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A dynamic optimization method for power distribution network operation with high ratio photovoltaics

IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第21期16卷 4417-4432页

作者： Liu, Keyan Zhan, Huiyu Wei, Yuhan Kang, Tianyuan China Elect Power Res Inst 15 Xiaoying E Rd Beijing 100192 Peoples R China

With the high ratio distributed photovoltaics (PVs) penetration to the distribution network (DN), the bearing capacity, integration capacity, consuming capacity and controlling capacity of DN for PVs are all facing great challenges. Enhancing the flexible regulation and operation optimization capabilities of DN have become key factors to satisfy these new challenges. For the operation optimization problem of high ratio PVs penetration, a novel quasi-equal curtailment ratio (QCR) constraint of PVs is proposed in the dynamic optimization method for DN operation. Firstly, the uncertainty of high ratio PVs and their active power deviations are discussed. And a dynamic optimization framework of PVs based on optimal power flow (OPF) has been proposed. Secondly, the optimization model for high ratio PVs is formulated with multi-objective of maximum PVs output, minimum voltage deviation and minimum line loss, under the equality and inequality constraints of power flow, PVs power generation limit, QCR of PVs and so on. Then the multi-objective optimization problem (MOP) is transformed into a single-objective optimization problem (SOP) by weighting coefficients, and solved by sequential quadratic programming (SQP) with trust-region (TR) searching algorithm. Finally, the proposed method is tested, verified and compared with the primal dual interior point (PDIP) and traditional SQP algorithms in IEEE 33-bus, PG&E 69-bus, 292-bus and 1180-bus test cases. The experimental results show the rapidity and robustness of the proposed method.

关键词： search problems PV power generation limit operation optimization problem optimal power flow SQP algorithm high ratio distributed photovoltaic penetration quadratic programming QCR multiobjective optimization problem single-objective optimization problem dynamic optimization framework minimum line loss primal dual interior point dynamic optimization method active power deviation bearing capacity trust-region searching algorithm controlling capacity photovoltaic power systems high ratio PVs penetration power generation economics IEEE 292-bus test case power distribution network operation IEEE 33-bus test case minimum voltage deviation distributed power generation PG&E 69-bus test case IEEE 1180-bus test case novel quasiequal curtailment ratio constraint optimization model sequential quadratic programming integration capacity consuming capacity load flow demand side management flexible regulation DN operation power distribution economics PDIP

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Resource allocation for multi-IRS-aided D2D communication underlying cellular networks

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IET COMMUNICATIONS 2022年第13期16卷 1628-1641页

作者： Forouzanmehr, Maliheh Akhlaghi, Soroush Khalili, Ata Shahed Univ Elect Engn Dept Tehran Iran Friedrich Alexander Univ Inst Digital Commun Erlangen Germany

This paper investigates the resource allocation design in device-to-device (D2D) communication underlying cellular networks, which is assisted by multiple intelligent reflecting surfaces (IRSs) deployed at the cell boundary to enhance desired signals and mitigate interference between D2D pairs and CUs. In this regard, a multi-objective optimization problem (MOOP) framework is formulated to jointly maximize the downlink sum-rate of the D2D pairs and cellular users (CUs). To doing so, the MOOP is first converted into a single-objective optimization problem (SOOP) by invoking the weighted sum method. Next, to facilitate the high-coupled non-convex SOOP formulation, it is decomposed into two sub-problems through the alternative optimization method. For the first sub-problem, the inner approximation and successive convex approximations are adopted for jointly allocating D2D transmission power, subcarrier assignment, and transmit beamforming matrix at the BS. For the second sub-problem, an iterative algorithm based on the penalty-based method as well as Fenchel's duality approach is adopted to design the passive beamforming matrices at IRSs, which is guaranteed to rapidly converge to a stationary point. Simulation results reveal that a non-trivial trade-off between the total throughput of CUs and D2D pairs exist. Furthermore, the proposed framework significantly outperforms existing works addressed in the literature.

关键词： weighted sum method multiIRS-aided D2D communication D2D transmission power cell boundary resource allocation cellular users multiple intelligent reflecting surfaces optimisation D2D pairs single-objective optimization problem alternative optimization method device-to-device communication array signal processing penalty-based method multiobjective optimization problem framework cellular networks approximation theory cellular radio downlink sum-rate inner approximation radiofrequency interference iterative methods nonconvex SOOP formulation resource allocation design IRSs MOOP successive convex approximations transmit beamforming matrix mobile radio

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3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with Multi-objective and Multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy

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Guidance, Navigation and Control 2024年第1期4卷 149-175页

作者： Pengyang Xie Ben Ma Bingbing Wang Jian Chen Gang Xiao College of Engineering China Agricultural UniversityBeijing 100083P.R.China State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310027P.R.China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhou 310058P.R.China Key Laboratory of Smart Agricultural Technology(Yangtze River Delta) Ministry of Agriculture and Rural AffairsNanjing 210044P.R.China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural AffairsGuangzhou 510642P.R.China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191P.R.China

In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous *** into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow efficiency,and minimizing regional risk ***,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex *** simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous *** results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.

关键词： Solar powered UAV multi-objective optimization problem single-objective optimization problem multi-constraint sparrow search algorithm Levy flight strategy

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To Invest or Not to Invest: The Exploration of Preferred Investment Strategies in Corporate Renewable Power Purchase Agreements

To Invest or Not to Invest: The Exploration of Preferred Inv...

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作者： Li, Daiwei PennState University Libraries

学位级别：Master of Science

The modern society has seen a continuously growing electricity consumption and its associated environmental consequences. With recent technology advancements, renewable energy has been considered by many as a source of electricity that is both economically feasible and environmentally friendly. The investment of renewable energy projects can be intriguing, however. This research first developed a theoretical model using Multi-objective optimization problem to determine the preferred investment strategies that considers both the economic and environmental benefit of a special kind of investment in renewable energy projects – Corporate Renewable Power Purchase Agreement (PPA). The proposed methods were implemented on the case study of The Pennsylvania State University in central Pennsylvania, United States. The general version of the Multi-objective optimization problem required making significant assumptions that reduced the computation complexity. The study explored the uncertainty in future Wholesale Electricity Prices, which was assumed to be the source of electricity for the investors of these renewable energy projects had there been no investments made. The use of Binomial Lattice Pricing Model, Monte Carlo Simulation, and Unit Commitment produced the feasible solutions of the Multi-objective optimization problem in which the corresponded Pareto Set was identified. The simplified version of the proposed Multi-objective optimization problem was reduced into several single-objective optimization problems of the economic benefits of PPA investments, in which they also represent some Real Option Valuation problems under specific conditions. While making other assumptions to maintain the tractability of these problems, the optimal solutions of the single-objective optimization problem and the Value of Options were identified. One of these single-objective optimization problem monetized the environmental benefits of PPA investments using Social Cost of Carbon publishe

关键词： Multi-objective optimization problem single-objective optimization problem Real Option Valuation Power Purchase Agreement Greenhouse Gas Emission Wholesale Electricity Price Binomial Lattice Pricing Method Unit Commitment Monte Carlo Simulation single-objective optimization problem Real Option Valuation Power Purchase Agreement Greenhouse Gas Emission Wholesale Electricity Price Binomial Lattice Pricing Method Unit Commitment Monte Carlo Simulation

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Analysis and prediction model based on ARMI-GARCH and single objective optimization 23

Analysis and prediction model based on ARMI-GARCH and single...

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Proceedings of the 2023 6th International Conference on E-Business, Information Management and Computer Science

作者： Yan Xulong Zuo Cheng Yang Junjun Li Zhipeng Zhu Derong Yan Junjie Tongda College of Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9798400709333

This paper mainly conducts correlation analysis on the relationship between the sales volume, cost and pricing of vegetable commodities in fresh supermarket. Based on the sales and wholesale situation of each vegetable category, the distribution law of the sales volume of each vegetable category and single item is obtained by using the analysis of the breakdown table and correlation analysis model. The replenishment quantity and pricing strategy of vegetable commodities were obtained by using partial least squares fitting, time series and single objective optimization mode.

关键词： ARIMA-GARCH Contingency table analysis Genetic Algorithm Pearson Chi-square test single-objective optimization problem

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Diversity Improvement in Decomposition-Based Multi-objective Evolutionary Algorithm for Many-objective optimization problems

Diversity Improvement in Decomposition-Based Multi-Objective...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Zhenan He Gary G. Yen School of Electrical and Computer Engineering Oklahoma State University

ISBN: (纸本)9781479938414

Decomposition-Based Multi-objective Evolutionary Algorithms (DBMOEA), such as Multiple single objective Pareto Sampling (MSOPS) and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), have been successfully applied in finding Pareto-optimal fronts in Multiobjective optimization problems (MOPs), two or three-objective in general. DBMOEA decomposes one MOP into multiple single-objective optimization problems (SOPs) where the convergence of approximated front is facilitated by finding the optimal solution of each SOP and its diversity is preserved by a group of well distributed SOPs. However, when solving problems with many objectives, one single solution can be the optimal solution of multiple SOPs which inadvertently leads to a severe loss of population diversity. In this paper, we propose a new diversity improvement method incorporated into a modified DBMOEA to directly handle this challenge. The design includes two steps. First, a few number of weight vectors guide the whole population towards a small number of solutions nearby the true Pareto front. Afterwards, initialize a subpopulation around each solution and diversify them toward well distribution. As a case study, a new algorithm based on this design is compared with three state-of-the-art DBMOEAs, MOEA/D, MSOPS, and MO-NSGA-II. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the chosen competitors for solving many-objective optimization problems.

关键词： Pareto optimisation evolutionary computation sampling methods DBMOEA MO-NSGA-II algorithm MOEA/D algorithm MSOPS algorithm Pareto-optimal front decomposition-based multiobjective evolutionary algorithm diversity improvement method many-objective optimization problems multiobjective evolutionary algorithm based on decomposition multiple single objective Pareto sampling algorithm single-objective optimization problem Diversity methods Evolutionary computation Measurement optimization Sociology Statistics Vectors

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