We propose and experimentally validate a process to dispatch the operation of a distribution feeder with heterogeneous prosumers according to a trajectory with 5 min resolution, called dispatch plan, established the day before the operation. The controllable element is a utility-scale grid-connected battery energy storage system (BESS) integrated with a minimally pervasive monitoring infrastructure. The process consists of two stages: day-ahead, where the dispatch plan is determined by using forecast of the aggregated consumption and local distributed generation (prosumption), and real-time operation, where the mismatch between the actual prosumption realization and dispatch plan is compensated for thanks to adjusting the real power injections of the BESS with model predictive control (MPC). MPC accounts for BESS operational constraints thank to reduced order dynamic grey-box models identified from online measurements. The experimental validation is performed by using a grid-connected 720 kVA/500 kWh BESS to dispatch the operation of a 20-kV distribution feeder of the École Polytechnique Fédérale de Lausanne campus with both conventional consumption and distributed photo-voltaic generation.

In this paper, we propose a control framework for a battery energy storage system (BESS) to provide simultaneously multiple services to the electrical grid. The objective is to maximize the battery exploitation from these services in the presence of uncertainty (load, stochastic distributed generation, and grid frequency). The framework is structured in two phases. In a period-ahead phase, we solve an optimization problem that allocates the battery power and energy budgets to the different services. In the subsequent real-time phase the control set-points for the deployment of such services are calculated separately and superimposed. The control framework is first formulated in a general way and then casted in the problem of providing dispatchability of a medium voltage feeder in conjunction to primary frequency control. The performance of the proposed framework are validated by simulations and real-scale experiments, performed with a grid-connected 560 kWh/720 kVA Li-ion BESS.

Distributed energy resources (DERs) installed in active distribution networks (ADNs) can be exploited to provide both active and reactive power reserves to the upper-layer grid (i.e., sub-transmission and transmission systems) at their connection point. This paper introduces a method to determine the capability area of an ADN for the provision of both active and reactive power reserves while considering the forecast errors of loads and stochastic generation, as well as the operational constraints of the grid and DERs. The method leverages a linearized load flow model and introduces a set of linear scenario-based robust optimization problems to estimate the reserve provision capability (RPC) area of the ADN. It is proved that, under certain assumptions, the RPC area is convex. The performance of the proposed method is tested on a modified version of the IEEE 33-bus distribution test system.

We consider the problem of estimating the unobserved amount of photovoltaic (PV) generation and demand in a power distribution network starting from measurements of the aggregated power flow at the point of common coupling and local global horizontal irradiance (GHI). The estimation principle relies on modeling the PV generation as a function of the measured GHI, enabling the identification of PV production patterns in the aggregated power flow measurements. Four estimation algorithms are proposed: the first assumes that variability in the aggregated PV generation is given by variations of PV generation, the next two use a model of the demand to improve estimation performance, and the fourth assumes that, in a certain frequency range, the aggregated power flow is dominated by PV generation dynamics. These algorithms leverage irradiance transposition models to explore several azimuth/tilt configurations and explain PV generation patterns from multiple plants with nonuniform installation characteristics. Their estimation performance is compared and validated with measurements from a real-life setup including four houses with rooftop PV installations and battery systems for PV self-consumption.

Due to the increasing proportion of distributed photovoltaic (PV) production in the generation mix, the knowledge of the PV generation capacity has become a key factor. In this paper, we propose to compute the PV plant maximum power starting from the indirectly estimated irradiance. Three estimators are compared in terms of ability to compute the PV plant maximum power, bandwidth, and robustness against measurements noise. The approaches rely on measurements of the DC voltage, current, and cell temperature, and on a model of the PV array. We show that the considered methods can accurately reconstruct the PV maximum generation even during curtailment periods, i.e., when the measured PV power is not representative of the maximum potential of the PV array. Performance evaluation is carried out by using a dedicated experimental setup on a 14.3 kWp rooftop PV installation. Results also proved that the analyzed methods can outperform pyranometer-based estimations with a simple sensing system. We show how the obtained PV maximum power values can be applied to train time series based solar maximum power forecasting techniques. This is beneficial when the measured power values, commonly used as training, are not representative of the maximum PV potential.

In this paper, we propose and experimentally validate a scheduling and control framework for distributed energy resources (DERs) that achieves to track a day-ahead dispatch plan of a distribution network hosting controllable and stochastic heterogeneous resources while respecting the local grid constraints on nodal voltages and lines ampacities. The framework consists of two algorithmic layers. In the first one (day-ahead scheduling), we determine an aggregated dispatch plan. In the second layer (real-time control), a distributed model predictive control (MPC) determines the active and reactive power setpoints of the DERs so that their aggregated contribution tracks the dispatch plan while obeying to DERs operational constraints as well as the grid's ones. The proposed framework is experimentally validated on a real-scale microgrid that reproduces the network specifications of the CIGRE microgrid benchmark system.