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Stateoftheart, challenges, and future trends in security constrained optimal power flow
 ELECTRIC POWER SYSTEMS RESEARCH
, 2011
"... This paper addresses the main challenges to the security constrained optimal power flow (SCOPF) computations. We first discuss the issues related to the SCOPF problem formulation such as the use of a limited number of corrective actions in the postcontingency states and the modeling of voltage an ..."
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Cited by 19 (2 self)
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This paper addresses the main challenges to the security constrained optimal power flow (SCOPF) computations. We first discuss the issues related to the SCOPF problem formulation such as the use of a limited number of corrective actions in the postcontingency states and the modeling of voltage and transient stability constraints. Then we deal with the challenges to the techniques for solving the SCOPF, focusing mainly on: approaches to reduce the size of the problem by either efficiently identifying the binding contingencies and including only these contingencies in the SCOPF or by using approximate models for the postcontingency states, and the handling of discrete variables. We finally address the current trend of extending the SCOPF formulation to take into account the increasing levels of uncertainty in the operation planning. For each such topic we provide a review of the state of the art, we identify the advances that are needed, and we indicate ways to bridge the gap between the current state of the art and these needs.
Active network management: planning under uncertainty for exploiting load modulation
 In Proceedings of the 2013 IREP Symposium  Bulk Power Systems Dynamics and Control  IX, Rethymnon
, 2013
"... This paper addresses the problem faced by a distribution system operator (DSO) when planning the operation of a network in the shortterm. The problem is formulated in the context of high penetration of renewable energy sources (RES) and distributed generation (DG), and when flexible demand is avail ..."
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Cited by 7 (4 self)
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This paper addresses the problem faced by a distribution system operator (DSO) when planning the operation of a network in the shortterm. The problem is formulated in the context of high penetration of renewable energy sources (RES) and distributed generation (DG), and when flexible demand is available. The problem is expressed as a sequential decisionmaking problem under uncertainty, where, in the first stage, the DSO has to decide whether or not to reserve the availability of flexible demand, and, in the subsequent stages, can curtail the generation and modulate the available flexible loads. We analyze the relevance of this formulation on a small test system, discuss the assumptions made, compare our approach to related work, and indicate further research directions.
SensingDriven Energy Purchasing in Smart Grid . . .
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
, 2012
"... Distributed and renewableenergy resources are likely to play an important role in the future energy landscape as consumers and enterprise energy users reduce their reliance on the main electricity grid as their source of electricity. Environmental or ambient sensing of parameters such as temperatu ..."
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Cited by 4 (0 self)
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Distributed and renewableenergy resources are likely to play an important role in the future energy landscape as consumers and enterprise energy users reduce their reliance on the main electricity grid as their source of electricity. Environmental or ambient sensing of parameters such as temperature and humidity, and amount of sunlight and wind, can be used to predict electricity demand from users and supply from renewable sources, respectively. In this paper, we describe a Smart Grid CyberPhysical System (SGCPS) comprising sensors that transmit realtime streams of sensed information to predictors of demand and supply of electricity and an optimizationbased decision maker that uses these predictions together with realtime grid electricity prices and historical information to determine the quantity and timing of grid electricity purchases throughout the day and night. We investigate two forms of the optimizationbased decision maker, one that uses linear programming and another that uses multistage stochastic programming. Our results show that sensingdriven predictions combined with the optimizationbased purchasing decision maker hosted on the SGCPS platform can cope well with uncertainties in demand, supply, and electricity prices and make grid electricity purchasing decisions that successfully keep both the occurrence of electricity shortfalls and the cost of grid electricity purchases low. We then examine the computational and memory requirements of the aforementioned prediction and optimization algorithms and find that they are within the capabilities of modern embedded system microprocessors and, hence, are amenable for deployment in typical households and communities.
Scenario Trees and Policy Selection for Multistage Stochastic Programming using Machine Learning
 Informs J. on Computing
, 2013
"... I n the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vectorvalued recourse decisions optimized using scenariotree techniques from multistage stochastic pr ..."
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Cited by 2 (0 self)
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I n the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vectorvalued recourse decisions optimized using scenariotree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenariotree solution and are selected by outofsample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution (policy) found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain nearoptimal solutions, starting with a "weak" scenariotree generator that randomizes the branching structure of the trees.
1Cautious operation planning under uncertainties
"... Abstract—This paper deals with dayahead power systems security planning under uncertainties, by posing an optimization problem over a set of power injection scenarios that could show up the next day and modeling the next day’s realtime control strategies aiming at ensuring security with respect to ..."
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Abstract—This paper deals with dayahead power systems security planning under uncertainties, by posing an optimization problem over a set of power injection scenarios that could show up the next day and modeling the next day’s realtime control strategies aiming at ensuring security with respect to contingencies by a combination of preventive and corrective controls. We seek to determine whether and which dayahead decisions must be taken so that for scenarios over the next day there still exists an acceptable combination of preventive and corrective controls ensuring system security for any postulated contingency. We formulate this task as a threestage feasibility checking problem, where the first stage corresponds to dayahead decisions, the second stage to preventive control actions, and the third stage to corrective postcontingency controls. We propose a solution approach based on the problem decomposition into successive optimal power flow (OPF) and securityconstrained optimal power flow (SCOPF) problems of a special type. Our approach is illustrated on the Nordic32 system and on a 1203bus model of a reallife system. Index Terms—power systems security, operation planning under uncertainty, worstcase analysis, securityconstrained optimal power flow, nonlinear programming I.
F.: Optimized lookahead tree policies: a bridge between lookahead tree policies and direct policy search
 International Journal of Adaptive Control and Signal Processing
, 2013
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"... This thesis investigates different issues related to the issuance of debt by sovereign bodies such as governments, under uncertainty about the future interest rates. Several dynamic models of interest rates are presented, along with extensive numerical experiments for calibration of models and compa ..."
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This thesis investigates different issues related to the issuance of debt by sovereign bodies such as governments, under uncertainty about the future interest rates. Several dynamic models of interest rates are presented, along with extensive numerical experiments for calibration of models and comparison of performance on real financial market data. The main contribution of the thesis is the construction and demonstration of a stochastic optimisation model for debt issuance under interest rate uncertainty. When the uncertainty is modeled using a model from a certain class of single factor interest rate models, one can construct a scenario tree such that the number of scenarios grows linearly with time steps. An optimization model is constructed using such a one factor scenario tree. For a real government debt issuance remit, a multistage stochastic optimization is performed to choose the type and the amount of debt to be issued and the results are compared with the real issuance. The currently used simulation models by the government, which are in public domain, are also reviewed. Apparently, using an optimization model, such as the one proposed in this