In district heating(DH) system with gas-fired peak load regulating boiler in the secondary network,by prolonging run time of base load plants under rated condition,the mean energy efficiency could be increased. The fu...In district heating(DH) system with gas-fired peak load regulating boiler in the secondary network,by prolonging run time of base load plants under rated condition,the mean energy efficiency could be increased. The fuels of the system,including coal and gas,would cause different environmental impacts. Meanwhile,the reliability of the heating networks would be changed because the peak load regulating boiler could work as a standby heat source. A model for assessment of heating system was established by value analysis to optimize this kind of system. Energy consumption,greenhouse gas emission,pollution emission and system reliability were selected as functional assessment indexes in the model. Weights of each function were determined by analytical hierarchy process (AHP) and experts consultation. Life cycle cost was used as the cost in the model. A real case as an example was discussed to obtain the optimal base load ratio. The result shows that the optimal base load ratio of the case is 0.77.展开更多
The upgrading of the DH （district heating） system through installing WSN （wireless sensor networks）--a technology by which to monitor and control quality operation of the DH system will lead to more effective use ...The upgrading of the DH （district heating） system through installing WSN （wireless sensor networks）--a technology by which to monitor and control quality operation of the DH system will lead to more effective use of thermal energy, enabling also the provision of quality customer services, as the data concerning the status of the existing networks is available in a timely manner, and in the stated amounts. Over the last decades, the use of WSN systems in enabling quality monitoring of heat production and supply process has been widely discussed among various researchers and industry experts, but has been little deployed in practice. These researchers and industry experts have analysed the advantages and constraints related to the use of the WSN in district heating. A pilot project conducted by Riga Heat （the main heating supplier in Riga, Latvia） has allowed to gain a real life experience as to the use of the WSN system in district in-house heating substations, and is deemed to be a major step towards future development of WSN technologies.展开更多
The integrated electricity-heat-hydrogen system(IEHHS)facilitates the efficient utilization of multiple energy sources,while the operational flexibility of IEHHS is hindered by the high heat inertia of alkaline electr...The integrated electricity-heat-hydrogen system(IEHHS)facilitates the efficient utilization of multiple energy sources,while the operational flexibility of IEHHS is hindered by the high heat inertia of alkaline electrolyzers(AELs)and the variations of renewable energy.In this paper,we propose a robust scheduling of IEHHS considering the bidirectional heat exchange(BHE)between AELs and district heating networks(DHNs).First,we propose an IEHHS model to coordinate the operations of AELs,active distribution networks(ADNs),and DHNs.In particular,we propose a BHE that not only enables the waste heat recovery for district heating but also accelerates the thermal dynamics in AELs.Then,we formulate a two-stage robust optimization(RO)problem for the IEHHS operation to consider the variability of renewable energy in ADNs.We propose a new solution method,i.e.,multi-affine decision rule(MADR),to solve the two-stage RO problem with less conservatism.The simulation results show that the operational flexibility of IEHHS with BHE is remarkably improved compared with that only with unidirectional heat exchange(UHE).Compared with the traditional affine decision rule(ADR),the MADR effectively reduces the IEHHS operating costs while guaranteeing the reliability of scheduling strategies.展开更多
With the advancement of clean heating projects and the integration of large-scale distributed heat pumps into rural distribution networks in northern China,power grid companies face tremendous pressure to invest in po...With the advancement of clean heating projects and the integration of large-scale distributed heat pumps into rural distribution networks in northern China,power grid companies face tremendous pressure to invest in power grid upgrades,which bring opportunities for renewable power generation integration.The combination of heating by distributed renewable energy with the flexible operation of heat pumps is a feasible alternative for dealing with grid reinforcement challenges resulting from heating electrification.In this paper,a mathematical model of the collaborative planning of distributed wind power generation(DWPG)and distribution network with large-scale heat pumps is developed.In this model,the operational flexibility of the heat pump load is fully considered and the requirements of a comfortable indoor temperature are met.By applying this model,the goals of not only increasing the profit of DWPG but also reducing the cost of the power grid upgrade can be achieved.展开更多
To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the character...To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.展开更多
This paper proposes a neural network based feasible region approximation model of a district heating system(DHS),and it is intended to be used for optimal operation of integrated electricity and heating system(IEHS)co...This paper proposes a neural network based feasible region approximation model of a district heating system(DHS),and it is intended to be used for optimal operation of integrated electricity and heating system(IEHS)considering privacy protection.In this model,a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear constraints.Based on the received approximation models of DHSs and detailed electricity system model,the electricity operator conducts centralized optimization,and then sends specific heating generation plans back to corresponding heating operators.Furthermore,subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation plan.In this scheme,optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system parameters.Case studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.展开更多
电力和热力系统的耦合能促进可再生能源消纳、提升能源系统综合效率。先进绝热压缩空气储能(advanced adiabatic compressed air energy storage,AA-CAES)是一种大规模的清洁储能技术,具有多能联储、联供的潜力,可以作为集成电力和热力...电力和热力系统的耦合能促进可再生能源消纳、提升能源系统综合效率。先进绝热压缩空气储能(advanced adiabatic compressed air energy storage,AA-CAES)是一种大规模的清洁储能技术,具有多能联储、联供的潜力,可以作为集成电力和热力系统的能源枢纽。但当前其作为独立主体参与电力与热力市场的竞标机制尚未明确,且传统建模多采用类似电池储电模型,导致经济效益难以精准衡量。为此,提出了一种考虑热电联供的AA-CAES主从博弈竞标策略。首先,建立考虑各组件运行特性的AA-CAES热电联供数学模型;其次,建立单领导者-双追随者的主从博弈框架,上层以AA-CAES收益最大化为目标优化竞标标的,下层以社会福利最大化为目标实现市场出清;针对双层非线性模型的求解难题,采用Karush-Kuhn-Tucker(KKT)最优性条件和二进制扩展线性化方法转化为单层混合整数规划;最后,算例仿真表明,AA-CAES参与双重市场比参与电力市场收益提升了30.6%,其自身组件参数对收益影响明显,尤其是透平机等熵效率提升10%可使总收益增长28%,为AA-CAES的市场运营与参数优化提供关键参考。展开更多
The present study develops a data-based compact model for the prediction of the fluid temperature evolution in district heating-and-cooling pipeline networks.This model is based on an existing“reduced-order model”by...The present study develops a data-based compact model for the prediction of the fluid temperature evolution in district heating-and-cooling pipeline networks.This model is based on an existing“reduced-order model”by the authors obtained from reduction of the“full-order model”describing the spatio-temporal energy balance for each pipe segment to a semi-analytical input-output relation between the pipe outlet temperature and the pipe inlet and ground temperatures.The proposed model(denoted XROM)expands on the original reduced-order model by incorporating variable mass flux as an additional input and thus greatly increases its practical relevance.The XROM represents variable mass flux by step-wise switching between mass-flux levels and thereby induces a prediction error relative to the true full-order model evolution after each switching.Theoretical analysis rigorously demonstrates that this error always decays and the XROM invariably converges on the full-order model evolution and,consequently,affords the same prediction accuracy.Performance analyses reveal that prediction errors are restricted to short“convergence intervals”after each mass-flux switching and the XROM therefore can handle substantially faster operating schemes than the current ones based on hourly monitoring and control.Convergence intervals of O(minutes)are namely typically sufficient-and thus switching frequencies up to O(minutes 1)permissible during dynamic operation and control actions-for reliable predictions.Quantification of these convergence intervals by an easy-to-use empirical relation furthermore enables a priori determination of the conditions for reliable predictions.Moreover,the XROM can capture the full 3D system dynamics(provided incompressible flow and heat-transfer mechanisms depending linearly on temperature)versus the essentially 1D approximation of current compact pipe models yet at similar computational cost.These attributes advance(parts of)district heating and cooling networks demanding prediction accuracies beyond 1D as its primary application area.This makes the XROM complementary to said pipe models and thereby expands the modelling capabilities for handling the growing complexity of(next-generation)networks.展开更多
District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency i...District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.展开更多
文摘In district heating(DH) system with gas-fired peak load regulating boiler in the secondary network,by prolonging run time of base load plants under rated condition,the mean energy efficiency could be increased. The fuels of the system,including coal and gas,would cause different environmental impacts. Meanwhile,the reliability of the heating networks would be changed because the peak load regulating boiler could work as a standby heat source. A model for assessment of heating system was established by value analysis to optimize this kind of system. Energy consumption,greenhouse gas emission,pollution emission and system reliability were selected as functional assessment indexes in the model. Weights of each function were determined by analytical hierarchy process (AHP) and experts consultation. Life cycle cost was used as the cost in the model. A real case as an example was discussed to obtain the optimal base load ratio. The result shows that the optimal base load ratio of the case is 0.77.
文摘The upgrading of the DH （district heating） system through installing WSN （wireless sensor networks）--a technology by which to monitor and control quality operation of the DH system will lead to more effective use of thermal energy, enabling also the provision of quality customer services, as the data concerning the status of the existing networks is available in a timely manner, and in the stated amounts. Over the last decades, the use of WSN systems in enabling quality monitoring of heat production and supply process has been widely discussed among various researchers and industry experts, but has been little deployed in practice. These researchers and industry experts have analysed the advantages and constraints related to the use of the WSN in district heating. A pilot project conducted by Riga Heat （the main heating supplier in Riga, Latvia） has allowed to gain a real life experience as to the use of the WSN system in district in-house heating substations, and is deemed to be a major step towards future development of WSN technologies.
基金supported by the Science and Technology Project of State Grid“Research and Application of Wide Area Multi energy Storage Collaborative Optimization and Control Technology in Provincial Power Grid”.
文摘The integrated electricity-heat-hydrogen system(IEHHS)facilitates the efficient utilization of multiple energy sources,while the operational flexibility of IEHHS is hindered by the high heat inertia of alkaline electrolyzers(AELs)and the variations of renewable energy.In this paper,we propose a robust scheduling of IEHHS considering the bidirectional heat exchange(BHE)between AELs and district heating networks(DHNs).First,we propose an IEHHS model to coordinate the operations of AELs,active distribution networks(ADNs),and DHNs.In particular,we propose a BHE that not only enables the waste heat recovery for district heating but also accelerates the thermal dynamics in AELs.Then,we formulate a two-stage robust optimization(RO)problem for the IEHHS operation to consider the variability of renewable energy in ADNs.We propose a new solution method,i.e.,multi-affine decision rule(MADR),to solve the two-stage RO problem with less conservatism.The simulation results show that the operational flexibility of IEHHS with BHE is remarkably improved compared with that only with unidirectional heat exchange(UHE).Compared with the traditional affine decision rule(ADR),the MADR effectively reduces the IEHHS operating costs while guaranteeing the reliability of scheduling strategies.
文摘With the advancement of clean heating projects and the integration of large-scale distributed heat pumps into rural distribution networks in northern China,power grid companies face tremendous pressure to invest in power grid upgrades,which bring opportunities for renewable power generation integration.The combination of heating by distributed renewable energy with the flexible operation of heat pumps is a feasible alternative for dealing with grid reinforcement challenges resulting from heating electrification.In this paper,a mathematical model of the collaborative planning of distributed wind power generation(DWPG)and distribution network with large-scale heat pumps is developed.In this model,the operational flexibility of the heat pump load is fully considered and the requirements of a comfortable indoor temperature are met.By applying this model,the goals of not only increasing the profit of DWPG but also reducing the cost of the power grid upgrade can be achieved.
基金supported by the Scientific Development Pro-gram of Shandong Province(Grant No.2012GGB01071)the Doctoral Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XNBS1225)the School Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XN110108)
文摘To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.
基金financially supported by China Scholarship Council(CSC)(No.201804910516 and No.202106070041)。
文摘This paper proposes a neural network based feasible region approximation model of a district heating system(DHS),and it is intended to be used for optimal operation of integrated electricity and heating system(IEHS)considering privacy protection.In this model,a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear constraints.Based on the received approximation models of DHSs and detailed electricity system model,the electricity operator conducts centralized optimization,and then sends specific heating generation plans back to corresponding heating operators.Furthermore,subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation plan.In this scheme,optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system parameters.Case studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.
文摘电力和热力系统的耦合能促进可再生能源消纳、提升能源系统综合效率。先进绝热压缩空气储能(advanced adiabatic compressed air energy storage,AA-CAES)是一种大规模的清洁储能技术,具有多能联储、联供的潜力,可以作为集成电力和热力系统的能源枢纽。但当前其作为独立主体参与电力与热力市场的竞标机制尚未明确,且传统建模多采用类似电池储电模型,导致经济效益难以精准衡量。为此,提出了一种考虑热电联供的AA-CAES主从博弈竞标策略。首先,建立考虑各组件运行特性的AA-CAES热电联供数学模型;其次,建立单领导者-双追随者的主从博弈框架,上层以AA-CAES收益最大化为目标优化竞标标的,下层以社会福利最大化为目标实现市场出清;针对双层非线性模型的求解难题,采用Karush-Kuhn-Tucker(KKT)最优性条件和二进制扩展线性化方法转化为单层混合整数规划;最后,算例仿真表明,AA-CAES参与双重市场比参与电力市场收益提升了30.6%,其自身组件参数对收益影响明显,尤其是透平机等熵效率提升10%可使总收益增长28%,为AA-CAES的市场运营与参数优化提供关键参考。
文摘The present study develops a data-based compact model for the prediction of the fluid temperature evolution in district heating-and-cooling pipeline networks.This model is based on an existing“reduced-order model”by the authors obtained from reduction of the“full-order model”describing the spatio-temporal energy balance for each pipe segment to a semi-analytical input-output relation between the pipe outlet temperature and the pipe inlet and ground temperatures.The proposed model(denoted XROM)expands on the original reduced-order model by incorporating variable mass flux as an additional input and thus greatly increases its practical relevance.The XROM represents variable mass flux by step-wise switching between mass-flux levels and thereby induces a prediction error relative to the true full-order model evolution after each switching.Theoretical analysis rigorously demonstrates that this error always decays and the XROM invariably converges on the full-order model evolution and,consequently,affords the same prediction accuracy.Performance analyses reveal that prediction errors are restricted to short“convergence intervals”after each mass-flux switching and the XROM therefore can handle substantially faster operating schemes than the current ones based on hourly monitoring and control.Convergence intervals of O(minutes)are namely typically sufficient-and thus switching frequencies up to O(minutes 1)permissible during dynamic operation and control actions-for reliable predictions.Quantification of these convergence intervals by an easy-to-use empirical relation furthermore enables a priori determination of the conditions for reliable predictions.Moreover,the XROM can capture the full 3D system dynamics(provided incompressible flow and heat-transfer mechanisms depending linearly on temperature)versus the essentially 1D approximation of current compact pipe models yet at similar computational cost.These attributes advance(parts of)district heating and cooling networks demanding prediction accuracies beyond 1D as its primary application area.This makes the XROM complementary to said pipe models and thereby expands the modelling capabilities for handling the growing complexity of(next-generation)networks.
文摘District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.
