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Modelling and simulation of a predictive BESS controller based on load forecasting in a South European island power system

Chapaloglou, Spyridon; Nesiadis, Athanasios; Atsonios, Konstantinos; Nikolopoulos, Nikos; Grammelis, Panagiotis; Kakaras, Emmanuel


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    <dct:title>Modelling and simulation of a predictive BESS controller based on load forecasting in a South European island power system</dct:title>
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    <dcat:keyword>Artificial Neural Networks</dcat:keyword>
    <dcat:keyword>Battery Energy Storage System</dcat:keyword>
    <dcat:keyword>Energy Management System</dcat:keyword>
    <dcat:keyword>Load Forecast</dcat:keyword>
    <dcat:keyword>Peak Shaving</dcat:keyword>
    <dcat:keyword>Renewable Energy</dcat:keyword>
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    <dct:description>&lt;p&gt;Modern isolated power grids are constantly evolving to adopt smart grid concepts that can permit higher&lt;br&gt; renewable energy penetration and energy management optimization, in the view of a sustainable RES based&lt;br&gt; energy production EU policy with reduced pollutant emissions. Nevertheless, many islandic power systems&lt;br&gt; like the islands in Southern Europe are still depending on oil-fired diesel engines, while the renewable energy&lt;br&gt; production is limited due to financial, technical and environmental reasons. In this study, the power system of&lt;br&gt; a typical non-interconnected South European island consisting of diesel generators and a PV farm is modelled&lt;br&gt; and simulated. Scope of this paper is to examine the ability of a Battery Energy Storage System (BESS) to&lt;br&gt; achieve load peak shaving combined with maximization of the PV power penetration into the grid leading to&lt;br&gt; pre-planned zero curtailment. For this purpose, a novel peak shaving algorithm is developed and implemented&lt;br&gt; into an Energy Management System (EMS), for optimal scheduling of the diesel engines. Thereinafter,&lt;br&gt; dynamic simulations of the island&amp;rsquo;s power system are carried out employing a predictive control strategy for&lt;br&gt; different time scales, ranging from a supervisor BESS controller based on load forecasting, to a real-time&lt;br&gt; battery power regulation. The predictive BESS controller is based on future consumption values forecasting,&lt;br&gt; which in turn result from an Artificial Neural Network (ANN) and an optimization procedure taking into account&lt;br&gt; PV power generation and a peak shaving threshold. Thus, a new diesel engine scheduling is obtained capable&lt;br&gt; of replacing the maximum peak power demand with renewable power while at the same time load curve&lt;br&gt; smoothening and reduced diesel generators ramps-up are achieved. The simulations are executed in APROS&lt;br&gt; (Advanced Process Simulator) dynamic simulation platform, using built-in components for the BESS modelling,&lt;br&gt; an external model for load forecasting and a user-developed EMS structure.&lt;/p&gt;</dct:description>
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