Compilation of real-time control strategies for heterogeneous resources at MV and LV

Description of deliverable and goal 1.1. Executive summary The core of this activity is to provide distribution system operators with tools for the operation of utility-scale distributed battery energy storage systems (BESSs) in order to optimize the integration of stochastic distributed generation. The main goal of this deliverable is to assess two possible strategies for the real-time control of a utility-scale BESS to follow a day-ahead computed dispatch plan. In particular, one solution is based on a grid-aware optimal power flow (OPF)-based control accounting for both grid and BESS operational constraints (thoroughly described in D1.4.4c) [1], whereas the second one is based on the COMMELEC (thoroughly described in D1.2.3c) [2], [3]. The goal of the first method is to achieve the real-time dispatch plan tracking using a grid-aware model predictive control (MPC) to determine the active and reactive power set-points of the BESS so that the aggregated power of all the resources connected to a medium voltage power grid contribution track the dispatch plan while obeying to BESS’s operational constraints as well as the grid’s ones. The grid constraints are modelled using the Augmented Relaxed OPF [4]. COMMELEC is a framework proposed in the literature ([2], [3]) for the real-time control of power grids. It uses a hierarchy of agents to compute explicit active and reactive power setpoints for the resources connected to the grid. Each resource is equipped with a resource agent (RA) whose job is to translate the internal state of the resource into a device-independent format (advertisement). The advertisements are collected by the grid agent (GA), which computes the optimal power setpoints that optimize a global objective. The global objective is the weighted sum of various objectives, including tracking a predetermined dispatch plan at the slack bus, minimizing grid’s nodal voltage deviations from the nominal value, limiting the line currents below the respective ampacities and achieving target internal states for the resources. The proposed control frameworks are validated by dispatching the operation of a 12kV/20MVA MV distribution network in Aigle, Switzerland (i.e. the REeL demonstrator) using a 1.5 MW/2.5 MWh BESS, which is controlled in real-time given the online grid state estimation enabled by the deployed distributed PMU-based sensing infrastructure.

Description of deliverable and goal 1.1. Executive summary The core of this activity is to provide distribution system operators with tools for the operation of utility-scale distributed battery energy storage systems (BESSs) in order to optimize the integration of stochastic distributed generation. The main goal of this deliverable is to assess two possible strategies for the real-time control of a utility-scale BESS to follow a day-ahead computed dispatch plan. In particular, one solution is based on a grid-aware optimal power flow (OPF)-based control accounting for both grid and BESS operational constraints (thoroughly described in D1.4.4c) [1], whereas the second one is based on the COMMELEC (thoroughly described in D1.2.3c) [2], [3]. The goal of the first method is to achieve the real-time dispatch plan tracking using a grid-aware model predictive control (MPC) to determine the active and reactive power set-points of the BESS so that the aggregated power of all the resources connected to a medium voltage power grid contribution track the dispatch plan while obeying to BESS’s operational constraints as well as the grid’s ones. The grid constraints are modelled using the Augmented Relaxed OPF [4]. COMMELEC is a framework proposed in the literature ([2], [3]) for the real-time control of power grids. It uses a hierarchy of agents to compute explicit active and reactive power setpoints for the resources connected to the grid. Each resource is equipped with a resource agent (RA) whose job is to translate the internal state of the resource into a device-independent format (advertisement). The advertisements are collected by the grid agent (GA), which computes the optimal power setpoints that optimize a global objective. The global objective is the weighted sum of various objectives, including tracking a predetermined dispatch plan at the slack bus, minimizing grid’s nodal voltage deviations from the nominal value, limiting the line currents below the respective ampacities and achieving target internal states for the resources. The proposed control frameworks are validated by dispatching the operation of a 12kV/20MVA MV distribution network in Aigle, Switzerland (i.e. the REeL demonstrator) using a 1.5 MW/2.5 MWh BESS, which is controlled in real-time given the online grid state estimation enabled by the deployed distributed PMU-based sensing infrastructure.

Compilation of real-time control strategies for heterogeneous resources at MV and LV

Unified Harmonic Power-Flow and Stability Analysis of Power Grids with Converter-Interfaced Distributed Energy Resources

Analytical Computation of the Sensitivity Coefficients in Hybrid AC/DC Networks

Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients

Unified Harmonic Power-Flow and Stability Analysis of Power Grids with Converter-Interfaced Distributed Energy Resources

Analytical Computation of the Sensitivity Coefficients in Hybrid AC/DC Networks

Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients