Peak Energy Consumption Research Articles

Distributed real-time demand response for energy management scheduling in smart grid

Demand response is a promising technology to cope with the ever-increasing peak energy consumption in smart grid. For the smart home energy management system, it may choose to charge the battery when the electricity price is low, and discharge the battery to supply the home load when the electricity price is high. Under this circumstance, the temporally and spatially coupled constraints coexist in the energy demand, battery charging and discharging energy, and energy supply. The coupled constraints make the demand response problem more challenging than the existing ones and thus the existing distributed algorithms cannot be employed to find the optimal solution of the problem. In this paper, we focus on the real-time two-way communications between utility company and multiple residential users, and each user is regarded as a smart home energy management system. A distributed real-time algorithm is proposed to find the optimal energy management scheduling scheme for each user and utility company to maximize the social welfare. To overcome the obstacle brought by the temporally and spatially coupled constraints, dual decomposition technique is employed and the primal problem is decoupled into several independent subproblems which can be solved in a distributed way by each user and utility company without revealing or exchanging their private information. Simulation results are provided to demonstrate that the proposed distributed algorithm can bring potential benefits to the society.

Peak Power Load and Energy Costs Using the Example of the Startup and Idling of a Grinding Machine

Countries worldwide are experiencing changes in their electricity generating portfolio, from base load sources – coal or nuclear – to more sustainable but more volatile and intermittent sources – wind or photovoltaics. Industrial, high wattage consumers with machines that have frequent short-term peak power loads can cause grid failure when a demand peak coincides with an available power lull. These demand peaks are fulfilled by fossil-fuel-based peaking power plants. To avoid additional energy and cost expenditures of peaking power plants and reduce the lifecycle impact of production, peak demand needs to be mitigated. A procedure is proposed to reduce the peak loads of a grinding machine by controlling machine parameters. First, spindle energy and power data is measured while varying ramp up time to evaluate peak load and energy consumption trade-offs. Then, a model is developed with three different objectives: minimize energy, peak power, or cost.

Trend appraisal of Nigeria's energy sector and its implication on living standards

Utility capacity and energy consumption in Nigeria over a 10-year period 2005-2014 were analysed. Improvement was recorded from 2010 through 2014 for yearly and monthly peak load demand and energy consumption. The coming on stream of some NIPP plants, reduction in vandalism of gas infrastructure following the presidential amnesty to Niger Delta militant in August 2009, the launching of power sector road map in 2010 which saw to the eventual privatisation of power generation and distribution and mean monthly reservoir inflow for Kainji Dam respectively were seen to influence the yearly and monthly energy distribution. By regression technique, per capita electricity consumption (EC/capita) was used to predict to a reasonable degree of accuracy the living standard indicators-infrastructure availability and purchasing power parity. The implication of this therefore is that for there to be a positive shift in the economic and social well-being of the populace attention must be given to the provision of functional energy infrastructure.

Grid-Tied Photovoltaic and Battery Storage Systems with Malaysian Electricity Tariff—A Review on Maximum Demand Shaving

Under the current energy sector framework of electricity tariff in Malaysia, commercial and industrial customers are required to pay the maximum demand (MD) charge apart from the net consumption charges every month. The maximum demand charge will contribute up to 20% of the electricity bill, and will hence result in commercial and industrial customers focussing on alternative energy supply to minimize the billing cost. This paper aims to review the technical assessment methods of a grid-connected solar photovoltaic (PV)—battery storage system—with respect to maximum demand shaving. An effective battery storage system can provide the extra energy needed during the peak energy consumption periods, as well as when renewable energy (RE) sources go offline. Based on the reviews, maximum demand shaving with good Return-of-Investment (ROI) can be achieved by considering the actual load profile, technical, and economic aspects of the solar PV-battery system and the Malaysian electricity tariff for commercial and industrial customers.

Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

Optimizing the power and energy consumption of powered prosthetic ankles with series and parallel elasticity

Several works have shown that series and parallel elasticity can reduce peak power and energy consumption in prosthetic ankles. Setting the right stiffness of the elastic elements is essential to unlock this potential. In this work, we perform a thorough optimization of series and parallel elastic elements for a prosthetic ankle driven by a geared DC motor. Through simulation, we study the effect of drivetrain limitations and compare different mechanical and electrical optimization objectives. The results highlight the importance of selecting a motor and gearbox in an early stage of the design process. Drivetrain inertia causes peaks in electrical power in the swing phase, which would go unnoticed in an optimization based solely on mechanical power. Furthermore, limitations of the drivetrain and controller reduce the range of applicable springs. This has a direct influence on the optimized spring stiffness values, which, as a result, are different from other works. Overall, the results suggest that, by integrating motor selection into the early stages of the design process, designs can be made lighter, more compact and more efficient.

An Agent-Based Model to Project China’s Energy Consumption and Carbon Emission Peaks at Multiple Levels

To assess whether China’s emissions will peak around 2030, we forecast energy consumption and carbon emissions in China. We use an agent-based model driven by enterprises’ innovation. Results show some differences in both energy consumption peaks and carbon emission peaks when we compare trends at different levels. We find that carbon emissions and energy consumption will peak in 2027 and 2028, respectively. However, the primary, secondary, and tertiary industries will reach energy consumption in different years: 2023, 2029, and 2022, respectively, and reach carbon emission peaks in 2022, 2028, and 2022, respectively. At the sectoral level, we find a wider range of energy consumption peaks and carbon emission peaks. Peak energy consumption occurs between 2020 and 2034, and peak carbon emissions between 2020 and 2032. Commercial and catering businesses, utilities and resident services, and finance and insurance achieve peak energy consumption and carbon emissions earliest in 2020, while building materials and other non-metallic mineral products manufacturing and metal products manufacturing are the two latest sectors to reach peak energy consumption and emissions, in 2034 and 2032, respectively.

Use of technology to facilitate physical activity in children with autism spectrum disorders: A pilot study

Deficits in social behavior and communication skills are correlated with reduced gross motor skills in children with autism spectrum disorders (ASD). The ExerciseBuddy application (EB app) was designed to communicate these motor skills to those with ASD and integrates evidence-based practices such as visual support and video modeling supported by The National Professional Development Center on Autism Spectrum Disorders. The purpose of this study was to determine the effectiveness of the EB app in facilitating increased physiologic responses to physical activity via a continuous measurement of energy expenditure and heart rate versus practice-style teaching methods in children with ASD. Six children, ages 5 to 10years, diagnosed with ASD were recruited. Each participant performed a variety of locomotor or object control skills as defined by the Test of Gross Motor Development-2 once per week for 4weeks. Motor skills were communicated and demonstrated using either practice-style teaching methods or the instructional section of the EB app. Energy expenditure and heart rate were measured continuously during each 12-minute session. A Wilcoxon signed-rank test was performed to assess any differences between the use of the app and practice-style teaching methods. The use of the EB app elicited greater values for peak energy expenditure (p=0.043) and peak heart rate response (p=0.028) while performing locomotor skills but no differences were observed while performing object control skills. Similarities were observed with average physiologic responses between the use of the EB app and practice-style teaching methods. The use of the EB app may allow for a greater peak physiologic response during more dynamic movements and a similar average cardiovascular and metabolic response when compared to practice-style teaching methods in children with ASD.

IGreen: green scheduling for peak demand minimization

Home owners are typically charged differently when they consume power at different periods within a day. Specifically, they are charged more during peak periods. Thus, in this paper, we explore how scheduling algorithms can be designed to minimize the peak energy consumption of a group of homes served by the same substation. We assume that a set of demand/response switches are deployed at a group of homes to control the activities of different appliances such as air conditioners or electric water heaters in these homes. Given a set of appliances, each appliance is associated with its instantaneous power consumption and duration, our objective is to decide when to activate different appliances in order to reduce the peak power consumption. This scheduling problem is shown to be NP-Hard. To tackle this problem, we propose a set of appliance scheduling algorithms under both offline and online settings. For the offline setting, we propose a constant ratio approximation algorithm (with approximation ratio \(\frac{1+\sqrt{5}}{2}+1\)). For the online setting, we adopt a greedy algorithm whose competitive ratio is also bounded. We conduct extensive simulations using real-life appliance energy consumption data trace to evaluate the performance of our algorithms. Extensive evaluations show that our schedulers significantly reduce the peak demand when compared with several existing heuristics.

Uncertainty and sensitivity analysis of spatio-temporal occupant behaviors on residential building energy usage utilizing Karhunen-Loève expansion

Occupant behaviors (e.g., switch/dim lights, adjust thermostat, etc.) have a big impact on building energy consumption. However, the occupant behavior in buildings is not well understood and oversimplified. In addition, a majority of current researches just focused on the occupant number variation and ignored the temporal variation (e.g., the occupant presence duration and arrive/leave time). This paper aims to use a whole building simulation program (i.e., EnergyPlus) to investigate the impacts of spatio-temporal occupant behaviors on residential building energy usage utilizing a simple static schedule-based behavior model through uncertainty analysis (UA) and sensitivity analysis (SA).A residential prototype building EnergyPlus model with two thermal zones from the U.S. Department of Energy is used for the case study. The chosen building type is a single-family detached house with three locations in U.S. 3000 EnergyPlus Monte Carlo simulations using a Karhunen-Loève (KL) expansion based sampling method for each location is conducted. Occupancy behaviors related parameters including thermostat setpoints, occupancy and lighting schedules are randomly perturbed using a KL truncated expansion, which results in a 3.76% overall average coefficient of variation of selected input parameters. UA with such perturbation shows about a 4% impact on the HVAC annual and peak energy consumption, Annual building and HVAC electricity consumption, and hourly peak building and HVAC electricity consumption are selected for the study of impacts of the spatio-temporal occupant behaviors on building energy usage using Sobol and PEAR sensitivity index. SA Results show that thermostats setpoints have big influences on residential building energy usage.

Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables

Plug-in electric vehicle penetration is increasing due to technical advancements and environmental concerns. Along with residential plug-in electric vehicle charging, the public charging infrastructure is much needed to reduce plug-in electric vehicles' range anxiety and foster their adoption. Renewable energy and demand management programs are considered viable options that can reduce the impacts of widespread plug-in electric vehicle penetration on the electric grid. This research studies the impacts of plug-in electric vehicle direct current fast charging on a simulated standalone retail building's peak demand and energy consumption, and presents the ability of renewable energy and demand management options to reduce their impacts. Additionally, insights into a public charge station usage are presented by monitoring different types of plug-in electric vehicle charge behaviors at a retail site. Research findings indicate that demand management of building end-use loads along with the use of solar photovoltaic can contribute to absorbing plug-in electric vehicle penetration at the building level ranging from the average of 7% for the demand management option alone to an average of 38% for the combination of demand management and solar photovoltaic, and contributing to shifting building peak demand to late evening hours.

Daily Energy Expenditure and Its Relation to Health Care Costs in Patients Undergoing Ambulatory Electrocardiographic Monitoring

Our increasingly sedentary lifestyle is associated with a heightened risk of obesity, diabetes, heart disease, and cardiovascular mortality. Using the recently developed heart rate index formula in 843 patients (mean ± SD age 62.3 ± 15.7years) who underwent 24-hour ambulatory electrocardiographic (ECG) monitoring, we estimated average and peak daily energy expenditure, expressed as metabolic equivalents (METs), and related these data to subsequent hospital encounters and health care costs. In this cohort, estimated daily average and peak METs were 1.7 ± 0.7 and 5.5 ± 2.1, respectively. Patients who achieved daily bouts of peak energy expenditure ≥5 METs had fewer hospital encounters (p= 0.006) and median health care costs that were nearly 50% lower (p <0.001) than their counterparts who attained <5 METs. In patients whose body mass index was ≥30kg/m2, there were significant differences in health care costs depending on whether they achieved <5 or ≥5 METs estimated by ambulatory ECG monitoring (p= 0.005). Interestingly, patients who achieved ≥5 METs had lower and no significant difference in their health care costs, regardless of their body mass index (p= 0.46). Patients with previous percutaneous coronary intervention who achieved ≥5 METs had lower health care costs (p= 0.044) and fewer hospital encounters (p= 0.004) than those who achieved <5 METs. In conclusion, average and peak daily energy expenditures estimated from ambulatory ECG monitoring may provide useful information regarding health care utilization in patients with and without previous percutaneous coronary intervention, irrespective of body habitus. Our findings are the first to link lower intensities of peak daily energy expenditure, estimated from ambulatory ECG monitoring, with increased health care utilization.

Building thermal design for solar photovoltaic air-conditioning in Australian climates

Residential buildings should be places in which their occupants feel comfortable and content. In an energy conscious world the challenge of offering improved indoor comfort is contrast with the desire to minimise grid energy consumption (both from an efficient distribution perspective and to minimise fossil fuel consumption) as well as the associated cost. Occupant behaviour is one of the key drivers of energy consumption, and in particular peak energy consumption, which is largely the result of air-conditioner usage. Selecting the most cost effective building designs or modifications that met these dual goals regardless of occupant behaviour is a non-trivial task. One way of approaching this problem that guarantees no grid connected energy usage is to use an off-grid PV-battery driven air-conditioner. Here we use a detailed simulation model to investigate the suitability of a small off-grid PV-battery system to power an air-conditioner to provide occupant comfort for a range of different building thermal designs across Australian climates. The relative importance of different building parameters on the ability of the off-grid system to provide comfort is discussed. Results show that even in tropical climates, there are certain building thermal designs that lead to indoor temperatures <25°C at all times with a modest size PV-battery system.

Stochastic–multiobjective market equilibrium analysis of a demand response program in energy market under uncertainty

In the electricity market, demand response programs are designed to shift peak demand and enhance system reliability. A demand response program can reduce peak energy demand, power transmission congestion, or high energy-price conditions by changing consumption patterns. The purpose of this research is to analyze the impact of a demand response program in the energy market, under demand uncertainty. A stochastic–multiobjective Nash–Cournot competition model is formulated to simulate demand response in an uncertain energy market. Then, Karush–Kuhn–Tucker optimality conditions and a linear complementarity problem are derived for the stochastic Nash–Cournot model. Accordingly, the linear complementarity problem is solved and its stochastic market equilibrium solution is determined by using a general algebraic modeling system. Additionally, the case of the Taiwanese electric power market is taken up here, and the results show that a demand response program is capable of reducing peak energy consumption, energy price, and carbon dioxide emissions. The results show that demand response program decreases electricity price by 2–10%, total electricity generation by 0.5–2%, and carbon dioxide emissions by 0.5–2.5% in the Taiwanese power market. In the simulation, demand uncertainty leads to an 2–7% increase in energy price and supply risk in the market. Additionally, tradeoffs between cost and carbon dioxide emissions are presented.

Demonstration of a day-night rhythm in human skeletal muscle oxidative capacity

ObjectiveA disturbed day-night rhythm is associated with metabolic perturbations that can lead to obesity and type 2 diabetes mellitus (T2DM). In skeletal muscle, a reduced oxidative capacity is also associated with the development of T2DM. However, whether oxidative capacity in skeletal muscle displays a day-night rhythm in humans has so far not been investigated. MethodsLean, healthy subjects were enrolled in a standardized living protocol with regular meals, physical activity and sleep to reflect our everyday lifestyle. Mitochondrial oxidative capacity was examined in skeletal muscle biopsies taken at five time points within a 24-hour period. ResultsCore-body temperature was lower during the early night, confirming a normal day-night rhythm. Skeletal muscle oxidative capacity demonstrated a robust day-night rhythm, with a significant time effect in ADP-stimulated respiration (state 3 MO, state 3 MOG and state 3 MOGS, p < 0.05). Respiration was lowest at 1 PM and highest at 11 PM (state 3 MOGS: 80.6 ± 4.0 vs. 95.8 ± 4.7 pmol/mg/s). Interestingly, the fluctuation in mitochondrial function was also observed in whole-body energy expenditure, with peak energy expenditure at 11 PM and lowest energy expenditure at 4 AM (p < 0.001). In addition, we demonstrate rhythmicity in mRNA expression of molecular clock genes in human skeletal muscle. ConclusionsOur results suggest that the biological clock drives robust rhythms in human skeletal muscle oxidative metabolism. It is tempting to speculate that disruption of these rhythms contribute to the deterioration of metabolic health associated with circadian misalignment.

Series and Parallel Elastic Actuation: Impact of natural dynamics on power and energy consumption

This paper provides a detailed analysis of the power and mechanical/electrical energy consumption of Series Elastic Actuators (SEAs) and Parallel Elastic Actuators (PEAs). The study is done by imposing a sinusoidal motion to a pendulum load, such that the natural dynamics automatically present itself in the power and energy consumption. This allows to link the actuators' dynamics to their loss mechanisms, revealing interesting characteristics of series and parallel elastic elements in actuator designs. Simulations demonstrate that the SEA and PEA allow to decrease both peak power and energy consumption, provided that the stiffness of their elastic element is tuned properly. For the SEA, both are minimized by tuning the elastic element to the antiresonance frequency of the actuator. For the PEA, peak power is minimal at the link's resonance frequency, but the optimal stiffness for minimal electrical energy consumption cannot be determined by a theoretical resonance and needs to be calculated using a complete system model. If these guidelines are followed, both types of elastic actuators can provide significant energetic benefits at high frequencies. This was confirmed by experiments, which demonstrated energy reductions of up to 78% (SEA) and 20% (PEA) compared to rigid actuators.

An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings

Demand Response (DR) applications along with strategically deployed solar photovoltaic (PV) and ice storage systems at the building level can help reduce building peak demand and energy consumption. Research shows that no work has been carried out to study the impact of integrated control of PV and ice storage on improving building operation and energy savings in demand responsive buildings. This can enable building operators to take advantage of different electricity prices and enable utilities to spread the demand over whole day. This research presents a model to study coordinated control of building end-use loads including cooling, lighting and plug loads, together with PV and ice storage integrated with packaged air conditioning (AC) units. This is used to study their impacts on peak demand and energy consumption in a simulated medium-sized office building located in Virginia/Maryland, U.S. area. Research findings provide an improved understanding of the contribution of DR, solar PV and ice storage systems towards reducing building peak electricity demand and energy consumption while being sensitive to occupant thermal and lighting needs.

Trust based reliable transmissions strategies for smart home energy consumption management in cognitive radio based smart grid

Reliability of smart home energy management (SHEM) is enhanced due to the use of opportunistic cognitive radio based communications for reliable transmission of peak period energy utility data. Cognitive radio networks are apt communication solutions capable of offering reliable opportunistic data transmissions through spectrum reuse. Reliable opportunistic data transmissions ensure strategic decision making and to execute critical control operations for sustainable energy utilizations. Accurate spectrum sensing and efficient spectrum sharing are vital to aspects of reliable peak energy consumption data transmission. Cooperative spectrum sensing has been proposed as a more reliable method for gaining accurate spectrum availability detection. Trustworthiness of the local decisions of cooperating users is vital for the accuracy of the final spectrum availability decision. However, measures which can assess the trustworthiness of cooperating users are given less attention. In this paper, we describe a novel multi-attribute trust based framework to facilitate reliable spectrum sensing and priority based spectrum access allocation to enhance delay sensitive data transmissions. We have evaluated our solutions using extensive simulation experiments. Furthermore, we comparatively analyzed the reliability of the proposed user selection method for known spectrum sensing data falsification (SSDF) attack behaviors to accurately identify the non-malicious users. As evidenced by the results proposed novel multi-attribute trust based transmission strategies offer greater reliability in ensuring timely availability of peak period energy utility data for SHEM.

On the Use of Train Braking Energy Regarding the Electrical Consumption Optimization in Railway Station

Nowadays, many projects have been conducted in order to reduce CO2 emissions, with the objective of reducing energy consumption. In the context of the urban railway area, the energy consumption is huge. It is respectively split into 70% for the traction and 30% for station consumers. Many works have already been carried out on traction systems, but very few of them were oriented towards the station energy problematic. This paper describes the project led by “Efficacity” Institute which concerns the use of the braking energy to manage and optimize the railway station energy consumption.Efficacity investigates energetic concepts in order to store the braking energy of the trains with a stationary electrical saving system, and to reutilize it for the power supply of electric and thermal consumers or actuators in a railway station thanks to a microgrid. The idea is to store train braking energy in hybrid storage system (composed of batteries and super-capacitors cells) and to restate it judiciously at different moments of the day (during peak or low energy consumption hours) to various kind of station loads.

Online on-board Optimization of Cutting Parameter for Energy Efficient CNC Milling

Energy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a workshop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parameters to reduce the overall machining time while at the same time minimizing the peak energy consumption.