In this thesis we propose a methodology to assess the value of future measurements as a first step towards the development of a framework to optimize the design of reservoir surveillance plans. We also investigate alternatives to improve current reservoir management approaches by recommending actions which anticipate the availability of future information and account for the impact of immediate decisions on the decisions to be made in the future.

Throughout the chapters, we discuss how to combine a variety of topics (e.g., model-based optimization, data assimilation, uncertainty quantification) with other unusual ingredients (e.g., plausible truths, clairvoyance, flexible plans) to develop a methodology which can be applied in many problems involving decision making and learning. Despite being motivated by a real application, this research addresses abstract concepts such as value and information, but always from an engineering perspective. This makes us approach the problem in a different way, which, we hope, will inspire innovative solutions in the future.","value of information; closed-loop reservoir management; reservoir surveillance; geological uncertainty; robust optimization; data assimilation; plausible truths; representative models; clustering; stochastic programming","en","doctoral thesis","","978-94-6366-009-9","","","","","","","","","","","","" "uuid:40a5911e-14bd-4ea2-a854-e7cc3703c966","http://resolver.tudelft.nl/uuid:40a5911e-14bd-4ea2-a854-e7cc3703c966","Hidden Power System Inflexibilities imposed by traditional unit commitment formulations","Morales-Espana, G. (TU Delft Algorithmics); Ramirez Elizondo, L.M. (TU Delft DC systems, Energy conversion & Storage); Hobbs, Benjamin F. (Johns Hopkins University)","","2017","Approximations made in traditional day-ahead unit commitment model formulations can result in suboptimal or even infeasible schedules for slow-start units and inaccurate predictions of actual costs and wind curtailment. With increasing wind penetration, these errors will become economically more significant. Here, we consider inaccuracies from three approximations: the use of hourly intervals in which energy production from each generator is modeled as being constant; the disregarding of startup and shutdown energy trajectories; and optimization based on expected wind profiles. The results of unit commitment formulations with those assumptions are compared to models that: (1) use a piecewise-linear power profiles of generation, load and wind, instead of the traditional stepwise energy profiles; (2) consider startup/shutdown trajectories; and (3) include many possible wind trajectories in a stochastic framework. The day-ahead hourly schedules of slow-start generators are then evaluated against actual wind and load profiles using a model real-time dispatch and quick-start unit commitment with a 5 minute time step. We find that each simplification usually causes expected generation costs to increase by several percentage points, and results in significant understatement of expected wind curtailment and, in some cases, load interruptions. The inclusion of startup and shutdown trajectories often yielded the largest improvements in schedule performance.","Energy-based unit commitment; Power-based unit commitment; Reserves; Stochastic programming; Unit commitment (UC); Wind power","en","journal article","","","","","","","","","","","Algorithmics","","","" "uuid:e8dbb294-dd57-4c10-b733-b4aded62607c","http://resolver.tudelft.nl/uuid:e8dbb294-dd57-4c10-b733-b4aded62607c","Strategies, Methods and Tools for Solving Long-term Transmission Expansion Planning in Large-scale Power Systems","Fitiwi, D.Z. (TU Delft Energy & Industry)","Herder, P.M. (promotor); Rivier Abbad, M. (promotor)","2016","","transmission expansion planning; uncertainty and variability; optimization; stochastic programming; moments technique; clustering","en","doctoral thesis","","978-84-608-9955-6","","","","","","","","","Energy & Industry","","","" "uuid:857094e3-35f7-42d7-a96d-8ce3387e44f0","http://resolver.tudelft.nl/uuid:857094e3-35f7-42d7-a96d-8ce3387e44f0","On the Dynamics and Statics of Power System Operation: Optimal Utilization of FACTS Devices and Management of Wind Power Uncertainty","Nasri, A.","Ghandhari, M. (promotor); Herder, P.M. (promotor)","2014","Nowadays, power systems are dealing with some new challenges raised by the major changes that have been taken place since 80’s, e.g., deregulation in electricity markets, significant increase of electricity demands and more recently large-scale integration of renewable energy resources such as wind power. Therefore, system operators must make some adjustments to accommodate these changes into the future of power systems. One of the main challenges is maintaining the system stability since the extra stress caused by the above changes reduces the stability margin, and may lead to rise of many undesirable phenomena. The other important challenge is to cope with uncertainty and variability of renewable energy sources which make power systems to become more stochastic in nature, and less controllable. Flexible AC Transmission Systems (FACTS) have emerged as a solution to help power systems with these new challenges. This thesis aims to appropriately utilize such devices in order to increase the transmission capacity and flexibility, improve the dynamic behavior of power systems and integrate more renewable energy into the system. To this end, the most appropriate locations and settings of these controllable devices need to be determined. This thesis mainly looks at (i) rotor angle stability, i.e., small signal and transient stability (ii) system operation under wind uncertainty. In the first part of this thesis, trajectory sensitivity analysis is used to determine the most suitable placement of FACTS devices for improving rotor angle stability, while in the second part, optimal settings of such devices are found to maximize the level of wind power integration. As a general conclusion, it was demonstrated that FACTS devices, installed in proper locations and tuned appropriately, are effective means to enhance the system stability and to handle wind uncertainty. The last objective of this thesis work is to propose an efficient solution approach based on Benders’ decomposition to solve a network-constrained ac unit commitment problem in a wind-integrated power system. The numerical results show validity, accuracy and efficiency of the proposed approach.","Trajectory sensitivity analysis (TSA); transient stability; small signal stability; flexible AC transmission system (FACTS) devices; critical clearing time (CCT); optimal power flow (OPF); network-constrained ac unit commitment (ac-UC); wind power uncertainty; wind power spillage; stochastic programming; Benders decomposition","en","doctoral thesis","","","","","","","","","Technology, Policy and Management","Technology, Policy and Management Engineering, Systems and Services","","","","" "uuid:78b5ae1b-a809-442a-bd55-baea330ff25e","http://resolver.tudelft.nl/uuid:78b5ae1b-a809-442a-bd55-baea330ff25e","Optimal Control of Water Systems Under Forecast Uncertainty: Robust, Proactive, and Integrated","Raso, L.","Van de Giesen, N. (promotor)","2013","Water systems consist of natural and man-made objects serving multiple essential purposes. They are affected by many types of meteorological disturbances. In order to deal with these disturbances and to serve the desired objectives, infrastructures have been built and managed by societies for specific purposes. Given a water system, and its purposes, the control of the existing infrastructures is the subject of operational water management. The system controller, either a natural person or a mathematical algorithm, takes his recursive decisions observing the state of the system and trying to bring it to the desired condition. Model Predictive Control (MPC) is an advanced method for the control of complex dynamic systems. When applied to water systems operation, MPC provides integrated and optimal management. If disturbance forecasts are available, this information can be integrated in the control policy and water management becomes proactive. Before the realization of the disturbance, the MPC controller sets the system to a state which is optimal to accommodate the expected disturbance. A typical example is lowering the water level of a reservoir before an expected storm event in order to avoid floods. In proactive control of open water systems, the main uncertainty is generally related to the difficulty of producing good forecasts. Weather and hydrological processes are difficult to predict, and meteorological or rainfall-runoff models can be wrong. Especially when using only one deterministic estimate, the control is more vulnerable to forecast uncertainty, running the risk of taking action against a predicted event that will not occur. The research question of this thesis is: How to use existing forecasting methods in optimal control schemes, thereby enhancing robustness in the face of forecasting uncertainty? In open water systems, such as rivers, canals, or reservoirs, the available forecast is generally the natural inflow, which is the output of a deterministic rainfall-runoff model. The model produces a point estimate, which is the expected value of the variable of interest. Nevertheless, the nonlinearity of the control problem requires the forecast of the entire probability distribution. When residuals are assumed independent, identically distributed, zero-mean, and Gaussian, then the variance is the only extra parameter required to build up the entire distribution, and its value can be estimated from the data. However, residuals of rainfall-runoff models are in fact heteroscedastic (i.e. the variance changes in time) and autocorrelated. In Chapter 2 it is shown how to deal with both deficiencies. Dynamic modelling of predictive uncertainty is built up by regression on absolute residuals, and applied to two test cases: the Rhone River, in Switzerland, and Lake Maggiore, at the border between Italy and Switzerland. When the information on the catchment state does not offer sufficient anticipation, for example because the catchment dynamics are fast compared to the controlled system, it is necessary to include weather forecasts. Meteorological agencies produce not only a deterministic trajectory of the future state of the weather system, but a set of them, called ensemble, to communicate the forecast uncertainty. The algorithm presented in Chapter 3, called Tree-Based Model Predictive Control (TB-MPC), exploits the information contained in the ensemble, setting up a Multistage Stochastic Programming (MSP) problem within the MPC framework. MSP is a stochastic optimization scheme that takes into account not only the present uncertainty, but its resolution in time as well. Going on in time along the control horizon, information will enter the system. Consequently, uncertainty will be reduced, and the control strategy after uncertainty reduction will change according to the occurring ensemble member. The key idea of TB-MPC is producing a tree topology from the ensemble data and using this tree in the following MSP optimization. A tree specifies in fact the moments when uncertainties are resolved. Generating a tree from ensemble data is both difficult and of critical importance. It has been considered an open problem until now, especially regarding the tree branching structure, which also strongly affects control performance. Chapter 4 shows a new methodology that produces a tree topology from ensemble data. The proposed method models the information flow to the controller. This implies the explicit definition of the available observations and their degree of uncertainty. Chapter 5 summarizes the contribution of my PhD and the research directions that, in my opinion, deserve more investigation.","Operational Water Management; Optimal Control; Model Predictive Control; Ensemble Forecasting; Uncertainty; Multistage Stochastic Programming; Closed Loop Formulation; Robustness; Predictive Uncertainty; Heteroschedasticity","en","doctoral thesis","","","","","","","","2013-09-23","Civil Engineering and Geosciences","Water Management","","","",""