Transforming electricity access through smart sensors and grid efficiency algorithms

f86c5642-ba4d-4da6-abec-1c42dbb49c97

Closed

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

£552,460

Feb. 1, 2018

July 31, 2019

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.

Potential Impact:
Energy systems contain many stakeholders including energy consumers, network operators, aggregators, technology suppliers, energy suppliers and generators. In addition, energy markets, technology supplier, and the operation of network operators are usually strongly influenced by government policy and regulatory bodies. All of these stakeholders are invested in solving the energy trilemma in their own way, balancing the needs of security, cost and sustainability. This project will deliver impact for all these stakeholders as follows.

The energy trilemma is most directly aligned with the needs of energy consumers; energy consumers are perhaps most traditionally concerned with cost and security of supply, but increasingly consumers are also concerned about environmental impact. This project will enable network operators to install cost-effective monitoring equipment that will unlock the potential of smart grid services such as demand response, energy storage and renewable generation by providing planning information and real-time control signals to allow such technology to improve the running of the grid and defer or in some cases mitigate the need for conventional and costly network upgrades. Network investment deferral reduces the impact of carbon emissions, and more renewable generation can be connected to the grid when supported by monitoring and smart grid systems; both of these actions will have demonstrable benefits for the environment and sustainability. By being able to monitor networks and deploy appropriate smart grid solutions, network operators will be able to provide a more secure and reliable energy supply, and will be able to do so more less cost compared to traditional interventions. As a result of this project OrxaGrid will have a commercial offering that can immediately lead to benefits for network operators and so benefits for energy consumers.

Accurate and real-time substation monitoring data coupled with analytics and control algorithms can provide immediate benefits to aggregators and other technology suppliers. For example, although the rise of electric vehicles (EVs) is by most measures objectively good for consumers and the environment, they present significant challenges to the electrical energy system. It is likely that the uptake of EVs will not be uniform with clusters of vehicles appearing on in specific streets and neighbourhoods before others. Therefore, even at lower levels of national penetration, distribution networks will feel the strain first. Controlled charging, such that the number of concurrently charging vehicles can be influenced, is likely to become mandatory but such systems will require knowledge of real-time substation capacity in order to run in an optimal manner. The substation monitoring equipment and data platform provided by OrxaGrid and developed by this project will provide that real-time knowledge.

The academic outputs from the project will enable other researchers to further develop the field and so enable further innovations in energy systems research. The academic team will disseminate their work through workshops, seminars and academic publications. The Technologies for Sustainable Built Environments centre within the School of the Built Environment at the University of Reading has an established track record of running energy-focused seminar series for academic and industrial partners as well as the public and this forum will be used amongst others.