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Day-Ahead Scheduling for Renewable Energy Generation Systems considering Concentrating Solar Power Plants
- Source :
- Mathematical Problems in Engineering, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- With the advent of the new types of electrical systems that attach more importance to the renewability of the energy resource, issues arising out of the randomness and volatility of the renewable energy resource, such as the safety, reliability, and economic operation of the underlying power generation system, are expected to be challenging. Generally speaking, the power generation company can do a reasonable dispatch of each unit according to weather forecast and load demand information. Focusing on concentrating solar power (CSP) plants (wind power, photovoltaic, battery energy storage, and thermal power plants), this paper proposes a day-ahead scheduling model for renewable energy generation systems. The model also considers demand response and related generator set constraints. The problem is described as a mixed-integer nonlinear programming (MINLP) problem, which can be solved by the CPLEX solver to obtain an optimal solution. At the same time, the paper compares and analyzes the impact of concentrating solar power plants on other renewable energy generation and thermal power operation systems. The results show that the renewable energy generation system can lower power generation costs, reduce load fluctuation, and enhance the energy storage rate.
- Subjects :
- Wind power
Article Subject
business.industry
Computer science
General Mathematics
Photovoltaic system
General Engineering
Thermal power station
Engineering (General). Civil engineering (General)
Energy storage
Automotive engineering
Renewable energy
Demand response
Electricity generation
QA1-939
TA1-2040
business
Mathematics
Solar power
Subjects
Details
- ISSN :
- 15635147 and 1024123X
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Mathematical Problems in Engineering
- Accession number :
- edsair.doi.dedup.....fa691663cc8d4193662b7c5798de5e49