As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy o...As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.展开更多
The depletion of fossil energy and the deterioration of the ecological environment have severely restricted the development of the power industry.Therefore,it is extremely urgent to transform energy production methods...The depletion of fossil energy and the deterioration of the ecological environment have severely restricted the development of the power industry.Therefore,it is extremely urgent to transform energy production methods and vigorously develop renewable energy sources.It is therefore important to ensure the stability and operation of a large multi-energy complementary system,and provide theoretical support for the world’s largest single complementary demonstration project with hydro-wind-PV power-battery storage in Qinghai Province.Considering all the multiple power supply constraints,an optimization scheduling model is established with the objective of minimizing the volatility of output power.As particle swarm optimization(PSO)has a problem of premature convergence and slow convergence in the latter half,combined with niche technology in evolution,a niche particle swarm optimization(NPSO)is proposed to determine the optimal solution of the model.Finally,the multiple stations’coordinated operation is analyzed taking the example of 10 million kilowatt complementary power stations with hydropower,wind power,PV power,and battery storage in the Yellow River Company Hainan prefecture.The case verifies the rationality and feasibility of the model.It shows that complementary operations can improve the utilization rate of renewable energy and reduce the impact of wind and PV power’s volatility on the power grid.展开更多
The application of multi-energy hybrid power systems is conducive to tackling global warming and the low-carbon transition of the power system.A capacity allocation model of a multi-energy hybrid power system includin...The application of multi-energy hybrid power systems is conducive to tackling global warming and the low-carbon transition of the power system.A capacity allocation model of a multi-energy hybrid power system including wind power,solar power,energy storage,and thermal power was developed in this study.The evaluation index was defined as the objective function,formulated by normalizing the output fluctuation,economic cost,and carbon dioxide emissions.Calculations under different initial conditions and output electric power scenarios were carried out with genetic algorithm.The capacity allocation model was validated with the literature results,with errors of less than 5%.Results indicate that the capacity allocation modes of the multi-energy hybrid power system can be divided into thermal power dominated mode,multi-energy complementary mode,and renewable power dominated mode.In addition,the division of capacity allocation modes is not affected by the weather conditions and energy storage ratio.The capacity factor decreases from 0.4 to 0.24 as the power system changes from the thermal power dominated mode to the renewable power dominated mode.When the output electric power is 240 MW,300 MW,and 340 MW,the optimal energy storage ratio is 10%,18%,and 16%,respectively.The model developed in this study not only enriches the theory of multi-energy complementary power generation but also guides the engineering design of the wind-photovoltaics-thermal-storage system targeting smart grid and be beneficial for the middle-long-term planning of the green and low-carbon transition of the power system.展开更多
针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud contr...针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud control system,IPPCCS)解决方案.基于智能电厂云控制系统,针对绿色能源发电波动性强、抗扰能力差的问题,利用机器学习算法对采集到的风电、光伏输出功率进行短时预测,获知未来风、光机组功率输出情况.在云端使用经济模型预测控制(Economic model predictive control,EMPC)算法,通过实时滚动优化得到水轮机组的功率预测调度策略,保证绿色能源互补发电的鲁棒性,充分消纳风、光两种能源,减少水轮机组启停和穿越振动区次数,在为用户清洁、稳定供电的同时降低了机组寿命损耗.最后,一个区域云数据中心的供电算例表明了本文方法的有效性.展开更多
基金funded by the National Key R&D Program of China,grant number 2019YFB1505400.
文摘As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.
文摘The depletion of fossil energy and the deterioration of the ecological environment have severely restricted the development of the power industry.Therefore,it is extremely urgent to transform energy production methods and vigorously develop renewable energy sources.It is therefore important to ensure the stability and operation of a large multi-energy complementary system,and provide theoretical support for the world’s largest single complementary demonstration project with hydro-wind-PV power-battery storage in Qinghai Province.Considering all the multiple power supply constraints,an optimization scheduling model is established with the objective of minimizing the volatility of output power.As particle swarm optimization(PSO)has a problem of premature convergence and slow convergence in the latter half,combined with niche technology in evolution,a niche particle swarm optimization(NPSO)is proposed to determine the optimal solution of the model.Finally,the multiple stations’coordinated operation is analyzed taking the example of 10 million kilowatt complementary power stations with hydropower,wind power,PV power,and battery storage in the Yellow River Company Hainan prefecture.The case verifies the rationality and feasibility of the model.It shows that complementary operations can improve the utilization rate of renewable energy and reduce the impact of wind and PV power’s volatility on the power grid.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No.XDA29010500)。
文摘The application of multi-energy hybrid power systems is conducive to tackling global warming and the low-carbon transition of the power system.A capacity allocation model of a multi-energy hybrid power system including wind power,solar power,energy storage,and thermal power was developed in this study.The evaluation index was defined as the objective function,formulated by normalizing the output fluctuation,economic cost,and carbon dioxide emissions.Calculations under different initial conditions and output electric power scenarios were carried out with genetic algorithm.The capacity allocation model was validated with the literature results,with errors of less than 5%.Results indicate that the capacity allocation modes of the multi-energy hybrid power system can be divided into thermal power dominated mode,multi-energy complementary mode,and renewable power dominated mode.In addition,the division of capacity allocation modes is not affected by the weather conditions and energy storage ratio.The capacity factor decreases from 0.4 to 0.24 as the power system changes from the thermal power dominated mode to the renewable power dominated mode.When the output electric power is 240 MW,300 MW,and 340 MW,the optimal energy storage ratio is 10%,18%,and 16%,respectively.The model developed in this study not only enriches the theory of multi-energy complementary power generation but also guides the engineering design of the wind-photovoltaics-thermal-storage system targeting smart grid and be beneficial for the middle-long-term planning of the green and low-carbon transition of the power system.
文摘针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud control system,IPPCCS)解决方案.基于智能电厂云控制系统,针对绿色能源发电波动性强、抗扰能力差的问题,利用机器学习算法对采集到的风电、光伏输出功率进行短时预测,获知未来风、光机组功率输出情况.在云端使用经济模型预测控制(Economic model predictive control,EMPC)算法,通过实时滚动优化得到水轮机组的功率预测调度策略,保证绿色能源互补发电的鲁棒性,充分消纳风、光两种能源,减少水轮机组启停和穿越振动区次数,在为用户清洁、稳定供电的同时降低了机组寿命损耗.最后,一个区域云数据中心的供电算例表明了本文方法的有效性.
