Flexible dispatch of a building energy system using building thermal storage and battery energy storage

Niu, Jide; Tian, Zhe; Lu, Yakai; Zhao, Huabin

doi:10.1016/j.apenergy.2019.03.187

Cited by 98 publications

(27 citation statements)

References 35 publications

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“…Of the data-driven modeling approaches available, autoregressive-exogenous (ARX) [31] and autoregressive moving average (ARMAX) [32] models are the most commonly used for simulation of building energy consumption. Reviews of data-driven modeling approaches in buildings include numerical models for the prediction of electrical loads in buildings [33], regression models [34], energy prediction in buildings [35] as well as large-scale datadriven modeling of energy in buildings [36].…”

Section: Ahu Fan Modelmentioning

confidence: 99%

“…Modeling the electrical response of thermal systems in the building is a complex process that typically requires detailed thermal modeling of the building physics, known as white-box models [40]. As was seen in the previous section, data-driven models, sometimes referred to as numerical or black-box models, have also been used [31][32][33][34][35][36] but (i) parameter identification may not map to real parameters in the building, e.g., thermal conductivity, and (ii) prediction is reliant on the quality and diversity of the data available. In recent years, a number of grey box modeling approaches have been developed which combine knowledge of building physics with some data-driven aspects.…”

Section: Thermal Systems Modelmentioning

confidence: 99%

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Development of a Framework for Activation of Aggregator Led Flexibility

O’Connell

Keane

2021

Energies

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This paper presents a novel framework architecture for an online, real-time flexibility assessment and activation platform targeted at unlocking the flexibility potential of commercial buildings and smaller industrial sites, thereby enabling greater levels of renewable grid integration. Renewable integration targets in Europe of up to 40% of power generation from renewable sources by 2030 and over 90% by 2050 aim to decarbonize the electrical grid and increase electrification of transport, industry, and buildings. As renewable integration targets increase, participation in flexibility programs will be required from a much greater range of buildings and sites to balance grids hosting high levels of renewable generation. In this paper, an online implementation of a standardized flexibility assessment methodology, previously developed for offline contract negotiations between stakeholders, is modified to automate the assessment. The automated assessment is then linked to an aggregator-based multi-building or site optimization stage, enabling increased participation of multiple buildings and sites. To implement the assessment, models for individual flexible systems were reviewed, selected, and adapted, including physics-based, data-driven, and grey-box models. A review of optimization for flexibility found mixed-integer linear programming to be the optimal approach for the selection of flexible systems for demand response events.

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Section: Ahu Fan Modelmentioning

confidence: 99%

Section: Thermal Systems Modelmentioning

confidence: 99%

Development of a Framework for Activation of Aggregator Led Flexibility

O’Connell

Keane

2021

Energies

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“…However, it should be noted that these energy sources actually consume a large amount of energy and emit emissions during their life cycle. Furthermore, energy production from renewable sources is intermittent [8], fluctuates [9], difficult to forecast [10], and requires strategies for balancing supply and demand [11]. In addition, electrification of residential heating and transportation can increase peak loads and require both greater generation and grid capacity [12].…”

Section: Introduction 1building Electrificationmentioning

confidence: 99%

Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes

Homaei

Hamdy

2021

Energies

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All-electric buildings are playing an important role in the electrification plan towards energy-neutral smart cities. Batteries are key components in all-electric buildings that can help the demand-side energy management as a flexibility asset and improve the building survivability in the case of power outages as an active survivability asset. This paper introduces a novel methodology and indexes for determining cost-effective battery sizes. It also explores the possible trade-off between energy flexibility and the survivability of all-electric buildings. The introduced methodology uses IDA-ICE 4.8 as a building performance simulation tool and MATLAB® 2017 as a post-processing calculation tool for quantifying building energy flexibility and survivability indexes. The proposed methodology is applied to a case study of a Norwegian single-family house, where 10 competitive designs, 16 uncertainty scenarios, and 3 dynamic pricing tariffs suggested by the Norwegian regulators are investigated. The methodology provides informative support for different stakeholders to compare various building designs and dynamic pricing tariffs from the flexibility and survivability points of view. Overall, the results indicate that larger cost-effective batteries usually have higher active survivability and lower energy flexibility from cost- effectiveness perspective. For instance, when the time of use tariff is applied, the cost-effective battery size varies between 40 and 65 kWh (daily storage). This is associated with a cost-effective flexibility index of 0.4–0.55%/kWh and an active survivability index of 63–80%.

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“…These mechanisms will allow buildings to shift from passive elements to active players by cooperating in the network operation. However, this cooperation will require a closer relationship between the building and the energy sector [5,14,26] as well as the ability of the building to store energy, for instance with batteries [27]. A recent case study highlighted that establishing a proper regulatory framework and financial motives was deemed to be of the essence towards making battery storage attractive and cost-effective to potential investors [28].…”

Section: Introductionmentioning

confidence: 99%

Dispatch Strategies for the Utilisation of Battery Storage Systems in Smart Grid Optimised Buildings

et al. 2021

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This study investigates Smart Grid Optimised Buildings (SGOBs) which can respond to real-time electricity prices by utilising battery storage systems (BSS). Different building design characteristics are assessed to evaluate the impact on energy use, the interaction with the battery, and potential for peak load shifting. Two extreme cases based on minimum and maximum annual energy consumption were selected for further investigation to assess their capability of utilising BSS to perform arbitrage, under real-time pricing. Three operational dispatch strategies were modelled to allow buildings to provide such services. The most energy-efficient building was capable of shifting a higher percentage of its peak loads and export more electricity, when this is allowed. When using the biggest battery (220 kWh) to only meet the building loads, the energy-efficient building was able to shift 39.68% of its original peak loads in comparison to the 33.95% of the least efficient building. With exports allowed, the shifting percentages went down to 31.76% and 29.46%, respectively, while exports of 18.08 and 16.34 kWh/m2 took place. The formation of a regulatory framework is vital in order to establish proper motives for buildings to undertake an active role in the smart grid.

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Flexible dispatch of a building energy system using building thermal storage and battery energy storage

Cited by 98 publications

References 35 publications

Development of a Framework for Activation of Aggregator Led Flexibility

Development of a Framework for Activation of Aggregator Led Flexibility

Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes

Dispatch Strategies for the Utilisation of Battery Storage Systems in Smart Grid Optimised Buildings

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