This paper presents a practical method for estimate the state of charge (SoC) of customer-owned solar PV and battery assets on a site-by-site basis in real-time. Starting from the assumption that there is a preceding agreement between customers and network operator, the tool makes a day-ahead estimation of the SoC of the battery. This considers the local PV generation and consumption of the customer and assumes the system is operating to optimize performance with regards to tariffs, operating costs and network agreement. A model-based observer is used to predict SoC 24 hours ahead. The model considers customer consumption and PV production which are separately forecasted and used to estimate the SoC of the battery one day ahead. Since the forecasts are uncertain, a Monte Carlo approach has been considered in order to estimate an upper and lower bound of the SoC over time. With this information the utility can make its choices on Ancillary Services resources allocation one day ahead. Simulation results are presented using input data from a real system. © 2017 IEEE.

Development of a planning tool for network ancillary services using customer-owned solar and battery storage

Del Giudice, A.
2018

Abstract

This paper presents a practical method for estimate the state of charge (SoC) of customer-owned solar PV and battery assets on a site-by-site basis in real-time. Starting from the assumption that there is a preceding agreement between customers and network operator, the tool makes a day-ahead estimation of the SoC of the battery. This considers the local PV generation and consumption of the customer and assumes the system is operating to optimize performance with regards to tariffs, operating costs and network agreement. A model-based observer is used to predict SoC 24 hours ahead. The model considers customer consumption and PV production which are separately forecasted and used to estimate the SoC of the battery one day ahead. Since the forecasts are uncertain, a Monte Carlo approach has been considered in order to estimate an upper and lower bound of the SoC over time. With this information the utility can make its choices on Ancillary Services resources allocation one day ahead. Simulation results are presented using input data from a real system. © 2017 IEEE.
9781538619537
battery management;SoC estimation;solar power;optimal scheduling;Energy storage
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12079/5579
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