Robust scheduling of hydrogen based smart micro energy hub with integrated demand response

Mansour-Saatloo, Amin; Agabalaye-Rahvar, Masoud; Mirzaei, Mohammad Amin; Mohammadi‐Ivatloo, Behnam; Abapour, Mehdi; Zare, Kazem

doi:10.1016/j.jclepro.2020.122041

Cited by 152 publications

(57 citation statements)

References 35 publications

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“…In HSS technology, the converted electricity to hydrogen, denoted as

P_{t, s}^{P 2 H}

, is the charging equivalent power while the hydrogen converted to electricity, that is,

P_{t, s}^{H 2 P}

, is the discharging equivalent electric power, which is restricted by Equations (48) and (49), respectively. Also, the other constraints (i.e., (50)–(53)) are precisely the same as the previously defined energy storage systems [10,22]. It should be noted that H2P conversion via a fuel cell is a clean process with zero carbon emission; however, supplying hydrogen to the gas network and then to the CHP unit to generate power causes carbon emission, which has environmental impacts.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Also, in order to consider the gas storage system (GSS), constraints (42)-(47) are outstanding and determine the charging and discharging power of GSS for each hour and scenario [22,31]. are the charging and discharging gas power of GSS unit at time t and scenario s; G min,chr,GS and G max,chr,GS are the minimum and maximum state of charging power and also G min, dischr,GS and G max, dischr,GS are the minimum and maximum state of discharging gas power by GSS unit; I chr;GS t;s and I dischr;GS t;s are the binary decision variables for charging and discharging power by GSS unit at time t and scenario s; η chr, GS and η dischr,GS are the efficiencies of charging and discharging gas power modes; E min, GS and E max, GS are the minimum and maximum gas energy stored level; Δt is the interval time for calculating gas energy level of GSS unit; E GS t;s is the level of stored gas energy in GSS unit at time t and scenario s.…”

Section: Stochastic Approachmentioning

confidence: 99%

“…(20) (21) (22), respectively. These limitations determine that the starting up and shutting F I G U R E 3 Gas and electricity price profiles MANSOUR-SATLOO ET AL.…”

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confidence: 99%

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A hybrid robust‐stochastic approach for optimal scheduling of interconnected hydrogen‐based energy hubs

et al. 2021

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The energy hub (EH) concept is an efficient way to integrate various energy carriers. In addition, demand response programmes (DRPs) are complementary to improving an EH's efficiency and increase energy system flexibility. The hydrogen storage system, as a green energy carrier, has an essential role in balancing supply and demand precisely, similar to other storage systems. A hybrid robust-stochastic approach is applied herein to address fluctuations in wind power generation, multiple demands, and electricity market price in a hydrogen-based smart micro-energy hub (SMEH) with multi-energy storage systems. The proposed hybrid approach enables the operator to manage the existing uncertainties with more flexibility. Also, flexible electrical and thermal demands under an integrated demand response programme (IDRP) are implemented in the proposed SMEH. The optimal scheduling of the hydrogen-based SMEH problem considering wind power generation and electricity market price fluctuations, as well as IDRP, is modelled via a mixed-integer linear programming problem. Finally, the validity and applicability of the proposed model are verified through simulation and numerical results. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In HSS technology, the converted electricity to hydrogen, denoted as

P_{t, s}^{P 2 H}

, is the charging equivalent power while the hydrogen converted to electricity, that is,

P_{t, s}^{H 2 P}

Section: Problem Formulationmentioning

confidence: 99%

Section: Stochastic Approachmentioning

confidence: 99%

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A hybrid robust‐stochastic approach for optimal scheduling of interconnected hydrogen‐based energy hubs

et al. 2021

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“…Moreover, the solution result is conservative and cannot be adequately applied to the comprehensive energy system operational planning problem to promote the utilization of renewable energy. [20] focuses on the concept of hydrogen-based smart micro energy hub (SMEH) considering integrated demand response (IDR) and fuel cell-based hydrogen storage system (HSS) and introduces IDR to control consumers' electrical and heat demand patterns. [21] proposes an integrated energy infrastructure based on power to hydrogen technology and applies a hybrid robust-stochastic optimization approach to manage uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Optimal Planning Design of a District-Level Integrated Energy System Considering the Impacts of Multi-Dimensional Uncertainties: A Multi-Objective Interval Optimization Method

et al. 2021

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Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-objective interval optimization framework for the energy hub (EH) planning problem from the perspective of the source load synergy, while considering the impacts of both supply-and demand-side uncertainties. For this aim, based on an in-depth analysis of the adjustable characteristics of various loads in EH and their effect on RES absorption, an interval model is first established to describe the responsiveness of users' load demand to real-time energy price variations and its associated uncertainties. In view of the natural contradiction between the system's economic and environmental benefits, a multi-objective interval optimization model for the EH planning problem is developed, wherein the minimization of the system's economic costs and the maximization of the RES utilization rate are considered as the dual objectives to be optimized simultaneously. Moreover, this study takes into account the uncertainties of RES availability and demand-side behaviors by using interval numbers and properly considering their impacts in the context of long-term planning. According to the features of the proposed model, the interval order relation and possible degree method are jointly used to transform the model into a deterministic optimization problem first, and then an improved non-dominant sorting genetic algorithm is used to derive the optimal solution to the problem. The results show that the proposed method can effectively improve the economy of EH and the utilization efficiency of RES and flexibly meet different planning requirements, giving better engineering practicability.INDEX TERMS demand-side management, improved non-dominated sorting genetic algorithm, multiobjective interval optimization model, source load synergy, uncertainty analysis

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“…In [32], a demand response mechanism of electro-thermal IES considering electrical and thermal load is proposed, and a multi-objective operation optimization model is established based on the DR mechanism to improve the economic and energy efficiency of IES. In [33], the authors addressed the concept of a hydrogen-based smart micro-energy hub considering IDR and a fuel cell-based hydrogen storage system. IDR is introduced to manage consumers' load patterns not only by shifting their electrical loads, but also by shifting their heat loads.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multi-Energy Microgrid Operation in the Presence of PV, Heterogeneous Energy Storage and Integrated Demand Response

Wang

Liang

et al. 2021

Applied Sciences

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In this paper, a model is proposed for the optimal operation of multi-energy microgrids (MEMGs) in the presence of solar photovoltaics (PV), heterogeneous energy storage (HES) and integrated demand response (IDR), considering technical and economic ties among the resources. Uncertainty of solar power as well as the flexibility of electrical, cooling and heat load demand are taken into account. A p-efficient point method is applied to compute PV power at different confidence levels based on historical data. This method converts the uncertain PV energy from stochastic to deterministic to be included in the optimization model. The concept of demand response is extended and mathematically modeled using a linear function based on the quantized flexibility interval of multi-energy load demand. As a result, the overall model is formulated as a mixed-integer linear program, which can be effectively solved by the commercial solvers. The proposed model is implemented on two typical summer and winter days for various cases. Results of case studies show the important benefits for maximum PV utilization, energy efficiency and economic system operation. Moreover, the influence of the different confidence levels of PV power and effectiveness of IDR in the stochastic circumstances are addressed in the optimization-based operation.

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Robust scheduling of hydrogen based smart micro energy hub with integrated demand response

Cited by 152 publications

References 35 publications

A hybrid robust‐stochastic approach for optimal scheduling of interconnected hydrogen‐based energy hubs

A hybrid robust‐stochastic approach for optimal scheduling of interconnected hydrogen‐based energy hubs

Optimal Planning Design of a District-Level Integrated Energy System Considering the Impacts of Multi-Dimensional Uncertainties: A Multi-Objective Interval Optimization Method

Optimization of Multi-Energy Microgrid Operation in the Presence of PV, Heterogeneous Energy Storage and Integrated Demand Response

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