A network-based virtual slack bus model for energy conversion units in dynamic energy flow analysis

Integrated energy system(IES) is a viable route to “carbon peak and carbon neutral”. As the basis and cornerstone of economic operation and security of IES, energy flow calculation(EFC) has been widely studied. Traditional EFC focuses on the single or distributed slack bus models, which results in the lack of unlimited power to maintain system operation, especially for electric power grid working in islanded or coupled mode. To deal with this problem, this paper proposes a network-based virtual-slack bus(VSB) model in EFC. Firstly, considering the anticipated growth of energy conversion units(ECUs) with power adjustment capacity, the generators and ECUs are together modeled as a virtual slack bus model to reduce the concentrated power burden of IES. Based on this model, a power sensitivity method is designed to achieve the power sharing among the ECUs, where the power can be allocated adaptively based on the network conditions. Moreover, the method is helpful to maintain the voltage and pressure profile of IES. With these changes, a dynamic energy flow analysis including virtual slack bus types is extended for IES.It can realize the assessment of the system state. Finally, simulation studies illustrate the beneficial roles of the VSB model.

Comparison of optimal capacitor placement methods in radial distribution system with load growth and ZIP load model

In this paper, a combined power loss sensitivity （PLS） index-based approach is proposed to determine the optimal location of the capacitors in the radial distribution system （RDS） based on the real and reactive combined loss sensitivity index, as capacitor placement not only reduces real power loss with voltage profile improvement but also reduces reactive power loss due to the reactive power compensation in the network. The results have been obtained with the existing methods of power loss index （PLI） and index vector （IV） method for comparison. Besides, the optimal placement has been obtained with the proposed method as well as existing methods and the total kVar support has been obtained. In addition, the results of net cost savings for the 10-, 34-, and 69-bus systems are obtained for comparison. Moreover, the results have been obtained for a large system of 85 buses to validate the results with combined sensitivity based approach. Further- more, the load growth factor has been considered in the study which is essential for the planning and expansion of the existing systems, whereas the impact of the realistic load model as ZIP load model has been considered for the study of all the systems.

6T SRAM cell analysis for DRV and read stability

The present paper analyzes the hold and read stability with temperature and aspect ratio variations. To reduce the power dissipation, one of the effective techniques is the supply voltage reduction. At this reduced supply voltage the data must be stable. So, the minimum voltage should be discovered which can also retain the data. This voltage is the data retention voltage（DRV）. The DRV for 6T SRAM cell is estimated and analyzed in this paper.The sensitivity analysis is performed for the DRV variation with the variation in the temperature and aspect ratio of the pull up and pull down transistors. Cadence Virtuoso is used for DRV analysis using 45 nm GPDK technology files. After this, the read stability analysis of 6T SRAM cell in terms of SRRV（supply read retention voltage） and WRRV（wordline read retention voltage） is carried out. Read stability in terms of RSNM can be discovered by accessing the internal storage nodes. But in the case of dense SRAM arrays instead of using internal storage nodes,the stability can be discovered by using direct bit line measurements with the help of SRRV and WRRV. SRRV is used to find the minimum supply voltage for which data can be retained during a read operation. Similarly, WRRV is used to find the boosted value of wordline voltage, for which data can be retained during read operation. The SRRV and WRRV values are then analyzed for different Cell Ratios. The results of SRRV and WRRV are then compared with the reported data for the validation of the accuracy of the results.