检索结果-内蒙古大学图书馆

A mixed-integer convex programming Algorithm for Security-Constrained Unit Commitment of Power System with 110-kV Network and Pumped-Storage Hydro Units

引用

ENERGIES 2019年第19期12卷

作者： Lin, Shunjiang Fan, Guansheng Lu, Yuan Liu, Mingbo Lu, Yi Li, Qifeng South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Guangdong Peoples R China Shenzhen Power Supply Bur Power Dispatching Control Ctr Shenzhen 518001 Peoples R China Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

The secure operation of 110-kV networks should be considered in the optimal generation dispatch of regional power grids in large central cities. However, since 110-kV lines do not satisfy the premise of R << X in the direct current power flow (DCPF) model, the DCPF, which is mostly applied in the security-constrained unit commitment (SCUC) problem of high-voltage power grids, is no longer suitable for describing the active power flow of regional power grids in large central cities. Hence, the quadratic active power flow (QAPF) model considering the resistance of lines is proposed to describe the network security constraints, and an SCUC model for power system with 110-kV network and pumped-storage hydro (PSH) units is established. The analytical expressions of the spinning reserve (SR) capacity of PSH units are given considering different operational modes, and the SR capacity of PSH units is included in the constraint of the SR capacity requirement of the system. The QAPF is a set of quadratic equality constraints, making the SCUC model a mixed-integer nonlinear non-convex programming (MINNP) model. To reduce the computational complexity of solving the model when applied in actual large-scale regional networks, the QAPF model is relaxed by its convex hull, and the SCUC model is transformed into a mixed-integer convex programming (MICP) model, which can be solved to obtain the global optimal solution efficiently and reliably by the mature commercial solver GUROBI (24.3.3, GAMS Development Corporation, Guangzhou, China). Test results on the IEEE-9 bus system, the PEGASE 89 bus system and the Shenzhen city power grid including the 110-kV network demonstrate that the relaxed QAPF model has good calculation accuracy and efficiency, and it is suitable for solving the SCUC problem in large-scale regional networks.

关键词： security-constrained unit commitment quadratic active power flow mixed-integer convex programming convex hull relaxation pumped storage hydro spinning reserve

来源：评论

学校读者我要写书评

暂无评论

Robust energy management inactive distribution networks using mixed-integer convex optimization

引用

ELECTRIC POWER SYSTEMS RESEARCH 2025年 241卷

作者： Useche-Arteaga, Mario Gil-Gonzalez, Walter Gomis-Bellmunt, Oriol Cheah-Mane, Marc Lacerda, Vinicius Univ Politecn Cataluna Ctr Innovacio Tecnol Convertidors Estat & Accionam Dept Engn Elect Barcelona Spain Univ Tecnol Pereira Fac Engn Dept Elect Engn Pereira 660003 Colombia

Active distribution systems represent a significant evolution in power distribution infrastructure, characterized by their capability to integrate and manage a diverse array of distributed energy resources and advanced reactive power compensation technologies. This paper proposes a robust mixed-integer second-order cone model for the energy management problem in these active distribution systems. The proposed strategy takes advantage of various sources for reactive power management inactive distribution systems, including capacitor banks, photovoltaic systems, wind generators, and modern devices for continuous reactive power compensation, such as thyristor-switched capacitors and distribution static synchronous compensators. The objective of the proposed energy management approach is to minimize the total network generation costs through dynamic active and reactive power dispatch. This approach leverages the significant capacity of active distribution networks to regulate reactive power and efficiently coordinates the active power generation of distributed energy resources. The nonlinear exact model of the energy management problem is reformulated using a mixed-integer second-order cone relaxation, addressed through the branch-and-cut algorithm combined with the interior-point method via the YALMIP toolbox in MATLAB. The paper also addresses uncertainties in modern power systems by applying robust programming to the mixed-integer second-order cone model with YALMIP, accounting for unpredictable variations in power demand, and wind and solar energy generation, ensuring optimal scheduling for both reactive and active power dispatch in worst-case scenarios. The performance of the proposed methodology was double-checked using MATPOWER and SDP programming, demonstrating high accuracy with a maximum error of approximately 0.002%. The results show a reduction inactive energy losses by more than 35% and a 2% reduction in generation costs using the proposed EMP approach for

关键词： Active distribution systems Distributed energy resources Energy management problem mixed-integer convex programming Renewable energy integration Robust optimization Second-order cone programming Uncertainties

来源：评论

学校读者我要写书评

暂无评论

Tractable Data-Driven Model Predictive Control Using One-Step Neural Networks Predictors

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 6740-6751页

作者： Menegatti, Danilo Giuseppi, Alessandro Pietrabissa, Antonio Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Rubert I-00185 Rome Italy

Model Predictive Control (MPC) is a popular control strategy that relies on the availability of a prediction model to estimate future system trajectories over a finite time horizon. Recently, researchers have introduced Neural Networks (NNs) into the MPC framework for the development of data-driven prediction models. In MPC, the control actions are computed by solving iteratively, at each time-step, an optimization problem subject to state and input constraints. Finding the optimal solution to such a problem is a crucial challenge in the data-driven setting, due to the complexity and black-box nature of data-driven models such as NNs. This paper addresses this challenge by proposing a hierarchical deep NN formed by a set of cascading one-step NN predictors whose combination constitutes an interpretable prediction model over the entire prediction horizon. Thanks to the proposed NN architecture, it is shown that the resulting optimal control problem is tractable, as it can be solved by employing efficient iterative algorithms, and interpretable, so that input and state constraints can be enforced seamlessly. The effectiveness of the proposed method is validated through numerical simulations.

关键词： Artificial neural networks Predictive models Optimization Predictive control Computational modeling Heuristic algorithms Computer architecture Model predictive control deep neural networks mixed-integer convex programming data-driven control

来源：评论

学校读者我要写书评

暂无评论

SHORTEST PATHS IN GRAPHS OF convex SETS

引用

SIAM JOURNAL ON OPTIMIZATION 2024年第1期34卷 507-532页

作者： Marcucci, Tobia Umenberger, Jack Parrilo, Pablo Tedrake, Russ MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

Given a graph, the shortest -path problem requires finding a sequence of edges with minimum cumulative length that connects a source vertex to a target vertex. We consider a variant of this classical problem in which the position of each vertex in the graph is a continuous decision variable constrained in a convex set, and the length of an edge is a convex function of the position of its endpoints. Problems of this form arise naturally in many areas, from motion planning of autonomous vehicles to optimal control of hybrid systems. The price for such a wide applicability is the complexity of this problem, which is easily seen to be NP -hard. Our main contribution is a strong and lightweight mixed -integer convex formulation based on perspective operators, that makes it possible to efficiently find globally optimal paths in large graphs and in high -dimensional spaces.

关键词： shortest-path problem graph problems with neighborhoods mixed-integer convex programming perspective formulation optimal control

来源：评论

学校读者我要写书评

暂无评论

Optimal Reconfiguration for Active Distribution Networks Incorporating a Phase Demand Balancing Model

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第5期39卷 6183-6195页

作者： Fu, Long Wang, Wei Dong, Zhao Yang Li, Yaran NARI Grp Corp StateGrid Elect Power Res Inst Nanjing 211106 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore State Grid Jiangsu Elect Power Res Inst Nanjing 211103 Peoples R China

Optimally reconfiguring an active distribution network (ADN) during power outages has been regarded as a reasonable approach to facilitate system secure operation and reliability. Nevertheless, most existing studies for the reconfiguration virtually focus on taking actions from the generation- and network-side, in which the potential achievement from the demand-side is underestimated. Moreover, the phase-unbalance and voltage violation in ADNs should be restricted to avoid extreme conditions of distributed generators (DGs) that jeopardize system reliability. To bridge the gap, a new approach to reconfigure ADNs under multiple faults is proposed in this paper, incorporating a phase demand balancing (PDB) model to improve dispatch performance. The model regulates asymmetrical loads to mitigate the phase-unbalance issue from the demand-side, co-optimized with step voltage regulators (SVRs) and DG dispatching to enhance reliability and flexibility in reconfiguring ADNs. The derived optimization is a challenging mixed-integer non-convex programming (MINCP), which is reformulated as an efficiently solvable mixed-integer second-order cone programming (MISOCP) via exact equivalence and accurate approximation techniques. Case studies based on modified IEEE benchmark systems validate the effectiveness and advantages of the proposed method.

关键词： Switches Reliability Voltage control Power system reliability programming profession Load modeling Load flow Active distribution network reconfiguration demand-side management phase-unbalance voltage regulation mixed-integer convex programming electric vehicle

来源：评论

学校读者我要写书评

暂无评论

CoCo: Online mixed-integer Control Via Supervised Learning

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 1447-1454页

作者： Cauligi, Abhishek Culbertson, Preston Schmerling, Edward Schwager, Mac Stellato, Bartolomeo Pavone, Marco Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA Stanford Univ Dept Mech Engn Stanford CA 94305 USA Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

Many robotics problems, from robot motion planning to object manipulation, can be modeled as mixed-integer convex program (MICPs). However, state-of-the-art algorithms are still unable to solve MICPs for control problems quickly enough for online use and existing heuristics can typically only find suboptimal solutions that might degrade robot performance. In this work, we turn to data-driven methods and present the Combinatorial Offline, convex Online (CoCo) algorithm for quickly finding high quality solutions for MICPs. CoCo consists of a two-stage approach. In the offline phase, we train a neural network classifier that maps the problem parameters to a logical strategy, which we define as the discrete arguments and relaxed big-M constraints associated with the optimal solution for that problem. Online, the classifier is applied to select a candidate logical strategy given new problem parameters;applying this logical strategy allows us to solve the original MICP as a convex optimization problem. We show through numerical experiments how CoCo finds near optimal solutions to MICPs arising in robot planning and control with 1 to 2 orders of magnitude solution speedup compared to other data-driven approaches and solvers.

关键词： Optimization and optimal control data-driven optimization mixed-integer convex programming

来源：评论

学校读者我要写书评

暂无评论

PRISM: Recurrent Neural Networks and Presolve Methods for Fast mixed-integer Optimal Control 4

PRISM: Recurrent Neural Networks and Presolve Methods for Fa...

引用

4th Annual Conference on Learning for Dynamics and Control (L4DC)

作者： Cauligi, Abhishek Chakrabarty, Ankush Di Cairano, Stefano Quirynen, Rien Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA

While mixed-integer convex programs (MICPs) arise frequently in mixed-integer optimal control problems (MIOCPs), current state-of-the-art MICP solvers are often too slow for real-time applications, limiting the practicality of MICP-based controller design. Although supervised learning has been proposed to hasten the solution of MICPs via convex approximations, they are not designed to scale well to problems with >100 decision variables. In this paper, we present PRISM: Presolve and Recurrent network-based mixed-integer Solution Method, to leverage deep recurrent neural network (RNN) architectures such as long short-term memory (LSTMs) networks, in conjunction with numerical optimization tools to enable scalable acceleration of MICPs arising in MIOCPs. Our key insight is to learn the underlying temporal structure of MIOCPs and to combine this with presolve routines employed in MICP solvers. We demonstrate how PRISM can lead to significant performance improvements, compared to branch-and-bound (B&B) methods and to existing supervised learning techniques, for stabilizing a cart-pole with contact dynamics, and a motion planning problem under obstacle avoidance constraints.

关键词： mixed-integer convex programming optimal control deep learning learning for optimization recurrent architectures LSTM contact dynamics motion planning MPC

来源：评论

学校读者我要写书评

暂无评论

mixed-integer convex Nonlinear Optimization with Gradient-Boosted Trees Embedded

引用

INFORMS JOURNAL ON COMPUTING 2021年第3期33卷 1103-1119页

作者： Mistry, Miten Letsios, Dimitrios Krennrich, Gerhard Lee, Robert M. Misener, Ruth Imperial Coll London Dept Comp South Kensington SW7 2AZ England BASF SE Ludwigshafen Germany

Decision trees usefully represent sparse, high-dimensional, and noisy data. Having learned a function from these data, we may want to thereafter integrate the function into a larger decision-making problem, for example, for picking the best chemical process catalyst. We study a large-scale, industrially relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk. This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pretrained regression tree models. Decision makers may wish to optimize discrete models to repurpose legacy predictive models or they may wish to optimize a discrete model that accurately represents a data set. We develop several heuristic methods to find feasible solutions and an exact branch-and-bound algorithm leveraging structural properties of the gradient-boosted trees and penalty functions. We computationally test our methods on a concrete mixture design instance and a chemical catalysis industrial instance.

关键词： gradient-boosted trees branch-and-bound mixed-integer convex programming decomposition catalysis

来源：评论

学校读者我要写书评

暂无评论

Optimal Restoration of an Unbalanced Distribution System Into Multiple Microgrids Considering Three-Phase Demand-Side Management

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第2期36卷 1350-1361页

作者： Fu, Long Liu, Bin Meng, Ke Dong, Zhao Yang Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Sectionalizing a distribution system with distributed energy resources (DERs) into multiple microgrids in the case of power outages has been recognized as a promising solution to restore critical loads. Nevertheless, most existing literatures focus on restoring loads by optimally dispatching distributed generators (DGs), where the controllability on the demand side is usually neglected. Moreover, unbalances in the restored microgrids should be limited in specified levels to prevent DGs from tripping to enhance the operational reliability. To address these issues, this paper proposes a novel method to restore a distribution system into multiple microgrids whilst taking the three-phase demand-side management (T-DSM) into account. The T-DSM exploits optimal phase positions of switchable loads to alleviate the unbalance issue from the demand side, which can be coordinated with network partition and DG dispatching to improve the controllability of microgrids during the restoration process. The problem is formulated as a mixed-integer second-order cone programming (MISOCP) which enables applying the off-the-shelve solvers. To improve computational performance, a multi-timescale strategy is developed to enhance its practicality. Simulation results based on the IEEE 34- and 123-bus feeders demonstrate the effectiveness and efficiency of the proposed method.

关键词： Microgrids Switches Power system reliability Dispatching Reliability Demand-side management Controllability Demand-side management distribution system restoration microgrid mixed-integer convex programming optimal power flow

来源：评论

学校读者我要写书评

暂无评论

Dual-DNN Assisted Optimization for Efficient Resource Scheduling in NOMA-Enabled Satellite Systems

Dual-DNN Assisted Optimization for Efficient Resource Schedu...

引用

IEEE Global Communications Conference (GLOBECOM)

作者： Wang, Anyue Lei, Lei Lagunas, Eva Chatzinotas, Symeon Ottersten, Bjorn Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Luxembourg Luxembourg

ISBN: (纸本)9781728181042

In this paper, we apply non-orthogonal multiple access (NOMA) in satellite systems to assist data transmission for services with latency constraints. We investigate a problem to minimize the transmission time by jointly optimizing power allocation and terminal-timeslot assignment for accomplishing a transmission task in NOMA-enabled satellite systems. The problem appears non-linear/non-convex with integer variables and can be equivalently reformulated in the format of mixedinteger convex programming (MICP). Conventional iterative methods may apply but at the expenses of high computational complexity in approaching the optimum or near-optimum. We propose a combined learning and optimization scheme to tackle the problem, where the primal MICP is decomposed into two learning-suited classification tasks and a power allocation problem. In the proposed scheme, the first learning task is to predict the integer variables while the second task is to guarantee the feasibility of the solutions. Numerical results show that the proposed algorithm outperforms benchmarks in terms of average computational time, transmission time performance, and feasibility guarantee.

关键词： Satellite communications NOMA deep learning transmission time minimization mixed-integer convex programming

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：