A scheme for building demand response based on a comprehensive load profile

2012 ◽  
Author(s):  
Weixuan Lin ◽  
Jinchi Han ◽  
Rui Xia ◽  
Dawei He
Keyword(s):  
Demand Response ◽  
Load Profile

A new quantitative load profile measure for demand response performance evaluation

2020 ◽  
Vol 121 ◽  
pp. 106073 ◽  
Author(s):  
Ehsan Saeidpour Parizy ◽  
Ali Jahanbani Ardakani ◽  
Arash Mohammadi ◽  
Kenneth A. Loparo
Keyword(s):  
Performance Evaluation ◽  
Demand Response ◽  
Load Profile ◽  
Response Performance

scholarly journals Demand Response Economic Assessment with the Integration of Renewable Energy for Developing Electricity Markets

2020 ◽  
Vol 12 (7) ◽  
pp. 2653 ◽  
Author(s):  
Abdul Conteh ◽  
Mohammed Elsayed Lotfy ◽  
Oludamilare Bode Adewuyi ◽  
Paras Mandal ◽  
Hiroshi Takahashi ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Demand Response ◽  
Peak Load ◽  
Load Factor ◽  
Gas Emissions ◽  
Load Profile ◽  
Profile Modification ◽  
Load Demand

Electricity disparity in sub-Saharan Africa is a multi-dimensional challenge that has significant implications on the current socio-economic predicament of the region. Strategic implementation of demand response (DR) programs and renewable energy (RE) integration can provide efficient solutions with several benefits such as peak load reduction, grid congestion mitigation, load profile modification, and greenhouse gas emissions reduction. In this research, an incentive and price-based DR programs model using the price elasticity concepts is proposed. Economic analysis of the customer benefit, utility revenue, load factor, and load profile modification are optimally carried out using Freetown (Sierra Leone) peak load demand. The strategic selection index is employed to prioritize relevant DR programs that are techno-economically beneficial for the independent power producers (IPPs) and participating customers. Moreover, optimally designed hybridized grid-connected RE was incorporated using the Genetic Algorithm (GA) to meet the deficit after DR implementation. GA is used to get the optimal solution in terms of the required PV area and the number of BESS to match the net load demand after implementing the DR schemes. The results show credible enhancement in the load profile in terms of peak period reduction as measured using the effective load factor. Moreover, customer benefit and utility revenues are significantly improved using the proposed approach. Furthermore, the inclusion of the hybrid RE supply proves to be an efficient approach to meet the load demand during low peak and valley periods and can also mitigate greenhouse gas emissions.

Impact of Load Profile on Dynamic Interactions Between Energy Markets: A Case Study of Power Exchange and Demand Response Exchange

2019 ◽  
Vol 15 (11) ◽  
pp. 5855-5866 ◽  
Author(s):  
Srikanth Reddy Konda ◽  
Ameena Saad Al-Sumaiti ◽  
Lokesh Kumar Panwar ◽  
Bijaya Ketan Panigrahi ◽  
Rajesh Kumar
Keyword(s):  
Demand Response ◽  
Energy Markets ◽  
Dynamic Interactions ◽  
Load Profile ◽  
Power Exchange

scholarly journals Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1348 ◽  
Author(s):  
Eunjung Lee ◽  
Jinho Kim ◽  
Dongsik Jang
Keyword(s):  
Demand Response ◽  
Customer Segmentation ◽  
Stable Operation ◽  
Peak Time ◽  
Self Organizing Map ◽  
Daily Consumption ◽  
Large Variability ◽  
Load Profile ◽  
Residential Demand ◽  
Profile Segmentation

Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers.

Investigating the Impact of Load Profile Attributes on Demand Response Exchange

2018 ◽  
Vol 14 (4) ◽  
pp. 1382-1391 ◽  
Author(s):  
Srikanth Reddy Konda ◽  
Lokesh Kumar Panwar ◽  
Bijaya Ketan Panigrahi ◽  
Rajesh Kumar
Keyword(s):  
Demand Response ◽  
Load Profile ◽  
On Demand ◽  
The Impact
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