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Publication# Operation of the battery storage systems for grid control, feeder dispatching (RE Demo)

Résumé

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.

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Chargement

Chargement

Chargement

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.

Mario Paolone, Paola Pongiglione, Fabrizio Sossan

This paper presents a method to determine the optimal location, energy capacity, and power rating of distributed battery energy storage systems at multiple voltage levels to accomplish grid control and reserve provision. We model operational scenarios at a one-hour resolution, where deviations of stochastic loads and renewable generation (modeled through scenarios) from a day-ahead unit commitment and violations of grid constraints are compensated by either dispatchable power plants (conventional reserves) or injections from battery energy storage systems. By plugging-in costs of conventional reserves and capital costs of converter power ratings and energy storage capacity, the model is able to derive requirements for storage deployment that achieve the technical-economical optimum of the problem. The method leverages an efficient linearized formulation of the grid constraints of both the HV (High Voltage) and MV (Medium Voltage) grids while still retaining fundamental modeling aspects of the power system (such as transmission losses, effect of reactive power, OLTC at the MV/HV interface, unideal efficiency of battery energy storage systems) and models of conventional generator. A proof-of-concept by simulations is provided with the IEEE 9-bus system coupled with the CIGRE’ benchmark system for MV grids, realistic costs of power reserves, active power rating and energy capacity of batteries, and load and renewable generation profile from real measurements.

2020Sherif Alaa Salaheldin Fahmy, Rahul Kumar Gupta, Mario Paolone

The penetration of electric vehicle (EV) charging stations (CSs), along with the progressive connection of stochastic distributed generation, is increasing the probability of violating the power distribution grid operational constraints and deteriorate the quality of power supply. To this end, the paper proposes a real-time control scheme for allocating power set-points to EV CSs while accounting for the grid operational requirements. In the proposed problem formulation the grid and the power injections are modelled accounting for their unbalanced 3-phase nature, thus enabling to formulate the problem objective and its constraints adopting the sequence decomposition. The EVs’ users need, along with the stochastic nature of other uncontrollable injections (e.g. loads and generation from photovoltaic generation units), are also taken into account. A distributed control scheme, with a minute-scale control horizon, is proposed where local controllers, operating at EV aggregation level, compute EV battery-secure power set-points. These controllers send their set-points to a central controller operating at the grid aggregation level. The central controller solves a scenario-based linearised optimal power flow accounting for grid operational and power quality constraints. Then, it sends back its solution to the respective local controllers. The obtained iterative algorithm is efficiently solved until convergence. We analyse the performance of the proposed control scheme via a simulation ran on the IEEE-34 feeder. Comparisons with two other control algorithms, a grid-unaware local controller and a myopic maximum power controller, are included to benchmark the proposed control scheme.

2020