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Economical Real-Time Energy Management For Microgrids Via Nilm And With User Decision Support

Creative Commons 'BY-NC-ND' version 4.0 license

With time-of-use pricing of electrical energy, real-time energy management is being economically incentivized for all. Consumers with renewable sources are among the first to recognize this, and those with the capability to operate in “island” mode as a micro grid find real-time energy management a necessity.

A real-time energy management system (EMS) requires real-time data that enables immediate identification of electrical loads. Non-Intrusive Load Monitoring (NILM) is the process of identification of loads from an aggregate power interface using disaggregation algorithms, thus providing load data economically.

Application of NILM in residential settings has been hampered by limited data availability. Utility billing smart meters provide very sparse (time) sampling of energy use, yielding data that is not adequate for quantifying fundamental harmonics of the waveform. For research and deployment of NILM, there is a critical need for a low-cost sensor system to collect energy data with fast sampling and significant precision.

We first identify the current status, methodologies and challenges of NILM in residential settings. NILM has advanced substantially in recent years due to improvement in algorithms and methodologies. Currently, the important challenges facing residential NILM are inaccessibility of electricity meter high sampling data, and lack of reliable high resolution datasets.

We introduce SEADS (Smart Energy Analytic Disaggregation System) which provides a powerful and flexible system, supporting user configuration of sampling rates and amplitude resolution up to 65KHz and up to 24 bits respectively. The SEADS internal processor is capable of implementing NILM algorithms in real time on the sampled measurements.

An Intelligent Energy Management System (IEMS) has been introduced. Since SEADS has the load information instantaneously, it can be part of a real time command and control system of a microgrid. IEMS proposed integrates SEADS into a Decision Support System (DSS). DSS helps consumers make informed realtime decisions, especially when microgrid is operating in the island mode. Prolonging the stay on the battery and renewable sources, can reduce or eliminate the need for use of a local fossil fuel generator. A combination of automatic and user driven load shedding is necessary in a microgrid for interactively responding to the intermittency of renewable sources. This is possible by controlling only a limited number of loads in parallel with using a battery storage system.

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