A shrinking-horizon optimization framework for energy hub scheduling in the presence of wind turbine and integrated demand response program

Salehimaleh, Mohammad; Akbarimajd, Adel; Dejamkhooy, Abdolmajid

doi:10.1016/j.scs.2022.103689

Cited by 15 publications

(13 citation statements)

References 40 publications

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“…Besides, RTP-DRP for thermal demand (RTP-TDRP) is developed according to (41) to (47), similarly. 55 Equations (41) and (42) express the total and average level of thermal demand, respectively.…”

Section: Rtp-drpmentioning

confidence: 99%

“…The shrinking-horizon scheme has been used for modeling the uncertainties related to wind generation and electricity prices. 47 To sum up, References [6,7,9,10,[12][13][14][15][16][17][18] did not consider uncertainties in their studies. Although, References studied the uncertainty of input variables in EHs, but they did not pay attention to the effect of the correlation among uncertain variables on the optimization process of the EHs.…”

Section: Introductionmentioning

confidence: 99%

“…A bi‐level forecasting algorithm has been used for taking into account the uncertainties of the customer load demands and wind generation in [46]. Optimal scheduling of an EH has been discussed in [47], considering wind energy and DRP. The shrinking‐horizon scheme has been used for modeling the uncertainties related to wind generation and electricity prices 47 …”

Section: Introductionmentioning

confidence: 99%

“…Optimal scheduling of an EH has been discussed in [47], considering wind energy and DRP. The shrinking‐horizon scheme has been used for modeling the uncertainties related to wind generation and electricity prices 47 …”

Section: Introductionmentioning

confidence: 99%

“…To sum up, References [6,7,9,10,12–18] did not consider uncertainties in their studies. Although, References [19–47] studied the uncertainty of input variables in EHs, but they did not pay attention to the effect of the correlation among uncertain variables on the optimization process of the EHs. There is usually a significant amount of correlation between uncertain variables, which can considerably affect the SEH operational decisions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Allahvirdizadeh

Galvani

Shayanfar

et al. 2022

Energy Science & Engineering

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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.

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