Browsing by Author "Vaccaro, Alfredo"

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A Knowledge-Based Framework for Power Flow and Optimal Power Flow Analyses
(Institute of Electrical and Electronics Engineers (IEEE), 2016-04-07) Vaccaro, Alfredo; Canizares, Claudio A.
This paper proposes the application of formal methods for knowledge discovery from large quantity of data to reduce the complexity of power flow (PF) and optimal power flow (OPF) problems. In particular, a knowledge-based paradigm for PF and OPF analyses is used to extract complex features, hidden relationships, and useful hypotheses potentially describing regularities in the problem solutions from operation data-sets. This is realized by designing a knowledge-extraction process based on principal components analysis. The structural knowledge extracted by this process is then used to project the problem equations into a domain in which these equations can be solved more effectively. In this new domain, the cardinality of the PF and OPF problem is sensibly reduced and, consequently, the problem solutions can be obtained more efficiently. The effectiveness of the proposed framework is demonstrated with numerical results obtained for realistic power networks for several operating conditions.
A Novel Affine Arithmetic Method to Solve Optimal Power Flow Problems With Uncertainties
(Institute of Electrical and Electronics Engineers (IEEE), 2014-05-02) Pirnia, Mehrdad; Canizares, Claudio A.; Bhattacharya, Kankar; Vaccaro, Alfredo
An affine arithmetic (AA) method is proposed in this paper to solve the optimal power flow (OPF) problem with uncertain generation sources. In the AA-based OPF problem, all the state and control variables are treated in affine form, comprising a center value and the corresponding noise magnitudes, to represent forecast, model error, and other sources of uncertainty without the need to assume a probability density function (pdf). The proposed AA-based OPF problem is used to determine the operating margins of the thermal generators in systems with uncertain wind and solar generation dispatch. The AA-based approach is benchmarked against Monte Carlo simulation (MCS) intervals in order to determine its effectiveness. The proposed technique is tested and demonstrated on the IEEE 30-bus system and also a real 1211-bus European system.
A Range Arithmetic-Based Optimization Model for Power Flow Analysis Under Interval Uncertainty
(Institute of Electrical and Electronics Engineers (IEEE), 2012-09-18) Vaccaro, Alfredo; Canizares, Claudio A.; Bhattacharya, Kankar
This paper presents a novel framework based on range arithmetic for solving power flow problems whose input data are specified within real compact intervals. Reliable interval bounds are computed for the power flow problem, which is represented as an optimization model with complementary constraints to properly represent generator bus voltage controls, including reactive power limits and voltage recovery processes. It is demonstrated that the lower and upper bounds of the power flow solutions can be obtained by solving two determinate optimization problems. Several numerical results are presented and discussed, demonstrating the effectiveness of the proposed methodology and comparing it to a previously proposed affine arithmetic based solution approach.
An Affine Arithmetic-Based Framework for Uncertain Power Flow and Optimal Power Flow Studies
(Institute of Electrical and Electronics Engineers (IEEE), 2016-05-10) Vaccaro, Alfredo; Canizares, Claudio A.
This paper proposes a unified framework based on affine arithmetic for computing reliable enclosures of uncertain power flow (PF) and optimal power flow (OPF) solutions. The main idea is to formulate a generic mathematical programming problem under uncertainty by means of equivalent deterministic problems, and to identify the affine forms describing the data uncertainty by means of a signal processing technique based on principal components analysis. Compared to existing solution algorithms, this formulation presents greater flexibility, as it allows to find feasible solutions and inclusion of multiple equality and inequality constraints, and reduce the approximation errors to obtain better PF and OPF solution enclosures. Detailed numerical results are presented and discussed using a variety of realistic test systems, demonstrating the effectiveness of the proposed methodologies and comparing it to existing techniques for uncertain PF and OPF analysis.
An Affine Arithmetic-Based Method for Voltage Stability Assessment of Power Systems With Intermittent Generation Sources
(Institute of Electrical and Electronics Engineers (IEEE), 2013-08-21) Munoz, Juan; Canizares, Claudio; Bhattacharya, Kankar; Vaccaro, Alfredo
This paper presents a novel method based on affine arithmetic (AA) for voltage stability assessment of power systems considering uncertainties associated with operating conditions, which may be attributed to intermittent generation sources, such as wind and solar. The proposed AA-based method reduces the computational burden as compared to Monte Carlo (MC) simulations, and also improves the accuracy as compared to some analytical approaches. The proposed method is tested using two study cases: first, a 5-bus test system is used to illustrate the proposed technique in detail, and thereafter a 2383-bus test system to demonstrate its practical application. The results are compared with those obtained using MC simulations to verify the accuracy and computational burden of the proposed AA-based method, and also with respect to a previously proposed technique to estimate parameter sensitivities in voltage stability assessment.
An Affine Arithmetic-Based Methodology for Reliable Power Flow Analysis in the Presence of Data Uncertainty
(Institute of Electrical and Electronics Engineers (IEEE), 2009-11-17) Vaccaro, Alfredo; Canizares, Claudio A.; Villacci, Domenico
Power flow studies are typically used to determine the steady state or operating conditions of power systems for specified sets of load and generation values, and is one of the most intensely used tools in power engineering. When the input conditions are uncertain, numerous scenarios need to be analyzed to cover the required range of uncertainty. Under such conditions, reliable solution algorithms that incorporate the effect of data uncertainty into the power flow analysis are required. To address this problem, this paper proposes a new solution methodology based on the use of affine arithmetic, which is an enhanced model for self-validated numerical analysis in which the quantities of interest are represented as affine combinations of certain primitive variables representing the sources of uncertainty in the data or approximations made during the computation. The application of this technique to the power flow problem is explained in detail, and several numerical results are presented and discussed, demonstrating the effectiveness of the proposed methodology, especially in comparison to previously proposed interval arithmetic's techniques.