Organic Rankine Cycles(ORCs) are an effective way to produce electricity from low-grade heat sources, which cannot be effectively obtained using conventional high-temperature Rankine cycles. Due to the lack of availab...Organic Rankine Cycles(ORCs) are an effective way to produce electricity from low-grade heat sources, which cannot be effectively obtained using conventional high-temperature Rankine cycles. Due to the lack of available information regarding the real Organic Rankine Cycle units on industrial level, off-design simulation under diversified operating conditions plays a significant role for both the system performance prediction and control strategy design. This paper summarizes the theoretical basis, modeling approaches and tools for ORC off-design simulations. Firstly, a review was conducted on the individual state-of-the-art convective heat transfer correlations and void fraction models. Secondly, different kinds of modeling approaches and simulation tools were proposed, highlighting their relevant characteristics, and were categorized for their specific applications. Moreover, an in-depth analysis of technical challenges related to various applications and focusing on the model accuracy and complexity, computational efficiency, as well as the model compatibility were extensively described and discussed. Finally, the current research trends in this field and the development for further investigations were presented.展开更多
The fluctuations of renewable energy and various energy demands are crucial issues for the optimal design and operation of combined cooling,heating and power(CCHP)system.In this paper,a novel CCHP system is simulated ...The fluctuations of renewable energy and various energy demands are crucial issues for the optimal design and operation of combined cooling,heating and power(CCHP)system.In this paper,a novel CCHP system is simulated with advanced adiabatic compressed air energy storage(AA-CAES)technology as a join to connect with wind energy generation and an internal-combustion engine(ICE).The capital cost of utilities,energy cost,environmental protection cost and primary energy savings ratio(P E S R)are used as system performance indicators.To fulfill the cooling,heating and power requirements of a district and consider the thermal-electric coupling of ICE and AA-CAES in CCHP system,three operation strategies are established to schedule the dispatch of AA-CAES and ICE:ICE priority operation strategy,CAES priority operation strategy and simultaneous operation strategy.Each strategy leads the operation load of AA-CAES or ICE to improve the energy supply efficiency of the system.Moreover,to minimize comprehensive costs and maximize the P E S R,a novel optimization algorithm based on intelligent updating multi-objective differential evolution(MODE)is proposed to solve the optimization model.Considering the multi-interface characteristic and active management ability of the ICE and AA-CAES,the economic benefits and energy efficiency of the three operation strategies are compared by the simulation with the same system configuration.On a typical summer day,the simultaneous strategy is the best solution as the total cost is 3643 USD and the P E S R is 66.1%,while on a typical winter day,the ICE priority strategy is the best solution as the total cost is 4529 USD and the P E S R is 64.4%.The proposed methodology provides the CCHP based AA-CAES system with a better optimized operation.展开更多
With the rapid growth of urban population,the contradiction between energy consumption and environment protection has begun to restrict the development of the ecological civilization.The distributed energy network sys...With the rapid growth of urban population,the contradiction between energy consumption and environment protection has begun to restrict the development of the ecological civilization.The distributed energy network system,which combines the advantages of renewable energy and distributed energy,has gradually emerged and become one of the main development directions of the next-generation energy system.Distributed energy network system is a new type of intelligent energy network system that has evolved from the distributed energy system with integration of multiple energy networks and information networks.By connecting multiple renewable energy and natural gas in the region with end users in a distributed way,it can realize the instant and bidirectional transmission of cold,heat and electricity,and achieve ordering of the entire network through integration and cooperative control of the material flow,energy flow and information flow.展开更多
With the increasing demand for clean renewable energy and electric cars,people have put forward higher requirement for the energy storage system.One of the most successful lithium-ion batteries with a cathode combinat...With the increasing demand for clean renewable energy and electric cars,people have put forward higher requirement for the energy storage system.One of the most successful lithium-ion batteries with a cathode combination of lithium nickel manganese cobalt oxide(also called NCM lithium-ion battery),has been playing an increasingly important role.So far,numerous research has been done on the fabrication of cathode material with optimization of its composition,design,and assembly of the battery system in order to improve the energy storage performance.However,most of the previous studies were conducted based on relatively short cycling time of testing,with limited charge-discharge cycles of no more than 1000.Thus the conclusions were insufficient to be applied in the practical working condition.In this work,by using the developed NCM523 lithium-ion batteries,we have performed a series of ultra-long cycling tests on the individual cell and its module,with a comprehensive study on the relationship between the retained capacity after long cycling time and the depth of discharge(DOD),charge-discharge rate and operating temperature.Optimization of the charge-discharge strategies on a single cell and the whole module was also made to effectively improve the overall energy storage efficiency.This experimental study offers a guideline for the efficient use of similar types of lithium-ion batteries in the practical working condition.The developed batteries together with the optimized charge-discharge strategy proposed here are promising to meet the requirements for applications of stationary energy storage and electric cars.展开更多
In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable t...In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.展开更多
基金financially supported by the National Key Basic Research Program of China 973 Program(Grant No.2014CB249201)
文摘Organic Rankine Cycles(ORCs) are an effective way to produce electricity from low-grade heat sources, which cannot be effectively obtained using conventional high-temperature Rankine cycles. Due to the lack of available information regarding the real Organic Rankine Cycle units on industrial level, off-design simulation under diversified operating conditions plays a significant role for both the system performance prediction and control strategy design. This paper summarizes the theoretical basis, modeling approaches and tools for ORC off-design simulations. Firstly, a review was conducted on the individual state-of-the-art convective heat transfer correlations and void fraction models. Secondly, different kinds of modeling approaches and simulation tools were proposed, highlighting their relevant characteristics, and were categorized for their specific applications. Moreover, an in-depth analysis of technical challenges related to various applications and focusing on the model accuracy and complexity, computational efficiency, as well as the model compatibility were extensively described and discussed. Finally, the current research trends in this field and the development for further investigations were presented.
基金The work was supported by the National Fundamental Research Program of China 973 project(2014CB249201).
文摘The fluctuations of renewable energy and various energy demands are crucial issues for the optimal design and operation of combined cooling,heating and power(CCHP)system.In this paper,a novel CCHP system is simulated with advanced adiabatic compressed air energy storage(AA-CAES)technology as a join to connect with wind energy generation and an internal-combustion engine(ICE).The capital cost of utilities,energy cost,environmental protection cost and primary energy savings ratio(P E S R)are used as system performance indicators.To fulfill the cooling,heating and power requirements of a district and consider the thermal-electric coupling of ICE and AA-CAES in CCHP system,three operation strategies are established to schedule the dispatch of AA-CAES and ICE:ICE priority operation strategy,CAES priority operation strategy and simultaneous operation strategy.Each strategy leads the operation load of AA-CAES or ICE to improve the energy supply efficiency of the system.Moreover,to minimize comprehensive costs and maximize the P E S R,a novel optimization algorithm based on intelligent updating multi-objective differential evolution(MODE)is proposed to solve the optimization model.Considering the multi-interface characteristic and active management ability of the ICE and AA-CAES,the economic benefits and energy efficiency of the three operation strategies are compared by the simulation with the same system configuration.On a typical summer day,the simultaneous strategy is the best solution as the total cost is 3643 USD and the P E S R is 66.1%,while on a typical winter day,the ICE priority strategy is the best solution as the total cost is 4529 USD and the P E S R is 64.4%.The proposed methodology provides the CCHP based AA-CAES system with a better optimized operation.
文摘With the rapid growth of urban population,the contradiction between energy consumption and environment protection has begun to restrict the development of the ecological civilization.The distributed energy network system,which combines the advantages of renewable energy and distributed energy,has gradually emerged and become one of the main development directions of the next-generation energy system.Distributed energy network system is a new type of intelligent energy network system that has evolved from the distributed energy system with integration of multiple energy networks and information networks.By connecting multiple renewable energy and natural gas in the region with end users in a distributed way,it can realize the instant and bidirectional transmission of cold,heat and electricity,and achieve ordering of the entire network through integration and cooperative control of the material flow,energy flow and information flow.
基金This work was financially supported by the National K ey Basic Research Program of China(2014CB249200).
文摘With the increasing demand for clean renewable energy and electric cars,people have put forward higher requirement for the energy storage system.One of the most successful lithium-ion batteries with a cathode combination of lithium nickel manganese cobalt oxide(also called NCM lithium-ion battery),has been playing an increasingly important role.So far,numerous research has been done on the fabrication of cathode material with optimization of its composition,design,and assembly of the battery system in order to improve the energy storage performance.However,most of the previous studies were conducted based on relatively short cycling time of testing,with limited charge-discharge cycles of no more than 1000.Thus the conclusions were insufficient to be applied in the practical working condition.In this work,by using the developed NCM523 lithium-ion batteries,we have performed a series of ultra-long cycling tests on the individual cell and its module,with a comprehensive study on the relationship between the retained capacity after long cycling time and the depth of discharge(DOD),charge-discharge rate and operating temperature.Optimization of the charge-discharge strategies on a single cell and the whole module was also made to effectively improve the overall energy storage efficiency.This experimental study offers a guideline for the efficient use of similar types of lithium-ion batteries in the practical working condition.The developed batteries together with the optimized charge-discharge strategy proposed here are promising to meet the requirements for applications of stationary energy storage and electric cars.
基金This work was supported by the National Science Foundation of China(Grant 2014CB249200)the National Natural Science Foundation of China(Grant 61873162)the Shanghai Pujiang Program(Grant 18PJ1405500).
文摘In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.
