A Hybrid Customer Baseline Load Estimator for Small and Medium Enterprises

Ramar, Gururaghav; Peng, Jimmy Chih-Hsien

doi:10.1109/iecon.2018.8591759

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In [26] an accurate baseline calculation for fair payment to the industrial customers has been proposed compared with well-known regression based methods. The authors in [27] studied the challenge of refining the estimation methods to robustly handle the higher demand variations exhibited by small and medium enterprises through improving day-matching technique based on customer-grouping per DR event (similarity theory). In [28], the authors proposed a model for industrial refrigerated warehouses with heating, ventilation and air conditioning (HVAC) systems.…”

Section: Baselines Prequalificationmentioning

confidence: 99%

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

et al. 2020

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Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction. INDEX TERMS Multiple demand response programs, MOPSO, group method of data handling (GMDH), Kmeans, clustering, smart grid, industrial customers.

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Section: Baselines Prequalificationmentioning

confidence: 99%