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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Scenario-Based Distributionally Robust Unit Commitment Optimization Involving Cooperative Interaction with Robots.

Xuanning Song¹, Bo Wang¹, Pei-Chun Lin²

¹School of Management and Engineering, Nanjing University, Nanjing, 210093 China.

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|September 19, 2022

Summary

This summary is machine-generated.

This study introduces a robotic assistance approach for power system operators facing renewable energy uncertainty. It uses distributionally robust unit commitment (DR-UC) and a hybrid algorithm to aid decision-making, improving operational efficiency.

Keywords:

Distributionally robust unit commitment Hybrid solution algorithm Renewable generation Robotic assistance Scenario-based ambiguity set

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Area of Science:

Electrical Engineering
Operations Research

Background:

Increasing renewable energy integration introduces significant operational uncertainty in power systems.
Traditional optimization methods like stochastic optimization (SO), robust optimization (RO), and distributionally robust optimization (DRO) require experienced operators, posing challenges during staff shortages.

Purpose of the Study:

To propose a novel robotic assistance decision-making approach for power system operators.
To address the challenges of uncertainty management in power systems due to renewable energy penetration.

Main Methods:

Utilizing advanced clustering and reduction techniques to generate renewable generation scenarios.
Constructing a scenario-based ambiguity set for distributionally robust unit commitment (DR-UC).
Developing a hybrid algorithm combining improved particle swarm optimization (IPSO) and a mathematical solver to solve the DR-UC model.
Integrating the DR-UC model and solution algorithm into a robotic assistance system.

Main Results:

Demonstrated the effectiveness of the proposed robotic assistance approach through experiments on IEEE test systems.
Successfully generated renewable generation scenarios and constructed a time-series ambiguity set.
Developed and validated a hybrid IPSO-based algorithm for solving the DR-UC model.

Conclusions:

The proposed robotic assistance system effectively aids power system operators in making decisions under uncertainty.
The integration of DR-UC with robotic assistance offers a viable solution for managing renewable energy integration challenges.
The approach enhances operational efficiency and decision-making reliability in power systems.