Times Of Peak Demand Research Articles

Impacts of Domestic Solar Water Heating (DSWH) systems on the cost of a hot shower in low-income dwellings in Brazil

This work presents a method to evaluate, from the distribution utility's perspective, the impacts of adopting Domestic Solar Water Heating (DSWH) systems in Brazil, based on demand measurements carried out on two groups of residential households. For the 12 months period evaluated, the case-study shows that using DSWH results in savings of 198 kWh (38%), and active power demand savings at peak times of 860 W (42%) per individual unit. For the distribution utility and per individual unit, using DSWH leads to avoided costs of $ 27.59 (38%), avoided costs due to peak time demand reduction of $ 170.2 (42%), and annual avoided costs of $ 197.79 (41%). This work clearly shows that in the residential section the tariff model and structure is strongly distorted, since it only accounts for the energy consumption per residential unit, and completely disregards the costs associated with power demand availability. Our results show that in Brazil the distribution utility charges only some 15% of the real cost of a hot shower based on electric showerheads.

Distributed control of thermostatically controlled loads in distribution network with high penetration of solar PV

High penetration of solar energy can result in voltage rise in midday, while growth in residential air conditioning is the main contributor of overloading and voltage drop issues during peak demand time. This paper provides a hierarchical control scheme to coordinate multiple groups of aggregated thermostatically controlled loads to regulate network loading and voltage in a distribution network. Considering the limited number of messages that can be exchanged in a realistic communication environment, an event-triggered distributed control strategy is proposed in this paper. Through intermittent on and off toggling of air conditioners, the required active power adjustment is shared among participating aggregators to solve the issue. A case study is conducted and simulation results are presented to demonstrate the performance of the proposed control scheme.

Task scheduling algorithms for multi-cloud systems: allocation-aware approach

Cloud computing has gained enormous popularity for on-demand services on a pay-per-use basis. However, a single data center may be limited in providing such services, particularly in the peak demand time as it may not have unlimited resource capacity. Therefore, multi-cloud environment has been introduced in which multiple clouds can be integrated together to provide a unified service in a collaborative fashion. However, task scheduling in such environment is much more challenging than that is used in the single cloud environment. In this paper, we propose three allocation-aware task scheduling algorithms for a multi-cloud environment. The algorithms are based on the traditional Min-Min and Max-Min algorithm and extended for multi-cloud environment. All the algorithms undergo three common phases, namely matching, allocating and scheduling to fit them in the multi-cloud environment. We perform extensive simulations on the proposed algorithms and test with various benchmark and synthetic datasets. We evaluate the performance of the proposed algorithms in terms of makespan, average cloud utilization and throughput and compare the results with the existing algorithms in such system. The comparison results clearly demonstrate the efficacy of the proposed algorithms.

A field study using an adaptive in-house pricing model for commercial and industrial customers in Korea

Demand response programs provide customers with economic incentives for load reductions at times of high market prices and system reliability constraints. One type of demand response programs, price-based program, induces customers to respond to changes in product rates. However, some large-scale customers find it difficult to change their electricity consumption patterns, even with rate changes, because their electricity demands are commercial and industrial. This study proposes an adaptive in-house pricing model for large-scale customers, particularly those with multiple business facilities, for self-regulating price-based program. The adaptive in-house pricing model charges higher rates to customers with lower load factors by employing a peak-to-off-peak usage ratio in order to reduce usage at times of high prices at each facility. This study analyzes the daily electricity consumption patterns of large-scale customers through a field trial of the proposed pricing model at a telecom company with 447 offices and worksites for one month. The results show that the pricing model corresponds to average reductions of 3.54–28.69% during peak-demand times for four different types of workplaces. However, reductions in electricity consumption during off-peak periods did not show a significant difference. The results of this study prove that this proposed pricing model can be successfully applied to large-scale operations.

Distributed Energy Storage Control for Dynamic Load Impact Mitigation

The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation.

Modeling and optimization of resources in multi-emergency department settings with patient transfer

The increasing number of non-emergency patients in emergency departments (EDs) and long wait times caused by their overcrowding are some of the main concerns in health systems research. If an ED reaches an overcrowding state, there may be a need to transfer patients to other hospitals, which may or not belong to the same hospital system. Therefore, this paper investigates the effect of transferring non-emergency patients to other hospitals in multi-hospital settings. The strategy of transferring patients between hospitals can be used to minimize patient wait times without increasing the number of required resources. In this research, hospital capacity allocation is modeled using capacitated network design models, wherein transferring patients between hospitals is allowed. To investigate the effect of various system parameters, a comprehensive set of experiments are conducted for the proposed integer programming model. The experimental results showed that transferring patients is an effective way to decrease the number of patients waiting, especially in cases when minimum overlap exists in peak demand times of hospitals or when there is high uncertainty in the rate of patient arrivals. The experiments proved that the proposed patient transfer strategy can reduce the number of patients waiting by up to 35%. It was also shown that transferring patients between hospitals can decrease the resource requirements in each hospital by about 10%.

Development of Hollow Steel Ball Macro-Encapsulated PCM for Thermal Energy Storage Concrete.

The application of thermal energy storage with phase change materials (PCMs) for energy efficiency of buildings grew rapidly in the last few years. In this research, octadecane paraffin was served as a PCM, and a structural concrete with the function of indoor temperature control was developed by using a macro-encapsulated PCM hollow steel ball (HSB). The macro-encapsulated PCM-HSB was prepared by incorporation of octadecane into HSBs through vacuum impregnation. Test results showed that the maximum percentage of octadecane carried by HSBs was 80.3% by mass. The macro-encapsulated PCM-HSB has a latent heat storage capacity as high as 200.5 J/g. The compressive strength of concrete with macro-encapsulated PCM-HSB at 28 days ranged from 22 to 40 MPa. The indoor thermal performance test revealed that concrete with macro-encapsulated octadecane-HSB was capable of reducing the peak indoor air temperature and the fluctuation of indoor temperature. It can be very effective in transferring the heating and cooling loads away from the peak demand times.

Hybrid Algorithm for Profit Maximization in Hybrid Cloud

When customers own computing resources are not sufficient users utilize Infrastructure as a Service (IaaS) from public clouds. But the scheduling of task to the resources especially at the time of peak demand is a challenging issue. Also achieving better profit for the cloud provider is a problem. Previous proposed methods faced a challenge in the economical benefit and also hard to practice in real environment. The mathematical programming models done already experienced a slow response when the size of the problem scales up. The main objective is to improve the economic benefit of the IaaS provider with guaranteed Quality of Service (QoS). Hence to solve this problem, a hybrid algorithm incorporating Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) is proposed for allocating user's task to the resources in hybrid cloud environment. The results show the improvement in the profit of cloud provider with fast response and better utilization of resources.

水道送水システムにおける電力デマンドレスポンスのためのポンプ運転スケジューリング方式

Environmental concern has recently led to saving of electric power for water transmission and distribution systems in water utilities. To cope with the problem, effective water supply operation and appropriate management of water pressure have been researched. Moreover, after a large earthquake in eastern Japan, the problem of electricity shortage has been evident. To address the problem, the water saving which results in the electricity saving has been encouraged and the driving of waterworks facilities has been shifted from a peak demand time zone to a low demand time zone in order to achieve power peak cut. We propose a transmission pump operation scheduling method that minimizes the maximum value of power consumption in a time zone of demand response request. The optimal operational plan of pumps is made by mathematical programming so that the power consumption is minimized under constraints that the each reservoir keeps water level to enable 12-hour consecutive water supply and the water level does not go under lower limit during and after a time period of demand response. Simulation tests showed that the proposed method reduced power consumption by more than 65% compared with the current pump operation.

Managing peak loads in energy grids: Comparative economic analysis

One of the key issues in modern energy technology is managing the imbalance between the generated power and the load, particularly during times of peak demand. The increasing use of renewable energy sources makes this problem even more acute. Various existing technologies, including stationary battery energy storage systems (BESS), can be employed to provide additional power during peak demand times. In the future, integration of on-board batteries of the growing fleet of electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) into the grid can provide power during peak demand hours (vehicle-to-grid, or V2G technology).This work provides cost estimates of managing peak energy demands using traditional technologies, such as maneuverable power plants, conventional hydroelectric, pumped storage plants and peaker generators, as well as BESS and V2G technologies. The derived estimates provide both per kWh and kWyear of energy supplied to the grid. The analysis demonstrates that the use of battery storage is economically justified for short peak demand periods of <1h. For longer durations, the most suitable technology remains the use of maneuverable steam gas power plants, gas turbine,reciprocating gas engine peaker generators, conventional hydroelectric, pumped storage plants.

A Simulated Level Loading of Supply and Demand for Beds in a Tertiary Care Children's Hospital Reduces Overall Bed Requirements.

Anticipating throughput and allocating resources effectively in children's hospitals have unique challenges relative to adult inpatient centers. The seasonal and daily variation can be difficult to anticipate in terms of impact and creating plans for adequate preparation. Discrete event simulation methodology can be helpful in determining appropriate allocation of resources and has been increasingly appropriated in health care from industry. A representative sample set was abstracted from the Cohen Children's Medical Center census tracking system to describe the present state. A larger data set was used to determine the appropriate level load. The total work performed each hour from 8 AM to 8 PM was evaluated against the level load plan of 11.5%. During the initial hours of the working period when the total work was low, more discharges were added. For each discharge added, an equal quantity of discharges was subtracted from the later hours of the day to bring the total work below 11.5% for each hour. Once the simulated state discharges were determined, a new aggregate bed supply line was created. These values were then added to the original visualization to show improvement. Our analysis suggests that a large part of the discharge/transfer activity and bed demand activity occurs in the pediatric intensive care unit in a roughly 4- to 5-hour window. Our simulation analysis suggests that level loading of this resource-intensive activity period has a potential to reduce bed occupancy, increase bed availability in peak bed demand times, and improve efficiency and throughput throughout the hospital. Discrete event simulation can be an effective tool for pediatric inpatient centers to determine appropriate allocation of resources to enhance patient safety and throughput without significant, costly expansion of bed capacity.

Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources

Abstract Demand-side management programs such as load shifting utilise the flexibility in the load consumption pattern of consumers to enable the effective capacity utilisation of renewable energy sources. The locational and temporal characteristics of renewable energy sources cause forecasting and operational challenges in the implementation of such a renewable energy program. In this paper, a dynamic load-shifting program using real-time data in a cloud computing framework is proposed to address the aforementioned issues. A new dynamic renewable factor is proposed to facilitate on-time incentive based load shifting program. The effectiveness of the dynamic load-shifting program was evaluated using simulated case studies. The case study indicates that PV energy utilisation at the consumer side is increased by 18% by the application of the proposed load-shifting program. The study result in Kerala, India, consisting of more than 7.5 million domestic consumers, indicates that demand reduction of 250–300 MW at times of peak demand can be achieved by using load shifting in the domestic sector.

An Incentive-based Optimal Energy Consumption Scheduling Algorithm for Residential Users

Smart Grid is the most promising concept which is more reliable, flexible, controllable and environment friendly. Home energy management (HEM) system is an important part of the smart grid that provides a number of benefits to the end users such as savings in the electricity bill, reduction in peak demand and meeting the demand side requirements. Demand Response (DR) and Time-of-Use (ToU) pricing refer to programs which offer incentives to the end users who curtail their energy use during times of peak demand. This paper proposes an energy efficient optimization model based on Binary Particle Swarm Optimization (BPSO) for residential electricity consumers. The proposed model optimally schedules the electricity consumption of different household appliances in a dynamic pricing environment to benefice the user by minimizing electricity cost. Simulation results illustrate that the proposed method efficiently shifts the appliances operation time from high peak to low peak hours and leads to significant electricity bill saving.

A comparison of the technological, economic, public policy, and environmental factors of HVDC and HVAC interregional transmission

The design of an interregional high-voltage transmission system in the US is a revolutionary technological concept that will likely play a significant role in the planning and operation of future electric power systems. Historically, the primary justification for building interregional high-voltage transmission lines in the US and around the world has been based on economic and reliability criteria. Today, the implementation renewable portfolio standards, carbon emission regulations, the improvements in the performance of power electronic systems, and unused benefits associated with capacity exchange during times of non-coincident peak demand, are driving the idea of designing an interregional high-voltage transmission system in the US. However, there exist challenges related to technical, economic, public policy, and environmental factors that hinder the implementation of such a complex infrastructure. The natural skepticism from many sectors of the society, in regards to how will the system be operated, how much will it cost, and the environmental impact that it could potentially create are among the most significant challenges to its rapid implementation. This publication aims at illustrating the technological, environmental, economic, and policy challenges that interregional HV transmission systems face today in the US, looking specifically at the Clean Line Rock Island project in Iowa.

A Novel Approach in Demand Side Management for Smart Home

Abstract The development of smart grid technology acts as new brain to the power generation and distribution network. Through this technology, the network is not only going to get integrated and turn communicable all over the country but also the demand will be monitored by the control centers and at peak time the usage of electricity is going to cost higher than the nominal rate with intimation given to the customer through sms and mail. Hence it's time for the consumers to act smart or else they will end up receiving highly billed statements. This project presents an idea of having a power hub which has control over all the loads individually. The hub detects the peak time demand and reduces the load usage by switching off unwanted loads with a priority that is being set by the consumer based on game theory's algorithm which schedules load usage by creating several possible schedule vector for consumers such that demand is never raised. This could be done both individually and among multiple users of a community or area. The power hub can also monitor our energy usage and creates an order of importance among the loads, thus providing intelligence to the consumers.

Safety in numbers: lack of evidence to indicate the number of physicians needed to provide safe acute medical care

Patient safety in hospital is dependent on a multitude of factors. Recent reports into the failings of healthcare organisations in the UK have highlighted low staffing levels as a significant factor. There is research into the impact of nurse-to-patient ratios on patient safety, but our literature search found little published data that would allow healthcare providers to define a minimum number of physician staff and skills mix that would assure safety in the largest hospital specialty: unscheduled (acute) medicine. Future work should focus on the evaluation of existing data on hospital mortality rates and physician staffing levels as well as on empirical time and motion studies to ascertain the resources required to undertake safe medical care at times of peak demand.

Economic analysis of Indian pumped storage schemes

Pumped-hydro energy storage schemes (PHES) are developed for improving the net efficiency of the base load thermal power plants. These schemes are operated only at times of peak demand and during major power plant outages. In India, the first pumped storage plant was commissioned during 1985 and till today eleven plants with on aggregate installed capacity of 4804MW have been in operation. However all these plants up to the year 2003 have generated energy less than the projected figures. The major reason for less output from the PHES in India, than that envisaged in the planning stage is deficit of off-peak power availability for pumping operation in almost all the regional power grids except north-eastern grid. But gradual increase in efficiency has been realized from PHES operation after introduction of the availability based tariff (ABT) in 2003. Further, the increase in peak power demand, requirement of spinning reserve, increased generation from renewable energy sources have singly and combinedly triggered the necessity for installation of more pumped storage schemes in the country. Under these circumstances, this analysis addresses the economics of pumped storage schemes in India with special reference to Kadamparai PHES. Various costs involved in pumped storage operation are analyzed in the Indian context. The case study reveals considerable techno-economic benefits obtained by more operational hours of the PHES in the state grid after 2003. The analysis and results would be useful for investors and researchers to understand the economics of Indian pumped storage schemes.

Recent Advances in Inexpensive Aqueous Batteries for Large Scale Electrical Energy Storage

The integration of electrical energy generated from renewable sources such as solar and wind power into the electricity grid faces the challenge of intermittent electricity output from these renewable sources. Storing the electricity during times of excess production and releasing the electrical energy to the grid during times of peak demand is an obvious solution. Rechargeable batteries are very attractive for energy storage because of their high energy efficiency and scalability. [1-3] Since grid-scale electrical energy storage at a global scale requires hundreds of gigawatt-hours to be stored, the batteries for this application must be inexpensive, robust, safe and sustainable. None of today’s mature battery technologies meet all of these requirements. In this presentation, we will summarize the recent research advancements in three aqueous battery systems that have the potential to meet the demanding requirements of grid-scale energy storage: (1) alkaline iron-air battery, (2) the iron-chloride redox flow battery and (3) a new aqueous organic redox flow battery. These three battery systems satisfy the primary criterion of using of inexpensive or abundantly-available and sustainable materials for energy storage. The use of toxic heavy metals is completely avoided. By careful selection of additives, the iron electrode of the alkaline iron-air battery can now be charged at high as C-rate with no more than 5% loss in faradaic efficiency to parasitic hydrogen evolution. The iron electrode also has a utilization 0.3 Ah/g and can be discharged continuously at rates as high as 3C. Such a high-performance iron electrode has also been cycled over 500 times without the loss of capacity or change in faradaic efficiency. Separated air electrodes based on carbon and spinel oxides can be charged and discharged with a significant reduction with overpotential losses not exceeding 250 mV at 10 mA/cm2. [4,5] The electro-deposition efficiency of the iron-chloride flow battery has been improved to as high as 95% with use of additives for complexing the iron (II) in solution and by controlling the pH to be higher than 2. An iron-chloride flow cell that uses an anion-exchange membrane for the transport of chloride ions has been operated successfully over multiple charge discharge cycles.We have advanced a new type of Organic Redox Flow Battery (ORBAT) that employs two different water-soluble organic redox couples on the positive and negative side of a flow battery. Compounds such as quinones are particularly suitable as redox couples. No precious metal catalyst is needed because of the fast proton-coupled electron transfer processes. The ORBAT cell uses a membrane-electrode assembly configuration similar to that used in polymer electrolyte fuel cells and can be charged and discharged multiple times at high faradaic efficiency without any noticeable degradation of performance. The ORBAT configuration presents a unique opportunity for developing an inexpensive and sustainable metal-free rechargeable battery for large-scale electrical energy storage. AcknowledgementsThe authors thank ARPA-E, US Army RDECOM, the University of Southern California, and the Loker Hydrocarbon Research Institute for funding the research.

Simulation of a stand-alone renewable hydrogen system for residential supply

Computer simulation is a first logical step before taking a project to physical construction beside it is a powerful tool in energy network design. Combined systems are used to improve availability for energy supplied by renewable systems. The main inconvenient of some renewable energy sources is their highly seasonal nature, with great variations over time that can impede their use as the basis for consumption and limits them to peak demand times. The aim of this work is to simulate to verify the energetic sufficiency of a family house with renewable energies (wind, solar-photovoltaic) using a hybrid system of batteries and hydrogen. For that, Simulink®-Matlab® program was used considering meteorological data provided by CINAM (Galician Center for Environmental Research and Information). The model can be applied to determine the feasibility of implementing an energy network in specific places, and to predict energy flows and system behavior throughout the year.

A New Intelligent Neuro–Fuzzy Paradigm for Energy-Efficient Homes

Demand response (DR), which is the action voluntarily taken by a consumer to adjust amount or timing of its energy consumption, has an important role in improving energy efficiency. With DR, we can shift electrical load from peak demand time to other periods based on changes in price signal. At residential level, automated energy management systems (EMS) have been developed to assist users in responding to price changes in dynamic pricing systems. In this paper, a new intelligent EMS (iEMS) in a smart house is presented. It consists of two parts: a fuzzy subsystem and an intelligent lookup table. The fuzzy subsystem is based on its fuzzy rules and inputs that produce the proper output for the intelligent lookup table. The second part, whose core is a new model of an associative neural network, is able to map inputs to desired outputs. The structure of the associative neural network is presented and discussed. The intelligent lookup table takes three types of inputs that come from the fuzzy subsystem, outside sensors, and feedback outputs. Whatever is trained in this lookup table are different scenarios in different conditions. This system is able to find the best energy-efficiency scenario in different situations.