Impact of Load Profile on Dynamic Interactions Between Energy Markets: A Case Study of Power Exchange and Demand Response Exchange

2019 ◽  
Vol 15 (11) ◽  
pp. 5855-5866 ◽  
Author(s):  
Srikanth Reddy Konda ◽  
Ameena Saad Al-Sumaiti ◽  
Lokesh Kumar Panwar ◽  
Bijaya Ketan Panigrahi ◽  
Rajesh Kumar
Keyword(s):  
Demand Response ◽  
Energy Markets ◽  
Dynamic Interactions ◽  
Load Profile ◽  
Power Exchange

scholarly journals Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3361 ◽  
Author(s):  
Venkat Durvasulu ◽  
Timothy Hansen
Keyword(s):  
Demand Response ◽  
Power Market ◽  
Electric Utility ◽  
Energy Markets ◽  
Power Markets ◽  
High Price ◽  
Test Case ◽  
Market Prices ◽  
Bulk Power

In most U.S. market sponsored demand response (DR) programs, revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. This paper presents an aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX). Using the DRX structure, DR providers can participate in energy markets as a service to benefit bulk-power market entities. The benefits and challenges to each market entity using DR-as-a-service are presented in an extended review. The DRX model in this study is a market entity that operates with the day-ahead market to select DR offers that minimize electric utility payments. A case study was performed using the proposed DRX model on the IEEE 24-bus system, augmented to represent actual bulk-power market prices to study factors that influence utility payments under the DRX-market paradigm. Two high-price days of the PJM market were simulated, and it was shown for a single day on the augmented test case that spending $69,955 for DR-as-a-service results in a reduction of utility payments of $864,199. The day-ahead generator supply curve, network congestion, and DR curtailment were found to be the most influencing factors that impact the benefit of using DR-as-a-service.

scholarly journals Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4343
Author(s):  
Yunbo Yang ◽  
Rongling Li ◽  
Tao Huang
Keyword(s):  
Data Analysis ◽  
Peak Load ◽  
Smart Meter ◽  
Load Shifting ◽  
Load Profile ◽  
Transition Season ◽  
Morning Peak ◽  
Heating Load ◽  
Heating Loads

In recent years, many buildings have been fitted with smart meters, from which high-frequency energy data is available. However, extracting useful information efficiently has been imposed as a problem in utilizing these data. In this study, we analyzed district heating smart meter data from 61 buildings in Copenhagen, Denmark, focused on the peak load quantification in a building cluster and a case study on load shifting. The energy consumption data were clustered into three subsets concerning seasonal variation (winter, transition season, and summer), using the agglomerative hierarchical algorithm. The representative load profile obtained from clustering analysis were categorized by their profile features on the peak. The investigation of peak load shifting potentials was then conducted by quantifying peak load concerning their load profile types, which were indicated by the absolute peak power, the peak duration, and the sharpness of the peak. A numerical model was developed for a representative building, to determine peak shaving potentials. The model was calibrated and validated using the time-series measurements of two heating seasons. The heating load profiles of the buildings were classified into five types. The buildings with the hat shape peak type were in the majority during the winter and had the highest load shifting potential in the winter and transition season. The hat shape type’s peak load accounted for 10.7% of the total heating loads in winter, and the morning peak type accounted for 12.6% of total heating loads in the transition season. The case study simulation showed that the morning peak load was reduced by about 70%, by modulating the supply water temperature setpoints based on weather compensation curves. The methods and procedures used in this study can be applied in other cases, for the data analysis of a large number of buildings and the investigation of peak loads.

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.

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.

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

scholarly journals Optimal Control of Multiple Microgrids and Buildings by an Aggregator

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1058 ◽  
Author(s):  
Giulio Ferro ◽  
Riccardo Minciardi ◽  
Luca Parodi ◽  
Michela Robba ◽  
Mansueto Rossi
Keyword(s):  
Distributed Generation ◽  
Electric Vehicles ◽  
Demand Response ◽  
Electrical Grid ◽  
Energy Management Systems ◽  
New Energy ◽  
Systems Models ◽  
Fair Assignment ◽  
Demand Response Programs

The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.

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