Abstract
This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units' costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units' structure and characteristics, RESs' correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.
Highlights
This paper addressed the effect of storage units on the optimal operation of a MG, using an effective stochastic framework called modified bird mating optimization (MBMO)-reduced unscented transformation (RUT)
Different operational and structural characteristics of storage units along with the high uncertainties of solar irradiation, wind fluctuations, market price (MP) and demand variations were studied in a 24-hour period
The results indicated that the best solution is dependent on the smart selection of the battery type, its capacity, the initial and final charge values and correlation consideration
Summary
Wöhler curve function Capital cost ($) Battery capital cost ($) Covariance matrix. Population change Depth of discharge (%) Constant voltage of the battery model (V) Storage unit energy (kWh) Representing the exponential zone dynamics of the battery (V) Market price vector Operation and maintenance cost ($). Description Charging power at hour " " (kW) Discharging power at hour " " (kW). Grid power of the “ith” unit at hour " " (kW) Maximum grid power of the “ith” unit at hour " " (kW). The “ith” storage unit power at hour " " (kW) The diagonal elements of the power matrix Load power vector Power of the correlated units Wind power vector Fuel cost ($/kWh) Representing uncertain parameters Battery charging mode { 1, 0,1} Representing the on/off state of dispatchable units {0,1}. Storage units set (kW) Fuel cell unit set (kW) Generation units set (kW) Grid set (kW) Load set (kW) Microturbine unit set (kW) Nonrenewable units cluster set (kW) Photovoltaic units set (kW) Renewable units cluster set (kW) Source set (kW) Time set (s) Wind power set (kW) Electrical efficiency (%) Charging efficiency (%) Discharging efficiency (%)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.