Modeling and optimization of a network of energy hubs to improve economic and emission considerations

Maroufmashat, Azadeh; Elkamel, Ali; Fowler, Michael; Sattari, Sourena; Roshandel, Ramin; Hajimiragha, Amir H.; Walker, Sean B.; Entchev, Evgueniy

doi:10.1016/j.energy.2015.10.079

Cited by 153 publications

(63 citation statements)

References 25 publications

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“…Various studies have been done on the modeling, energy optimization, and optimal planning of energy hubs. Then, renewable energies and electrical/thermal storages integration in the energy hubs have been investigated . PV and wind power stochastic nature led to uncertainty modeling studies in Huo et al and Brahman et al Multiple energy sources enable the customers to participate in DR programs and supply the load with lower cost.…”

Section: Cloud‐based Energy Management‐related Modelsmentioning

confidence: 99%

A survey on cloud computing in energy management of the smart grids

Allahvirdizadeh

Moghaddam

Shayanfar

2019

Int Trans Electr Energ Syst

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Summary Smart grid mitigates the environmental concerns associated with the global warming and climate shifting and reduces the dependence of the power generation on conventional fossil fuels. This is done by energy efficiency improvement and renewable energy sources exploitation at large scales in the form of centralized systems as well as small‐scale systems in the form of microgrids and nanogrids. However, smart grid technology brings new challenges to the big data management of the new power system construction. Additionally, due to the large‐scale intermittent distributed renewable resources contribution and stochastic electric vehicles integration, the current energy management systems must undergo some improvements to handle the smart grid requirements. Cloud computing, as an intermittent based on‐demand computing model, serves an emerging solution to the aforementioned challenges. As a result, this paper presents a survey study on the smart grid and state of art energy management methods and the necessitation of the cloud computing incorporation in smart grid energy management. Then, the application of the cloud computing in energy management, demand side management programs, building energy management systems, energy hubs, and power dispatching systems have been discussed and associated models are addressed.

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Section: Cloud‐based Energy Management‐related Modelsmentioning

confidence: 99%

A survey on cloud computing in energy management of the smart grids

Allahvirdizadeh

Moghaddam

Shayanfar

2019

Int Trans Electr Energ Syst

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“…This situation becomes clear when solar-based RETs are considered, such as photovoltaic panels and solar thermal collectors, because they consume an energy resource that has zero cost and zero emissions. Nevertheless, it is also interesting to analyze the various methods employed in the literature to determine the average CO 2 emission factors: the most common approach is to consider the electricity power mix of a region or a country, 31,[41][42][43][44][45][46] but Casisi et al 47 adopted the region's main thermoelectric plant, Wang et al 29 considered a coal power plant, and Conci et al 48 employed the average between the measured value in 2015 and the forecast value for 2050. Third, several studies disregard the effect of dynamic climatic conditions, such as hourly and seasonal variations in the ambient temperature and solar radiation, on the performance of solar-based RETs. 32 Second, to the best of the authors' knowledge, timebased electricity CO 2 emission factors have never been taken into account in energy systems optimization studies for buildings applications.…”

Section: Introductionmentioning

confidence: 99%

A multiperiod multiobjective framework for the synthesis of trigeneration systems in tertiary sector buildings

2019

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This paper develops a multiperiod multiobjective optimization procedure to determine the optimal configuration and operational strategy of a trigeneration system assisted with solar-based technologies and thermal energy storage. The optimization model, formulated as mixed integer linear programming problem, incorporates dynamic operating conditions through time-dependent local climatic data, energy resources, energy demands, electricity prices, and electricity CO 2 emission factors. The methodology is applied to a case study of a residential building in Spain. First, the single-objective solutions are obtained, highlighting their fundamental differences regarding the installation of cogeneration (included in the optimal total annual cost solution) and solar-based technologies (included in the optimal total annual CO 2 emissions solution). Then, the Pareto curve is generated, and a decision-making approach is proposed to select the preferred trade-off solutions based on the marginal cost of CO 2 emissions saved. Additionally, sensitivity analyses are performed to investigate the influence of key parameters concerning energy resources prices, investment costs, and rooftop area. The analyses of the trade-off solutions verify the enormous potential for CO 2 emissions reduction, which can reach 32.3% with only 1.1% higher costs by displacing cogeneration in favor of the heat pump and the electric grid. Besides, with a modest cost increase of 7.3%, photovoltaic panels are incorporated, promoting an even greater CO 2 emissions reduction of 45.2%.

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“…With the emergence of electricity and natural gas distribution networks, urban and more populated areas, the cooperation of different energy converters in supplying different consumer demands such as electric, heating, and cooling is feasible. Concepts such as EHs, VPPs, heating‐cooling networks, and smart energy networks have been formed due to this feasibility.…”

Section: Introductionmentioning

confidence: 99%

Scheduling a smart energy hub-based virtual power plant using benders decomposition to considering power system constraints

Moghaddam

2018

Int Trans Electr Energ Syst

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Summary In this paper, a model is presented for scheduling of a virtual power plant (VPP) which consists of several energy hubs. With a collaborative management of energy hubs as a VPP, collaboration of energy hubs for supplying local loads is managed greatly. Furthermore, the amount of energy exchange with energy distribution networks and limitations of local distribution networks between energy hubs is done via VPP manager. In the presented model, energy hubs can be connected to each other via heat and electricity distribution networks. Using Benders decomposition, scheduling of this VPP is decomposed into a master problem and a slave problem. The master problem defines working points of energy hubs, and the slave problem considers the limitations of energy hubs. The proper performance of the presented model is verified using a case study, and the results are presented in detail.

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Modeling and optimization of a network of energy hubs to improve economic and emission considerations

Cited by 153 publications

References 25 publications

A survey on cloud computing in energy management of the smart grids

A survey on cloud computing in energy management of the smart grids

A multiperiod multiobjective framework for the synthesis of trigeneration systems in tertiary sector buildings

Scheduling a smart energy hub-based virtual power plant using benders decomposition to considering power system constraints

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