Optimal Demand-Side Response to Electricity Spot Prices for Storage-Type Customers

Daryanian,; Bohn, Roger E.; Tabors, Richard D.

doi:10.1109/mper.1989.4310873

Cited by 52 publications

(58 citation statements)

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“…The necessity of large consumer participating in electricity market is elaborated in [7]. In [8], the optimal response of electricity consumer to the pool prices is presented. The optimal procurement and bidding strategies in the electricity markets are presented in [9] for the decision maker of consumer.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program

Nojavan

Aalami

2015

Energy Conversion and Management

128

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program

Nojavan

Aalami

2015

Energy Conversion and Management

128

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“…In particular, we assign 30 % of the entities to use the clothes washer and dryer during morning hours and remaining during evening hours. For the morning use, we set a preferred schedule of Uniform (7,9) for clothes washer and Uniform (8, 10) for dryer. The evening schedule is set as Uniform (16,18) for clothes washer, Uniform (17,19) for dryer and Uniform (20, 22) for dishwasher.…”

Section: Data Collection and Scenario Designmentioning

confidence: 99%

“…However, to date most DR literature assumes that in the presence of financial incentives consumers' decision on energy consumption will be driven purely by cost minimization. For example, Daryanian et al [7] propose one of the earliest model on DR, in which they develop an optimization model for energy users to determine an optimal response to spot prices with the objective of minimizing total electricity cost. More recently, Mohsenian-Rad et al [13] discuss the use of a distributed algorithm on smart meters to find optimal consumption schedules for subscribers to minimize total energy cost.…”

Section: Introductionmentioning

confidence: 99%

A simulation model with multi-attribute utility functions for energy consumption scheduling in a smart grid

et al. 2015

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When consumers' use of electricity is mainly driven by convenience, coincident demand occurs, resulting in electric load peaks. Consequently, the undesirable large gaps between peak and off-peak loads will adversely affect the system's efficiency due to unused capacity during off-peak hours and extra ancillary generators required during peak hours. Demand response (DR) has long been proposed to reduce peak load by providing incentives to encourage consumers to shift their peak loads to off-peak periods. In most DR literature, incentive schemes are purely financial in assuming that cost is the only parameter to influence consumers' load-shifting behavior. In this paper, we assume that in addition to cost, convenience of energy usage is also an important factor when consumers respond to DR programs. Hence, we use multi-attribute utility functions consisting of both cost and convenience factors to model consumer behavior on energy consumption for home appliances. The "convenience" herein is defined as being able to use an appliance during one's preferred time window. A simulation model is developed to study a residential population consisting B Lihui Bai 123 Khadgi et al.of heterogeneous households with varying preference of convenience over cost. We study the effects of time-of-use pricing structure on users' utility-based load shifting behaviors and subsequently on system-wide performances such as peak to average ratio (PAR) and load variance (LV). We also describe a method of design of experiment (DOE) for determining an optimal time-of-use rate structure that minimizes both PAR and LV for the system.

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“…The presence of an appliance within a household and the decision whether and when these will be activated is determined using a pseudo random number generator in combination with Tables 3, 4, Eqs. (1) and (2). The consumption of appliances is be picked from a set of prior performed measurements and added to the overall consumption of the household.…”

Section: Consumption Curve Creationmentioning

confidence: 99%

“…Often, papers describe DSM as a reaction on varying prices of electricity [2][3][4][5][6]. A drawback of this approach is, that if the price falls under a certain threshold, many households will activate appliances with complex programs, like washing machines, simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Quality-functions for an uniform and comparable analysis of demand side management algorithms

et al. 2014

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Due to renewable energies, the feed-in to the power grid will fluctuate increasingly. As long as no highly efficient storage technology is found, the importance of demand side management (DSM) will grow. Different algorithms have been proposed for managing the consumers. However, they cannot be easily compared due to a lot of different assumptions while generating the consumption curves and pursuing different goals in each DSMalgorithm. This paper describes a method to transparently generate a data-basis as input for the DSM-algorithms to produce comparable results. A number of quality-functions (Q-functions) are mathematically described and discussed. The Q-Functions allow a consistent evaluation of DSMalgorithms by analysing managed and unmanaged (original) consumption curves.

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Optimal Demand-Side Response to Electricity Spot Prices for Storage-Type Customers

Cited by 52 publications

References 0 publications

Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program

Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program

A simulation model with multi-attribute utility functions for energy consumption scheduling in a smart grid

Quality-functions for an uniform and comparable analysis of demand side management algorithms

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