Dynamic multi-period optimal power flow considering renewable energy degradation and temperature derating.

Area of Science:

• Electrical Engineering
• Climate Science
• Optimization Theory

Background:

• Renewable energy sources (solar, wind) are crucial for reducing carbon emissions but introduce variability into power grids.
• Optimal power flow (OPF) modeling must account for this variability for reliable grid operations.
• Climate change, particularly temperature variations, affects renewable energy system performance.

Purpose of the Study:

• To investigate the impact of climate change, specifically temperature variations, on the integration of wind and solar energy into power systems.
• To develop single and multi-objective optimization models for optimal power flow (OPF) considering temperature-dependent de-rating effects.
• To introduce a carbon credit concept to enhance renewable energy reliance and reduce overall costs.

Main Methods:

• Application of the Mayfly Algorithm (MA) for single-objective OPF on the IEEE-30 bus system.
• Development of Stochastic OPF (SOPF) models incorporating temperature-dependent wind and solar energy generation.
• Multi-period analysis (25-year lifetime) using dynamic Multi-Period SOPF (MPOPF) to evaluate long-term impacts of degradation and temperature rise.
• Utilizing a fuzzy-based Pareto front for multi-objective optimization solutions.

Main Results:

• The Mayfly Algorithm achieved reductions in fuel cost (0.6%) and carbon emissions (0.5%) compared to PSO in single-objective OPF.
• Incorporating carbon credits reduced total cost by 0.8%.
• At 40°C, total cost and emissions increased by 21% and 45.67% respectively (cost minimization) and 9.67% and 45.7% (emission minimization).
• Multi-period analysis showed significant cost and emission increases (up to 24.96% and 51.8% respectively) after 25 years at 40°C.
• Multi-objective optimization at 40°C indicated cost and emission increases of 16.65% and 41.87% (compromise solution), and up to 20.16% cost and 60.1% emission increase after 25 years.

Conclusions:

• Climate change and component degradation negatively impact renewable energy integration, leading to increased reliance on thermal power.
• Accurate modeling of temperature-dependent de-rating effects and long-term degradation is essential for realistic power system planning.
• Sustainable energy infrastructure requires informed planning, considering climate change impacts and potential re-powering strategies for renewable resources.