Optimal active and reactive power rescheduling in wind-integrated power systems using intelligent metaheuristic techniques with demand response

Abstract

Demand response (DR) is a widely used control strategy to reduce electricity demand by encouraging consumers to change their consumption patterns, especially during peak usage. Participants in DR programs adjust their power consumption by lowering their loads during peak hours or shifting them to off-peak periods, often in exchange for lower rates or cash incentives. This approach decreases the need for additional infrastructure, enhances grid reliability, and maintains the supply-demand balance. Power system operators utilize optimal power flow to effectively manage and regulate the power grid. In this context, the goal is to increase voltage levels in wind-integrated systems while optimizing real power losses and system stability. Due to its inherent variability and lack of automatic dispatchability, wind power (WP) in a hybrid power network faces several constraints. Energy management becomes more difficult when WP constitutes a significant portion of the generation mix. This paper proposes a novel strategy for applying incentive-based DR in electricity markets and for effectively integrating wind generation. Simulations on the modified IEEE 30-bus system confirm the validity of the proposed method. A multi-objective analysis reveals that the cost minimization problem involves a trade-off between reducing voltage deviation and lowering overall costs. Two metaheuristic algorithms, the Whale Optimization Algorithm and the Multi-objective Mantis Search Algorithm, were employed to solve the problem. The results show that these algorithms outperform traditional methods.