10,937 research outputs found
Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty
In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed
An exact solution method for binary equilibrium problems with compensation and the power market uplift problem
We propose a novel method to find Nash equilibria in games with binary
decision variables by including compensation payments and
incentive-compatibility constraints from non-cooperative game theory directly
into an optimization framework in lieu of using first order conditions of a
linearization, or relaxation of integrality conditions. The reformulation
offers a new approach to obtain and interpret dual variables to binary
constraints using the benefit or loss from deviation rather than marginal
relaxations. The method endogenizes the trade-off between overall (societal)
efficiency and compensation payments necessary to align incentives of
individual players. We provide existence results and conditions under which
this problem can be solved as a mixed-binary linear program.
We apply the solution approach to a stylized nodal power-market equilibrium
problem with binary on-off decisions. This illustrative example shows that our
approach yields an exact solution to the binary Nash game with compensation. We
compare different implementations of actual market rules within our model, in
particular constraints ensuring non-negative profits (no-loss rule) and
restrictions on the compensation payments to non-dispatched generators. We
discuss the resulting equilibria in terms of overall welfare, efficiency, and
allocational equity
Vulnerability Assessment of Large-scale Power Systems to False Data Injection Attacks
This paper studies the vulnerability of large-scale power systems to false
data injection (FDI) attacks through their physical consequences. Prior work
has shown that an attacker-defender bi-level linear program (ADBLP) can be used
to determine the worst-case consequences of FDI attacks aiming to maximize the
physical power flow on a target line. This ADBLP can be transformed into a
single-level mixed-integer linear program, but it is hard to solve on large
power systems due to numerical difficulties. In this paper, four
computationally efficient algorithms are presented to solve the attack
optimization problem on large power systems. These algorithms are applied on
the IEEE 118-bus system and the Polish system with 2383 buses to conduct
vulnerability assessments, and they provide feasible attacks that cause line
overflows, as well as upper bounds on the maximal power flow resulting from any
attack.Comment: 6 pages, 5 figure
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