Optimisation of photovoltaic system and storage

Industry representative: Chris White, Ergon Energy

Moderators: John Boland, University of South Australia; Fanny Boulaire, Queensland University of Technology

The problem

The electricity industry is changing, driven by multiple disruptive forces. This presents great transformation opportunities for both industry participants and consumers. We are shifting from a centralised energy model to a future energy model that is more dynamic. The changes are driven by:

• increased availability and falling costs of technologies including rooftop photovoltaic (PV) generation, home energy management systems, and electric cars
• the Internet of Things, which is changing the way people interact with technology
• policies, tariffs and incentives designed to change our use of energy and our carbon emissions
• increased competition and new business models, such as technology options with no upfront costs.

Customers want choice, predictability, certainty and control. They are also telling retailers that they want a simple, transparent and effortless ex- perience with their energy provider.

Ergon Energy is an electricity distributor and retailer that provides electricity to about 700,000 households in Queensland. They wanted to know how they can estimate the value to individual customers of various combinations of PV and storage.

Storage

Electrical energy storage systems are starting to become available, but are still relatively expensive. It could still be possible to achieve a reasonable return on investment by monetising the various value streams that storage can create, including:

• storing low-cost energy from PV systems and from off-peak periods of Time of Use (ToU) tariffs, for later use when the cost of electricity is high
• managing storage at scale as a Virtual Power Plant (VPP), to import and export power at specific times to provide services to electricity networks and retailers.

To fully utilise investment in storage it needs to be managed as part of an ecosystem that includes customer behaviour, home energy management technologies, advanced analytics and tariffs. Understanding how this ecosystem works and how the benefits of storage can be accessed by customers, retailers and networks is a key objective of a residential storage pilot programme currently being conducted by Ergon Energy. The pilot project is run over three locations—Cannonvale, Toowoomba and Townsville—using a Virtual Power Plant capable system to explore the multiple value streams. The Australian Renewable Energy Agency (ARENA) has provided $400,000 towards this pilot.

The MISG project team delineated various aspects to solving the problem of how to estimate the value to the customer of various combinations of PV, storage and tariffs. A simulation-based solution was opted for, and different groups were formed to work on a literature review, analysis of Ergon data, and simulation.

There are few papers in the literature that use real PV and demand data with high temporal resolution from households. However, UniSA has good 1-minute data from 30-50 low-energy homes at Lochiel Park.

Analysis of Ergon data

Load data for Toowoomba and Townsville

Ergon provided half-hour load profiles for generic households in Toowoomba and Townsville, for a full year. This data is of interest because it shows different patterns of consumption over a year; Townsville peak consumption occurs over the summer months due to air-conditioning, whereas the Toowoomba peak occurs over the winter months due to heating. Both were significantly different from the National Electricity Market (NEM) load profiles of Queensland. The NEM data will be useful for determining how revenue might change for a retailer if energy storage is adopted widely, whereas the other two data sets can be used to understand how batteries and home energy management systems can help individual customers save electricity.

Load according to socio-demographic groups

A question arose regarding the possibility of grouping customers according to their consumption so that Ergon could identify clusters of people for whom, for example, a particular configuration of PV and battery might be suitable. Work on clustering of electricity consumer had already been done in a previous MISG [1], so that question was not thoroughly investigated.

The data provided by Ergon contained information about which of four socio-demographic groups each household belongs to, enabling us to determine variability between groups and also within groups.

Home Energy Management Systems

Home Energy Management System (HEMS) data at 15-minute intervals was available as an average day for each month from July 2015 to January 2016, for different loads including air-conditioning and pool pumps. HEMS are used to best manage the household loads, and are important when batteries are installed. However, with the right sizing of PV and control of appliances, HEMS might also be able to manage loads and reduce the need for storage.

Simulations

During the MISG workshop we simulated the operation of a household with PV and storage to assess the impact of PV size, storage size and tariff. Simulations were done using a year of PV and load data; eventually they could be extended to use many years of synthetic data.

Simulations were done using an existing PV and battery simulation model developed for the CRC for Low Carbon Living and based on data collected by the University of South Australia from instrumented houses at Lochiel Park. The original code was modified to change the time step from 1-minute to 30-minutes, to incorporate new battery management protocols, and to incorporate Queensland tariffs including a flat tariff and a time-of-use tariff.

The strategy for charging and discharging the energy storage depended on the tariff. For a flat tariff, if solar power exceeded load then the storage system was charged, or the energy exported if the storage system was full; if solar power was less than the load then the storage system was discharged to meet the load, and extra energy imported if necessary.

The strategy for a time-of-use tariff was more complicated. Except for two time periods, the strategy was the same as for the flat tariff. During the peak period, solar and discharge are used to meet loads before importing energy. The goal before the peak is to fill the storage system, taking into account the charing rate of the storage system and expected solar and loads.

The simulation was a viable tool for making decisions about PV, storage and tariff, and were also useful for evaluating different storage control profiles. We found that a control strategy that attempted to fill the battery before a peak period was beneficial, and almost as good as a strategy with perfect knowledge of future PV generation and load.

Conclusions

During MISG we modified an existing simulation tool so that it could calculate the energy cost savings given half-hour PV generation and customer loads, for any combination of PV size, storage capacity and tariff. Ergon can use this simulation method, coupled with its estimates of the costs of the various configurations, to determine which the best combination of PV, storage and tariff for a particular customer’s load profile. For customers who already have PV, it is also possible to develop an empirical model that predicts storage requirement based on how much they export.

Acknowledgments

We would like to thank Chris White and Ergon Energy for the chance to work on this problem at MISG. The following people worked on this project during MISG and should also be acknowledged for their ideas and their participation: Kirrilee Rowe, Luigi Cirocco, Silvio Tarca, Minh Tran, Soorena Ezzati, Joon Heo, Adrian Grantham, Manju Agrawal, Noel Thompson, Charles Ling, Kaye Marion.

References

[1] McDonald B., Pudney P. and Rong J. (2014). “Pattern recognition and segmentation of smart meter data”, ANZIAM 54: 105-150.