Optimal combined scheduling of generation and demand response with demand resource constraints

Kwag, Hyung-Geun; Kim, Jin-O

doi:10.1016/j.apenergy.2011.12.075

Cited by 55 publications

(40 citation statements)

References 12 publications

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“…In [20], combined scheduling of generation and DR has been studied by considering DR as virtual generation and hence, similar to constraint of Generation, DR constraint have been considered based on customer registration and participation.…”

Section: F Methods Of Demand Responsementioning

confidence: 99%

A critical review on Demand Response scenario

Tambe

Roy

2012

2012 11th International Conference on Environment and Electrical Engineering

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Section: F Methods Of Demand Responsementioning

confidence: 99%

A critical review on Demand Response scenario

Tambe

Roy

2012

2012 11th International Conference on Environment and Electrical Engineering

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“… ρ DR and ρ EE are the prices that must be paid by retailer to consumers for their participation in DR and EE programs. C DR , C EE , C PENd , d , and ρ are DR and EE programs implementation cost, penalties for nonfulfillment of the commitments, amount of purchased capacity and the price of this capacity. Equations to also show the cost functions for implementing DR and EE programs and their constraints, respectively. These two functions are quadratic functions of DSM resources implementation.…”

Section: Modeling Of the Retailer's Capacity Productmentioning

confidence: 99%

“…Numerical studies have been done about one retailer's modeling and its trading with consumers and the capacity market on modified IEEE 30‐bus test system . The information is rare about DSM's cost functions and scenarios of uncertain parameters . In the cases of data shortage, reasonable estimations have been considered.…”

Section: Numerical Studiesmentioning

confidence: 99%

Modeling of retailer's behavior for participation in the capacity market

Jalili

Eslami

Moghaddam

et al. 2019

Int Trans Electr Energ Syst

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Summary Due to economic reasons, only way for demand side management resources participation in capacity market is their participation by load serving entities, energy service companies, or retailers. To achieve the optimal participation of retailers in the capacity market, it is crucial to model the effective parameters in retailers’ behavior model and how they trade with the independent system operator and the consumers. So a novel method is proposed in this paper for modeling retailers’ behavior, considering demand side management resources availability for their participation in long term commitments. The capacity product of the retailer and its behavior are comprehensively modeled to trade in the capacity market. The competition of retailers is also considered and its effects on the profit of the retailer and consumers are analyzed. A genetic algorithm has been used to solve this problem due to its ability for solving large‐scale real nonlinear Non‐deterministic Polynomial‐time hard (NP‐hard) optimization problems.

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“…Moreover, generation scheduling is a crucial challenge in power systems, especially under new environment of liberalization of the electricity industry [6]. After restructuring, the performance of power systems has changed a lot that one of these changes is related to using Demand Response (DR) programs and spot pricing [7]. In the previous decades, the generation scheduling of power plants has been obtained by considering the constant value of demand, whereas after restructuring, the value of demand could be changed according to different electricity prices [8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of elasticity of electricity demand in Iran: New empirical evidence using aggregate data

Zin

Moradi

Khairuddin

et al. 2015

2015 IEEE Student Conference on Research and Development (SCOReD)

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as electricity markets are liberalized, consumers become exposed to more volatile electricity prices and may decide to modify the profile of their demand to reduce their electricity costs. Therefore, accurate prediction of consumers' reaction to the electricity price changes can be effective in order to increase the profits of producers and consumers. This paper classifies the variety of consumers' reaction to the electricity price changes. Then, the price elasticity is estimated for a real network in Iran based on collected data from statistical analysis. To achieve this, the consumption period (1 day) is divided into three groups (Peak, Off-peak, Low) and mathematical definition model of elasticity is applied to estimate price elasticity. Results revealed that the highest elasticity in peak period belongs to residential customers due to their highest demand-level in the peak period.

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Optimal combined scheduling of generation and demand response with demand resource constraints

Cited by 55 publications

References 12 publications

A critical review on Demand Response scenario

A critical review on Demand Response scenario

Modeling of retailer's behavior for participation in the capacity market

Estimation of elasticity of electricity demand in Iran: New empirical evidence using aggregate data

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