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American Control Conference (ACC)

作者： Ingalalli, Aravind Robinson, Jonathan Nandola, Naresh N. Siemens Technol Princeton NJ 08540 USA Siemens Technol Munich Germany

ISBN: (纸本)9798350328066

Lifetime of the photovoltaic (PV) inverters is influenced by its power profile. The reliability of such PV inverters is affected by the thermal fatigue cycles witnessed by the underlying components. However, there is a trade-off between the inverter efficiency and the fatigue witnessed by its components. With a systematic formulation of this trade-off, a real-time nonlinear optimization problem is formulated to generate the appropriate reactive power set-points to the PV inverter controller. The proposed approach improves the lifetime of the inverters while keeping its efficiency above desired threshold value. The time domain loss and damage models that uses PV power profile as an input are critical to the proposed optimization framework. The proposed framework provides an option to the customer to operate the PV inverter with an objective of lifetime improvement under the acceptable losses by flattening the component thermal fatigue cycles. The framework is evaluated using the PV power profile of a 10 kVA PV inverter with various simulated case studies.

关键词： nonlinear programming

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Time Series Based Decision Making Problem of Vegetable Replenishment and Pricing in Superstores 3

Time Series Based Decision Making Problem of Vegetable Reple...

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3rd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Xiao, Kun Luo, Haoyuan Song, Kangfei College of Software Xinjiang University Urumqi China

ISBN: (纸本)9798350380989

With the progress of living standards, people are more and more thirsty for green and healthy life, for the purchase of vegetables also has a higher pursuit of hope to buy fresh and cheap vegetables, and due to the short freshness period of vegetables, seasonal characteristics, super sales space is very limited. In this paper, through the quantification of the previous sales data, we explored the relationship between vegetable categories and analyzed the vegetable pricing strategy. First of all, the sales volume of vegetable categories is analyzed from the perspective of year, season and month, and the correlation is verified by Spearman's test, and the sales volume of individual products is divided according to the month, and then clustering analysis is carried out by using K-Means cluster analysis based on PSO particle swarm algorithm. Then, the final replenishment quantity is obtained by time series forecasting using the combined time series model based on LSTM-XGBoost, the average wholesale price is obtained by using the ARIMA time series forecasting model, a nonlinear planning model is built out, and finally the single-item sales strategy is derived by a heuristic algorithm. Finally, taking whether to stock the vegetable on the shelves on a single day as the decision variable, this paper maximizes revenue as the objective function, defines the sales space and the loss rate threshold as the constraints, establishes a 0-1 planning model, and solves to obtain the single-day sales strategy by using the heuristic algorithm. The vegetable replenishment and pricing model established in this paper can be closely linked with the actual situation, can be combined with the actual situation to solve the problem, the model has good generality and popularization. © 2024 IEEE.

关键词： cluster analysis correlation test LSTM-XGBoost nonlinear programming time series forecasting

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A Cooperative Edge Offloading Strategy for New Power System Services 13th

A Cooperative Edge Offloading Strategy for New Power System ...

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13th International Conference on Computer Engineering and Networks, CENet 2023

作者： Shen, Fang Zhang, Jiale Ji, Yutong Duan, Chengyu Wang, Zhen State Grid Jibei Electric Power Co. Ltd. Information and Communication Branch Beijing China

ISBN: (纸本)9789819992386

Multi-access edge computing (MEC) is a critical technology for 5G networks. Computing tasks can be processed in edge servers instead of cloud servers by deploying computing servers at the edge of integrated power communication networks. With the expanding scale of the power grid, the delay-sensitive and computation-intensive services are growing rapidly, and MEC-enabled base stations can meet the demand for low-delay and highly reliable power services. Because the capacity of base stations is limited, most existing schemes use queuing or retransmission to reduce the pressure of base stations. However, the quality of service (QoS) will deteriorate due to queuing and retransmission. In this paper, we study the cooperation between MEC base stations to improve the computing service capability by offloading computing tasks to other MEC base stations. An optimization strategy based on the Q-learning algorithm is proposed to reduce the total delay of power user equipment (UE) within the coverage of the base station. We consider the limitations of terminal battery capacity and the computing capacity of base stations. The simulation results show that the strategy effectively reduces the system delay and achieves the load balancing of the base station. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： nonlinear programming

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Meta-heuristic Approach for Flood Control in Reservoir Operation

Meta-heuristic Approach for Flood Control in Reservoir Opera...

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International Conference on Innovation in Smart and Sustainable Infrastructure, ISSI 2022

作者： Chauhan, Priya Narulkar, Sandeep M. Shri G. S. Institute Of Technology And Science Indore India

ISBN: (纸本)9789819935567

Besides irrigation and power generation, reservoirs in India also play a vital role in protecting the country from floods. During flood, reservoirs store and release the excess water according to its capacity and future requirement. It is imperative that the reservoir has to be operated effectively to conserve water so that competing goals can be accomplished. This requires an extensive investigation and the development of new system analysis approaches using meta-heuristic techniques of soft computing. The Omkareshwar Sagar Project (OSP) Reservoir in India has been designed to satisfy annual demands, protect against flooding, and produce maximum power generation, which is used for analysis in the current study. The present study incorporates one conventional optimization method the nonlinear programming (NLP), a semi- conventional method the genetic algorithm (GA) and a meta-heuristic technique the teaching–learning-based optimization (TLBO) to solve the problem of optimum operation in a flood scenario. A flood-release protocol has to be developed keeping the objective of maximum power output and reach the specified storage target at the end of the operation. Present study has been a successful attempt in getting the desired results, and the adoptability of the TLBO for complex problem is also proven. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： nonlinear programming

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Optimal Reconfiguration of Electric Power Distribution Networks 1

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13th International Conference on Information Systems and Advanced Technologies, ICISAT 2023

作者： Segovia, Edwin Freire, Luigi O. Quinatoa, Carlos Castillo, Jessica N. Universidad Técnica de Cotopaxi Latacunga Ecuador

ISBN: (数字)9783031605949

ISBN: (纸本)9783031605932

The study proposes a methodology to address the reconfiguration of electrical distribution networks, formulating it as a mixed integer nonlinear optimization model. The complexity arises from disconnectors, with binary behavior, and nonlinear power flow equations. The lack of efficient and reliable programs to solve these challenges is highlighted. A solvable approach is proposed as a mixed integer convex model, implemented in Julia with JuMP for optimization and the solvers Ipopt and CPLEX. It is compared with the standard operation of the system to validate the proposed formulations, including relaxed models of convex nature. The implementation is validated on two case studies, one created for the research and the other using a 118-bar IEEE radial system. The results show significant advantages in computational speed and response quality, improving the operating cost of the distribution system between 1% and 12%. It is important to highlight that the modification of disconnectors does not imply additional costs in the operation of the system. This work contributes with efficient convex models to solve reconfiguration problems in electrical networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： nonlinear programming

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Deep Reinforcement Learning Based Double-layer Optimization Method for Energy Management of Microgrid 5

Deep Reinforcement Learning Based Double-layer Optimization ...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Yu, Qinglei Xu, Wei Lv, Jianhu Wang, Ying Zhang, Kaifeng Southeast Univ Key Lab Measurement & Control CSE Minist Educ Nanjing Peoples R China State Grid Jiaxing Power Supply Co Jiaxing Zhejiang Peoples R China China Elect Power Res Inst Nanjing Peoples R China

ISBN: (纸本)9781665490542

Microgrid provides an effective way to integrate renewable energy into power grid. However, the uncertainty of renewable energy and load demand bring great challenges to the energy management of microgrid. Therefore, this paper proposes a double-layer optimization method based on deep reinforcement learning (DRL) to solve this problem. The upper DRL agent takes Soft actor-critic algorithm to fully explore the regulation ability of the energy storage system. The lower nonlinear programming solver optimizes the output of other controllable equipment based on the output of the upper layer, and constantly revises the network parameters of the upper layer according to the optimization results. By combining DRL with traditional nonlinear programming, the convergence speed of the algorithm can be improved and the design difficulty of the DRL reward function can be reduced. Case studies show that the double-layer collaborative optimization method can provide real-time high-quality solutions for energy management of the microgrid only based on the immediate information of the microgrid and can effectively accelerate the convergence speed of the model.

关键词： microgrid energy management deep reinforcement learning nonlinear programming

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Algorithm for Computing the Parameters of a Low-Impact Sonic Boom Demonstrator Aircraft in Accordance with Specified Technical Requirements During Preliminary Aerodynamic Design Under Epistemic Uncertainty 17

Algorithm for Computing the Parameters of a Low-Impact Sonic...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Veresnikov, Georgy Bashkirov, Igor Sobolev, Danila Sapronov, Daniil V.A. Trapeznikov Institute of Control Sciences Laboratory of Decision Support Systems Moscow Russia Central Aerohydrodynamic Institute Zhukovsky Russia

ISBN: (纸本)9798350375718

An algorithm for calculating the aerodynamic layout and power plant parameters according to specified technical requirements (TR) at the stage of preliminary aerodynamic design (PAD) has been developed. A low-impact sonic boom demonstrator aircraft X-59 was chosen as a model. We use the uncertainty theory to account for non-deterministic data. © 2024 IEEE.

关键词： nonlinear programming

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On sparse optimal regression trees

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期299卷 1045-1054页

作者： Blanquero, Rafael Carrizosa, Emilio Molero-Rio, Cristina Morales, Dolores Romero Inst Matemat Univ Sevilla IMUS Seville Spain Copenhagen Business Sch CBS Frederiksberg Denmark

In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desirable properties for the regression task, such as cost-sensitivity and fairness. Thanks to the smoothness of the predictions, we can derive local explanations on the continuous predictor variables. The computational experience reported shows the outperformance of our approach in terms of prediction accuracy against standard benchmark regression methods such as CART, OLS and LASSO. Moreover, the scalability of our approach with respect to the size of the training sample is illustrated. (c) 2021 Published by Elsevier B.V.

关键词： Machine learning Classification and regression trees Optimal regression trees Sparsity nonlinear programming

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GA based energy efficiency resource allocation in heterogeneous networks with MRC

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IET COMMUNICATIONS 2022年第4期16卷 369-377页

作者： Qi, Xiaomin Khattak, Shahid Mustafa, Syed Shahid Khan, Imdad COMSATS Univ Islamabad Dept Elect & Comp Engn Abbottabad Campus Abbottabad Pakistan

The energy efficiency (EE) resource allocation problem for downlink orthogonal frequency division multiple access (OFDMA) heterogeneous networks (HetNets) of wireless communication is investigated in this article. A coordinated multipoint (CoMP) network consisting of a microcell and multiple picocells interconnected via a backhaul network is considered. The mathematical form of EE maximization is a fractional, mixed-integer, nonlinear programming, and NP-hard to solve. Genetic algorithm (GA) based scheme is developed for solving the formulated maximization problem. For GA based scheme, a normal GA is used for resource blocks (RBs) allocation and for the power distribution process, instead of transforming the fractional objective function into a parametric subtractive form, a multi-objective optimization problem (MOOP) is formed and then non-dominated sorting genetic algorithm II (NSGA-II) is applied to tackle the MOOP. Finally, the simulation process is provided to verify the effectiveness of the proposed method and the results show that the performance of GA based scheme outperforms the benchmark scheme.

关键词： picocellular radio mathematical form downlink orthogonal frequency division multiple access heterogeneous networks genetic algorithms mixed-integer microcell fractional integer resource allocation multiobjective optimization problem telecommunication power management frequency division multiple access normal GA resource blocks allocation nonlinear programming formulated maximization problem parametric subtractive form integer programming genetic algorithm based scheme energy conservation multiple picocells wireless communication energy efficiency resource allocation problem sorting genetic algorithm II OFDM modulation coordinated multipoint network backhaul network fractional objective function Pareto optimisation GA based energy efficiency resource allocation

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nonlinear Optimal Guidance for Intercepting Stationary Targets with Impact-Time Constraints

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第9期45卷 1614-1626页

作者： Wang, Kun Chen, Zheng Wang, Han Li, Jun Shao, Xueming Zhejiang Univ Hangzhou 310027 Zhejiang Peoples R China Sch Aeronaut & Astronaut Hangzhou Peoples R China

This paper is concerned with devising nonlinear optimal guidance for intercepting a stationary target with a desired impact time. According to Pontryagin's maximum principle, some optimality conditions for the solutions of the nonlinear optimal interception problem are established;and the structure of the corresponding optimal control is presented. By employing the optimality conditions, we formulate a parameterized system so that its solution space is the same as that of the nonlinear optimal interception problem. As a consequence, a simple propagation of the parameterized system, without using any optimization method, is sufficient to generate enough sampled data for the mapping from the current state and time-to-go to the optimal guidance command. By virtue of the universal approximation theorem, a feedforward neural network, trained by the generated data, is able to represent the mapping from the current state and time-to-go to the optimal guidance command. Therefore, the trained network eventually can generate impact-time-constrained nonlinear optimal guidance within a constant time. Finally, the developed nonlinear optimal guidance is exemplified and studied through simulations, showing that the nonlinear optimal guidance law performs better than existing interception guidance laws.

关键词： Pontryagin's Maximum Principle Guidance System Feedforward Neural Network nonlinear programming Heading Angle Lyapunov Stability Theory nonlinear Kinematics Powered Descent Guidance Cartesian Coordinate System Numerical Simulation

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