Risk-constrained probabilistic optimal scheduling of FCPP-CHP based energy hub considering demand-side resources

Karkhaneh, Javad; Allahvirdizadeh, Yousef; Shayanfar, Heidarali; Galvani, Sadjad

doi:10.1016/j.ijhydene.2020.04.131

Cited by 33 publications

(37 citation statements)

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“…This section is devoted to the riskrelated constraints as seen in relation ( 59) to (61). 29 Where VAR presents the value at risk, α is the confidential level, γ s is an auxiliary variable for risk assessment and PR s is the probability of the s th scenario. Equation (59) demonstrates the risk cost of the SEH.…”

Section: Risk Constraintsmentioning

confidence: 99%

“…This study aims to minimize the total operation and emission costs of the SEH as well as its risk cost, as presented in Equation (62). 29 The first target in the scheduling cost is the minimization of the operation and emission cost. The second target is the risk control.…”

Section: Objective Functionmentioning

confidence: 99%

“…It is reminded that the objective function is according to Equations ( 62)-(78) and the constraints are according to Equations ( 1)-( 62) and ( 79)-(87). The expected value of electrical, thermal and gas demands are demonstrated in Figure 3, which is a modified version of data in [29].…”

Section: Numerical Studiesmentioning

confidence: 99%

“…As discussed before, the CVaR method is used to control the variations in the operation and emission cost of the SEH. This section is devoted to the risk‐related constraints as seen in relation () to () 29 . Where VAR presents the value at risk,

\alpha

is the confidential level,

{\gamma }_{s}

is an auxiliary variable for risk assessment and

{{PR}}_{s}

is the probability of the s th scenario.…”

Section: Formulation and Modelingmentioning

confidence: 99%

“…In [28], TOU-based DRPs have been considered for all electrical, heating, and cooling demands in an EH framework. In [29], a conditional value at risk (CVaR)-constrained probabilistic approach has been applied for a FC/CHP based SEH. They have applied DRPs for both electrical and thermal load demands in this study.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Risk Constraintsmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

Section: Numerical Studiesmentioning

confidence: 99%