Flexibility Potential of Space Heating Demand Response in Buildings for District Heating Systems

Romanchenko, Dmytro; Nyholm, Emil; Odenberger, Mikael; Johnsson, Filip

doi:10.3390/en12152874

Cited by 22 publications

(9 citation statements)

References 31 publications

(43 reference statements)

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“…Although DH-related DR is not as well researched and even less put into practice, Romanchenko et al [15] have studied the potential for flexible space heating demand as well as its effects on the heating demand in a case study in Sweden. The study [15] stated that DR from buildings connected to DH is identified as being the most effective during the period when daily heat demand variations are the greatest (spring and autumn months). It is clear that this causes changes in the space heating demand profile during a couple of days.…”

Section: Introductionmentioning

confidence: 99%

Demand Response in District Heating Market—Results of the Field Tests in Student Apartment Buildings

Ala-Kotila

Vainio

Heinonen³

2020

Smart Cities

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This article presents the field test of the developed demand response system installed in the central heating systems of existing apartment buildings. The buildings are occupied by students and located in Tampere, Finland, which is within the northern climate zone. The studied buildings are connected to the local district heating network. The presented demand response system takes into account weather forecasts, indoor temperatures and decreases in space heating temperatures when demand for domestic hot water is the highest. The owner of the buildings benefits from peak demand control and can save in fixed fees. If enough buildings would have this kind of demand response control system, there would be a decreased need for utility companies to use peak power plants that typically use fossil fuels for heat production. In this field test, the peak load decrease was 14%–15% on average. During the test, the heating period of February and March, the normalized energy consumption of eight buildings was reduced by 11%, which represents a 9% annual cut in energy, costs and greenhouse gas emissions. Demand Response (DR) heating aims to help in reaching the objectives of the National Energy and Climate Strategy for 2030.

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Section: Introductionmentioning

confidence: 99%

Demand Response in District Heating Market—Results of the Field Tests in Student Apartment Buildings

Ala-Kotila

Vainio

Heinonen³

2020

Smart Cities

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“…The equilibrium between heat supply and demand is maintained not just on an annual basis but also on a subannual basis, or in each time slice. The approach used to separate the space heating demand profile from the hot water demand profile using a single profile from the DH substations has been described in the literature [80]. The hourly electricity price profiles and the CO2 emission factors for electricity consumption and generation in the price region of Sweden were obtained from the NordPool wholesale electricity market [81] and BALMORAL modeling performed by EA Energy Analysis A/S [82].…”

Section: Eskilstunamentioning

confidence: 99%

Comparative Modeling of Cost-Optimal Energy System Flexibility for Swedish and Austrian Regions

Mata

Garcia

Suna

et al. 2023

Preprint

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This study develops a reproducible method for estimating the cost-efficient flexibility potential of a local or regional energy system. Future scenarios that achieve ambitious climate targets and estimate the cost-efficient flexibility potential of demonstration sites were defined. Flexible potentials for energy system assessment are upscaled from the demonstration sites in Eskilstuna (Sweden) and Lower Austria (Austria). For example, Eskilstuna uses the building-stock model ECCABS and the TIMESCity-heat model for upscaling, whereas Lower Austria uses the Mixed Integer Linear Programming (MILP) optimization model, the BALMORAL power system model, and the Integrated MARKAL-EFOM System energy system modeling tool. According to the modeling, HPs will dominate Eskilstuna’s heating sector by 2040. Building rehabilitation reduces energy use in Lower Austria, where residential fossil fuel use ends in 2040. Heat pumps and district heating are critical for future heat demand. These findings are explained by the postulated technological-economic parameters, energy prices, and CO2 prices. We conclude that future electricity prices will determine future heating systems: either a high share of centralized heat pumps (low electricity prices) or a high share of combined heat-and-power (high electricity prices). Large-scale energy storage may be crucial. Furthermore, district heating companies have demand-side flexibility, but centralized flexible systems are the most cost effective.

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“…An integrated demand-supply co-optimization methodology is presented in Romanchenko et al (2019) [88] and applied to the DH system of Gothenburg (Sweden). The space heating demand in buildings is modeled with one thermal zone and the dispatch of heat production units and internal temperature set point variation in the buildings (only upwards) is obtained by mixed-integer linear programming in GAMS with hourly time resolution.…”

Section: Demand-side Management (Dsm) In Dhc Systemmentioning

confidence: 99%

Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review

et al. 2021

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Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.

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Flexibility Potential of Space Heating Demand Response in Buildings for District Heating Systems

Cited by 22 publications

References 31 publications

Demand Response in District Heating Market—Results of the Field Tests in Student Apartment Buildings

Demand Response in District Heating Market—Results of the Field Tests in Student Apartment Buildings

Comparative Modeling of Cost-Optimal Energy System Flexibility for Swedish and Austrian Regions

Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review

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