Solving AC Optimal Power Flow with Discrete Decisions to Global Optimality

Aigner, Kevin-Martin; Burlacu, Robert; Liers, Frauke; Martín, Alexander

doi:10.1287/ijoc.2023.1270

Cited by 6 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering this factor in a nonlinear AC-OPF can significantly increase computing time. To address this issue, some studies have employed a DC-based model to determine optimal discrete decisions, which are then used as parameters for the AC-based model [34]. Therefore, this paper uses a linearized DC-OPF to determine the commitment of the generators.…”

Section: A Ac-opf Formulationmentioning

confidence: 99%

Quantifying Benefit of Well-Located Distributed Energy Resources

Choi,

Lee,

Kim

et al. 2024

IEEE Trans. on Enrgy Mrkts, Pol and Reg

View full text Add to dashboard Cite

In recent years, there has been a global acceleration in the adoption of distributed energy resources (DERs), due to their potential to decrease net demand and minimize costs associated with transmission and distribution networks. In practice, however, many of them are not situated in load areas, but in remote areas for return of investment, i.e., mostly characterized by high solar radiation, abundant wind resources, and relatively low land-use fees. As a result, the locational mismatches can lead to excessive network construction, significant congestion, and loss costs. To achieve cost-effective grid operation and planning results, it is crucial to locate DERs considering their system level impacts. Since the locational benefits of DERs are not fully assessed for and reflected in their field deployment process today, DERs are not induced to the appropriate sites. To fill this gap, this study quantifies the benefits of diverse DER deployment scenarios using Monte Carlo simulations and provides policy recommendations for utilities and authorities. To estimate the benefits, we conducted a long-term analysis using the transmission expansion planning approach and a short-term analysis based on the optimal power flow methodology. The proposed analysis reveals that the upper 10% scenario of the experimental group with better DER locations can achieve 27% cost reduction than that of the control group. The noteworthy improvement of the well-located scenario for the same amount of DER deployment accounts for a benefit of 1519M$ in the Korean power system case study.

show abstract

Section: A Ac-opf Formulationmentioning

confidence: 99%

Quantifying Benefit of Well-Located Distributed Energy Resources

Choi,

Lee,

Kim

et al. 2024

IEEE Trans. on Enrgy Mrkts, Pol and Reg

View full text Add to dashboard Cite

show abstract

“…ac networks have been studied intensively in the literature, e.g. in Carpentier (1962), Mary et al (2012), Aigner et al (2021a), also in their linearised version as DC networks, see e.g. Aigner et al (2021).…”

Section: Stability Of Lvdc Networkmentioning

confidence: 99%

Optimization over decision trees: a case study for the design of stable direct-current electricity networks

et al. 2023

View full text Add to dashboard Cite

In many real-world mixed-integer optimization problems from engineering, the side constraints can be subdivided into two categories: constraints which describe a certain logic to model a feasible allocation of resources (such as a maximal number of available assets, working time requirements, maintenance requirements, contractual obligations, etc.), and constraints which model physical processes and the related quantities (such as current, pressure, temperature, etc.). While the first type of constraints can often easily be stated in terms of a mixed-integer program (MIP), the second part may involve the incorporation of complex non-linearities, partial differential equations or even a black-box simulation of the involved physical process. In this work, we propose the integration of a trained tree-based classifier—a decision-tree or a random forest, into a mixed-integer optimization model as a possible remedy. We assume that the classifier has been trained on data points produced by a detailed simulation of a given complex process to represent the functional relationship between the involved physical quantities. We then derive MIP-representable reformulations of the trained classifier such that the resulting model can be solved using state-of-the-art solvers. At the hand of several use cases in terms of possible optimization goals, we show the broad applicability of our framework that is easily extendable to other tasks beyond engineering. In a detailed real-world computational study for the design of stable direct-current power networks, we demonstrate that our approach yields high-quality solutions in reasonable computation times.

show abstract

Enhancements of discretization approaches for non-convex mixed-integer quadratically constrained quadratic programming: Part I

Beach,

Burlacu,

Bärmann

et al. 2024

Comput Optim Appl

View full text Add to dashboard Cite

We study mixed-integer programming (MIP) relaxation techniques for the solution of non-convex mixed-integer quadratically constrained quadratic programs (MIQCQPs). We present MIP relaxation methods for non-convex continuous variable products. In this paper, we consider MIP relaxations based on separable reformulation. The main focus is the introduction of the enhanced separable MIP relaxation for non-convex quadratic products of the form $$z=xy$$ z = x y , called hybrid separable (HybS). Additionally, we introduce a logarithmic MIP relaxation for univariate quadratic terms, called sawtooth relaxation, based on Beach (Beach in J Glob Optim 84:869–912, 2022). We combine the latter with HybS and existing separable reformulations to derive MIP relaxations of MIQCQPs. We provide a comprehensive theoretical analysis of these techniques, underlining the theoretical advantages of HybS compared to its predecessors. We perform a broad computational study to demonstrate the effectiveness of the enhanced MIP relaxation in terms of producing tight dual bounds for MIQCQPs. In Part II, we study MIP relaxations that extend the MIP relaxation normalized multiparametric disaggregation technique (NMDT) (Castro in J Glob Optim 64:765–784, 2015) and present a computational study which also includes the MIP relaxations from this work and compares them with a state-of-the-art of MIQCQP solvers.

show abstract

Solving AC Optimal Power Flow with Discrete Decisions to Global Optimality

Cited by 6 publications

References 39 publications

Quantifying Benefit of Well-Located Distributed Energy Resources

Quantifying Benefit of Well-Located Distributed Energy Resources

Optimization over decision trees: a case study for the design of stable direct-current electricity networks

Enhancements of discretization approaches for non-convex mixed-integer quadratically constrained quadratic programming: Part I

Contact Info

Product

Resources

About