Decentralized prioritization of demand response programs in multi-area power grids based on the security...

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Optimization

Background:

• Deregulation of electrical energy grids (EEGs) introduces challenges in secure operation due to distributed generations like wind energy.
• Integrating demand response programs (DRPs) and managing multi-area EEGs (MEEGs) further increases operational complexity.
• Fluctuating wind energy sources necessitate techno-economic mitigation strategies, often involving energy storage systems (ESSs).

Purpose of the Study:

• To propose a method for the secure operation of MEEGs incorporating DRPs for demand-supply balancing.
• To mitigate techno-economic impacts of fluctuating wind energy by coordinating with ESSs.
• To develop a decentralized probabilistic DC-SCOPF model for MEEGs with ESS-coordinated wind energy and DRPs.

Main Methods:

• A decentralized probabilistic DC-SCOPF model is developed using the optimality condition decomposition (OCD) algorithm.
• The model decomposes the MEEG operation into smaller mixed-integer non-linear programming (MINLP) problems for each area.
• A priority list for DRPs is generated based on operational cost, security (contingencies), load shedding, and peak-to-valley ratio (PVR).

Main Results:

• The decentralized model effectively manages MEEGs with integrated DRPs and wind energy sources coordinated with ESSs.
• Numerical simulations on the New England 39-bus testbed demonstrate the model's capability in ensuring secure and efficient grid operation.
• The generated DRP priority list aids in optimizing system performance based on defined criteria.

Conclusions:

• The proposed decentralized approach enhances the secure operation of complex MEEGs with renewable energy sources and demand-side participation.
• Coordinating ESSs with wind energy and strategically deploying DRPs are crucial for grid stability and economic efficiency.
• The method provides a robust framework for operators to manage modern, decentralized electrical energy grids.