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As Climate Change Worsens, Update NERC MOD-031-2 — Demand and Energy Data

Continuing our series on NERC’s Reliability Standards, this week the focus is on MOD-031-2 — Demand and Energy Data. This standard focuses on standardizing the collection of data for reliability assessments needed to identify the root cause of blackouts, be they neighborhood or wide area blackouts.


As climate change continues to worsen, as is evident in this summer’s record-breaking heat waves across the globe, collection of key data for reliability analysis is more important than ever. The risk of blackouts linked to extreme heat continues to grow as the climate warms.


Updates to reliability standard MOD-031-2 can help to establish a more reliable electric power grid in the face of climate change. By improving the standards for data collection, NERC can guide electric utilities on the path to significantly lowering the risk of wide area blackouts.


Follow along to read about concerns and recommended improvements with MOD-031-2.


MOD-031-2 Stated Purpose


The stated purpose of NERC Reliability Standard MOD-031-2 is:


“To provide authority for applicable entities to collect Demand, energy and related data to support reliability studies and assessments and to enumerate the responsibilities and obligations of requestors and respondents of that data.”


Concerns with Reliability Standard MOD-031-2


Every electric utility has a treasure trove of customer load data. Unfortunately, most electric utility data is focused on outdated reliability metrics, such as SAIFI, SAIDI, and CAIDI. These indicators are useful to Wall Street investors, but not necessarily to creating a more reliable electric energy grid.


Instead, data collection should be focused on relevant load, trigger, and distributed energy data, all of which impact an electric utility’s ability to create accurate models for future energy demand and supply.


Additionally, like other reliability standards, MOD-031-2 would benefit from updating the jargon used throughout from legalese to that of the user, in this case, transmission system operators.


Let’s take a closer look at three concerns with MOD-031-2.


Concern 1: Inadequate Load Data


The electric energy grid is designed and operated assuming that all loads can be modelled as either constant resistance loads or constant power loads. However, this is not always the case. Electric utilities would benefit from the collection of additional load data for use in other modeling applications, such as the amount of air conditioning and corresponding torque values.


Concern 2: Missing Trigger Data


Load forecasters in Energy Control Centers use historical data and weather forecasts to address concerns associated with peak load periods when demand can exceed supply. To avoid wide area blackouts, load forecasters recommend that electric utilities schedule outages, bring additional generators on-line, and issue appeals for consumers to reduce consumption.


However, specific data about the triggers of historical wide area blackouts is often missing. Without this data, electric utilities are apt to implement rolling blackouts or engage dirty fossil fuel generating plants unnecessarily.


Concern 3: Lack of Distributed Energy Data


The electric energy grid has been designed to accommodate energy production at large, remote, central energy production facilities. However, as the renewable energy sector continues to grow, more and more energy will be produced at small, distributed locations.


The electric energy grid is not designed to accommodate energy production from distributed energy facilities. Moreover, data from this growing sector is often not tracked by electric utilities. Data from distributed generation locations must be collected and integrated into models for the future electric energy grid.


How to Improve NERC MOD-031-2


The following actions will substantially improve the effectiveness of MOD-031-2.


1. Add the requirement that electric utilities report key variables on an hourly basis via a common database. Variables may include:


a. Air conditioning usage.

b. Electric vehicle charging.

c. Distributed energy production and storage.


Basis:

As the climate continues to change, the number of residences equipped with air conditioners, heat pumps, electric vehicles, solar panels, and energy storage facilities will grow.


Today’s constant resistance and constant power representations of these loads and energy sources are inadequate. In addition, it’s important to understand on / off durations and real world considerations, such as consumer preferences and environmental parameters for these expanding technologies.


2. Add the requirement that electric utilities include trigger data in demand and energy data.

Basis:

On the demand side, consumers are known to react to environmental triggers. For example, during the first day of a heat wave, many customers will set their air conditioners to a comfortable 72 degrees, while some will forgo air conditioning. By the third day, every air conditioner will be used, some set to an even cooler temperature, and reluctant consumers will finally activate or invest in A/C.


Figure 1 represents the daily summer load for an all-electric home with air conditioning and an electric vehicle. Note, this household relies entirely on electric energy to fuel the car, cool the home, cook, etc., and therefore has a high demand for electricity especially during peak load periods.

Figure 1 is a bar graph showing the daily summer load for an all-electric home with  air conditioning and an electric vehicle.

Figure 1 represents the daily summer load for an all-electric home with air conditioning and an electric vehicle.


On the production side, renewable energy production varies hourly and seasonally. Although solar panels produce maximum output between 10 AM and 2 PM, as illustrated in Figure 2, clouds, trees, shadows, and other obstructions reduce energy production.

Figure 2 is a bar graph showing the daily energy production of a solar farm (actual data).

Figure 2 shows the daily energy production of a solar farm (actual data).


Energy produced by wind turbine generators varies with atmospheric conditions as illustrated by weather forecasts for Smithville, WA, in Figure 3. Smithville is located in the Columbia River Gorge, a popular location for wind farms.


This data is vital for electric utilities to make informed decisions about peak load conditions and avoid wide area blackouts.

Figure 3 is a bar graph showing the variable nature of wind forecasts in the town of Smithville, WA.

Figure 3 shows the variable nature of wind forecasts in the town of Smithville.


3. Add the requirement that electric utilities collate data on a distribution substation basis.

Basis:

Recording demand and production values is a starting point. Providing details that enable data representation in distinct locations is essential. In addition, this data should include environmental and economic conditions that enable the utilization of data during all seasons and all climatic conditions.


A Better Purpose Statement for NERC MOD-031-2


A better purpose statement for NERC Reliability Standard MOD-031-2 would be:


“To provide authority for applicable assure that responsible entities to collect Demand, energy and related data and share such data to support reliability studies and assessments and to enumerate the responsibilities and obligations of requestors and respondents of that data.”


The Data is Available


Unlike Fox Mulder, the believer, and Dana Scully, the skeptic, electric utilities do not need to travel the world to investigate bewildering events. Electric utilities can account for every kilowatt of energy that was purchased from producers and sold to consumers in their territory.


It is time that electric utilities begin using this data to increase the reliability of the electric energy grid, especially during the global transition towards carbon-free energy production.


In our next article in this series, I’ll outline concerns and improvements to NERC Reliability Standard, MOD-032-1 — Data for Power System Modeling and Analysis.


Questions about my expertise or recommendations? Please email me directly or submit our contact us form.


This article was written in collaboration with Prescient's Lead Editor Alyssa Sleva-Horine.

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