A Case Study of Traffic Demand Response to Broadband Service-Plan Upgrades

2016 ◽  
pp. 124-135
Author(s):  
Sarthak Grover ◽  
Roya Ensafi ◽  
Nick Feamster
Keyword(s):  
Demand Response ◽  
Traffic Demand ◽  
Broadband Service ◽  
Service Plan

scholarly journals KPI Evaluation Framework and Tools Performance: A Case Study from the inteGRIDy Project

2021 ◽  
Vol 11 (1) ◽  
pp. 23
Author(s):  
Chris Ogwumike ◽  
Huda Dawood ◽  
Tariq Ahmed ◽  
Bjarnhedinn Gudlaugsson ◽  
Nashwan Dawood
Keyword(s):  
Demand Response ◽  
Performance Indicators ◽  
Storage System ◽  
Energy Storage System ◽  
Heat Pumps ◽  
Evaluation Framework ◽  
Pilot Site ◽  
Network Operator ◽  
Case Evaluation

This paper presents an assessment of the impacts of the different tools implemented within the inteGRIDy project through the analysis of key performance indicators (KPIs) that appropriately reflect the technical and economic domains of the inteGRIDy thematic pillars, comprising demand response and battery storage systems. The evaluation is based on improvements brought about by individual components of the inteGRIDy-enabled smart solution across the Isle of Wight (IOW) pilot site. The analyses and the interpretation of findings for the pilot use case evaluation are presented. The results indicate that the smart solution implementation across the IOW pilot site resulted in achieving the inteGRIDy set objectives. Overall, a 93% reduction in energy consumption, equivalent to 643 kWh was achieved, via the M7 energy storage system and heat pumps developed as part of inteGRIDy solution. Additionally, the grid efficiency and demand flexibility contribution to the distribution network operator (DNO)-triggered DR services, based on a 10% increase/decrease in demand, resulted in stabilizing the grid efficiency.

scholarly journals Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6261
Author(s):  
Alexander Brem ◽  
Ken Bruton ◽  
Paul D. O’Sullivan
Keyword(s):  
Demand Response ◽  
Response Times ◽  
Industrial Sector ◽  
Industrial Building ◽  
Factors Affecting ◽  
Ambient Temperatures ◽  
Thermal Environments ◽  
Industrial Sites ◽  
Electricity Grids

Increasing participation in demand response within the industrial sector may be crucial to growing the levels of available flexible capacity required to reliably control national electricity grids as renewable generation increases to satisfy emission targets. This research aims to assist the uptake of demand response in the industrial sector by investigating risk to indoor thermal environments on industrial sites offering air handling unit capacity for demand response. This evaluation uses a systematic model-based approach, calibrated and validated with empirical data from a relevant case study industrial building to assess risk through a number of scenarios. The conditions investigated cover several relevant grid response times and durations, and national and international extreme external ambient temperatures in the past, present and future under a variety of temperature limits. The study demonstrated that there is very low risk to the case study site participating in demand response, with only 15 of 264 initial and 284 of 936 total scenarios triggering any risk. The major factors affecting risk levels identified were more stringent temperature limits and the influence of more extreme climates. The development and implementation of this concept has considerable potential to benefit industrial participants and the wider national electricity grids.

Demand Response Process Assessment Model: Development and Case Study Assessment

2021 ◽  
pp. 103609
Author(s):  
İLİSULU Fadime ◽  
Ayça KOLUKISA TARHAN ◽  
Kubilay KAVAK
Keyword(s):  
Demand Response ◽  
Model Development ◽  
Assessment Model ◽  
Process Assessment ◽  
Response Process

Evaluating the Operational Efficiency of Two Versions of Super Diverging Diamond Interchange Design: A Case Study in Denver, Colorado

2021 ◽  
pp. 036119812110372
Author(s):  
Muhammad Tahmidul Haq ◽  
Amirarsalan Mehrara Molan ◽  
Khaled Ksaibati
Keyword(s):  
Research Effort ◽  
Traffic Signals ◽  
Operational Efficiency ◽  
Traffic Demand ◽  
Operational Analysis ◽  
Signal Coordination ◽  
Traffic Volumes ◽  
Microsimulation Models ◽  
Better Than

This paper aims to advance the current research on the new super diverging diamond interchange (super DDI) design by evaluating the operational efficiency using real-world locations. As part of a comprehensive research effort on improving the performance of failing service interchanges in the mountain-plains region, the study identified three interchanges (Interstate 225 and Mississippi Avenue, Interstate 25 and 120th Avenue, and Interstate 25 and Hampden Avenue) at Denver, Colorado as the potential candidates to model for future retrofit. Four interchange designs (i.e., existing CDI [conventional diamond interchange], DDI, super DDI-1, and super DDI-2) were tested in this study. The operational analysis was conducted using VISSIM and Synchro. Several microsimulation models (120 scenarios with 600 runs in total) were created with three peak hours (a.m., noon, and p.m.) for existing (the year 2020) and projected (the year 2030) traffic volumes. The study considered two simulation networks: (1) when no adjacent traffic signal exists, to determine how the four interchange designs would perform if there were no adjacent signals or they were far away from the interchange; and (2) when there are two adjacent traffic signals, to evaluate the performance of the four interchanges in a bigger corridor with signal coordination needed. An important finding is that super DDI designs outperformed DDI with adjacent signals and higher traffic demand, while DDI performed similarly to or sometimes insignificantly better than super DDI if no adjacent intersections were located in the vicinity and if the demand was lower than the DDI’s capacity.

Developing Guidelines for Implementing Transit Signal Priority and Freight Signal Priority Using Simulation Modeling and a Decision Tree Algorithm

2021 ◽  
pp. 036119812110575
Author(s):  
Shahadat Iqbal ◽  
Taraneh Ardalan ◽  
Mohammed Hadi ◽  
Evangelos Kaisar
Keyword(s):  
Decision Tree ◽  
Signal Control ◽  
Signal Timing ◽  
Transit Signal Priority ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Traffic Demand ◽  
Capacity Ratio ◽  
The Impact

Transit signal priority (TSP) and freight signal priority (FSP) allow transportation agencies to prioritize signal service allocations considering the priority of vehicles and, potentially, decrease the impact signal control has on them. However, there have been no studies to develop guidelines for implementing signal control considering both TSP and FSP. This paper reports on a study conducted to provide such guidelines that employed a literature review, a simulation study, and a decision tree algorithm based on the simulation results. The guideline developed provides recommendations in accordance with the signal timing slack time, the proportion of major to minor street hourly volume, hourly truck volume per lane for the major street, hourly truck volume per lane for the minor street, the proportion of major to minor street hourly truck volume, the proportion of major to minor street hourly bus volume, the volume-to-capacity ratio for the major street, and the volume-to-capacity ratio for the minor street. The guideline developed was validated by implementing it for a case study facility. The validation result showed that the guideline works correctly for both high and low traffic demand.

scholarly journals Road-safety recognition and network equilibrium with perceived route-choice sets

2019 ◽  
Vol 1 (2) ◽  
pp. 126-134
Author(s):  
Mao-sheng Li ◽  
He-lai Huang
Keyword(s):  
Convex Combination ◽  
Route Choice ◽  
User Equilibrium ◽  
Equilibrium Analysis ◽  
Combination Method ◽  
Crash Frequency ◽  
Traffic Demand ◽  
Additional Costs ◽  
Choice Set

Abstract Safety is regarded as the second basic need in Maslow’s hierarchy of needs (1943), and safety recognition and circumvention behaviour in the route-choice decision-making process should therefore be accommodated in network-traffic equilibrium analysis frameworks. This paper proposes a framework by which crash frequency, forecasted using the safety-analysis method or compiled from historical data for intersections, is used to measure the safety consciousness of drivers. Drivers are then classified into different groups according to their acceptable-risk thresholds, and each group has its own route-choice set. Decision behaviour whereby drivers are willing to bear additional costs in order to circumvent travel risk is incorporated into the variational inequality model based on the user equilibrium in the perceived route-choice set (UE-PRCS), which is an extension of Wardrop’s first principle. The Frank–Wolfe algorithm, based on the convex combination method, is employed to obtain the solution. A small road network is used as a case study to illustrate the proposed framework, incorporating risk recognition and circumvention behaviour under different combinations of traffic demand and risk-sensitivity group ratio. The results show that the standard user equilibrium is a special case of the UE-PRCS, but that the UE traffic state is more common than the UE-PRCS under different parameters.

scholarly journals Demand Response Model Development for Smart Households Using Time of Use Tariffs and Optimal Control—The Isle of Wight Energy Autonomous Community Case Study

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 541 ◽  
Author(s):  
Sourav Khanna ◽  
Victor Becerra ◽  
Adib Allahham ◽  
Damian Giaouris ◽  
Jamie M. Foster ◽  
...  
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Energy Transport ◽  
Model Development ◽  
Software Tool ◽  
Load Flow ◽  
Smart Devices ◽  
Smart Meters ◽  
Isle Of Wight

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households.

Mean-Variance Model for the Build-Operate-Transfer Scheme Under Demand Uncertainty

2003 ◽  
Vol 1857 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Anthony Chen ◽  
Kitti Subprasom ◽  
Zhaowang Ji
Keyword(s):  
Demand Uncertainty ◽  
Network Design Problem ◽  
Transfer Scheme ◽  
Nondominated Solutions ◽  
Variance Model ◽  
Traffic Demand ◽  
Simulation Based ◽  
Capacity Selection ◽  
Mean Variance

A mean-variance model was developed for determining the optimal toll and capacity in a build-operate-transfer (BOT) roadway project subject to traffic demand uncertainty. This mean-variance model involves two objectives: maximizing mean profit and minimizing the variance (or standard deviation) of profit. The variance associated with profit is considered as a risk. Because maximizing expected profit and minimizing risk are often conflicting, there may not be a single best solution that can simultaneously optimize both objectives. Hence, it is necessary to explicitly consider this as a multiobjective problem so that a set of nondominated solutions can be generated. In this study, the optimal toll and capacity selection for the BOT problem under demand uncertainty is formulated as a special case of the stochastic network design problem. A simulation-based multiobjective genetic algorithm was developed to solve this stochastic bilevel mathematical programming formulation. Numerical results are also presented as a case study.

Freight Planning Typology

1998 ◽  
Vol 1613 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Reginald Souleyrette ◽  
T. H. Maze ◽  
Tim Strauss ◽  
David Preissig ◽  
Ayman G. Smadi
Keyword(s):  
Full Range ◽  
Freight Transportation ◽  
System Capacity ◽  
Demand Model ◽  
Transportation Demand ◽  
Traffic Demand ◽  
Demand Modeling ◽  
Freight Traffic ◽  
Freight Transportation Demand

A layered architecture for freight transportation demand modeling entails the construction of a statewide freight transportation demand model by separately simulating traffic for one commodity at a time. Layers can then be added together to construct a comprehensive model that includes the most significant freight flows. Most state or regional economies are dominated by a few economic sectors, and models can be constructed for those sectors that generate the most freight traffic and/or are the most important to the regional economy. Freight traffic demand modeling in intercity applications is more likely to focus on economic development, local infrastructure improvements, maintenance, and similar policy and planning concerns than on system capacity issues. Thus, it is more important to understand changes in traffic growth by economic sector than as the composite of all freight traffic. This method is less data intensive and more easily understood by transportation professionals than previous approaches. The layered approach is therefore more likely to achieve the desired objectives than would general models, which attempt to forecast heterogeneous freight transportation demands simultaneously. This approach is demonstrated through a case study using the meat products and farm machinery industries in Iowa. Other commodities will be added in the future to complete a model of Iowa’s statewide freight transportation demand. A framework is presented for organizing and identifying planning goals, key issues, and predominant commodities for intercity freight transportation. Although examples are provided, specific recommendations addressing the full range of issues, data collection activities, tools, and urban applications are suggested for further study. A case study demonstrates the approach used for one issue, one mode, and two commodities, which could be repeated elsewhere for similar applications.

Modeling the long-term impact of demand response in energy planning: The Portuguese electric system case study

Energy ◽  
2018 ◽  
Vol 165 ◽  
pp. 456-468 ◽  
Author(s):  
João Anjo ◽  
Diana Neves ◽  
Carlos Silva ◽  
Abhishek Shivakumar ◽  
Mark Howells
Keyword(s):  
Demand Response ◽  
Energy Planning ◽  
Electric System ◽  
Long Term Impact
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