Customer reward-based demand response program to improve demand elasticity and minimise financial risk during price spikes

2018 ◽  
Vol 12 (15) ◽  
pp. 3764-3771 ◽  
Author(s):  
Dao H. Vu ◽  
Kashem M. Muttaqi ◽  
Ashish P. Agalgaonkar ◽  
Abdesselam Bouzerdoum
Keyword(s):  
Demand Response ◽  
Financial Risk ◽  
Demand Elasticity ◽  
Demand Response Program ◽  
Price Spikes

scholarly journals Demand response model for TOU (Time of Use) analysis

2016 ◽  
Vol 19 (4) ◽  
pp. 5-13
Author(s):  
Binh Thi Thanh Phan ◽  
Thao Thi Thu Huynh
Keyword(s):  
Demand Response ◽  
Demand Elasticity ◽  
Analytical Models ◽  
Cost Share ◽  
The Third ◽  
Knowing That ◽  
The Neural Network ◽  
Demand Response Program ◽  
The Cost ◽  
Electrical Consumption

The demand response program is focused on changing the electrical consumption as the response to the time of use tariff changing. This program is considered by utilities currently. To estimate the effectiveness of TOU changing, the works try to find the analytical models expressing the changing of electrical consumption and electrical prices. All models are based on the assumption about the optimal response. This paper proposed three ways to find the models. The first way is based on the cost-share function knowing that the response is optimal. The second way is an approximately estimation of demand elasticity coefficients. The third is based on the neural network. The two first ways tried to find the analytical model, the third focused on the consumption response by prices of day.

scholarly journals Modelling Demand Response to Alleviate Congestion in Active Power Market

2021 ◽  
Vol 10 (2) ◽  
pp. , 1277-1283
Keyword(s):  
Demand Response ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Power Market ◽  
Congestion Management ◽  
Demand Elasticity ◽  
Willingness To Participate ◽  
Optimal Power ◽  
Demand Response Program ◽  
The Cost

A new method of congestion management in deregulated and competitive power system based on a combination of Demand Response (DR) program and generation re-dispatch is proposed in this research. One of DR program called Emergency Demand Response Program (EDRP) is carried out through customer's willingness to participate in this program in order to reduce their consumption during congestion. EDRP is modeled based on demand elasticity of the load and considering incentives. Different level of demand elasticity values is introduced to the customers to observe their contribution in congestion relief. The proposed method is examined on IEEE 30 bus system by using the Optimal Power Flow tool and it indicates that by integrating the customer’s elasticity for EDRP can decrease the cost to relieve the congestion and lead more benefit for all participants. The obtained results are the cost to manage congestion problem and optimal re-dispatch of generators by involving the participation of customers in EDRP.

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 Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1378
Author(s):  
Ildar Daminov ◽  
Rémy Rigo-Mariani ◽  
Raphael Caire ◽  
Anton Prokhorov ◽  
Marie-Cécile Alvarez-Hérault
Keyword(s):  
Ambient Temperature ◽  
Demand Response ◽  
Thermal Model ◽  
Critical Period ◽  
Optimization Problem ◽  
Integrated Management ◽  
Critical Periods ◽  
Network Access ◽  
Maximal Amplitude ◽  
Demand Response Program

(1) Background: This paper proposes a strategy coupling Demand Response Program with Dynamic Thermal Rating to ensure a transformer reserve for the load connection. This solution is an alternative to expensive grid reinforcements. (2) Methods: The proposed methodology firstly considers the N-1 mode under strict assumptions on load and ambient temperature and then identifies critical periods of the year when transformer constraints are violated. For each critical period, the integrated management/sizing problem is solved in YALMIP to find the minimal Demand Response needed to ensure a load connection. However, due to the nonlinear thermal model of transformers, the optimization problem becomes intractable at long periods. To overcome this problem, a validated piece-wise linearization is applied here. (3) Results: It is possible to increase reserve margins significantly compared to conventional approaches. These high reserve margins could be achieved for relatively small Demand Response volumes. For instance, a reserve margin of 75% (of transformer nominal rating) can be ensured if only 1% of the annual energy is curtailed. Moreover, the maximal amplitude of Demand Response (in kW) should be activated only 2–3 h during a year. (4) Conclusions: Improvements for combining Demand Response with Dynamic Thermal Rating are suggested. Results could be used to develop consumer connection agreements with variable network access.

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