Received December 16, 2019, accepted January 3, 2020, date of publication January 15, 2020, date of current version January 24, 2020. Digital Object Identifier 10.1109/ACCESS.2020.2966736 Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution CHARLES IBRAHIM 1 , IMAD MOUGHARBEL 2 , (Member, IEEE), HADI Y. KANAAN 1 , (Senior Member, IEEE), SEMAAN W. GEORGES 3 , (Member, IEEE), NIVINE ABOU DAHER 4 , AND MAAROUF SAAD 2 1 Faculty of Engineering (ESIB), Saint-Joseph University of Beirut, Campus des Sciences et Technologies, Beirut 1107 2050, Lebanon 2 Electrical Engineering Department, Ecole de Technologie Supérieure, Montreal, QC H3C 1K3, Canada 3 Department of Electrical, Computer and Communication Engineering, Notre Dame University–Lebanon, Zouk Mosbeh 25126, Lebanon 4 Institut de Recherche d’Hydro-Québec, Varennes, QC J3X 1S1, Canada Corresponding author: Charles Ibrahim (charles.ibrahim@net.usj.edu.lb) This work was supported by the Research Council of Saint-Joseph University of Beirut. ABSTRACT 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. I. INTRODUCTION Various types of Demand Response Programs (DRPs) can be offered for residential, commercial and industrial con- sumers on the electrical network. Each of these categories can account for roughly one-third of the electricity use as in USA as per United States Environmental Protection Agency (EPA) and United States Energy Information Administration (EIA) sites [1], [2]. Every category and its related sub-categories have relevant impacts on DRPs. The topology of conventional electricity distribution separates usually industrial zones from other consumers’ zones but they are managed at a spe- cific level by one master player like the distribution ser- vice operator (DSO). Their management at similar specific layer becomes a necessity while coordinating their various The associate editor coordinating the review of this manuscript and approving it for publication was Sotirios Goudos . contributions in the DRP processes. In smart grid topologies with distributed resources, these consumers’ categories are no more considered separated because more than one DSO may distribute energy to more than one category of consumers independently from their geographical and electrical location on the grid. The industrial loads are considered as virtual resources with flexible capacities that can be managed to handle the network stress that might result from aggregated loads through optimization and system harmonization. They offer ancillary services such as regulation and load following. Their processes can be adjusted dynamically according to production plans. These facts help the energy system to adapt to change as their contribution can impact significantly and achieve quick response to stress on the grid in addition to investments elimination for some cases. In [3], a multi-agent system is used to connect these aggregated categories to one higher level through multiple agents. In [4], DSO, producing VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ 14901