An overview of Demand Response: Key-elements and international experience

Paterakis, Nikolaos G.; Erdinç, Ozan; Catalào, João P. S.

doi:10.1016/j.rser.2016.11.167

Cited by 486 publications

(238 citation statements)

References 115 publications

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“…In fact, most of the recent and relevant literature in demand response, namely review papers, still insist in the demand response opportunities and flexibility options that are more and more evident with the increase of technology that supports demand response by providing examples of practical evidence of DR implementations and identifying the most relevant barriers without referring to possible innovative approaches for aggregation and remuneration [6,27]. Most of the identified barriers are related to market structures and incentives regarding the incentivizing consumers to participate in DR programs [28].…”

Section: Related Literaturementioning

confidence: 99%

“…Demand response is divided in two types including price and incentive-based where the first corresponds to the response of consumers given a price signal (price variation) and, the latter, to the response of consumers given monetary incentives (tax relief, payment) [5][6][7][8][9][10]. These two types of demand response are used by different entities and to distinct consumers.…”

Section: Introductionmentioning

confidence: 99%

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Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

2018

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Demand response aggregators have been developed and implemented all through the world with more seen in Europe and the United States. The participation of aggregators in energy markets improves the access of small-size resources to these, which enables successful business cases for demand-side flexibility. The present paper proposes aggregator's assessment of the integration of distributed energy resources in energy markets, which provides an optimized reschedule. An aggregation and remuneration model is proposed by using the k-means and group tariff, respectively. The main objective is to identify the available options for the aggregator to define tariff groups for the implementation of demand response. After the first schedule, the distributed energy resources are aggregated into a given number of groups. For each of the new groups, a new tariff is computed and the resources are again scheduled according to the new group tariff. In this way, the impact of implementing the new tariffs is analyzed in order to support a more sustained decision to be taken by the aggregator. A 180-bus network in the case study accommodates 90 consumers, 116 distributed generators, and one supplier.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

2018

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“…Very poor (VP) (0,0,1) Poor (P) (0,1,3) Medium poor (MP) (1,3,5) Fair (F) (3,5,7) Medium good (MG) (5,7,9) Good (G) (7,9,10) Excellent (E) (9,10,10) More procedures of the modified approach are shown as:…”

Section: Linguistic Variables Tfnsmentioning

confidence: 99%

Evaluating the Comprehensive Performance of Demand Response for Commercial Customers by Applying Combination Weighting Techniques and Fuzzy VIKOR Approach

Dong¹,

Huo²,

Liu³

et al. 2017

Sustainability

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Abstract:In order to guarantee the sustainability of power industries, demand response is widely developed in China with the improvement of power markets. Massive potential flexible resources in the commercial sector are valuable to carry out continuous demand response programs. This paper presented a hybrid framework to evaluate the performance of such programs. Considering that assessment processes involve multiple decisions for massive criteria under fuzzy conditions, we proposed a fuzzy multi-criteria decision making model to evaluate the performance of commercial demand response based on the concepts of a fuzzy Vlsekriterijumska Optimizacijia I Kompromisno Resenje method and a L2-metric distance. The weighting determination process in the model was modified by integrating subjective opinions and objective information according to a fuzzy Analytic Hierarchy Process and Criteria Importance Through Intercriteria Correlation methods. Then a comprehensive evaluation index system for demand response performance was established by using a fuzzy Delphi method based on experts' opinions, including the five aspects of economy, society, technology, environment and management. Finally, the practicality of the proposed hybrid framework was verified through an empirical analysis of five such programs in Chinese commercial buildings. Their comprehensive performances were ranked effectively. Sub-criteria affiliated with society and environment should be more attention than the other evaluation criteria based on experts' judgments and objective information. Moreover, a set of sensitivity analyses were performed to confirm the robustness and effectiveness of the proposed framework and the evaluation results. The study findings can offer references for the improvement of demand response and relevant policy formulation.

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“…Due to this issue, GENCOs are sometimes not able to meet the customer demands, hence making them unsatisfied or prompting them to terminate their contracts. Some of the growing issues associated with power system operation include limited supply of system resources that in turn forces the operators to operate their systems at their maximum capacity, resulting in regular price hikes in the electricity market [2]. All the aforementioned limitations motivate us to search for and explore novel ways to increase the efficiency of resource utilization in power operations.…”

Section: Introductionmentioning

confidence: 99%

Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit

2017

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Over the recent years there has been an immense growth in load consumption due to which, Load Management (LM) has become more significant. Energy providers around the world apply different load management concepts and techniques to improve the load profile. In order to reduce the stress over the load management, Demand Response Unit Commitment (DRUC), a new concept, has been implemented in this paper. The main feature of this concept is that both the energy providers and consumers must participate in order to get mutual benefits hence maximizing each of their profits. In this paper we discuss the time-based Demand Response Program since there is no penalty observed in this program. When the Demand Response was combined with Unit Commitment and compiled it was observed that a satisfactory solution resulted, which is proved to be mutually beneficial for both Generating Companies (GENCOs) and their customers. Here, we have used a Cat Swarm Optimization (CSO) technique to find the solution for the DRUC problem. The results are obtained using CSO technique for UC problem with and without DR program. This is compared with the results obtained using other conventional methods. The test system considered for the study is IEEE39 bus system.

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An overview of Demand Response: Key-elements and international experience

Cited by 486 publications

References 115 publications

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

Evaluating the Comprehensive Performance of Demand Response for Commercial Customers by Applying Combination Weighting Techniques and Fuzzy VIKOR Approach

Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit

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