smart grids: from renewable sources to machine learning

CRICOS Provider Number: 03567C | Higher Education Provider Number: 14008 | RTO Provider Number: 51971

Smart Grids: from Renewable Sources to Machine Learning

Technical Topic Webinar

Dr Yuanyuan Fan| EIT Lecturer & Course Coordinator

PRESENTED BY

Wednesday 31 March


View Recording Here

https://vimeo.com/531534088/e0028525aa

CRICOS Provider Number: 03567C | Higher Education Provider Number: 14008 | RTO Provider Number: 51971CRICOS Provider Number: 03567C | Higher Education Provider Number: 14008 | RTO Provider Number: 51971

1 Introduction to Smart Grids

2 Renewable Energy

3 Machine Learning for Smart Grids

4 Case Studies

Agenda


What is Smart Grids?


Traditional Power Grid


• Centralized, bulk generation

• Heavy reliance on coal and oil

• Limited automation

• Limited situational awareness

• Consumers lack data to manage energy usage


Smart Grid



Definition of Smart Grids


A smart grid is an electricity network that uses digital and other advanced

technologies, such as cyber-secure communication technologies, automated and

computer control systems, in an integrated fashion to be able to monitor and

intelligently and securely manage the transport of electricity from all generation

sources to economically meet the varying electricity demands of end-users.

Smart grids is not a ‘thing’, but a ‘vision’!


Characteristics of the smart grid based on functionality approach


1. Optimize asset utilization and operating efficiency.

2. Accommodate all generation and storage options.

3. Provide power quality for the range of needs in a digital economy.

4. Anticipate and respond to system disturbances in a self-healing manner.

5. Operate resiliently against physical and cyber-attacks and natural disasters.

6. Enable active participation by consumers.

7. Enable new products, services, and markets.

People talk about smart girds from various aspects, such as asset management, data communication and metering technologies, information/data security, instrumentation and automation, electricity market and demand management, power conversion, energy storage, electric vehicles, HVDC and FACTS, power quality, renewable integration, artificial intelligence, etc..


Renewable Energy


Renewable Sources


Image source: https://www.dreamstime.com/illustration/non-renewable-energy.html


Solar Energy Conversion


A solar power plant works on the concept of conversion of sunlight into

electricity either directly using:

•Photovoltaic(PV) cells or solar panels; or

•Concentrated Solar Power (CSP)

•CSP systems use lenses or mirrors and tracking systems to focus a large

area of sunlight into a small beam.

•Photovoltaics converts light into electric current using the photoelectric effect.


Solar Panels


A photovoltaic system typically includes:

▪Solar Panel or an array of solar modules: To absorb and directly convert sunlight into

electricity.

▪An inverter: To change the output electrical current from DC to AC

▪A battery

▪A solar tracker

▪Mounting, interconnecting cabling, and other accessories


Solar Energy


Image source: https://www.pv-magazine-australia.com/2020/06/19/south-australias-rooftop-solar-could-be-switched-off-on-the-way-to-100-renewables/

Image source: https://reneweconomy.com.au/its-not-easy-to-build-a-solar-farm-in-australia-any-more-59308/

Image source:https://www.ecomagazine.com/news/industry/the-new-solar-frontier-offshore-and-coastal-solar-applications


Wind Farms


Image source: https://www.ferrostaal.com/en/top-references-ferrostaal/onshore-wind-farms/

Image source: https://www.prysmiangroup.com/en/press-releases/prysmian-secures-dolwin5-grid-connection-offshore-wind-farms-germany

Image source: https://e360.yale.edu/features/will-floating-turbines-usher-in-a-new-wave-of-offshore-wind


Wind Turbine


Main parts of a wind turbine:

1. Base

2. Tower

3. Nacelle

4. Blades

5. *Gearbox

GE’s 12 MW Haliade-X: Height: 260 m; Rotor diameter: 220 metersVestas launched the V236-15 MW turbine, the sweep area of which is +43,000 m2, equivalent to the surface of 6 football fields.


A Turbine Blade


Image source: https://s3.amazonaws.com/bncore/wp-content/uploads/2018/05/1010281637-LOCAL-TURBIN5-1260x800.jpg


Why the Size?


Wind energy is captured by the blades of a wind turbine and converted into mechanical power, which is calculated by

• Locate wind turbines in regions with higher wind speeds, eg, offshore;• Get larger sweep areas;• Achieve MPPT

ρ - air density, A - cross-sectional area through which the wind passes/sweep area, vw - wind speed,Cp - the rotor efficiency of the blade


MPPT – Wind Power


The TSR is the ratio of the blade tip speed to the wind speed:

The optimal TSR is a constant for a given blade.So, ωM has to follow Vw!

ωM – blade rotating speed, rb – radius of rotor blades, vw – wind speed


MPPT – Wind Power Contd…


Image source: https://www.researchgate.net/figure/Wind-turbine-MPPT-configuration_fig1_331989450

Source: Bin Wu, Yongqiang Lang, Navid Zargari, Samir Kouro, Power Conversion and Control of Wind Energy Systems, 2011.


MPPT - Solar


The principle is that if the output from the cell can be regulated to the voltage and current levels needed to achieve a power output at this point, then the power generated by the solar cell will be used most efficiently.

Image source: https://www.eurekaselect.com/node/162545/article/comparison-of-maximum-power-point-tracking-mppt-algorithms-to-control-dc-dc-converters-in-photovoltaic-systems

Image source: A. Podder, ‘MPPT methods for solar PV systems: a critical review based on tracking’, IET Renewable Power Generation, 2019.


Global Warming Urgency


“We need to be installing around 180 GW per year to get to where we need to be. Every year we fall short, the mountain to climb gets higher.” - Ben Backwell, CEO, Global Wind Energy Council


Hydrogen – Green/Renewable Hydrogen


• Green hydrogen and Fuel cells


Hydrogen Stations


• According to IEA, the power sector accounts for nearly 40% of CO2 emissions worldwide, and this share is declining; transport and industry make up nearly half of remaining global emissions, with buildings comprising around 10%.

• While the other European countries focus on publicly accessible passenger car refuelling stations, most of the French stations aim at the refuelling of buses and delivery vehicle fleets.

Source: H2stations.org by LBST

Not as competitive as battery based EVs atm…


Using Hydrogen


• Heating and cooking – can be blended with natural gas, can use existing gas networks

• Powering vehicles – faster refuelling than battery; needs refuelling stations

• Generating electricity – fuel cells or being burnt

• Energy storage – ammonia; (compressed) liquid hydrogen

Low density – hard to compress;

Cost – expensive to produce;

Implementation – needs fuel cells


Energy Storage


Hydrogen

Electrical energy must be produced almost when it is required for consumption and has no inherent self-life. However, renewable power is not dispatchable!

Batteries are increasingly affordable and popular. How about other options for long duration storage?


Power-to-X


Stored electricity can be electrolysed into hydrogen to be used as• feedstock, to produce bulk chemicals like methanol or ammonia for industrial processes (Power-

to-Gas or Power-to-Chemicals) or• combined with captured CO2 to make carbon neutral liquid fuels such as crude, gasoline, diesel

and aviation fuels (Power-to-Liquid Fuels).Stored green power can• generate heat through electric boilers for houses and factories (Power-to-Heat), or • contained in underground formations such as salt domes and fed back to the gas grid or

transformed into electricity when needed (Power-to-Heat and Power-to-Power).

Reference: GWEC Global Wind Report - 2021


Distributed Generation


•Distributed generation (DG) refers to power generation at the point of

consumption.

• Distributed energy resource (DER) systems are small-scale power generation

units in the range of 1 kW to 10,000 kW.

• DER systems typically are characterized by high initial capital costs per kilowatt.

• DER systems also serve as storage device and are often called Distributed

energy storage systems (DESS).

• Generating power on-site eliminates cost, complexity, interdependencies, and

inefficiencies associated with transmission and distribution.

• Distributed generation shifts control to the consumer.

• Earlier Distributed generation meant combustion generators, e.g. Diesel

Generator (DG) systems. Recently, solar has become a popular distributed

generation option.


Smart Grids


• Smart grid values– More reliable -- More economic

-- More efficient– More secure -- More environmentally

friendly -- Safer• Smart grid principal characteristics

– Enable active participation by consumers -demand response

– accommodate all generation and storage options – distributed generation

– enable new products, services, and markets –peer to peer energy trading

– optimize asset utilization and operate efficiently - VPP

– anticipate and respond to system disturbances – outage prediction and self-healing

• Sensors

• Communication infrastructure

• Control algorithms

• Actuators


Machine Learning


What is Machine Learning?


➢Machine learning is a form of artificial intelligence (AI) that enables a system to learn from data rather than through explicit programming.

➢ Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes.

• Supervised learning: classification; regression

• Unsupervised learning: Clustering, associations, anomaly detection, sequencing, recommendation systems, market basket analysis;

• Reinforcement learning: learns from trial and error, egrobot in a maze


Big Data in Smart Grids


Big data is a term for large and complex data sets that cannot be adequately processed with traditional data processing application software. Real-time data from the smart grid could be generated in terabytes due to the massive scale of the power grid:

➢ field measurements obtained by substation

➢ feeder intelligent electronic devices to specialized commercial and/or government

➢ state databases,

➢ Weather data of different types,

➢ lightning detection data,

➢ seismic data,

➢ electricity market data

➢ ….

Internal data External data

• Metering data• PMU data• Power line monitoring data • Etc.

• Weather forecast• Social networks• Stock markets• Etc.


What Can Machine Learning Do to Power Systems?


• Failure prediction• Power outage prediction• Dynamic energy management – demand response; peer-

to-peer electricity trading etc• Demand side management• Theft detection• Predictive maintenance• Real-time customer billing• Optimising asset management• Enhancing customer experience• Demand and renewable forecasting• Condition monitoring

Data Engineering

and Data Science


Demand Side Management


Demand side management is a set of measures to use loads and local generation to support network operation/management and improve the quality of power supply:

➢ Direct load control: Customers’ electrical appliances

(e.g. air conditioner, water heater, space heating) are

controlled remotely.

➢ Emergency demand response programs: Provide

incentive payments to customers for load reduction

during periods when the system is short of reserve

➢ Ancillary services market program: Customers bid load

curtailments in local independent markets. Image: Courtesy of AEMO


Energy Forecasting in Demand Response


To model the schedule of appliances in a house more precisely, one can extract useful

information from the schedule of residential customers by getting answers to the

following questions:

➢ When do they work?

➢ How far are they away from work?

➢ How do they commute?

➢ What do they do outside after work?

➢ What do they do at home?


Peer to Peer Energy Trading


• Some customers have installed Distributed Generation (DG) and energy storage. Such customers do not only consume electricity, but also can supply power to the grid.

• In P2P energy trading, the main agent is the prosumer, who produces and consumes energy and exchanges with other prosumers for surplus electricity that is overproduced after consumption.


Peer to Peer Energy Trading Contd…


• In stock trading, the agents aim to maximize their gains by trading at the optimum time using the market price of stocks that fluctuate in real time;

• Differences in electricity trading: electricity reserve can change in real time when they are not traded; the actual trading volume is related to power losses.

Reference: J. Kim and B. Lee, ‘Automatic P2P energy trading model based on reinforcement learning using long short term delayed reward’, Energies, 2020.


Predictive Maintenance (Condition Monitoring)


• Time series?

https://archive.ics.uci.edu/ml/index.php


Predictive Maintenance (Condition Monitoring) Contd…


• Supervised or unsupervised learning?

• Classification or regression?

Classification: will the equipment fail or not within a period?

Regression: what is the remaining useful time of the equipment?

Clustering: does the wind turbine data fit the normal way?


Predictive Maintenance Contd…


• DBSCAN: Density Based Spatial Clustering of Applications with Noise

Image source: https://bookdown.org/ndirienzo/ista_321_data_mining/intro-to-anomaly-detection.html


What else can we do?


• Using natural language processing to assess how smart grid strategies are working, eg, participants’ survey;

• Predictive control;

• Optimum power generation distribution among power sources and storage systems;

• Health status of equipment based on appearance

• …


Case Studies


Green Hydrogen and Floating Offshore Wind


• The Dolphyn project: The idea of using excess wind energy to make hydrogen or use all to make hydrogen!!

https://www.evwind.es/2021/02/13/the-global-race-to-produce-hydrogen-with-offshore-wind-energy/79337


Hornsdale Power Reserve


• What can we do here: price forecasting; battery usage forecasting

https://hornsdalepowerreserve.com.au/


Hydrogen – Wind – Solar – Battery System


• EIT master student thesis


1. Feng Ye, Yi Qian, Rose Qingyang Hu, Smart Grid Communication Infrastructures: Big Data, Cloud Computing, and Security, 2018, ISBN: 978-1-119-24016-7/Chapter 8.

2. Panagiotis D. Diamantoulakis, Vasileios M. Kapinas, George K. Karagiannidis, Big Data Analytics for Dynamic Energy Management in Smart Grids, Big Data Research, Volume 2, Issue 3, 2015, Pages 94-101, https://doi.org/10.1016/j.bdr.2015.03.003.

3. Syed Saqib Ali and Bong Jun Choi: State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review, Electronics 2020, 9, 1030; doi:10.3390/electronics9061030

References

https://doi.org/10.1016/j.bdr.2015.03.003


Q&A


www.eit.edu.au

1031 Wellington Street West PerthPerth, WA 6005

Inside Australia: 1300 138 522Outside Australia: +61 8 9321 1702

[email protected]

Website

Head Office

Email

Phone

smart grids: from renewable sources to machine learning

Documents