Data-Driven Targeting of Customers for Demand Response

Kwac, Jungsuk; Rajagopal, Ram

doi:10.1109/tsg.2015.2480841

Cited by 68 publications

(32 citation statements)

References 27 publications

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“…Valero et al propose the capability of self‐organizing maps to classify customers, and their response potential, in day‐ahead or real‐time products, using distributor, energy trader, or customer electrical demand database was analyzed, focusing on customer targeting. Kwac and Rajagopal propose that the use of large‐scale data from smart meters for customer targeting was analyzed and tested experimentally with data from more than 58 000 residential households. The targeting problem is formulated as a knapsack problem, composed by a simple linear response modeling and a fast heuristic selection algorithm.…”

Section: A Brief Overview Of Price‐based Demand Response Techniquesmentioning

confidence: 99%

“…The multiplier indexes w 1 , w 2 , and w 3 perform the weighting between the goals. In (13), the first term is aimed to meet the electrical load shifting goals, in other words, the objective of the DR program (active power flow reduction on the head of the distribution feeder, ie, the output from power substation, on critical periods). The second term represents the monthly revenue variation estimated for the power utility with the application of the DR program.…”

Section: Bus-based Approachmentioning

confidence: 99%

“…being OF bus described in (13). Basically, the global approach performs a single optimization for all eligible customers.…”

Section: Global Approachmentioning

confidence: 99%

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Customer targeting optimization system for price-based demand response programs

Siebert

Aoki

Fernandes

et al. 2018

Int Trans Electr Energ Syst

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Summary In a demand‐side management (DSM) program, a set of activities are designed to influence electricity consumption in a way that may produce desired changes in the utility's load shape. Detailed analysis when planning DSM programs is crucial because the utility intends to achieve technical and economic benefits but does not wish to lose revenue unnecessarily. To assist utilities in planning for price‐based demand response (DR) programs (an approach to DSM), this paper proposes an optimization system focused on the targeting of residential customers to enroll in a new time‐of‐use tariff. The developed system uses optimal power flow to estimate the reduction needed to achieve the proposed objectives of the DR program in each bus, followed by the selection of the customers that will provide this reduction via binary particle swarm optimization. The optimization system has been tested using real data acquired from a Brazilian utility including customers' load curves, radial distribution feeder data, a time‐of‐use tariff, and estimated elasticity matrices from the literature. The main results show that the system can be of great value supporting power utilities' implementation of price‐based DR programs.

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Section: A Brief Overview Of Price‐based Demand Response Techniquesmentioning

confidence: 99%

Section: Bus-based Approachmentioning

confidence: 99%

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Customer targeting optimization system for price-based demand response programs

Siebert

Aoki

Fernandes

et al. 2018

Int Trans Electr Energ Syst

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show abstract

“…Peak load pricing (PLP)- [61,62] Variable price Fixed price- [26] Time of Use (TOU)- [26,[57][58][59][60] Flat pricing- [35,57] Ancillary service- [56] Capacity market- [28] Emergency binding- [28] Demand binding- [28] Interruptible load- [55] Direct load control (DLC)- [48][49][50][51][52][53][54] …”

Section: Customer Participationmentioning

confidence: 99%

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Divshali

Choi

2016

Energies

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Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new electrical demand sectors, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionary new power grid system, called smart grid, has been developed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since conventional distribution networks lack smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of the electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG.

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“…In terms of non-regulated services or third-party services, several works in the literature describe data-driven services and business cases to boost demand response potential and promote energy efficiency. Smart meter data can be used by a third-party for segmentation of customers and identification of temporal consumption patterns [10], predicting customer response to price signals [17], estimating the price elasticity of customers [13] and derive optimal bidding strategies under dynamic electricity tariffs [27]. In fact, total energy consumption at the residential level is enough to derive a ranking of thermal-load flexibility and sub-metering is not required [2].…”

Section: Introductionmentioning

confidence: 99%

Data Economy for Prosumers in a Smart Grid Ecosystem

Bessa

Rua

Abreu

et al. 2018

Proceedings of the Ninth International Conference on Future Energy Systems

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Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.

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Data-Driven Targeting of Customers for Demand Response

Cited by 68 publications

References 27 publications

Customer targeting optimization system for price-based demand response programs

Customer targeting optimization system for price-based demand response programs

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Data Economy for Prosumers in a Smart Grid Ecosystem

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