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Security-constrained unit commitment: modeling, solutions and evaluations

APPLIED ENERGY 2025年 390卷

作者： Latify, Mohammad Amin Mokhtari, Ali Alavi-Eshkaftaki, Amin Najafabadi, Fatemeh Rajaei Hashemian, Seyed Nasrollah Khaleghizadeh, Ali Nezamabadi, Hossein Ramandi, Mostafa Yousefi Mozdawar, Seyed Alireza Hatziargyriou, Nikos D. Dolatabadi, Sarineh Hacopian Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Iran Grid Management Co Deputy Elect Market Tehran Iran Natl & Tech Univ Athens Sch Elect & Comp Engn Athens Greece Univ Isfahan Dept Econ Esfahan *** Iran

Unit commitment has been widely studied in the last few decades due to its importance in power systems operation. To ensure the reliability and feasibility of the operational constraints of the transmission network, the system operator needs to solve this problem, considering the security constraints. In this problem, the total operational costs during each day are minimized while considering all operational constraints of the generation units and the transmission network under various contingencies. Solving this problem in large networks is associated with many challenges, such as solution time and accuracy. From the perspective of the optimization literature, it is a high-dimensional non-convex problem. Different methods have been presented to solve the security-constrained unit commitment (SCUC). This paper presents a comprehensive and practical overview of the SCUC as a critical decision-making problem to determine optimal power generation schedules in the power system by investigating the most critical studies on this topic and providing a detailed classification of both modeling and problem-solving approaches for the existing real electricity markets. It also outlines the existing research challenges in this field by implementing test benchmarks on different types of SCUC problems and provides deep and novel insights. First, the various models available for this problem are summarized. Then, the different methods proposed to solve this problem are explained based on their characteristics and application scopes, are comprehensively categorized, and the corresponding solution methods are discussed. Finally, various methods are evaluated and compared through numerical case studies on benchmark formulations, and the potential future research directions in this field are proposed.

关键词： Security-constrained unit commitment (SCUC) problem modeling Solution methods Survey

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How People Visually Represent Discrete Constraint problems

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020年第8期26卷 2603-2619页

作者： Zhu, Xu Nacenta, Miguel A. Akgun, Ozgur Nightingale, Peter Univ St Andrews Sch Comp Sci Jack Cole Bldg St Andrews KY16 9SX Fife Scotland Univ York Dept Comp Sci Deramore Lane York YO10 5GH N Yorkshire England

problems such as timetabling or personnel allocation can be modeled and solved using discrete constraint programming languages. However, while existing constraint solving software solves such problems quickly in many cases, these systems involve specialized languages that require significant time and effort to learn and apply. These languages are typically text-based and often difficult to interpret and understand quickly, especially for people without engineering or mathematics backgrounds. Visualization could provide an alternative way to model and understand such problems. Although many visual programming languages exist for procedural languages, visual encoding of problem specifications has not received much attention. Future problem visualization languages could represent problem elements and their constraints unambiguously, but without unnecessary cognitive burdens for those needing to translate their problem's mental representation into diagrams. As a first step towards such languages, we executed a study that catalogs how people represent constraint problems graphically. We studied three groups with different expertise: non-computer scientists, computer scientists and constraint programmers and analyzed their marks on paper (e.g., arrows), gestures (e.g., pointing) and the mappings to problem concepts (e.g., containers, sets). We provide foundations to guide future tool designs allowing people to effectively grasp, model and solve problems through visual representations.

关键词： Visualization Software Computer languages Tools problem-solving problem visualization problem modeling problem solving constraint programming visual programming languages

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Automatic error function learning with interpretable compositional networks

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2023年 1-29页

作者： Richoux, Florian Baffier, Jean-Francois AIST Tokyo Japan JFLI CNRS Tokyo Japan IIJ Res Lab Tokyo Japan

In Constraint Programming, constraints are usually represented as predicates allowing or forbidding combinations of values. However, some algorithms can exploit a finer representation: error functions. By associating a function to each constraint type to evaluate the quality of an assignment, it extends the expressiveness of regular Constraint Satisfaction problem/Constrained Optimization problem formalisms. Their usage comes with a price though: it makes problem modeling significantly harder, since users must provide a set of error functions that are not always easy to define. Here, we propose a method to automatically learn an error function corresponding to a constraint, given its predicate version only. This is, to the best of our knowledge, the first attempt to automatically learn error functions for hard constraints. In this paper, we also give for the first time a formal definition of combinatorial problems with hard constraints represented by error functions. Our method aims to learn error functions in a supervised fashion, trying to reproduce either the Hamming or the Manhattan distance, by using a graph model we named Interpretable Compositional Networks. This model allows us to get interpretable results. We run experiments on 7 different constraints to show its versatility. Experiments show that our system can learn functions that scale to high dimensions, and can learn fairly good functions over incomplete spaces. We also show that learned error functions can be used efficiently to represent constraints in different classic problems.

关键词： Combinatorial satisfaction Combinatorial optimization Constraint programming problem modeling Error function Interpretable learning

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A Data-Driven Approach to Constraint Optimization

A Data-Driven Approach to Constraint Optimization

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International Conference on Automation (AUTOMATION)

作者： Wikarek, Jaroslaw Sitek, Pawel Kielce Univ Technol Dept Informat Syst Kielce Poland

ISBN: (纸本)9783030132736;9783030132729

Many problems occurring in production, transport, supply chains and everyday life problems can be formulated in the form of constraint optimization problems (COPs). Most often these are issues related to planning and scheduling, distribution of resources, fleet selection, route and network optimization, configuration of machines and manufacturing systems, timetabling, etc. In the vast majority of cases, these are discrete problems of a combinatorial nature. Significant difficulties in modelling and solving COPs are usually the magnitude of real problems, which translates into a large number of variables and constraints as well as high computational complexity (usually NP-hard problems). The article proposes a data-driven approach, which allows a significant reduction in the magnitude of modelled problems and, consequently, the possibility of solving many real problems in an acceptable time.

关键词： Data-driven Constraint optimization Mathematical programing problem modeling

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NSGA-II Algorithm in Path Planning of Multiple Unmanned Aerial Vehicle Collaborative Search Tasks 24

NSGA-II Algorithm in Path Planning of Multiple Unmanned Aeri...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Huang, Xin Tianjin Vocat Inst Sch Elect Mech Engn & Automat Tianjin 300410 Peoples R China

ISBN: (纸本)9798400718144

In response to the problem of single objective optimization in collaborative search path planning for multiple unmanned aerial vehicles, this paper aims to use the NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm for optimization. The first step of this article is to conduct multi-objective optimization modeling on the path planning problem, clarifying that the optimization objectives include search area coverage and flight time. To find the best path planning solution, the NSGA-II algorithm is used in the second stage to enhancing the dynamic environment's adaptability and the drone's flexibility and search efficiency in complex surroundings constitute the third step. Finally, by comparing the performance indicators with traditional algorithms, it was found that the algorithm used in this paper performs well in terms of search area coverage, with the highest average coverage, up to 87%. At the same time, it requires the least flight time to perform search tasks. In summary, the multi-objective optimization method based on NSGA-II algorithm proposed in this article has shown significant performance advantages in multi-UAV (Unmanned Aerial Vehicle) collaborative search path planning.

关键词： Multiple Drones in a Coordinated Search Path Planning NSGA-II Algorithm problem modeling Objective Function

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Agent-Based Natural Domain modeling for Cooperative Continuous Optimization

Agent-Based Natural Domain Modeling for Cooperative Continuo...

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5th International Conference on Computational Collective Intelligence (ICCCI)

作者： Jorquera, Tom George, Jean-Pierre Gleizes, Marie-Pierre Regis, Christine Univ Toulouse 3 Inst Rech Informat Toulouse F-31062 Toulouse France

ISBN: (纸本)9783642404955;9783642404948

While multi-agent systems have been successfully applied to combinatorial optimization, very few works concern their applicability to continuous optimization problems. In this article we propose a framework for modeling a continuous optimization problems as multi-agent system, which we call NDMO, by representing the problem as an agent graph, and complemented with optimization solving behaviors. Some of the results we obtained with our implementation on several continuous optimization problems are presented.

关键词： Multi-Agent System Continuous Optimization problem modeling

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Levels? 18

Levels?

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18th International Conference on Information Fusion (Fusion)

作者： Steinberg, Alan N. Snidaro, Lauro Univ Udine Dept Math & Comp Sci Udine Italy

ISBN: (纸本)9780982443866

This paper challenges the familiar hierarchical partitioning of data fusion problems into "levels". The JDL data fusion model and its variants are seen as a method to partition a problem space in a way that tends to support different types of solutions. The layered view of fusion presented in these models is a rough engineering-based representation of a domain that has been addressed in analytically - and empirically-based models developed over centuries by philosophers and cognitive scientists. These ontological and cognitive models involve distinctions that are not all necessarily hierarchical or sequential. A hierarchical partitioning - while often convenient in characterizing fusion problems - should not be an impediment to fusion solutions that span the levels. A more flexible and comprehensive partitioning scheme is suggested.

关键词： epistemology JDL data fusion model problem modeling model-based recognition anomaly-based detection abductive learning context-based feasibility

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Integration of Constraint Programming and ModelBased Approach for System Synthesis 15

Integration of Constraint Programming and ModelBased Approac...

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15th Annual IEEE International Systems Conference (SysCon)

作者： Pierre-Alain, Yvars Laurent, Zimmer Inst Super Mecan Paris SupMeca EA7393 QUARTZ St Ouen France Dassault Aviat Direct Prospect St Ouen France

ISBN: (纸本)9781665444392

Most of the work in the field of Model-Based System Engineering for the design of technical systems consists of implementing solution-oriented approaches. Several system modeling languages are available to represent fully defined systems from several points of view. It is also possible to link these descriptions with simulation or analysis tools to evaluate the solutions thus described. After having studied the limits of this way of designing system, we propose in this paper an approach oriented to the description of the design problem to be solved, through an adapted formalism called DEPS. This formalism allows a model-based approach for architecture and system synthesis. DEPS (Design problem Specification) addresses problems of sizing, configuration, resource allocation and more generally of architecture generation or synthesis encountered in system design. The systems considered can be physical systems, software-intensive systems or mixed systems (embedded, mechatronical, cyber-physical). This language combines structural modeling features specific to object-oriented languages with problem specification features from constraint programming. We also present an integrated approach through the DEPS Studio environment, allowing DEPS modeling, model compilation and solving using an integrated constraint programming solver. This integration allows, among other things, the development and the debugging of models directly in DEPS rather than in the language of an external solver. The approach is illustrated on a simple case of electrical system synthesis.

关键词： constraint programming model based system synthesis problem modeling problem solving

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Efficient Representations and Conversions of Planning problems

Efficient Representations and Conversions of Planning Proble...

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作者： Toropila, Daniel Charles University of Prague

Title E cient Representations and Conversions of Planning problems Author Daniel Toropila Department Department of Theoretical Computer Science and Mathematical Logic Supervisor prof. RNDr. Roman Barták, Ph.D. Abstract The e ciency of all types of planning systems is strongly dependent on the in- put formulation, the structure of which must be exploited in order to provide an improved e ciency. Hence, the state-variable representation (SAS+ ) has be- come the input of choice for many modern planners. As majority of planning problems is encoded using a classical representation, several techniques for trans- lation into SAS+ have been developed in the past. These techniques, however, ignore the instance-specific information of planning problems. Therefore, we in- troduce a novel algorithm for constructing SAS+ that fully utilizes the information from the goal and the initial state. By performing an exhaustive experimental evaluation we demonstrate that for many planning problems the novel approach generates a more e cient encoding, providing thus an improved solving time. Finally, we present an overview and performance evaluation of several constraint models based on SAS+ and finite-state automata, showing that they represent a competitive alternative in the category of constraint-based planners. Keywords...

关键词： artificial intelligence domain-independent planning problem modeling state variables constraint satisfaction

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Efficient Representations and Conversions of Planning problems

Efficient Representations and Conversions of Planning Proble...

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作者： Toropila, Daniel Charles University of Prague

关键词： artificial intelligence domain-independent planning problem modeling state variables constraint satisfaction

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