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 gr...
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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 optimizationproblem 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-objectiveoptimizationproblem (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.
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 bo...
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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-objectiveoptimizationproblem (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.
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 ***...
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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.
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 o...
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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-objectiveoptimizationproblem 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-objectiveoptimizationproblem 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-objectiveoptimizationproblem in which the corresponded Pareto Set was identified. The simplified version of the proposed Multi-objectiveoptimizationproblem 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
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 vegetabl...
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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 singleobjectiveoptimization mode.
Decomposition-Based Multi-objective Evolutionary Algorithms (DBMOEA), such as Multiple singleobjective Pareto Sampling (MSOPS) and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), have been succ...
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ISBN:
(纸本)9781479938414
Decomposition-Based Multi-objective Evolutionary Algorithms (DBMOEA), such as Multiple singleobjective Pareto Sampling (MSOPS) and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), have been successfully applied in finding Pareto-optimal fronts in Multiobjectiveoptimizationproblems (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-objectiveoptimizationproblems.
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