Abstract
In association with the development of intermittent renewable energy generation (REG), dynamic multiobjective dispatch faces more challenges for power system operation due to significant REG uncertainty. To tackle the problems, a day-ahead, optimal dispatch problem incorporating energy storage (ES) is formulated and solved based on a robust multiobjective optimization method. In the proposed model, dynamic multistage ES and generator dispatch patterns are optimized to reduce the cost and emissions. Specifically, strong constraints of the charging/discharging behaviors of the ES in the space-time domain are considered to prolong its lifetime. Additionally, an adaptive robust model based on minimax multiobjective optimization is formulated to find optimal dispatch solutions adapted to uncertain REG changes. Moreover, an effective optimization algorithm, namely, the hybrid multiobjective Particle Swarm Optimization and Teaching Learning Based Optimization (PSO-TLBO), is employed to seek an optimal Pareto front of the proposed dispatch model. This approach has been tested on power system integrated with wind power and ES. Numerical results reveal that the robust multiobjective dispatch model successfully meets the demands of obtaining solutions when wind power uncertainty is considered. Meanwhile, the comparison results demonstrate the competitive performance of the PSO-TLBO method in solving the proposed dispatch problems.
Highlights
The traditional economic dispatch method aims to determine a generation schedule that minimizes total generation cost while being subjected to generator and system operating limits [1,2,3]
It is clear that as the wind power (WP) prediction error increases, the gap between the robust Pareto fronts in Case 2 and original front in Case 1 enlarges. This reveals that more accurate WP prediction techniques result in lower costs and emissions
The increasing development and penetration of renewable energy generation (REG) are a major challenge for transmission and distribution networks
Summary
The traditional economic dispatch method aims to determine a generation schedule that minimizes total generation cost while being subjected to generator and system operating limits [1,2,3]. In the initial studies [6,7,8], researchers attempted to transform the EED problem into a single-objective model based on a linear combination of different objectives as a weighted sum This method cannot obtain Pareto front solutions in a single run and does not address how to select weighting factors for the system operators. Carbon capture and storage are considered in the model formulation to reduce carbon emission, and a multiobjective bacterial colony chemotaxis method is adopted to solve the proposed robust EED model. None of these papers consider energy storage (ES) integration.
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