Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs
Abstract In this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained resul...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-04-01
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| Series: | Energy Science & Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ese3.1104 |
| _version_ | 1817989193443311616 |
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| author | Yousef Allahvirdizadeh Sadjad Galvani Heidarali Shayanfar Mohsen Parsa Moghaddam |
| author_facet | Yousef Allahvirdizadeh Sadjad Galvani Heidarali Shayanfar Mohsen Parsa Moghaddam |
| author_sort | Yousef Allahvirdizadeh |
| collection | DOAJ |
| description | Abstract In this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively. |
| first_indexed | 2024-04-14T00:43:08Z |
| format | Article |
| id | doaj.art-25ee622e3cd44643b2159a087a070e24 |
| institution | Directory Open Access Journal |
| issn | 2050-0505 |
| language | English |
| last_indexed | 2024-04-14T00:43:08Z |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj.art-25ee622e3cd44643b2159a087a070e242022-12-22T02:22:06ZengWileyEnergy Science & Engineering2050-05052022-04-011041343137210.1002/ese3.1104Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programsYousef Allahvirdizadeh0Sadjad Galvani1Heidarali Shayanfar2Mohsen Parsa Moghaddam3Department of Electrical Engineering, Center of Excellence for Power Systems Automation and Operation Iran University of Science and Technology Tehran IranDepartment of Power Engineering, Faculty of Electrical and Computer Engineering Urmia University Urmia IranDepartment of Electrical Engineering, Center of Excellence for Power Systems Automation and Operation Iran University of Science and Technology Tehran IranDepartment of Electrical and Computer Engineering Tarbiat Modares University Tehran IranAbstract In this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively.https://doi.org/10.1002/ese3.1104correlationdemand responsereal‐time pricingrisk managementsmart energy hubtime of use pricing |
| spellingShingle | Yousef Allahvirdizadeh Sadjad Galvani Heidarali Shayanfar Mohsen Parsa Moghaddam Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs Energy Science & Engineering correlation demand response real‐time pricing risk management smart energy hub time of use pricing |
| title | Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs |
| title_full | Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs |
| title_fullStr | Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs |
| title_full_unstemmed | Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs |
| title_short | Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs |
| title_sort | risk averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real time pricing based demand response programs |
| topic | correlation demand response real‐time pricing risk management smart energy hub time of use pricing |
| url | https://doi.org/10.1002/ese3.1104 |
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