Peak Consumption Research Articles

Feasibility Study of Integrating Renewable Energy Generation System in Sark Island to Reduce Energy Generation Cost and CO2 Emissions

The island of Sark, located in the English Channel, has endured an electricity distribution crisis for the past few years, resulting in high electricity costs almost six times higher than UK mainland energy prices. This article is focused on a methodology for finding the best renewable energy system with the lowest levelized cost of energy (LCOE) in comparison to the current energy rate of 66 p/kWh. Three different main cases of study have been compared in performance for different levels of renewable energy integration and energy storage, evaluating the estimated size of the system, installation cost and CO2 emissions. The results, which depend on the assumptions outlined, show that Case 2 renewable energy generation system is the most suitable in terms of reduction of CO2 emissions and expected earnings from a lower LCOE. Uncertainty in the results could be minimized if actual data from the island is made available by following the same methodology to find the best solution to the island’s current energy generation problem. Due to non-available data for the load profiles and wind velocity a set of assumption were required to be implemented. As such, two different load profiles were selected—one with a peak of energy consumption in winter and the other with a summer peak.

Heat exposure and global air conditioning

Air conditioning adoption is increasing dramatically worldwide as incomes rise and average temperatures go up. Using daily temperature data from 14,500 weather stations, we rank 219 countries and 1,692 cities based on a widely used measure of cooling demand called total cooling degree day exposure. India, China, Indonesia, Nigeria, Pakistan, Brazil, Bangladesh and the Philippines all have more total cooling degree day exposure than the United States—a country that uses 400 terawatt-hours of electricity annually for air conditioning. Adoption of air conditioning is increasing globally, leading to peaks in electricity consumption and related environmental concerns. Compiling recent data on population and temperature, this study ranks 219 countries and 1,692 cities based on a measure of cooling demand to improve our understanding of future trends.

Economic model predictive control of space heating and dynamic solar shading

Dynamic solar shading devices are an effective mean to avoid overheating in buildings due to excessive solar heat gains. They can also be used to affect the heat balance of buildings; they can be on e.g. during nighttime to reduce space heating or retracted to increase night cooling. The shading control system can utilize this feature to obtain energy cost savings and reduce peak consumption. However, it is difficult to define optimal rule-based control strategies that minimize overheating and energy costs simultaneously. In this paper, we therefore propose an economic model predictive control (E-MPC) scheme for space heating where solar shading is included as an additional control variable. The proposed scheme employs black-box models, as they are ‘cheap’ to create from data; this is important for a widespread deployment of E-MPC in practice. The study was based on co-simulation experiments where EnergyPlus models represented the actual building and it was hereby demonstrated that the proposed E-MPC outperformed two rule-based controllers with respect to reducing overheating, energy costs and peak consumption. Shading also turned out to be valuable for north facing rooms where it reduced costs and peak consumptions compared to an MPC without shading. The energy-related benefits of the proposed E-MPC scheme can be regarded as ‘an added value’ that can be used for economic justification of an investment in dynamic solar shading devices.

A 24-MB Embedded Flash System Based on 28-nm SG-MONOS Featuring 240-MHz Read Operations and Robust Over-the-Air Software Update for Automotive Applications

This letter presents an embedded flash (eFlash) system based on 28-nm split-gate MONOS (SG-MONOS) with high- ${k}$ metal gate CMOS process technology for automotive applications. It contains the world’s largest 24-MB memory capacity (4-MB code flash macro (CF) $\times6$ ) and achieves 240-MHz random read access at Tj from −40 °C to 170 °C, and read throughput reaches 46 GB/s by reading 6 CFs in parallel. The peak current consumption during over-the-air software update (OTA) is reduced by 55% in low-noise program mode. A high-speed program mode for factory program and test achieves 6.5-MB/s program throughput by programming 2 CFs in parallel. In OTA, the eFlash system realizes ~1 ms software switching and robustness against unintentional OTA termination.

An Economical Energy Management Strategy for Viable Microgrid Modes

In the last couple of decades, numerous energy management strategies have been devised to mitigate the effects of greenhouse gas emission, hence introducing the concept of microgrids. In a microgrid, distributed energy generators are used. Microgrid enables a point which ameliorates in exchanging power with the main grid during different times of day. Based on the system constraints, in this work, we aim to efficiently minimize the operating cost of the microgrid and shave the power consumption peaks. For this purpose, we introduce an improved binary bat (iBBat) algorithm which helps to schedule the load demand of smart homes and energy generation from distributed generator of microgrid to the load demand and supply. The proposed energy management algorithm is applied to both grid-connected and islanded modes of the microgrid. The constraints imposed on the algorithm ensure that the load of electricity consumer does not escalate during peak hours. The simulation results are compared with BBat and binary flower pollination algorithm, which validate that the iBBat reflects substantial reduction in operating cost of microgrid. Moreover, results also show a phenomenal reduction in the peak-to-average ratio of load demand from main the main grid.

Trends in Antiarrhythmic Drug Use in Denmark Over 19 Years

Antiarrhythmic drugs are widely used in the treatment of supraventricular and ventricular arrhythmias. Yet, nationwide long-term utilization trends remain unexplored. We examined 19-year trends in the use of antiarrhythmic drugs in Denmark. Using nationwide prescription data, we obtained information on hospital and primary healthcare use of Class I-V antiarrhythmic drugs from 1999 to 2017. Data was stratified according to sex and age groups. From 1999 to 2017, the total use of antiarrhythmic drugs per 1000 inhabitants/day increased 16% from 36.3 in 1999 to 41.9 in 2017 with peak consumption in 2008 (46.5). In primary healthcare, Class I usage decreased from 0.8 to 0.5 defined daily doses (DDD) per 1000 inhabitants/day, driven by a decreased prescription rate of propafenone (0.4 to 0.1) whereas prescription of flecainide (Class Ic) increased from 0.3 to 0.4 DDD per 1000 inhabitants/day (mainly in men of age 45 to 79 years). Class II usage increased from 15.4 to 33.6 DDD per 1000 inhabitants/day. Class III usage decreased from 2.6 to 1.1 DDD per 1000 inhabitants/day, reflecting reduced prescription rate of sotalol (2.1 to 0.2) whereas amiodarone increased from 0.5 to 0.9 (mainly due to increased prescription among men and women >80 years). Class IV usage declined from 8.6 to 2.8 DDD per 1000 inhabitants/day. Finally, Class V drugs decreased 8.1 to 3.3 DDD per 1000 inhabitants/day. In conclusion, during the past 2 decades considerable changes in prescription rate of antiarrhythmic drugs have occurred, most notably a reduction in sotalol and increased usage of flecainide, Class II drugs, and amiodarone.

Compression ratio energy and exergy analysis of a developed Brayton-based power cycle employing CAES and ORC

Energy consumption growth in the world is one of the primary concerns of researchers in the energy fields. Providing demanded power, especially in peak consumption times besides less emission production, is always been the goal of power plants designers. Utilizing auxiliary devices or cycles such as compressed air energy system or organic Rankine cycle can help them to achieve their goal. However, the performance of these auxiliary instruments should be evaluated having the best design achieving the target. In this research, employing compressed air energy besides utilizing an organic Rankine cycle is proposed for improving the performance of a Brayton power cycle; moreover, optimum operating condition for compression ratio of each cycle is found with energy–exergy analysis. Various working fluids for organic Rankine cycle are explored, and the best conditions are introduced based on energy and exergy parameters, namely the first- and second-law efficiencies, power and exergy destruction. Results show that the optimum compression ratio of Brayton cycle is 7.5 for all considered organic fluids, and optimum pressure ratio of organic Rankine cycle is 5.5 for isopentane and n-pentane. Isopentane has the least exergy destruction, while the maximum first- and second-law efficiencies are achieved by R123.

Comparison of methodologies for generation of future weather data for building thermal energy simulation

In the last decade, building energy simulation (BES) became a central component in building energy systems’ design and optimization. For each building location, BES requires one year of hourly weather data. Most buildings are designed to last 50+ years, consequently, the building design phase should include BES with future weather files considering climate change. This paper presents a comparative study of two methods to produce future climate hourly data files for BES: Morphing and typical meteorological year of future climate (F-TMY). The study uses data from a high-resolution (9 km) regional climate atmospheric model simulation of Iberia, spanning 10 years of historical and future hourly data. This study compares both methods by analyzing anomalies in air temperature, and the impact in BES predictions of annual and peak energy consumption for space heating, cooling and ventilation in 4 buildings. Additionally, this study performs a sensitivity analysis of morphing method. The analysis shows that F-TMY is representative of the multi-year simulation for BES applications. A high-quality Morphed TMY weather file has a similar performance compared to F-TMY (average difference: 8% versus 7%). Morphing based on different baseline climates, low-grid resolution and/or outdated climate projections leads to BES average differences of 16%-20%.

Multi-agent reinforcement learning for adaptive demand response in smart cities

Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such that they react to price signals and coordinate with each other to shave the peaks of electricity consumption. We explore the use of multi-agent deep deterministic policy gradient (DDPG), an adaptive and model-free reinforcement learning control algorithm, for coordination of several buildings in a demand response scenario. We conduct our experiment in a simulated environment with 10 buildings.

Designing an additional freshwater source infrastructure to ensure the environmental sustainability of coastal areas

The work is based on the search for additional sources of supply of coastal areas with fresh water based on resource-saving technologies and the fundamental rationale for the design and application of the proposed structures in real time. The article identifies potential areas for the placement of aboveground and underground reservoirs of freshwater resources in the coastal areas of the Krasnodar region. The article attempts to develop an innovative project for the seasonal freshwater basin, which allows accumulating the necessary volume during the peak consumption season to meet the needs of the population. The emphasis for placing the pool is made in the mountain gorges, taking into account the geographical features of the landscape of the region. The results of the analytical analysis will subsequently help to optimize constructive solutions, with the aim of further practical implementation of reasonable and proposed measures.

Dilution effect of the building area on energy intensity in urban residential buildings

Urban residential buildings make large contributions to energy consumption. Energy consumption per square meter is most widely used to measure energy efficiency in urban residential buildings. This study aims to explore whether it is an appropriate indicator. An extended STIRPAT model was used based on the survey data from 867 households. Here we present that building area per household has a dilution effect on energy consumption per square meter. Neglecting this dilution effect leads to a significant overestimation of the effectiveness of building energy savings standards. Further analysis suggests that the peak of energy consumption per square meter in China’s urban residential buildings occurred in 2012 when accounting for the dilution effect, which is 11 years later than it would have occurred without considering the dilution effect. Overall, overlooking the dilution effect may lead to misleading judgments of crucial energy-saving policy tools, as well as the ongoing trend of residential energy consumption in China.

Sustained perfusion of revascularized bioengineered livers heterotopically transplanted into immunosuppressed pigs.

Implanted bioengineered livers have not exceeded three days of continuous perfusion. Here, we show that decellularized whole porcine livers revascularized with human umbilical endothelial cells and implanted heterotopically into immunosuppressed pigs whose spleen has been removed can sustain perfusion for up to 15 days. We identified peak glucose consumption rate as a main predictor of the patency of the revascularized bioengineered livers (rBELs). On heterotopic implantation of the rBELs into pigs in the absence of anticoagulation therapy led to sustained perfusion for 3 days, followed by significant immune responses directed against the human endothelial cells. A 10-day steroid-based immunosuppression protocol and a splenectomy at time of rBEL implantation reduced the immune responses and resulted in continuous perfusion of the rBELs for over two weeks. We also show that the human endothelial cells in the perfused rBELs colonize the liver sinusoids and express sinusoidal endothelial markers similar to those in normal liver tissue. Revascularized liver scaffolds that can maintain blood perfusion at physiological pressures might eventually help overcome the chronic shortage of transplantable human livers.

Comparison of Direct, Selective Oxidation of Methane by N2O over Fe-ZSM-5, Fe-Beta, and Fe-FER Catalysts

In this study, the activity and selectivity to Fe-ZSM-5, Fe-Beta, and Fe-FER catalysts were studied at 350 °C for the partial oxidation of methane using N2O as oxidant. Spectroscopic and solid characterization tools including H2-TPR, in situ IR, N2 gas adsorption, CO chemisorption, and TGA-MS were used in the investigation. Ammonia adsorption data suggested that among the studied zeolites, H-FER zeolite contained the highest concentration of framework Al atoms, which are essential for the formation of active extra-framework Fe species. The oxidation state and redox active species of Fe-ZSM-5, Fe-Beta, and Fe-FER catalysts were studied by H2-TPR, which disclosed the presence of a unique reduction peak (originating from N2O pretreatment) centered at approximately 235 °C over the samples. The hydrogen consumption peak was more prominent over Fe-FER than other catalysts, demonstrating that the Fe-FER catalyst contained more active sites for N2O conversion in comparison to Fe-Beta and Fe-ZSM-5 catalysts. For I...

A Trustworthy Communication Hub for Cyber-Physical Systems

The motivation for this research arises from the challenges in the trustworthy communications related operation of cyber-physical systems (CPS), especially in the energy and mobility domains. The increasing amount of distributed energy resources (DERs) of prosumers and electric vehicles requires new ways for CPS communications to enable information exchanges for smart operation in peak consumption hours and balancing power levels in the energy grids in order to lower the energy cost. The huge number of mobile appliances and the related service providers do not serve properly the privacy of the owners, owing to the vertical silo type of operating model in industries. As the results of this research, we provide a trustworthy communication hub for CPS (CPS hub) for solving the challenges related to trustworthy communications between physical resources owned by different stakeholders. The CPS hub realizes the communication spaces concept, and enables combined trust and communications processes when dynamic resources owned by different stakeholders are exchanging information. The evaluations showed that the provided CPS hub enable information exchanges between distributed energy resources of different stakeholders, so that they can join the aggregation process for more flexible and efficient resource usage in energy markets. The CPS hub enable interaction between heterogeneous physical devices of multiple stakeholders to exchange information so that, for example, authorities can see the situation in the emergency area and, simultaneously, the policies of the owners can be taken into concern. Despite limited evaluation scenarios, it is shown that consideration of the ownership issues in the trustworthy communication for information exchanges between heterogeneous physical resources (devices) is possible and feasible. Several future research items, such as, for example, scalability; real-time and streams based operation; as well as consideration of the security, privacy, trust, and safety challenges, were detected. However, the evaluations showed that the constructed CPS hub contribute a set of very essential technical enablers for future smart CPS systems and create strong a basis for such future research towards a future smart society.

An IoT-Based Data Driven Precooling Solution for Electricity Cost Savings in Commercial Buildings

The buildings sector is one of the largest energy consuming entities today, accounting for about 40% of global energy consumption. Alongside, electricity prices are increasing in several nations worldwide, putting pressure on facility managers to reduce the expense incurred in operating their buildings. In this paper, we focus on building heating, ventilation, and air conditioning and make the following contributions. First, inspired by the deployment of Internet of Things in buildings, we propose a data driven gray box model for zone thermal dynamics and use it to develop an optimization framework for precooling a building. The goal is to determine a precooling strategy that not only shifts the peak air conditioning power to low electricity tariff regimes, but also reduces the peak power, energy consumption, and electricity costs associated with cooling a building. Second, the optimal precooling problem turns out to be nonlinear. To enable ease of use of our framework in the real-world without undermining its efficacy, we develop a linearized version of the problem and show that it has comparable performance to the original formulation. Third, we quantify the benefits of the precooling optimization framework by applying it to building management system data, across multiple days, obtained from a large office building in Australia. The evaluations reveal that optimal precooling lowers the peak power, energy consumption, and electricity costs for cooling the building by up to 30% while ensuring that the thermal comfort of the occupants is maintained. We conclude by describing two applications of the proposed optimization framework for use in developing countries. Our precooling solution incurs no capital expense and permits facility managers to take decisions from a data driven point of view for improving the energy efficiency of their buildings.

An innovative energy management framework for cooperative operation management of electricity and natural gas demands

Introduction of the power to gas and combined heat and power technologies, has led to remarkable interdependency between electrical and gas systems. By taking advantage of this interdependency, cleaner and more efficient energy management systems could be implemented. This paper attempts to establish an innovative energy management framework which takes advantage of this interdependency and devices like power to gas and combined heat and power to simultaneously smoothen electricity and natural gas demand profiles for a year ahead. The method outperforms this task using the electricity profile valleys to reduce peak natural gas consumption and using natural gas profile valleys to shave electricity consumption peak. In this way, the stress on both networks for supplying demand in peak periods is released. The proposed method is able to achieve demand smoothness and cost reduction objectives considering penalty factors for demand variance. For this purpose, multiple integrated energy hubs are used to simulate the energy consumption of an area. The designed mixed integer linear model is handled by General Algebraic Modeling Software and CPLEX solver. The results demonstrate that by applying the proposed method, the system is able to save 16.92% in the energy cost and decrease electricity and natural gas demand standard deviations by 8.34% and 66.64%, respectively.

Assessing the impact of residential load profile changes on electricity distribution utility revenues under alternative rate structures

Decentralized energy resources (DERs) may affect the revenues of distribution utilities, depending on their rate structure. Based on a typical household load profile, we model the impacts of rooftop photovoltaics (PV) and electric vehicles (EV) on revenues of electricity distribution utilities under alternative rate structures. Our case study finds that EVs and PVs decouple peak load and energy consumption, which is problematic with volumetric rates for distribution utilities. The optimization of EV charging load can create a new peak, rendering time-of-use (ToU) counterproductive. Our findings suggest that charges to recover capacity-related costs help shield distribution utilities from undesirable revenue impacts.

Has China's coal consumption actually reached its peak? National and regional analysis considering cross-sectional dependence and heterogeneity

To investigate whether China's coal consumption has actually peaked, this study tests the national and regional coal Kuznets curve (CKC) hypothesis by using a panel dataset of 30 provinces covering 2000 to 2016. To fully capture the trends of coal consumption at the national, regional, and provincial levels, this study proposes a novel regional division method based on coal dependence and economic level. Considering the potential cross-sectional dependence and slope homogeneity, the newly developed methods allowing for heterogeneous slope coefficients are employed. The whole panel and subpanel results validate the CKC hypothesis for China, and province-specific results are mixed. The subpanel results reveal that only in the coal-dependent developing region has the peak of coal consumption not been reached, and for other regions, coal consumption displays a downward trend along with gross domestic product (GDP) increases. Furthermore, the province-specific results suggest that coal consumption will continue to increase slightly in certain provinces. This study implies that to reduce coal consumption, the coal-dependent developing region and provinces with a future turning point should act with great urgency to achieve a balance of economic growth and environmental responsibility. In addition, policymakers formulating coal consumption reduction policy in China must consider the remarkable differences across regions and provinces.

The Influence of Argon, Air and Hydrogen Gas on Thermal Conductivity of Gas Diffusion Layers and Temperature Gradients in PEMFCS

Many renewable electricity sources (e.g., wind and solar) are intermittent, and in many cases peak production does not align with peak consumption. One way to store this excess renewable energy is by means of hydrogen gas through the process of water electrolysis. The stored energy in hydrogen gas can be recovered with a fuel cell when needed. [1] This way fuel cells can be an energy buffer and help to stabilize power systems based on renewable energy sources. A proton exchange membrane fuel cell (PEMFC) consists of several components. The membrane electrode assembly (MEA) is sandwiched between gas diffusion layers (GDLs) on each side. GDLs in fuel cells have to satisfy a range of conflicting demands. They must deliver good mass transport, but also good electrical and thermal transport. In this work the thermal conductivity of two commercial GDLs was measured with either hydrogen, air, or argon present inside their pores. The results show an increase of up to 19% with regard to the thermal conductivity for the Freudenberg H1410 GDL with hydrogen present in the pores as opposed to measurements with air present. The thermal conductivity in the Sigracet 10BA GDL was also enhanced, with an increase of 15% with hydrogen present in the pores. This correlates with the thermal conductivity of hydrogen gas, which is higher than that of air. Furthermore, the results suggest that the GDL materials have a lower thermal conductivity with argon gas present in the pores. The thermal conductivity for Freudenberg H1410 increased from 0.119 ± 0.011 WK-1m-1 for air to a thermal conductivity of 0.140 ± 0.015 WK-1m-1 for hydrogen gas at a compaction pressure of 10 bar. The thermal conductivity of Sigracet 10BA increased from 0.30 ± 0.05 WK-1m-1 for air to a thermal conductivity of 0.32 ± 0.03 WK-1m-1 for hydrogen gas at a compaction pressure of 10 bar. These results suggest that the gas present in the pores has a significant influence on the thermal conductivity of the GDL. Additionally, a 2D thermal model has been constructed to represent the impact of the results on the temperature distribution inside a fuel cell, see figure 1. The considerable temperature gradients inside the single cells need to be considered when assessing lifetime and degradation effects [1-4]. Judging by the scarcity of data on this in the literature this is not the case as of today. Figure 1: PEMFC land temperature profiles saturated with air, argon, or hydrogen on the anode side. The dashed vertical lines represent the border between different materials in the MEA References [1] O. S. Burheim, Engineering Energy Storage, 1st edition, Academic Press, 2017. [2] R. Bock, A.D. Shum, X. Xiao, H. Karoliussen, F. Seland, I.V. Zenyuk and O.S. Burheim, Thermal conductivity and compaction of GDL-MPL interfacial composite material, J. Electrochem. Soc. 2018 165(7): F514-F525 [3] R. Bock, H. Karoliussen, B. G. Pollet, M. Secanell, F. Seland, D. Stanier and O. S. Burheim, The influence of graphitization on the thermal conductivity of catalyst layers and temperature gradients in proton exchange membrane fuel cells, International Journal of Hydrogen Energy, In-press 2018 [4] R. Bock, A. Shum, T. Khoza, F. Seland, N. Hussain, I. V Zenyuk and O. S. Burheim Experimental Study of Thermal Conductivity and Compression Measurements of the GDL-MPL Interfacial Composite Region, ECS Trans. 2016 75(14): 189-199 Figure 1

Bitcoin mining: A global review of energy and power demand

After its introduction in 2008, increasing Bitcoin prices and a booming number of other cryptocurrencies lead to a growing discussion of how much energy is consumed during the production of these currencies. Being the most expensive and the most popular cryptocurrency, both the business world and the research community have started to question the energy intensity of Bitcoin mining. This paper only focuses on computational power demand during the proof-of-work process rather than estimating the whole energy intensity of mining. We make use of 160GB of Bitcoin blockchain data to estimate the energy consumption and power demand of Bitcoin mining. We considered the performance of 269 different hardware models (CPU, GPU, FPGA, and ASIC). For estimations, we defined two metrics, namely; minimum consumption and maximum consumption. The targeted time span for the analysis was from 3 January 2009 to 5 June 2018. We show that the historical peak of power consumption of Bitcoin mining took place during the bi-weekly period commencing on 18 December 2017 with a demand of between 1.3 and 14.8 GW. This maximum demand figure was between the installed capacities of Finland (∼16 GW) and Denmark (∼14 GW). We also show that, during June 2018, energy consumption of Bitcoin mining from difficulty recalculation was between 15.47 and 50.24 TWh per year.