Procuring load curtailment from local customers under uncertainty.

Area of Science:

• Energy Systems Engineering
• Operations Research
• Power Systems Management

Background:

• Demand side response (DSR) is vital for managing power network constraints, especially with uncertain future asset utilization.
• Procurement processes for DSR from customers introduce significant uncertainty.
• Optimizing DSR procurement is essential for grid flexibility and economic efficiency.

Purpose of the Study:

• To develop economically optimal procurement policies for DSR from heterogeneous, localized customers.
• To address uncertainty in DSR procurement using probabilistic modeling and chance constraints.
• To ensure the adequacy of procured DSR for managing constrained power network assets.

Main Methods:

• Combination of probabilistic modeling, simulation, and optimization techniques.
• Development of a mathematical framework involving a search over permutations.
• Illustrative example implementation and case study for practical validation.

Main Results:

• Identification of economically optimal DSR procurement strategies under uncertainty.
• Demonstration of the effectiveness of the proposed approach in a case study.
• Quantification of the impact of chance constraints on DSR adequacy.

Conclusions:

• The combined approach of probabilistic modeling, simulation, and optimization effectively determines optimal DSR procurement policies.
• This methodology enhances power network asset management by providing flexibility under uncertain conditions.
• The findings offer valuable insights for energy management, risk mitigation, and optimization in power systems.