Design of incentive price for voluntary Demand Response Programs using fuzzy system

Gadham, Kumar Raja; Ghose, T. K.

doi:10.1109/icepes.2016.7915958

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…In order to determine which of those methodologies produces the most accurate results and reduces systematic errors, a large number of studies have been conducted. 4,5 The best baseline methodology is determined by the type of customers that are inducted in the program. End-users with demand that highly interacts with the outdoor temperature can be precisely determined using a common methodology that is based on temperature.…”

Section: Energy Demand and Electricity Marketsmentioning

confidence: 99%

An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

Panagiotidis

Effraimis²,

Xydis

2018

Energy & Environment

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The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

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Section: Energy Demand and Electricity Marketsmentioning

confidence: 99%

An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

Panagiotidis

Effraimis²,

Xydis

2018

Energy & Environment

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“…One way of achieving this is to dispatch proper messages which include suggestions for operating specific devices during proper time slots. Despite the potential of load shifting in residential customers, a key factor for the success of a DR event is the responsiveness of customers to different kinds of incentives [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…contains the optimisation variables; it is a T × N • P N -sized matrix with binary elements, where N is the number of shiftable appliances in a household, T is the number of total time slots of the optimisation horizon (e.g., 24, 96 with hourly, quarterly resolution etc.) and P N is the number of total possible permutations of all devices; the dashed separators of the table signify the c n columns corresponding to appliance n and the total number of possibilities in which it can appear; o i,j is equal to 1 if the device is selected to be operating, otherwise it is 0; the indices t,c correspond to time slot and the permutation of appliance n;…”

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confidence: 99%

Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration

et al. 2021

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In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.

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“…It also a necessity to include charging network when users would like to switch from 3G to 4G network [7,28,29]. That is why in this paper, the improved IRC [30,31] model by determining the base cost as a decision variable by using the BER QoS attribute is attempted to be designed. This QoS attribute pays important role in measuring the quality of network in terms of bit error per unit time.…”

Section: Introductionmentioning

confidence: 99%

Models of improved multilink reverse charging network by utilizing the bit error rate QoS attribute

Puspita

Rohania

Yuliza

et al. 2021

IJEECS

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<span>In this paper, a modification model for single-link reverse charging of internet is formed on a multi-link wireless network. The pricing scheme also takes into account the base costs and quality of services provided by the service provider. Bit error rate (BER) was utilized as one of the well-known quality of service (QoS) attribute that can guarantee best performance for internet service provider (ISP) and users. The base price is determined as a decision variable to help ISP to maximize profit. This optimization model can be solved using the LINGO 13.0 program to gain optimal values. The computational results show that by setting costs as constants and service quality as variables, optimal results are obtained for ISPs. This can make ISP considerations in determining the base price that can benefit the ISP and according to the services provided</span>.

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Design of incentive price for voluntary Demand Response Programs using fuzzy system

Cited by 7 publications

References 13 publications

An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration

Models of improved multilink reverse charging network by utilizing the bit error rate QoS attribute

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