Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles
[Display omitted] •Uncertainties of PHEV’s load demand is incorporated into renewable DG planning.•An unaccustomed type of point estimate method is used to handle uncertainties.•Pareto optimal front is obtained considering different penetration levels of PHEVs.•Chance-constrained programming is used...
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| Vydané v: | International journal of electrical power & energy systems Ročník 119; s. 105885 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.07.2020
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| Predmet: | |
| ISSN: | 0142-0615, 1879-3517 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | [Display omitted]
•Uncertainties of PHEV’s load demand is incorporated into renewable DG planning.•An unaccustomed type of point estimate method is used to handle uncertainties.•Pareto optimal front is obtained considering different penetration levels of PHEVs.•Chance-constrained programming is used to handle smooth constraints.•Robustness evaluations made by MC simulation prove the efficiency of the method.•Ignoring the uncertainties can severely increase constraint violation probability.
The increasing penetration of solar distributed generations (SDGs) and wind distributed generations (WDGs) together with plug-in electric vehicles (PEVs) will lead to a promising amount of reduction in greenhouse gas emissions. Nevertheless, they bring about adversities such as uncertainty in production-load sides, augmented power loss, and voltage instability in the power system, which should be carefully addressed to increase the reliability. In this concern, this paper proposes a multi-objective optimization methodology for sizing and siting of SDGs, WDGs, and capacitor banks (CBs) in the power system considering uncertainties stemmed from PEVs load demand, solar irradiance, wind speed, and the conventional load. The understudy objectives are the voltage stability index, green-house gas emissions, and the total cost. An unconventional point estimate method (PEM) is used to handle the related uncertainties, and chance-constrained programming method is deployed to deal with smooth constraints. The corresponding probability distribution functions of output variables are estimated by the maximum entropy method. Furthermore, robustness analysis is made by Monte Carlo simulation (MCS). The proposed methodology is applied to a typical radial distribution network. The results show that the presence of PEV’s significantly increases the load demand, which results in voltage collapse in the distribution system without the presence of distributed generations. However, the proposed probabilistic method ensures the safe operation of the distribution system with the optimal allocation of renewable distributed generations and CBs. Moreover, the results of deterministic and probabilistic cases are compared under different penetration levels of PEVs. The best tradeoff solution of the Pareto front is selected by the fuzzy satisfying method. |
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| ISSN: | 0142-0615 1879-3517 |
| DOI: | 10.1016/j.ijepes.2020.105885 |