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Robust LNG sales planning under demand uncertainty: A data-driven goal-oriented approach

DIGITAL CHEMICAL ENGINEERING 2023年 9卷

作者： Feng, Yulin Li, Xianyu Liu, Dingzhi Shang, Chao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China Petrochina Co Ltd PetroChina Planning & Engn Inst Beijing 100083 Peoples R China

This paper addresses the liquefied natural gas (LNG) sales planning problem over a pipeline network with a focus on uncertain demands. Generically, the total profit is maximized by seeking optimal transportation and inventory decisions, and robust optimization (RO) has been a viable decision-making strategy to this end, which is however known to suffer from over-conservatism. To circumvent this, a new goal-oriented data-driven RO approach is proposed. First, we adopt data-driven polytopic uncertainty sets based on kernel learning, which yields a compact high-density region from data and assures tractability of RO problems. Based on this, a new goal-oriented RO formulation is put forward to satisfy to the greatest extent the target profit while tolerating slight constraint violations. In contrast to traditional min-max RO scheme, the proposed scheme not only ensures a flexible trade-off but also yields parameters with clear interpretation. The resulting optimization problem turns out to be equivalent to a mixed-integer linear program that can be effectively handled using off-the-shelf solvers. We illustrate the merit of the proposed method in satisfying a prescribed goal with optimized robustness by means of a case study.

关键词： Robust optimization Uncertainty set Data-driven decision-making Support vector clustering LNG sales planning mixed-integer linear programming

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Flexibility enhancement in active distribution networks through a risk-based optimal placement of sectionalizing switches

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 201卷 106985-106985页

作者： Moradijoz, Mahnaz Moradijoz, Saeed Moghaddam, Mohsen Parsa Haghifam, Mahmoud-Reza Tarbiat Modares Univ Elect & Comp Engn Dept Tehran Iran Shahid Beheshti Univ Fac Management & Accounting Tehran Iran

The advent of distributed energy resources (DERs) in distribution networks, referred to as active distribution networks (ADNs), brings in new opportunities for distribution network operators (DNOs) to improve the network reliability. The contribution of DERs in reliability enhancement generally depends on the island mode operation of DERs. Optimal placement of sectionalizing switches (SSs) enables ADNs to be operated more reliably by islanding the faulted part of the system in the form of flexible micro-grids (FMGs). However, practical methods are needed to handle the complex problem of the SS placement in ADNs. This paper presents a risk-based two-stage mixed-integer linear programming (MILP) model for the optimal placement of the network-driven flexibility providers, i.e. SSs, in ADNs taking into account the limits of the resource-driven flexibility providers, i.e. DERs. The performance of the proposed model is evaluated on various test distribution networks, and its effectiveness demonstrated through several case studies.

关键词： Active distribution networks Distributed energy resources Sectionalizing switches mixed-integer linear programming

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Unit-commitment via logarithmic aggregated convex combination in multi-unit hydro plants

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 189卷 106784-106784页

作者： Brito, Brunno H. Finardi, Erlon C. Takigawa, Fabricio Y. K. Fed Inst Tocantins Palmas Brazil Univ Fed Santa Catarina Florianopolis SC Brazil INESC P&D Brasil Santos SP Brazil Fed Inst Santa Catarina Florianopolis SC Brazil

We present a new strategy to solve the hydro unit commitment (HUC) in plants with many generating units (GUs), where the nonlinear Hydro Production Function (HPF) is linearized via Logarithmic Aggregated Convex Combination (LACC). Although the LACC is an efficient technique, the solution procedure via mixed-integer linear programming (MILP) solver in plants with several GUs is still a challenging task. Thus, this paper proposes a solution strategy that is divided into two steps. In the first step, the identical GUs are aggregated into an equivalent one, and the resulting MILP is resolved for obtaining the starting values of the gross head (GH). In the second step, the GH is used to determine the optimal dispatch considering the individualized HPF for each UG. The performance of the proposed strategy is analyzed in a Brazilian hydro plant composed of 50 GUs of two different types.

关键词： Hydro unit commitment Hydro production function Logarithmic aggregated convex combination mixed-integer linear programming Two-step strategy

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A Generic Convex Model for a Chance-Constrained Look-Ahead Economic Dispatch Problem Incorporating an Efficient Wind Power Distribution Modeling

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第2期35卷 873-886页

作者： Khorramdel, Benyamin Zare, Alireza Chung, C. Y. Gavriliadis, Panagiotis Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada Natl Tech Univ Athens Dept Naval Architecture & Marine Engn Athens 15780 Greece

Power systems with high penetration of wind resources must cope with significant uncertainties originated from wind power prediction error. This uncertainty might lead to wind power curtailment and load shedding events in the system as a big challenge. Efficient modeling and incorporation of wind power uncertainty in generation and reserve scheduling can prevent these events. This paper presents a new framework for wind power cumulative distribution function (CDF) modeling and its incorporation in a new chance-constrained economic dispatch (CCED) problem. The proposed CDF modeling uses few moments of wind power random samples. To validly capture the actual features of the wind power distribution such as main mass, high skewness, tails, and especially boundaries from the moments, an efficient moment problem is presented and solved using the beta kernel density representation (BKDR) technique. Importantly, a new polynomial cost function for efficient modeling of wind power misestimation costs is proposed for the CCED problem that eliminates the need for an analytical CDF and enables the use of an accurate piece-wise linearization technique. Using this technique, the non-linear CCED problem is converted to a mixed-integer linear programming (MILP)-based problem that is convex with respect to the continuous variables of the problem. Therefore, it is solved via off-the-shelf mathematical programming solvers to reach more optimal results. Numerical simulations using the IEEE 118-bus test system show that compared with conventional approaches, the proposed MILP-based model leads to lower power system total cost, and thereby is suggested for practical applications.

关键词： Chance-constrained optimization mixed-integer linear programming wind power probability distribution

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A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106790-106790页

作者： Tirkolaee, Erfan Babaee Aydin, Nadi Serhan Ranjbar-Bourani, Mehdi Weber, Gerhard-Wilhelm Mazandaran Univ Sci & Technol Dept Ind Engn Babol Iran Istinye Univ Dept Ind Engn Istanbul Turkey Univ Sci & Technol Mazandaran Dept Ind Engn Behshahr Iran Poznan Univ Tech Fac Engn Management Poznan Poland Middle East Tech Univ Inst Appl Math Ankara Turkey

This paper proposes a novel bi-objective mixed-integer linear programming (MILP) model for allocating and scheduling disaster rescue units considering the learning effect. When a natural phenomenon (e.g., earthquake or flood) occurs, the presented decision support model is expected to help decision-makers of emergency relief centers to provide efficient planning for rescue units to minimize the total weighted completion time of rescue operations, as well as the total delay in rescue operations. The problem has some features in common with unrelated parallel machine scheduling (UPMS) problem and traveling salesman problem (TSP). To deal with the inherent uncertainty and bi-objective nature of the problem, an uncertainty-set based robust optimization technique and multi-choice goal programming (MCGP) with utility functions are applied. To demonstrate the applicability of the proposed model, a real case study in Mazandaran province in Iran is presented. The computational results confirm the high complexity of the problem and the significant impacts of the uncertainty on the solution. Moreover, the analytical results provide useful insights to decision-makers for disastrous situations.

关键词： Disaster rescue units Resource allocation and scheduling Multi-choice goal programming with utility functions mixed-integer linear programming Robust Optimization

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On integer and bilevel formulations for the k-vertex cut problem

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MATHEMATICAL programming COMPUTATION 2020年第2期12卷 133-164页

作者： Furini, Fabio Ljubic, Ivana Malaguti, Enrico Paronuzzi, Paolo Univ Paris 09 LAMSADE Paris France ESSEC Business Sch Paris Cergy France Univ Bologna DEI Bologna Italy

The family of critical node detection problems asks for finding a subset of vertices, deletion of which minimizes or maximizes a predefined connectivity measure on the remaining network. We study a problem of this family called the k-vertex cut problem. The problem asks for determining the minimum weight subset of nodes whose removal disconnects a graph into at least k components. We provide two new integer linear programming formulations, along with families of strengthening valid inequalities. Both models involve an exponential number of constraints for which we provide poly-time separation procedures and design the respective branch-and-cut algorithms. In the first formulation one representative vertex is chosen for each of the k mutually disconnected vertex subsets of the remaining graph. In the second formulation, the model is derived from the perspective of a two-phase Stackelberg game in which a leader deletes the vertices in the first phase, and in the second phase a follower builds connected components in the remaining graph. Our computational study demonstrates that a hybrid model in which valid inequalities of both formulations are combined significantly outperforms the state-of-the-art exact methods from the literature.

关键词： Vertex cut mixed-integer linear programming Bilevel programming Branch-and-cut algorithm

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A prediction-based optimization strategy to balance the use of diesel generator and emergency battery in the microgrid

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2020年第7期44卷 5425-5440页

作者： Liu, Zhonghua Zhang, Huihui Dong, Jichang Yu, Hua Beijing Univ Chem Technol Coll Econ & Management Beijing 100029 Peoples R China China Elect Power Res Inst Informat & Commun Dept Beijing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing Peoples R China

This paper presents a prediction-based optimization strategy (POS) for the Energy Management System to balance the use of diesel generator (DG) and emergency battery (EB) in the microgrid. The POS is developed by combing two operating strategies, the "predictive analysis" and "optimal operation" in each scheduling period for the microgrid. Based on the predicted microgrid state and energy demand, a multi-objective mixed-integer nonlinear programming model (MOMINP) is constructed to minimize the fuel consumption and the regularization of battery charge/discharge subject to the practical constraints in the microgrid. This paper proposes a detailed scheme to deal with the multiple objectives and nonlinear constraints in the MOMINP, then the MOMINP is successfully converted into a mixed-integer linear programming model (MILP). And an adjustment strategy is designed to obtain the nearoptimal solution of the MOMINP based on the optimal solution of the MILP solved by using the CPLEX Optimizer. Experimental results show that in a basic scheduling period, the working time of DG in the POS-softmax regression strategy is shorter than the current operation, and the fuel consumption reduction ratio is about 15.3% with the same battery SoC value at the end of the scheduling. At the same time, the fuel consumption in the POS-accurate prediction strategy can be reduced by up to 54.9% compared with the POS-softmax regression strategy and can be reduced by 61.8% compared to the current operation. Based on the comparative analysis of the actual case data of a micro-grid in 6 months, it can be seen that on average the POS works better than the current operation, with an approximately 23.6% decrease in the objective function and an additional 16.2% decrease with an accurate prediction.

关键词： energy management microgrid mixed-integer linear programming multi-objective mixed-integer nonlinear programming optimal operation

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Technical Paper Dual mode control strategy for the energy efficiency of complex and flexible manufacturing systems

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JOURNAL OF MANUFACTURING SYSTEMS 2020年 56卷 104-116页

作者： Diaz, Jenny L. C. Ocampo-Martinez, Carlos Olaru, Sorin Univ Politecn Cataluna Inst Robot & Informat Ind CSIC UPC Automat Control Dept Llorens i Artigas 4-6Planta 2 Barcelona 08028 Spain Univ Paris Saclay Univ Paris Sud Lab Signals & Syst L2S UMR CNRS 8506Cent SupelecCNRS F-91190 Gif Sur Yvette France

The manufacturing industry is shifting towards smart manufacturing, in which both energy efficiency and flexibility are some of the main objectives of this digital transformation. In this regard, the control strategies for manufacturing systems should be able to support the requirements of this transformation with a low computational burden towards their implementation in real time. To this end, in this paper, a dual mode control strategy based on two control approaches is proposed to minimise the energy consumption of manufacturing systems without affecting their productivity, even when scenarios of flexible manufacturing are considered. The first control mode is based on model predictive control to determine an optimisation-based strategy for the constrained behaviour of the system. Then, the second mode builds on the assumption that the system exhibits a periodic behaviour and, thus, it will be able to switch to an autonomous control mode that avoids the resolution of an optimisation problem online. The proposed control strategy is tested in a manufacturing process line in which changes in the production programs are considered with the aim to test the performance in flexible manufacturing scenarios. The obtained results show that the computational burden could be significantly reduced while reducing global energy consumption without affecting the system productivity.

关键词： Dual mode control strategy Model predictive control mixed-integer linear programming Smart manufacturing systems Flexible manufacturing

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An A-Posteriori Multi-Objective Optimization Method for MILP-Based Distribution Expansion Planning

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IEEE ACCESS 2020年 8卷 60279-60292页

作者： Gholizadeh-Roshanagh, Reza Zare, Kazem Marzband, Mousa Univ Tabriz Fac Elect & Comp Engn Tabriz *** Iran Northumbria Univ Fac Engn & Environm Dept Math Phys & Elect Engn Newcastle Upon Tyne NE1 8ST Tyne & Wear England King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21589 Saudi Arabia

Distribution expansion planning (DEP) is a mixed integer nonlinear programming (MINLP) problem and contains various options to be considered where the nonlinearity makes it difficult to solve. Over the years, different heuristic and classical optimization methods have been introduced to solve this problem. In a few classical optimization methods, this problem has been linearized. In this contribution, an epsilon-constraint based multi-objective method was proposed for mixed integer linear programming (MILP) based DEP model considering network loss. Operational network loss was linearized and incorporated in the model, then the operational loss cost was obtained by modeling the production cost. In order to model the production cost, the well-known supply price curve was considered, where the price of energy for each loading condition was obtained based on a linear model. Using the epsilon-constraint method, the Pareto optimal front was formed, which provides the decision maker with a range of solutions showing relationship among conflicting objectives. Then, a Fuzzy satisfying method was utilized to obtain the best compromised solution. Simulations were performed on an 18-node primary distribution network. Results showed the effectiveness of the proposed method for the MILP-based multi-objective DEP models.

关键词： mixed-integer linear programming distribution expansion planning power market operational network loss multi-objective optimization E-constraint optimization a-posteriori

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Minimum-Fuel Energy Management of a Hybrid Electric Vehicle via Iterative linear programming

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第12期69卷 14575-14587页

作者： Robuschi, Nicolo Salazar, Mauro Viscera, Nicola Braghin, Francesco Onder, Christopher H. Politecn Milan Dept Mech Engn Via Giuseppe La Masa 1 I-20156 Milan Italy Eindhoven Univ Technol TU e Control Syst Technol Grp NL-5600 MB Eindhoven Netherlands Swiss Fed Inst Technol Inst Dynam Syst & Control Sonneggstr CH-8092 Zurich Switzerland

This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle.

关键词： Hybrid electric vehicles energy management supervisory control mixed-integer linear programming convex optimization linear programming

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