Minimizing the supply temperature at the district heating plant – dynamic optimization

Чичерин, Станислав; Junussova, Lyazzat; Junussov, Timur

doi:10.1051/e3sconf/201911802004

Cited by 11 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, it is expected that real-time minimization of the DH operational characteristics will help to reduce heat loss and required pump power. The real-time minimization can be classified in the following categories: (1) associated with demand side measures (DSM, e.g., made by Tunzi et al [7]), (2) applicable for both district heating and cooling networks (e.g., [8]), or simplified for the supply temperature at a DH plant only [9]. The research topic is also of great importance for cold regions.…”

Section: Prior Researchmentioning

confidence: 99%

Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy

Чичерин

Mašatin²,

Siirde

et al. 2020

Energies

Self Cite

View full text Add to dashboard Cite

The goal of this paper was to evaluate heat loss and the demand of district heating (DH) in the context of the fourth generation DH concept using a data-driven approach. The heat loss profile was calculated with GIS Zulu© (software (8.0.0.7539, Politerm, LLC, St.Petersburg, Russia) using eight various states of insulation, detailed information on thermal conductivity, internal heat transfer coefficient, and geometry of the concrete trench. There is a strong correlation between the heat sold and the average annual outdoor temperatures. The outstanding episodes are extremely rare, and the difference in the overall pattern is elusive. The results of the annual heat production and annual heat loss analyses were compared using three different estimation methods. The new method was the only one that showed a positive effect after the complete modernization of thermal insulation. The actual proportion of heat loss is much higher at 16%, while the actual heat delivery is less than anticipated at 85–86% only. The trend of the normative approach is correct but cannot determine changes in network heat loss due to aging. The method focuses on the effects of the state of insulation and actual supply temperature levels. The transition to smart energy systems includes strategic and progressive energy planning, as well as new pricing rules and tariffs. Thus, the method presented is the first step in the transition towards the fourth generation DH networks.

show abstract

Section: Prior Researchmentioning

confidence: 99%

Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy

Чичерин

Mašatin²,

Siirde

et al. 2020

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Case study object is a high temperature DH system in Omsk [22], which includes heat distribution system with head pipes of 800-1000 mm of diameter and operates at design temperatures of 70°C in return line and 150°C in supply one, and pressure between 8-14 bar [23]. Several buildings were opted for the thorough study.…”

Section: Materialsandmethodsmentioning

confidence: 99%

Specifying DHW heat demand profiles according to operational data: enhancing quality of a DH system model

et al. 2021

Self Cite

View full text Add to dashboard Cite

For a DH network a meticulous analysis is required to detect a correlation of a reduction in energy demand from one year to another. The factors, which lead to such inconsistency, force an energy company (1) to modernize equipment at a consumer side and (2) to lower network operating temperatures. It results into so called fourth generation district heating (4GDH). The current research focuses on large-scale DH systems and DHW as second largest share of heat demand. The heat delays, thermal inertia and DHW consumption patterns are specified further since they might represent a natural heating accumulator. In this case, daily flow changes are considered, as they influence a DH system performance and desirable TES capacity. However, more precise profiles can be achieved by detecting the actual flow curve, and measuring the temperature difference between substation supply and return line. The dimensioning of DH systems requires comprehensive understanding of simultaneity factors. Thus, we consider substations with DHW preparation to choose the optimal size of the heat distribution network according to the new method. Case study is a DH system in Omsk, which includes residential houses (both SH and DHW coverage), and university buildings (more demand results from process heat). The operation of the system was studied for the period from the 1st of January to 31st of December 2020. We suggest a TES with a capacity of 0.04 MWh; based on the traditional temperature range, the volume is about 0.5 m3. Daily compensation time is 2-3 hours, when there is a reduction in the supply flow rate of 1500 t/h with minimum DH plant make-up. The entire DH system requires about 400 t of hot water make-up to reach the quasi-steady state conditions after the night DHW shutdown. Using the threshold of the traditional model, it hardly fits an operational value - it is better set according to novel method (0.1 MW). For similar relations between circulation and DHW flow rates, the systems with a HE result in higher circulating flows than the substations with no one. The consumer benefit from consuming DHW and heat according to more accurate profiles accounts 1.72 billion USD. It is quantified by considering avoiding using a back-up electricity source to ensure DHW service when a DH plant supplies enough heat. Moreover, if a TES is controlled according to the method detailed, it alleviates the stress for intermittent operation by compensating the transients of SH and DHW loads. 4GDH concept should be considered according to: (1) the operational data, (2) new DHW demand assessments, and (3) using TES to buffer peaks.

show abstract

“…In this work, as well as in Ref. [15] the way of choosing minimal outdoor temperature is used because in this case the effect of curve manipulation is better visible. The temperature profiles of the heating network, as it can be seen from Fig.…”

Section: Methodsmentioning

confidence: 99%

Advanced Control of a District Heating System with High Residential Domestic Hot Water Demand

2020

Self Cite

View full text Add to dashboard Cite

Proper adjustment of domestic hot water (DHW) load structure can balance energy demand with the supply. Inefficiency in primary energy use prompted Omsk DH company to be a strong proponent of a flow controller at each substation. Here the return temperature is fixed to the lowest possible value and the supply temperature is solved. Thirty-five design scenarios are defined for each load deviation index with equally distributed outdoor temperature ranging from +8 for the start of a heating season towards extreme load at temperature of -26°C. All the calculation results are listed. If a flow controller is installed, the customers might find it suitable to switch to this type of DHW supply. Considering an option with direct hot water extraction as usual and a flow controller installed, the result indicates that the annual heat consumption will be lower once network temperatures during the fall or spring months are higher. The heat load profiles obtained here may be used as input for a simulation of a DH substation, including a heat pump and a tank for thermal energy storage. This design approach offers a quantitative way of sizing temperature levels in each DH system according to the listed methodology and the designer's preference.

show abstract

Minimizing the supply temperature at the district heating plant – dynamic optimization

Cited by 11 publications

References 30 publications

Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy

Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy

Specifying DHW heat demand profiles according to operational data: enhancing quality of a DH system model

Advanced Control of a District Heating System with High Residential Domestic Hot Water Demand

Contact Info

Product

Resources

About