The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Amirmohammad Behzadi - One of the best experts on this subject based on the ideXlab platform.
-
multi criteria optimization of an Integrated Energy System with thermoelectric generator parabolic trough solar collector and electrolysis for hydrogen production
International Journal of Hydrogen Energy, 2018Co-Authors: Ali Habibollahzade, Pouria Ahmadi, Ehsan Gholamian, Amirmohammad BehzadiAbstract:Abstract In this research paper, a newly Energy System consisting of parabolic trough solar collectors (PTSC) field, a thermoelectric generator (TEG), a Rankine cycle and a proton exchange membrane (PEM) is proposed. The integration is performed by establishing a TEG instead of the condenser as power generation and cooling unit thereafter surplus power output of the TEG is transferred to the PEM electrolyzer for hydrogen production. The Integrated renewable Energy System is comprehensively modeled and influence of the effective parameters is investigated on exergy and economic indicators through the parametric study to better understand the System performance. Engineering equation solver (EES) as a potential engineering tool is used to simulate the System and obtain the desired results. In order to optimize the System, a developed multi-objective genetic algorithm MATLAB code is applied to determine the optimum operating conditions of the System. Obtained results demonstrate that at optimum working condition from exergy viewpoint, exergy efficiency and total cost are 12.76% and 61.69 $/GJ, respectively. Multi-objective optimization results further show that the final optimal point which is well-balanced between exergy efficiency and total cost, has the maximum exergy efficiency of 13.29% and total cost of 63.96 $/GJ, respectively. The corresponding values for exergy efficiency and total cost are 10.01% and 60.21 $/GJ for optimum working condition from economic standpoint. Furthermore, hydrogen production at well-balanced operating condition would be 2.28 kg/h. Eventually, the results indicate that establishing the TEG unit instead of the condenser is a promising method to optimize the performance of the System and reduce total cost.
-
multi criteria optimization of an Integrated Energy System with thermoelectric generator parabolic trough solar collector and electrolysis for hydrogen production
International Journal of Hydrogen Energy, 2018Co-Authors: Ali Habibollahzade, Pouria Ahmadi, Ehsan Gholamian, Amirmohammad BehzadiAbstract:Abstract In this research paper, a newly Energy System consisting of parabolic trough solar collectors (PTSC) field, a thermoelectric generator (TEG), a Rankine cycle and a proton exchange membrane (PEM) is proposed. The integration is performed by establishing a TEG instead of the condenser as power generation and cooling unit thereafter surplus power output of the TEG is transferred to the PEM electrolyzer for hydrogen production. The Integrated renewable Energy System is comprehensively modeled and influence of the effective parameters is investigated on exergy and economic indicators through the parametric study to better understand the System performance. Engineering equation solver (EES) as a potential engineering tool is used to simulate the System and obtain the desired results. In order to optimize the System, a developed multi-objective genetic algorithm MATLAB code is applied to determine the optimum operating conditions of the System. Obtained results demonstrate that at optimum working condition from exergy viewpoint, exergy efficiency and total cost are 12.76% and 61.69 $/GJ, respectively. Multi-objective optimization results further show that the final optimal point which is well-balanced between exergy efficiency and total cost, has the maximum exergy efficiency of 13.29% and total cost of 63.96 $/GJ, respectively. The corresponding values for exergy efficiency and total cost are 10.01% and 60.21 $/GJ for optimum working condition from economic standpoint. Furthermore, hydrogen production at well-balanced operating condition would be 2.28 kg/h. Eventually, the results indicate that establishing the TEG unit instead of the condenser is a promising method to optimize the performance of the System and reduce total cost.
Qinkun Tan - One of the best experts on this subject based on the ideXlab platform.
-
combined electricity heat cooling gas load forecasting model for Integrated Energy System based on multi task learning and least square support vector machine
Journal of Cleaner Production, 2020Co-Authors: Zhongfu Tan, Hongyu Lin, Shenbo Yang, Liling Huang, Qinkun TanAbstract:Abstract Accurate forecasting of the combined loads of electricity, heat, cooling and gas in the Integrated Energy System is the key to improve the comprehensive efficiency and gain more economic benefits of various types of Energy. As an important part of the new generation of Energy Systems, the Integrated Energy System contains Energy subSystems such as electricity, heat, cooling and gas, and each subSystem employs Energy supply, conversion and storage equipment. This form of Energy System achieves the coupling of different types of Energy in different links. Based on this, this paper firstly combs the coupling relations among different Integrated Energy subSystems. Secondly, with the help of the weight sharing mechanism in the multi-task learning and the idea of least square support vector machine, a combined forecasting model of electricity, heat, cooling and gas loads based on the multi-task learning and least square support vector machine is constructed. Finally, in order to verify the effectiveness of the forecasting model proposed in this paper, the actual data from the Integrated Energy System in Suzhou Industrial Park are selected for a case study. The results show that: (1) the combined forecasting model based on multi-task learning and least square support vector machine can accurately predict the electricity, heat, cooling and gas loads of the park Integrated Energy System. (2) Compared with extreme learning machine and least square support vector machine, the combined forecasting model based on the multi-task learning and least square support vector machine increased the forecasting accuracy of a workday and a weekend by 18.00% and 19.19%, and the average forecasting accuracy increased by 18.60%. (3) Compared with extreme learning machine and least square support vector machine, the combined forecasting model can effectively shorten the training time which is reduced by 58.1% and 35.22%. The results further reflect the application effect of the multi-task learning in Energy demand forecasting of the Integrated Energy System and have a very broad reference value.
Zhongfu Tan - One of the best experts on this subject based on the ideXlab platform.
-
combined electricity heat cooling gas load forecasting model for Integrated Energy System based on multi task learning and least square support vector machine
Journal of Cleaner Production, 2020Co-Authors: Zhongfu Tan, Hongyu Lin, Shenbo Yang, Liling Huang, Qinkun TanAbstract:Abstract Accurate forecasting of the combined loads of electricity, heat, cooling and gas in the Integrated Energy System is the key to improve the comprehensive efficiency and gain more economic benefits of various types of Energy. As an important part of the new generation of Energy Systems, the Integrated Energy System contains Energy subSystems such as electricity, heat, cooling and gas, and each subSystem employs Energy supply, conversion and storage equipment. This form of Energy System achieves the coupling of different types of Energy in different links. Based on this, this paper firstly combs the coupling relations among different Integrated Energy subSystems. Secondly, with the help of the weight sharing mechanism in the multi-task learning and the idea of least square support vector machine, a combined forecasting model of electricity, heat, cooling and gas loads based on the multi-task learning and least square support vector machine is constructed. Finally, in order to verify the effectiveness of the forecasting model proposed in this paper, the actual data from the Integrated Energy System in Suzhou Industrial Park are selected for a case study. The results show that: (1) the combined forecasting model based on multi-task learning and least square support vector machine can accurately predict the electricity, heat, cooling and gas loads of the park Integrated Energy System. (2) Compared with extreme learning machine and least square support vector machine, the combined forecasting model based on the multi-task learning and least square support vector machine increased the forecasting accuracy of a workday and a weekend by 18.00% and 19.19%, and the average forecasting accuracy increased by 18.60%. (3) Compared with extreme learning machine and least square support vector machine, the combined forecasting model can effectively shorten the training time which is reduced by 58.1% and 35.22%. The results further reflect the application effect of the multi-task learning in Energy demand forecasting of the Integrated Energy System and have a very broad reference value.
-
operation optimization and income distribution model of park Integrated Energy System with power to gas technology and Energy storage
Journal of Cleaner Production, 2020Co-Authors: Shenbo Yang, Zhongfu Tan, Rui Zhao, Fengao ZhouAbstract:Abstract Power-to-gas (P2G) technology is considered as a new approach for clean Energy consumption and Energy conversion. However, because this technology must be combined with other Energy Systems to build a stable Energy System, a reasonable income distribution method is necessary to guarantee this integration. Firstly, the operation optimization model of the park Integrated Energy System (PIES) and park independent Energy System (PINES) with P2G are constructed for the first stage optimization, with the objective of maximizing net income. Secondly, the Energy System performance evaluation indicators are developed to assess the System quantitatively in terms of the economic and environmental aspects. Thereafter, the income distribution models based on Shapley value and the improved Shapley value with operational risk factor are created, and the total income is optimally distributed at the second stage based on the first-stage optimization results. Finally, an industrial park in a province of China is selected for case analysis. The results show that (1) PIES can complement different Systems, integrate the demands in heat, electricity, and gas, while realizing the electricity-gas-heat/electricity conversion and heat-electricity complementarity. (2) Income distribution using an improved Shapely value method is proposed, it overcomes the one-sidedness of transaction volume and lack of differences among participants, reflects the actual operational risk and the degree of contribution of participants to the whole System, and promotes the incentives of cooperation. (3) Based on the improved income distribution model, the income of the PS, P2G, and HS are redistributed. The income of P2G increased by ¥7,450, which reflects the important collaborative value of P2G. The willingness of System cooperation increased by 742.392%. Therefore, the proposed operation optimization and income distribution model can enhance the incentives of participants want to cooperate under the premise of ensuring the maximum net income of the PIES. Moreover, it can be used as reference for the formulation of an optimal operation plan and income distribution of the complex Energy System and it can provide a way to promote clean Energy production.
Xiurong Zhang - One of the best experts on this subject based on the ideXlab platform.
-
estimating the failure probability in an Integrated Energy System considering correlations among failure patterns
Energy, 2019Co-Authors: Xiurong Zhang, Zheng QiaoAbstract:Abstract Hydrocarbons in the form of natural gas can be used in long-term Energy production and could strengthen the defossilisation of the Energy sector. However, operational reliability will decrease due to gas shortages in Integrated Energy Systems. Correlations exist among the multiple failure patterns such as overloads of the transmission lines, overloads of the combined heat and power generator, and over-limit operations of gas compressors. The Energy reliability assessment problem becomes more difficult to resolve for Integrated Energy Systems than for power Systems. This paper proposes an analytical method to address the multiple correlated risks that are caused by the Energy network interactions in an Integrated Energy System. Three case results in MATLAB demonstrate the accuracy and computational efficiency of the proposed method. Compared to the Monte Carlo algorithm, which required approximately 550 s to obtain a precise failure probability with 50 thousand samples, the proposed method takes approximately 12.7 s to obtain an exact failure probability. The advantage is that, different from wide-bound theory and narrow-bound theory, the proposed method can offer a certain value rather than two bounds with respect to the failure probability.
-
failure probability estimation of the gas supply using a data driven model in an Integrated Energy System
Applied Energy, 2018Co-Authors: Xueqian Fu, Gengyin Li, Xiurong Zhang, Zheng QiaoAbstract:Probabilistic security evaluation is one of the academic frontiers in the research on Energy System reliability. It is very important to evaluate the impact of gas Systems on the power/heat System for practical engineering in gas turbine engine-based Integrated Energy Systems. This paper proposes a data-driven model instead of a physical model to estimate the probabilities of the incident of insufficient gas supply suffered from weather uncertainty, which affects the reliability of gas turbine engine-based Integrated Energy Systems. According to actual Energy projections, it can be assumed that the uncertainty of intermittent wind power, load fluctuations, and variations in gas deliverability derives from fluctuating weather conditions such as the temperature and wind. The wind power, load, and gas consumption data in the Integrated Energy System and the gas supply data of the station are sufficient to accurately build a data-driven model. Traditional methods based on physical models include the Iman and Stein methods, the first-order reliability method, and the mixed Monte Carlo algorithm to judge the effectiveness of the proposed method. The results from three cases are a testimony to the accuracy and engineering feasibility of the proposed method. The calculation of a data-driven model is easier than that of a physical model, and its simplification is conducive to failure probability estimation in a real application.
-
failure probability estimation of gas supply using the central moment method in an Integrated Energy System
Applied Energy, 2018Co-Authors: Xiurong ZhangAbstract:Abstract The reliable supply of natural gas ensures the safe and stable operation of an Integrated Energy System. Operational uncertainty of the Integrated Energy System (IES) such as a surge in heating load can exacerbate an unreliable supply situation of natural gas. This paper examines gas supply reliability. A central moment method is proposed to estimate the failure probabilities for the gas supply in a combined heat- and power-based microIntegrated Energy System. To make the calculation more relevant to an actual project, we considered the Energy network constraints and Energy stochastic characteristics. To demonstrate the use of the central moment method in the estimation of failure probability of a gas supply, the results were validated using sufficient comparisons with traditional methods. Classic traditional methods include the Iman and Stein methods, the first order reliability method, the mixed algorithm based on Latin hypercube sampling, the Cholesky decomposition and the Nataf transformation, and the principle of maximum entropy method. An actual Integrated Energy System is provided to promote practical solutions for failure probability estimation of the gas supply.
-
estimation of the failure probability of an Integrated Energy System based on the first order reliability method
Energy, 2017Co-Authors: Qinglai Guo, Xiurong Zhang, Hongbin Sun, Li WangAbstract:Abstract In this paper, we investigate the impacts of intermittent renewable Energy sources (RESs) and stochastic Energy loads on the operation and uncertainties of an Integrated Energy System (IES). In our analysis, we use a first order reliability method (FORM) to estimate the failure probabilities, which are crucial for ensuring the reliability of the gas supply and surplus power absorption. The Hasofer Lind and Rackwitz Fiessler (HLRF) algorithm is introduced to solve the FORM optimization model while considering the stochastic behaviours and dependencies of multiple Energy sources. A mathematical case is presented to demonstrate the use of the FORM in the estimation of failure probability, and the results are validated using the Latin hypercube sampling theories, including the Iman and Stein methods. The results of a failure probability analysis for an ideal IES are provided to illustrate the proposed technique. The failure probability can be used to improve IES operation and planning and ensure better reliability.
Ali Habibollahzade - One of the best experts on this subject based on the ideXlab platform.
-
multi criteria optimization of an Integrated Energy System with thermoelectric generator parabolic trough solar collector and electrolysis for hydrogen production
International Journal of Hydrogen Energy, 2018Co-Authors: Ali Habibollahzade, Pouria Ahmadi, Ehsan Gholamian, Amirmohammad BehzadiAbstract:Abstract In this research paper, a newly Energy System consisting of parabolic trough solar collectors (PTSC) field, a thermoelectric generator (TEG), a Rankine cycle and a proton exchange membrane (PEM) is proposed. The integration is performed by establishing a TEG instead of the condenser as power generation and cooling unit thereafter surplus power output of the TEG is transferred to the PEM electrolyzer for hydrogen production. The Integrated renewable Energy System is comprehensively modeled and influence of the effective parameters is investigated on exergy and economic indicators through the parametric study to better understand the System performance. Engineering equation solver (EES) as a potential engineering tool is used to simulate the System and obtain the desired results. In order to optimize the System, a developed multi-objective genetic algorithm MATLAB code is applied to determine the optimum operating conditions of the System. Obtained results demonstrate that at optimum working condition from exergy viewpoint, exergy efficiency and total cost are 12.76% and 61.69 $/GJ, respectively. Multi-objective optimization results further show that the final optimal point which is well-balanced between exergy efficiency and total cost, has the maximum exergy efficiency of 13.29% and total cost of 63.96 $/GJ, respectively. The corresponding values for exergy efficiency and total cost are 10.01% and 60.21 $/GJ for optimum working condition from economic standpoint. Furthermore, hydrogen production at well-balanced operating condition would be 2.28 kg/h. Eventually, the results indicate that establishing the TEG unit instead of the condenser is a promising method to optimize the performance of the System and reduce total cost.
-
multi criteria optimization of an Integrated Energy System with thermoelectric generator parabolic trough solar collector and electrolysis for hydrogen production
International Journal of Hydrogen Energy, 2018Co-Authors: Ali Habibollahzade, Pouria Ahmadi, Ehsan Gholamian, Amirmohammad BehzadiAbstract:Abstract In this research paper, a newly Energy System consisting of parabolic trough solar collectors (PTSC) field, a thermoelectric generator (TEG), a Rankine cycle and a proton exchange membrane (PEM) is proposed. The integration is performed by establishing a TEG instead of the condenser as power generation and cooling unit thereafter surplus power output of the TEG is transferred to the PEM electrolyzer for hydrogen production. The Integrated renewable Energy System is comprehensively modeled and influence of the effective parameters is investigated on exergy and economic indicators through the parametric study to better understand the System performance. Engineering equation solver (EES) as a potential engineering tool is used to simulate the System and obtain the desired results. In order to optimize the System, a developed multi-objective genetic algorithm MATLAB code is applied to determine the optimum operating conditions of the System. Obtained results demonstrate that at optimum working condition from exergy viewpoint, exergy efficiency and total cost are 12.76% and 61.69 $/GJ, respectively. Multi-objective optimization results further show that the final optimal point which is well-balanced between exergy efficiency and total cost, has the maximum exergy efficiency of 13.29% and total cost of 63.96 $/GJ, respectively. The corresponding values for exergy efficiency and total cost are 10.01% and 60.21 $/GJ for optimum working condition from economic standpoint. Furthermore, hydrogen production at well-balanced operating condition would be 2.28 kg/h. Eventually, the results indicate that establishing the TEG unit instead of the condenser is a promising method to optimize the performance of the System and reduce total cost.