In this paper we propose a method for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant, to be used for generation forecasting purposes. We address this problem in a scenario where measurements of meteorological variables (i.e. solar irradiance and temperature) at the plant site are not available. The proposed approach efficiently exploits only power generation measurements and theoretical clear-sky irradiance, and is characterized by very low computational effort. Experimental validation is also presented. The proposed procedure is currently being deployed at several DSO control centres in Italy

Enel Distribuzione and Siemens are developing a new system called "MAGO" (Monitoring and control of Active distribution Grid Operation) to collect and process data regarding distributed generation. The scope of the project is forecasting the distributed generation (DG) on MV networks, aggregating data according to the different sources (solar, wind, hydro, thermal and others). Forecasted production data and real-time measurements will be provided to the operators of Distribution and transmission (TSO) control centres to help them in "Active" network operation.

This paper presents a heuristic method for the estimation of a model of a photovoltaic plant to be used for power generation forecasting purposes. The problem is addressed in a scenario where measurements of meteorological variables (i.e., irradiance and temperature) at the plant site are not available. The proposed approach efficiently exploits only power generation measurements and theoretical clear-sky irradiance, and is characterized by very low computational effort. A real-world application is presented to validate the procedure, which is also currently in pre-deployment phase at a Distribution System Operator control center in Northern Italy

The development of the Dispersed Generation (DG) in Distribution Networks (DN) requires new planning and operational tools. The Distribution Company takes the role of the Distribution System Operator (DSO) that has to manage the network from many points of view; in particular, DSOs need advanced tools to manage DNs with respect to security, reliability and quality constraints, and to improve the Hosting Capacity (HC) of the system. To fulfill these goals, it is necessary to estimate the state of the network in different operating conditions, also considering the DG. Moreover, the new standard framework requires that the DG provides, among others, reactive support. The paper presents the preliminary results of the INGRID 2 project, which is the product of the collaboration among Politecnico di Milano, SIEMENS SpA and Università degli Studi di Milano and represents a tool developed to answer the above mention needs of the DSO.Index Terms--Smart grid, Distribution networks, Distribution System Operator. I. INTRODUCTIONThe Ingrid project was started in 2010 thanks to a collaboration among Politecnico di Milano, University of Milan and Siemens SpA. The goals are to develop new tools for DSO Control Center useful for both off-line analysis and real-time operations. In the first part of the project, the attention was put on the data exchange problem with existing SCADA system [1]-[3]. Moreover, power flow (PF) and short-circuit computational tools were developed. In this context, it was also necessary to overcome the problem related to the estimation of real and reactive power consumptions of the customers connected to the MV system. In particular, starting from standard profiles of different customers, the real and reactive power for each hour is estimated according to the energy consumption of the customer in the last year. In this context, the Ingrid tool needs to be implemented in a server that manages all data exchange among different components and users.Moreover, the information given by the power flow and short-circuit solutions allow to implement new procedures in order to improve the generation units operation; in this way, the capacity of the existing system to host further generator injection will increase [4]. In particular, in Ingrid project new approaches about the reactive support management of DG are developed; it represents one of the most critical issues that impacts on the hosting capacity of the distribution grid.

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