Energy management and flexibility quantification in a discrete event distribution grid simulation

TL;DR

The paper tackles the increasing complexity of distribution grids with high renewable penetration by introducing a discrete-event simulation framework built on SIMONA that models energy management and flexibility. It formalizes system participants as DEVS-based SPMS and includes battery storage and EV charging station components to enable realistic EMS testing. A hierarchical flexibility communication protocol allows aggregation and disaggregation of flexibility across EMU levels, enabling EMS strategies such as maximizing own consumption. Demonstrations on a modified SimBench grid show improved voltage stability and scalable performance, supporting the framework's value for grid planning and operational studies.

Abstract

Distribution grid operation faces new challenges caused by a rising share of renewable energy sources and the introduction of additional types of loads to the grid. With the increasing adoption of distributed generation and emerging prosumer households, Energy Management Systems, which manage and apply flexibility of connected devices, are gaining popularity. While potentially beneficial to grid capacity, strategic energy management also adds to the complexity of distribution grid operation and planning processes. Novel approaches of time-series-based planning likewise face increasingly complex simulation scenarios and rising computational cost. Discrete event modelling helps facilitating simulations of such scenarios by restraining computation to the most relevant points in simulation time. We provide an enhancement of a discrete event distribution grid simulation software that offers fast implementation and testing of energy management algorithms, embedded into a feature-rich simulation environment. Physical models are specified using the Discrete Event System Specification. Furthermore, we contribute a communication protocol that makes use of the discrete event paradigm by only computing flexibility potential when necessary.

Figures (5)

• Figure 1: Relationships between various types of data classes of SPMs. An arrow indicates that data at its origin is used as input for the determination of data at its destination. Affiliations of data to the former, current and future time step are annotated at the bottom. Data classes involved with flexibility communication (highlighted in gray) are only applicable if the SPM is EM-controlled.
• Figure 2: Schema of an exemplary energy management setup using the proposed flexibility protocol.
• Figure 3: The Energy Management Strategy interface with its relationships to input and output data. Features highlighted in gray are planned for future implementation.
• Figure 4: Cumulative sum of produced and consumed power by connected entity (top) and the associated flexibility potential and realized power (bottom).
• Figure 5: Empirical cumulative distribution function of the voltage at the low voltage side of the transformer. The usage of energy management leads to improved voltage stability.