaction, and benefits both the customers and suppliers by reducing the energy consumption cost and the peak load demand, which effectively improve the supply quality and enrich the energy consumption patterns.

Power Outages and Community Health: a Narrative Review.

PURPOSE OF REVIEW: Power outages, a common and underappreciated consequence of natural disasters, are increasing in number and severity due to climate change and aging electricity grids. This narrative review synthesizes the literature on power outages and health in communities. RECENT FINDINGS: We searched Google Scholar and PubMed for English language studies with titles or abstracts containing "power outage" or "blackout." We limited papers to those that explicitly mentioned power outages or blackouts as the exposure of interest for health outcomes among individuals living in the community. We also used the reference list of these studies to identify additional studies. The final sample included 50 articles published between 2004 and 2020, with 17 (34%) appearing between 2016 and 2020. Exposure assessment remains basic and inconsistent, with 43 (86%) of studies evaluating single, large-scale power outages. Few studies used spatial and temporal control groups to assess changes in health outcomes attributable to power outages. Recent research linked data from electricity providers on power outages in space and time and included factors such as number of customers affected and duration to estimate exposure. The existing literature suggests that power outages have important health consequences ranging from carbon monoxide poisoning, temperature-related illness, gastrointestinal illness, and mortality to all-cause, cardiovascular, respiratory, and renal disease hospitalizations, especially for individuals relying on electricity-dependent medical equipment. Nonetheless the studies are limited, and more work is needed to better define and capture the relevant exposures and outcomes. Studies should consider modifying factors such as socioeconomic and other vulnerabilities as well as how community resiliency can minimize the adverse impacts of widespread major power outages.

Optimal vehicle size and driving condition for extended-range electric vehicles in China: A life cycle perspective.

Many researchers use life cycle assessment methodology to investigate the energy and environmental impacts of energy-saving and new energy vehicles. However, in the context of China, the life cycle energy-saving and emission-reduction effects of extended-range electric vehicles (EREVs), and the optimal applicable vehicle size and driving conditions for EREVs have been rarely studied. In this study, based on the life cycle assessment theory, the resource consumption, energy exhaustion, and environmental impact of EREVs were comprehensively analyzed. In addition, a differential evaluation model of ecological benefits was established for comparing EREVs with other vehicles with different power sources. Finally, scenario analysis was performed in terms of different vehicle sizes and driving conditions. The results have shown that EREV has great advantages in reducing mineral resource consumption and fossil energy consumption. The consumption of mineral resources of EREV is 14.68% lower than that of HEV, and the consumption of fossil energy is 34.72% lower than that of ICEV. In terms of environmental impact, EREV lies in the middle position. The scenario analysis has revealed that, for EREV in China, the optimal vehicle size is the passenger car and the optimal driving condition is the suburban condition. This work helps to understand the environmental performance of EREVs in China and may provide a decision-making reference for the government.

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning.

To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.

Foreign body ingestion in children presenting to a tertiary paediatric centre in South Africa: A retrospective analysis focusing on battery ingestion.

BACKGROUND: Ingestion of foreign bodies remains a frequent reason for presentation to paediatric emergency departments worldwide. Among the variety of objects ingested, button batteries are particularly harmful owing to their electrochemical properties, which can cause extensive injuries if not diagnosed and treated rapidly. International trends show an increasing incidence of button battery ingestion, leading to concern that this pattern may be occurring in South Africa. Limited local data on paediatric foreign body ingestion have been published. OBJECTIVES: To assess battery ingestion rates in a tertiary paediatric hospital. We hypothesised that the incidence has increased, in keeping with international trends. Secondary objectives included describing admission rates, requirements for anaesthesia and surgery, and promoting awareness of the problems associated with battery ingestion. METHODS: We performed a retrospective, descriptive analysis of the Red Cross War Memorial Children's Hospital trauma database, including all children under 13 years of age seen between 1 January 2010 and 31 December 2015 with suspected ingestion of a foreign body. The ward admissions database was then examined to find additional cases in which children were admitted directly. After exclusion of duplicate records, cases were classified by type of foreign body, management, requirement for admission, anaesthesia and surgery. Descriptive statistics were used to analyse the data in comparison with previous studies published from this database. RESULTS: Patient age and gender patterns matched the literature, with a peak incidence in children under 2 years of age. Over the 6-year period, 180 patients presented with food foreign bodies, whereas 497 objects were classified as non-food. After exclusion of misdiagnosed cases, the remaining 462 objects were dominated by coins (44.2%). Batteries were the causative agent in 4.8% (22/462). Although the subtypes of batteries were not reliably recorded, button batteries accounted for at least 64% (14/22). Most children who ingested batteries presented early, but more required admission, anaesthesia and surgery than children who ingested other forms of foreign body. CONCLUSIONS: The study demonstrated that the local incidence of button battery ingestion may be increasing, although data are still limited.Admission, anaesthesia and surgery rates for batteries were higher in this cohort than for all other foreign bodies. As button batteries can mimic coins, with much more dire consequences on ingestion, our ability to expedite diagnosis and management hinges on a high index of suspicion. It is imperative to increase awareness among healthcare workers and parents.

Power Outage: An Ignored Risk Factor for COPD Exacerbations.

BACKGROUND: COPD is the third leading cause of death in the United States, with 16 million Americans currently experiencing difficulty with breathing. Power outages could be life-threatening for those relying on electricity. However, significant gaps remain in understanding the potential impact of power outages on COPD exacerbations. RESEARCH QUESTION: The goal of this study was to determine how power outages affect COPD exacerbations. STUDY DESIGN AND METHODS: Using distributed lag nonlinear models controlling for time-varying confounders, the hospitalization rate during a power outage was compared vs non-outage periods to determine the rate ratio (RR) for COPD and its subtypes at each of 0 to 6 lag days in New York State from 2001 to 2013. Stratified analyses were conducted according to sociodemographic characteristics, season, and clinical severity; changes were investigated in numerous critical medical indicators, including length of stay, hospital cost, the number of comorbidities, and therapeutic procedures between the two periods. RESULTS: The RR of COPD hospitalization following power outages ranged from 1.03 to 1.39 across lag days. The risk was strongest at lag0 and lag1 days and lasted significantly for 7 days. Associations were stronger for the subgroup with acute bronchitis (RR, 1.08-1.69) than for cases of acute exacerbation (RR, 1.03-1.40). Compared with non-outage periods, the outage period was observed to be $4.67 thousand greater in hospital cost and 1.38 greater in the number of comorbidities per case. The average cost (or number of comorbidities) was elevated in all groups stratified according to cost (or number of comorbidities). In contrast, changes in the average length of stay (-0.43 day) and the average number of therapeutic procedures (-0.09) were subtle. INTERPRETATION: Power outages were associated with a significantly elevated rate of COPD hospitalization, as well as greater costs and number of comorbidities. The average cost and number of comorbidities were elevated in all clinical severity groups.

Physical wastage of immunobiological products in the state of Ceará, Brazil, 2014-2016. / Perdas físicas de imunobiológicos no estado do Ceará, 2014-2016.

OBJECTIVE: to describe discarded wasted immunobiological products provided by the National Im-munization Program (PNI) to the State of Ceará between 2014 and 2016, and the costs of discarded doses. METHODS: this was a descriptive study using data from suspect im-munobiological product evaluation forms and data from disposal approval forms. RESULTS: a total of 317 forms were included, 72.0% of which had a disposal approval form, and 160,767 discarded doses were identified, at a total cost of BRL 1,834,604.75; wastage accounted for 0.45%, 0.93% and 0.53% of the total cost of vaccines in 2014, 2015 and 2016, respectively; the main reason for the wastage identified was electric power shortage (54.9%). CONCLUSION: we identified a large number of discarded wasted doses, with high absolute cost; tighter control is necessary, as failures in conservation dynamics may interfere with the supply of immunobiologicals.

Bayesian Network-Based Risk Assessment of Single-Phase Grounding Accidents of Power Transmission Lines.

With the increasing demand for electricity transmission and distribution, single-phase grounding accidents, which cause great economic losses and casualties, have occurred frequently. In this study, a Bayesian network (BN)-based risk assessment model for representing single-phase grounding accidents is proposed to examine accident evolution from causes to potential consequences. The Bayesian network of single-phase grounding accidents includes 21 nodes that take into account the influential factors of environment, management, equipment and human error. The Bow-tie method was employed to build the accident evolution path and then converted to a BN. The BN conditional probability tables are determined with reference to historical accident data and expert opinion obtained by the Delphi method. The probability of a single-phase grounding accident and its potential consequences in normal conditions and three typical accident scenarios are analyzed. We found that "Storm" is the most critical hazard of single-phase grounding, followed by "Aging" and "Icing". This study could quantitatively evaluate the single-phase grounding accident in multi-hazard coupling scenarios and provide technical support for occupational health and safety management of power transmission lines.

The Medical, Public Health, and Emergency Response to the Impact of 2017 Hurricane Irma in Cuba.

In 2017, Cuba was pummeled by Hurricane Irma, one of the strongest and most devastating Atlantic basin hurricanes in history. Twelve of Cuba's 15 provinces and 90 percent of the population were affected, and there was island-wide loss of electrical power. Despite the significant damage, ongoing economic hardships, and the political realities that required Cuba to handle the situation without response support from other nations, Cuba's recovery was swift and effective. Cuba's disaster self-sufficiency and timely response to Hurricane Irma was grounded on 5 decades of disaster planning coupled with ongoing evolution of disaster risk reduction and management strategies. While the central command center, with local dispatch response teams, and mandated citizen engagement are features unique to Cuba's political structure, in this study, we highlight 5 defining attributes of Cuba's hurricane response that can constructively inform the actions of other island and coastal nations vulnerable to Atlantic tropical cyclones. These attributes are: (1) actively learning and incorporating lessons from past disaster events, (2) integrating healthcare and public health professionals on the frontlines of disaster response, (3) proactively engaging the public in disaster preparedness, (4) incorporating technology into disaster risk reduction, and (5) infusing science into risk planning. In terms of hurricane response, as a geopolitically isolated nation, Cuba has experienced particular urgency when it comes to protecting the population and creating resilient infrastructure that can be rapidly reactivated after the onslaught of storms of ever-increasing intensity. This includes planning for worsening future disaster scenarios based on a clear-eyed appreciation of the realities of climate change.

Identifying Driving Factors of Jiangsu's Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method.

The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th "Five-Year Plan" period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004-2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized.

Planning and Design of Low-Power-Consuming Full-Outer-Air-Intake Natural Air-Conditioning System.

A person stays indoors for about 85%∼90% time of his lifetime, and the need for a comfortable indoor environment is getting higher; thus, the air-conditioning dependency becomes intense too. Nowadays, residents focus on both the comfortable living environment and indoor air quality. A closed environment will become hazardous because of carbon dioxide released during respiration and toxic organic solvent vapor released from interior decoration. In order to improve the indoor air quality (IAQ), we must allow outer fresh air into the indoor space and release the dirty air out. But while taking in fresh air, the heat and factory/vehicle exhaust are also introduced. Indoor CO2, HCHO, and VOCs and outer dirty gas threaten human health badly. To solve this problem, we bring up an innovative low-power-consuming full-outer-air-intake natural air-conditioning system that completely separates intake and exhaust air, which is a solution for cross-contamination and makes mass/energy exchange by means of air and water. Design airflow exceeds 300∼500 CFM, steam evaporation mass rate reaches 3.13∼3.88 kg/hr, and heat exchange capacity becomes 1,855∼2,300 kcal/hr. The sensible heat effectiveness is 71%∼112%, and EER exceeds 14.05∼17.42 kcal/W·h. In addition, the system under design can be of positive or negative pressure status according to the user's or work's requirement. It creates a comfortable and healthy living environment by supplying clean and fresh outer ambient air with low power consumption.

A decoupled circuital model methodology for calculating DC currents in AC grids induced by HVDC grounding current.

Large currents are injected into the earth from grounding poles of HVDC systems under monopole ground return mode. The currents change the earth surface potential and result in DC currents in AC systems. This paper proposes a computationally efficient decoupled circuital calculation method for assessing the unwanted DC currents in AC grids. Firstly, the earth resistive network is acquired by simulating the DC grounding current distribution using Finite Element Method (FEM). Secondly, the earth resistive network and AC grid are combined to develop a decoupled circuital model of the overall system. The acquired model is used to calculate the DC currents in AC grids by solving a set of linear equations. The proposed method is computationally more efficient as compared to field-circuit coupled methods. In addition, its accuracy is proved by showing a close agreement between our results and field-circuit coupled model as well as the actual measurements. Finally, in Shanghai area power grid the DC currents are calculated using the proposed technique. Based on these calculations, remedial measures for reducing the DC currents in AC grid are suggested. Our research results indicate that DC currents in AC systems can be reduced by operating the two HVDC projects with opposite polarities.

Calculating deep brain stimulation amplitudes and power consumption by constrained optimization.

OBJECTIVE: Deep brain stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurological and psychiatric conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization. APPROACH: Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings. RESULTS: It was observed that clinical settings were generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values. SIGNIFICANCE: The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered safety constraints based on brain anatomy depend on the patient and further research into it is needed. This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of considering power consumption in DBS optimization, since it increases with the square of the stimuli amplitude and also critically affects battery life through pulse frequency and duty cycle.

The Effect of Electrical Load Shedding on Pediatric Hospital Admissions in South Africa.

BACKGROUND: South Africa faced repeated episodes of temporary power shutdowns, or load shedding, in 2014/2015. The effect of load shedding on children's health is unknown. METHODS: We determined periods of load shedding using Twitter, Facebook, and data from the City of Cape Town. We obtained the number of unscheduled hospital admissions between June 2014 and May 2015 from Red Cross Children's Hospital, Cape Town, and weather data from the South African Weather Service. We used quasi-Poisson regression models to explore the relationship between number of hospital admissions and load shedding, adjusted for season, weather, and past admissions. Based on assumptions about the causal process leading to hospital admissions, we estimated the average treatment effect, that is, the difference in expected number of admissions per day had there been load shedding each day or on any of the preceding 2 days compared with if there had not been any load shedding. RESULTS: We found a 10% increase (95% confidence interval: 4%, 15%) in hospital admissions for days where load shedding was experienced on the same day, or no more than 2 days prior, compared with when there was no load shedding in the past 2 days. The increase was more pronounced during weekdays (12% [7%, 18%] vs. 1% [-9%, 11%]), and for specific diagnoses (e.g., respiratory system: 14% [2%, 26%]). The average treatment effect was estimated as 6.50 (5.12, 7.87) highlighting that about 6 additional admissions a day could be attributed to load shedding. CONCLUSIONS: The association we measured is consistent with our hypothesis that failures of the power infrastructure increase risk to children's health. See video abstract at, http://links.lww.com/EDE/B409.

Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile.

Estimation of remaining capacity is essential for ensuring the safety and reliability of lithium-ion batteries. In actual operation, batteries are seldom fully discharged. For a constant current-constant voltage charging mode, the incomplete discharging process affects not only the initial state but also processed variables of the subsequent charging profile, thereby mainly limiting the applications of many feature-based capacity estimation methods which rely on a whole cycling process. Since the charging information of the constant voltage profile can be completely saved whether the battery is fully discharged or not, a geometrical feature of the constant voltage charging profile is extracted to be a new aging feature of lithium-ion batteries under the incomplete discharging situation in this work. By introducing the quantum computing theory into the classical machine learning technique, an integrated quantum particle swarm optimization-based support vector regression estimation framework, as well as its application to characterize the relationship between extracted feature and battery remaining capacity, are presented and illustrated in detail. With the lithium-ion battery data provided by NASA, experiment and comparison results demonstrate the effectiveness, accuracy, and superiority of the proposed battery capacity estimation framework for the not entirely discharged condition.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

An energy-efficient failure detector for vehicular cloud computing.

Failure detectors are one of the fundamental components for maintaining the high availability of vehicular cloud computing. In vehicular cloud computing, lots of RSUs are deployed along the road to improve the connectivity. Many of them are equipped with solar battery due to the unavailability or excess expense of wired electrical power. So it is important to reduce the battery consumption of RSU. However, the existing failure detection algorithms are not designed to save battery consumption RSU. To solve this problem, a new energy-efficient failure detector 2E-FD has been proposed specifically for vehicular cloud computing. 2E-FD does not only provide acceptable failure detection service, but also saves the battery consumption of RSU. Through the comparative experiments, the results show that our failure detector has better performance in terms of speed, accuracy and battery consumption.

Hierarchical and hybrid energy storage devices in data centers: Architecture, control and provisioning.

Recently, a new approach has been introduced that leverages and over-provisions energy storage devices (ESDs) in data centers for performing power capping and facilitating capex/opex reductions, without performance overhead. To fully realize the potential benefits of the hierarchical ESD structure, we propose a comprehensive design, control, and provisioning framework including (i) designing power delivery architecture supporting hierarchical ESD structure and hybrid ESDs for some levels, as well as (ii) control and provisioning of the hierarchical ESD structure including run-time ESD charging/discharging control and design-time determination of ESD types, homogeneous/hybrid options, ESD provisioning at each level. Experiments have been conducted using real Google data center workloads based on realistic data center specifications.