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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Xiyan Jian Zhanbo Xu Xiangxiang Dong Jiang Wu Kun Liu Xiaohong Guan MOEKLINNS Xi’an Jiaotong University Xi’an China

Recently, more attention is attracted on renewable energy sources (RESs) that could alleviate energy and environmental issues. However, with the fast development of RESs, vast power curtailment arouses due to the mismatch of energy demand and renewable supply in time and spatial horizon, leading to low renewable energy sources utilization efficiency. In this paper, we propose a coordinated scheduling problem of a wind-solar-hydrogen integrated energy system (WSH-IES) to address the problem mentioned above. The curtailed RESs power can be converted into hydrogen in supply subsystem, and such hydrogen can be delivered to demand subsystem through tube trailers to meet multi-energy demand. The problem is formulated into a mixed integer linear programming problem. Numerical results show the developed method can achieve 100% RESs utilization efficiency and reduce 23.4% of the operation cost through the coordinated scheduling of all the energy devices and the hydrogen interaction between the demand and supply subsystems.

关键词： Renewable energy sources Costs Supply and demand Hydrogen Optimal scheduling Electron tubes mixed integer linear programming

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Multi-agent Optimization Model of Power Spot Market under Renewable Portfolio Standard

Multi-agent Optimization Model of Power Spot Market under Re...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Xiaofan Lin Jinchun Chen Simin Chen Wanqing Chen Han Chen Yuanfei Li Power Economic Research Institute of State Grid Fujian Electric Power Company Fuzhou China

China officially began to implement the Renewable Portfolio Standard (RPS) in 2021, which requires power users to take the actual consumption of renewable energy electricity as the main way to achieve the target. At the same time power users can also purchase Renewable energy certificate (REC) and surplus renewable energy consumption as a supplementary means. Based on the above framework, a bilevel multi-agent decision optimization model including renewable energy day-ahead market, conventional energy day-ahead market and real-time market is established under the RPS, and transformed by the Karush-Kuhn-Tucker condition, strong duality theorem and binary expansion method. It is transformed into mixed integer linear programming to find the market equilibrium. The effects of assessment index of RPS, REC price and renewable power penetration on market equilibrium characteristics are discussed. The results show that in the market equilibrium state, the day-ahead market clearing price of renewable energy is equal to the conventional energy clearing price plus the REC price, and with the increase of the penetration rate of renewable energy, the power spot market clearing price of electricity shows a downward trend.

关键词： Renewable energy sources Supply and demand System integration Market research linear programming Real-time systems mixed integer linear programming

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Interpreting Deep-Learned Error-Correcting Codes

Interpreting Deep-Learned Error-Correcting Codes

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IEEE International Symposium on Information Theory

作者： N. Devroye N. Mohammadi A. Mulgund H. Naik R. Shekhar Gy. Turá n Y. Wei M. Ž efran University of Illinois at Chicago Chicago IL USA MTA-SZTE Research Group on Artificial Intelligence ELRN Szeged Hungary

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

Deep learning has been used recently to learn error-correcting encoders and decoders which may improve upon previously known codes in certain regimes. The encoders and decoders are learned "black-boxes", and interpreting their behavior is of interest both for further applications and for incorporating this work into coding theory. Understanding these codes provides a compelling case study for Explainable Artificial Intelligence (XAI): since coding theory is a well-developed and quantitative field, the interpretability problems that arise differ from those traditionally considered. We develop post-hoc interpretability techniques to analyze the deep-learned, autoencoder-based encoders of TurboAE-binary codes, using influence heatmaps, mixed integer linear programming (MILP), Fourier analysis, and property testing. We compare the learned, interpretable encoders combined with BCJR decoders to the original black-box code.

关键词： Training Deep learning Neural networks Turbo codes Robustness Decoding mixed integer linear programming

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Improved Genetic programming for Symbolic Regression: Case Studies on Practical Applications

Improved Genetic Programming for Symbolic Regression: Case S...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Quang Huynh Hemant Singh Tapabrata Ray Akira Oyama School of Enginerring and Information Technology The University of New South Wales at ADFA Canberra Australia Institue of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara Japan

ISBN: (纸本)9781665487696

Genetic programming (GP), especially Semantic GP (SGP), has shown significant potential in solving numerical benchmarks in Symbolic Regression (SR) domain in recent years. However, its application on real-world problems has been less explored due to the large sizes of the resulting expressions, which are prone to over-fitting and are difficult to interpret. In this paper, we propose a method that incorporates customization for real-world data sets based on a combination of two operators of GP. The first operator utilizes the concept of Semantic Backpropagation, a noteworthy method in SGP, to create short expressions which are highly correlated with the outputs. The second operator makes use of mixed integer linear programming (MILP) to combine short expressions into the overall expression with good accuracy. The proposed approach is tested on one synthetic data set and two practical applications which are challenging for conventional GP. The experimental results are very promising, with further scope of improvement.

关键词： Backpropagation Sensitivity analysis Semantics Genetic programming Benchmark testing mixed integer linear programming Computational intelligence

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Distributionally Robust Decision-Dependent Generation and Transmission Expansion Planning for 100% Renewable Energy Utilization

Distributionally Robust Decision-Dependent Generation and Tr...

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IEEE International Conference on Energy Internet (ICEI)

作者： Yangqing Dan Lu Qiu Yalong Li Economic Research Institute State Grid Zhejiang Electric Power Company LTD. Hangzhou China School of Mechanical Electronic & Information Engineering China University of Mining and Technology-Beijing Beijing China

Renewable energy is the fundamental approach to reducing the carbon emission of power systems. To fully utilize renewable energy sources in the planning stage, a co-optimization of generation and transmission expansion planning (G&TEP) strategy is proposed, where the system security is guaranteed under uncertainties. To address the long-term uncertainties of loads, renewable energy output, and component failures across the planning horizon, a novel decision-dependent ambiguity set is proposed using total variation distance, where the renewable energy output and component failures are affected by planning. A multi-year G&TEP problem is formulated as a two-stage distributionally robust optimization problem with decision-dependent ambiguity sets, where the renewable energy, energy storage systems (ESSs), and transmission lines are jointly optimized. Using Lagrange duality, this problem is further reformulated as a mixed-integer linear programming problem, which can be solved by the off-the-shelf solvers. The simulations are performed on a modified Garver 6 test system. The effectiveness of the proposed strategy is verified by the numerical results.

关键词： Renewable energy sources Power transmission lines Uncertainty Wind farms Planning mixed integer linear programming Numerical models

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Integrating Security in Resource-Constrained Cyber-Physical Systems

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ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2020年第3期4卷 1–27页

作者： Lesi, Vuk Jovanov, Ilija Pajic, Miroslav Duke Univ Durham NC 27708 USA

Defense mechanisms against network-level attacks are commonly based on the use of cryptographic techniques, such as lengthy message authentication codes (MAC) that provide data integrity guarantees. However, such mechanisms require significant resources (both computational and network bandwidth), which prevents their continuous use in resource-constrained cyber-physical systems (CPS). Recently, it was shown how physical properties of controlled systems can be exploited to relax these stringent requirements for systems where sensor measurements and actuator commands are transmitted over a potentially compromised network;specifically, that merely intermittent use of data authentication (i.e., at occasional time points during system execution), can still provide strong Quality-of-Control (QoC) guarantees even in the presence of false-data injection attacks, such as Man-in-the-Middle (MitM) attacks. Consequently, in this work, we focus on integrating security into existing resource-constrained CPS, in order to protect against MitM attacks on a system where a set of control tasks communicates over a real-time network with system sensors and actuators. We introduce a design-time methodology that incorporates requirements for QoC in the presence of attacks into end-to-end timing constraints for real-time control transactions, which include data acquisition and authentication, real-time network messages, and control tasks. This allows us to formulate a mixed integer linear programming-based method for direct synthesis of schedulable tasks and message parameters (i.e., deadlines and offsets) that do not violate timing requirements for the already deployed controllers, while adding a sufficient level of protection against network-based attacks;specifically, the synthesis method also provides suitable intermittent authentication policies that ensure the desired QoC levels under attack. To additionally reduce the security-related bandwidth overhead, we propose the use of cumula

关键词： Cyber-physical systems security real-time scheduling quality-of-control mixed integer linear programming

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A MILP algorithm for the optimal sizing of an off-grid hybrid renewable energy system in South Tyrol

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ENERGY REPORTS 2020年 6卷 21-26页

作者： Alberizzi, Jacopo C. Rossi, Mose Renzi, Massimiliano Free Univ Bozen Bolzano Fac Sci & Technol Piazza Univ 5 I-39100 Bozen Bolzano Italy

The exploitation of renewable energy sources through sustainable energy technologies are taking the field to decrease the pollutions' emissions into the Earth's environment. To offset the limitations of such resources, hybrid energy systems are becoming fundamental in grid-connected applications as well as in off-grid ones. However, the unsteady behavior of renewable sources, such as Sun and Wind, complicates the prediction of the energy production's trend. The main factors and components involved in the design of hybrid energy systems are: (i) type of generators, (ii) their optimal number, (iii) storage systems and (iv) optimal management strategies. All of them have to be considered simultaneously to develop the optimal solution aimed at either reducing the dependence from fossil fuels or granting the supply of energy. In this paper, a methodology based on the mixed integer linear programming (MILP) is presented and adopted to meet the electric demand of a mountain lodge located in a remote area in South-Tyrol (Italy). The methodology has been developed implementing an algorithm through the Matlab (c) software. The algorithm is capable of evaluating the optimal size of a hybrid off-grid Solar-Wind system with battery storage in order to replace an Internal Combustion Engine (ICE) fueled by diesel. (C) 2019 Published by Elsevier Ltd.

关键词： Hybrid off-grid energy system mixed integer linear programming Matlab (c) Optimization algorithm Renewable energy

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Predictive Control Based Energy Management of a Residential Hybrid AC-DC Nanogrid

Predictive Control Based Energy Management of a Residential ...

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Electrical Engineering and Control Technologies (CEECT), Conference on

作者： Sergio Bruno Giovanni Giannoccaro Muhammad Muzammal Islam Cosimo Iurlaro Massimo La Scala Marco Menga Carmine Rodio DEI - Politecnico di Bari Bari Italy

ISBN: (纸本)9781665499002

The depletion of conventional energy sources and the spread of renewable distributed ones are rapidly drawing attentions towards new smart systems and solutions for energy efficiency and saving energy cost in residential sector. In order to meet these needs, a model predictive control based energy management algorithm has been proposed and applied to a residential hybrid AC-DC nanogrid in this paper. Residential hybrid AC-DC nanogrid is characterized by an innovative conductive flat tape, while a mixed-integer linear programming approach has been considered in the design of proposed algorithm. Furthermore, the simulation tests have been performed to check the effectiveness of proposed algorithm for residential hybrid AC-DC nanogrid by considering various kinds AC and DC loads.

关键词： Renewable energy sources Costs Switches Predictive models Prediction algorithms Museums mixed integer linear programming

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Disjoint-Path Segment Routing: Network Reliability Perspective

Disjoint-Path Segment Routing: Network Reliability Perspecti...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Yaser Al Mtawa Anwar Haque Greg Sidebottom The University of Winnipeg Winnipeg MB Canada Western University London ON Canada Juniper Networks Kanata ON Canada

Achieving five-nines reliability in communication networks is of paramount importance to network operators. Adopting multipath Segment Routing (SR) in networking offers several key advantages in load balancing, optimal link utilization, reducing congestion, and enhancing reliability. Equal-cost multipath (ECMP) routing falls short to effectively utilize a multipath approach because ECMP may still forward traffic through the specific paths resulting in decreased network reliability. In this paper, we propose a novel two-phase Disjoint-path SR-based reliability framework to enhance the current network reliability. This framework drives network traffic flows through disjoint paths, resulting in effective load balancing and improving network reliability to meet stringent requirements of various services. Our proposed framework includes the following: a) Phase I: Optimization phase uses mixed-integer linear programming (MILP) formulation to find the optimal multipath that minimizes the maximum utilization of a given network while transmitting traffic between two terminal nodes, and b) Phase II: This phase splits the multipath of phase I into two disjoint multipaths, namely working and backup. The proposed framework is implemented, and its validation and effectiveness are demonstrated through simulation results.

关键词： Wireless communication Simulation Telecommunication traffic Routing Load management Reliability engineering mixed integer linear programming

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A Backbone Grid Planning Method against Disasters in Transmission Grid

A Backbone Grid Planning Method against Disasters in Transmi...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Yuchen Tang Yi Lin Yishan Shi Ruipeng Guo Wei Wu Department of Power Grid Planning State Grid Fujian Economic Research Institute Fuzhou China Zhejiang University Hangzhou China

The frequent occurrence of natural disasters in recent years has posed a great threat to the power system, especially to the transmission grid. The backbone power grid consists of transmission lines with higher design standard, and is assumed to be able to stand against natural disasters. A backbone grid planning model against disasters is proposed to deal with this problem. This model aims to minimize the cost of differentiated strengthened lines and ensure critical loads supply. Moreover, the backbone grid should meet both the constraints of normal operating state and the secure operating constraints under disasters. Meanwhile, the model adopts "virtual flow" to ensure the connectivity of the backbone grid. The method of inequality constraint separation is used to convert the model into a 0-1 mixed integer linear programming model. The validity of the proposed model and algorithm is verified through simulation tests in IEEE 118 bus system.

关键词： Technological innovation Power transmission lines Costs Design standards Power grids Planning mixed integer linear programming

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