# Smart Grid

The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR).

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This file is Matlab extended (.Mex). There is a frequency file measured at one second intervals.

The frequency regulation lithium battery takes into account the nonlinearity of the life and inputs the operating range of the SOC for the optimal design. The detailed calculation process will be presented in a separate paper.

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This work focuses on using the full potential of PV inverters in order to improve the efficiency of low voltage networks. More specifically, the independent per-phase control capability of PV three-phase four-wire inverters, which are able to inject different active and reactive powers in each phase, in order to reduce the system phase unbalance is considered. This new operational procedure is analyzed by raising an optimization problem which uses a very accurate modelling of European low voltage networks.

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Smart Grids (SG) are a novel paradigm introduced for optimizing the management of the power generation, transmission, distribution and consumption. A SG system can efficiently work only if all the components are connected through a communication network able to satisfy the SG applications requirements. Wireless communications are the most appropriate candidates for handling SG requirements due to their flexibility.

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This folder contains two csv files and one .py file. One csv file contains NIST ground PV plant data imported from https://pvdata.nist.gov/. This csv file has 902 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST ground PV plant data.

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When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system's state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model.

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For submission

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This dataset is in support of my 3 research papers - '** Comparative SoC Analysis using Non-Linear Kalman Estimation in 8RC ECM of 72Ah LIB - Part I**', '

*and '*

**Comparative SoC Analysis using Non-Linear Kalman Estimation in 8RC ECM of 72Ah LIB - Part I****I' ,**

**Comparative SoC Analysis using Non-Linear Kalman Estimation in 8RC ECM of 72Ah LIB - Part I****II'.**- Categories:

This dataset is in support of my 2 research papers - '* Short Circuit Analysis of 72Ah Li-Ion BMC - Part I*' and '

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*Short Circuit Analysis of 72Ah Li-Ion BMC - Part II*** Faults and datasets can be copied to submit in fire cause investigation reports or thesis. **

**This dataset is a collection of data of battery and BMC faults. **

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