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Modelling and optimising the marginal expansion of an existing district heating network

ENERGY 2017年第Part1期140卷 209-223页

作者： Delangle, Axelle Lambert, Romain S. C. Shah, Nilay Acha, Salvador Markides, Christos N. Imperial Coll London Ctr Proc Syst Engn Dept Chem Engn South Kensington Campus London SW7 2AZ England

Although district heating networks have a key role to play in tackling greenhouse gas emissions associated with urban energy systems, little work has been carried out on district heating networks expansion in the literature. The present article develops a methodology to find the best district heating network expansion strategy under a set of given constraints. Using a mixed-integer linear programming approach, the model developed optimises the future energy centre operation by selecting the best mix of technologies to achieve a given purpose (e.g. cost savings maximisation or greenhouse gas emissions minimisation). Spatial expansion features are also considered in the methodology. Applied to a case study, the model demonstrates that depending on the optimisation performed, some building connection strategies have to be prioritised. Outputs also prove that district heating schemes' financial viability may be affected by the connection scenario chosen, highlighting the necessity of planning strategies for district heating networks. The proposed approach is highly flexible as it can be adapted to other district heating network schemes and modified to integrate more aspects and constraints. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： District heating network expansion mixed-integer linear programming Investment schedule Low carbon heat

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Heuristic approach for the economic optimisation of combined heat and power (CHP) plants: Operating strategy, heat storage and power

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ENERGY 2017年 121卷 66-77页

作者： Vogelin, Philipp Koch, Ben Georges, Gil Boulouchos, Konstatinos Swiss Fed Inst Technol Aerothermochem & Combust Syst Lab Sonneggstr 3 CH-8092 Zurich Switzerland

CHP plants produce power and heat in parallel and operate between power and heat consumers as well as prices for their supply. The profitability of a plant with given power depends on operating strategy and heat storage size. In this paper, we present a fast heuristic algorithm to determine the most profitable setting for given boundary conditions, as a substitute for the state-of-the-art approach based on mixed integer linear programming (MILP). The presented heuristic algorithm is appropriate for thousands of plant calculations or real-time operation planning of single as well as interconnected plants. We used hourly resolved full-year data reflecting electricity market price and heat demand, which was simulated using a typical multi-family house configuration. The heuristic algorithm found the optimal operating strategy and heat storage size 34 times faster than the M1LP solver. Additional optimisation runs of the operating pattern at various fixed heat storage sizes, resulted in equal solutions of both approaches. The heuristic algorithm found in this case the operating patterns around 200 times faster. Furthermore, the heuristic algorithm can be enhanced to find optimal CHP power, heat storage size and operating strategy in one process using characteristic curves for plant efficiencies and costs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Combined heat and power Thermal energy storage Economic optimisation Heuristic algorithm mixed-integer linear programming Building heat demand

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Two-Stage Adaptive Restoration Decision Support System for a Self-Healing Power Grid

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2017年第6期13卷 2802-2812页

作者： Golshani, Amir Sun, Wei Zhou, Qun Zheng, Qipeng P. Tong, Jianzhong Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL 32816 USA PJM Interconnect LLC Audubon PA 19403 USA

Power outages cost American industries and businesses billions of dollars and jeopardize the lives of hospital patients. The losses can be greatly reduced with a fast, reliable, and flexible self-healing tool. This paper is aimed to tackle the challenging task of developing an adaptive restoration decision support system (RDSS). The proposed RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. The comprehensive formulation encompasses practical constraints including ac power flow, dynamic reserve, and load modeling. The combinatorial problem is decomposed into a two-stage formulation solved by an integer L-shaped algorithm. The two stages are then executed online in the RDSS framework employing a sliding window method. The IEEE 39-bus system has been studied under normal and contingency conditions to demonstrate the effectiveness and efficiency of the proposed online RDSS.

关键词： Adaptive restoration dynamic reserve integer L-shaped algorithm mixed-integer linear programming two-stage optimization

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MILP models and metaheuristic for balancing and sequencing of mixed-model two-sided assembly lines

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2017年第3期11卷 353-379页

作者： Nilakantan, J. Mukund Li, Zixiang Tang, Qiuhua Nielsen, Peter Aalborg Univ Dept Mat & Prod Aalborg Denmark Wuhan Univ Sci & Technol Ind Engn Dept Wuhan 430081 Hubei Peoples R China

mixed-model assembly lines are becoming increasingly popular due to flexibility of producing customised products. In a mixed-model assembly line, line balancing and model sequencing problems are tightly interrelated and very important for efficiency. This paper proposes a new assembly line configuration based on paced mixed-model two-sided assembly lines where balancing and sequencing problems are considered simultaneously. Minimal work has been reported considering both problems simultaneously for this type of assembly line configuration. Two mixed-integer linear programming (MILP) models are developed and a restarted SA algorithm with new encoding, decoding and neighbourhood procedures is developed. The parameters of the proposed algorithm are selected based on a statistical technique and the performance of it is tested on a set of new benchmark problems. The computational results demonstrate the effectiveness of the MILP models and the high efficiency of the proposed algorithm. The proposed algorithm outperforms the comparative original SA algorithm.

关键词： assembly line balancing model sequencing mixed-model two-sided assembly line mixed-integer linear programming MILP simulated annealing

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On handling indicator constraints in mixed integer programming

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2016年第3期65卷 545-566页

作者： Belotti, Pietro Bonami, Pierre Fischetti, Matteo Lodi, Andrea Monaci, Michele Nogales-Gomez, Amaya Salvagnin, Domenico FICO Birmingham W Midlands England IBM Corp Madrid Spain Univ Padua Padua Italy Univ Bologna Bologna Italy Ecole Polytech Montreal Montreal PQ Canada Huawei France R&D Math & Algorithm Sci Lab Paris France IBM Corp Milan Italy

mixed integer programming (MIP) is commonly used to model indicator constraints, i.e., constraints that either hold or are relaxed depending on the value of a binary variable. Unfortunately, those models tend to lead to weak continuous relaxations and turn out to be unsolvable in practice;this is what happens, for e.g., in the case of Classification problems with Ramp Loss functions that represent an important application in this context. In this paper we show the computational evidence that a relevant class of these Classification instances can be solved far more efficiently if a nonlinear, nonconvex reformulation of the indicator constraints is used instead of the linear one. Inspired by this empirical and surprising observation, we show that aggressive bound tightening is the crucial ingredient for solving this class of instances, and we devise a pair of computationally effective algorithmic approaches that exploit it within MIP. One of these methods is currently part of the arsenal of IBM-Cplex since version 12.6.1. More generally, we argue that aggressive bound tightening is often overlooked in MIP, while it represents a significant building block for enhancing MIP technology when indicator constraints and disjunctive terms are present.

关键词： mixed-integer linear programming mixed-integer quadratic programming Indicator constraints

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Optimal switchable load sizing and scheduling for standalone renewable energy systems

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SOLAR ENERGY 2017年 144卷 707-720页

作者： Habib, Abdulelah H. Disfani, Vahid R. Kleissl, Jan de Callafon, Raymond A. Univ Calif San Diego Dept Mech & Aerosp Engn 9500 Gilman Dr MC 0411 La Jolla CA 92093 USA

The variability of solar energy in off-grid systems dictates the sizing of energy storage systems along with the sizing and scheduling of loads present in the off-grid system. Unfortunately, energy storage may be costly, while frequent switching of loads in the absence of an energy storage system causes wear and tear and should be avoided. Yet, the amount of solar energy utilized should be maximized and the problem of finding the optimal static load size of a finite number of discrete electric loads on the basis of a load response optimization is considered in this paper. The objective of the optimization is to maximize solar energy utilization without the need for costly energy storage systems in an off-grid system. Conceptual and real data for solar photovoltaic power production provides the power input to the off-grid system. Given the number of units, the following analytical solutions and computational algorithms are proposed to compute the optimal load size of each unit: mixed-integer linear programming and constrained least squares. Based on the available solar power profile, the algorithms select the optimal on/off switch times and maximize solar energy utilization by computing the optimal static load sizes. The effectiveness of the algorithms is compared using one year of solar power data from San Diego, California and Thuwal, Saudi Arabia. It is shown that the annual system solar energy utilization is optimized to 73% when using two loads and can be boosted up to 98% using a six load configuration. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Inequality-constrained least square Load management mixed-integer linear programming Off-grid solar energy

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Non-prespecified Starting Depot Formulations for Minimum-Distance Trajectory Optimization in Patrolling Problem

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2017年第3-4期87卷 699-710页

作者： Ghadiry, Walaaeldin Habibi, Jalal Aghdam, Amir G. Zhang, Youmin Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada McGill Univ Fac Engn Montreal PQ Canada Concordia Univ Dept Mech & Ind Engn Montreal PQ Canada

In this paper, two new formulations are presented for trajectory optimization in the patrolling problem. It is assumed that the starting depot is not prespecified;an assumption that distinguishes the present work from the existing literature. A number of viewpoints are assigned to be visited in a certain sequence to minimize the total travel distance. The problem turns out to be a variant of the well-known Traveling Salesmen Problem (TSP), namely the Single depot multiple Traveling Salesmen Problem (mTSP). Comparisons between the commonly-used prespecified starting depot approach and the proposed formulations are performed and the efficacy of the results is presented through simulations. It is noted that by using the new approach, the total travel distance can be improved by an average of about 20 % compared to the case where the starting depot is prespecified, and by about 40 % in the worst-case scenario (in terms of the starting depot).

关键词： Single depot multiple Travelling Salesman problem (mTSP) Patrolling problem Non-prespecified starting depot Return trip mixed-integer linear programming

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Head Dependence of Pump-Storage-Unit Model Applied to Generation Scheduling

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第4期32卷 2869-2877页

作者： Chen, Cheng-Hung Chen, Nanming Luh, Peter B. Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10607 Taiwan Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

This paper presents a head-dependent model for pump storage units (PSUs) in a power system for short-term generation scheduling over one week. A hydraulic system with upper and lower reservoirs, each having their own in and out river flows, is considered. Hydraulic conditions, as well as head effects, are explicitly modeled for both generation and pumping modes of the PSUs. The problem is solved by the branch-and-cut method to obtain a near-optimal solution. Test results of an hourly generation schedule, including comparisons of pump efficiencies, hydraulic conditions, and operating costs of the PSUs for a real power system, are presented.

关键词： Discharge generation scheduling head effect mixed-integer linear programming pump storage unit reservoir

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Two-phase decomposition method for the last train departure time choice in subway networks

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2017年 104卷 568-582页

作者： Kang, Liujiang Meng, Qiang Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore

An urban subway network with a number of service lines forms the backbone of the public transport system for a large city of high population, such as Singapore, Hong Kong and Beijing. Passengers in these large cities heavily rely on urban subway networks for their daily life. The departure times of the last trains running on different lines of an urban subway network should be well coordinated in order to serve more passengers who can successfully transfer from one line to another, which is referred to as the last train departure time choice problem. This study aims to develop a global optimization method that can solve the last train departure time choice problem for large-scale urban subway networks. To do so, it first formulates a mixed-integer linear programming (MILP) model by introducing auxiliary binary and integer decision variables. For the real-life and large-scale instances, however, the formulated MILP model cannot be solved directly by the global optimization methods such as branch-and-bound algorithm invoked by CPLEX one of the powerful optimization solvers because of the instance sizes. An effective two-phase decomposition method is thus proposed to globally solve the large-scale problems by decomposing the original MILP into two MILP models with small sizes. Finally, a real case study from the Beijing subway network is conducted to assess the efficiency and applicability of the two-phase decomposition method and perform the necessary sensitivity analysis of the operational parameters involved in the last train departure time choice problem. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Urban subway networks Last train departure time choice mixed-integer linear programming Global optimization method Two-phase decomposition method

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Optimal shale oil and gas investments in the United States

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ENERGY 2017年 141卷 398-422页

作者： Tan, Siah Hong Barton, Paul I. MIT Proc Syst Engn Lab 77 Massachusetts Ave Cambridge MA 02139 USA

We present a comprehensive supply chain optimization model to determine optimal shale oil and gas infrastructure investments in the United States. The model encompasses multiple shale plays, commodities, plant locations, conversion technologies, transportation modes and both local and foreign markets. The dynamic evolution of supply, demand and price parameters and the uncertainty in parameter realizations are fully taken into account. Imposing two different scenario sets over a time horizon of twenty-five years, the model maximizes the expected net present value of the entire undertaking. We analyze the features of the optimal infrastructure investments and associated operating decisions, perform case studies which highlight the importance of incorporating uncertainty into the model and analyze the stability of the stochastic solutions as the degree of uncertainty changes. The overall opportunity set of investments is sparse, and there is a tendency for over-investment in new liquefied natural gas capacity when the uncertainties in future oil prices are not taken fully into account. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Shale oil and gas Infrastructure investments Supply chain optimization Stochastic programming mixed-integer linear programming

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