• Energy Research

We first comprehensively analyze determining the generating capacities of each type, then discuss the heat distribution across interconnected district-level heat energy systems (“the hubs”) containing both low-scale and centralized heat plants; discussion is presented from the standpoint of primary energy consumption and costs. The applied tool includes a flexible heat generation model and an accurate objective function. The set of constraints and the Jacobian matrix are configured manually. We apply the fixed-point iteration approach to solve an equation system. In order to evaluate the consequences of all possible scenarios, we analyze a novel district heating (DH) system with low-scale solar collectors and heat pumps against central combined heat-and-power (CHP) plant fueled with fossil fuels and biomass. The proposed system is evaluated from the standpoint of the state-of-the-art, future prospects, and environmental impact. For each specific type of capacities, we herein present maximum achievable net generation of heat. System layout sketch is based on the data on the actual districts of Canberra, Australia. All the hubs are studied as a whole with due account of heat redistribution. The configuration made implies construction renewable energy facilities with a heat generation of 6.5+ MW, which will suffice to cover about a fifth of the peak load. This approach is quite novel for Australia. With greater electricity and heat supplies, renewable resources can cover an even greater share of such supplies. To sum up, a significant difference between the costs of a natural gas-based DHS and a coal-fired DH one lies in the fixed costs of operating the CHPPs and in the fuel costs. The self-generation of a hub may be inconsistent with the total energy balance, which is the total of eigen-production, energy inflows and outflows. The total system performance is considerably better when coal-fired CHPPs account for the bulk of net production.

We first comprehensively analyze determining the generating capacities of each type, then discuss the heat distribution across interconnected district-level heat energy systems (“the hubs”) containing both low-scale and centralized heat plants; discussion is presented from the standpoint of primary energy consumption and costs. The applied tool includes a flexible heat generation model and an accurate objective function. The set of constraints and the Jacobian matrix are configured manually. We apply the fixed-point iteration approach to solve an equation system. In order to evaluate the consequences of all possible scenarios, we analyze a novel district heating (DH) system with low-scale solar collectors and heat pumps against central combined heat-and-power (CHP) plant fueled with fossil fuels and biomass. The proposed system is evaluated from the standpoint of the state-of-the-art, future prospects, and environmental impact. For each specific type of capacities, we herein present maximum achievable net generation of heat. System layout sketch is based on the data on the actual districts of Canberra, Australia. All the hubs are studied as a whole with due account of heat redistribution. The configuration made implies construction renewable energy facilities with a heat generation of 6.5+ MW, which will suffice to cover about a fifth of the peak load. This approach is quite novel for Australia. With greater electricity and heat supplies, renewable resources can cover an even greater share of such supplies. To sum up, a significant difference between the costs of a natural gas-based DHS and a coal-fired DH one lies in the fixed costs of operating the CHPPs and in the fuel costs. The self-generation of a hub may be inconsistent with the total energy balance, which is the total of eigen-production, energy inflows and outflows. The total system performance is considerably better when coal-fired CHPPs account for the bulk of net production.

Newer buildings have a lower but smoother profile of indoor temperature, while older buildings are less energy efficient. Sometimes, the indoor temperature is unreasonably high, being 25–30 °C. There are buildings where the indoor temperature does not correlate with the outdoor one. Correction factors adjusting convective heat transfer coefficients are suggested. Energy demand is defined using the rate of heat loss and internal heat gains for the given building construction and design consumption profile. We suggest adjusting the setpoints of the secondary supply temperature to keep indoor and return temperatures lower. Correcting a traditional approach when designing a building may minimize energy consumption by 23.3% and increase the annual performance by up to 14.1%. The reductions of thermal peak resulting from a new type of controller adjustment (for instance, discrete) compared to the traditional operation range from roughly 10 to 30%, respectively. A better understanding of the system operation is a necessary step to switch to fourth-generation district heating (4GDH). This methodology is especially helpful in shaving daily peaks of heat demand. Building envelopes ease the charging, maximum storage capacity, and balance of the given generation and demand profiles, which are key factors in achieving the reduction in greenhouse gas (GHG) emissions. Once the heat demand is covered according to the maximum storage capacity for the given generation and demand profile, fewer efforts to modernize a district heating network are required.

Newer buildings have a lower but smoother profile of indoor temperature, while older buildings are less energy efficient. Sometimes, the indoor temperature is unreasonably high, being 25–30 °C. There are buildings where the indoor temperature does not correlate with the outdoor one. Correction factors adjusting convective heat transfer coefficients are suggested. Energy demand is defined using the rate of heat loss and internal heat gains for the given building construction and design consumption profile. We suggest adjusting the setpoints of the secondary supply temperature to keep indoor and return temperatures lower. Correcting a traditional approach when designing a building may minimize energy consumption by 23.3% and increase the annual performance by up to 14.1%. The reductions of thermal peak resulting from a new type of controller adjustment (for instance, discrete) compared to the traditional operation range from roughly 10 to 30%, respectively. A better understanding of the system operation is a necessary step to switch to fourth-generation district heating (4GDH). This methodology is especially helpful in shaving daily peaks of heat demand. Building envelopes ease the charging, maximum storage capacity, and balance of the given generation and demand profiles, which are key factors in achieving the reduction in greenhouse gas (GHG) emissions. Once the heat demand is covered according to the maximum storage capacity for the given generation and demand profile, fewer efforts to modernize a district heating network are required.

Research focus (problem description, short background):: The energy efficiency of a district heating (DH) system can be improved by fine-tuning supply and return temperatures and flow rates without need for invasive renovation of the end user heating systems. Research methods:: We employ a correlation coefficient to examine the relationship between the local supply temperature and the use of heat; research relies on data hourly heat generation records from each heat generation facility in the DH system of Omsk, Russia, 2016–2017. Key research results/findings:: The correlation declines the further we go from winter. Firstly, the relationship between the transition states and the outside temperature results from the relationship between external conditionings and heat parameters in DH. Secondly, both the energy consumption and the supply temperature are affected by the infrastructure, other weather conditions, and energy price; they are also highly dependent on consumer behavior, which is arbitrary, heterogeneous, and interdependent. Thirdly, the regional heating load is influenced by the characteristics of a building cluster. Although nearly all DH plants have a control system in place, we find that human factor jeopardizes the performance of such systems. Main conclusions and recommendations:: To generalize these results to other systems, we emphasize that the effective diffusion coefficients and effective heat-loss coefficients are influenced by the thermal dynamics in the network and change on typical time scale (one year), hence their uncertainty. This study has produced a technique for visualizing the relationship that exists in a DH system between supply temperature and heat consumption; this technique is expected to contribute to the dynamic modeling, control, and planning of future integrated energy systems. The load profiles are dynamic; a thermal storage could be used to shift the timing of heating loads to minimize the flow through DH networks.

Research focus (problem description, short background):: The energy efficiency of a district heating (DH) system can be improved by fine-tuning supply and return temperatures and flow rates without need for invasive renovation of the end user heating systems. Research methods:: We employ a correlation coefficient to examine the relationship between the local supply temperature and the use of heat; research relies on data hourly heat generation records from each heat generation facility in the DH system of Omsk, Russia, 2016–2017. Key research results/findings:: The correlation declines the further we go from winter. Firstly, the relationship between the transition states and the outside temperature results from the relationship between external conditionings and heat parameters in DH. Secondly, both the energy consumption and the supply temperature are affected by the infrastructure, other weather conditions, and energy price; they are also highly dependent on consumer behavior, which is arbitrary, heterogeneous, and interdependent. Thirdly, the regional heating load is influenced by the characteristics of a building cluster. Although nearly all DH plants have a control system in place, we find that human factor jeopardizes the performance of such systems. Main conclusions and recommendations:: To generalize these results to other systems, we emphasize that the effective diffusion coefficients and effective heat-loss coefficients are influenced by the thermal dynamics in the network and change on typical time scale (one year), hence their uncertainty. This study has produced a technique for visualizing the relationship that exists in a DH system between supply temperature and heat consumption; this technique is expected to contribute to the dynamic modeling, control, and planning of future integrated energy systems. The load profiles are dynamic; a thermal storage could be used to shift the timing of heating loads to minimize the flow through DH networks.

The tightening of the requirements to the hazardous emissions from fossil fuel fired boiler houses motivates a search for new technical solutions for heat generation. The four-year experience of operating an industrial heating boiler house converted from expensive liquid fuel to synthetic gas is described in as much detail as possible. The emergency situations that can occur during the operation of gas generators are described. The composition of synthetic gas and the quantitative composition of hazardous emissions in flue gases are presented.

The tightening of the requirements to the hazardous emissions from fossil fuel fired boiler houses motivates a search for new technical solutions for heat generation. The four-year experience of operating an industrial heating boiler house converted from expensive liquid fuel to synthetic gas is described in as much detail as possible. The emergency situations that can occur during the operation of gas generators are described. The composition of synthetic gas and the quantitative composition of hazardous emissions in flue gases are presented.