Long-term forecasting process is necessary for the correct operation of electric utilities, especially concerning planning purposes. There is an ongoing attention towards the improvement of this task, on which only few works have been done in recent past, since the focus of the studies was mainly on short-term load forecasting. Generally, the forecasting process deals with the load demand. This paper aims to estimate not only the long-term load behaviour, but the distributed generation too. In fact, the large spread of renewable energy sources make their production significant and no more negligible. The actual forecasting process allows to roughly estimate the “net energy transfer” at primary substation level, measured at the MV busbar of the HV/MV transformer, identifying reverse power flow conditions and potential critical situations, particularly concerning transformers saturation. An alternative long-term forecasting model is here presented. The proposed solution is an algorithmic framework that provides yearly trends for the long-term forecasting of both load and generation plants, with a resolution of 15 minutes. Thus, the goal of the algorithm is to forecast the net energy transfer at MV busbar of the HV/MV transformer in primary substation. In order to achieve this result, it is necessary to provide production curves of distributed generation (DG) and typical load curves: putting together all these data, the result is the net energy which flows through the transformer. Distributed energy resources have been broken down by typology of energy source, identifying different production models. Historical production values have been investigated and analysed, source by source. Then, statistical analyses have been performed on them, in order to set up different generation profiles. The only exception is the estimation of solar production, which is not based on historical values. It is forecasted doing specific studies on its production trend, which depends on external factors, such as solar irradiation (i.e., sun position) and weather conditions. Once completed, the algorithm has been tested over different primary substations (PSs). This validation phase is divided into two stages. The first one consists in defining the reference scenario and tuning all the parameters involved. After that, knowing all the new producer installations and considering that the configuration of the loads is quite similar respect to the reference scenario, the algorithm is run in order to estimate the net energy transfer. The forecasting process consists in the definition of different possible scenarios, based on the hypotheses of either the same DG production (business-as-usual scenario, with a load variation within -2% and +2%) or an increase of renewable energy resources (RESs) production (two different levels of increase, still considering a load variation within -2% and +2%). The forecasts have been made for each PS analysed. In this way, it is possible to define the most likely scenario and the energy fluxes in those conditions. Moreover, evaluating the results ex-post, the model gives the possibility to make hypotheses on the reasons of why something strange has happened in a particular year.

Long-term forecasting model for energy and power flow estimation at Primary substation level

Samuele Grillo;
2018-01-01

Abstract

Long-term forecasting process is necessary for the correct operation of electric utilities, especially concerning planning purposes. There is an ongoing attention towards the improvement of this task, on which only few works have been done in recent past, since the focus of the studies was mainly on short-term load forecasting. Generally, the forecasting process deals with the load demand. This paper aims to estimate not only the long-term load behaviour, but the distributed generation too. In fact, the large spread of renewable energy sources make their production significant and no more negligible. The actual forecasting process allows to roughly estimate the “net energy transfer” at primary substation level, measured at the MV busbar of the HV/MV transformer, identifying reverse power flow conditions and potential critical situations, particularly concerning transformers saturation. An alternative long-term forecasting model is here presented. The proposed solution is an algorithmic framework that provides yearly trends for the long-term forecasting of both load and generation plants, with a resolution of 15 minutes. Thus, the goal of the algorithm is to forecast the net energy transfer at MV busbar of the HV/MV transformer in primary substation. In order to achieve this result, it is necessary to provide production curves of distributed generation (DG) and typical load curves: putting together all these data, the result is the net energy which flows through the transformer. Distributed energy resources have been broken down by typology of energy source, identifying different production models. Historical production values have been investigated and analysed, source by source. Then, statistical analyses have been performed on them, in order to set up different generation profiles. The only exception is the estimation of solar production, which is not based on historical values. It is forecasted doing specific studies on its production trend, which depends on external factors, such as solar irradiation (i.e., sun position) and weather conditions. Once completed, the algorithm has been tested over different primary substations (PSs). This validation phase is divided into two stages. The first one consists in defining the reference scenario and tuning all the parameters involved. After that, knowing all the new producer installations and considering that the configuration of the loads is quite similar respect to the reference scenario, the algorithm is run in order to estimate the net energy transfer. The forecasting process consists in the definition of different possible scenarios, based on the hypotheses of either the same DG production (business-as-usual scenario, with a load variation within -2% and +2%) or an increase of renewable energy resources (RESs) production (two different levels of increase, still considering a load variation within -2% and +2%). The forecasts have been made for each PS analysed. In this way, it is possible to define the most likely scenario and the energy fluxes in those conditions. Moreover, evaluating the results ex-post, the model gives the possibility to make hypotheses on the reasons of why something strange has happened in a particular year.
2018
2018 CIGRÉ session
978-2-85873-497-9
Long-term forecasting, Distributed Generation, Net energy transfer, Renewable Energy Sources, Primary Substation, Load profile
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1076225
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