The Future of Grid Planning

Tomorrow's Energy System is Built on Today's Planning

Over the next 20 years electric utilities will need to build as much electric infrastructure as they have in the past 150 years. They face an opportunity to shape the energy system of the future, a system that could be affordable, reliable, and clean.

The reasons for this opportunity are varied and known:

• Revolutions in battery-electric technology enable more efficient vehicles, heating solutions, and assemblies powered by electrons instead of combustion,
• Governments, corporations, and individuals have set ambitious decarbonization targets which rely on electrification,
• New computing frontiers require enormous amounts of electricity,
• The widespread availability and cost competitiveness of solar PV and batteries is enabling energy to be created and stored at the edge for similar costs as the bulk system,
• Governments and corporations are increasingly recognizing that our economy is capped by the growth of the energy system, and an emerging consensus that now is a time to build and grow.

However, to expand their role, electric utilities must overcome several challenges:

• Critical uncertainties in the degree and speed of electrification facing the system, and the grid modernization required or feasible to meet electrification needs
• An increasingly complex grid across new bulk system and demand-side resources, including a customer shift to consumer-producers who actively participate in energy markets and real-time demand management programs
• Component, supply chain, and labour shortages and corresponding cost escalation for key electric infrastructure

But the change is happening rapidly, ranging from 29% of all cars on the road in Norway being electric, PV providing 40% of power to Texas’s grid during peak hours, 10-year queues for new 1-10MW connections in the Netherlands, and data centre operators increasingly turning to gas gensets for as much as 4GW of capacity.

In this context, planners, engineers, and electricity system leaders, need new analytical and simulation tools which allow them to address and embed uncertainty into their planning processes and confidently make no-regrets investments that protect reliability, manage affordability, and prepare the grid for the future.

What are the Principles of Modern Grid Planning?

Expanding planning requirements for the energy system, both regionally and continentally, are limited by a lack of consistency in the data, analytics, and other capabilities of sector participants. Historically, DSOs operating in an environment of relatively flat and predictable demand have neither possessed nor required the capabilities to rapidly assess and provide varying demand scenarios or pathways for meeting local energy needs.

Meeting this opportunity requires a new approach across the asset planning lifecycle:

#1

From Static Planning...

Traditional grid planning processes design for a predictable and centralized grid – a ‘fit-and-forget’ approach. Some utilities assess low-base-high simulations and take dozens to hundreds of engineering hours to incorporate new projections on how demand is changing or what the impact of non-wires alternatives could be.

To Rapid Scenario Planning

With unpredictable load growth and the pace of decentralization / DER integration, utilities must be able to conduct rapid scenario planning on their grid across a spectrum of plausible futures. By creating data-driven scenarios with energy-economic and physical system modelling, utilities can simulate grid conditions and required investments under a range of possible futures and identify no-regrets investments in a matter of minutes.

#2

From Bulk System or Sub-station Level Forecasting...

Traditional planning processes assess load growth at the sub-station or bulk system level based on residential, commercial, and industrial developments and population growth.

To Bottom-up Demand Forecasting with a Focus on Your Customers

Meter-level load forecasts that are based on a deep understanding of customers (demographic, existing adoption of EVs, heat pumps, etc) and system context (e.g., weather) reveal temporal and geospatial load patterns and asset-level system constraints that are missed by system-level models. By identifying the drivers of load evolution at the meter, feeder, transformer, or substation level, utilities can identify which and when assets are becoming congested and deploy more targeted demand-side management solutions and physical upgrades. Below is an example of how leading utilities model long-term system peaks.

#3

From Prioritization of Physical Infrastructure Upgrades...

Traditional grid planning processes determine where physical grid infrastructure upgrades are required to meet new demand. Some static non-wires alternatives have been applied across energy systems (e.g., time-of-use rates) to help manage peak demand.

To Active Assessment of Wires and Non-Wires Alternatives

Utilities lack the capital, time, or resources to expand the grid the way we historically have. To manage affordability and reliability, grid planning processes need to rapidly simulate and compare the impact of a range of possible congestion solutions, both wired and non-wired. Leading utilities:

• Rapidly compare the costs and benefits of different solutions in addressing grid needs
• Share results with regulators and policymakers to quickly align on required investments and are rewarded for optimizing capital deployment to benefit ratepayers

#4

From Participant-led Planning...

Electricity distribution grid planning has historically been done in isolation from transmission / bulk system planning, gas network planning, policymaking, and system operations, integrated only when necessary and through time-consuming manual processes.

To Integrated Energy Planning

To model demand across increasingly integrated energy systems, distribution utilities must be able to support integrated planning across four dimensions:

• The electricity infrastructure value chain,
• Energy vectors and systems,
• Policy, economic, and physical impacts,
• Interprovincial / interstate / inter-regional and international borders,

…enabled through shared, data-based energy forecasts across energy types. Optimized demand forecasting data that can be shared across regions and stakeholders will enable more effective allocation of resources and long-term infrastructure planning.

#5

From Siloed Grid Planning...

Traditional grid planning processes take dozens to hundreds of hours to incorporate perspectives from system stakeholders in formal rate filings. As well, policymakers often don’t consider the holistic grid impact of potential policy directives.

To Stakeholder Input Integrated at Every Step of the Planning Cycle

To address emerging energy needs, leading utility planners develop tools and processes to collect and share data on demand forecasts and the potential grid impact of new loads, enabling productive discussions on where, when, and what investments need to be made across the energy system, with all key stakeholders.
‍
For utility planners, building these tools and processes improves:

• Collaboration cross-functionally within organizations (e.g., with Customer or Asset Management teams)
• Relationships with regulators and reduces the likelihood of rate case rejection by pre-auditing load growth and solution methodologies
• Modelling and sharing the grid impact of policy decisions (e.g., heat pump incentives, net zero targets) with policymakers
• Response time to customer connection requests

What steps can utilities start taking to improve their planning capabilities?

With new analytical tools that enable the future of planning, what can be achieved?

A blueprint for the future could look like…

In a week…

• Establish your base state view of the future, across population growth, new development, and existing constraints on assets into your bottom-up digital twin of the grid, taking input from meter, SCADA, GIS, EAM, and customer data systems
• Solicit perspectives on the forces that impact the grid, and the range of potential futures that are worthwhile to stress test
  
  • These perspectives can be generated internally—teams across planning, regulatory, operations, customer, and strategy
  • And also externally—gas system operators, electric system operators, policymakers, regulators, and independent forecasters

In a month…

• Level set on assumptions and areas of uncertainty with key stakeholders—noting any disagreements for future pressure-testing
• Parameterize these areas of uncertainty into integrated scenarios, with consistent logic and behaviour models on how for example the price of battery storage will interact with the price of electric vehicles, and how those prices influence customer adoptionThose disagreements on the future now form the basis for different scenarios that can be compared and contrasted

In a quarter…

• Understand how those scenarios—dozens and hundreds of them—affect grid infrastructure, and where there are consistently areas of congestion or supply constraints
  
  • The solutions to these constraints become no regrets investments, with a strong case for why those investments are needed under a wide range of scenarios and sensitivities
• Explore how non-wires alternatives—rates, storage, markets—can also resolve these constraints and how they compare to typical wired solutions
• Engage other ecosystem stakeholders—policymakers, market platforms, industry—on what needs to change to enable the lowest cost and highest-reliability electric system of the future
• Proactively engage regulators to audit scenario methodology and spend time debating underlying assumptions instead of outcomes

In a year…

• Design bottom-up customer programs and flexibility solutions, acknowledging that the right rate or incentive may differ by region, sub-station, or transformer, and hyper-target initiatives to where they’ll deliver the highest value
• Re-allocate your planning team to new areas of highest and best use of their time, recognizing that much more planning will need to be done in the future, and now you’ve equipped your team to do it effectively for every asset in the system