Applications of Heuristic Methods to Electrical Power Engineering
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: closed (15 January 2020) | Viewed by 9211
Special Issue Editor
Interests: power system modelling; control and stability analysis; stochastic and functional differential algebraic equations; software architecture and parallel computing for power system analysis
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Special Issue Information
Dear Colleagues,
Heuristic methods are a crucial aspect of any complex algorithm. Power systems analysis and operation are no exception to this rule. Whoever has implemented a routine to solve the power flow analysis through the Newton-Raphson method, for example, knows well that the choice of the initial guess and the convergence criterion are based on heuristics. However, heuristic methods are, more often than not, associated with artificial intelligence and other black-box techniques that do not attempt to investigate the functioning of algorithms, unravel the inner details of theoretical models, or understand the physical behaviour that is described and the assumptions and simplifications that are implied by such models.
With this Special Issue, we are looking for works that genuinely attempt to understand the behaviour of power system models, either static or dynamic, and discuss how heuristic techniques can help to solve implementation issues and practical problems. For example, it is well known that properly splitting nonlinear constraints can help the convergence of non-convex optimization problems, or that defining additional algebraic constraints, while increasing the size of the Jacobian matrices, can also increase their sparsity and hence accelerate their factorization. We are also looking for heuristic techniques that, while based on a mathematically-sound approach, are able to solve NP-hard problems (e.g., mixed-integer nonlinear programming problems), or problems whose size or sources of uncertainty are so vast that they cannot be efficiently solved with conventional deterministic techniques.
Please note that this special section solicits exclusively original work that is not under consideration for publication in other venues.
Prof. Dr. Federico Milano
Guest Editor
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Keywords
- non-convex optimization methods (MIP, NLP, mixed integer programming, nonlinear programming problems, and mixed-integrar nonlinear programming problems)
- time domain integration
- stability analysis of nonlinear equations
- NP-hard problems
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