Individual Thermal Generator and Battery Storage Bidding Strategies Based on Robust Optimization

Vidan, Matea; D’Andreagiovanni, Fabio; Pandžić, Hrvoje

doi:10.1109/access.2021.3076872

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Rui et al [14] combined Mixed Integer Programming and Stackelberg games for defining an optimal energy scheduling in a microgrid context. Vidan et al [15] proposed a computationally efficient Robust Optimization method for tackling energy price data uncertainty while optimally managing production and battery storage actions. However, it should be noted that existing single-objective optimization methods for energy management rarely consider the control of controllable loads, and most of them only utilize existing optimization methods without designing proprietary methods for the complex and high-dimensional constraints present in energy management problems.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Constraint-Handling Multi-Objective Optimization Method for Resilient Microgrid Energy Management

Lv,

Li,

Zhao

et al. 2024

Applied Sciences

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In recent years, renewable energy has seen widespread application. However, due to its intermittent nature, there is a need to develop energy management systems for its scheduling and control. This paper introduces a multi-stage constraint-handling multi-objective optimization method tailored for resilient microgrid energy management. The microgrid encompasses diesel generators, energy storage systems, renewable energy sources, and various load types. The intelligent management of generators, batteries, switchable loads, and controllable loads ensures a reliable power supply for the critical loads. Beyond operational costs, our model also considers grid dependency as a key objective, making it particularly suited for energy management in extreme environments such as islands, border regions, and military bases. Managing complex controls of generators, batteries, switchable loads, and controllable loads presents challenging constraints that the management strategy must meet. To tackle this challenge, we propose an multi-objective optimization algorithm with multi-stage constraint-handling strategy to handle the high-dimensional complex constraints of the resilient energy management problem. Our proposed approach demonstrates superior performance compared to nine leading constrained multi-objective optimization algorithms across various test scenarios. Furthermore, the benefits of our method become increasingly evident as the complexity of the problem increases. Compared to the classical NSGA-II, the proposed NSGA-II-MC method achieved a 49.7% improvement in the Hypervolume metric on large-scale problems.

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Section: Introductionmentioning

confidence: 99%