Optimization Framework for Short-Term Control of Energy Storage Systems

Short-term control of energy storage systems (ESS) aims to find the optimal control action for the next time step in a demand management system. Several optimization models and solution strategies are presented in literature for accomplishing this task. However, there is no framework available, which enables prototyping and flexible definition of optimization problems according to changing conditions and constellation of components in real time applications and that is deployable in different embedded systems. The present work analyses the requirements imposed by the EU project Storage4Grid (S4G) and uses them as a basis for the design of an optimization framework to combine data from various sources and offer a flexible optimization-setting environment. The architecture includes modules for management and signal processing of sensor data, linking of predictive algorithms to deliver inputs to the optimization model, optimization modeling, linking of a solver, an optimization controller and a post-processer module for formatting the results or creating events. The framework is tested on three scenarios of a deterministic optimization problem and its output interface was linked to an open source power flow simulator OpenDSS to validate the results.