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Optimal hierarchical modeling of power to X stations through a chance constrained Two-Stage stochastic programming

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 161卷

作者： Son, Yeong Geon Acquah, Moses Amoasi Kim, Sung-Yul Hanyang Univ Dept Elect Engn Seoul South Korea Keimyung Univ Dept Elect Energy Engn Daegu South Korea

The carbon neutrality policy aiming for net zero carbon emissions has led to a significant increase in the use of renewable energy sources (RES) globally. However, due to their uncertain nature, RES can cause imbalances in power demand. Recently, Power-to-X (P2X) station technology has gained attention as a solution to the uncertainties of RES and as a means to enhance the capacity and efficiency of RES operations. P2X stations can be utilized when power demand imbalances occur due to the uncertain output of RES or when the power system cannot accommodate the power supply from RES due to various stability issues. Specifically, when supply disruptions occur in the power system due to RES uncertainties, P2X stations contribute to preventing RES curtailment by supplying power to electric vehicle (EV) fuel sources, producing heat using electric heat pumps (EHP), or producing hydrogen using electrolyzers (ELZ), thus improving the uncertain financial benefits for independent power producers (IPP). This paper proposes a mixed-integer linear programming (MILP) based chance-constrained two-stage stochastic optimization (CCTS) approach to address imbalances in power demand from RES and to enhance the profitability of IPP by finding the optimal planning and operational solutions for P2X stations. The proposed method provides hierarchical level results, demonstrating that economic benefits can increase by up to 60.2% with the application of P2X stations and that curtailed energy from RES can be reduced by up to 76.5%. The proposed methodology is also validated for its superior performance by being compared with both the non-linear stochastic chance constraint method and the stochastic method.

关键词： Wind farm Power to gas Distributionally robust optimization Ambiguity set mixed-integer linear programming

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Site and capacity selection for on-site production facilities in a nationwide hydrogen supply chain deployment plan

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartB期50卷 968-987页

作者： Lee, Seungyeop Kim, Hyunjoon Kim, Byung-In Song, Minseok Lee, Deoksang Ryu, Hyunyoung Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn Gyeongbuk 37673 South Korea Hanyang Univ Dept Ind & Management Engn Ansan 15588 Gyeonggi Do South Korea

The Republic of Korea intends to establish a nationwide hydrogen supply chain (HSC) infrastructure to promote the adoption of hydrogen fuel cell vehicles. As the hydrogen demand for mobility may be higher than the estimated production capacity of existing facilities, additional on-site hydrogen refueling stations (HRSs) are required. To address infrastructure deployment problems, a two-stage (demand and supply stages) optimization approach is proposed based on mixed-integer linear programming models. The maximal covering location and p-median models at the demand stage determine the location and chargers of the HRSs and the demand allocation to the stations. At the supply stage, the supply model determines the installation of additional on-site production facilities for the deployed HRSs and the supply allocation to the stations by minimizing the installation and transportation costs. We estimated highresolution demand data using public sources and utilized investigated supply data from reputable institutions representing the hydrogen economy in the Republic of Korea. The proposed approach provides realistic HSC infrastructure deployment plans, satisfying over 90% of the hydrogen demand in the years 2025 and 2030. Additionally, a sensitivity analysis indicated the effects of various parameters on the deployment plan, including the demand coverage and the need for additional on-site HRSs to address potential hydrogen supply shortages. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen supply chain Hydrogen refueling station Hydrogen supply shortage On-site production mixed-integer linear programming

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Learning to Optimize for mixed-integer Nonlinear programming

arXiv

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arXiv 2024年

作者： Tang, Bo Khalil, Elias B. Drgoňa, Ján Department of Mechanical and Industrial Engineering University of Toronto TorontoONM5S 1A1 Canada Pacific Northwest National Laboratory RichlandWA99354 United States The Ralph O’Connor Sustainable Energy Institute Johns Hopkins University BaltimoreMD21218 United States

mixed-integer nonlinear programs (MINLPs) arise in domains as diverse as energy systems and transportation but are notoriously difficult to solve, particularly at scale. While learning-to-optimize (L2O) methods have been successful at continuous optimization, extending them to MINLPs is challenging due to the integer constraints. To overcome this, we propose a novel L2O approach with two integer correction layers to ensure solution integrality and a projection step to improve solution feasibility. Our experiments show that our methods efficiently solve MINLPs with up to tens of thousands of variables, providing high-quality solutions within milliseconds, even for problems where traditional solvers and heuristics fail. This is the first general L2O method for parametric MINLPs, finding solutions to some of the largest instances reported to date. Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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Developing a Transactive Charging Control Framework for EV Parking Lots Equipped With Battery and Photovoltaic Panels: A MILP Approach

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IEEE ACCESS 2024年 12卷 108731-108743页

作者： Alanazi, Mohana Alanazi, Abdulaziz Alruwaili, Mohammed Salem, Mohamed Ueda, Soichiro Senjyu, Tomonobu Mohamed, Faisal A. Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Saudi Arabia Northern Border Univ Coll Engn Dept Elect Engn Ar Ar 73222 Saudi Arabia Univ Sains Malaysia USM Sch Elect & Elect Engn Nibong Tebal 14300 Penang Malaysia Libyan Author Sci Res Tripoli Libya Univ Ryukyus Fac Engn Okinawa 9030213 Japan

Electric vehicle (EV) drivers aim to charge their vehicles cost-effectively and with minimal charging time. Meanwhile, the ever-increasing number of EVs without charging control strategies could result in a massive surge in peak demand, potentially overloading distribution equipment and violating voltage constraints. To tackle this challenge, this paper introduces a transactive energy market (TEM) framework for an EV parking lot (EVPL) equipped with photovoltaic (PV) panels and battery systems (BSs), considering the preferences of both EV drivers and the EVPL operator. In this framework, EVs parked in the EVPL participate in the TEM by submitting their charging flexibility through response curves, which indicate the compensation required for different values of flexibility. Furthermore, the proposed model allows the EVPL operator to utilize the flexibility of EVs in the vehicle-to-grid (V2G) program by incentivizing EV drivers, considering their preferences and the degradation cost of EV batteries. The study employs the stochastic programming method to model uncertainties in PV output, electricity prices, and EV availability. It also incorporates BS degradation costs and carbon emissions constraints into the EVPL scheduling problem. linearization techniques are then applied to transform the non-linear optimization problem into a mixed-integer linear programming (MILP) model. Finally, applying the model to a case study validates its superiority in satisfying the preferences of both EV drivers and EVPL.

关键词： Uncertainty Costs Carbon dioxide Electric vehicle charging Vehicles Vehicle-to-grid Batteries Transactive energy market electric vehicle parking lot vehicle-to-grid (V2G) operation uncertainty modeling mixed-integer linear programming carbon emissions constraint

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Scaling mixed-integer programming for Certification of Neural Network Controllers Using Bounds Tightening

arXiv

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arXiv 2024年

作者： Sosnin, Philip Tsay, Calvin Department of Computing Imperial College London United Kingdom

Neural networks offer a computationally efficient approximation of model predictive control, but they lack guarantees on the resulting controlled system’s properties. Formal certification of neural networks is crucial for ensuring safety, particularly in safety-critical domains such as autonomous vehicles. One approach to formally certify properties of neural networks is to solve a mixed-integer program based on the network. This approach suffers from scalability issues due to the complexity of solving the resulting mixed-integer programs. Nevertheless, these issues can be (partially) mitigated via bound-tightening techniques prior to forming the mixed-integer program, which results in tighter formulations and faster optimization. This paper presents bound-tightening techniques in the context of neural network explicit control policies. Bound tightening is particularly important when considering problems spanning multiple time steps of a controlled system, as the bounds must be propagated through the problem depth. Several strategies for bound tightening are evaluated in terms of both computational complexity and tightness of the bounds. Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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Block-based state-expanded network models for multi-activity shift scheduling

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JOURNAL OF SCHEDULING 2024年第4期27卷 341-361页

作者： Roemer, Michael Bielefeld Univ Decis Analyt Grp Univ Str 25 D-33615 Bielefeld Germany

This paper presents new mixed-integer linear programming formulations for multi-activity shift scheduling problems (MASSP). In these formulations, the rules governing shift feasibility are encoded in block-based state-expanded networks in which nodes are associated with states and arcs represent assignments of blocks of work or break periods inducing state transitions. A key advantage of these formulations is that for the anonymous MASSP in which all employees are considered as equal only a single network with integer flow variables is needed as long as the network encodes all shift composition rules. A challenging aspect is that the networks can become very large, yielding huge models that are hard to solve for large problem instances. To address this challenge, this paper proposes two exact modeling techniques that substantially reduce the size of the model instances: First, it introduces a set of aggregate side constraints enforcing that an integer flow solution can be decomposed into paths representing feasible shifts. Second, it proposes to decouple the shift composition from the assignment of concrete activities to blocks of work periods, thereby removing a large amount of symmetry from the original model. In a computational study with two MASSP instance sets from the literature dealing with shift scheduling problems, we demonstrate the effectiveness of these techniques for reducing the both size of the model instances and the solution time: We are able to solve all instances, including more than 70 previously open instances, to optimality-the vast majority of them in less than 30 min on a notebook computer.

关键词： Multi-activity shift scheduling State-expanded networks mixed-integer linear programming

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Performance-Based Capacity Allocation of DERs to Improve Aggregated Flexibility Provision

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IEEE Transactions on Smart Grid 2025年第4期16卷 2968-2990页

作者： Hamidi, Amir Hamzeh, Mohsen Bozorg, Mokhtar Cherkaoui, Rachid School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Institute of Energy HEIG-VD Yverdon-les-Bains Switzerland Power System Group École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Importance of aggregating distributed energy resources (DERs) capacity to procure high-performance frequency regulation services ensuring adequate flexibility for renewable-rich power systems is increasing. Meanwhile, disregarding response performance variety arising from different technical characteristics and inherent uncertainties of DERs managed by an aggregator adversely impacts aggregated response (AR) from three viewpoints: technical feasibility of AR delivery, economic profit-making and fairness of capacity allocation. To tackle these challenges, this paper introduces a new probabilistic flexibility response index (FRI) to integrally evaluate DERs' performance considering four factors: structure, control, communication, aggregation and uncertainties and interactions of these factors. Also, a multi-objective stochastic bidding strategy framework in the day-ahead energy and regulation markets is developed, in which the DERs capacity allocation is devised using the evaluation of their FRIs as the second objective. This framework lies within a mixed-integer linear programming model, considering uncertainties of resources, communication systems, AR, and market prices. The multi-objective structure of problem is handled through augmented ϵ -constraint and Fuzzy decision-making approaches. To validate the efficiency, scalability, and generality of the developed framework, it is implemented on three aggregators of different numbers and types of DERs participating in the ERCOT market context. The results show fundamental improvements regarding three viewpoints. © 2010-2012 IEEE.

关键词： mixed-integer linear programming

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Impact of energy price scheme and rewarding strategies on mobilizing the flexibility of residential end-users and aggregator's profit

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 158卷

作者： Rasouli, Vahid Gomes, Alvaro Antunes, Carlos Henggeler Univ Coimbra INESC Coimbra Dept Elect & Comp Engn Rua Luis Reis SantoPolo 2 P-3030788 Coimbra Portugal

In incentive-based demand response programs, the aggregator sends reward signals to the consumers to stimulate consumption changes for mobilizing their flexibility in energy end-uses, which can then be traded in different markets. Therefore, the amount of flexibility available for being collected and traded depends on reward signals, among other factors. Hence, the aggregator needs to anticipate how the end-users will respond to the reward signals. In the presence of time-of-day tariffs, from the end-users' perspective, those reward signals can change the impact of the cost per unit of energy in different periods of the day. Thus, the aggregation of residential flexibility requires grasping how the energy price scheme (the prices at which the end-user purchases the energy from the retailer) interacts with the reward signals. In this study, the retailer and the aggregator are separate entities. Therefore, the aggregator does not have direct access to the end-users' energy pricing data. This paper presents a study to show the impact of different energy pricing schemes on mobilizing the flexibility of a group of residential end-users and the aggregator's profit. Accordingly, different end-use profiles, a range of reward values, and two different rewarding strategies are considered. Two separate mixed integer linear programing models are proposed, which are associated with the end-user's and the aggregator's problems. The objective of the aggregator's problem is to maximize the profit by identifying which end-users' flexibility will be selected to form the tradable aggregated flexibility. From the end-users' perspective, the objectives are to minimize the energy cost and the potential discomfort inflicted by changing the regular operation of appliances. The results show the effectiveness of the proposed methodology to analyze the impact of energy price scheme and rewarding strategies on assembling flexibility and the aggregation profitability.

关键词： Demand-side management Demand-side flexibility Demand response Smart grid mixed-integer linear programming

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A mathematical programming model and dispatching rules for the repatriation program scheduling problem

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PUBLIC TRANSPORT 2025年第1期17卷 101-120页

作者： Al-Shihabi, Sameh Al-Abdallat, Yousef Saidan, Motasem N. Univ Sharjah Ind Engn & Engn Management Dept POB 27272 Sharjah U Arab Emirates Univ Jordan Sch Engn Ind Engn Dept Amman 11942 Jordan Univ Sharjah Modeling & Optimizat Sustainable Operat & Supply C Sharjah U Arab Emirates Univ Jordan Sch Engn Chem Engn Dept Amman 11942 Jordan

Commercial flights were temporarily suspended due to the Corona Virus Disease (COVID-19) pandemic in March 2020. Subsequently, several countries had to repatriate their citizens from hosting countries. Typically, flights are planned ahead of time in commercial aviation, after which passengers buy their seats;however, in repatriation, the opposite happens. Citizens express their wish to return to their country first, and flights are scheduled accordingly. Due to the suspension of commercial aviation, decision makers had to rely on their flag-carrying airlines to schedule repatriation flights. These flights were constrained by limitations like the airline's fleet capacity, the availability of quarantine locations, and pilots' availability, while the decision makers' objective was to return the most vulnerable citizens first. The present paper examines this problem, which we call the repatriation program scheduling problem (RPSP). The RPSP objective is to minimize the social impacts of keeping a country's citizens abroad by assigning flights to different countries to repatriate stranded citizens. Therefore, we develop a mixed-integer linear program to model the RPSP and solve the RPSP using a commercial solver. We also suggest and test several dispatching rules that decision makers can use to schedule repatriation flights. We then model a case resembling an actual case and study the heuristics' performances. Heuristics are found to lead to reasonable solutions for large quarantine capacities and limited pilot-airplane assignment options;otherwise, decision makers need to rely on exact solutions.

关键词： Scheduling Repatriation mixed-integer linear programming Dispatching rules Flights Quarantine

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Models for two-dimensional bin packing problems with customer order spread

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2024年第1期48卷 8-8页

作者： Martin, Mateus Yanasse, Horacio Hideki Santos, Maristela O. Morabito, Reinaldo Univ Fed Sao Carlos Dept Engn Prod Rodovia Joao Leme St BR-18052780 Sorocaba SP Brazil Univ Fed Sao Paulo Inst Ciencia & Tecnol Ave Cesare Mansueto Giulio Lattes 1201 BR-12247014 Sao Jose Dos Campos SP Brazil Univ Sao Paulo Inst Ciencias Matemat & Comput Ave Trabalhador Sao carlense 400 BR-13566590 Sao Carlos SP Brazil Univ Fed Sao Carlos Dept Engn Prod Via Washington Luiz km 235 BR-13565905 Sao Carlos SP Brazil

In this paper, we address an extension of the classical two-dimensional bin packing (2BPP) that considers the spread of customer orders (2BPP-OS). The 2BPP-OS addresses a set of rectangular items, required from different customer orders, to be cut from a set of rectangular bins. All the items of a customer order are dispatched together to the next stage of production or distribution after its completion. The objective is to minimize the number of bins used and the spread of customer orders over the cutting process. The 2BPP-OS gains relevance in manufacturing environments that seek minimum waste solutions with satisfactory levels of customer service. We propose integer linear programming (ILP) models for variants of the 2BPP-OS that consider non-guillotine, 2-stage, restricted 3-stage, and unrestricted 3-stage patterns. We are not aware of integrated approaches for the 2BPP-OS in the literature despite its relevance in practical settings. Using a general-purpose ILP solver, the results show that the 2BPP-OS takes more computational effort to solve than the 2BPP, as it has to consider several symmetries that are often disregarded by the traditional 2BPP approaches. The solutions obtained by the proposed approaches have similar bin usage and significantly better metrics of customer satisfaction concerning the approaches that neglect the customer order spread.

关键词： Cutting & Packing mixed-integer linear programming Non-guillotine pattern 2-stage and 3-stage patterns Order spread minimization

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