Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response

Rakipour, Davood; Barati, Hassan

doi:10.1016/j.energy.2019.02.021

Cited by 154 publications

(54 citation statements)

References 39 publications

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“…To address the second problem, many scenario reduction methods have been proposed. Reference [13] employs MCS to only generate 10 different scenarios. Reference [14] assumes that the solar irradiance follows the Beta distribution and generates 1000 scenarios initially.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Home Energy Scheduling Strategy Under the Uncertainty in Photovoltaic Generations

Wang

Zhang

et al. 2020

IEEE Access

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To address the uncertainty in photovoltaic (PV) outputs for day-ahead home energy scheduling in hourly timescale, a novel stochastic optimization strategy based data-driven method is proposed. Based on available historical PV outputs, the Gaussian mixture model (GMM) algorithm combined with improved prediction strength of clustering method is applied to establish the forecasted probabilistic PV outputs model. Based on the seven-step approximation model of Gaussian distribution, only PV outputs with larger probability level at each hour are used to generate scenarios. Then the typical scenario set can be constructed by scenario reduction method. By finding the solution in typical scenario set using mixed-integer nonlinear programming (MINLP), the scheduling strategy will be closer to real cases. Test results verify the effectiveness of proposed probabilistic PV output model and solution method. INDEX TERMS Home energy scheduling, stochastic optimization, photovoltaic (PV) outputs, Gaussian mixture model (GMM), prediction strength of clustering method. NOMENCLATURE ABBREVIATION

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Section: Introductionmentioning

confidence: 99%

“…scenarios are generated. The methods in [13]- [20] obviously improve calculation efficiency. However, the reserved scenarios still depend on the initial scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Home Energy Scheduling Strategy Under the Uncertainty in Photovoltaic Generations

Wang

Zhang

et al. 2020

IEEE Access

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“…The design of a management tool for a district energy system for polygeneration relying on an agent-based approach is discussed in [12]. Concerning the uncertainty analysis, various works can be found in the literature [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Optimal Integration of Renewable Sources and Latent Heat Storages for Nearly Zero-Energy Buildings

Guelpa¹,

Mancò²,

Verda³

2020

The First World Energies Forum—Current and Future Energy Issues

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A crucial way to reach a future sustainable society concerns the path towards nearly zero-energy buildings because of large amounts of energy at stake. The present work proposes an approach for the optimal integration of small-scale technologies (renewable and traditional) to enhance the pathway of existing and inefficient buildings towards low-carbon systems in a cost–benefit effective manner. Operation optimization, as well as an innovative combined design, is investigated with the goal of selecting the capacity of the technologies to be installed depending on the expected operations. The renewable technologies are integrated with proper storage units, such as batteries and latent thermal storage, which allows for reducing the space required for the installation. Two different non-linear programming approaches are used with the aim of finding an optimal solution. The optimization allows for reducing operation costs of 22% for renewable energy sources (RES)-fed dwellings. The combined operation and design optimization lead to a reduction in installation and operating costs by 7%. In the analyzed case, the adoption of the advanced optimization approach shows that latent heat storage is more suitable to be installed than electric storage (−2.5% cost).

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“…To solve this challenging problem, the adoption of stochastic optimization methods, based on both sampling and analytical techniques, has been proposed in the literature [21][22][23][24]. Although these methods allow insight into energy hub optimization problems and making more reliable decisions in complex situations, their deployment in realistic operation scenario is not immune from drawbacks and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization of Energy Hubs Operation Based on Extended Affine Arithmetic

2019

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Traditional energy systems were planned and operated independently, but the diffusion of distributed and renewable energy systems led to the development of new modeling concepts, such as the energy hub. The energy hub is an integrated paradigm, based on the challenging idea of multi-carrier energy systems, in which multiple inputs are conditioned, converted and stored in order to satisfy different types of energy demand. To solve the energy hub optimal scheduling problem, uncertainty sources, such as renewable energy production, price volatility and load demand, must be properly considered. This paper proposes a novel methodology, based on extended Affine Arithmetic, which enables the solving of the optimal scheduling problem in the presence of multiple and heterogeneous uncertainty sources. Realistic case studies are presented and discussed in order to show the effectiveness of the proposed methodology.

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Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response

Cited by 154 publications

References 39 publications

A Data-Driven Home Energy Scheduling Strategy Under the Uncertainty in Photovoltaic Generations

A Data-Driven Home Energy Scheduling Strategy Under the Uncertainty in Photovoltaic Generations

Optimal Integration of Renewable Sources and Latent Heat Storages for Nearly Zero-Energy Buildings

Robust Optimization of Energy Hubs Operation Based on Extended Affine Arithmetic

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