Assessment of the potential of multifarious demand response programs in reducing transformer loss of life

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Nonika Loitongbam ◽  
Kumar Raja Gadham ◽  
T. Ghose
Keyword(s):  
Demand Response ◽  
Entropy Method ◽  
Loss Of Life ◽  
Average Ratio ◽  
Multi Attribute Decision Making ◽  
Transformer Model ◽  
Demand Response Programs ◽  
The Impact ◽  
Ranking Index ◽  
Spot Temperature

AbstractTransformer may enter the ageing cycle sooner if it is loaded more than the rated value for longer periods of time in its life cycle. This paper exploits demand response as a way to improve transformer life by reducing the hottest spot temperature (HST) which is caused due to better load profile. The aim of the paper is to investigate the impact of various types of price-based and incentive-based demand response programs (DRPs) on the transformer life and other attributes like energy consumption, peak to average ratio, etc. Entropy method is used to determine the weights of multi-attributes in a multi-attribute decision-making (MADM) model formed by the various attributes and the multifarious demand response programs. Using these weights, the various DRPs are ranked using Program Ranking Index to assist the utility in deciding which DRP is to be employed. IEEE transformer model is used to calculate the transformer ageing for two cases with and without demand response programs. The simulation results validate the effectiveness of demand response in mitigating transformer loss of life. Furthermore, the economic and technical benefits of employing demand response are quantified.

scholarly journals Demand Response Programs in Multi-Energy Systems: A Review

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4332
Author(s):  
Morteza Vahid-Ghavidel ◽  
Mohammad Sadegh Javadi ◽  
Matthew Gough ◽  
Sérgio F. Santos ◽  
Miadreza Shafie-khah ◽  
...  
Keyword(s):  
Energy Storage ◽  
Demand Response ◽  
Storage System ◽  
Energy Systems ◽  
Energy Storage System ◽  
Consumer Participation ◽  
Diverse Range ◽  
Good Communication ◽  
Demand Response Programs ◽  
The Impact

A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.

scholarly journals The impact of demand response programs on UPFC placement

2019 ◽  
Vol 27 (6) ◽  
pp. 4624-4639
Author(s):  
Abbas SHARIFI NASAB ANARI ◽  
Mehdi EHSAN ◽  
Mahmud FOTUHI FIRUZABAD
Keyword(s):  
Demand Response ◽  
Demand Response Programs ◽  
The Impact

scholarly journals Managing server clusters on intermittent power

2015 ◽  
Vol 1 ◽  
pp. e34
Author(s):  
Navin Sharma ◽  
Dilip Krishnappa ◽  
Sean Barker ◽  
David Irwin ◽  
Prashant Shenoy
Keyword(s):  
Demand Response ◽  
Distributed Applications ◽  
Energy Footprint ◽  
Activation Policy ◽  
Application Characteristics ◽  
Demand Response Programs ◽  
Server Clusters ◽  
The Impact ◽  
To Receive ◽  
Significant Attention

Reducing the energy footprint of data centers continues to receive significant attention due to both its financial and environmental impact. There are numerous methods that limit the impact of both factors, such as expanding the use of renewable energy or participating in automated demand-response programs. To take advantage of these methods, servers and applications must gracefully handle intermittent constraints in their power supply. In this paper, we propose blinking—metered transitions between a high-power active state and a low-power inactive state—as the primary abstraction for conforming to intermittent power constraints. We design Blink, an application-independent hardware–software platform for developing and evaluating blinking applications, and define multiple types of blinking policies. We then use Blink to design both a blinking version of memcached (BlinkCache) and a multimedia cache (GreenCache) to demonstrate how application characteristics affect the design of blink-aware distributed applications. Our results show that for BlinkCache, a load-proportional blinking policy combines the advantages of both activation and synchronous blinking for realistic Zipf-like popularity distributions and wind/solar power signals by achieving near optimal hit rates (within 15% of an activation policy), while also providing fairer access to the cache (within 2% of a synchronous policy) for equally popular objects. In contrast, for GreenCache, due to multimedia workload patterns, we find that a staggered load proportional blinking policy with replication of the first chunk of each video reduces the buffering time at all power levels, as compared to activation or load-proportional blinking policies.

scholarly journals Optimal Portfolio Selection Methodology for a Demand Response Aggregator

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7923
Author(s):  
Pedro Nel Ovalle ◽  
José Vuelvas ◽  
Arturo Fajardo ◽  
Carlos Adrián Correa-Flórez ◽  
Fredy Ruiz
Keyword(s):  
Demand Response ◽  
Optimal Portfolio ◽  
Equilibrium Constraints ◽  
Indirect Control ◽  
Optimal Portfolio Selection ◽  
Upper Level ◽  
Selection Methodology ◽  
Demand Response Programs ◽  
The Impact

This paper presents a methodology for determining the optimal portfolio allocation for a demand response aggregator. The formulation is based on Day-Ahead electricity prices, in which the aggregator coordinates a set of residential consumers that are recruited through contracts. Four types of contracts are analyzed, considering both direct and indirect demand response programs. The objective is to compare different scenarios for contract portfolios in order to establish the benefits of each market agent. An optimization problem is formulated to capture the interactions between the aggregator and end consumers. The model is formulated as a mathematical program with equilibrium constraints: At the upper level, the aggregator maximizes its benefits, whereas the lower level represents the consumers’ contracts. By applying the developed methodology, the characterization of the consumers’ behavior is established in order to forecast their responses to the generation of punctual incentives, both for usual scenarios and peak events, as well as to evaluate the impact that direct and indirect control contracts have on the performance of the aggregator as the energy price varies.

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