Peak Load Cutting in District Heating Network

Hietaharju, Petri; Ruusunen, Mika

doi:10.3384/ecp1714299

Cited by 3 publications

(6 citation statements)

References 5 publications

(6 reference statements)

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“…However, the median peak load cut over all the 201 buildings was 15%, and in some buildings the peak loads could not be cut at all. These results are comparable to the earlier study [28] where 70% peak load cut was achieved in an apartment building while in another apartment building 30% cut was too much to maintain the desired indoor temperature.…”

Section: Cutting Of Peak Loadssupporting

confidence: 87%

“…This means that a maximum of 70% peak load cut was assumed to be possible. This is consistent with the results in [28]. The pattern search algorithm from the Global Optimization Toolbox of MATLAB ® was utilized in the optimization.…”

Section: Peak Load Cuttingsupporting

confidence: 83%

“…In the earlier work, the cost function for peak load cutting minimized the heat demand [28]. However, in the case were no measured indoor temperatures are available and with the assumption that the simulated indoor temperature is the set point temperature, minimizing the heat demand would mean that the indoor temperature would just be decreased to the minimum level.…”

Section: Peak Load Cuttingmentioning

confidence: 99%

“…However, the optimization was not considered in these works. In another work, peak load cutting was studied in two different buildings utilizing the indoor temperature model and it was found that buildings can have very different heat storage capabilities and the energy savings or peak load cuts achieved in a single building are not necessarily the indication of the larger scale performance [28]. Consequently, the optimization of individual buildings alone is of no interest when considering the overall benefits to the energy system [29,30].…”

Section: Introductionmentioning

confidence: 99%

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Predictive Optimization of the Heat Demand in Buildings at the City Level

et al. 2019

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Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

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Section: Cutting Of Peak Loadssupporting

confidence: 87%

Section: Peak Load Cuttingsupporting

confidence: 83%

Section: Peak Load Cuttingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Optimization of the Heat Demand in Buildings at the City Level

et al. 2019

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“…In previous studies [42,43], the physical constants of a similar dynamic model were calculated using area information on the buildings. However, information about the floor, wall and window area of a building is not always easily available and in the case of hundreds or thousands of buildings it could be laborious to collect.…”

Section: Modelling and Analysis Methodsmentioning

confidence: 99%

Enabling Demand Side Management: Heat Demand Forecasting at City Level

2019

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Implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. Demand side management offers means to involve individual buildings in the optimization of the heat demand at city level to improve energy efficiency. In this work, two models were applied to forecast the heat demand from individual buildings up to a city-wide area. District heating data at the city level from more than 4000 different buildings was utilized in the validation of the forecast models. Forecast simulations with the applied models and measured data showed that, during the heating season, the relative error of the city level heat demand forecast for 48 h was 4% on average. In individual buildings, the accuracy of the models varied based on the building type and heat demand pattern. The forecasting accuracy, the limited amount of measurement information and the short time required for model calibration enable the models to be applied to the whole building stock. This should enable demand side management and lead to the predictive optimization of heat demand at city level, leading to increased energy efficiency.

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Smart Integration of Energy Production

Juuso

2017

Linköping Electronic Conference Proceedings

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Heat is primarily important in the world's total energy consumption and especially, peak loads and seasonal variations result in problems which are difficult to efficiently come up with electricity networks. Renewable energy sources bring important new possibilities, especially for the heat energy. This paper focuses on heat energy: compact parametric models are essential in the smart integration of production in the district heating and efficient collection of the solar thermal energy. Thermal masses of the buildings are used in the peak load cutting as energy storages. The physical parameters calculated from the building information facilitate the use of different types of buildings simultaneously in the calculations. Smart adaptive control solutions extend feasible operating periods in collecting solar thermal energy. A combination of multiple control actions is essential in keeping the system in control during strong fluctuations in cloudiness and energy demand. Heat storages increase the collecting power and extend the utilisation of the solar energy utilisation over daily and seasonal periods.

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Peak Load Cutting in District Heating Network

Cited by 3 publications

References 5 publications

Predictive Optimization of the Heat Demand in Buildings at the City Level

Predictive Optimization of the Heat Demand in Buildings at the City Level

Enabling Demand Side Management: Heat Demand Forecasting at City Level

Smart Integration of Energy Production

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