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Details of Grant 

EPSRC Reference: EP/R035792/1
Title: Transforming electricity access through smart sensors and grid efficiency algorithms
Principal Investigator: Potter, Dr BA
Other Investigators:
Coker, Dr PJ
Researcher Co-Investigators:
Project Partners:
Department: Built Environment
Organisation: University of Reading
Scheme: Technology Programme
Starts: 01 February 2018 Ends: 31 July 2019 Value (£): 110,492
EPSRC Research Topic Classifications:
Energy Efficiency Sustainable Energy Networks
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:  
Summary on Grant Application Form
Electricity distribution network operators (DNOs) in both developed and developing countries are facing significant challenges to address the energy trilemma- offering clean, affordable and secure energy. Increased demand for electricity coupled with the rapid shifting of supply to distributed generation requires DNOs to increase monitoring, analytics and optimisation in order to continue to provide a cost-effective service. However, in developing countries such as India, the challenges can be more extreme namely: over $16Bn of the $89Bn lost annually to electricity theft comes from India, which when coupled with significant technical losses due to aging infrastructure, result in increasing electricity prices and frequent power outages; the grid is unable to effectively integrate the growing renewables (380% growth planned in India by 2027) due to the uneven, variable and bidirectional nature of renewables; electricity supply is unable to meet the growing demand for electricity (71% non-OECD growth expected by 2040) in developing markets which is leading to peak deficits (3.2% in India for 2015-16), power shortages and routine blackouts.

Instead of installing expensive legacy SCADA systems or making costly expansions to the network, OrxaGrid provides a smarter, lower cost alternative monitoring system for improving grid efficiency. OrxaGrid works on the principle of 'Monitor, Analyse and Optimize' by providing smart IoT sensors that are retrofitted on critical nodes of distribution electricity grids. Realtime monitored data is sent to the cloud via cellular/internet/LoRa for analytics. The research team at the University of Reading will analyse this data in order to determine trends and to develop both a forecast model and a classification engine that can both predict future substation energy use and also detect important events as they occur.

Extracting value from data to enable smart grid services with a sustainable business model that also meets the needs of the energy trilemma is a significant challenge. The research in this project will first perform data analytics on the raw substation data to identify trends and patterns. The research team will then identify within the data key events that are important to the energy system such as when energy demand is approaching operational limits of the substation or when power supplies become disrupted. By creating a library of such events, future events can be automatically detected and identified.

Key smart grid systems such as energy storage require scheduling. As the future state of an energy storage systems depends on its past state, energy storage systems can not simply adjust to meet a given requirement in a given moment. For example, a battery that is already charged to full capacity can not continue to charge. Therefore, to use such systems effectively, some expectation of future requirements is necessary. This project will develop a forecast model that will use historical substation data to predict future requirements. However, in practice forecasts must be designed with a specific application in mind. In this work, the forecasts will be used, alongside the library of important events, to detect when something unusual has happened and identify the cause.

Once a forecast model is in place and key events can be detected, the platform will be able to provide recommended schedules for smart grid systems such as energy storage, demand response and electric vehicle charging. Algorithms will be developed to determine these recommended schedules. Simulations based on real substation data will be run to demonstrate the impact of these algorithms running in conjunction with the recommended portfolios of low-carbon technologies. In addition, these algorithms will attempt to detect electrical energy theft, where expected household can be compared to actual demand to determine the likelihood of whether any unmetered energy is being used.
Key Findings
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Organisation Website: http://www.rdg.ac.uk