Power quality studies for distribution networks are very important for future network expansions realized by utility companies, so the accuracy of such studies is critical. Load data, including information on load imbalance, could have in many situations a significant influence on the correct estimation of many power quality indicators. This paper investigates the impact of load imbalance on several phase imbalance indicators and voltage quality indicators by comparing the values of these indicators, as calculated in a power quality study using, sequentially, different sets of load data characterized by different load imbalances. The results of this study confirm the original hypothesis, showing that the use of inaccurate consumption profiles for loads leads to an inaccurate estimation of some power quality indicators. In addition, the results highlight the difficulty of approximating the actual consumption profiles of electrical loads so that this approximation does not affect the correctness of the estimation of phase imbalance and voltage quality indicators.

Data integration from heterogeneous data sources in low-voltage (LV) power distribution grids will be valuable to distribution system operators (DSOs). The power measurements from customer premises need to be processed with other data such as grid topology, line parameters etc., to deploy smart grid applications (SGA) such as real-time grid monitoring and voltage regulation. The most challenging task for DSOs is to collect and integrate data from several sources as several entities are involved in the data management and access to databases are restricted. This paper presents an op E n common information ${M}$ odel (CIM) BA sed sma ${R}$ t grid application framewor ${K}$ ( EMBARK ) to address the above-mentioned challenge. EMBARK is developed to be an efficient, modular and scalable architecture for extracting relevant grid related information from various asset management databases. A novel data management functionality is a part of EMBARK that enables data-driven update of settings and parameters of the algorithms behind smart grid applications. The proposed approach is demonstrated and numerically validated using grid data from a medium-sized distribution grid operator in Denmark. The architecture developed and presented in this paper can support all the phases from planning to the actual smart grid operation, i.e., automatically building the models to perform load flows, grid impact studies, planning, asset management etc.

The installation of smart meters at customer premises provides opportunities for the monitoring of distribution grids. This paper addresses the problem of improving the observability of low-voltage distribution grids using smart metering infrastructure. In particular, this paper deals with the application of state estimation algorithm using smart meter measurements for near-real-time monitoring of low-voltage distribution grids. This application is proposed to use a nonlinear weighted least squares method-based algorithm for estimating the node voltages from minimum number of smart meter measurements. This paper mainly deals with sensitivity analysis of the state estimation algorithm with respect to multiple uncertainties for, e.g., measurements errors, line parameter errors, and pseudo-measurements. Simulation studies are conducted to estimate the accuracy of the DSSE under various operating scenarios of a real-life low-voltage grid, and cost-effective ways to improve the accuracy of the state estimation algorithm are also evaluated. The paper concludes that by using smart meter measurements from few locations, voltage profiles of the low-voltage grid can be estimated with reasonable accuracy in near-real-time.

The cryogenic carbon capture (CCC) process is a promising post-combustion CO₂ removal method. This method is very novel compared with conventional and well-developed methods. However, cryogenic carbon capture is not yet commercially available despite its techno-economic benefits. Thus, a model-based design approach for this process can provide valuable information. This paper will first introduce the cryogenic carbon capture process. Then, a comprehensive literature overview that focuses on different methods for modeling the process at the component level will be given. The modelling methods which are deemed most effective are presented more in depth for each of the key system components. These methods are compared with each other in terms of complexity and accuracy and the simplest methods with an acceptable level of precision for modelling a specific component in the CCC process are recommended. Furthermore, potential research areas in modeling and simulation of the CCC process are also highlighted.

This paper focuses on the sizing of a battery energy storage system providing frequency containment reserves in a power system with a large wind power penetration level. A three-stage sizing methodology including the different aspect of battery energy storage system performance is proposed. The first stage includes time-domain simulations, investigating battery energy storage system dynamic response and its capability of providing frequency reserves. The second stage involves lifetime investigation. An economic assessment of the battery unit is carried out by performing the last stage. The main outcome of the proposed methodology is to choose the suitable battery energy storage system size for providing frequency containment reserve from augmented wind power plants while fulfilling relevant evaluation criteria imposed for each stage.