Combined domestic demand response and energy hub optimisation with renewable generation uncertainty

Huo, Da; Gu, Chenghong; Gang, Yang; Blond, Simon Le

doi:10.1016/j.egypro.2017.12.399

Cited by 6 publications

(15 citation statements)

References 17 publications

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“…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. Therefore, the researchers studied the energy hub optimization process in the presence of the DR programs . Figure shows the principle of the energy hub …”

Section: Cloud‐based Energy Management‐related Modelsmentioning

confidence: 99%

“…Therefore, the researchers studied the energy hub optimization process in the presence of the DR programs . Figure shows the principle of the energy hub …”

Section: Cloud‐based Energy Management‐related Modelsmentioning

confidence: 99%

“…Then, renewable energies and electrical/thermal storages integration in the energy hubs have been investigated. [120][121][122][123][124] PV and wind power stochastic nature led to uncertainty modeling studies in Huo et al and Brahman et al 125,126 Multiple energy sources enable the customers to participate in DR programs and supply the load with lower cost. Therefore, the researchers studied the energy hub optimization process in the presence of the DR programs.…”

Section: Figure 10mentioning

confidence: 99%

“…Therefore, the researchers studied the energy hub optimization process in the presence of the DR programs. 125,126 Figure 11 shows the principle of the energy hub. 125 Smart energy hubs enable the customers to take part in demand side management programs by feeding the must run loads with converting the natural gas into electricity via CHPs in peak load periods.…”

Section: Figure 10mentioning

confidence: 99%

“…125,126 Figure 11 shows the principle of the energy hub. 125 Smart energy hubs enable the customers to take part in demand side management programs by feeding the must run loads with converting the natural gas into electricity via CHPs in peak load periods. As a result, in this approach with changing the supply source of the customer, the energy demand of the utilities is reduced.…”

Section: Figure 10mentioning

confidence: 99%

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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%

“…Therefore, the researchers studied the energy hub optimization process in the presence of the DR programs . Figure shows the principle of the energy hub …”

Section: Cloud‐based Energy Management‐related Modelsmentioning

confidence: 99%

Section: Figure 10mentioning

confidence: 99%

Section: Figure 10mentioning

confidence: 99%

Section: Figure 10mentioning

confidence: 99%

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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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Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

Abo‐Elyousr

Sharaf²,

Darwish

et al. 2022

Energy Science & Engineering

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Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility-owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost-effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day-ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self-adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K-means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE-24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near-optimal day-ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.

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