Recent developments of distribution systems take advantages of new technical issues and networks equipments such as: DG (Distributed Generation), Smart Grids, Smart Metering, etc.. The aim of the paper is to propose an approach for load/generation control in distribution grid based on the solution of an OPF (Optimal Power Flow). The OPF dispatch local generation (DG), power injected by feeders and assess the load that needs to be curtailed in emergency conditions (isolated system, lack of, transport capacity onto the feeders, etc.). The proposed approach investigates about the possibility for Distribution Companies to reduce customer load up to 50% using signals coming from smart meters.The analysis also considers compensation costs for customers' supply interruption based on the actual Italian regulation.
The paper describes the methodology used for developing an electric load microforecasting module to be integrated in the Energy Management System (EMS) architecture designed and tested within the “Energy Router” (ER) project. This Italian R&D project is aimed at providing non-industrial active customers and prosumers with a monitoring and control device that would enable demand response through optimization of their own distributed energy resources (DERs). The optimal control of resources is organized with a hierarchical control structure and performed in two stages. A cloud-based computation platform provides global control functions based on model predictive control whereas a closed-loop local device manages actual monitoring and control of field components. In this architecture, load forecasts on a small scale (a single residential or tertiary building) are needed as inputs of the predictive control problem. The microforecasting module aimed at providing such inputs was designed to be flexible, adaptive, and able to treat data with low time resolution. The module includes alternative forecasting techniques, such as autoregressive integrated moving average (ARIMA), neural networks, and exponential smoothing, allowing the application of the right forecasting strategy each time. The presented test results are based on a dataset acquired during a monitoring campaign in two pilot systems, installed during the ER Project in public buildings.
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