Power System Hosting Capacity Under Probabilistic DER Adoption
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Solution Overview
Problem
Current methods for calculating hosting capacity in power systems with distributed energy resources (DERs) and electrification are inefficient, requiring extensive Monte Carlo simulations and failing to accurately approach the minimum DER penetration level that causes power system problems, leading to computational inefficiencies and inaccurate infrastructure upgrade requirements.
Innovation Solution
A probabilistic capacity planning method that uses probability distribution functions to calculate hosting capacity and infrastructure requirements by solving an optimization problem with a reasonability constraint, constraining DER and electrification distributions within a defined confidence level, allowing for closed-form calculations and improved computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulations are used to calculate hosting capacity, then the calculation reflects realistic DER distribution randomness, but the computational time increases significantly
Solution Approach 1:
The patent transforms the hosting capacity calculation from a simulation-based approach to a closed-form mathematical solution by changing the parameters from random sampling to deterministic optimization with probabilistic constraints. This allows achieving accurate results without the computational burden of extensive Monte Carlo simulations.
Solution Approach 2:
The patent replaces the mechanical simulation process (Monte Carlo method) with a mathematical optimization framework. Instead of repeatedly simulating random DER distributions, the system uses an optimization problem with probabilistic constraints to directly compute the hosting capacity, substituting computational simulation with analytical calculation.
2Measurement precision
If Monte Carlo simulations are used to find minimum DER penetration level, then random distributions are represented, but the true minimum cannot be approached due to vast number of permutations
Solution Approach 1:
The patent inverts the traditional approach by instead of searching for the minimum DER penetration through random sampling, it formulates an optimization problem that directly identifies the minimum penetration level by considering the worst-case distribution within probabilistic constraints. This inversion transforms an intractable search problem into a solvable optimization problem.
Solution Approach 2:
The patent introduces probabilistic constraints as an intermediary element that bridges the gap between random DER distribution representation and deterministic optimization. These constraints act as a mediator that captures the essence of random distributions without requiring actual random sampling, enabling the optimization to find the true minimum DER penetration level.
3Reliability
If hosting capacity is calculated as minimum DER penetration level, then ideal safety is achieved, but the calculation becomes extremely challenging due to vast number of distribution permutations
Solution Approach 1:
The patent introduces dynamics by incorporating probabilistic constraints that adapt to different confidence levels. The optimization problem dynamically adjusts based on the specified confidence level, allowing the system to balance between reliability and computational feasibility by finding the minimum DER penetration level that satisfies the probabilistic safety requirements.
Data Source
AI summary
A method is disclosed for distributed energy resource (DER) and/or electrification capacity planning in a power system. The method includes obtaining, for each of multiple electrical nodes in a circuit model of the power system, parameters of a probability distribution function describing respective probabilities of different amounts of DERs and/or electrification being added at the electrical node. The method further comprises calculating an existing hosting capacity of the power system and/or infrastructure requirements to achieve a target hosting capacity of the power system, by solving an optimization problem that is subject to a reasonability constraint. The reasonability constraint constrains a distribution of amounts of DERs and/or electrification added at respective electrical nodes to being within a space of reasonable distributions which, according to the obtained parameters, each are within a defined confidence level. The method may also comprise reporting information associated with the existing hosting capacity and/or the infrastructure requirements.