Development in the field of electronics and communication set up new paradigm. The need of making the grid smart is increasing due to environmental constrain and effective use of the available electricity. The use of digital information and controls technology to improve reliability, security, and efficiency of the electric grid.

Smart grid

This article proposes a forecasting-aided medium-voltage (MV) distribution system state estimation (SE) method using a robust ensemble Kalman filter (REnKF). In the proposed solution, low-voltage (LV) measurements at the LV side of secondary substations are used together with the mathematical model of the MV/LV substations to derive new equivalent MV measurements. This yields the improvement of measurement redundancy and robustness of the REnKF to bad data and unknown system process noise. Specifically, we rely on the temporal correlations of the constructed innovation vector as well as the projection statistics (PS) to detect and modify the measurement error covariance matrix. Furthermore, the system process noise covariance matrix is updated adaptively to mitigate the impacts of uncertainties ON-state forecasting and measurement filtering. Extensive comparisons have been carried out with both the traditional EnKF and other SE formulations under balanced and unbalanced distribution system conditions. The results reveal that our proposed REnKF is able to obtain accurate SE results under various operation conditions, including the presence of intermittent renewable energy sources, bad data, and system unbalance.

Robust Ensemble Kalman Filter for Medium-Voltage Distribution System State Estimation

http://manuscript.elsevier.com/S0306261922000885/pdf/S0306261922000885.pdf

Multi-service battery energy storage system optimization and control

Due to the increasing penetration of distributed energy resources (DERs) required for the sustainable distribution system, new voltage control strategy is needed by utilities Traditional voltage control strategy can not support the increasing number of DERs in a coordinated and scalable manner to meet the operational voltage regulation requirement Supported by the power electronics converter, the energy storage system can provide fast, smooth, and flexible voltage control services In this paper, an effective and easy to implement sensitivity-based voltage control strategy is developed for the energy storage system The developed control strategy is validated using an industrial feeder data in Northwest Washington The proposed strategy can mitigate the voltage unbalance issue, improve the voltage profile, and correct power factors while supporting sustainable distribution system operation

Voltage Control Strategy for Energy Storage System in Sustainable Distribution System Operation

The past few years have witnessed significant growth of distributed energy resources (DERs) at the customer level. Such growth requires a paradigm change in modeling distribution systems in more detail. Existing solutions only focus on modeling the primary backbone, aggregating loads at the primary of the customer transformer, or approximating or ignoring the secondary network model. Modeling the secondary network in detail is essential to assess distribution systems with high penetrations of DERs. In this paper, a detailed model of an actual feeder in Arizona is developed. For the primary circuit, distribution transformers, cables, and large loads are modeled. For the secondary circuit, actual conductors to each house, as well as loads and solar photovoltaic (PV) units at each premise are represented. In addition, accurate active and reactive power time series profiles are designed for both loads and PVs. All simulations are validated against field measurements.

Detailed Primary and Secondary Distribution System Feeder Modeling Based on AMI Data

The electric distribution system is becoming more complex with increasing visibility, and a variety of control devices for voltage and power factor control. These advancements provide improved observability of system status and let the operator control the system in more efficient ways. Smart meters are measurement devices that allow the operator to acquire accurate information about the end consumers, and state of the system. Smart meters provide the opportunity to revisit traditional distribution system models in an effort to improve them with more accurate information. Smart meter data enables the creation of time-varying load based distribution system models, enabling testing of various control algorithms. This paper purposes a method for developing a time-series distribution power-flow model using text-based AMI load data which may contain calculation failures. As a test case, a real distribution feeder is modeled in the open source distribution software GridLAB-D, with seasonal loading scenarios. The results are verified by DMS data obtained from the utility. The proposed method does not require GIS data for creating distribution system model. and also provides ways to deal with had data.

Thomas J. Morrell

Papers

Modeling of Electric Distribution Feeder Using Smart Meter Data

Development and Analysis of Control Strategies for a 1 MW/3.2 MWh Energy Storage System at Avista Utilities