Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables

Ying-Yi Hong, Yuan-Ming Lai, Yung-Ruei Chang, Yih- Der Lee and Pang-Wei Liu
Additional contact information
Ying-Yi Hong: Department of Electrical Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Taoyuan 32023, Taiwan
Yuan-Ming Lai: Department of Electrical Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Taoyuan 32023, Taiwan
Yung-Ruei Chang: Division of Smart Grid, Institute of Nuclear Energy Research, Longtan 32546, Taiwan
Yih- Der Lee: Division of Smart Grid, Institute of Nuclear Energy Research, Longtan 32546, Taiwan
Pang-Wei Liu: Division of Smart Grid, Institute of Nuclear Energy Research, Longtan 32546, Taiwan

Energies, 2015, vol. 8, issue 4, 1-20

Abstract: This paper explores real power generation planning, considering distributed generation resources and energy storage in a small standalone power system. On account of the Kyoto Protocol and Copenhagen Accord, wind and photovoltaic (PV) powers are considered as clean and renewable energies. In this study, a genetic algorithm (GA) was used to determine the optimal capacities of wind-turbine-generators, PV, diesel generators and energy storage in a small standalone power system. The investment costs (installation, unit and maintenance costs) of the distributed generation resources and energy storage and the cost of fuel for the diesel generators were minimized while the reliability requirement and CO 2 emission limit were fulfilled. The renewable sources and loads were modeled by random variables because of their uncertainties. The equality and inequality constraints in the genetic algorithms were treated by cumulant effects and cumulative probability of random variables, respectively. The IEEE reliability data for an 8760 h load profile with a 150 kW peak load were used to demonstrate the applicability of the proposed method.

Keywords: optimal capacity; reliability; renewable; energy storage; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.mdpi.com/1996-1073/8/4/2473/pdf (application/pdf)
https://www.mdpi.com/1996-1073/8/4/2473/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:8:y:2015:i:4:p:2473-2492:d:47504

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().