In the previous articles, I discussed how future demand shapes grid investment and how different stakeholders use load forecasts for planning decisions. Before moving deeper into base load forecasting, it is important to first discuss one key methodological question:

At what level of detail should a load forecast be prepared?

This question is usually described as forecast model granularity.

In simple terms, granularity means the level of detail used in the forecast. A forecast can be developed at system level or feeder level. It can be annual, monthly, daily, hourly, or even sub-hourly. It can represent one large region, one load zone, one transmission node, one distribution substation, or one customer class.

This matters because a forecast is only useful if its level of detail matches the planning question. A forecast that is good enough for long-term resource planning may not be detailed enough for distribution planning. Similarly, a feeder-level forecast may be too detailed for market-wide adequacy studies unless it is properly aggregated.

The ESIG report highlights that modern load and DER forecasting increasingly requires both temporal granularity and geographic granularity to capture changing demand patterns, DER adoption, electrification, and localized load growth [1].

2. What Is Forecast Model Granularity?

Forecast model granularity can be understood in two dimensions:

1. Temporal granularity

This refers to how demand is represented over time, e.g.

• annual peak demand

• monthly energy demand

• daily load profile

• hourly load profile

• 8,760 hourly time-series forecast

2. Spatial granularity

This refers to where demand is represented in the network, e.g.

• system level

• load zone level

• transmission node level

• distribution substation level

• feeder level

• customer or meter level

Both dimensions are important.

A planner may know the total demand growth of a region, but if the forecast does not show where the demand appears, it may miss local constraints. Similarly, a planner may know the annual peak demand, but if the forecast does not show when the demand appears, it may miss ramping needs, midday minimum load, evening peaks, or seasonal operating stress.

3. Why Temporal Granularity Matters

Traditional long-term load forecasts often focused on annual energy and annual peak demand. This was useful when electricity demand was relatively predictable and the system was dominated by centralized generation.

However, modern demand is becoming more dynamic.

Electric vehicles may charge outside traditional peak periods. Rooftop PV may reduce midday net load. Batteries may shift demand between hours. Heat pumps may create new winter peaks. Data centers may introduce large and relatively flat demand profiles. Extreme weather may create short-duration but high-impact demand events.

This is why hourly forecasting is becoming more important.

An 8,760 hourly forecast represents every hour of the year. It allows planners to see not only the annual peak, but also:

• hourly load shape

• seasonal variation

• minimum load conditions

• evening ramps

• non-coincident local peaks

• DER impact on net load

• EV charging behaviour

• weather-driven demand patterns

This does not mean every study always needs an 8,760 forecast. It means the time resolution should be selected based on the planning purpose as shown in below table.

The key point is simple: as the grid becomes more dynamic, time resolution becomes more important.

4. Why Spatial Granularity Matters

Spatial granularity is equally important.

A system-level forecast may show that the overall grid has enough capacity, but this does not mean every local area is secure. A specific substation, feeder, industrial zone, data center cluster, or EV charging corridor may become constrained even if the wider system appears adequate.

This is especially important for:

• EV fast-charging hubs

• data center clusters

• industrial electrification

• rooftop PV concentration

• behind-the-meter BESS

• heat pump adoption

• new urban developments

• distribution-level load pockets

For example, a 500 MW increase in demand spread across a large region may be manageable. But the same 500 MW concentrated near one transmission node or one group of substations may require major reinforcement.

This is why modern planning increasingly needs forecasts that can move between different geographic levels:

• system forecast

• load zone forecast

• transmission node forecast

• substation forecast

• feeder forecast

• customer class forecast

The forecast must be detailed enough to reveal the planning issue.

5. The Trade-Off: Detail Versus Complexity

Higher granularity improves visibility, but it also increases complexity.

A highly detailed forecast needs better data, more assumptions, stronger modelling capability, and more validation. For example, an hourly forecast at feeder level over a 20-year horizon creates a large amount of data. A customer-level DER adoption forecast may be very detailed, but it may also carry significant uncertainty.

Therefore, planners must balance:

• accuracy

• data availability

• modelling complexity

• computational effort

• planning horizon

• stakeholder needs

• decision impact

The objective is not to make every forecast as detailed as possible. The objective is to make the forecast fit for purpose.

A financial planning forecast may be acceptable at customer-class or service-territory level. A transmission planning forecast may need allocation to transmission nodes. A distribution planning forecast may need substation or feeder-level detail.

6. Aggregation and Disaggregation

Another important methodological concept is the movement of forecasts between different levels of the grid.

• Aggregation means combining lower-level data into a higher-level forecast.

For example:

• Disaggregation means allocating a higher-level forecast down to more detailed grid locations.

For example:

Both approaches are used in planning.

However, both can create errors if not handled carefully. A top-down system forecast may miss local load pockets. A bottom-up distribution forecast may overestimate total system demand if diversity and coincidence are not properly considered.

This is why reconciliation is important. Planners need to check whether system-level, transmission-level, and distribution-level forecasts are consistent enough for the intended planning use.

7. Matching Granularity with the Planning Use Case

A good forecasting methodology starts by asking:

What decision will this forecast support?

Different planning studies need different forecast resolution.

This shows why a single forecast cannot answer every planning question. The same demand outlook may need to be represented differently depending on whether the question is about adequacy, congestion, voltage, system strength, distribution reinforcement, tariff design, or grid connection.

8. Why This Comes Before Base Load Forecasting

Before developing a base load forecast, planners should first understand the required forecast resolution.

This is important because the base load forecast may be built differently depending on the intended use. A system-level base forecast may rely on economic, demographic, and weather-normalized trends. A feeder-level forecast may require SCADA, AMI, customer connection data, and local development information.

Therefore, the correct sequence is:

1. Define the planning use case

2. Select the temporal and spatial granularity

3. Collect and validate historical data

4. Develop the base load forecast

5. Add known new customer loads

6. Add DERs, electrification, and flexible demand

7. Apply scenarios and uncertainty analysis

8. Produce the final planning forecast

This sequence helps avoid one common problem: developing a forecast first and only later discovering that it does not have the right resolution for the planning study.

9. Key Takeaway

Forecast model granularity is not just a modelling detail. It is a planning decision.

If the forecast is too broad, it may hide local constraints. If it is too coarse in time, it may miss ramping needs, minimum load conditions, or non-coincident peaks. If it is too detailed without good data, it may create false confidence.

The key message is simple:

A load forecast must match the planning question. The right level of time and location detail is essential for reliable planning, efficient investment, and effective grid connection decisions.

In the next article, the discussion can move into the components of the long-term load and DER forecasting process, including top-down and bottom-up approaches, base load forecasting, known new customer loads, DERs, electrification, uncertainty, and scenario planning.

Reference

[1] Energy Systems Integration Group, Long-Term Load and DER Forecasting, A Report by the Energy Systems Integration Group’s Long-Term Load and DER Forecasting Task Force, Aug. 2025.

A load forecast is not prepared for one department, one study, or one decision. In modern power systems, it is a shared planning input used by utilities, system operators, regulators, developers, consultants, and other stakeholders to decide how the grid should evolve.

In the previous article, I discussed how future demand shapes grid investment and connection opportunities.

The next logical question is:

who actually uses these forecasts, and why do different stakeholders need them?

The answer is important because load forecasting is no longer limited to estimating annual energy demand and peak load. It now supports resource planning, transmission planning, distribution planning, financial planning, customer programs, market design, DER integration, and grid connection strategy.

As demand patterns become more complex due to electrification, data centers, EV charging, distributed solar, battery storage, flexible demand, and extreme weather, the value of a forecast depends not only on its accuracy, but also on how well it is understood, shared, and coordinated across planning stakeholders [1].

2. Load Forecasting as a Shared Planning Function

In a traditional power system, load forecasting was often treated as a relatively narrow planning function. Planners estimated future peak demand and annual energy, and those values were used to guide generation and network investment. That approach is no longer sufficient.

Today, the same forecast may be used by different teams for different purposes. A system operator may use it for resource adequacy. A transmission planner may use it to assess network reinforcement. A distribution utility may use it to identify overloaded feeders or substations. A financial planning team may use it to estimate revenue requirements. A customer program team may use it to design demand response, energy efficiency, EV charging, or DER programs.

This means load forecasting is not just a technical calculation. It is a coordination point between different parts of the electricity sector.

A useful way to think about it is:

If the forecast is inconsistent, incomplete, or poorly coordinated, the planning decisions that depend on it may also become inconsistent.

3. Key Stakeholders in Load Forecasting

Long-term load forecasting is used by several stakeholders, each with a different role in the planning process. Utilities use forecasts for distribution planning, infrastructure investment, customer programs, financial planning, and regulatory submissions. System operators, ISOs, and RTOs use forecasts for resource adequacy, market design, transmission planning, and system reliability studies.

Regulators and government agencies influence the forecast through policy targets, electrification programs, DER incentives, and planning requirements. Developers and large customers also shape the forecast when new generation, BESS, data centers, industrial loads, or hydrogen projects enter the planning pipeline. Consultants, vendors, and research organizations support the process through modelling tools, datasets, scenario studies, and technical analysis.

In simple terms, load forecasting is not owned by one stakeholder. It is a shared planning input used across the electricity sector to guide technical, commercial, regulatory, and investment decisions.

4. Main Use Cases of Load Forecasting

Different stakeholders use the same forecast in different ways. This is why the forecast must be clear, traceable, and suitable for the intended planning purpose.

4.1. Resource Planning and Market Design

Resource planners use load forecasts to determine the future need for generation, storage, capacity, reserves, and flexibility. The forecast helps answer questions such as:

• How much capacity will be needed?

• What type of generation mix is required?

• How much storage is needed?

• What level of reserve margin is required?

• How will demand shape affect dispatch and market design?

As load patterns become more variable, resource planning increasingly needs hourly or time-series forecasts, not only annual energy and peak demand values.

4.2.Transmission Planning

Transmission planners use load forecasts to identify future power flow patterns, network constraints, transfer needs, and reinforcement requirements. Forecasts help determine whether the system needs:

• new transmission lines

• transformer upgrades

• reactive power support

• revised transfer limits

• system strength solutions

• new substations or switching stations

• reinforcement near large load centers

Transmission planning usually requires a longer-term view because major network projects take many years to plan, approve, permit, and build.

4.3. Distribution Planning

Distribution planners use forecasts at a more granular level. They are concerned with local substations, feeders, transformers, cables, voltage levels, protection limits, and local hosting capacity.

This is where geographic detail becomes very important. A system may look adequate at the transmission level, while a local feeder or substation may be overloaded because of EV charging, rooftop solar, heat pumps, or a new commercial development.

Distribution planning therefore needs local forecasts that can identify load pockets and non-coincident peaks.

4.4. Financial Planning and Tariff

Forecasts also support utility financial planning and tariff design.

If energy sales, peak demand, and customer behavior change, the revenue requirement and cost recovery structure may also change. This becomes especially important when customers install behind-the-meter PV, batteries, EV chargers, or participate in demand response and time-varying rates. In this context, load forecasting supports:

• revenue projections

• tariff design

• cost-of-service studies

• investment planning

• customer class analysis

4.5. Customer Programs and DER Planning

Customer-facing teams use forecasts to design programs for:

• energy efficiency

• demand response

• EV charging

• rooftop solar

• behind-the-meter BESS

• building electrification

• flexible demand

These programs can reduce, shift, or reshape demand. Therefore, they are not only outputs of planning; they also become inputs into future forecasts.

5. Why Coordination Is Becoming More Important

The major challenge is that different stakeholders often produce different forecasts for the same or overlapping regions. For example:

• a system operator may prepare a system-level forecast

• a utility may prepare a service-territory forecast

• a distribution team may prepare feeder-level forecasts

• a financial team may use customer-class forecasts

• a regulator may use policy-driven adoption scenarios

• a consultant may develop project-specific assumptions

All of these forecasts may be valid for their own purpose, but they can create problems if they are not coordinated.

The ESIG report highlights that multiple entities often produce forecasts across overlapping areas and planning horizons, which can create discrepancies in assumptions, methods, and data. This is why coordination, data-sharing, and consistent assumptions are becoming essential in modern grid planning [1]. The following Figure describe the Data Sharing and Coordination in a Region or State with Market Operators

6. What Happens When Forecasts Are Not Aligned

When forecasts are not aligned, the consequences can be serious:

A transmission planner may underestimate local growth if distribution-level data is not reflected. A distribution planner may overbuild if a local forecast is too conservative. A resource planner may miss future adequacy needs if large loads are not properly included. A system operator may underestimate future ramping needs if DERs and electrification are not represented correctly.

Misalignment can lead to:

• underinvestment

• overinvestment

• delayed grid connections

• poor timing of network upgrades

• missed local load pockets

• inconsistent planning assumptions

• duplicated or double-counted demand

• resource adequacy gaps

• transmission bottlenecks

• higher costs for customers

This is why a forecast should not be treated as a single number. It should be treated as a shared planning dataset with clear assumptions, ownership, resolution, and use case.

7. Moving Toward Integrated Planning

Modern power system planning requires better integration between forecasting entities.

This means improving:

• data sharing between utilities, system operators, and regulators

• consistency between transmission and distribution forecasts

• coordination between customer account teams and planning teams

• transparency of assumptions for DERs and large loads

• alignment between policy targets and technical models

• reconciliation between top-down and bottom-up forecasts

• scenario planning for uncertainty

Integrated planning does not mean that every stakeholder uses the same model. It means that the assumptions, data flows, and planning outputs are coordinated enough to avoid major misalignment. This is especially important as future demand becomes more uncertain and more location-specific.

8. Key Takeaway

The value of a load forecast depends not only on its technical accuracy, but also on how well it is coordinated across the stakeholders who use it.

A forecast that is accurate at system level but disconnected from local distribution realities may miss load pockets. A forecast that is detailed at feeder level but not reconciled with system planning may create confusion. A forecast that ignores large loads, DERs, electrification, or customer behavior may lead to poor investment decisions.

Load forecasting is not only a planning input. It is a coordination tool that connects stakeholders, aligns assumptions, and shapes the future development of the power system.

9. Reference

[1] Energy Systems Integration Group, Long-Term Load and DER Forecasting, A Report by the Energy Systems Integration Group’s Long-Term Load and DER Forecasting Task Force, Aug. 2025.

Every power system planning process begins with one fundamental question: how much electricity will be needed, where will it be needed, and when will it be needed?

This question may look simple, but it sits at the centre of almost every major planning decision in the electricity sector. Future demand influences how much generation capacity is required, where transmission reinforcements may be needed, which substations and feeders may become constrained, and where new connection opportunities may emerge.

Traditionally, long-term load forecasting was mainly focused on two key indicators:

1. total annual energy demand and annual peak demand. This approach was generally sufficient when the grid was dominated by large centralized generation and relatively predictable customer demand patterns.

2. However, the modern power system is changing quickly. Electrification, data s, manufacturing growth, customer-sited DERs, EV charging, building electrification, behind-the-meter solar, battery storage, and extreme weather are changing not only the amount of demand, but also its timing, location, and operating characteristics [1].

This is why load forecasting is no longer just a statistical exercise. It is now one of the most important inputs to generation planning, transmission planning, distribution planning, market design, infrastructure investment, and grid connection strategy.

2. Future Demand as the First Planning Signal

Future demand is often the first signal that the power system may need to change. If demand is expected to grow in a particular region, planners must ask whether the existing network can serve that growth reliably. If demand is expected to shift because of EV charging, heat pumps, or data centres, planners must assess whether existing peak assumptions are still valid. If behind-the-meter PV or battery storage changes the shape of net load, planners must understand whether the system will need more ramping capability, storage, flexible demand, or operational tools.

The references reports highlights that long-term load and DER forecasting supports many planning functions, including generation capacity, energy markets, grid infrastructure, DER planning, customer rates, and programs. In simple terms, the forecast becomes the foundation on which future system needs are assessed [1] - [4].

A useful way to understand this is:

When the load forecast changes, almost every downstream planning decision can change with it. A higher demand forecast may create a need for new generation or storage. A geographically concentrated forecast may create a need for local network reinforcement. A changing hourly demand shape may create a need for flexibility, demand response, or BESS. A new large load may trigger a transmission connection, substation expansion, or system impact study.

3. Why Traditional Forecasting Is No Longer Enough

Traditional load forecasting often worked well when demand followed relatively stable historical patterns. In that environment, planners could rely heavily on economic growth, population growth, historical load trends, and weather-normalized peak demand.

That approach is now becoming less sufficient. Modern demand is being reshaped by several new drivers:

These drivers do not simply increase or decrease total electricity demand. They change the shape of demand. They affect when demand appears during the day, which season produces the highest stress, whether the system peak occurs in summer or winter, and whether a local feeder or substation peaks at the same time as the wider system.

For this reason, modern demand forecasting emphasizes the growing need for high-resolution, time-based forecasting, including 8,760 hourly forecasts. Annual energy and peak demand values may still be useful, but they may miss important operational details such as midday minimum load, evening ramping, local non-coincident peaks, weekend or seasonal peaks, and the interaction between weather, demand, and renewable generation [1] - [4].

In modern planning, the question is not only: How much demand will there be?

It is also: Where will the demand appear? When will it peak? What will drive it? How flexible is it? How certain is it? And how will it interact with DERs and the network?

4. How Demand Drives Network, Generation and Investment Decisions

Once future demand is forecast, planners begin to assess whether the existing system can accommodate it.

1. For generation planning, demand forecasts help determine how much capacity, energy, storage, reserve, and flexibility may be required. If demand grows faster than expected, additional generation or storage may be needed to maintain adequacy. If demand becomes more variable, the system may require more flexible resources. If electrification creates winter peaks, the generation portfolio may need to be assessed under different seasonal conditions.

2. For transmission planning, demand forecasts influence power flow patterns, transfer requirements, congestion risk, voltage performance, and reinforcement needs. A new industrial zone, data cluster, or electrified transport corridor may shift loading patterns across the transmission network. This can create a need for new lines, transformer upgrades, reactive support, system strength solutions, or revised operating limits.

3. For distribution planning, demand forecasts are even more location-specific. A system may appear adequate at the bulk level while individual feeders, substations, or transformers become constrained. This is particularly important where EV charging, heat pumps, rooftop PV, behind-the-meter BESS, or new commercial loads are concentrated in specific areas. ESIG notes that greater geographic granularity can help planners identify localized demand variations and load pockets that may otherwise be missed [1].

A forecast is not just a prediction. It becomes a signal for:

• new generation capacity

• BESS and flexibility services

• transmission reinforcement

• substation expansion

• feeder upgrades

• voltage support

• system strength solutions

• demand response programs

• connection capacity

• grid modernization

In other words, demand drives the need, and the need drives the investment.

5. From Load Forecast to Connection Opportunity

Grid connection opportunities often appear where the forecast reveals a future system need.

• If a region is expected to face load growth, developers may see opportunities for generation, BESS, or hybrid projects. If a corridor is expected to become congested, transmission reinforcement or local generation may become valuable. If a distribution area is expected to experience EV or data growth, utilities may need new substations, feeders, transformers, or flexibility solutions. If the system requires more ramping or peak-shaving capability, BESS and demand-side resources may become attractive.

• Large loads are becoming especially important in this discussion. The references report identifies data s as a major component of growth in utility load forecasts and highlights the challenges of forecasting large load growth, including uncertainty in realization rates, regional variation, and load clustering effects
  [1] - [4].

• A data may not only create a new connection request; it may also change the planning outlook for generation, transmission, resource adequacy, and local grid infrastructure.

This is why the connection process should not be viewed only as an application submitted by a project developer. The opportunity often begins earlier, when load forecasting identifies where the system is likely to need capacity, flexibility, reinforcement, or operational support.

A simple example is a data cluster. If several large loads are forecast in one region, the system may need new transmission capacity, additional local transformation, reactive power support, backup supply arrangements, or dedicated connection studies. This may create opportunities not only for the data customer, but also for network owners, generation developers, BESS providers, OEMs, EPC contractors, consultants, and system operators. The same logic applies to electrified transport corridors, industrial zones, hydrogen production hubs, renewable energy zones, and urban distribution networks with high DER adoption.

6. Key Takeaway

Future demand is not just an input to a planning model. It is one of the earliest indicators of where the power system may need to evolve. A well-developed load forecast helps planners understand future stress points, identify network and generation requirements, assess uncertainty, and prepare investment decisions. A weak or outdated forecast can lead to underinvestment, overinvestment, connection delays, poor allocation of resources, or missed opportunities.

The key message is simple:

Load forecasting is the starting point of power system planning, and power system planning is the starting point of many grid investment and connection opportunities.

In the next article, the discussion can move deeper into the main components of load forecasting: base load, new customer loads, DERs, electrification, large loads, uncertainty, and scenario planning.

7. Reference

[1] ESIG, Long-Term Load and DER Forecasting, Aug. 2025.
[2] AEMO, 2025 Electricity Statement of Opportunities, Aug. 2025.
[3] AESO, 2025 Long-Term Transmission Plan, Jan. 2025.
[4] PJM, 2026 Long-Term Load Forecast Report, Jan. 2026.

The grid connection projects do not begin with a substation drawing, an OEM data sheet, or a grid study. They usually begin much earlier inside a planning cycle run by a transmission system operator, distribution network operator, independent system operator, market operator, or regulator. That planning cycle may be called an integrated system plan, annual planning outlook, long-term outlook, network development plan, network investment plan or reliability assessment, depending on the region. The terminology changes from market to market, but the purpose is broadly the same: identify future system needs early enough that generation, storage, transmission, demand-side resources, and large new loads can be connected in a reliable and economic way [1]–[6].

In this article, the term annual planning is used in a broad sense to describe the recurring process through which a power system looks ahead, tests future scenarios, identifies emerging constraints, and signals where investment or interconnection opportunities may arise. In practice, these planning outputs are not always issued on a strictly annual basis. Like Ontario’s IESO, for example, publishes an Annual Planning Outlook, whereas AEMO’s Integrated System Plan is updated on a multi-year cycle, and AESO prepares its Long-Term Transmission Plan every two years using inputs from its broader Long-Term Outlook. In Europe, ENTSO-E publishes the Ten-Year Network Development Plan, while in North America wider reliability assessments are also undertaken through institutions such as NERC, alongside regulated transmission planning processes under FERC [1]–[6].

How the Power System Planning Cycle Works;

Before examining how projects emerge, it is useful to consider at a high level, how electricity markets function. Each market has its own legal, regulatory, and commercial design, yet the physical realities of electricity remain universal. Electricity must be produced, transported, balanced, and consumed in real time. Because demand evolves, generation portfolios change, networks age, public policy develops, and new technologies continue to enter the system, the power sector cannot rely solely on the existing asset base. It requires a forward-looking planning process capable of translating uncertain futures into practical actions, including transmission reinforcements, additional interconnection capacity, flexibility procurements, new operational tools, and, in some cases, entirely new technical requirements for connection [1]–[6].

This is why planning documents are so important. They are not merely administrative reports or records of institutional thinking. Rather, they serve as the bridge between long-term system needs and real project opportunities. They reveal how system planners interpret the future and where they expect technical, operational, or economic pressures to arise. In this sense, planning documents often provide the earliest meaningful signals to the market [1]–[6].

So how is this planning actually done?

At its core, every planning cycle begins with a fundamental question: what kind of power system will be required in the future, and what factors are most likely to place that system under stress? To answer this, planners construct future scenarios using a combination of demand forecasts, anticipated generator retirements, project development pipelines, fuel assumptions, policy targets, technology cost trajectories, weather and climate considerations, electrification trends, industrial growth, transmission constraints, and reliability criteria [1]–[6].

The principal drivers behind these plans are remarkably similar across all boundaries, even if the language used to describe them differs. Common drivers include load growth, generator retirement, renewable energy integration, storage deployment, industrial electrification, data centre expansion, decarbonization targets, transmission congestion, resilience concerns, and reliability margins. Although each market frames these issues in its own way, the planning challenge is essentially the same: to determine how the future system can remain secure, operable, and economically sustainable under changing conditions [1]–[6].

How Grid Connection Opportunities Emerge;

Once these future scenarios have been developed, planners do not simply publish them as forecasts. They test them. Power flow studies, contingency assessments, transfer analyses, stability studies, adequacy assessments, and, increasingly, combinations of operational and market modelling are carried out to determine where the system may face limitations or new requirements. It is at this stage that where opportunities begin to emerge [1]–[6].

Most grid connection opportunities do not originate as incidental or self-contained ideas. Rather, they arise when the planning process reveals a technical, economic, or reliability need within the system. In many cases, planning identifies one or more deficiencies or requirements: a region may need greater transfer capability, additional voltage support, more dispatchable capacity, faster frequency response, improved system strength, new local supply, or a more balanced relationship between generation and demand. In some markets, such needs are translated into transmission plans or procurement pathways. In others, they appear through congestion signals, hosting-capacity limitations, network expansion, policy-supported development zones, or specific connection windows. The form may differ, but the logic remains consistent: the system first identifies the need, and projects subsequently emerge or compete in response to it. [1]–[6].

That is why developers, OEMs, EPCs, consultants, and major load customers should read planning documents carefully. These reports often tell the market where the system expects future stress, which corridors may need reinforcement, where new capacity is likely to be valuable, what kinds of projects may face stronger scrutiny, and which technical issues are becoming more important. Even when a report does not list a project explicitly, it may signal the type of opportunity that will later appear as an interconnection wave: utility-scale renewables, BESS, synchronous support, HVDC links, flexible demand, or large load connections such as data centers. This is an inference from how the planning frameworks are structured, but it is strongly supported by the fact that these documents are designed to identify needs, evaluate candidate solutions, and inform subsequent investment and regulatory actions [1]–[6].

The most important takeaway is this: grid connection projects do not start at the connection application stage. They start much earlier, when the system begins asking what it will need in five, ten, or twenty years. That is where demand growth, policy, reliability, economics, and engineering first come together. By the time a specific PV plant, BESS project, HVDC scheme, or data centre appears in the queue, it is usually entering a system context that has already been shaped by a planning cycle. Understanding that planning cycle is the first step to understanding the full project lifecycle [1]–[6].

In the next article, the follow-on is to map the stakeholders in that lifecycle: who does what, when the developer enters, where the OEM becomes critical, how consultants shape the technical path, and why the system operator’s early planning assumptions can materially affect the final connection route.

References

[1] Australian Energy Market Operator, 2024 Integrated System Plan (ISP), Melbourne, VIC, Australia, Jun. 2024.

[2] Australian Energy Market Operator, ISP Methodology, Jun. 2025.

[3] Independent Electricity System Operator, Annual Planning Outlook, Ontario, Canada, Apr. 2025.

[4] Alberta Electric System Operator, 2025 Long-Term Transmission Plan, Alberta, Canada, Jan. 2025.

[5] ENTSO-E and ENTSOG, TYNDP 2024 Scenarios Report, Jan. 2025.

[6] North American Electric Reliability Corporation, 2025 Long-Term Reliability Assessment, Atlanta, GA, USA, Jan. 2026.