Consumer-Centric Electricity System Planning

While policymakers may drive the pace of decarbonization overall, the depth, timing, and location of consumer adoption of Behind the Meter (BTM) resources, i.e. rooftop solar PV systems, battery storage, electric vehicles, and appliances, are the key drivers of the future energy system in Australia.
While policymakers may drive the pace of decarbonization overall, the depth, timing, and location of consumer adoption of Behind the Meter (BTM) resources, i.e. rooftop solar PV systems, battery storage, electric vehicles, and appliances, are the key drivers of the future energy system in Australia.
Energeia research and modeling showing potential customer decisions (Example)
Figure 1 – Potential Customer Decisions (Example) Source: Energeia research and modeling
While policymakers may drive the pace of decarbonization overall, the depth, timing, and location of consumer adoption of Behind the Meter (BTM) resources, i.e. rooftop solar PV systems, battery storage, electric vehicles, and appliances, are the key drivers of the future energy system in Australia.
Figure 2 – Average Hourly Load Before Optimisation (excel PV and Battery). Source: Energeia modeling. Note: WH = Water Heating, SC = Space Cooling
Figure 3 illustrates the same customer following grid integration to maximize their load factor, i.e. to maximize the utilization of the grid capacity and minimize the amount of grid capacity. While this is an extreme example, it is technical feasible, and with the right rules and regulations, will be economic.
Figure 3 – Average Hourly Load After Optimisation (excel. PV) Source: Climate College (2021)
Once consumers transition to full electrification of appliances and transportation and have installed rooftop PV (including over-parking and other structures) and storage to minimize their energy costs, their grid needs will be completely different:
While this may take 20 years or more to realize due to the rate of turnover of appliances and vehicle stock and declines in the cost of battery storage, grid investments such as conductors and transformers typically last over 50 years. Therefore, to avoid the risk of significant stranding of today’s grid investments, the electricity industry needs to anticipate future consumer needs today with as much accuracy and precision as possible.

The State of the Art in Consumer Behavior and Load Impact Forecasting

The electricity forecasting community has been slow to change in the face of significant change in the things they are forecasting. Below shows the Australian Energy Market Operator’s forecast of peak demand over the 2011 and 2012 timeframe, when consumers starting using more solar PV.
Figure 4 – AEMO’s Forecast of Peak Demand Source: Climate College (2021)
Figure 4 – AEMO’s Forecast of Peak Demand Source: Climate College (2021)

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The industry’s track record in forecasting electric vehicle adoption has not been much better, as shown in Figure 5 below.
Figure 5 – Various Attempts at Forecasting EV Adoption in Australia Source: Climate College (2021)
A key reason for the forecasting errors seen above is due, in Energeia’s view, to the use of forecasting methods that were no longer fit for purpose. Regression based methods, which largely project the future using the past, were the tools used in the above examples. As new technologies emerged, and new behaviors emerged as a result, the past was a poor indicator of the future. Luckily, the state of the art in consumer behavior forecasting has come a long way, and it is possible to not only estimate what the future state looks like down to the LV transformer level, but also how key strategies can optimise it over time. Figure 6 below compares a range of forecasting methods, with the older but easier to use methods like regression, etc. shown at the top. These methods are less robust, and therefore less well suited to forecast an evolving and emergent factor like consumer behavior.
Figure 6 below compares a range of forecasting methods, with the older but easier to use methods like regression, etc. shown at the top. These methods are less robust, and therefore less well suited to forecast an evolving and emergent factor like consumer behavior.
The more complex, less easy to use methods can all be seen to be more robust to typical forecasting risks, but come at the cost of more effort, more complexity and computational complexity. Of these, Agent Based Modeling (ABM) stands out for its relative ease.

Agent Based Simulation 101

ABM is a category of computational models that invoke dynamic action, reaction, and intercommunication protocols amongst the agents in their shared environment to derive insights about their behaviour and emergent properties, as illustrated by the diagram in Figure 7.
ABM has grown in popularity with leading edge forecasting practitioners due to it s ability to address key complexities seen in modeling and forecasting consumer behavior:
Figure 7 – Agent-Based Modelling Source: ResearchGate, Macro Galbiati, 2021
Figure 7 – Agent-Based Modelling Source: ResearchGate, Macro Galbiati, 2021
ABM is originally based on the work of Jon Von Newman, a Hungarian polymath, who first invented the method. It’s first application by someone other than Von Neumann was in the social sciences in the 1970s, which used it to model the behavior of migrating populations based on their level of tolerance. It expanded into other areas from the 1990s, and its first identified application in the electricity industry was in 1999. From the 2010s, it has become more widely applied in the electricity industry.
Figure 8 – History of Agent Based Modelling Source: Energeia research
Figure 8 – History of Agent Based Modelling Source: Energeia research
With a more robust and accurate consumer behavior forecasting method, which can not only address decisions around new technology adoption, but also address participation in electricity industry programs, and therefore the development and operation of distributed resources, it is now possible to integrate this behavior into electricity system planning.

Truly Integrated, Consumer-Centric System Planning

The State of the Art in electricity planning is truly integrated system planning, which includes consumer resources and the distribution system as an endogenously modeled element. This stands in contrast to typical electricity planning, sometimes referred to as Integrated Resource Planning, which endogenously models and co-optimizes transmission and generation, with consumer and distribution factors treated as exogenous, potentially varying according to a range of scenarios, without any feedback loops.

The figure illustrates conceptually the difference between an IRP and a true[1] ISP in terms of how different elements of the electricity interact dynamically. For an IRP, you can see the generation and transmission elements interacting. A true ISP integrates each of the systems, modeling their interactions, and feedback loops.

illustrates conceptually the difference between an IRP and a true ISP in terms of how different elements of the electricity interact dynamically.
Figure 9 – Illustrative Value of Truly Integrated System Planning Source: Energeia
Figure 10 – Impact of Truly Integrated, Whole-of-System Optimization Source: Energeia modeling
Figure 10 – Impact of Truly Integrated, Whole-of-System Optimization Source: Energeia modeling
The differences in the level of optimisation between the two are growing in significance due to the potential role of consumer resources to reduce generation and grid costs. Figure 10 shows the difference between a business as usual (BaU) level of system planning and a true ISP level of system optimization, using consumer centric, agent-based simulation methods.
Interestingly, true ISP not only identifies lower cost pathways where consumer resources are used to offset utility investments, but it can also identify opportunities for reducing the level of uneconomic consumer investment in behind-the-meter resources, as shown in this example.
Despite the potential benefits of employing a true ISP approach, the significant complexity involved in setting up more sophisticated methods like ABM have limited its popularity to date. The table below summarises the results of Energeia’s recent review of international system planning methods. Our research found that California utilities are one of the few examples of true ISP at the moment.
Figure 11 – Benchmarking of Leading System Planning Efforts Source: Energeia research
Figure 11 – Benchmarking of Leading System Planning Efforts Source: Energeia research
[1] A number of plans refer to themselves as integrated, but do not endogenously model consumer behavior.

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