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Distribution system resilience enhancement by microgrid formation considering distributed energy resources

ENERGY 2020年第0期191卷 116442-000页

作者： Gilani, Mohammad Amin Kazemi, Ahad Ghasemi, Mostafa Iran Univ Sci & Technol Dept Elect Engn Ctr Excellence Power Syst Automat & Operat Tehran Iran

Due to increasing in natural disasters in the recent years, the issue of distribution network resilience has become highly important. Microgrids with different types of distributed energy resources have the capabilities to improve distribution network resilience under extreme events. In this paper, we present a mixed-integer linear program to restore prioritized loads while satisfying topology and operational constraints. In the presented model, we study dynamic microgrid formation and optimal management of various smart grid technologies such as distributed generations, demand response programs, wind turbines and energy storage units. We also investigate the significant impact of renewable energy resources, loads and their uncertainty on distribution system resilience. In addition, we determine the required emergency budgets of operation to restore a distribution system from extreme events. We also intend to examine the effects of demand response programs on improving the distribution system performance in the recovery period. Finally, we verify the effectiveness of the presented model on a modified IEEE 33-node test system and a real distribution system. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Distribution system resilience Microgrids Distributed energy resources Emergency operation budgets mixed-integer linear program

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Optimal allocation of battery energy storage systems for peak shaving and reliability enhancement in distribution systems

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JOURNAL OF ENERGY STORAGE 2024年 95卷

作者： Inaolaji, Adedoyin Wu, Xuan Roychowdhury, Rajarshi Smith, Raiford Univ Buffalo Dept Elect Engn Buffalo NY 14260 USA AES US Util 1 Monument Circle Indianapolis IN 46204 USA

Increasing demand for electricity and frequent power outages are common factors that are necessitating power utility companies to refurbish the existing power distribution systems. To avoid such expensive upgrades, a practical and more viable alternative solution is to use a battery energy storage system (BESS) that can participate in peak shaving requirements and function as an additional power source during power outages. In this context, this work develops an optimization model to optimally determine the size and site of a BESS connected to the distribution network for the purpose of two critical service options, namely peak shaving and reliability improvement, under both system normal and outage scenarios. Nonlinearities due to the sizing and siting variables of the BESS are linearized using the big -M method, and the resulting model becomes a mixed-integer linear programming (MILP) problem. The decisions made during planning, namely the size and location of the BESS, directly impact operational decisions regarding how the BESS supports peak shaving and outage management, if applicable. By considering a utility's historical load and outage data, the efficacy of the proposed model is validated on a realistic distribution circuit that is located at the edge of a service territory with no circuit ties. The optimal BESS allocation obtained from the model achieves up to 93.5% enhancement in the system's reliability and also decreases the feeder's peak demand by about 17% for a one -year planning horizon. The case studies demonstrate that the model is computationally scalable and efficient, and electric distribution planners may benefit from this study and use this as a framework for possible BESS integration to defer expensive peak capacity-related upgrades while adding an extra source for backup power supply.

关键词： Distribution grid reliability Battery energy storage system Outages Peak shaving Optimization mixed-integer linear program

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Interference-aware high-throughput channel allocation mechanism for CR-VANETs

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2016年第1期2016卷 1页

作者： Tabassum, Madiha Razzaque, Md Abdur Hassan, Mohammad Mehedi Almogren, Ahmad Alamri, Atif Univ Dhaka Dept Comp Sci & Engn Green Networking Res GNR Grp Dhaka Bangladesh King Saud Univ Coll Comp & Informat Sci Riyadh 11543 Saudi Arabia

The incorporation of cognitive radio (CR) technology in vehicular ad hoc networks (VANETs) has given birth to a new network, namely CR-VANET, which facilitates the vehicular network to achieve communication efficiency in many resource-demanding applications including video and audio streaming, collision warning, gaming, etc. One of the primary challenges in this CR-VANET network is to allocate high-throughput licensed channels to the application requests in face of interference between the primary users (PUs) and the secondary users (SUs) and among the SUs on the channels. In this paper, we address the channel allocation problem in CR-VANET with the objective of system-wide throughput maximization while maintaining the application quality-of-service (QoS) requirements in terms of channel throughput and packet delivery delay for data transmission. We develop conflict graphs of link-band pairs to describe the interference relationship among source-destination vehicle pairs on different channels and determine independent sets of vehicle pairs that can communicate simultaneously to maximize the spatial reuse of the licensed channels. Finally, we formulate a high-throughput channel allocation problem as a mixed-integer linear programming (MILP) problem. Through extensive simulations, we demonstrate that the proposed interference-aware high-throughput channel allocation mechanism (HT-CAM) provides with better network performances compared to state-of-the-art protocols.

关键词： CR-VANET Spatial reusability Throughput Channel utilization mixed-integer linear program

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Economic model predictive control of space heating and dynamic solar shading

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ENERGY AND BUILDINGS 2020年第0期209卷 109661-000页

作者： Knudsen, M. D. Petersen, S. Aarhus Univ Dept Engn Inge Lehmanns Gade 10 DK-8000 Aarhus Denmark

Dynamic solar shading devices are an effective mean to avoid overheating in buildings due to excessive solar heat gains. They can also be used to affect the heat balance of buildings;they can be on e.g. during nighttime to reduce space heating or retracted to increase night cooling. The shading control system can utilize this feature to obtain energy cost savings and reduce peak consumption. However, it is difficult to define optimal rule-based control strategies that minimize overheating and energy costs simultaneously. In this paper, we therefore propose an economic model predictive control (E-MPC) scheme for space heating where solar shading is included as an additional control variable. The proposed scheme employs black-box models, as they are 'cheap' to create from data;this is important for a widespread deployment of E-MPC in practice. The study was based on co-simulation experiments where EnergyPlus models represented the actual building and it was hereby demonstrated that the proposed E-MPC outperformed two rule-based controllers with respect to reducing overheating, energy costs and peak consumption. Shading also turned out to be valuable for north facing rooms where it reduced costs and peak consumptions compared to an MPC without shading. The energy-related benefits of the proposed E-MPC scheme can be regarded as 'an added value' that can be used for economic justification of an investment in dynamic solar shading devices. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Subspace system identification Price-based demand response State-space model Black box modelling mixed-integer linear program Optimal control

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Simple and Accurate Model of Thermal Storage with Phase Change Material Tailored for Model Predictive Control

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ENERGIES 2023年第19期16卷 6849页

作者： Vrbanc, Filip Vasak, Mario Lesic, Vinko Univ Zagreb Fac Elect Engn & Comp Lab Renewable Energy Syst Unska 3 Zagreb 10000 Croatia

Thermal heat storage is becoming important in systems with renewable energy sources. Their largest benefit is smoothing the intermittent production and reduction in the site peak demand. The advantages of thermal energy storage with phase-change material are storing energy at a lower temperature for reduction in thermal losses, and enabling energy transfer at a constant temperature, which reduces the risk of equipment damage. In this paper, a low-order model of latent thermal energy storage, derived in a state-space form by using the mixed logical dynamical approach, is proposed. The model is compared to a stratified model and shows significant improvements of physical accuracy and execution time. Finally, a model predictive control algorithm suited for the real case study is designed, implemented and compared to classical rule-based control. The obtained results show significant energy savings of 8.43%, and improvements in user comfort and equipment duration.

关键词： heating system mixed logical dynamical model mixed-integer linear program model predictive control phase-change material thermal energy storage

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Online routing and spectrum allocation in elastic optical networks based on dueling Deep Q-network

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第0期173卷

作者： Zhang, Jiawei Qian, Fengchen Yang, Junqiang Natl Univ Def Technol Coll Informat & Commun Xian 710106 Peoples R China

The study of online routing and spectrum allocation (RSA) problem has assumed increasing importance due to the exponential growth of dynamic traffic with uncertainty in elastic optical networks (EONs). This paper first formulates offline RSA as a mixed-integer linear program (MILP) with the consideration of the time attribute, and then models online RSA by a carefully designed Markov decision process (MDP), including the state, action and reward. To deal with such a complex dynamic optimization problem, a novel algorithm based on the classic deep reinforcement learning (DRL) framework, Deep Q-network (DQN), is developed. For further promoting the training efficiency, a dueling network architecture, an improved E-greedy strategy and a series of parameter adjustments are applied. Simulation results demonstrate the effectiveness of the proposed algorithm and show its superiority in reducing the blocking probability compared with the state of the art, which further exhibits the potential of applying DRL to solve such complex real-time decision-making problems.

关键词： Online routing and spectrum allocation Elastic optical networks mixed-integer linear program Markov decision process Deep reinforcement learning Deep Q-network

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Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

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ENERGIES 2020年第21期13卷 5683页

作者： Wang, Jing Garifi, Kaitlyn Baker, Kyri Zuo, Wangda Zhang, Yingchen Huang, Sen Vrabie, Draguna Univ Colorado Dept Civil Environm & Architectural Engn 1111 Engn Dr Boulder CO 80309 USA Univ Colorado Dept Elect Comp & Energy Engn 425 UCB 1B55 Boulder CO 80309 USA Renewable & Sustainable Energy Inst 027 UCB Suite N321 Boulder CO 80309 USA Natl Renewable Energy Lab 15013 Denver W Pkwy Golden CO 80401 USA Pacific Northwest Natl Lab 902 Battelle Blvd Richland WA 99354 USA

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

关键词： resilient community optimal operation load scheduling renewable resource allocation model predictive control mixed-integer linear program

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mixed Logical Dynamical Modelling of a Stratified Storage with Phase-Change Material

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IFAC-PapersOnLine 2022年第20期55卷 145-150页

作者： Filip Vrbanc Filip Rukavina Vinko Lešić Mario Vašak University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Zagreb Croatia

Thermal storages that use phase-change materials are increasingly applied in heating systems with renewable energy sources. Their main advantage is storing energy of a high density while keeping the storage at lower temperatures. To exploit the full potential, optimal control is applied, which requires an appropriately designed model of the heating system. This paper proposes a model of the latent stratified storage in a mixed logical dynamical form. The proposed model is developed and verified on a real heating pilot system with a latent heat storage. Thermodynamic behaviour of this system is simulated, both by using mixed logical dynamical model and a non-linear thermodynamical model. Obtained results show high accuracy of the models.

关键词： Heating system stratified thermal energy storage phase-change material mixed logical dynamical modelling mixed-integer linear program

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Evaluating the Applicability of Advanced Techniques for Practical Real-time Train Scheduling

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Transportation Research Procedia 2014年 3卷 279-288页

作者： Andrea D’Ariano Marcella Samà Paolo D’Ariano Dario Pacciarelli Università degli Studi Roma Tre Dipartimento di Ingegneria Sezione Informatica e Automazione Via della Vasca Navale 79 - 00146 Roma Italy

This paper reports on the practical applicability of published techniques for real-time train scheduling. The final goal is the development of an advanced decision support system for supporting dispatchers’ work and for guiding them toward near-optimal real-time re-timing, re-ordering and re-routing decisions. The paper focuses on the optimization system AGLIBRARY that manages trains at the microscopic level of block sections and block signals and at a precision of seconds. The system outcome is a detailed conflict-free train schedule, being able to avoid deadlocks and to minimize train delays. Experiments on a British railway nearby London demonstrate that AGLIBRARY can quickly compute near-optimal solutions.

关键词： Optimal Railway Operations Disruption and Delay Management mixed-integer linear program Disjunctive programming

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The Line-less Mobile Assembly System Simultaneous Scheduling and Location Problem

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Procedia CIRP 2022年 106卷 203-208页

作者： A.F. Buckhorst M.K.B. do Canto R.H. Schmitt Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Production Technology (IPT) Steinbachstrasse 17 52074 Aachen Germany

Line-less Mobile Assembly Systems (LMAS) operations management requires a global planning approach to benefit from the paradigm’s beneficial resilience and flexibility. LMAS’s unique property is the spatio-temporal assessment of assembly stations by mobile multipurpose resources. In mixed-integer programming (MIP), the simultaneous scheduling location problem (ScheLoc) combines two essential objectives desired for LMAS global planning and control: Finding optimal machine locations on the shop floor and finding optimal schedules for each of these machines subject to production and location constraints. However, recent ScheLoc formulations do not map the LMAS properties entirely and only respect either a single machine location for planar regions (P-ScheLoc) or parallel machines’ locations for discrete regions (D-ScheLoc). Therefore, this paper deduces the LMAS ScheLoc problem extending recent mixed-integer programs to customized production orders and spatio-temporal station assessment with individual capabilities by mobile robots for discrete environments. The model’s objective is to minimize the makespan respecting scheduling-specific, location-specific, and joining constraints. Since LMAS ScheLoc is computationally heavy, we propose the necessary criteria for input datasets. If they are not fulfilled, a solution’s existence for a given problem is precluded. Further, we present an upper bound estimate for the makespan C Make estimate for practical implementations. The analysis is executed based on an industrial sewage pump assembly use case. Test datasets exhibit different flexibility levels ultimately highlight LMAS ScheLoc’s benefits.

关键词： Line-less Mobile Assembly Systems Simultaneous Scheduling Location Flexibility in Assembly mixed-integer linear program

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