In the present study, a predictive battery energy storage system (BESS) for application in geographical non-interconnected islands with high renewable energy penetration is proposed, capable of performing load levelling. The system under consideration is composed of diesel and heavy oil generators, a photovoltaic farm and a small wind turbine. The proposed solution integrates machine learning (ML) methods for the forecasting of load and intermittent solar and wind power productions, alongside a custom scheduling algorithm, which calculates the necessary BESS setpoints that accomplish the desired levelling effect. An important feature of the scheduling algorithm is that the charge and discharge energy amounts of each day are by design equal and independent of the forecasts' accuracy. This aspect enables economic investigations to identify the appropriate BESS capacity for the particular system, taking also into account the battery's capacity degradation. The overall system is modelled and simulated utilizing the open-source languages Python and Modelica. Simulations presented a 9.8% peak-to-mean ratio (PMR) reduction of the thermal plant's load. Furthermore, economic investigations estimated a marginal BESS cost of 287.1 €/kWh revealing the financial viability of the proposed integrated system, in at least the case of geographical islands.

The defossilization of power generation is a prerequisite goal in order to reduce greenhouse gas emissions and transit for a sustainable economy. Achieving this goal requires increasing the penetration of renewable energy sources (RESs) such as solar and wind power. The gradual shrinking of conventional generation units in an energy map introduces new challenges to the stability of power systems as there is a considerable reduction of stored rotational energy in the synchronous generators (SGs) and the capability to control their power output, which has been taken for granted until today. Inertia and primary reserve reduction have a substantial effect on the ability of the power system to maintain its security and self-resilience during contingency events. Such issues become more evident in the case of non-interconnected islands (NII) as they have unique features associated with their small size and low inertia. The present study examines in depth the NII system of Madeira, which is composed of thermal, hydro, solid-waste, wind and solar generation units, and additional RES integration is planned for the near future. Electromagnetic transient (EMT) simulations are performed for both the current and future states of the system, including the installation of planned variable RES capacities. To alleviate the stability issues that occurred in the high-RES scenario, the introduction of a utility-scale battery energy storage system (BESS), capable of mitigating the active power imbalance due to the power system’s disturbances resultant of RES penetration, is examined. In addition, a comparison between a flywheel energy storage system (FESS) and BESS is shortly investigated. The grid has been modeled and simulated utilizing the open-source, object-oriented modeling language Modelica. The dynamic simulation results proved that battery storage is a promising technology that can be a solution for transitioning to a sustainable power system, maintaining its self-resilience under severe disturbances such as rapid load changes, the tripping of generation units and short-circuits.

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