Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming

Lu, Yuehong; Wang, Shengwei; Sun, Yongjun; Yan, Chengchu

doi:10.1016/j.apenergy.2015.02.060

Cited by 173 publications

(57 citation statements)

References 28 publications

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“…PV panels and wind turbines) are weather-dependent and therefore difficult to be controlled to follow the building demand during operation [96], thus the control issue of these generation systems themselves is not discussed in this paper. In contrast, other types of energy production systems can be controlled effectively to improve the efficiency of energy conversion and particularly alleviate the peak load on the grid [97,98].…”

Section: Control Of High Efficiency Generation Systemsmentioning

confidence: 98%

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

2015

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Section: Control Of High Efficiency Generation Systemsmentioning

confidence: 98%

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

2015

Self Cite

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“…Most of the literature in smart building prospective uses energy forecasting models with more physical meanings for building as well as distributed energy generation and storage system operation optimization [3]. For example, Lu et al [4] developed a serial of energy forecasting models for a building with a PV panel system, a combined cooling and power system, and a thermal storage system. Upon these energy forecasting models, a mixed-integer nonlinear programming based optimal scheduling framework is developed to reduce the energy cost.…”

Section: Introductionmentioning

confidence: 99%

An operation optimization and decision framework for a building cluster with distributed energy systems

Wen

Malkawi

2016

Applied Energy

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h i g h l i g h t sDeveloped and validated energy forecasting models for a building cluster with distributed energy systems. Proposed an operation optimization and decision framework for the building cluster. Optimized the operation strategies through particle swarm algorithm based multi-objective optimizations. Derived the Pareto frontiers to balance the energy cost saving and thermal comfort. a b s t r a c tDriven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1-58.3% under different electricity pricing plans and thermal comfort requirements.

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“…[5] -Take into account the impact of different equipment on the operational cost of the BIES -Cooling load is not considered -No detailed discussion on equipment [6,7] -Improve the optimization method of micro-grid -Cooling load is not considered [8] -propose the framework of NZEB -Lack of study case [9] -Take into account the factor of comfort when optimizing the BIES -Take room temperature as a factor -The fact that human beings are heat sources is not considered.…”

Section: Paper Pros Consmentioning

confidence: 99%

“…Reference [5] improved the efficiency of energy consumption and reduced the operation cost by coordinating the energy contributions of different energy units within the BIES. References [6,7] …”

Section: Introductionmentioning

confidence: 99%

Optimized Daily Dispatching Strategy of Building- Integrated Energy Systems Considering Vehicle to Grid Technology and Room Temperature Control

Wang

Tang²,

Chen

et al. 2018

Energies

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Abstract:With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are not only a promising transportation solution, but also can be used as mobile energy storage and spinning reserves, which play an important role in building-integrated energy systems (BIESs) and can further promote energy efficiency. Considering the space-time characteristics of EVs and the room temperature demand, this study establishes a planning model using V2G technology to minimize daily dispatch cost. Four kinds of control modes were proposed, combining the technology of the resident-owned and staff-owned EVs, in combination with the internal heating and power supply system. In this paper, the operating state of the system and the interaction of the equipment are analyzed under different charging and discharging control modes of EVs. The economics of the microgrid of the comprehensive energy building under four control modes are also discussed. Simulation results indicate that the combined control mode of residential vehicle and office vehicle is optimal for building an integrated energy microgrid, and the room temperature requirements can also be used as an important income source for building the microgrid.

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Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming

Cited by 173 publications

References 28 publications

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

An operation optimization and decision framework for a building cluster with distributed energy systems

Optimized Daily Dispatching Strategy of Building- Integrated Energy Systems Considering Vehicle to Grid Technology and Room Temperature Control

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