Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

Li, Yuanzheng; Cai, Yihan; Zhao, Tianyang; Liu, Yun; Wang, Jian; Wu, Lei; Zhao, Yong

doi:10.35833/mpce.2020.000109

Cited by 18 publications

(11 citation statements)

References 40 publications

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“…In Eq (5), I indicates the actual size of the population. Therefore, the crossover probability proposed based on differentiated neighborhood density is obtained by using Eq (1) to obtain the individual's neighborhood weight, and Eq (2) to obtain the individual's neighborhood density.…”

Section: Plos Onementioning

confidence: 99%

“…With the development of communication technology, the industry has put forward higher requirements for the performance of power amplifiers. Therefore, the application of MOO algorithms in power amplifiers has gradually attracted the attention of scholars at home and abroad [ 5 ]. Mengozzi M et al proposed a generalized dual input digital early warning distortion method suitable for power amplifier modulation using MOO method to address the frequent problem of poor frequency modulation signal and distortion in traditional power amplifiers.…”

Section: Introductionmentioning

confidence: 99%

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A multi-objective optimization based doherty power amplifier and its matching network optimization method

Sun

2023

PLoS ONE

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In the actual design process of traditional power amplifiers, there is a problem of being cumbersome and unable to simultaneously meet low power and saturation modes. Therefore, an improved multi-objective optimization algorithm proposed by decomposition is introduced to optimize its matching network to achieve overall optimization design of power amplifiers. The algorithm, matching network, and optimized power amplifier performance are simulated and verified. The experimental outcomes denote that on the logic function with Zener diode transistor, the proposed algorithm has a mean generation distance index of 5.03E-3, which is lower than most algorithms. Its overall comprehensive performance is better than the comparison algorithm, and compared to the comparison algorithm, it converges more quickly in the early stage of iteration on 1 and 2, and tends to stabilize in the 40th generation, and completes convergence in the 80th generation. In addition, the optimal solution has already begun to appear around the 25th generation and reached saturation around the 70th generation. At the same time, in the actual working bandwidth, the optimized power amplifier saturation efficiency reaches 51.5%~61.9%, and the efficiency at 6dB power backoff is about 44.4%~56.5%. Overall, the algorithm proposed in the study is effective in optimizing power amplifiers and their matching networks, effectively solving the problem of insufficient efficiency in low power modes in traditional designs.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization based doherty power amplifier and its matching network optimization method

Sun

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The optimization model constructed in this paper is highly nonlinear and has two conflicting objectives that can hardly be solved. The GA has proven to be effective in solving multi-objective problems [25] and thus is adopted in this paper. To reduce the computational burden of the model and accelerate convergence, a two-step solution strategy is proposed based on the time-series correlation of renewable generations or loads and the iterative principle of the GA.…”

Section: Two-step Solution Strategymentioning

confidence: 99%

Multi-objective Day-ahead Polarity Switching Optimization for Voltage Unbalance in Bipolar DC Distribution Networks

Huang¹,

Wang²,

Chen³

et al. 2023

Journal of Modern Power Systems and Clean Energy

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Bipolar direct current (DC) distribution networks can effectively improve the connection flexibility for renewable generations and loads. In practice, concerns regarding the potential voltage unbalance issue of the distribution networks and the frequency of switching still remain. This paper proposes a day-ahead polarity switching strategy to reduce voltage unbalance by optimally switching the polarity of renewable generations and loads while minimizing the switching times simultaneously in the range of a full day. First, a multi-objective optimization model is constructed to minimize the weighted sum of voltage unbalance factors and the sum of number of switching actions in the day based on the power flow model. Second, a two-step solution strategy is proposed to solve the optimization model. Finally, the proposed strategy is validated using 11-node and 34-node distribution networks as case studies, and a switching and stabilizing device is designed to enable unified switching of renewable generations and loads. Numerical results demonstrate that the proposed strategy can effectively reduce the switching times without affecting the improvement of voltage balance.

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“…Although this study examines the impact of charging stations on the RPS, it does not address environmental objectives to reduce pollutant emissions. Xing et al [21] integrated a PV power generation system into a centralized battery replacement charging system, proposed a centralized battery replacement charging system to minimize the total operating cost with a smooth load fluctuation many-objective optimization operation method, and also proposed a many-objective optimization algorithm that modifies the Non-dominated Sorting Genetic Algorithm Ⅲ (NSGA-III) cross variance operator to solve the model. However, they propose a method that does not consider the environmental pollution caused by energy originating from the grid side.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Electric Vehicle Integrated Energy Station Using a Novel Many-Objective Stochastic Competitive Optimization Algorithm

Yin,

Liao,

Qian

et al. 2023

IEEE Access

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The construction of the Electric Vehicle Integrated Energy Station (EV-IES) is a prerequisite for the rapid development of the EV industry. However, how to optimize the operation of the EV-IES is a problem worthy of study. Therefore, this paper designs an EV-IES model with PV and Energy Storage System (ESS). Fully consider the peak-valley time-of-use electricity price, user traffic flow, PV output, and other factors. On this basis, the three objectives of the maximum daily revenue of the EV-IES, the minimum exchanged energy between the EV-IES and the Regional Power System (RPS), and the minimum pollutant emission are optimized at the same time. Secondly, this paper proposes a MOSCO algorithm, which is utilized to assess the DTLZ1-7 benchmark functions and the optimization scheduling problem of EV-IES. By comparing its simulation results with those of five other optimization algorithms, it is evident that the MOSCO algorithm outperforms the other five in terms of IGD, GD, HV, and Spread values. This indicates the effectiveness of the MOSCO algorithm in addressing many-objective optimization problems. Finally, in order to illustrate the feasibility of designing the EV-IES model, three comparative cases were designed. The

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

Cited by 18 publications

References 40 publications

A multi-objective optimization based doherty power amplifier and its matching network optimization method

A multi-objective optimization based doherty power amplifier and its matching network optimization method

Multi-objective Day-ahead Polarity Switching Optimization for Voltage Unbalance in Bipolar DC Distribution Networks

Optimal Scheduling of Electric Vehicle Integrated Energy Station Using a Novel Many-Objective Stochastic Competitive Optimization Algorithm

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