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Practical approximations and heuristic approaches for managing shiftable loads in the multi-period optimal power flow framework

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 190卷

作者： Avramidis, Iason-Iraklis Capitanescu, Florin Deconinck, Geert Luxembourg Inst Sci & Technol LIST Environm Res & Innovat ERIN Dept 41 Rue Brill L-4422 Belvalux Luxembourg Katholieke Univ Leuven ESAT Electa Kasteelpk Arenberg 10 B-3001 Leuven Belgium

The presence of smart buildings (SBs) is expected to grow manifold in the next decade, leading to both challenges and opportunities in managing increasingly "active" distribution systems. SBs are host to a number of devices and technologies capable of providing residential flexibility in order to maintain acceptable operating conditions within the distribution system. A prevalent class of devices with significant potential for flexibility provision are shiftable loads (SLs), which represent a large amount of potential SB-provided flexibility. However, the accurate modelling of SLs within the multi-period optimal power flow (MP-OPF) framework results in complex non-convex mixed-integer nonlinear programming (MINLP) problems. For realistic feeders, MINLP problems are intractable and hence require some heuristic form of circumvention. This work evaluates the limitations of the MINLP formulation and proposes new practical alternatives, namely a flexible NLP approximation and a multi-faceted heuristic algorithm. The proposed approaches are generic and applicable for any problem size or type of SL, while they are shown to work efficiently, both speed-wise and solution-quality-wise, outperforming state-of-the-art MINLP solvers. A simple scoring and ranking scheme is also proposed in order to make a direct comparison between the different approaches.

关键词： mixed-integer nonlinear programming multi-period optimal power flow residential flexibility shiftable loads smart buildings

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A Resource Constraint Approach for One Global Constraint MINLP 1

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20th International Conference on Computational Science and Its Applications (ICCSA)

作者： Muts, Pavlo Nowak, Ivo Hendrix, Eligius M. T. Hamburg Univ Appl Sci Hamburg Germany Univ Malaga Comp Architecture Malaga 29080 Spain

ISBN: (数字)9783030588083

ISBN: (纸本)9783030588083;9783030588076

Many industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, where low-dimensional sub-problems are linked by a (linear) knapsack-like coupling constraint. This paper investigates exploiting this structure using decomposition and a resource constraint formulation of the problem. The idea is that one outer approximation master problem handles sub-problems that can be solved in parallel. The steps of the algorithm are illustrated with numerical examples which shows that convergence to the optimal solution requires a few steps of solving sub-problems in lower dimension.

关键词： Decomposition Parallel computing Column generation Global optimization mixed-integer nonlinear programming

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Generalized Benders Decomposition Method to Solve Big mixed-integer nonlinear Optimization Problems with Convex Objective and Constraints Functions

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ENERGIES 2021年第20期14卷

作者： Karbowski, Andrzej Natl Res Inst Res & Acad Comp Network NASK Ul Kolska 12 PL-01045 Warsaw Poland

The paper presents the Generalized Benders Decomposition (GBD) method, which is now one of the basic approaches to solve big mixed-integer nonlinear optimization problems. It concentrates on the basic formulation with convex objectives and constraints functions. Apart from the classical projection and representation theorems, a unified formulation of the master problem with nonlinear and linear cuts will be given. For the latter case the most effective and, at the same time, easy to implement computational algorithms will be pointed out.

关键词： optimization mixed-integer nonlinear programming decomposition convex problems bilinear problems integer programming Generalized Benders Decomposition hierarchical optimization branch and cut

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An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities

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BUILDINGS 2021年第6期11卷

作者： Nazemi, Seyyed Danial Jafari, Mohsen A. Zaidan, Esmat Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08854 USA Qatar Univ Coll Arts & Sci Dept Int Affairs Doha 999043 Qatar

The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model's objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete-continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk's method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model's effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants' satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 re

关键词： demand response load scheduling incentives inconvenience mixed-integer nonlinear programming smart building community multi-objective optimization

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An Analysis of Decision to Retrofit Coal Based Power Plant with Carbon Capture Technology Having Uncertain Parameters

An Analysis of Decision to Retrofit Coal Based Power Plant w...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Kumari, Shalini Bera, Sasadhar Indian Inst Management Ranchi Dept Operat Management Ranchi Bihar India

ISBN: (纸本)9781538672204

The increasing industrialization since its inception has drawn attention towards the impact of industrial activities on the global environment. The increasing concern of global warming and rising earth's temperature has driven the institution of the Paris Agreement to examine the threshold limit of emission of carbon dioxide, which is the major component of greenhouse gases (GHG). Carbon capture and storage (CCS) technologies play a vital role in achieving net-zero carbon emission. The motivation behind this paper is to review the execution of CCS innovation in thermal power plants with the help of a mixed-integer nonlinear programming model. The problem of uncertainty of emission information in the thermal power plant is solved using the fuzzy technique. The results presented here demonstrate the option of selection of technology in a coalfired power plant.

关键词： Carbon capture and storage Fuzzy optimization Greenhouse gases mixed-integer nonlinear programming

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Security-Constrained Energy Management System for Microgrids Under Uncertainty

Security-Constrained Energy Management System for Microgrids...

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IEEE PES Transmission and Distribution Conference and Exhibition - Latin America (T and D LA)

作者： Alves da Silva, Jessica Alice Lopez, Juan Camilo Arias, Nataly Banol Rider, Marcos Julio Pereira da Silva, Luiz Carlos Univ Estadual Campinas Dept Syst & Energy DSE UNICAMP Campinas SP Brazil

ISBN: (纸本)9781728141558

This paper presents a stochastic mixed-integer nonlinear programming model (MINLP) for the security-constrained energy management system (EMS) of microgrids under demand and renewable generation uncertainties. Considering a balanced single-phase representation of the network, the proposed MINLP model is transformed into a mixed-integer second-order cone programming (MISOCP) model that can be solved via off-the-shelf convex programming solvers. The proposed formulation considers photovoltaic (PV) generation, energy storage systems (ESS), direct load control (DLC) and a diesel generator (genset), which can be turned on when the microgrid is operating in isolated mode. The proposed model minimizes the average operational costs for the day-ahead scheduling. Constraints consider the operation in either grid-connected or isolated mode due to a predefined set of plausible contingencies, hence the term security-constrained. The proposed model is tested using data of the real microgrid Laboratory of Intelligent Electrical Networks (LabREI), located at the UNICAMP facilities. Results show that the proposed model is suitable for real-world applications since it provides cost-efficient and contingency-robust solutions.

关键词： Microgrids mixed-integer nonlinear programming mixed-integer second-order cone programming security-constrained energy management system

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Designing a drone delivery network with automated battery swapping machines

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COMPUTERS & OPERATIONS RESEARCH 2021年 129卷

作者： Cokyasar, Taner Dong, Wenquan Jin, Mingzhou Verbas, Ismail Omer Argonne Natl Lab Energy Syst Div Lemont IL 60439 USA Univ Tennessee Dept Ind & Syst Engn Knoxville TN USA

Drones are projected to alter last-mile delivery, but their short travel range is a concern. This study proposes a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). A sensitivity analysis identifies that ABSM service times and costs are the key parameters impacting the long-term adoption of drone delivery. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Drone delivery Network optimization mixed-integer nonlinear programming Queueing theory

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An integrated approach of train scheduling and rolling stock circulation with skip-stopping pattern for urban rail transit lines

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 128卷

作者： Zhao, Shuo Yang, Hai Wu, Yinghui China Acad Railway Sci Corp Ltd Inst Comp Technol Beijing 100081 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Clear Water Bay Hong Kong Peoples R China Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang 212100 Jiangsu Peoples R China

Integrated planning for urban rail transit (URT) systems is a significant technique in URT operation to balance service level and operating cost. It is generally oriented towards transportation efficiency and solved in a sequential way due to the complexity, but ignores the service experiences among equitable passengers and the utilization balance among equal rolling stocks. This paper aims to study the integrated planning for an oversaturated URT line, incorporating the fairness in train scheduling and workload balance in rolling stock circulation that are measured by two defined min-max indexes. A mixed-integer nonlinear programming (MINLP) model is established to formulate the integrated planning of stop planning, train scheduling and rolling stock circulation, explicitly considering time-dependent origin-destination demand, first-come-first-serve principle and rigid capacity constraints. Due to the model complexity, an iterative searching approach (ISA) is proposed to solve the model, involving adjusting stop patterns, departure times and connections at the depot terminal. The performance of the ISA is evaluated by three cases with different time lengths based on the practical data from No.8 URT line in Guangzhou. The results are compared with the practical plans and the solutions solved by the two-stage approach respectively. It shows that the ISA can not only mitigate unfairness and balance workload, but also improve transportation efficiency. We also investigate the optimization effects and application conditions of different combinations of searching strategies in the ISA. Furthermore, the study on the typical case during peak hours shows more details about the optimization effects and mechanism of the ISA, which can reach a uniform state in both service level and resource utilization.

关键词： Integrated planning Fairness Workload balance Urban rail transit mixed-integer nonlinear programming Iterative searching approach

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Delivery drone route planning over a battery swapping network

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Procedia Computer Science 2021年 184卷 10-16页

作者： Taner Cokyasar Argonne National Laboratory Energy Systems Division 9700 S. Cass Ave. Lemont IL 60439 USA

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

关键词： Drone delivery drone routing drone battery swapping mixed-integer nonlinear programming optimization

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Early field planning using optimisation and considering uncertainties Study case: Offshore deepwater field in Brazil

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 207卷

作者： Sales, Leonardo Jaschke, Johannes Stanko, Milan Dept Geosci & Petr SP Andersens Veg 15 N-7031 Trondheim Norway Dept Chem Engn Sem Saelands Vei 6 N-7034 Trondheim Norway

During early phases of oil field development, field planners must decide upon the optimal number of wells and optimal field plateau rate, usually by performing sensitivity studies. These design choices are then "frozen"in subsequent development stages. However, they often end up being suboptimal when the field is built and produced and the uncertainty is reduced. In this work, we employ non-linear numerical optimisation, latin hypercube sampling and the Schwartz & Smith oil price model to compute probability distributions of the optimal number of wells, plateau rate and project value. We also employ an analytical model to compute production profiles and project value and consider uncertainties in in-place oil volume, well productivity and oil price. Then, we study how do these distributions change from early field planning until when the field is abandoned, when uncertainties are reduced to a minimum. The variation in time of the in-place oil volume uncertainty is modelled with a random walk. The well productivity is a step function altered randomly after production startup. The actual oil price trajectory is picked randomly from possible trajectories computed with the Schwartz and Smith model. The results show that the distributions of the optimal number of wells, plateau rate and project value depend greatly on the uncertainties in the input data. Field designs based on the average of the distributions during the early phase are profitable, but suboptimal. A potential upside of such designs is that they entail less capital investment and therefore less financial risk when compared against the optimal field design.

关键词： Field development planning Optimisation Uncertainty analysis mixed-integer nonlinear programming

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