scholarly journals Real-time electricity pricing for demand response using online convex optimization

ISGT 2014 ◽  
2014 ◽  
Author(s):  
Seung-Jun Kim ◽  
Geogios B. Giannakis
Keyword(s):  
Convex Optimization ◽  
Real Time ◽  
Demand Response ◽  
Electricity Pricing ◽  
Online Convex Optimization

A Research into Probabilistic Electricity Load Prediction Based on Demand Response Feature under Smart Grid Environment

2013 ◽  
Vol 380-384 ◽  
pp. 3098-3102
Author(s):  
Ning Lu ◽  
Ying Liu
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Electricity Market ◽  
Electricity Price ◽  
Electricity Pricing ◽  
Load Prediction ◽  
Characteristics Analysis ◽  
System Risk ◽  
Probabilistic Load ◽  
Demand Changes

The construction of grid plays an important role in national economic development, social stability and peoples life. In case that electricity market adopts real time electricity price, users active participation and real time response to electricity price will change the traditional load prediction from rigid forecasting to flexible forecasting which takes electricity demand response into consideration. By using wavelet analysis and error characteristics analysis, the researches into the probabilistic predicting method for demand changes under the real time electricity pricing is carried out. The probabilistic load prediction result shall enable decision makers to better understand the load change range in the future and make more reasonable decision. Meanwhile, it shall provide support to electricity system risk analysis.

scholarly journals The Design of an Optimal Demand Response Controller Under Real Time Electricity Pricing

2013 ◽  
Vol 8 (3) ◽  
pp. 436-445 ◽  
Author(s):  
Young Gyu Jin ◽  
Tae-Seop Choi ◽  
Sung Chan Park ◽  
Yong Tae Yoon
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Electricity Pricing

scholarly journals Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response

2021 ◽  
Vol 13 (6) ◽  
pp. 3400
Author(s):  
Jia Ning ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Bin Chen ◽  
Yi Tang
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Demand Response ◽  
System Operation ◽  
Real Time Scheduling ◽  
High Penetration ◽  
Time Scheduling ◽  
Grid Load ◽  
Grid Side ◽  
Scheduling Models

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.

scholarly journals Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4597
Author(s):  
Zi-Xuan Yu ◽  
Meng-Shi Li ◽  
Yi-Peng Xu ◽  
Sheraz Aslam ◽  
Yuan-Kang Li
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Positive Impact ◽  
Environmental Aspects ◽  
Component Size ◽  
Cost Of Energy ◽  
Four Seasons ◽  
Demand Response Program ◽  
Real Time Pricing

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

scholarly journals Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6620
Author(s):  
Arman Alahyari ◽  
David Pozo ◽  
Meisam Farrokhifar
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Tracking Problem ◽  
Smart Meters ◽  
Set Point ◽  
Point Tracking ◽  
Power Set ◽  
Decision Making Framework

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

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