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2024 International Annual Conference of the Italian Association of Electrotechnics

作者： Saguatti, Lorenzo Bovera, Filippo Gabba, Marco Martoriello, Gaia Zatti, Matteo LEAP scarl Piacenza Italy Politecn Milan Dept Energy Milan Italy

ISBN: (纸本)9798350378498;9788887237627

The development of energy communities would benefit from the use of optimisation tools to improve their economic performance. However, collecting members' data, forecasts and contracts in a single centralised optimisation problem endangers their privacy and autonomy. To tackle this issue, it is possible to leverage distributed optimisation techniques. The paper first presents a deterministic scheduling optimisation model for an energy community under the Italian regulatory framework;then, the alternating direction method of multipliers (ADMM) is used to separate it in one distinct optimisation problem for each member, and a master problem that provides coordination. Since the original centralised problem is nonconvex, ADMM can achieve only local optima;thus, global optimal solutions of the centralised model are used as a benchmark for ADMM performance. Results show that ADMM, with a constant penalty parameter, converges only when the parameter is set sufficiently large, and reaches local solutions far from the global optimum. Conversely, starting with a small penalty parameter and increasing it iteratively ensures convergence for any starting value and yields solutions closer to the global optimum.

关键词： Energy communities Shared energy mixed-integer linear program Distributed optimisation Alternating Direction Method of Multipliers

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Robust Cooperative Monitoring Problem 7

Robust Cooperative Monitoring Problem

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7th International Workshop on Reliable Networks Design Modeling (RNDM)

作者： Papadimitriou, Dimitri Fortz, Bernard Alcatel Lucent Bell Labs Antwerp Belgium Univ Libre Bruxelles Dept Informat Brussels Belgium

ISBN: (纸本)9781467380515

The cooperative monitoring problem refers to the placement and configuration of passive monitoring points (or monitors) to jointly realize a task of monitoring time-varying traffic flows. Given a traffic monitoring task, e.g., monitor a traffic flow percentage, the objective consists in minimizing the total monitoring cost to realize this task. We formulate the corresponding problem as a mixed-integer linear program (MILP). This formulation can also be dualized to determine the gain obtained when increasing the number of monitoring points (i.e., the monitoring installation cost) and the fraction of monitored traffic (i.e., the monitoring configuration cost). As traffic flows are subject to uncertainty, we extend the MIP formulations developed in [1] to their robust counter-part when the uncertainty in traffic demands is modeled by means of box+polyhedral and box+ellipsoidal perturbation sets.

关键词： cooperative communication integer programming linear programming telecommunication traffic MILP box+ellipsoidal perturbation set box+polyhedral perturbation set mixed-integer linear program passive monitoring point configuration passive monitoring point placement robust cooperative monitoring problem time-varying traffic flow monitoring Minimization Monitoring Optimization Robustness Routing Topology Uncertainty cooperative communication telecommunication traffic linear programming Traffic flow integer programming Network routing topology Monitoring problem monitoring point Uncertainty mixed integer linear programming Robustness MONITOR

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Decentralized Multi-Charger Coordination for Wireless Rechargeable Sensor Networks 34

Decentralized Multi-Charger Coordination for Wireless Rechar...

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34th IEEE International Performance, Computing, and Communications Conference (lPCCC)

作者： Mo, Lei You, Pengcheng Cao, Xianghui Song, Ye-Qiong Chen, Jiming Zhejiang Univ Dept Control Sci & Engn Hangzhou Zhejiang Peoples R China Southeast Univ Sch Automat Nanjing Jiangsu Peoples R China Univ Lorraine LORIA Nancy France

ISBN: (纸本)9781467385909

Wireless charging is a promising technology for provisioning dynamic power supply in wireless rechargeable sensor networks (WRSNs). The charging equipment can be carried by some mobile nodes to enhance the charging flexibility. With such mobile chargers (MCs), the charging process should simultaneously address the MC scheduling, the moving and charging time allocation, while saving the total energy consumption of MCs. However, the efficient solutions that jointly solve those challenges are generally lacking in the literature. First, we investigate the multi-MC coordination problem that minimizing the energy expenditure of MCs while guaranteeing the perpetual operation of WRSNs, and formulate this problem as a mixed-integer linear program (MILP). Second, to solve this problem efficiently, we propose a novel decentralized method which is based on Benders decomposition. The multi-MC coordination problem is then decomposed into a master problem (MP) and a slave problem (SP), with the MP for MC scheduling and the SP for MC moving and charging time allocation. The MP is being solved by the base station (BS), while the SP is further decomposed into several sub-SPs and being solved by the MCs in parallel. The BS and MCs coordinate themselves to decide an optimal charging strategy. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method.

关键词： Wireless rechargeable sensor networks mobile charger coordination perpetual operation mixed-integer linear program decentralized method

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A Mathematical Model for Strategic Generation Expansion Planning

A Mathematical Model for Strategic Generation Expansion Plan...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Tohidi, Yaser Hesamzadeh, Mohammad Reza KTH Royal Inst Technol Elect Market Res Grp Stockholm Sweden

ISBN: (纸本)9781509041688

This paper proposes a mathematical model for strategic generation expansion planning problem. The model is developed based on the the simultaneous-move game between Gencos. Gencos investment decisions are passed to the dispatch center which decides about the production level in operating scenarios considered. Using Karush-Kuhn-Tucker conditions (KKTs) and disjunctive linearization, the model is formulated as a mixed-integer linear program (MILP). The concepts of worst Nash equilibrium (WNE) and best Nash equilibrium (BNE) are introduced to handle multiple NE problem. The impact of uncertainty (scenarios) on equilibria band, i.e., the difference between WNE and BNE is discussed. The developed model is simulated on illustrative 2-node and 3-node example systems and also on IEEE-RTS96 test system.

关键词： mixed-integer linear program Nash equilibrium generation planning

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Strategic Generation Expansion Planning Using the Modified Benders Decomposition

Strategic Generation Expansion Planning Using the Modified B...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Tohidi, Yaser Hesamzadeh, Mohammad Reza Gautam, Himanshu Eindhoven Univ Technol Eindhoven Netherlands KTH Royal Inst Technol Elect Market Res Grp Stockholm Sweden

ISBN: (纸本)9781538677032

This paper proposes a mathematical model for solving the generation expansion problem of multiple Gencos. The model is developed assuming a simultaneous-move game between Gencos. Each Genco maximizes his total profit of selling the electricity to the market minus the investment cost of new generating units given the investment decisions of rival Gencos. We assume one market operator who dispatches the generators in the system. This problem is formulated as a mixed-integer linear program (MILP) using the Nash equilibrium and the strong duality theorem. To solve the proposed MILP, a modified Benders decomposition algorithm is implemented. The numerical results are carried on the 2-bus and 12-bus networks and the results show the effectiveness of the model and the solution algorithm.

关键词： Strategic Generation Expansion Planning mixed-integer linear program Benders Decomposition

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Accessible location of mobile labs for COVID-19 testing

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HEALTH CARE MANAGEMENT SCIENCE 2024年第1期27卷 1-19页

作者： Villicana-Cervantes, Dianne Ibarra-Rojas, Omar J. Univ Autonoma Nuevo Leon Av Univ s n San Nicolas De Los Garza Nuevo Leon Mexico

In this study, we address the problem of finding the best locations for mobile labs offering COVID-19 testing. We assume that people within known demand centroids have a degree of mobility, i.e., they can travel a reasonable distance, and mobile labs have a limited-and-variable service area. Thus, we define a location problem concerned with optimizing a measure representing the accessibility of service to its potential clients. In particular, we use the concepts of classical, gradual, and cooperative coverage to define a weighted sum of multiple accessibility indicators. We formulate our optimization problem via a mixed-integer linear program which is intractable by commercial solvers for large instances. In response, we designed a Biased Random-Key Genetic Algorithm to solve the defined problem;this is capable of obtaining high-quality feasible solutions over large numbers of instances in seconds. Moreover, we present insights derived from a case study into the locations of COVID-19 testing mobile laboratories in Nuevo Leon, Mexico. Our experimental results show that our optimization approach can be used as a diagnostic tool to determine the number of mobile labs needed to satisfy a set of demand centroids, assuming that users have reduced mobility due to the restrictions because of the pandemic.

关键词： Location Accessibility mixed-integer linear program BRKGA COVID-19 testing Operations research

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Robust Optimization for Home-Load Scheduling under Price Uncertainty in Smart Grids

Robust Optimization for Home-Load Scheduling under Price Unc...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Guo, Limei Wu, Hsiao-Chun Zhang, Honhgting Xia, Tian Mehraeen, Shahab Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China Louisiana State Univ Sch Elect Engn Baton Rouge LA 70803 USA

ISBN: (纸本)9781479969593

In this paper;the emerging porblem of residential energy scheduling for smart grid under the energy-price uncertainty is investigated, where the prices randomly vary arount nominal values with a known underlying distribution. The objective of the focused optimization problem is to minimize the user's cost of energy consumption. The robust optimization methodology will be used to deal with the uncertainty programming. We schedule the amounts of charged/discharged energy of the energy storage devices varied continuously within each time slot, which is more realistic and cost-effective. In addition, the mathematical model we adopts here is more practical because we consider the linear dynamical model of the temperature-related appliances. The involvement of the price uncertainty makes our devised algorithm more robust. Simulation results illustrate that our proposed new scheme is more effective than the conventional method without smart grid in terms of total energy cost.

关键词： Energy consumption scheduling mixed-integer linear program price uncertainty robust optimization smart grid

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Finite disjunctive programming methods for general mixed integer linear programs

Finite disjunctive programming methods for general mixed int...

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作者： Chen, Binyuan The University of Arizona

学位级别：Ph.D.

In this dissertation, a finitely convergent disjunctive programming procedure, the Convex Hull Tree (CHT) algorithm, is proposed to obtain the convex hull of a general mixed-integer linear program with bounded integer variables. The CHT algorithm constructs a linear program that has the same optimal solution as the associated mixed-integer linear program. The standard notion of sequential cutting planes is then combined with ideas underlying the CHT algorithm to help guide the choice of disjunctions to use within a new cutting plane method, the Cutting Plane Tree (CPT) algorithm. We show that the CPT algorithm converges to an integer optimal solution of the general mixed-integer linear program with bounded integer variables in finitely many steps. We also enhance the CPT algorithm with several techniques including a “round-of-cuts” approach and an iterative method for solving the cut generation linear program (CGLP). Two normalization constraints are discussed in detail for solving the CGLP. For moderately sized instances, our study shows that the CPT algorithm provides significant gap closures with a pure cutting plane method. Key words: mixed-integer linear program, disjunctive programming, convex hull, cutting plane, finite convergence.

关键词： cutting plane finite convergence convex hull disjunctive programming mixed-integer linear program

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A New Method to Finding All Nash Equilibria 5th

A New Method to Finding All Nash Equilibria

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5th International Conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Wu, Zhengtian Dang, Chuangyin Hu, Fuyuan Fu, Baochuan Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783319238623;9783319238616

It is a main concern in applications of game theory to effectively select a Nash equilibrium. All Nash equilibria is often required to be computed for this selection process. However, it is well known that the problem of finding only one mixed-strategy Nash equilibrium is a PPAD-complete process. Therefore, it is very hard to find all Nash equilibrium for a certain problem by traditional methods. By exploiting the properties of multilinear terms in the payoff functions, this paper presents a good approximation of the multilinear terms and develops a mixed-integer linear programming for finding all mixed-strategy Nash equilibria. An example of this method will be given too.

关键词： Game theory Nash equilibrium PPAD-complete Multilinear terms mixed-integer linear program

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OPTIMIZATION OF THE FORCE GENERATION PROCESS OF MARINE CORPS INFANTRY BATTALIONS

OPTIMIZATION OF THE FORCE GENERATION PROCESS OF MARINE CORPS...

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作者： Joseph E. Moeller NAVAL POSTGRADUATE SCHOOL

学位级别：硕士

The Marine Corps' Force Generation Process (FGP) requires multiple inputs to generate the combat forces necessary to accomplish the goals set in the National Defense Strategy (NDS). Current manpower assignment methods do not optimize the manning of Marine Corps infantry battalions to ensure maximum training readiness, manning, and deployability under the FGP. This thesis researches manpower trends, personnel timelines, deployment manpower levels, and unit deployment timelines of historical infantry battalion data from the Marine Corps Total Force Data Warehouse (TFDW). The Optimized Assignment Model (OAM), a mixed-integer linear program, optimizes manpower assignments in order to minimize the loss of available training days and improve unit manpower readiness timelines. Over a four-year time horizon, OAM improves the number of available training days for 28 of 31 rank and military occupational specialty (MOS) combinations, a median value of 44 additional days prior to deployment. Over the same horizon, OAM reduces the time required for infantry battalions to reach their deployable unit strength by nearly 85 days.

关键词： optimization manpower force generation infantry manning MAGTF USMC personnel mixed-integer linear program training readiness entry level assignment deployment training data warehouse

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