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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.

Massive penetration of plug-in electric vehicles (EVs) may create challenges in the near future for the distribution network. Moreover, this may lead to an increase of the transformers' aging rate and a reduction of the financial profits. In this paper, a novel approach is proposed, in which the operational margin of the transformer is optimized based on the transformer's internal characteristics, its loss of life, and the variation of the ambient temperature. This operational power limit should not be exceeded to guarantee that the loss of life of the transformer is equal to or less than the one provided by the manufacturer. For validation purposes, a comparative study between the conventional method and the suggested one is presented. This paper is applied to a parking lot for charging EVs, which is supplied by a distribution transformer. In contrary to the conventional method, the one suggested in this paper can guarantee a predefined transformer loss of life. Simulation results show that the proposed method increases the transformer lifetime, reduces the loss of life, and reduces its depreciation cost by 63% in certain conditions. In addition, it increases the financial profit for the parking lot's owner up to 10% during cold weather.