What is societal data, and why is it critical for power grid planning?

The transition to a net-zero future is placing entirely new demands on our power grids. These changes are primarily driven by shifts in societal behavior, such as the widespread adoption of electric vehicles, decentralized solar power generation, the electrification of industries and residential areas, the growth of new technologies like data centers and battery storage solutions, and the introduction of new policies like power tariffs and green subsidies.

These evolving societal dynamics are introducing significant risks and uncertainties for the future of power grids. This results in key grid planning questions for grid operators, such as:

• What impact will the approval of new projects and technologies have on our grid,
  both now and in the future? For instance, what would the implications be of approving a
  large-scale battery storage solution, both locally and across the grid? And how can it be
  optimized to maximize value for both the asset owner and the grid operator?

• Can our grid handle the increased demand, and what are the potential bottlenecks?

• What investments and substation upgrades are needed, and when?

• What are the needs for flexibility services and what flexibility availability do we have?

• How can we support societal development and electrification initiatives while
  ensuring the grid remains secure, reliable, and stable for all customers?

The question is, can you really answer the above questions based on analyzing historical
grid meter data, typically used for grid planning and forecasting purposes?

Let’s make an analogy.

You have a big celebration coming up and want to make a great dinner for your family for the special occasion. Where do you start? For most of us, we don’t open the fridge to see what we already have. Instead, we start by asking ourselves, what would be a great meal? After deciding what to cook, the next step is to check the fridge to see if everything is there, and if something is missing, we identify which new ingredients we need to purchase.

In grid planning, we should do the same. We should start asking ourselves the question, what do we need? When we know what we need, the next step is to see what we already have, and if there is something we don’t have, we look for where we can get it. When we start with looking at what we have, we are already jumping to the solution, without knowing what problem we should solve.

Historical grid-data won’t cut it. Instead, when society is the catalyst for change, societal
data and insights are critical for grid planning purposes. But what exactly is societal data?

To answer this, let’s look at a specific example:

Imagine you want to understand the demand electric trucks will place on your grid in the next ten years. To answer this question, you won’t find the answer by analyzing historic grid data, often used as a data source for grid forecasting projections. Instead, you need to need to figure out the following:

• What number and types of electric trucks will be located where?

• Where and when will electric trucks be on the road, and under what conditions?

• Where and how will these trucks charge? This question is multifaceted and includes
  various dependencies. A key factor is the geographic location and surrounding
  conditions of each potential charging event. We refer to this as local-variability, which is
  often overlooked in traditional grid planning models.

To answer these questions, you need to collect and analyze a vast amount of data relevant to future electricity demand, such as socio-economic attributes and the driving pattern of trucks. We call this societal data. This data is local, time-dependent, complex, and multi-sourced. Once gathered, advanced models or AI are required to combine and translate this data into actionable insights.

The example above demonstrates the impact that truck electrification will have on the grid.
However, to understand the full cumulative future demand on your grid, and to explore different potential future scenarios, you need to apply the same approach to all sectors affecting power demand. This involves analyzing how these factors interact locally, in terms of both time and space.

At first glance, using societal data for grid planning might seem complex, difficult, or even unfeasible. But in reality, it has never been easier. At Endre, we focus on simplifying this process. We’ve developed user-friendly software that automates data collection, AI modeling, and insights generation. Our solutions provide the answers needed for informed, long-term grid planning, whether you’re assessing the impact of grid connection requests, identifying current or future bottlenecks, determining substation upgrade needs, conducting flexibility assessments, and more.

Our services also streamline grid-planning workflows, offering visual clarity that enhances
decision-making. This enables faster, more reliable planning and decisions.

Feel free to reach out if you'd like to discuss how societal changes and behaviors are affecting the grid or if you want to explore how our solutions can simplify your grid planning activities and improve your grid expansion strategies.