A linear programming approach to the electricity contract capacity problem

Chen, Chiung-Yao; Liao, Ching‐Jong

doi:10.1016/j.apm.2011.02.032

Cited by 30 publications

(28 citation statements)

References 4 publications

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“…In mainland China, utilities allow large customers to adjust their contract demand monthly [35], if the peak demand does not exceed the contract demand, a fixed demand charge is levied; on the other hand, if the peak demand exceeds the contract demand, a penalty charge twice as the basic rate is levied. In Southern Africa, the contract demand is determined by the notified maximum demand [35]. Customers can temporarily or permanently increase/decrease their notified maximum demand, and their demand charge is based on the maximum of the measured demand and notified maximum demand.…”

Section: Literature Reviewmentioning

confidence: 99%

“…If the peak demand does not exceed the contract capacity, a fixed capacity charge is levied. On the other hand, if the peak demand exceeds the contract capacity, a penalty charge from two to three times the basic rate is levied [35]. Hence, choosing an excessively low contract capacity will impose high capacity charges, while choosing an excessively high contract capacity may result in an unnecessary basic capacity charge.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are different contributions to find the optimal contract capacity for energy consumers. To determine the electricity contract capacity for industrial customers in Taiwan Chen et al [35] used a linear programming approach. To calculate the optimal capacity for peak, semi-peak, and off-peak capacities respectively, Tsay et al [39] used meta-heuristic evolutionary programming method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, when the demand exceeds the upper or lower limits of the contract option, the double power price is charged on the excess quantity as introduced by Rodoplu et al [37]. In Taiwan, an excess within 10 % of the contract demand is penalized at twice the rate of the contract demand, whereas the excess over 10 % of the contract demand is charged at thrice the rate, and the contract demand can be changed by high-tension industrial customers each month as modeled by Chen and Liao [35].…”

Section: Contributionmentioning

confidence: 99%

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Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties

et al. 2019

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This study presents a mathematical formulation to optimize contracting capacity strategies of multisources of energy for institutional or industrial consumers considering demand uncertainties. The objective consists of minimizing the total costs composed of the different types of energy contract capacity costs, penalty price, and an ecofriendly factor. The penalty price is charged on the demand of energy exceeding the total contract capacities. The ecofriendly factor encourages the use of renewable energy and reduces the traditional energy used in the optimal mix of energy sources. The proposed model is tested based on demand of energy inspired from real data. These numerical experiments are analyzed to illustrate the impact of encouraging the use of renewable energy sources by introducing the ecofriendly factor and the influence of penalty price and uncertainty in the demand of energy. The results show that in the presence of low penalty price or low uncertainty a large amount of ecofriendly support is needed for using more renewable energy sources in the optimal contract capacity combination.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

See 2 more Smart Citations

Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties

et al. 2019

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“…Therefore, signing an extremely low capacity on the contract can impose high capacity charges, while signing an extremely high capacity on the contract may result in an unnecessary basic capacity charge. Hence, to decide a proper contract capacity have received significant attention from critical electricity consumers [1]. Furthermore, problems occur when consumers demand more than promises.…”

Section: Billing Of Electricity Usagementioning

confidence: 99%

Clustering of Complementary Electricity Consumers Based on Their Usage Patterns

et al. 2018

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In the electricity market, the real-time balance of electricity generation and consumption is a main task. In view of this, power providers usually sign contracts with their critical consumers (i.e., usually large-scale industrial companies) for managing their capacity demands. On the other hand, aggregators group commercial and residential consumers, and integrate their demands to negotiate with power providers. With a proper grouping of numerous electricity consumers, aggregators help to ensure stable electric supply, and reduce the burden of managing many consumers. In this work, we thus propose a novel data clustering approach to group complementary consumers based on their usage patterns (i.e., daily electricity consumption curves.) Furthermore, we incorporate the technique of discrete wavelet transform to speed up the clustering process. Specifically, approximations reconstructed from only a few wavelet coefficients may precisely capture the shape of original usage patterns. Experimental results based on a real dataset show that our approach is promising in practical applications.

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