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Application of polynomial regression and MILP for under-frequency load shedding scheme in islanded distribution system

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ALEXANDRIA ENGINEERING JOURNAL 2022年第1期61卷 659-674页

作者： Sarwar, Sohail Mokhlis, Hazlie Othman, Mohamadariff Shareef, Hussain Wang, Li Mansor, Nurulafiqah Nadzirah Khairuddin, Anis Salwa Mohd Mohamad, Hasmaini Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Dept Elect Engn Al Ain 155511 U Arab Emirates Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Univ Teknol MARA Sch Elect Engn Shah Alam 40450 Malaysia

Distributed Generation (DG) integration, especially based on renewable energy resources, has gained great attention by power utilities and frequently utilized in the electrical distribution systems. However, DG integration imposes some risks towards system stability which may lead to system blackouts. This mainly occurs when the grid is decoupled from a portion of the distribution system consisting DGs while the total load demand is greater than total DGs output power. In order to overcome this problem, load shedding technique can be adopted to stabilize the system frequency. However, existing load shedding techniques were unable to accurately estimate the power imbalance due the variation in system loading. This results in excessive/inadequate load shedding to stabilize the system frequency. Moreover, random selection of the loads without load prioritization might cause vital loads to be shed. Therefore, in this paper, a new load shedding strategy for islanded distribution system is proposed. Polynomial regression analysis estimates the power mismatch while MILP optimization estimates optimal load combination for shedding. Furthermore, load priority (i.e., vital, non-vital, and semi-vital) is also considered to avoid disconnecting vital loads. Efficiency of the proposed scheme is evaluated on three different test systems. Validation is performed by modelling the proposed load shedding on PSCAD/EMTDC software for dynamic analysis. From the results, it can be analyzed that the proposed technique is superior compared to other techniques proposed in the literature. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.

关键词： Frequency instability Load priority mixed integer linear programming Polynomial regression System blackout Under frequency load shedding

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Analytical Maneuver Library for Remote Inspection with an Underactuated Spacecraft

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第4期45卷 611-622页

作者： Jia-Richards, Oliver Lozano, Paulo C. MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

This paper presents an analytical library for maneuvering of an underactuated spacecraft around a central target with application to remote inspection. Free-flying small spacecraft are an attractive option for in situ remote inspection but require maneuvering strategies that are computationally simple, such that they can be implemented on a small spacecraft computer, while still allowing for constraints like plume impingement and underactuation to be included. An analytical maneuver library provides a suboptimal solution but can be computed without the need for numerical optimization. In addition, the low fuel cost of maneuvers means that the degree of suboptimality of the analytical maneuvers is marginal. To develop the library, forced circular motion is used in order to place the spacecraft in a controllable state such that external disturbances can be rejected. From there, trajectories can be designed in order to manipulate the circular motion and accomplish maneuvers such as joining the circular motion from rest or changing the circle's radius. Conditions under which plume impingement is guaranteed to be avoided are developed, and applications of the maneuver library to point-to-point maneuvering and landing on/docking to a rotating target are shown.

关键词： Spacecrafts mixed integer linear programming Propulsion System Propellant Thrust Vectoring Model Predictive Control Orbital Period CubeSat Low Thrust Propulsion Spacecraft Trajectories

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Optimizing emergency services for road safety using a decomposition method: a case study of Delhi

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OPSEARCH 2023年第1期60卷 155-173页

作者： Wajid, Shayesta Nezamuddin, N. Indian Inst Technol Delhi Dept Civil Engn New Delhi 110016 India

Road traffic crashes are among the top ten leading causes of deaths in India and emergency medical services play a vital role in reducing fatality after a road crash. Being one of the largest metropolitan areas in the world, Delhi has thousands of fatal crashes each year which makes it challenging to achieve a timely response to crashes. In this study, we demonstrated the efficiency of a district-level decomposition approach for solving various types of ambulance optimization problems (for coverage, for survivability, and by incorporating travel time uncertainty) for the city of Delhi, India. Three scenarios were compared to optimize the ambulance locations: the first scenario (S1) considered only the existing ambulance sites;the second (S2) and third (S3) scenarios considered a larger set of potential ambulance sites and relocated ambulances within districts and across districts, respectively. Results showed that the city-level coverage increased by at least 10% with the reallocation of ambulances across districts. For the coverage and survivability maximization models, the decomposition approach was between two to six times faster than the non-decomposition approach. The decomposition approach was critical in solving a robust coverage model accounting for travel time uncertainty and containing nearly half-a-million integer variables. It solved the robust model on a regular PC with 16 GB RAM in half an hour. The non-decomposition approach needed a workstation with 124 GB RAM to solve the same problem in 5 h.

关键词： Emergency medical services Double standard model mixed integer linear programming Decomposition method Facility location

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Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第2期59卷 421-433页

作者： Takubo, Yuji Chen, Hao Ho, Koki Georgia Inst Technol Aerosp Engn Atlanta GA 30332 USA

This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, and space transportation scheduling. This problem involves a high-dimensional design space and is challenging especially with uncertainty present. To tackle this challenge, the developed framework has a hierarchical structure with reinforcement learning and network-based mixed-integer linear programming (MILP), where the former optimizes campaign-level decisions (e.g., design of the vehicle used throughout the campaign, destination demand assigned to each mission in the campaign), whereas the latter optimizes the detailed mission-level decisions (e.g., when to launch what from where to where). The framework is applied to a set of human lunar exploration campaign scenarios with uncertain in situ resource utilization performance as a case study. The main value of this work is its integration of the rapidly growing reinforcement learning research and the existing MILP-based space logistics methods through a hierarchical framework to handle the otherwise intractable complexity of space mission design under uncertainty. This unique framework is expected to be a critical steppingstone for the emerging research direction of artificial intelligence for space mission design.

关键词： Reinforcement Learning Spaceflight In Situ Resource Utilization Space Transportation Interplanetary Mission Space Logistics Optimization mixed integer linear programming Artificial Intelligence Propellant Earth

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Optimal Scheduling of COVID-19 Pandemic Supply Disruptions Under Uncertainty

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IEEE ACCESS 2022年 10卷 114374-114392页

作者： Cao, Wei Wang, Xifu Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

With the outbreak of COVID-19 pandemic, the problem of supply chain emergency scheduling has had a great influence on economic benefits of enterprises. The uncertainty of the COVID-19 pandemic and uncertainty of supply chain have made the problem of emergency scheduling more complicated. In this study, a multiobjective multiperiod mixed-integer programming optimization model was developed, in which two conflicting benefit factors, cost and service level were taken as the optimization target of the model. Cost and service level were normalized, and the weighted sum was taken as the objective function, which transformed the problem into a multiperiod nonlinear one. Two algorithms of Bi-level Modified Hybrid Genetic Algorithm (BMHGA) and Bi-level Hybrid Genetic Algorithm (BHGA) were designed to solve the model. Three emergency strategies have been proposed, including enabling alternative suppliers, repairing failure nodes and enabling internal suppliers' flexibility. The parameter of disruption scenario and its solution method were designed. Finally, the practicability of the proposed model and algorithm was demonstrated through application to a case study of an electronics supply chain. The results indicated that the optimal solution of two algorithms was between 2%-5%, and BMHGA could obtain a better optimal solution.

关键词： Supply chain management COVID-19 Genetic algorithms Pandemics mixed integer linear programming Job shop scheduling Production facilities A multiobjective multiperiod mixed-integer programming optimization model supply chain emergency scheduling uncertain supply disruptions uncertain demand COVID-19 pandemic bi-level modified hybrid genetic algorithm (BMHGA) bi-level hybrid genetic algorithm (BHGA)

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Flexible open-shop problem for minimizing weighted total completion time

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 1353-1366页

作者： Mashizi, Iman Khosravi Kermani, Vahid Momenaei Shahsavari-Pour, Naser IslamicAzad Univ Dept Math Kerman Branch Kerman Iran Vali e Asr Univ Dept Ind Management Rafsanjan Iran

In this article, scheduling flexible open shops with identical machines in each station is studied. A new mathematical model is offered to describe the overall performance of the system. Since the problem enjoys an NP-hard complexity structure, we used two distinct metaheuristic methods to achieve acceptable solutions for minimizing weighted total completion time as the objective function. The first method is customary memetic algorithm (MA). The second one, MPA, is a modified version of memetic algorithm in which the new permutating operation is replaced with the mutation. Furthermore, some predefined feasible solutions were imposed in the initial population of both MA and MPA. According to the results, the latter action caused a remarkable improvement in the performance of algorithms.

关键词： Flexible open shop mixed integer linear programming memetic algorithm permutating operation taguchi method

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On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第1期59卷 33-48页

作者： du Jonchay, Tristan Sarton Chen, Hao Isaji, Masafumi Shimane, Yuri Ho, Koki Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

This paper proposes an on-orbit servicing logistics optimization framework capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-the-art on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. mixedinteger linear programming is leveraged to find the optimal operations of the servicers over a given period, whereas the rolling horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high- and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.

关键词： Low Thrust Propulsion Propulsion Technology mixed integer linear programming Logistics Operations Bipropellants Geosynchronous Satellite Manufacturing Cost Geosynchronous Equatorial Orbit Propulsion System Earth

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Multi-objective mathematical programming for optimally sizing and managing battery energy storage for solar photovoltaic system integration of a multi-apartment building

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ENGINEERING OPTIMIZATION 2022年第1期54卷 81-100页

作者： Amorosi, Lavinia Cedola, Luca Dell'Olmo, Paolo Lucchetta, Francesca Sapienza Univ Rome Dept Stat Sci Rome Italy Sapienza Univ Rome Dept Mech & Aerosp Engn Rome Italy

This article presents a novel mathematical formulation to solve the problem of optimally sizing and managing battery energy storage for the solar photovoltaic system integration of a multi-apartment building. The aim is the maximization of the collective self-consumption maintaining control over time of the energy sold and bought and the monitoring of the state of batteries while ranging from minimum to maximum levels. This is obtained through a new mathematical programming model with three different objective functions that can be tuned simultaneously to find Pareto optimal solutions to the problem. The computational results in detailed scenarios with real data verify the model's adequacy to deal with the real problem and give a measure of the saving of bought and sold energy.

关键词： Battery storage optimization energy system management mixed integer linear programming

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A slack-bus-independent loss sensitivity approach for optimal day-ahead generation scheduling

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ELECTRICAL ENGINEERING 2022年第2期104卷 421-434页

作者： Phuoc, Nguyen Huy Ongsakul, Weerakorn Manjiparambil, Nimal Madhu Sasidharan, Nikhil Asian Inst Technol Sch Environm Resources & Dev Dept Energy Environm & Climate Change Pathum Thani Thailand Amrita Vishwa Vidyapeetham Univ Ctr Excellence Computat Engn & Networking Coimbatore Tamil Nadu India Natl Inst Technol Calicut Dept Elect Engn Kozhikode Kerala India

In a power system, the incurred transmission loss and the associated sensitivity factors are dependent on the selection of the slack bus and the dispatch. In this paper, a fast two-stage, hydro-thermal generation scheduling process, which is also inclusive of the system power loss, is proposed. A novel approach towards estimating power loss sensitivity factors is presented which is independent of the choice/location of the slack bus in the network. In addition to this, the approach identifies the loss incurred by the operation of different market players including the generation and distribution companies for penalizing. mixed integer linear programming is used to model the said optimal day-ahead scheduling problem. The two-stage process employed is such that the first stage is applied only in the planning stage, while second is a fast responsive algorithm suitable for very short-term applications. A real network, Vietnam Power Grid, is used for testing this proposed approach, and the results obtained demonstrated that there is significant reduction in the electricity cost and total transmission loss.

关键词： Optimal day-ahead generation scheduling Independent slack bus Transmission loss allocation Loss sensitivity factor Wholesale electricity market mixed integer linear programming

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Resiliency constraint proactive scheduling against hurricane in multiple energy carrier microgrid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第20期16卷 4096-4114页

作者： Ghasemi, Ali Akbar Gitizadeh, Mohsen Kamwa, Innocent Ghareghani, Omid Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Laval Elect Engn Dept Quebec City PQ Canada

Hurricane, as one of the most frequent natural events, causes damage to the infrastructure of the upstream networks of multiple energy carrier microgrids (MECMs). Thus, the supply continuity of critical loads is threatened due to sudden islanding of MECMs from the upstream networks. Therefore, providing a framework to enhance the resilience of MECMs against this threat is necessary. In response to this issue, this paper presents a proactive optimal operation scheduling approach that is constrained to the feasible islanding of MECM from the upstream networks as well as the continuous suppling of the critical loads until the return of the upstream networks. Proposed resiliency constraint proactive scheduling is formulated as a mixed integer linear programming (MILP) that considers the interdependence between all three electric, gas and heat networks. The demand responsibility of all three electric, thermal and gas loads is used to consider the satisfaction of the consumer beside economic and resilient operation. Furthermore, both normal operation and contingency-based uncertainties are taken into account and captured using a robust optimization method. Then in order to evaluate the resilience of the MECM, feasible islanding and preparedness indices are proposed. Finally, the effectiveness of proposed approach is illustrated using an MECM test.

关键词： electric loads natural events multiple energy carrier microgrid gas loads upstream networks optimization method integer programming mixed integer linear programming heat networks resilient operation MILP economic operation power generation economics feasible islanding distributed power generation thermal loads resiliency constraint proactive scheduling hurricane power generation scheduling proactive optimal operation scheduling approach electric networks MECM

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