Multi-objective IGDT-based scheduling of low-carbon multi-energy microgrids integrated with hydrogen refueling stations and electric vehicle parking lots

Mansour-Saatloo, Amin; Ebadi, Ramin; Mirzaei, Mohammad Amin; Zare, Kazem; Mohammadi‐Ivatloo, Behnam; Marzband, Mousa; Anvari‐Moghaddam, Amjad

doi:10.1016/j.scs.2021.103197

Cited by 84 publications

(18 citation statements)

References 47 publications

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“…Furthermore, in [138], the integration of EVs with renewable-based microgrids in both stand-alone and grid-connected modes has been studied to minimize the operational costs, which contained the profit of EV owners. Moreover, EV parking lot, hydrogen refueling station, fuel cell, wind farm, and solar photovoltaic plant have been integrated with combined heat, power, and hydrogen microgrid in another study [139] to meet the electricity and thermal loads. Moreover, in [140], optimal design of a hybrid islanded system, including EV parking lot, battery storage, and renewable energy sources, is accomplished based on minimizing construction and operating costs as well as emission level of the system considering various uncertainties.…”

Section: Microgridmentioning

confidence: 99%

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Sadeghian

Oshnoei

Mohammadi‐Ivatloo

et al. 2022

Journal of Energy Storage

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162

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Section: Microgridmentioning

confidence: 99%

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Sadeghian

Oshnoei

Mohammadi‐Ivatloo

et al. 2022

Journal of Energy Storage

Self Cite

162

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“…Moreover, power to heat (P2H), power to hydrogen (P2H 2 ), CHP, energy storage devices, and a demand response program are included to enhance the flexibility of the system. e authors of [32] optimally manage the energy of a combined hydrogen, heat, and power MG (CHHP-MG) that has hydrogen stations. e aim is to supply the demand for electric vehicles and micro-CHPS.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, each flexible source, i.e., NRES, ESS, and DRP, includes both upward and downward flexibility services. ere is upward flexibility power (F + ) in scenario w for each FS if the difference between the power of FS in scenarios w and 1 (refers to the scenario that has forecasted values for uncertain parameters) is positive; otherwise, it is considered as downward flexibility power (F − ) with constraints ( 25)-( 27) [32]. Moreover, the flexibility energy is equal to the sum of upward and downward flexibility power of all FSs, as seen in (28).…”

Section: Ess Constraintsmentioning

confidence: 99%

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Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

Sabzalian

Pirouzi

Aredes

et al. 2022

International Transactions on Electrical Energy Systems

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With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.

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“…Due to the finite fossil fuel supplies, growing energy demand, and environmental issues, the utilization of RESs has become an attractive alternative for electricity generation [1]. Meanwhile, the combination of RESs with PEVs plays an important role in emissions reduction [2].…”

Section: Introductionmentioning

confidence: 99%

On the Role of Renewable Energy Policies and Electric Vehicle Deployment Incentives for a Greener Sector Coupling

et al. 2022

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Various incentives are introduced for the expansion of electric vehicle fleets and electricity generation from renewable energy resources. Although many researchers studied the effect of these policies on the related sector, there is no study investigating the indirect effect of renewable energy incentives on the deployment of electric vehicles or the indirect effect of electric vehicle adoption policies on the long-term integration of renewable energy resources. The main contribution of this paper is to analyze the impact of the specific incentives on both deployment of electric vehicles in the transportation system and investment in capacity generation in the electricity market. For this purpose, a new framework was designed to analyze the effect of policies on the electric vehicle deployment and development of DC charging stations based on the system dynamics approach. Then, this framework was combined with the existing dynamic models of the electricity market to study the interaction and behavior of both coupled systems from the policymakers' perspective. The effect of policies implementation was interpreted in a mathematical framework and the Net Present Value method was used for assessing the investment in charging infrastructures. Simulation results of a case study in the United States and sensitivity analysis illustrate that increasing the wind capacity incentives accelerated the electrification of the transportation system and increasing the incentives for electrification of transportation system influences wind capacity positively. Moreover, the sensitivity of the electric vehicle adoption to gas price is more than the sensitivity of the wind capac ity penetration to gas price. INDEX TERMSDC charging stations, Electricity market, Electric vehicles deployment policies, Plug-in electric vehicles, Renewable capacity incentive, System dynamics, Wind capacity investment. NOMENCLATURE Abbreviations ESS Energy storage system RES Renewable energy source PEV Plug-in electric vehicle EVSE Electric vehicle supply equipment HC Hard coal units CCGT Combined cycle gas turbines GT Gas turbines NPV Net Present Value Variables DUCH Number of PEVs that are supplied in DC charging station d Time step (day) TNPEV Total number of plug-in electric vehicles

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Multi-objective IGDT-based scheduling of low-carbon multi-energy microgrids integrated with hydrogen refueling stations and electric vehicle parking lots

Cited by 84 publications

References 47 publications

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

On the Role of Renewable Energy Policies and Electric Vehicle Deployment Incentives for a Greener Sector Coupling

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