The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost

Dorahaki, Sobhan; Abdollahi, Amir; Rashidinejad, Masoud; Moghbeli, Mohammadamin

doi:10.1016/j.est.2020.102022

Cited by 41 publications

(17 citation statements)

References 33 publications

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“…The significance of DRPs and electrical storage in the energy productivity of energy hub is highlighted in Ref. 28. The results of this study show a major increase in energy productivity of energy hub by incorporating DRPs.…”

Section: Introduction 1| Motivation and Literature Reviewmentioning

confidence: 63%

Smart energy hub optimization in presence of stochastically modeled renewables and loads

Qamar

Malik

2021

Intl J of Energy Research

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Integration of information technology with multi-carrier energy systems has paved the way for smart energy hubs (SEHs). Such energy hubs require stochastic modeling of intermittent renewable energy resources (RERs) and fluctuating demands to realize their optimal operation. Many excellent works are available related to their optimization but none of the existing works presents a comprehensive model which not only incorporates the stochastic nature of RERs and demands but also taps demand response potential in the presence of system storages. To fill this gap, this paper proposes a comprehensive model for an SEH which incorporates the stochastic nature of electrical, heating, and cooling demands in the presence of RERs, batteries, and thermal storages. In addition, optimal shifting of electrical, heating, and cooling loads are performed to further reduce the operational cost of SEH considering a price-based demand response program (DRP). The cost minimization objective is formulated as a mixed-integer linear programming (MILP) optimization problem.Five different cases are analyzed considering the inclusion and exclusion of storage and RERs with different percentages of shiftable loads for DRP. The proposed complex model is solved using CPLEX in GAMS environment. With RERs, cost reductions of 5.25% and 18.96% are observed in the operational cost of SEH without tapping demand response (DR) potential. When 10% and 20% loads are deemed to be shiftable for DR, operational costs reduce to 20.15% and 21.43% respectively with RERs and energy storages. Results of the study can support energy managers in the optimal operation of multi-carrier energy systems.

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Section: Introduction 1| Motivation and Literature Reviewmentioning

confidence: 63%

Smart energy hub optimization in presence of stochastically modeled renewables and loads

Qamar

Malik

2021

Intl J of Energy Research

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“…( 6). The first term in the objective function is conventional unit's operating cost, the next two terms are the incentive cost of IDRRs, the fourth term is gas storage cost, the fifth term assigns to the P2G operating cost, and the last term associates with the maintenance cost [8,18].…”

Section: Formulation Of Proposed Ufgms Idrrsmentioning

confidence: 99%

“…EDR and GDR participation have been limited by ( 13)- (16). The incentive rate for the EDR and GDR event in have been limited via (17) and (18), respectively. The maximum level of gas injection and release for gas storages are restricted via (19) and (20), respectively.…”

Section: Formulation Of Proposed Ufgms Idrrsmentioning

confidence: 99%

mentioning

confidence: 99%

“…In[17], an incentive-based DRRs has been implemented to reduce the emission and cost of operation. In[18], the impact of ESs and DRPs has been investigated on the effectiveness aspect of energy efficiency.According to recent studies in the field of flexibility, the evaluation of flexibility in the field of short-term timescale is…”

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confidence: 99%

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Integrated Electricity and Natural Gas Demand Response in Flexibility-Based Generation Maintenance Scheduling

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Increasing emission concerns about greenhouse gases have led to an increasing tendency to use renewable energy sources (RERs) in the power system. Nevertheless, the probabilistic nature of RERs has led to an enhanced require to flexibility provision. Hence, it is necessary to implement a flexibility-based generation maintenance scheduling. For this purpose, it has used the flexibility index of the system in order to evaluate the flexibility of the power system. In flexibility studies, modeling and predicting the variability of renewable resources is important. In this paper, the uncertainties of wind are considered through forecasting by deep learning method in Python. Gas-fired power plants are one of the most important suppliers of flexibility in the supply-side. Therefore, the reliable operation of power system depends on the of natural gas availability. Furthermore, gas demand is subject to various uncertainties, especially in cold seasons, which will have significant effects on power system. in this paper, power-to-gas (P2G) technology as energy storages is modeled to mitigate the impact of wind output and gas demand uncertainty. Meanwhile, integrated natural gas and electricity demand response such as event-based and time-based model has been applied as a flexibility provision from demand-side point of view. In this paper, the objectives of reducing emission and costs, leveling the reserve margin and increasing flexibility are considered as the objectives of optimizing generation maintenance scheduling. In order to solve the multi-objective problem, the augmented Epsilon constraint method has been used. The proposed model has been implemented on a modified IEEE RTS 24 bus.

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Overview of the Optimal Operation of Heat and Electricity Incorporated Networks

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The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost

Cited by 41 publications

References 33 publications

Smart energy hub optimization in presence of stochastically modeled renewables and loads

Smart energy hub optimization in presence of stochastically modeled renewables and loads

Integrated Electricity and Natural Gas Demand Response in Flexibility-Based Generation Maintenance Scheduling

Overview of the Optimal Operation of Heat and Electricity Incorporated Networks

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