Abstract
Photovoltaic (PV) systems constitute one of the most promising renewable energy sources, especially for warm and sunny regions like the southern-European islands. In such isolated systems, it is important to utilize clean energy in an optimal way in order to achieve high renewable penetration. In this operational strategy, a Battery Energy Storage System (BESS) is most often used to transfer an amount of the stored renewable energy to the peak hours. This study presents an integrated energy management methodology for a PV-BESS energy system targeting to make the load curve of the conventional fuel based units as smooth as possible. The presented methodology includes prediction modules for short-term load and PV production forecasting using artificial neural, and a novel, optimized peak shaving algorithm capable of performing each day’s maximum amount of peak shaving and smoothing level simultaneously. The algorithm is coupled with the overall system model in the Modelica environment, on the basis of which dynamic simulations are performed. The simulation results are compared with the previous version of the algorithm that had been developed in CERTH, and it is revealed that the system’s performance is drastically improved. The overall approach proves that in such islanding systems, a PV-BESS is a suitable option to flatten the load of the conventional fuel based units, achieve steadier operation and increase the share of renewable energy penetration to the grid.
Highlights
Smooth operation of the generation units is associated with power quality improvement and promotes the enhanced Renewable Energy Systems (RES) penetration to the energy system [1]
These indicators were calculated for the three cases: i) no Battery Energy Storage System (BESS) installation in the system as the reference case, ii) the previous algorithm’s version [9], and iii) the current improved version
Modelica simulations confirmed that such methodology is beneficial even when there are significant inaccuracies in the forecasting estimations, occurring due to insufficient data amounts
Summary
Smooth operation of the generation units is associated with power quality improvement and promotes the enhanced Renewable Energy Systems (RES) penetration to the energy system [1]. An important function that is encountered by these systems is load shifting or load shaving [2] In this procedure, the battery contributes to the formulation of a smooth. Based on this ability, smart algorithms produce the dispatch plan according to load forecasting and intermittent sources’ production. The methodology included forecasting and clustering of the load, followed by a custom power flow scheduling algorithm, responsible to perform peak shaving. The present study continues the development of the aforementioned EMS, while introduces the following advancements: a) complete, end-to-end methodology, b) integration of the PV forecasting module, c) revised peak shaving algorithm, maximizing the shaved amount of each day, d) shift to non-proprietary tools: Python and Modelica. The exploitation of the system’s strengths and weaknesses became possible
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