Control (management) | EPFL Graph Search

Control is a function of management that helps to check errors and take corrective actions. This is done to minimize deviation from standards and ensure that the stated goals of the organization are achieved in a desired manner. According to modern concepts, control is a foreseeing action; earlier concepts of control were only used when errors were detected. Control in management includes setting standards, measuring actual performance, and taking corrective action in decision making. Definition In 1916, Henri Fayol formulated one of the first definitions of control as it pertains to management: Control of an undertaking consists of seeing that everything is being carried out in accordance with the plan which has been adopted, the orders which have been given, and the principles which have been laid down. Its objective is to point out mistakes so that they may be rectified and prevented from recurring. According to EFL Brech: Control is checking current performance agai

Modelling and Forecasting of Photovoltaic Generation for Microgrid Applications: from Theory to Validation

Enrica Scolari

The penetration of stochastic renewable generation in modern power systems requires to reconsider conventional practices to ensure the reliable functioning of the electrical network. Decentralized control schemes for distributed energy resources (DERs) have gained attention to support the grid operation. In order to cope with the uncertainties of the DERs, predictive schemes that leverage on forecast of renewable generation recently came into prominence. The period of the control action typically depends on the availability of the reserve in the grid. For the case of microgrids, their limited physical extension and the lack of spatial smoothing imply fast power fluctuations and the necessity of coupling energy management strategies with real-time control. Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future available capacity, and consequently, solar resource assessment and power forecasting are of fundamental importance. This thesis focuses on developing forecasting methods and generation models to support the integration of photovoltaic systems in microgrids, considering short-term temporal horizons (below one hour) and fine spatial resolution (single site installations). In particular, we aim at computing probabilistic prediction intervals (PIs), i.e. we include information accounting for the intrinsic uncertainty of the prediction. In this respect, nonparametric tools to deliver PIs from sub-second to intra-hour forecasting horizons are proposed and benchmarked. They forecast the AC power and/or the global horizontal irradiance (GHI) by extracting selected endogenous influential variables from historical time series. The methods are shown to outperform available state-of-the-art techniques, and are able to capture the fastest fluctuations of small-scale PV plants. Then, we investigate how the inclusion of features from ground all-sky images can be used to improve time-series-based forecasting tools, thanks to identifying clouds movement. In this respect, we define a toolchain that allows predicting the cloud cover of the sun disk, through image processing and cloud motion identification. Furthermore, a methodology to estimate the irradiance from all-sky images is proposed, investigating the possibility of using an all-sky camera as an irradiance sensor. Next, we consider the problem of having power measurements that are corrupted by exogenous control actions (e.g. curtailment) and, therefore, not representative of the true potential of the PV plant. We propose a model-based strategy to reconstruct the maximum power production of a PV power plant thanks to integrating measurements of the PV cell temperature, system DC voltage and current. The strategy can improve time series-based direct power forecasting techniques when the production of the PV system is curtailed and thus the measured power does not correspond to the maximum available. The proposed methods to model and forecast the PV generation are then integrated in a single chain that allows to deliver power PIs that are able to account for the overall uncertainty of a PV system at a predefined confidence level. In the last part of the thesis, the proposed methods are experimentally validated in a real microgrid by considering possible applications in modern power systems.

EPFL2019

Real-time MPC - Stability through robust MPC design

Colin Neil Jones, Melanie Nicole Zeilinger

Recent results have suggested that online Model Predictive Control (MPC) can be computed quickly enough to control fast sampled systems. High-speed applications impose a hard real-time constraint on the solution of the MPC problem, which generally prevents the computation of the optimal controller. In current approaches guarantees on feasibility and stability are sacrificed in order to achieve a real-time setting. In this paper we develop a real-time MPC scheme based on robust MPC design that recovers these guarantees while allowing for extremely fast computation. We show that a simple warm-start optimization procedure providing an enhanced feasible solution guarantees feasibility and stability for arbitrary time constraints. The proposed method can be practically implemented and efficiently solved for dynamic systems of significant problem size. Implementation details for a real-time robust MPC method are provided that achieves computation times equal to those reported for methods without guarantees. A 12-dimensional problem with 3 control inputs and a prediction horizon of 10 time steps is solved in 2msec with a performance deterioration less than 1% and thereby allows for sampling rates of 500Hz.

IEEE2009

Modelling and Forecasting of Photovoltaic Generation for Microgrid Applications: from Theory to Validation

Enrica Scolari

EPFL2019