Abstract
In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.
Highlights
The deregulation process in the energy sector has encouraged a transformation of the ElectricPower Systems (EPS)
This approach is used in a microgrid, being the problem addressed by means of a hybrid optimal algorithm, which consists in decomposing the problem into Integer Programming (IP) to solve the Unit Commitment (UC) problem and Non-Linear Programming (NLP) to solve the Economic Dispatch (ED) problem
In contrast with Case Study 1, the fitness function took higher values. This fact is a consequence of the reserve power, which must cover the possible deviation in the demand forecasting
Summary
The deregulation process in the energy sector has encouraged a transformation of the Electric. Similar to linear programming based approaches, metaheuristic algorithms such as evolutionary algorithms allow dealing with non-linearities in an effective way since the search engine is based on genetic operations (selection, crossover, and mutation) among potential solutions Given these characteristics of the combined UC and ED problem, solving it in the most optimal way constitutes itself a challenge. A new methodology is introduced to deal with the generation schedule problem of a microgrid where the demand forecasting is affected by uncertainties This approach ensures the robustness of the algorithm at the expense of affecting the operational and environmental cost of the installation.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
