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Stochastic Incentive-Based Demand Response Program for Virtual Power Plant With Distributed Energy Resources

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第3期61卷 4862-4875页

作者： Harsh, Pratik Sun, Hongjian Das, Debapriya Rameshrao, Awagan Goyal Jiang, Jing Univ Durham Dept Engn Durham DH1 3LE England Indian Inst Technol Dept Elect Engn Kharagpur 721302 India Northumbria Univ Dept Math Phys & Elect Engn Newcastle NE1 8ST England

The growing integration of distributed energy resources (DERs) into the power grid necessitates an effective coordination strategy to maximize their benefits. Acting as an aggregator of DERs, a virtual power plant (VPP) facilitates this coordination, thereby amplifying their impact on the transmission level of the power grid. Further, a demand response program enhances the scheduling approach by managing the energy demands in parallel with the uncertain energy outputs of the DERs. This work presents a stochastic incentive-based demand response model for the scheduling operation of VPP comprising solar-powered generating stations, battery swapping stations, electric vehicle charging stations, and consumers with controllable loads. The work also proposes a priority mechanism to consider the individual preferences of electric vehicle users and consumers with controllable loads. The scheduling approach for the VPP is framed as a multi-objective optimization problem, normalized using the utopia-tracking method. Subsequently, the normalized optimization problem is transformed into a stochastic formulation to address uncertainties in energy demand from charging stations and controllable loads. The proposed VPP scheduling approach is addressed on a 33-node distribution system simulated using MATLAB software, which is further validated using a real-time digital simulator.

关键词： Batteries Stochastic processes Indexes Energy consumption Power grids optimization Job shop scheduling Costs Solar panels Renewable energy sources Demand response electric vehicles multi-objective optimization problem stochastic model virtual power plant (VPP)

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Parametric approach for multi-objective enhanced interval linear fractional programming problem

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ENGINEERING optimization 2024年第5期56卷 740-765页

作者： Patel, Mridul Behera, Jyotirmayee Kumar, Pankaj SRM Inst Sci & Technol Dept Math Chennai India

The design (decision) variables in the presented article of a multi-objective interval fractional optimization problem based on a linear function are assumed to take the form of a closed interval using the concept of the parametric form of an interval. The original problem is initially changed into equivalent multi-objective interval linear programming with the design variables as closed intervals. Further, it is made free from interval uncertainty by changing into a classical single-objective problem using the weighted-sum method. The solutions of the model are theoretically justified by its existence. Finally, a numerical example and a case study on the agricultural planting structure optimization problem with hypothetical data are presented to support the recommended technique for the model.

关键词： Fractional programming interval optimization multi-objective optimization problem linear programming problem interval analysis

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multi-objective Deployment optimization of UAVs for Energy-Efficient Wireless Coverage

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第6期72卷 3587-3601页

作者： Zhu, Xiumin Zhai, Linbo Li, Nianxin Li, Yumei Yang, Feng Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Recently, Unmanned Aerial Vehicles (UAVs) have attracted much attention due to their flexibility and low cost. However, there are limitations for multiple UAVs such as limited energy and collaborative coverage. To achieve a better coverage performance, each UAV needs to find the optimal position to cover many ground users while saving energy. However, there are trade-offs between coverage utility and energy consumption. In this paper, we study a multi-UAV communication scenario where multi-UAV array is deployed to provide wireless coverage for mobile ground users. Considering the number, 3D positions, and speeds of UAVs, we formulate a Coverage Utility and Energy multi-objective optimization problem (CUEMOP) to simultaneously maximize the total coverage utility and minimize the total energy consumption of UAVs. Due to the complexity and NP-hardness of the formulated CUEMOP, we propose Improved multi-objective Grey Wolf Optimizer (ImMOGWO) algorithm. In this algorithm, we design the Role Determination (RD) algorithm to cluster the ground users and prepare for initialization of UAV number and position. Hybrid solution initialization (HSI) algorithm is to initialize multi-dimensional variables and overcome algorithm inefficiency caused by random initialization. The Levy flight and Sin Cosine method based on the MOGWO algorithm (LSCMGA) is proposed to increase the diversity of solutions and ensure the convergence effect of the algorithm. Simulation results verify that proposed ImMOGWO algorithm has better performance than some other benchmark methods.

关键词： Autonomous aerial vehicles optimization Clustering algorithms Wireless communication Energy consumption Three-dimensional displays Base stations multiple UAVs multi-objective optimization problem coverage performance energy efficiency

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A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 117卷

作者： Harsh, Pratik Das, Debapriya Indian Inst Technol Elect Engn Dept Kharagpur 721302 West Bengal India

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi -objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentivebased DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33 -bus and 69 -bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

关键词： Demand response Electric vehicles Off-grid microgrid multi-objective optimization problem Reconfiguration

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Efficient solutions for vector optimization problem on an extended interval vector space and its application to portfolio optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期249卷

作者： Sahu, B. R. B. Bhurjee, A. K. Kumar, P. SRM Inst Sci & Technol Coll Engn & Technol Dept Math Kattankulathur 603203 Tamil Nadu India VIT Bhopal Univ Bhopal Indore Highway Sehore 466114 Madhya Pradesh India Natl Inst Technol Hamirpur DoMSC Hamirpur 177005 HP India

In this paper, a generalized interval vector space is investigated and defined as an ordered relation in the form of a bijective linear transformation of its onto a real vector space. The ordered relation is utilized to formulate an interval optimization problem in the same manner as a classical multi-objective programming problem. A methodology that addresses the existence of efficient solutions for the multi-objective interval optimization problem has also been discussed. Various numerical examples are described to illustrate and substantiate all developed concepts. Furthermore, the multi-objective portfolio rebalancing problem for a time horizon is designed based on the developed interval vector optimization. An algorithm using exhaustive solution technology has been proposed to achieve an efficient investment strategy. Finally, its applicability and efficacy are analyzed using Bombay Stock Exchange India data sets.

关键词： multi-objective optimization problem Interval vector space Interval analysis theory Interval optimization problem Efficient solution Portfolio selection

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Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios

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SOFT COMPUTING 2024年第6期28卷 5173-5196页

作者： Tabalvandani, Mohammad Ali Nezafat Shirvani, Mirsaeid Hosseini Motameni, Homayun Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran

The multi-cloud environment (MCE) serves users on-demand by presenting miscellaneous online web services. Each web service which is delivered by every cloud provider has its own quality of features and also own pricing scheme. In the web service composition technology, the integration of the services required by the users is done with the aim of producing the efficient solutions with the desired quality. In some businesses, continuity of activities is very important and a business that fails a lot cannot be trusted by subscribers. In these businesses, it is necessary to maximize the reliability of the system along with minimizing the overall monetary costs. To this end, two new reliability and cost models are presented. All of the network equipment, communication, and elements affecting the total cost and reliability of the system are taken into consideration in the proposed models. Then, the web service composition issue is formulated to a multi-objective optimization problem. To solve this combinatorial problem in large search space of MCE, the multi-objective particle swarm optimization algorithm is suggested to maximize reliability while minimizing the cost of services and make Pareto optimal points. The results of the evaluations show that in different scenarios, the proposed solution proves the amount of 48%, 46%, and 12% averagely improvement over other comparative MOGWO, NSGA-II, and MOEA/D approaches in terms of service failure rate, service implementation cost in cloud providers, and the execution time respectively.

关键词： Web service composition multi-cloud environment Reliability model Cloud cost model multi-objective optimization problem multi-objective particle swarm optimization algorithm

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Compact Dual-Band Dual-Sense Circularly Polarized Fragmental Patch Antenna Optimized by Improved Simulated-Annealing-Based Algorithm

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2024年第6期5卷 1847-1853页

作者： Shu, Tianyu Feng, Bowen Zhang, Long Chen, Chuyue Chen, Hui Chen, Yaling Zhang, Qinyu Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

This study presents a compact dual-band dual-sense circularly polarized (CP) fragmental patch antenna that advanced by an improved simulated-annealing-based optimization algorithm. This design replaces truncated corners of traditional truncated patch antennas with fragmental structures, generating dual-band dual-CP radiation with a small frequency ratio and a high front-to-back ratio. The proposed optimized framework tackles the multi-objective optimization problem through hierarchical optimization to achieve an optimal balance among various performance metrics. Furthermore, the simulated annealing is improved using a matrix-based dynamic step-size perturbation mechanism and a nested cyclic process, averting premature convergence and ensuring multifaceted objective enhancement. The measurement results reveal that the proposed antenna can operate with a small frequency ratio of 1.07, providing left-hand CP radiation from 4.99 to 5.02 GHz and right-hand CP radiation 5.34 to 5.41 GHz, respectively. This compact cost-efficient design demonstrates potential for diverse antenna applications.

关键词： Antennas optimization Dual band Patch antennas Perturbation methods Convergence Antenna arrays Circular polarization dual-band dual-sense multi-objective optimization problem simulated annealing

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Energy-Delay Tradeoff for Dynamic Trajectory Planning in Priority-Oriented UAV-Aided IoT Networks

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2023年第1期7卷 158-170页

作者： Cao, Hailin Zhu, Wang Chen, Zhengchuan Sun, Zhiwei Wu, Dapeng Oliver Chongqing Univ Chongqing Key Lab Space Informat Network & Intelli Chongqing 400044 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Unmanned aerial vehicles (UAVs) play a crucial role in emergency-oriented applications. However, in UAV-aided Internet of Things (IoT) networks, the sensor nodes (SNs) would be mobile which poses a big challenge for trajectory planning of the UAV. In this paper, we investigate priority-oriented UAV-aided time-sensitive data collection problems in an IoT network with movable SNs. By defining different levels of delay sensitivities for each SN, we jointly minimize the energy consumed by a UAV and the average delay of different SNs through optimizing the trajectory of the UAV. The problem is formulated as a multi-objective optimization problem (MOP). To solve the formulated problem, we first transform the MOP into a single-objective optimization problem based on the weighted sum method. Then, we propose a novel autofocusing heuristic trajectory planning algorithm based on reinforcement learning (AHTP-RL) which can be operated in an online manner. The proposed algorithm can well extract the network dynamic topology and the delay-priority of SN through an attention mechanism, hence can structure the UAV's trajectory efficiently. Extensive simulations results demonstrate that the proposed online AHTP-RL algorithm can achieve a superior balance between the communication delay and energy consumption for both low and high SN mobilities.

关键词： Unmanned aerial vehicles delay sensitivity dynamic network attention mechanism multi-objective optimization problem

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Strongly geodesic preinvexity and strongly invariant η-monotonicity on Riemannian manifolds and its application

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RAIRO-OPERATIONS RESEARCH 2023年第5期57卷 2687-2702页

作者： Iqbal, Aklad Hussain, Askar Bhat, Hilal Ahmad Aligarh Muslim Univ Dept Math Aligarh 202002 India

This paper introduces the concepts of strongly geodesic preinvexity, strongly eta-invexity of order m, and strongly invariant eta-monotonicity of order m on Riemannian manifolds. Additionally, it discusses an important characterization of these functions under a condition, known as Condition C (The Condition C is defined in Remark 1 of this article), defined by Barani and Pouryayevali [J. Math. Anal. Appl. 328 (2007) 767-779]. The paper provides various non-trivial examples to support these definitions. Furthermore, it presents a significant characterization of strict eta-minimizers (or eta-minimizers) of order m for multi-objective optimization problems and a solution to the vector variational-like inequality problem.

关键词： Strongly geodesic preinvexity strongly eta-invex functions invariant eta-monotone vector fields multi-objective optimization problem vector variational like-inequality problem Riemannian manifolds

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Utilizing a projection neural network to convex quadratic multi-objective programming problems

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2023年第7期37卷 1847-1865页

作者： Jahangiri, Mohammadreza Nazemi, Alireza Shahrood Univ Technol Fac Math Sci POB ***-316 Shahrood Iran

In this paper, we propose a projection neural network model for solving convex quadratic multi-objective optimization problem (CQMOP). The CQMOP is first converted into an equivalent convex nonlinear programming problem by the means of the weighted sum method, where the Pareto optimal solutions are calculated via different values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of the suggested model. Both theoretical and numerical approaches are studied. Numerical results are in good agreement with the proved theoretical concepts.

关键词： convergence convex non-linear programming problem multi-objective optimization problem neural networks Pareto optimal solution stability

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