Renewable generation has been increasingly utilized recently, and multi-microgrid (MMG) system shows great potential in absorbing renewable energy. In this paper, a two-stage distributionally robust model is proposed ...
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Renewable generation has been increasingly utilized recently, and multi-microgrid (MMG) system shows great potential in absorbing renewable energy. In this paper, a two-stage distributionally robust model is proposed for the optimal design and operation of islanded MMG which is seldom studied. Considering the uncertainty of renewable generation, the first-stage design strategy and second-stage operation decision are co-optimized by minimizing the investment cost and operation cost. To model the uncertainty of renewable power, a distance -based ambiguity set is employed to capture the unknown probability distribution. Then the two-stage problem is solved by a column and constraint generation (CCG) based method. Simulation experiments and results with an islanded MMG system demonstrate the effectiveness and performance of the proposed model.
Recently, lithium-ion batteries with fast-charging capability have been gradually equipped in electric products. Capacity estimation specially developed for fast-charging batteries is still an open question, and most ...
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Recently, lithium-ion batteries with fast-charging capability have been gradually equipped in electric products. Capacity estimation specially developed for fast-charging batteries is still an open question, and most of the existing works are proposed for batteries charged under the 1C rate. However, incorrect fast-charging strategies may result in Li plating leading to battery capacity aging rapidly and degradation mode variation. Moreover, unprecise sampling by BMS increases the difficulty of accurately estimating battery capacity in such scenarios. Thus, a fusion prognostic method based on ensemble learning is proposed for the above issues in fast-charging batteries. Firstly, measurement-based health features are extracted from the portion charging phase. Then, the accuracy of the validation dataset-based strategy is proposed to achieve time-varying weight allocation. Finally, a fusion prognostic model is constructed based on the above weight allocation strategy and health features. The effectiveness of the proposed fusion method is verified by a self-design fast-charging battery dataset that maintains stable and reliable performance under sparse sampling and degradation mode variation conditions. In addition, the robustness and adaptability are validated by the comparison experiments with traditional ensemble learning-based methods, in which accuracy is improved by 27.5% minimally.
The lithium-ion battery energy storage system currently widely used faces a problem of rapid degradation of electrical performance at very low temperatures (such as -40 degrees C), making it difficult to meet the powe...
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The lithium-ion battery energy storage system currently widely used faces a problem of rapid degradation of electrical performance at very low temperatures (such as -40 degrees C), making it difficult to meet the power supply requirements of high-power pulse loads in low-temperature environments. To address this issue, this paper proposes a multi-objective configuration optimization method for passive lithium-ion battery-supercapacitor hybrid energy storage systems (HESS) based on an electro-thermal-aging coupling model, in order to achieve non-preheating power supply for pulse loads under low temperatures. Firstly, an electro-thermal-aging coupling model for the passive HESS is established to accurately describe the dynamic characteristics during discharge. Subsequently, aiming at the low-temperature application requirements of high-power pulse loads, a multiobjective configuration optimization model is established based on the coupling model with the objectives of minimizing the mass and the minimum operating ambient temperature of the HESS;a solving method of the optimization model is designed based on the non-dominated sorting genetic algorithm with elite strategy. Finally, a case study is conducted on configuration optimization for a certain type of pulse load. The optimization results show that when the minimum operating temperature is consistent and below 0 degrees C, the passive HESS compared with the lithium-ion battery energy storage system can reduce the system mass by more than 23% and the acquisition cost by more than 18% while maintaining basically consistent single-pulse costs. When the minimum operating temperature is lower, the advantages of the HESS are even more significant.
In terms of generating offspring solutions, simulated binary crossover (SBX) and differential evolution (DE) are two of the most representative reproduction operators in evolutionary multi-objective algorithms (EMOAs)...
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In terms of generating offspring solutions, simulated binary crossover (SBX) and differential evolution (DE) are two of the most representative reproduction operators in evolutionary multi-objective algorithms (EMOAs). However, they are found as less effective on multi-objective problems (MOPs) with complicated Pareto optimal set (PS). Under mild conditions, the PS of an MOP is an ( m - 1) -dimensional piecewise continuous manifolds where.. is the number of objectives. Inspired from this regularity property, this study proposes a simple yet effective reproduction operator, namely, PCA-assisted reproduction (PCA-ar). Specifically, the PCA-ar first applies principal component analysis (PCA) method to construct a new decision space with reduced number of dimensions based on the information extracted from several well converged solutions. The PS is then estimated by a hyperplane in the new decision space. To this end, new offspring are sampled from the estimated PS, and then re-converted to the original decision space for fitness calculation. In order to systematically examine the effectiveness of the PCA-ar operator, we integrate it into NSGA-II and MOEA/D, and compare the derived algorithms, nNSGA-II and nMOEA/D, with their original versions (NSGA-II with SBX operator and MOEA/D with DE operator) as well as the regularity model based multi-objective estimated distribution algorithm (RM-MEDA) on the modified DTLZ benchmarks with up to 8 objectives. Experimental results show that nNSGA-II and nMOEA/D outperform the competitor EMOAs for most of problems, which indicate that the PCA-ar is effective. Lastly, the PCA-ar is also demonstrated to have good scalability to the number of decision variables.Y
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