Nodal price volatility reduction and reliability enhancement of restructured power systems considering demand–price elasticity

Goel, L.; Wu, Qiuwei; Wang, Peng

doi:10.1016/j.epsr.2008.02.012

Cited by 32 publications

(7 citation statements)

References 25 publications

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“…Modeling single period elastic loads For exponential structure of customer response function, the benefit function can be obtained by Taylor expansion of B(d(i)) as following [15]:…”

Section: Exponential Modelingmentioning

confidence: 99%

“…Obviously, there are many possible structural forms for customer response. Linear economic models of price responsive loads for DR programs have been developed in [1,[11][12][13][14][15][16]. Since the optimization problem of the customer profit is nonlinear, it is necessary to develop nonlinear economic models of price responsive loads for more realistic characterization of the demand.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of nonlinear models for time-based rates demand response programs

Aalami

Moghaddam

Yousefi

2015

International Journal of Electrical Power & Energy Systems

111

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“…Modeling single period elastic loads For exponential structure of customer response function, the benefit function can be obtained by Taylor expansion of B(d(i)) as following [15]:…”

Section: Exponential Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of nonlinear models for time-based rates demand response programs

Aalami

Moghaddam

Yousefi

2015

International Journal of Electrical Power & Energy Systems

111

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“…Under the real-time pricing environment, there will be some elasticity in electricity demand. According to the principles of economics, the relationship between the user demand D and the price  can be described by electricity demand curve [6] , which is shown in Figure 2. Figure 2 illustrates that, if the price is high, the customers will make a timely response according to their own electricity consumption characters, their production mode and schedule will be adjusted to reduce electricity expenses.…”

Section: Electricity Demand Elasticity Modelmentioning

confidence: 99%

Real-time pricing of distribution network based on node load sensitivity

Chen¹,

Chen²,

Wang³

et al. 2016

CIRED Workshop 2016

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The smart distribution network requires the customers to participate in the regulation of grid, optimizes the electric curve and realizes the peak load shifting by using the real-time pricing. According to the geographical distribution of loads in the distribution network, the sensitivity of the loss of distribution network to nodal load is derived, and the model of the sensitive relationship of load node is provided. Combing the model and the elasticity curve of electric demand, the influence of different kinds of loads on real-time pricing is analysed. And the model of real-time pricing for distribution network is proposed. The simulation result based on the IEEE69 nodes system shows that the longer power supply radius and the bigger the load density are, the bigger the sensitivity of nodal load is and the higher the real-time price of node is. At the same time, the bigger the elastic demand is, the more the real-time pricing changes. The result also shows that the peakvalley difference of the grid is reduced significantly.

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“…Kirschen & Strbac [15] propose a general scheme to incorporate the short-term elasticity in generation scheduling and price setting. This methodology has been employed by, amongst others, Goel et al [16] to analyse the impact of an elastic demand on nodal prices and system reliability. Wolfgang & Doorman [17] showed that when the demand for electricity becomes elastic, the effects on other markets should be incorporated, as customers shift towards or away from substitutes.…”

Section: Literature Study: Modelling Adrmentioning

confidence: 99%

Short-term demand response of flexible electric heating systems: The need for integrated simulations

Bruninx

Patteeuw

Delarue

et al. 2013

2013 10th International Conference on the European Energy Market (EEM)

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Active Demand Response (ADR) can contribute to a more (cost-)efficient operation of and investment in the electrical power system as it provides the needed flexibility to cope with the intermittent character of renewables. One of the promising demand side technologies in terms of ADR are electric heating systems as they allow to modify their electrical load pattern without affecting the thermal energy service they deliver, due to the thermal inertia in the system. However, these systems are hard to describe with traditional demand side models, since the performance depends on boundary conditions (occupants behaviour, weather conditions). Therefore, in this paper, an integrated system approach is applied, taking into account the dynamics and constraints of both electricity supply and heating systems. Only such an integrated system approach is able to simultaneously consider all technical and comfort constraints present in the system. The effects not captured by traditional approaches -such as price elasticities and virtual generator models -are identified and quantified, enabling the reader to select a modelling approach, weighing the computational effort against the required accuracy. In extensive power system studies, this approach can be used to assess the technical potential and all effects of flexible demand side technologies.

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Nodal price volatility reduction and reliability enhancement of restructured power systems considering demand–price elasticity

Cited by 32 publications

References 25 publications

Evaluation of nonlinear models for time-based rates demand response programs

Evaluation of nonlinear models for time-based rates demand response programs

Real-time pricing of distribution network based on node load sensitivity

Short-term demand response of flexible electric heating systems: The need for integrated simulations

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