D.3.4 Energy storage management and integration

Executive Summary

Energy system storages (ESS) can increase energy security by improving the self- sufficiency rate, providing uninterrupted power supply functionalities to maintain power supply in case of grid failures. Due to their fast response and high-power capabilities, batteries are among the best candidates to provide electrical power system stability.

To properly select the substitution candidates for the planned aluminium-ion technologies, a list of the main criteria for choosing an ESS in the REACT project has been determined. Preliminarily we have selected six proven and state-of-the-art storage technologies: Lead-carbon battery, Li-ion (LTO) battery, Li-ion (NMC) battery, Vanadium Redox Flow battery, Sodium-ion battery, and Hydrogen storage. The benchmarking shows that Sodium-ion and Lithium-ion batteries (both LTO and NMC) are suitable substitutes for the planned aluminium-ion technologies.

The report then discusses how control actions will be triggered and how they integrate into the REACT ICT system, and details of the implementation of control strategies using OpenMUC. The edge level of the REACT architecture is described in detail in block and sequence diagrams. For SMA and Victron devices, inverters and smart meters, the communication protocol is Modbus. The OpenMUC based battery controllers are presented along with their description, parameters, and channels. Finally, the battery controllers are tested and validated using a mock battery simulation. The data plotter of OpenMUC validates how a self-consumption optimisation controller can be implemented.

The report also develops a simulation tool that enables a user to determine the optimum size of the battery storage system (BSS). The consumption data from the REACT pilot sites are pooled together and run through the simulation code to have a collective result to determine the right size for the BSS. The simulation structure allows for all the possible combinations of the prices, photovoltaic sizes and capacity, initial investments, maintenance costs, feed-in tariffs, and all data profiles to be compiled together to find the best possible battery storage system size. The simulation provided for this part of the study can further be developed into an application where an end-user can provide the input data such as consumption data, tariffs and costs, and photovoltaic size and capacity to find the optimum BSS size.

Overall, the design, development, and implementation of the battery controllers, validation of the control strategies, and development of a novel tool to estimate the optimum battery size indicates the successful completion of Task 3.4 and paves the way for the relevant future tasks.