Optimal time-of-use pricing for residential load control

Datchanamoorthy, S.; Kumar, Sunil; Öztürk, Yusuf; Lee, G.

doi:10.1109/smartgridcomm.2011.6102350

Cited by 76 publications

(53 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To represent the complete population, a dummy subscription is added to charge2subs, which assigns the portion of the population that is not subscribed to TacTex-13's tariffs to the best competitor tariff. Finally, all the predicted subscriptions for this customer are added to a map (lines [14][15] that is returned by the algorithm.…”

Section: Tactex-13's Instantiation Of Latte's Phasementioning

confidence: 99%

“…For example, as of the current date, residential TOU pricing schemes are proposed to take effect in California starting January 2019 [91]. Existing work on TOU tariffs either has not considered competitive retail markets or has used more abstract, smaller-scale simulations compared with Power TAC [130,127,10,14,118,1,107,131,19]. In Power TAC, to the best of our knowledge the first broker that used TOU tariffs was Mertacor13 [66] (see Section 8.5).…”

Section: Time-of-use Tariffs and Demand-side Managementmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous trading in modern electricity markets

Urieli

2016

AI Matters

View full text Add to dashboard Cite

The smart grid is envisioned to be a main enabler of sustainable, clean, efficient, reliable, and secure energy supply (U.S. Department of Energy, 2003). One of the milestones in the smart grid vision will be programs for customer participation in electricity markets through demand-side management and distributed generation; electricity markets will incentivize customers to adapt their demand to supply conditions, which will help to utilize intermittent energy resources such as from solar and wind, and to reduce peak-demand.

show abstract

Section: Tactex-13's Instantiation Of Latte's Phasementioning

confidence: 99%

Section: Time-of-use Tariffs and Demand-side Managementmentioning

confidence: 99%

Autonomous trading in modern electricity markets

Urieli

2016

AI Matters

View full text Add to dashboard Cite

show abstract

“…Residential customers are faced with diverse pricing schemes implemented by utilities to stimulate demand response [1][2][3], and are hence faced with the subsequent problem on how to operate the household appliances to minimize the electricity payment under the premise of human comfort. The growing integration of household generation, mainly intermittent renewable generation, makes this problem even more complex.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization for Household Load Scheduling with Uncertain Parameters

Wang

Fang

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Home energy management systems (HEMS) face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers' comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.

show abstract

“…TOU, CPP and RTP are the main electricity pricing strategies in DR. In TOU pricing schemes, electricity prices vary during different periods, according to a regular daily pattern [6][7][8], and peak periods cost more than off-peak periods. Under TOU pricing schemes, CPP mechanisms set a higher rate than the peak rate during periods when it is more difficult to meet customer load [9,10].…”

Section: Introductionmentioning

confidence: 99%

Virtual electricity retailer for residents under single electricity pricing environment

Wang

Shen

et al. 2017

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

Various residential electricity pricing strategies provide diverse methods for calculating consumption costs. Due to the existence of electricity company monopolies and single residential electricity pricing systems, residents of certain areas have no option but to accept the electricity pricing offered to them. Based on local residential electricity pricing strategies, a virtual electricity retailer (VER) mechanism is put forward. The proposed VER mechanism includes a pricing package plan (PPP), a consumption-based plan, an add-on plan, and an exclusive plan. A PPP optimization pricing model was established to maximize VER profits when taking into account income, allowances from sponsors, expenditures and customer savings. Finally, payment processes were designed under a fixed pricing system and a time-of-use pricing environment. This case study shows the impact of PPPs and the allowance and demonstrates that the model helps customers save electricity while maximizing VER profits.

show abstract

Optimal time-of-use pricing for residential load control

Cited by 76 publications

References 12 publications

Autonomous trading in modern electricity markets

Autonomous trading in modern electricity markets

Robust Optimization for Household Load Scheduling with Uncertain Parameters

Virtual electricity retailer for residents under single electricity pricing environment

Contact Info

Product

Resources

About