Abstract
This paper proposes the development, implementation, and impact of an innovative real-time forecast model on the cost and operational revenues of a wind generation (WG) microgrid with an associated battery energy storage system (BESS). An economic dispatch scheme is formulated using a predictive optimization policy called receding horizon control, in order to sell energy to the electricity grid through an energy market. This power dispatch framework is able to incorporate multi-step ahead forecasts of wind power and energy price, needed to determine the income and operational profits of the WG microgrid. An innovative intelligent forecast model is presented using the radial-basis functional network (FN), that offers more accuracy as compared to benchmark and conventional intelligent models, and consequently, the income of the WG microgrid is maximized. Since the inaccuracy of the forecasting can also lead to inadequate BESS sizing which subsequently mitigates the operational profits. Hence, at one hand, this research work strongly advocates the impact of power forecast accuracy on the economic aspects of a WG microgrid, while on the other hand also provides the necessary tools to the wind power producers in order to maximize their profits.
| Original language | English |
|---|---|
| Article number | 8669746 |
| Pages (from-to) | 36819-36832 |
| Number of pages | 14 |
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| State | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2013 IEEE.
Keywords
- Battery energy storage system
- economic dispatch
- functional network
- neural functions
- wind power
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering