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International Symposium on Circuits and Systems Nano-Bio Circuit Fabrics and Systems (ISCAS 2010)

作者： Noguchi, Hiroki Tani, Junichi Shimai, Yusuke Kawaguchi, Hiroshi Yoshimoto, Masahiko Kobe Univ Kobe Hyogo 6578501 Japan

ISBN: (纸本)9781424453092

This paper describes parallel processor architecture for a mixed integer linear programming (MILP) solver to realize motion planning and hybrid system control in robot applications. It features pipeline architecture with an MILP-specific configuration and two-port SRAM. Based on the architecture, both FPGA and VLSI implementations have been done to solve sample problems including 16 variables. The FPGA implementation can reduce the power consumption to 13 W: an 85.4% reduction compared to a 3.0-GHz processor (Pentium 4;Intel Corp.). The VLSI solver further reduces the power to 6.4 W using 0.18-mu m CMOS technology.

关键词： mixed integer linear programming problem hardware low power

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Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

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APPLIED THERMAL ENGINEERING 2024年 257卷

作者： Wu, Xialai Qin, Jiabin Chen, Junghui Wang, Yongli Huzhou Univ Coll Engn Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Chung Yuan Christian Univ Chung Li R&D Ctr Membrane Technol Taoyuan 320 Taiwan Chung Yuan Christian Univ Chung Li Dept Chem Engn Taoyuan 320 Taiwan

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input-output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

关键词： mixed integer linear programming problem Organic Rankine cycle Predictive control Recurrent neural network

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Resiliency oriented integration of DSRs in transmission networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第8期11卷 2013-2022页

作者： Soroudi, Alireza Maghouli, Pouria Keane, Andrew Univ Coll Dublin Sch Elect Elect & Commun Engn Dublin Ireland Shahed Univ Dept Elect Engn Tehran Iran

Secure and reliable operation of power system in normal and contingency conditions is of great importance for system operator. Natural disasters can seriously threaten power systems' normal operation with catastrophic consequences. While hardening approaches may be considered for resiliency improvement, an application of a new and cost-effective technology is proposed in this study. A planning procedure is proposed for integrating distributed series reactors (DSR)into transmission grids for improving the resiliency against these disasters. DSRs are able to control power flows through meshed transmission grids and thus improve the power transfer capability and the penetration level of renewable generation. The problem of integrating DSRs into transmission grids is formulated as a mixed integer linear programming problem. Different load and wind profiles and a predefined number of disaster scenarios are considered in evaluating the impacts of DSRs on system's operational costs, wind curtailment and load shedding during disasters and normal condition. The uncertainty of wind generation can affect economic viability of DSRs deployment which is handled using information gap decision theory. It is implemented on the IEEE-RTS 24-bus test system and results show the functionality of DSRs in converting the conventional transmission grid into a flexible and dispatchable asset.

关键词： transmission networks disasters power grids integer programming linear programming resiliency oriented integration DSR transmission networks power system natural disasters planning procedure distributed series reactors transmission grids power flows mixed integer linear programming problem load shedding information gap decision theory-based method IEEE-RTS 24-bus test system

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Stochastic scheduling of compressed air energy storage in DC SCUC framework for high wind penetration

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第13期13卷 2747-2760页

作者： Gupta, Pranda Prasanta Jain, Prerna Sharma, Kailash Chand Bhakar, Rohit Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Banasthali Vidyapith Sch Automat Vanasthali Rajasthan India

High intermittent wind generation necessitates integration of bulk energy storage systems (ESSs) for maintaining security and reliability in power system operation. Considering this, stochastic security constrained unit commitment (SCUC) including compressed air energy storage (CAES) as bulk ESS for high wind penetration and with wind uncertainty modelling is addressed. Network constraints for pre- and post-line contingency are modelled using DC power flow. Injection sensitivity factors (ISFs) are conventionally used in power flow equations which, however, make N - 1 network security constrained formulation huge and computationally demanding for the proposed stochastic model. Therefore, this study proposes a line outage distribution factor (LODF) to reduce the number of coefficients of post contingency DC power flow equations. This is a compact formulation with the lower computational requirement. Wind uncertainty is modelled as probable scenarios. The proposed SCUC is a complex mixed integer linear programming problem and solved using Benders decomposition technique for IEEE 30-bus and 118-bus system. Simulation results to analyse the impact of CAES, wind uncertainty and line contingency with ISF and LODF on overall operation costs, CAES scheduling, wind curtailment, locational marginal price and computational time. Results show that the proposed model is computationally efficient for system operation under high wind penetration.

关键词： power generation scheduling power system security power generation dispatch load flow compressed air energy storage stochastic processes linear programming integer programming wind power plants power system reliability high intermittent wind generation bulk energy storage systems power system operation stochastic security constrained unit commitment compressed air energy storage wind uncertainty modelling post-line contingency line outage distribution factor wind curtailment stochastic scheduling DC SCUC framework wind penetration DC power flow equations IEEE 118-bus system N-1 network security constrained formulation injection sensitivity factors ISF LODF mixed integer linear programming problem CAES scheduling Benders decomposition technique IEEE 30-bus system

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Comparison of two algorithms for solving a two-stage bilinear stochastic programming problem with quantile criterion

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2015年第6期31卷 862-874页

作者： Kibzun, Andrey Moscow Inst Aviat Technol Sch Appl Math & Phys Dept Probabil Theory Moscow 125993 Russia

The paper is devoted to solving the two-stage problem of stochastic programming with quantile criterion. It is assumed that the loss function is bilinear in random parameters and strategies, and the random vector has a normal distribution. Two algorithms are suggested to solve the problem, and they are compared. The first algorithm is based on the reduction of the original stochastic problem to a mixed integer linear programming problem. The second algorithm is based on the reduction of the problem to a sequence of convex programming problems. Performance characteristics of both the algorithms are illustrated by an example. A modification of both the algorithms is suggested to reduce the computing time. The new algorithm uses the solution obtained by the second algorithm as a starting point for the first algorithm. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： stochastic programming two-stage problem quantile function Value-at-Risk criterion confidence method mixed integer linear programming problem convex programming

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Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs

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IET RENEWABLE POWER GENERATION 2019年第7期13卷 1037-1049页

作者： Arasteh, Farzad Riahy, Gholam H. Amikabir Univ Technol Dept Elect Engn Tehran Iran

The non-preventable ever-increasing rate of wind power generation in market-based power systems faces the operators with challenging situations for making optimal decisions. So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Moreover, the high-priority of cheap wind power utilisation increases the probability of transmission lines congestion. Therefore, different solutions such as transmission switching (TS) and demand response (DR) programs have been recently introduced to manage the intermittent wind power generations. Accordingly, this study addresses the social welfare maximisation problem with coordinated control of TS and DR facilities to handle the regarding uncertainties using yet another linear matrix inequality parser (YALMIP). In fact, rapid algorithm and powerful employed solvers as well as simplicity of use, make YALMIP a practical modelling and optimisation toolbox. In this respect, the MOSEK solver is preferred by YALMIP to solve the proposed mixed integer linear programming problem. In addition, wind power uncertainty is modelled using the discrete-time Markov chain approach and optimisations are performed on the 8-bus and the large-scale IEEE 118-bus test systems. Results show that the proposed control strategy is highly capable of maximising social welfare by determining the optimal control commands in a real-time manner.

关键词： power generation economics power generation scheduling optimal control Markov processes wind power plants optimisation power generation planning power markets linear programming power generation reliability probability linear matrix inequalities integer programming demand side management simultaneous coordination transmission switching demand response programs wind power generation market-based power systems challenging situation optimal decisions applicable control strategies corresponding control facilities high-priority cheap wind power utilisation transmission lines congestion different solutions TS intermittent wind power generations social welfare maximisation problem coordinated control DR facilities linear matrix inequality parser YALMIP powerful employed solvers practical modelling optimisation toolbox mixed integer linear programming problem wind power uncertainty optimisations large-scale IEEE 118-bus test systems control strategy optimal control commands market based wind integrated power systems

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Optimal scheduling of canned food plants including simultaneous sterilization

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JOURNAL OF FOOD ENGINEERING 2009年第1期90卷 53-59页

作者： Simpson, R. Abakarov, A. Univ Tecn Federico Santa Maria Dept Proc Quim Biotecnol & Ambientales Valparaiso Chile

Optimization of thermal process scheduling in plants producing canned food is of great interest because canning is a dominant operation within the food processing sector. Batch processing has been widely practiced but scarcely analyzed in the context of plants producing canned food. The objective of this research study was to solve the problem of optimizing scheduling for the case where given amounts of different canned food products, with specific quality requirements, would be sterilized within a minimum plant operation time. The proposed solution is based on simultaneous sterilization implementation, using the revisited general method (RGM) procedure for process calculations, and developing a mixed integer linear programming (MILP) model to optimize autoclave scheduling with the purpose of minimizing plant operation time. In order to maximize the output from a specific plant processing different products and container sizes, isolethal processes were identified for various products/containers from which a common set of process conditions could be chosen for simultaneous sterilization of different product lots in the same retort. An example involving the sterilization of 16 different products was solved to illustrate the MILP model developed here. The methodology proposed here is of special relevance to small and medium size canneries, normally processing many different products at the same time. (c) 2008 Published by Elsevier Ltd.

关键词： scheduling canned food plants simultaneous sterilization mixed integer linear programming problem isolethal processes

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Robust expansion co-planning of electricity and natural gas infrastructures for multi energy-hub systems with high penetration of renewable energy sources

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IET RENEWABLE POWER GENERATION 2019年第13期13卷 2287-2297页

作者： Karamdel, Shahab Moghaddam, Mohsen Parsa Tarbiat Modares Univ Dept Elect Engn Tehran Iran

High penetration of renewable energy sources will cause crucial challenges for future energy systems. This study presents a three-level model for adaptive robust expansion co-planning of electricity and natural gas infrastructures in multi-energy-hub networks, which is robust against uncertainties of maximum production of wind generation and gas-fired power plants as well as estimated load levels. The proposed min-max-min model is formulated as a mixed integer linear programming problem. The first level minimises the investment cost of electricity and natural gas infrastructures, the worst possible case is determined through the second level, and the third level minimises the overall operation cost under that condition. To solve this model, the final minimisation problem is replaced by its Karush-Kuhn-Tucker conditions and a two-level problem is determined. Finally, by using the column and constraint generation algorithm the original problem is decomposed to master and sub-problems and the optimal solution is derived iteratively. The proposed robust expansion co-planning model is tested on modified Garver's 6-hub, modified IEEE RTS 24-hub, and modified IEEE 118-hub test systems and numerical results show its effectiveness to cope with uncertainties with regard to control conservativeness of the plan.

关键词： load flow renewable energy sources linear programming minimax techniques integer programming power generation planning natural gas technology adaptive robust expansion co-planning electricity natural gas infrastructures multienergy-hub networks wind generation min-max-min model mixed integer linear programming problem final minimisation problem two-level problem robust expansion co-planning model modified IEEE 118-hub test systems multienergy-hub systems high penetration renewable energy sources future energy systems three-level model gas-fired power plants constraint generation algorithm estimated load levels

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Design optimization of oilfield subsea infrastructures with manifold placement and pipeline layout

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COMPUTERS & CHEMICAL ENGINEERING 2018年 108卷 163-178页

作者： Rosa, Vinicius Ramos Camponogara, Eduardo Martins Ferreira Filho, Virgilio Jose Petr Brasileiro SA Rio De Janeiro Brazil Univ Fed Santa Catarina Dept Automat & Syst Engn Florianopolis SC Brazil Univ Fed Rio de Janeiro Dept Ind Engn Rio De Janeiro Brazil

This paper presents a practical and effective optimization method to design subsea production networks, which accounts for the number of manifolds and platforms, their location, well assignment to these gathering systems, and pipeline diameter. It brings a fast solution that can be easily implemented as a tool for layout design optimization and simulation-based analysis. The proposed model comprises reservoir dynamics and multiphase flow, relying on multidimensional piecewise linearization to formulate the layout design problem as a MILP. Besides design validation, reservoir simulation serves the purpose of defining boundaries for optimization variables and parameters that characterize pressure decrease, reservoir dynamics and well production over time. Pressure drop in pipelines are modeled by piecewise-linear functions that approximate multiphase flow simulators. The resulting optimization model and approximation methodology were applied to a real oilfield with the aim of assessing their effectiveness. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Subsea infrastructure Manifold placement Pipeline design Piecewise-linear approximation mixed integer linear programming problem

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Stackelberg game-based energy management for a microgrid with commercial buildings considering correlated weather uncertainties

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第11期13卷 2102-2111页

作者： Wang, Jiaying Feng, Changsen Xu, Yan Wen, Fushuan Zhang, Lijun Xu, Chengbo Salam, Abdus Zhejiang Univ Sch Elect Engn Hangzhou 310027 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore State Grid Zhejiang Econ Res Inst Hangzhou 310008 Zhejiang Peoples R China Univ Teknol Brunei Dept Elect & Elect Engn BE-1410 Bandar Seri Begawan Brunei

This paper proposes a Stackelberg game-based optimal energy management model for a microgrid with commercial buildings (CBs), which include a cluster of flexible loads, such as a heating, ventilation, and air conditioning (HVAC) system and a lighting system. In particular, the microgrid operator (MGO) determines the optimal energy management scheme while the CBs enjoy a dynamic pricing tariff to adjust their consumption patterns for cost saving. The interactions between MGO and CBs are formulated as a bi-level optimisation problem where the MGO behaves as a leader and CBs act as followers. The proposed model is transformed into a mixed integer linear programming (MILP) problem by jointly using the Karush-Kuhn-Tucker (KKT) condition and the strong duality theory. Besides, the effects of correlated solar irradiance and solar illuminance uncertainties on the load profile are taken into account through invoking the Nataf transformation-based 2M + 1 point estimate method (PEM). Finally, case studies are served for demonstrating the feasibility and efficiency of the proposed method.

关键词： game theory duality (mathematics) distributed power generation HVAC linear programming pricing tariffs energy management systems optimisation integer programming CBs dynamic pricing tariff MGO bi-level optimisation problem mixed integer linear programming problem Karush-Kuhn-Tucker condition correlated solar irradiance solar illuminance uncertainties Nataf transformation-based Stackelberg game-based energy management commercial buildings correlated weather uncertainties Stackelberg game-based optimal energy management model flexible loads air conditioning system lighting system microgrid operator optimal energy management scheme size 2 0 m

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