Estimating the Profile of Incentive-Based Demand Response (IBDR) by Integrating Technical Models and Social-Behavioral Factors

Shi, Qingxin; Chen, Chien; Mammoli, Andrea; Li, Fangxing

doi:10.1109/tsg.2019.2919601

Cited by 88 publications

(40 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IBDR programs were proven successful in the residential sector in [13,14]. In the first study [13], 1575 residential units agree to a maximum of one curtailment event per day lasting for 45 minutes only, where the HVAC load is reduced to a predefined level. A one-time payment is made to all customers at the beginning of the exercise.…”

Section: Uc Model Binary Variablesmentioning

confidence: 99%

“…The study finds out that payments of $5 per month or $10 per month to each unit can defer the installation of a new gas power plant for 35 years or 12 years, respectively. However, the study in [13] does not consider making higher payments for longer curtailments per day or more than 1 brief activation per day, either. The study was implemented for the summer season only.…”

Section: Uc Model Binary Variablesmentioning

confidence: 99%

“…The successful ventures in [13,14] provide the following experience about the behavior of small consumers: small perunit rewards do not appeal to residential consumers. A significant one-time payment made upfront is much more attractive.…”

Section: Uc Model Binary Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Incentive Based Demand Response Program for Power System Flexibility Enhancement

Mohandes

Moursi

Hatziargyriou

et al. 2021

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Section: Uc Model Binary Variablesmentioning

confidence: 99%

Section: Uc Model Binary Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Incentive Based Demand Response Program for Power System Flexibility Enhancement

Mohandes

Moursi

Hatziargyriou

et al. 2021

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

“…Then, the load reduction will be conducted according to the latest load ratio. The load reduction is implemented by raising the temperature setting of air conditioners and turning off electric water heaters [31].…”

Section: B Demand Response Schemementioning

confidence: 99%

Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With Graph-Computing-Based Power Flow Calculation

et al. 2019

Self Cite

View full text Add to dashboard Cite

The voltage stability is an essential security concern when a system is operating in peak load hours or subjected to an N-1 contingency. Among various voltage stability indices, Local Thevenin Index (LTI) has been a popular one, but mostly applied to the measurement-based framework. The reason is that it requires significant computing effort if applied to model-based approach, even though the model-based LTI calculation can provide more accurate results. In this paper, a new model-based LTI calculation is proposed using graph-computing-based power flow calculation, which is fast enough to fully utilize the advantage of model-based LTI. Then, when the voltage stability index of one or multiple buses exceeds the security limit, a demand response scheme is activated to increase the voltage security margin at the lowest cost. The simulation study of a thousand-level-bus system verifies the computation accuracy and efficiency of the proposed model-based LTI. It also demonstrates that the demand response action can enhance the system security effectively. Thus, the proposed work has advantages if applied to a large-scale Energy Management System (EMS). INDEX TERMS Power system security, voltage stability monitoring, voltage stability index, graph computing, demand response, security enhancement, local Thevenin index.

show abstract

“…The DR program operator incentivises the DR participants based on their contribution to the energy balance of the grid through load curtailment. This is specifically facilitated by the incentive-based DR programs [3].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Krishnadas¹,

Kiprakis

2020

Energies

View full text Add to dashboard Cite

Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.

show abstract

Estimating the Profile of Incentive-Based Demand Response (IBDR) by Integrating Technical Models and Social-Behavioral Factors

Cited by 88 publications

References 34 publications

Incentive Based Demand Response Program for Power System Flexibility Enhancement

Incentive Based Demand Response Program for Power System Flexibility Enhancement

Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With Graph-Computing-Based Power Flow Calculation

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Contact Info

Product

Resources

About