A review of optimal planning active distribution system:Models, methods, and future researches

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders

Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise

State of charge (SOC) is one of the most important parameters in battery management system (BMS). There are numerous algorithms for SOC estimation, mostly of model-based observer/filter types such as Kalman filters, closed-loop observers, and robust observers. Modeling errors and measurement noises have critical impact on accuracy of SOC estimation in these algorithms. This paper is a comparative study of robustness of SOC estimation algorithms against modeling errors and measurement noises. By using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods (extended Kalman filter, PI-controlled observer, and H∞ observer) are compared on such robustness. The simulation and experimental results demonstrate that deterioration of SOC estimation accuracy under modeling errors resulted from aging and larger measurement noise, which is quantitatively characterized. The findings of this paper provide useful information on the following aspects: (1) how SOC estimation accuracy depends on modeling reliability and voltage measurement accuracy; (2) pros and cons of typical SOC estimators in their robustness and reliability; (3) guidelines for requirements on battery system identification and sensor selections

International Journal of Smart Grid and Clean Energy Research on battery storage system configuration in active distribution networks

Abstract In this paper, an optimizing model of battery energy storage system (BESS) capacity configuration for active distribution networks (ADNs) is proposed to mitigate the negative effect of distribution systems with distributed generations (DGs), in stability, economy and reliability perspectives. In details, this model involved the charging and discharging limitation and the operation constrains of BESS, as well as the power flow balance of distribution system. Moreover, three optimizing objectives have been presented for the optimal power profile of BESS, including minimizing the voltage fluctuation, reducing the feeder loss and maximizing the consecutive supplied power of ADN, respectively. After the optimization, the BESS capacity is calculated by estimation of the maximum charging and discharging energy. Finally, the simulation results are shown to illustrate the procedure of capacity configuration

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Estimation of state of charge of lithium-ion batteries usedin HEV using robust extended kalman filtering

A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latterfor SOC estimation

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM) and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters) and a nonparametric part [involving the open-circuit voltage (OCV)]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA) expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests

An Efficiency-Optimized Isolated Bidirectional DC-DC Converter with Extended Power Range for Energy Storage Systems in Microgrids

This paper proposes a novel extended-single-phase shift (ESPS) control strategy of isolated bidirectional full-bridge DC-DC converters (IBDCs) which are a promising alternative as a power electronic interface in microgrids with an additional function of galvanic isolation. Based on the mathematical models of ESPS control under steady-state conditions, detailed theoretical and experimental analyses of IBDC under ESPS control are presented. Compared with conventional single-phase-shift (CSPS) control, ESPS control can greatly improve the efficiency of IBDCs in microgrids through decreasing current stress and backflow power considerably over a wide input and output voltage range under light and medium loads. In addition, ESPS control only needs to adjust one single phase-shift angel to control transmission power, thus it retains implementation simplicity in comparison with dual-phase-shift (DPS) control for microgrid applications. Furthermore, an efficiency-optimized modulation scheme based on ESPS and CSPS control is developed in the whole power range of IBDC for power distribution in microgrids. A 10 kW IBDC prototype is constructed and the experimental results validate the effectiveness of the proposed control strategy, showing that the proposed strategy can enhance the overall efficiency up to 30%