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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.

Power electronic matrix converters are converters carrying out just one solitary transitional stage. AC-AC conversion automatism in matrix converters is attained through the exploitation of bidirectional controllable power switches associated with a structural matrix. The indirect matrix converter is an alternating preference to the direct matrix converter embodied by AC-DC converter and DC-AC inverter. In this paper, an assemblage of Pulse Width Modulation categories is used to control the dual stages of IMC. AC-DC converter is modulated through the well-known space vector algorithm, while DC-AC inverter is modulated through a novel algorithmic conception referred to as the digital scalar algorithm. Comparative analysis between the conventional DSVPWM modulation and the novel proposed modulation technique is escorted demonstrating that the latter is facilely comprehended and simply put into real-time practice. This paper comes forward with digital signal processor real-time enforcement, conceived for indirect matrix converter. Uniting both original approaches into one topological system entrained a significant decrease of 38% in real-time computation assesses. Throughout the paper, it is well proven that the proposed solution results in high-quality input/output waveforms, and averts from servicing supplementary devices that trigger silicon devices' gates. Numerous experimental enquires within the delivered research reveal advantageous aspects of the contemporary approach from overall simplicity to entire effectiveness.

Different types of Demand Response Programmes (DRPs) exist and can be simultaneously offered by the electrical utilities through established contracts with customers. Operating simultaneously multiple types of DRPs might lead to undesired results. DRPs might have different responses to objectives, time-based ones tend to maximise consumption during lowest tariffs periods while incentive-based ones tend to reduce the usage based on peak events, accordingly contradiction might occur. Thus, synchronising these DRPs and their parameters through an optimised process including customer selection for the appropriate one is a mandatory step. A fair allocation of the various types of DRPs including their execution's priority at a specific time is the main objective of this study. An original approach based on clustering technique for predicting customers' behaviour coupled with a particle swarm optimisation (PSO) to reach an optimal solution for relocation is presented. In this study, an optimal solution is developed; it provides the various DRPs with the most convenient parameters for the best demand/generation balance, utility profit maximisation and operational cost minimisation. The method is validated through a simulation applying a time-based with two incentive-based DRPs in the presence of conventional and renewable generation while using Kmeans clustering and PSO on Matlab.