17. Financial optimization routine for hybrid distributed power generation with battery storage system
Invited abstract in session WE-7: Optimization applications III, stream Optimization applications.
Wednesday, 14:10 - 15:50Room: M:I
Authors (first author is the speaker)
| 1. | Paulo Rotella Junior
|
| Faculty of Social Sciences, Charles University | |
| 2. | Arthur Leandro Guerra Pires
|
| Post-Graduate Program in Production Engineering and Systems - Federal University of Paraiba, Federal University of Paraiba | |
| 3. | Luiz Celio Souza Rocha
|
| Management, Federal Institute of Northern Minas Gerais | |
| 4. | Karel Janda
|
| Charles University |
Abstract
Intermittency is one of the main challenges associated with using renewable energy sources, but it can be mitigated through hybrid generation and battery storage systems. This study proposes a financial optimization routine to identify the optimal configuration for residential hybrid distributed generation systems (photovoltaic-wind), including battery energy storage, aiming to optimize the economic indicators Net Present Value and Levelized Cost of Energy simultaneously. Initially, the Design of Experiments approach, specifically the Response Surface Methodology, was adopted to model the objective functions, utilizing four input variables: X1: wind energy representativeness in the project (in %), X2: Demand Level, in kWh, X3: Battery Type: Lead-acid or Lithium-ion, and X4: Scenario Type: Peak/Intermediate or Total. The Desirability method was chosen for multi-objective optimization. The results that simultaneously optimize both responses are: X1 = 7.6%, X2 = 407.1 kWh, X3 = Lead-acid battery, and X4 = Total (Scenario Type). Finally, the findings emphasize the importance of incentives to promote residential wind power generation, as it is currently less cost-competitive than photovoltaic energy. Moreover, the higher cost of wind energy and the expense of batteries can render residential power generation projects unviable under certain circumstances.
Keywords
- Optimization in industry, business and finance
- Multi- and many-objective optimization
Status: accepted
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