Demand response: Understanding the rational behavior of consumers in a Peak Time Rebate Program

Vuelvas, José; Ruiz, Fredy

doi:10.1109/ccac.2015.7345196

Cited by 9 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another implementation of a price-based demand response algorithm would be a peak-time rebate mechanism [40], [41]. This structure relies on offering incentives to users to drop their collective consumption.…”

Section: Existing Spot Market Pricing Mechanismsmentioning

confidence: 99%

Decentralized Real-Time Pricing to Achieve Integrated Transactive and Physical Grids

Jinsiwale

Divan

2019

IEEE Access

View full text Add to dashboard Cite

With exponentially decreasing prices of PV the adoption of volatile and non-dispatchable sources into the grid has increased. Reliance on high speed communication and complex computations to handle these fast phenomena introduces points of failure and dramatically decreases the controllability and resiliency of the system. Real-time pricing mechanisms ensure that demand and source flexibility can be leveraged to hedge this volatility by balancing demand with available generation. Most pricing mechanisms are quasi real-time and rely on knowledge of system topology, feeder configuration and power flow patterns. This paper proposes a decentralized real-time pricing scheme that is dependent on system frequency. The global visibility of frequency as a pricing signal ensures that the dynamic price can be seen at every agent in the system. This gives every connected agent the ability to balance local needs while stabilizing the global system. By integrating the transactive and physical aspects of the grid, the system can be operated in a more optimal fashion. The approach demonstrates the ability to scale to islanded microgrids as well as larger market structures. By eliminating the need for low-latency communications and detailed network models, the system is also seen to be more resilient and flexible. The proposed approach shows the ability to dynamically balance generation and loads in real-time, while also achieving required market functions showing a truly integrated transactive and physical grid.INDEX TERMS Decentralized dispatch, fractal grids, PV penetration, real-time pricing, transactive energy.

show abstract

Section: Existing Spot Market Pricing Mechanismsmentioning

confidence: 99%

Decentralized Real-Time Pricing to Achieve Integrated Transactive and Physical Grids

Jinsiwale

Divan

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In the literature, there are few mathematical models in quantifying such manipulations. The customer's manipulation is confirmed as a rational behavior in [12], but their twostage model is too simple to represent the true scenario. An infinite-horizon model is established to investigate the customer's manipulation behavior [13].…”

Section: Introductionmentioning

confidence: 99%

To Overconsume or Underconsume: Baseline Manipulation in Demand Response Programs

Wang

Tang

2018

2018 North American Power Symposium (NAPS)

View full text Add to dashboard Cite

The customer baseline is required to assign rebates to participants in baseline-based demand response (DR) programs. The average baseline method has been widely accepted in practice due to its simplicity and reliability. However, the customer's baseline manipulation is little-known in the literature. We start from a customer's perspective and establish a Markov decision process to model the customer's payoff-maximizing problem. The behavior of a rational customer's underconsumption on DR days and overconsumption on non-DR days are revealed. Furthermore, we propose an approximated baseline method and show how the consumption distribution and program parameters affect the results. Due to the curse of dimensionality, a linear policy-based rollout algorithm is introduced to obtain a practical approximate solution. Finally, a case study is carried out to illustrate the baseline manipulation, where the simulation results confirm the effectiveness of the proposed methods and shed light on how to properly design baseline methods. Index Terms-Baseline manipulation, baseline method, demand response, dynamic programming, Markov decision process.

show abstract

“…For instance, Time-of-use (TOU) [3] program, where the day is divided into adjoining blocks of hours. The price of energy varies between blocks, but not within blocks; Critical peak pricing (CPP) [9], is related to TOU, unlike that it is only applied to a small number of event days; in Real-time pricing (RTP) [2], the price varies hourly according to the real-time market cost of delivering electricity; Direct load control [6], remote control of flexible loads; Emergency demand reduction [20], users receive incentive by diminishing energy consumption during emergency events; PTR [24], where customers receive electricity bill rebates by not consuming (relative to a previously established, household-specific baseline) during peak periods, which it is the mechanism studied in this paper; and many other mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Rational consumer decisions in a peak time rebate program

Vuelvas

Ruiz

2017

Electric Power Systems Research

Self Cite

View full text Add to dashboard Cite

A rational behavior of a consumer is analyzed when the user participates in a Peak Time Rebate (PTR) mechanism, which is a demand response (DR) incentive program based on a baseline. A multi-stage stochastic programming is proposed from the demand side in order to understand the rational decisions. The consumer preferences are modeled as a risk-averse function under additive uncertainty. The user chooses the optimal consumption profile to maximize his economic benefits for each period. The stochastic optimization problem is solved backward in time. A particular situation is developed when the System Operator (SO) uses consumption of the previous interval as the household-specific baseline for the DR program. It is found that a rational consumer alters the baseline in order to increase the well-being when there is an economic incentive. As results, whether the incentive is lower than the retail price, the user shifts his load requirement to the baseline setting period. On the other hand, if the incentive is greater than the regular energy price, the optimal decision is that the user spends the maximum possible energy in the baseline setting period and reduces the consumption at the PTR time. This consumer behavior produces more energy consumption in total considering all periods. In addition, the user with high uncertainty level in his energy pattern should spend less energy than a predictable consumer when the incentive is lower than the retail price.

show abstract

Demand response: Understanding the rational behavior of consumers in a Peak Time Rebate Program

Cited by 9 publications

References 9 publications

Decentralized Real-Time Pricing to Achieve Integrated Transactive and Physical Grids

Decentralized Real-Time Pricing to Achieve Integrated Transactive and Physical Grids

To Overconsume or Underconsume: Baseline Manipulation in Demand Response Programs

Rational consumer decisions in a peak time rebate program

Contact Info

Product

Resources

About