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Data-Driven Dispatchable Regions With Potentially Active Boundaries for Renewable Power Generation: Concept and Construction

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2022年第2期13卷 882-891页

作者： Liu, Yanqi Li, Zhigang Wei, Wei Zheng, J. H. Zhang, Hongcai South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Dept Elect & Comp Engn Macau 999078 Peoples R China

The dispatchable region of volatile renewable power generation (RPG) quantifies how much uncertainty the power system can handle at a given operating point. State-of-the-art dispatchable region (DR) research has studied how system operational constraints influence the DR but has seldom considered the effect of the uncertainty features of RPG outputs. The traditional DR is generally described by a large number of boundaries, and it is computationally intensive to construct. To bridge these gaps, a novel type of DR is defined, which is enclosed by potentially active boundaries (PABs) that consider the operational constraints and uncertainty features of RPG outputs. The proposed DR is easier to construct because the PABs are only a small part of the traditional DR boundaries. The procedure for constructing the proposed DR is described in terms of the progressive search for PABs, which is formulated as a mixed-integer linear program by incorporating the discrete observed data points of RPG outputs as an approximate distribution. A parallel solution paradigm is also developed to expedite the construction procedure when using a large observed dataset. Simulation tests on the IEEE 30-bus and 118-bus systems verify the effectiveness and scalability of the proposed DR and the efficiency of the proposed algorithm.

关键词： Renewable energy sources Uncertainty Power systems Wind power generation Mathematical models Indexes Optimization Column generation data driven dispatchable region mixed-integer linear program potentially active boundary renewable power generation

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Moment-based distributionally robust joint chance constrained optimization for service network design under demand uncertainty

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OPTIMIZATION AND ENGINEERING 2024年第3期25卷 1631-1683页

作者： Zang, Yongsen Wang, Meiqin Liu, Huiqiang Qi, Mingyao Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Shenzhen AI Rise Co Ltd Shenzhen 518066 Peoples R China Tsinghua Univ Res Ctr Modern Logist Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

This paper proposes a distributionally robust joint chance constrained (DRJCC) programming approach to optimize the service network design (SND) problem under demand uncertainty. The distributionally robust method does not need complete distribution information and utilizes restricted historical data knowledge, which is significant in scarce data situations. The joint consideration of chance constraints enables more effective control of event probability, by which network managers can realize the purpose of controlling the overall service level of multi-commodities in a service network. DRJCC optimization can also help decision-makers adjust the network's conservativeness, robustness, and service rates by setting the probability parameters of the chance constraints. We reformulate the DRJCC model by addressing the corresponding distributionally robust joint chance constraints with the worst-case Conditional Value-at-Risk method and Lagrange duality theory. The model is approximately reformulated as a mixed-integer linear program, which is easier to solve than the mixed-integer semi-definite programming model in existing literature. We also develop two benchmark approaches for comparison: Bonferroni inequality approximation and scenario-based stochastic program. Comparative numerical studies demonstrate the robustness and the validation of the proposed formulations. A case study is conducted to demonstrate the industrial performance of the uncertain SND under the DRJCC formulation. We explore the impact of the confidence level parameter on operational cost and real service level, reveal the general correlation between them. We also extract several risk-averse managerial insights for logistics fleet managers.

关键词： Service network design Uncertain demand Joint chance constraint Distributionally robust optimization mixed-integer linear program

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A Data-Driven Approach to Improve Wind Dispatchability

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第1期32卷 421-429页

作者： Qiu, Feng Li, Zhigang Wang, Jianhui Argonne Natl Lab Div Energy Syst Lemont IL 60439 USA South China Univ Technol Sch Elect Power Guangzhou 510641 Guangdong Peoples R China

Wind power has been treated as a non-dispatchable resource until recent development of active wind dispatch strategies in several electricity markets. In markets such as ISO New England, a dispatch range for each wind farm is determined based on security analysis. Wind power will be fully absorbed unless it is out of the ranges. This approach, though aiming at improving wind utilization with system security considered, relies solely on wind power forecasting, which could be inaccurate by nature, and might result in unnecessary wind curtailment. In our work, we recognize the discrepancy between wind power forecast and the actual wind power dispatched and develop a data-driven approach to better capture the uncertainties in wind power dispatch. The computational experiments demonstrate that the dispatch ranges determined by our data-driven approach can dispatch more wind without endangering the system security and that solution is also efficient.

关键词： Chance constraint data driven mixed-integer linear program wind dispatchability

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The Power Edge Set Problem

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NETWORKS 2016年第2期68卷 104-120页

作者： Poirion, Pierre-Louis Toubaline, Sonia D'Ambrosio, Claudia Liberti, Leo Ecole Polytech CNRS LIX F-91128 Palaiseau France

The automated real time control of an electrical network is achieved through the estimation of its state using phasor measurement units. Given an undirected graph representing the network, we study the problem of finding the minimum number of phasor measurement units to place on the edges such that the graph is fully observed. This problem is also known as the Power Edge Set problem, a variant of the Power Dominating Set problem. It is naturally modeled using an iteration-indexed binary linear program, whose size turns out to be too large for practical purposes. We use a fixed-point argument to remove the iteration indices and obtain a more compact bilevel formulation. We then reformulate the latter to a single-level mixed-integer linear program, which performs better than the natural formulation. Lastly, we provide an algorithm that solves the bilevel program directly and much faster than a commercial solver can solve the previous models. We also discuss robust variants and extensions of the problem. (C) 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(2), 104-120 2016

关键词： real time electrical network monitoring bilevel program mixed-integer linear program PMU placement problem observability power dominating set

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Collaborative traveling salesman problem with ground vehicle as a charger for unmanned aerial vehicle

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2023年第7期15卷 707-721页

作者： Cha, Hyungjoo Kim, DongKyun Eun, Joonyup Cheong, Taesu Korea Univ Sch Ind Management Engn Seoul South Korea Korea Univ Grad Sch Management Technol Seoul South Korea

Innovation in logistics industry is now implicit, and the use of unmanned aerial vehicles is at the center of logistics innovation. To effectively utilize unmanned aerial vehicles for last-mile delivery, collaborative delivery using ground vehicles and unmanned aerial vehicles has recently been investigated. However, previous studies neglected proper management of batteries, assuming their constant replacement whenever unmanned aerial vehicles land, and hence numerous batteries are required for unmanned aerial vehicles. Given this research gap, we present a new routing model for collaborative delivery wherein an unmanned aerial vehicle uses a ground vehicle as a power source. A mathematical formulation is presented, and noticing the complexity, a heuristic algorithm is developed. We present a case study to verify the operational efficiency of the model. We test the performance of the heuristic and draw insights based on geographical locations of customers via computational experiments.

关键词： Electric unmanned aerial vehicle traveling salesman problem collaborative delivery mixed-integer linear program Heuristics

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Optimising the regional mix of intermittent and flexible energy technologies

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JOURNAL OF CLEANER PRODUCTION 2019年 219卷 508-517页

作者： Haikarainen, Carl Pettersson, Frank Saxen, Henrik Abo Akad Univ Lab Thermal & Flow Engn Biskopsgatan 8 FI-20500 Turku Finland

Renewable energy sources could potentially satisfy a much larger part of the energy demand of human societies. When it comes to wind and solar energy, a key technological difficulty is the intermittent nature of these sources of energy. In order to ensure that energy is available at times when it is needed, these renewable energy technologies need to be backed up by storage systems and other more flexible energy systems such as gas engines. This paper presents a multi-period mixed-integer linear programming optimisation model of an energy system featuring multiple alternatives for satisfying variable demands of electricity and heat. The model is used to investigate how the intermittent renewable energy sources could work together with storage technologies, heat pumps, and flexible gas engines in a resource-efficient manner. Case studies are presented to illustrate how suitable technologies could be selected and operated in a region with high seasonal differences in the demands of heat and electricity. The case study suggests that heat pumps could play an important part in optimising the usage rate of wind power. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Optimisation mixed-integer linear program Distributed energy systems Regional energy mix Energy storage

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linear Reformulation of Polynomial Discrete programming for Fast Computation

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INFORMS JOURNAL ON COMPUTING 2017年第1期29卷 108-122页

作者： Li, Han-Lin Huang, Yao-Huei Fang, Shu-Cherng Natl Chiao Tung Univ Inst Informat Management Dept Informat Management & Finance Hsinchu Taiwan Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27695 USA

Polynomial discrete programming problems are commonly faced but hard to solve. Treating the nonconvex cross-product terms is the key. State-of-the-art methods usually convert such a problem into a 0-1 mixed-integer linear programming problem and then adopt the branch-and-bound scheme to find an optimal solution. Much effort has been spent on reducing the required numbers of variables and linear constraints as well as on avoiding unbalanced branch-and-bound trees. This study presents a set of equations that linearize the discrete cross-product terms in an extremely effective manner. It is shown that embedding the proposed "equations for linearizing discrete products" into those state-of-the-art methods in the literature not only significantly reduces the required number of linear constraints from O(h(3)n(3)) to O(hn) for a cubic polynomial discrete program with n variables in h possible values but also tighten these methods with much more balanced branch-and-bound trees. Numerical experiments confirm a two-order (10(2)-times) reduction in computational time for some randomly generated cubic polynomial discrete programming problems.

关键词： polynomial discrete program mixed-integer linear program linearization equation branch and bound

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Energy-Aware Multiple Mobile Chargers Coordination for Wireless Rechargeable Sensor Networks

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IEEE INTERNET OF THINGS JOURNAL 2019年第5期6卷 8202-8214页

作者： Mo, Lei Kritikakou, Angeliki He, Shibo Univ Rennes CNRS INRIA IRISA F-35042 Rennes France Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

Wireless charging provides dynamic power supply for wireless sensor networks (WSNs). Such systems, are typically considered under the scenario of wireless rechargeable sensor networks (WRSNs). With the use of mobile chargers (MCs), the flexibility of WRSNs is further enhanced. However, the use of MCs poses several challenges during the system design. The coordination process has to simultaneously optimize the scheduling, the moving time, and the charging time of multiple MCs under limited system resources (time and energy). Efficient methods that jointly solve these challenges are generally lacking in the literature. In this paper, we address the multiple MCs coordination problem under multiple system requirements. First, we aim at minimizing the energy consumption of MCs, guaranteeing that every sensor will not run out of energy. We formulate the multiple MCs coordination problem as a mixed-integer linear programming and derive a set of desired network properties. Second, we propose a novel decomposition method to optimally solve the problem, as well as to reduce the computation time. Our approach divides the problem into a subproblem for the MC scheduling and a subproblem for the MC moving time and charging time, and solves them iteratively by utilizing the solution of one into the other. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method in terms of solution quality and computation time.

关键词： Decomposition method mixed-integer linear program mobile charger (MC) coordination perpetual operation wireless rechargeable sensor networks (WRSNs)

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Modeling the capacitated p-cable trench problem with facility costs

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2021年第2期29卷 713-735页

作者： Schwarze, Silvia Lalla-Ruiz, Eduardo Voss, Stefan Univ Hamburg Inst Informat Syst IWI Hamburg Germany Univ Twente Dept Ind Engn & Business Informat Syst Enschede Netherlands Pontificia Univ Catolica Valparaiso Escuela Ingn Ind Valparaiso Chile

In this work, two network design concepts which have been proven to be relevant with respect to telecommunication applications, are joined within a unified approach. First, the cable trench problem searches for cost-minimizing network structures that take into account two types of edge costs appearing in the installation of wire-based networks, namely trenching costs and cable costs. Second, the facility location problem considers the placement of shared telecommunication equipment, like switches or concentrators, together with an assignment of entities to demand nodes. Following practical needs, we join these concepts within the new capacitated cable trench problem with facility costs and service capacity in terms of number of customers that can be served by facilities. Within this setting, facility location decisions in wire-based networks can be taken under a more realistic cost scenario. A mixed-integer linear program and valid inequalities are proposed. Experiments indicate a positive impact of the valid inequalities on computational time and integrality gap.

关键词： Cable-trench problem Facility location Valid inequalities mixed-integer linear program

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Reformulation, linearization, and a hybrid iterated local search algorithm for economic lot-sizing and sequencing in hybrid flow shop problems

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COMPUTERS & OPERATIONS RESEARCH 2019年 104卷 127-138页

作者： Zohali, Hassan Naderi, Bahman Mohammadi, Mohammad Roshanaei, Vahid Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran Univ Windsor Dept Mech Automot & Mat Engn Fac Engn Windsor ON Canada Univ Toronto Rotman Sch Management Dept Operat Management & Stat Toronto ON Canada

We study an integrated economic lot-sizing and sequencing problem (ELSP) in the hybrid flow shop manufacturing setting with unlimited intermediate buffers in a finite planning horizon. The ELSP entails making two simultaneous decisions regarding (i) the manufacturing sequences of products, and (ii) their production quantity. The objective is to minimize the total cost, consisting of inventory holding and set-up costs. To solve this problem, we first develop a novel mixed-integer nonlinear programming (MINLP) model that improves an existing MINLP model in the literature. We then present a novel linearization technique that transforms these two MINLP models into effective mixed-integer linear programming (MILP) models. Additionally, we develop an effective algorithm that hybridizes the iterated local search algorithm with an approximate function. We conduct comprehensive experiments to compare the performance of MILPs+CPLEX with that of MINLPs+BARON. Additionally, our proposed algorithm is compared with four existing metaheuristic algorithms in the literature. Computational results demonstrate that our novel MINLP formulation and its linearized variant significantly improve the solvability and optimality gap of an existing MINLP formulation and its linearized variant. We also show that our new hybrid iterated local search algorithm substantially improves computational performance and optimality gap of the mathematical models and the existing algorithms in the literature, on large-size instances of the problem. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Lot-sizing Sequencing Hybrid flow shop mixed-integer nonlinear program linearization mixed-integer linear program Hybrid iterated local search

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