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Personne# Antonio Zecchino

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Battery storage power station

A battery storage power station is a type of energy storage power station that uses a group of batteries to store electrical energy. Battery storage is the fastest responding dispatchable source of

Réseau de distribution électrique

Un réseau de distribution électrique est la partie d'un réseau électrique desservant les consommateurs. Un réseau de distribution achemine l'énergie électrique d'un réseau de transport (Haute tensio

Gestionnaire de réseau de transport

Dans l'Union européenne, un gestionnaire de réseau de transport (GRT) est une entreprise chargée de la gestion de tout ou partie d'un réseau de transport d'énergie (électricité ou gaz).
Le réseau d

Personnes menant des recherches similaires (82)

Plouton Grammatikos, Rahul Kumar Gupta, Mario Paolone, Antonio Zecchino

- 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.

Rahul Kumar Gupta, Mario Paolone, Antonio Zecchino

The core of this activity is to provide distribution system operators with tools for the optimal and grid-aware operation of utility-scale distributed battery energy storage systems (BESSs) in order to optimize the integration of stochastic distributed generation. The ultimate goal is the optimal control of active distribution networks with high penetration of stochastic (i.e., non-controllable) renewable-based generation. In particular, unscheduled fluctuations of the power exchanged with the upper grid level are minimised via the proposed control and scheduling framework, where we compute a dispatch plan in day-ahead using advanced forecasts of the aggregated prosumption and track it during the real-time operation using a grid-aware optimal power flow (OPF)-based control of the controllable BESS accounting for both grid constraints and BESS operational constraints. We experimentally validated the proposed control and scheduling strategy to dispatch the operation of a medium voltage active distribution network interfacing stochastic heterogeneous prosumers by using a grid-connected BESS as a controllable element coupled with a distributed monitoring infrastructure. In particular, the framework consists of two algorithmic layers. In the first one (day-ahead scheduling), an aggregated dispatch plan is determined, which is based on the day-ahead forecast of the prosumption and accounts for the operational constraints of grid and BESS state-of-energy. An adaptive data-driven scheme based on multi-variate Gaussian distribution is used to forecast the power consumption and photovoltaic generation and used as an input at the day-ahead stage. Then, the dispatch plan for the next 24 hours is computed using a scenariobased iterative AC OPF (Codistflow) algorithm, which accounts for forecasts of RESs and load profiles with 95% confidence interval with 1h time resolution. The second layer consists of real-time operation, where a grid-aware model predictive control determines the active and reactive power set-points of the BESS so that their aggregated contribution tracks 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 developed at the EPFL-DESL. The proposed control framework is 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.

Rahul Kumar Gupta, Mario Paolone, Ji Hyun Yi, Antonio Zecchino

Dispatching active distribution networks (ADNs) is an energy-intensive application that, if implemented via battery-energy storage systems (BESSs), can require a large capacity of these assets in order to fully balance the uncertainties caused by the stochastic demand and generation. The insufficient capacity of the BESSs often leads to their state-of-charge (SOC) saturation; this results in unreliable dispatch tracking. In this work, we propose and experimentally validate a real-time control scheme that achieves a highly-reliable dispatching of ADNs and ensures that the BESSs’ SOC is not saturated during the daily operation. Our proposed scheme uses a two-layer model predictive control (MPC). The upper-layer MPC, running every 5 minutes, optimizes the BESSs’ SOC trajectories while minimizing the tracking error, considering the prosumption forecast of the whole day. Then, the lower layer MPC, running every 30 seconds, takes the BESSs’ SOC trajectories as constraints while achieving a high-resolution tracking of the dispatch plan over the current 5-minute time horizon. Both layers account for the grid constraints by using the augmented relaxed optimal power-flow (AR-OPF) model; an exact convex relaxation of the original AC-OPF and used in this paper (for the first time in the literature) to solve a real-time constrained control problem for ADNs. Our proposed framework was experimentally validated using a 1.5 MVA/2.5 MWh BESS connected to an actual 24-node medium-voltage (MV) ADN that, in Aigle, Switzerland, hosted an uncontrollable 3.2 MWp distributed photovoltaic generation, 3.4 MVA hydro-power generations, and a 2.8 MW base demand.

2022