Peak Demand Hours Research Articles

Optimal Energy Trading for Renewable Energy Integrated Building Microgrids Containing Electric Vehicles and Energy Storage Batteries

The increasing penetration of renewable energy resources and volatility of energy prices cause huge challenges in planning and regulating energy generation, transport, and distribution. A possible solution can be a paradigm change of employing control actions from the demand side in addition to the conventional generation control. To realize such shifts, the primary stage should be a proper and robust analysis of the energy flexibility on the demand side. Recently, demand side control in buildings has become a major research issue because buildings share a substantial portion of the total electricity consumption. The increasing use of controllable devices in buildings combined with the advent of smart metering system has paved the way to exploit the potential flexibility of managing the energy generation and demand of buildings for optimal energy trading. In this paper, we investigate the benefits of demand resources in buildings for optimal energy trading in day-ahead and real-time energy markets. The building flexible demand resources considered are electric vehicles and batteries. The paper examines the combined optimization of EVs and batteries in the day-ahead and regulation electricity markets with the objective of maximizing the total profit of the building microgrid. It takes EVs driving pattern into consideration. The major contribution of the paper is the exploitation of the energy flexibility of buildings using EVs as dynamic energy storage device and batteries as manageable demand facility. The devised optimization problem is formulated as a double-stage mixed-integer linear programming (MILP) problem, and solved using the CPLEX solver. Several numerical results are presented to validate the effectiveness of the devised optimization framework using actual data of building electricity demand and local renewable generation in the Otaniemi area of Espoo, Finland. We demonstrate that the proposed optimization solution can achieve considerable increase in profit, reduce renewable energy curtailment and decrease power demand in peak hours, compared to uncontrolled or non-optimized operation.

Reversible Solid Oxide Cell (ReSOC) as flexible polygeneration plant integrated with CO2 capture and reuse

This work presents the concept of a Reversible Solid Oxide Cell (ReSOC) system localized in an urban residential district. The system is operated as a polygeneration plant that acts as interface between the electricity grid and the local micro-grid of the district. The ReSOC plant produces hydrogen via electrolysis during periods of low electricity demand (i.e., low-priced electricity). Hydrogen is used for multiple city needs: public mobility (H2 bus fleet), electricity production delivered to the micro-grid during peak-demand hours, and heat (accumulated in a storage) provided to the local district heating (DH) network. An additional option analyzed is the use of part of the H2 to produce DME using CO2 captured from biogas obtained from municipal solid wastes. The DME is used for fueling a fleet of trucks for the garbage collection in the residential district. A traditional CO2 removal process based on liquid MEA thermally integrated with the ReSOC system is studied. A time-resolved model interfaces the steady-state operating points with the thermal storage and the loads (electrical, H2 buses, DME trucks, heat), implementing constraints of thermal and H2 self-sufficiency on the system. Neglecting the DME option, the average daily roundtrip electric efficiency is about 38%, while the annual efficiency, which includes H2 mobility and thermal energy to DH, reaches 68%. When the DME option is considered, the thermal demand for CO2 removal and conversion process reduces the heat availability for DH, while the need for additional H2 for DME synthesis increases the electricity consumption for water electrolysis: both these phenomena imply a reduction of system efficiency (-9%) proportional to DME demand.

Assessing solar and wind complementarity in Texas

As wind and solar power installations proliferate, power grids will face new challenges in ensuring consistent coverage from variable renewable resources. One option to reduce variability is to integrate the output from wind and solar facilities with dissimilar temporal profiles of output. This study measured the complementarity of wind and solar resources sited in various regions of Texas. This study modeled solar and wind power output using the System Advisory Model with solar data from the National Solar Radiation Database and wind data from the Wind Integration National Dataset Toolkit. Half-hourly power production was assessed based on resource location, plant size, hourly load, inter-annual variability, and solar array design for all sites. We found that solar and wind resources exhibit complementary peaks in production on an annual and daily level and that West and South Texas wind resources also exhibit complementarity. Pairings of West Texas wind with solar power or South Texas wind sites yield the highest firm capacity. Solar farms are better suited for providing power during summertime hours of peak demand, whereas wind farms are better for winter. Taken together, our results suggest that Texas renewable power production can be made more reliable by combining resources of different types and locations.

Competition and substitution between public transport modes

The management and understanding of modal split between public transport (PT) modes is of interest for numerous reasons. It may, for example, be desirable to stimulate passengers to switch from crowded buses and over to higher capacity rail. This requires a good understanding of drivers of transit modal substitution.The evidence put forward in this paper is based on more than 150 empirically estimated cross elasticities between PT modes from over 20 sources collected from Australia, Europe and USA. These sources include scientifically published evidence as well as grey literature.This evidence is coded into a database from which our paper presents and analyses the available cross-PT-modal demand relations. We focus on evidence for how fares, travel time and service intervals on PT ‘mode A’ affect the demand for PT ‘mode B’.Despite generally low levels of substitution between PT modes, passengers are particularly sensitive to in-vehicle, access/egress and waiting time in choosing PT mode and less so for fare variations. In general, rail demand is less sensitive to changes in bus than bus demand is to changes in rail. We also find that peak-hour demand more markedly switches between PT modes than off-peak demand does.

Spatio-Temporal Coordinated V2V Energy Swapping Strategy for Mobile PEVs

A vehicle-to-vehicle (V2V) energy swapping strategy can provide an alternative fast charging way for plug-in electric vehicles (PEVs) to relieve the charging overload problem in the power system during peak-demand hours. The main challenges in designing an efficient V2V energy swapping strategy are: 1) to stimulate mobile PEVs to participate in an energy swapping transaction that balances supply with demand at aggregators and 2) to achieve optimal energy utilization and individual PEVs’ profits. In this paper, we present a novel smart grid architecture with enhanced communication capabilities for mobile PEVs, via a heterogeneous wireless network-enhanced smart grid. We propose an online V2V energy swapping strategy based on price control. Specifically, mobile PEVs with surplus energy are motivated by getting paid to contribute to a V2V energy swapping transaction at aggregators with energy-hungry PEVs. To evaluate the performance of the proposed V2V energy swapping strategy, a realistic suburban scenario is developed in VISSIM to track the PEVs’ mobility using the generated simulation traces. Extensive simulation results are given to demonstrate the efficacy of the proposed V2V energy swapping strategy.

Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem

Short term probabilistic load forecasting is essential for any power generating utility. This paper discusses an application of partially linear additive quantile regression models for predicting short term electricity demand during the peak demand hours (i.e. from 18:00 to 20:00) using South African data for January 2009 to June 2012. Additionally the bounded variable mixed integer linear programming technique is used on the forecasts obtained in order to find an optimal number of units to commit (switch on or off. Variable selection is done using the least absolute shrinkage and selection operator. Results from the unit commitment problem show that it is very costly to use gas fired generating units. These were not selected as part of the optimal solution. It is shown that the optimal solutions based on median forecasts (Q0.5 quantile forecasts) are the same as those from the 99th quantile forecasts except for generating unit g8c, which is a coal fired unit. This shows that for any increase in demand above the median quantile forecasts it will be economical to increase the generation of electricity from generating unit g8c. The main contribution of this study is in the use of nonlinear trend variables and the combining of forecasting with the unit commitment problem. The study should be useful to system operators in power utility companies in the unit commitment scheduling and dispatching of electricity at a minimal cost particularly during the peak period when the grid is constrained due to increased demand for electricity.

Giving parking the time of day: A case study of a novel parking occupancy measure and an evaluation of infill development and carsharing as solutions to parking oversupply

In the US, parking is oversupplied in both residential and commercial settings, a consequence of the widespread application of minimum parking standards, which typically supply excess parking for peak-hour demand. Yet the evidence base for specific parking reforms is thin, and alternatives to the peak-hour metric still under-explored. In this illustrative case study, I study time-of-day parking occupancy in a 146-unit apartment complex in Davis, CA to obtain preliminary results, demonstrate new data collection and analysis methods that can be applied at a wider scale, and explore policy analyses that are facilitated by this novel, detailed parking behavior information. First, I systematically observe parking occupancy in the apartment complex to understand hourly variation. I find that the peak and off-peak occupancy rates in the apartment complex are 55% and 34%. I calculate that the 45% of spaces left unused across a day could be converted to roughly 32 new townhouses. I also estimate a multilevel latent class analysis to identify distinct patterns of household parking use and find that 54% of households could be likely candidates for carshare adoption. I conclude by noting implications for housing availability, shared parking standards, the rise of shared and autonomous vehicles, and future research.

Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand

Firms and governments often use moral suasion and economic incentives to influence intrinsic and extrinsic motivations for economic activities. To investigate persistence of such interventions, we randomly assign households to moral suasion and dynamic pricing that stimulate energy conservation during peak-demand hours. We find significant habituation and dishabituation for moral suasion—the treatment effect diminishes after repeated interventions but can be restored to the original level by a sufficient time interval between interventions. Economic incentives induce larger treatment effects, little habituation, and significant habit formation. Our results suggest moral suasion and economic incentives produce substantially different short-run and long-run policy impacts. (JEL C93, D83, L94, L98, Q41, Q48)

Minimization of Network Losses With Financial Incentives in Voluntary Demand Response

This paper delivers a customer voluntary demand response (CVDR) program to help the load serving entity (LSE) curtail peak demand and cutoff carbon emission. LSE provides financial incentives to customers who are willing to curtail energy consumption during peak demand hours. A bilevel problem is proposed to determine the optimal power curtailment and financial incentives to achieve equivalent minimal cost for LSE and maximal utility function for customers simultaneously. The effects of the CVDR program are examined with two benchmark radial systems: 3-bus and the IEEE 8500-Node networks. All simulations are carried out with General Algebraic Modeling System and MATLAB. Numerical studies unveil that CVDR enhances customer’s willingness in demand response program and achieve economic savings and peak shaving for LSE.

Consumer Preferences and Soft Load Control on the Swedish Electricity Market

main purpose of the present report is to present the results of the project The electricity customer, a new power on the electricity market? main purpose of the project is to estimate lost values due to various restrictions on household electricity consumption, which gives us prices of schematic reductions in power through behavioral adaptations among Swedish households. Another purpose is to estimate households' costs for short power outages, which gives a price of a targeted disconnection of electricity. willingness of households to adjust their electricity consumption is governed by several factors - both economic and non-economic. An additional objective is therefore to analyze the extent to which households are willing to adapt for non-economic reasons, for example, to facilitate the integration of renewable electricity production such as solar and wind power. To achieve the objectives of the project, we analyze household habits and preferences for electricity usage in connection with daily demand peaks during winter time in Sweden. We have chosen an empirical approach where households are subjected to choose between hypothetical electricity contracts where different types of restrictions in the use of large-scale household appliances are included. different characteristics of the agreements or contracts relate to (1) maximum power usage in watts, (2) the duration of the restriction, (3) number of occasions of restriction and (4) the ability to change the selection of which electrical appliances to be used during the restriction. In addition to the above-mentioned approach, we also study how this relates to other electricity usage (e.g. heating, lighting, TV, etc.). This is done by asking households for compensation requirements to accept full power outages, i.e. black-outs. By studying the difference in compensation requirements between the limitation and the black-outs, the value of different loads can be estimated. The results reveal that households on average require a compensation of SEK 2000 - 3700 depending on the severity of electricity consumption constraint. Depending on how we define the potential loss in potential electricity usage for different scenarios, the results can be translated to be between SEK 20 and 40 per kWh. In the case of total power outages, the valuation is significantly higher and corresponds to SEK 3000 to 4600. This can in turn be translated to the equivalent of SEK 400 - 600 per kWh. results thus indicate a significant difference between the value of the load in a soft control DSM program, and the remaining load (e.g. heating, lighting and TV). Compared to previous literature on the value of lost load, VOLL, our estimates fall in the higher range, especially compared to Swedish studies. We believe this is in line with the context outlined in the present study with rather many occasions of disruptions at the peak demand hour. The results also show that a pro-environmental cheap talk make people more likely to opt into a DSM program with load controlled at many occasions. It did not, however, make people see more lenient on hard load controls in general. An immediate policy implication from the results is that specific policies aiming at stimulating behavioral changes probably are very ineffective and/or costly. As a result, policies to affect demand response should focus on automatization and passive response. A related policy implication is that it is far from obvious that demand response is always more cost effective than supply response, i.e., increasing production of electricity.

Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand

Firms and governments often use moral suasion and economic incentives to influence intrinsic and extrinsic motivations for economic activities. To investigate persistence of such interventions, we randomly assign households to moral suasion and dynamic pricing that stimulate energy conservation during peak-demand hours. We find significant habituation and dishabituation for moral suasion—the treatment effect diminishes after repeated interventions but can be restored to the original level by a sufficient time interval between interventions. Economic incentives induce larger treatment effects, little habituation, and significant habit formation. Our results suggest moral suasion and economic incentives produce substantially different short-run and long-run policy impacts.

Energy and Cost Analysis of a New Packed Bed Pumped Thermal Electricity Storage Unit

Renewable energy sources (RES) are quite capable to actively contribute to meet the today's energy demand. However, many of them have a time-dependent nature that constitutes their major disadvantage. To overcome this drawback, energy storage systems (ESS) need to be set up. In this way, the stored energy can be used in the absence of RES or under peak demand hours. High-temperature pumped thermal electricity storage (PTES) using packed bed constitutes an attractive solution but is characterized by high losses and irreversibilities. For this reason, in this paper, a new plant scheme is presented and its mathematical model built up. To predict the packed bed behavior, a one-dimensional two phase model of the hot and cold storages has been included, while the plant feasibility is evaluated using an energy and a cost analysis. Results show that the highest quantity of energy and round-trip efficiency are reached with a packed bed made of magnetite and titanium oxide.

Modelling of a new hydro-compressed air-storage system

ABSTRACTThe interest in energy storage systems is increasing, since it provides an excellent solution to store the low-cost excess energy from the energy sources, which are available at peak demand hours. This paper presents a new compressed-air storage system that combines ambient air and hydraulic oil, in order to store energy in compressed-air form and benefit from the advantages of both pneumatic and hydraulic systems. The process consists of charging and discharging cycles, however, this paper investigates the discharging cycle, where a new technique of Small-Scale Compressed Air Energy Storage (SS-CAES) system is realised. The new idea in RC-CAHES is to obtain higher efficiency in energy conversion machines during charging and discharging processes with numerous advantages over conventional types of energy storage systems. This study demonstrates the effectiveness of this technique by proving that it has higher efficiency than a similar Compressed Air Energy Storage (CAES) systems.

Cascaded Multilevel Active Rectifier Fed Three-Phase Smart Pump Load on Single-Phase Rural Feeder

Irrigation pumping system that marks a sizeable load on the grid is generally blind on assessment of peak demand hours, while in operation. Such operation if smartly controlled may avoid shedding of some connected loads to match power demand. Moreover, due to the accessibility of only single-phase supply, the rural pump three-phase loads are supplied through either by splitting ac single phase into three phases or by single phase ac–dc three-phase ac conversion. Such configuration suffers from power quality problems, higher capacitor voltages, and common mode voltage problems, etc. A seven-level cascaded multilevel active rectifier (CMAR) is proposed here to provide power through three different dc links to reduce the voltage rating of capacitors, feed vector-controlled open end winding induction motor pump in addition to variable illumination LED lighting. The control employed for CMAR enhances the grid support operation by flexibly controlling power on the dc links. The control is invoked by adjusting the speed of the pump by sensing the grid frequency using droop characteristics thereby acting as smart pump load. The effectiveness of the proposed configuration and control is investigated both through simulation and experimentation on same scale hardware prototype.

An agent-based model of building occupant behavior during load shedding

Load shedding enjoys increasing popularity as a way to reduce power consumption in buildings during hours of peak demand on the electricity grid. This practice has well known cost saving and reliability benefits for the grid, and the contracts utilities sign with their “interruptible” customers often pass on substantial electricity cost savings to participants. Less well-studied are the impacts of load shedding on building occupants, hence this study investigates those impacts on occupant comfort and adaptive behaviors. It documents experience in two office buildings located near Philadelphia (USA) that vary in terms of controllability and the set of adaptive actions available to occupants. An agent-based model (ABM) framework generalizes the case-study insights in a “what-if” format to support operational decision making by building managers and tenants. The framework, implemented in EnergyPlus and NetLogo, simulates occupants that have heterogeneous thermal and lighting preferences. The simulated occupants pursue local adaptive actions such as adjusting clothing or using portable fans when central building controls are not responsive, and experience organizational constraints, including a corporate dress code and miscommunication with building managers. The model predicts occupant decisions to act fairly well but has limited ability to predict which specific adaptive actions occupants will select.

A Systematic Approach to Evaluation of Performance Deficiencies in ED Triage

• Nurses are uniquely situated to critically evaluate quality and performance measures, identify opportunities for improvement, and recommend potential corrective inverventions within the emergency department. • Nurses can actively contribute to performance improvements that increase quality of care, improve patient outcomes and satisfaction, and reduce institutional liability and risk. • Establishment of a systematic process to identify deficiencies, evaluate problems, and develop solutions is vital to continuous quality improvement. Increasing ED crowding has resulted in greater demand and longer time-to-triage and time-to-provider wait times, making accurate triage more important than ever before to avoid poor patient outcomes and possible hospital liability. In one case, a 75-year-old patient presenting with chest pain became unconscious in the ED waiting area after initial registration but before triage. Although resuscitation was attempted, the patient did not survive. Continuous assessment and improvement are needed to streamline the triage process and improve accuracy and efficiency. Because nurses play a vital role in patient triage, they are uniquely positioned to evaluate and enhance the process. When staff at an 18-bed emergency department in a rural hospital suspected potential quality deficiencies related to extended wait times and patients leaving without being seen, a panel of nurses was formed to characterize the problem by conducting a chart review to develop potential solutions. The chart review identified a correlation between wait times and patients leaving without being seen during hours of peak demand. Accordingly, the panel collaborated and proposed several interventions to alleviate these problems. Formation of a panel of skilled nurses to assess problems and recommend potential solutions may represent a useful approach for active nurse participation in quality improvement in the emergency department, thus having a meaningful impact on patient outcomes and hospital liability.

Thermodynamic analysis of a solar–enhanced geothermal hybrid power plant using CO2 as working fluid

CO2-based Enhanced Geothermal Systems (EGS) and closed-loop supercritical CO2 Brayton cycles for solar thermal systems are both currently being developed for environmentally-friendly power plant systems. However, the recompressor needed in closed-loop supercritical CO2 Brayton cycles operates at high temperature and pressure with attendant higher manufacture and maintain cost. Here a solar thermal-EGS hybrid system is proposed where the geothermal power plant provides the base-load electrical power while the solar system increases the system capacity factor by generating additional electric power during peak demand hours. The two stand-alone systems and the hybrid system were modeled to predict the system efficiencies. It is found that comparison with the stand-alone CO2-EGS and CO2-solar thermal systems, the hybrid system has equal or even higher efficiency than the efficiency of the sum of two separate systems. Moreover, the operating pressure can be decreased and the recompression compressor can be removed in the hybrid system which reduces costs of the system installation and maintenance.

Impact of storage technologies on renewable energy integration in Kazakhstan

Utilizing electricity from renewables requires significant back-up generating capacity for the reason that solar and wind energy outputs could vary throughout the days, seasons and affected by weather conditions. This paper examines the impact of storage technologies integration to the power system of Kazakhstan based on optimization model. System components involve nodes and regions allowing the model to interact among these division sets trough transmission lines. Using the data elaborated by the Kazakhstani Electricity Grid Operating Company (KEGOC) the model allows to observe the nodal level interconnection of the spatial characteristics of the power system till 2050. Model results indicate that storage facilities could improve the reliability of power system and reduce electricity costs by storing electricity at the low electricity demand hours and low prices for utilizing stored electricity at peak demand hours. The proposed model determines the optimal way of implementation of energy storage technologies and renewable energy sources, their capacity and amount of investment. With the assumption that the generation from conventional power plants is fixed on the level of 2015 we have tested various levels of implementation of storage technologies. The resulting optimal ratio of installed capacity of storage to installed capacity of renewables is between 10-15%.

Pancho's Burritos

Pancho's Burritos is a high-end fast-food restaurant located in Charlottesville, Virginia. Due to a recent surge in customers, the lines at the restaurant during peak hours are becoming very long and causing excessive customer waiting. To improve current customer wait times and plan for future growth requirements, Francisco “Pancho” Escoba, the proprietor, wants to get a better understanding of the current operational capacity. The key decision Escoba must make is how to redesign his burrito-making process to increase the capacity and reduce customer wait time.Suitable for an undergraduate or MBA course, the case can be used in an introductory operations course to teach capacity analysis and queuing. The case provides a relatable and understandable setting for students without an operations background to gain a better grasp of basic course concepts and illustrates how interactions between capacity and queuing affect one another. As an exam or review case it works well because it contains standard process calculations that all students should know how to perform. Excerpt UVA-OM-1539 Rev. May 24, 2017 Pancho's Burritos It was Friday night, and Francisco “Pancho” Escoba, manager and owner of high-end fast-food restaurant Pancho's Burritos, was performing his regular restocking order when he noticed something odd. As he walked into the restaurant, he saw a number of customers walking out, frustrated with the long line that spread all the way across the restaurant. Customers leaving at the sight of a long line during peak demand hours was a recent trend that worried Escoba. In order to put an end to the fleeing customers and to improve the customer experience, Escoba decided to analyze his operations by taking some time studies of the burrito-making process. The Arrival of Pancho's Burritos Escoba was born an entrepreneur. At age 12, he started his first business, a home movie theater that served his friends and family. By age 16, he owned and operated a number of small businesses in his hometown of Guadalajara, Mexico, including a lawn-care service, a neighborhood food-delivery service, and an airport-ride service. Due in part to the stable income he received from his businesses, he was able to attend and pay for college at Universidad Panamericana. Following graduation, he returned to Guadalajara where he started a furniture design, manufacturing, and retail store. After laboring for several years just to break even, he decided to return to school for his MBA at the University of Virginia's Darden School of Business. During his first year at Darden, Escoba would regularly have friends over for dinner. He would always make them burritos using his grandmother's secret recipe. His friends could not get enough of the burritos. After seeing his friends' reactions, Escoba realized he might have the makings of another entrepreneurial venture. He knew that there had been a recent boom in the fast-food industry, but most of the establishments were serving unhealthy, overprocessed food. Escoba believed there was an opportunity to provide high-end fast food for price- and health-conscious consumers. In 2011, Escoba founded Pancho's Burritos in Charlottesville, Virginia. Since its opening, Pancho's Burritos had become a local mainstay, growing immensely popular, especially with students at the University of Virginia. Analyzing the Operations . . .

Pancho's Burritos (Abridged)

This case, an abridged version of (UVA-OM-1539). Pancho's Burritos is a high-end fast-food restaurant located in Charlottesville, Virginia. Due to a recent surge in customers, the lines at the restaurant during peak hours are becoming very long and causing excessive customer waiting. To improve current customer wait times and plan for future growth requirements, Francisco “Pancho” Escoba, the proprietor, wants to get a better understanding of the current operational capacity. The key decision Escoba must make is how to redesign his burrito-making process to increase the capacity and reduce customer wait time.Suitable for an undergraduate or MBA course, the case can be used in an introductory operations course to teach capacity analysis and queuing. The case provides a relatable and understandable setting for students without an operations background to gain a better grasp of basic course concepts and illustrates how interactions between capacity and queuing affect one another. As an exam or review case it works well because it contains standard process calculations that all students should know how to perform. Excerpt UVA-OM-1555 Rev. May 24, 2017 Pancho's Burritos (Abridged) It was Friday night, and Francisco “Pancho” Escoba, manager and owner of high-end fast-food restaurant Pancho's Burritos, was performing his regular restocking order when he noticed something odd. As he walked into the restaurant, he saw a number of customers walking out, frustrated with the long line that spread all the way across the restaurant. Customers leaving at the sight of a long line during peak demand hours was a recent trend that worried Escoba. In order to put an end to the fleeing customers and to improve the customer experience, Escoba decided to analyze his operations by taking some time studies of the burrito-making process. The Arrival of Pancho's Burritos Escoba was born an entrepreneur. At age 12, he started his first business, a home movie theater that served his friends and family. By age 16, he owned and operated a number of small businesses in his hometown of Guadalajara, Mexico, including a lawn-care service, a neighborhood food-delivery service, and an airport-ride service. Due in part to the stable income he received from his businesses, he was able to attend and pay for college at Universidad Panamericana. Following graduation, he returned to Guadalajara where he started a furniture design, manufacturing, and retail store. After laboring for several years just to break even, he decided to return to school for his MBA at the University of Virginia's Darden School of Business. . . .