ENERGY STORAGE2016-07-26 C-MADENS UPDATETEMPORAL RESOLUTION IN MODELLING

HOW AND WHY

TEMPORAL RESOLUTION

• Size of time interval between two measurements of a system’s state

• Want to determine:

- What discrepancies may be seen in energy storage purposes

- How this affects modelling or software optimization

• 1 to 60 minutes

• Longer the interval, the less calculations and the cheaper it is

Wright and Firth (2007): “longer than a minute underestimates import and export”

- But only considered seven houses and only two of those in depth

HOW AND WHY

INTEGRATED BRIGHT AND CREST MODELS

▸ Bright, J. open source solar model:

▸ Combines hourly historical cloud coverage measurements with HELIOSTAT model to generate stochastic minutely solar irradiation

▸ Validated against Leeds Church Fenton and Cambourne cloud coverage data

▸ CREST Integrated Demand Model

▸ Generates stochastic minutely solar irradiation (not used)

▸ Generates stochastic minutely household electricity demands based on survey data

▸ Storage heater use is function of month

▸ Validated against data collected from 22 households

LIMITATIONS

ASSUMPTIONS AND LIMITATIONS

▸ Value at time t is the average over the time interval and not the instantaneous measurement

http://blog2.ecoastsales.com/wp-content/uploads/2014/01/Warning.jpg

▸ CREST Model:

▸ Occupancy is not dependent on time of year (“This model under-represents seasonal variation”)

▸ Appliances are not dependent on number of people in the house*

▸ Bright Model:

▸ Solar Irradiation is based on a point. Household and neighbours have same irradiance

SIMULATION

METHOD

Using a COM (ActiveX) server to bridge MatLab and Excel

▸ Only way to run macros automatically from MatLab

Extended CREST model to calculate for a week

SIMULATION

SIMULATION PROCEDURE

1. PARAMETERS2. BRIGHT SOLAR MODEL

3. HOUSEHOLD ASSIGNMENT4. CREST DEMAND MODELS5. PROCESS AND PRESENT

SIMULATION

INPUT PARAMETERS

MatLab container script meant to be flexible

▸ PV system parameters based on (from Jacques et al. LiDAR survey and Ofgem FiT installation report)

▸ Actual panels based on commercial products

▸ Battery chosen is currently 6.4 kWh Tesla Powerwall (2 kW; Moixasystems under 1 kW)

SIMULATION

BRIGHT MODEL

Generates data for specified time length (currently one year)

▸ Moderate speed

▸ Fastest to store every day for t time into array and draw upon it

SIMULATION

HOUSEHOLD AND OCCUPANCY

Generates data for houses and occupancy models

▸ Define number of houses (scenarios: single, street, neighbourhood, etc.)▸ Each house is stochastically assigned appliances▸ Assignment based on various survey statistics

▸ Occupancy models (type of occupancy)▸ Stochastically generated based on UK 2000 Time Use Survey

▸ ”Houses” are stored and reused (will elaborate).

▸ Number of people per house stochastically assigned based on distribution found in 2011 General Lifestyle Survey

SIMULATION

DEMAND

Generates data based on occupancy and outdoor lighting

▸ Appliances▸ Probability of usage based on various survey statistics▸ Mostly a function of household occupancy (generated previously)▸ Some appliances run when people are not home

▸ Lightbulbs▸ Using probabilities from survey data▸ Function of household occupancy▸ Compares to outdoor irradiation; sets threshold for outdoor irradiation (normal

dis.; μ=60 Wm-2 σ=10)

▸ Slowest part of calculations

SIMULATION

ITERATIONS OF SIMULATION

DAYSHOUSES

DEMAND MODELS

SIMULATION

SUMMARY MODEL OF ITERATIONSINTERVAL 1

DAY 1

HOUSE 1

INTERVAL 2 INTERVAL V

DAY 2 DAY 7

…

… DAYS 1:7

HOUSE 1:H HOUSE 1:N

DEMAND DEMANDS…DEMAND DEMANDS

21 X 1:X

DEMANDS

1:X

DEMANDS

1:X

HOUSE H

SIMULATION

ECONOMICS

NET DEMANDGENERATION

SIMPLIFIED CHARGE / DISCHARGE DECISIONCALCULATE IMPORT/EXPORT

CALCULATE DIFFERENCES (NET AND %)

SIMULATION

SIMPLIFIED MODEL OF CHARGING AND DISCHARGING

PRODUCTION DEMAND

> 0

< 0

= 0

STORAGE

EXPORT

CHARGE

STORAGE

IMPORT

DISCHARGE

If not full

If not empty

SIMULATION

CHARGING MODEL

▸ System Constraints

- Charge Rate (Power / W)

- Discharge Rate (Power / W)

- Total Capacity (Energy / kWh)

▸ To Do:

- Continuous vs Peak?

- Roundtrip efficiency

- Does not account for grid economics

- Does not act to prolong life (assumes ideal storage device)

- Limited by manufacturer datasheets

WORK SO FAR

SIMULATION

PRELIMINARY RESULTS

Same as Wright and Firth (2007) found:

Increased resolution underestimates performance

Error vs time res. has good polynomial fit (R2 > 0.9975)

Need to validate and review calculations

Unknown ’aliasing’ effect arrising

SUMMARY

FUTURE WORK / RELEVANCE TO C-MADENS▸ Improve charge/discharge model more

comprehensive by incorporating:

▸ Arbitrage considerations

▸ Battery degradation economics

▸ Other predictive models (Maths guys)

▸ Validate CREST work against load profile data

▸ Jamie Bright’s PhD work includes better neighbourhood simulation

▸ Could be turned into a systems model with other sources and storage technologies.

QUESTIONS?

Johnny Appleseed

SUMMARY

SUMMARY

REFERENCES▸ Bright, J. M., Smith, C. J., Taylor, P. G., & Crook, R. (2015). Stochastic generation of synthetic minutely irradiance time series

derived from mean hourly weather observation data. Solar Energy, 115, 229–242. http://doi.org/10.1016/j.solener.2015.02.032

▸ Bright, J.M., Smith, C.J., Taylor, P.G., & Crook, R. (2015). The Bright Solar Resource Model. http://jamiembright.github.io/BrightSolarModel

▸ Jacques, D. A., Gooding, J., Giesekam, J. J., Tomlin, A. S., & Crook, R. (2014). Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK). Applied Energy, 124, 28–34. http://doi.org/10.1016/j.apenergy.2014.02.076

▸ Richardson, I., & Thomson, M. (2012). Integrated simulation of photovoltaic micro-generation and domestic electricity demand: a one-minute resolution open-source model. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 227(1), 73–81. http://doi.org/10.1177/0957650912454989

▸ Richardson, I., & Thomson, M. (2010). Domestic Electricity Demand Model —Integrated Domestic Electricity Demand and PV Micro-generation Model, Loughborough University Institutional Repository, 2010, http://hdl.handle.net/2134/7773

▸ Wright, A., & Firth, S. (2007). The nature of domestic electricity-loads and effects of time averaging on statistics and on-sitegeneration calculations. Applied Energy, 84(4), 389–403. http://doi.org/10.1016/j.apenergy.2006.09.008