How are utilities using probabilistic planning to tackle the rising complexity and uncertainty in grid operations due to distributed energy resources and fluctuating demand?

Nov 18, 2024 | Article

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We’ve partnered with utilities to tackle this question, and here are the insights we’ve gathered.

Model and simulate a variety of possible future scenarios, including demand growth with emerging technologies like heat pumps and Electric Vehicles, Distributed Energy Resource (DER) adoption, and system contingencies.
Identify potential constraints and capacity issues under different conditions.
Optimize the placement of sensors and the operation of advanced applications like Distribution System State Estimation (DSSE) to enhance grid visibility.
Evaluate the effectiveness of non-wired alternatives as a means to address or defer capital infrastructure investment to mitigate system constraints.
Consider transitioning from a typical deterministic approach for hosting capacity and interconnection analysis to a flexible interconnection with DER programmatic controls put in place to mitigate the identified constraints.

Key Uses of Probabilistic Planning:

1. Handling Dynamic Demand and Distributed Energy Resources (DERs):

As more customers adopt technologies like solar photovoltaic (PV) systems, electric vehicles (EVs), and battery storage, the demand and generation on the distribution network become more dynamic. Probabilistic planning helps utilities assess how these resources may impact the grid by analyzing a wide range of potential future scenarios. This includes looking at both additional demand (e.g., from EVs) and distributed generation (e.g., from solar PV) under different conditions. Scenarios can also include DER evaluation for customer programs such as dispatchable battery storage, EV charging, and changing smart inverter (IEEE 1547-2018) configuration settings.

2. Demand and Solar PV Generation Forecasting:

Statistical based probabilistic planning using Monte Carlo simulations for:

Load/Demand Curve Analysis: Historical kW data is used to calculate statistical measures such as the standard deviation and confidence intervals. These are then applied to project future demand scenarios, considering factors like the adoption of EVs or heat pumps, providing a range of possible planning load forecast scenarios and bandwidth specification for the forecast confidence (e.g., 90% confidence interval range).

Solar PV Output Analysis: Similarly, probabilistic simulations are run for different levels of solar PV capacity to forecast how solar generation will impact the grid at different times and under different weather or adoption conditions. By using a range of potential values rather than fixed inputs, utilities can plan for uncertainty and avoid under- or over-building infrastructure.

3. DER Adoption Forecasting:

Probabilistic analysis is used to predict where DERs are more likely to be adopted across the grid. DER adoption by customers has historically “clustered” in certain areas with demographic considerations. Analyzing these past trends can help proactively identify areas of the distribution grid that are more likely to experience DER proliferation. For example, utilities can use probabilistic models to assess the likelihood that customers in specific locations will install solar panels or adopt EVs. This is based on factors like rooftop area, economic indicators, and fleet electrification trends. By understanding the potential for DER adoption across different parts of the grid, utilities can better plan for future capacity needs and grid upgrades.

4. Assessing Grid Hosting Capacity:

Probabilistic planning is used to determine how much additional DER capacity the grid can host without causing power quality issues or exceeding the grid’s design limits. This advanced approach goes beyond traditional deterministic methods by analyzing a wide range of possible conditions, including fluctuations in DER output, load profiles, and network configurations. By considering these variables probabilistically, utilities can gain a realistic and comprehensive view of hosting capacity under diverse scenarios. This analysis evaluates the likelihood of issues such as voltage deviations, thermal overloads, and protection coordination challenges, accounting for factors like varying DER penetration levels and 8,760-hour analysis providing profiles for each hour of a given year. With probability-based outcomes, utilities are better positioned to understand potential risks and to prioritize grid upgrades or operational strategies accordingly. This comprehensive assessment equips utilities with valuable insights to enhance circuit performance while informing potential DER applicants about expected disconnection hours should they surpass current hosting limits. This approach enables utilities to optimize grid reliability and resilience in the face of increasing DER integration.

5. Distribution System State Estimation (DSSE):

DSSE is an application associated with Advanced Distribution Management Systems (ADMS) for distribution operations as well as distribution planning that provides distribution system situational awareness and DER visibility. It relies on probabilistic methods to determine the location, quantity, and type of telemetry points in the distribution systems.. Probabilistic planning helps ensure DSSE can accurately estimate system conditions under various operating states and contingencies, such as measurement errors or changes in generation/demand. This situational awareness allows utilities to optimize grid operations and prevent overloads or voltage violations without building new infrastructure.

6. Non-Wired Alternatives:

Probabilistic planning also supports the use of non-wired alternatives (NWAs), such as energy storage or demand response, to mitigate peak demand. By forecasting demand and generation under different probabilistic scenarios, utilities can determine when NWAs could be used to defer or avoid traditional infrastructure investments.

7. Flexible Interconnection:

Reassess the approach for hosting capacity and interconnection analysis from deterministic rejection or infrastructure upgrade being triggered for an interconnection request when a constraint is exceeded. With a flexible interconnection approach, DER programmatic controls can be put in place to mitigate the identified constraints (e.g., non-export times of day during specific months or participation in dispatchable DER programs).

Let’s shape the future of energy together. Contact EnerNex at info@enernex.com and solve energy challenges with experts by your side!

Connect with Us

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Contact our Authors

Jeremy Laundergan

jlaundergan@enernex.com

(865) 770-4866

Muhammad Humayun

mhumayun@enernex.com

(865) 770-4870

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