Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy couplin...Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy coupling equipment,and renewable energy.An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost,carbon emission and enhance the power supply reliability.Firstly,the lowcarbon mathematical model of combined thermal and power unit,carbon capture system and power to gas unit(CCP)is established.Subsequently,we establish a low carbon multi-objective optimization model considering system operation cost,carbon emissions cost,integrated demand response,wind and photovoltaic curtailment,and load shedding costs.Furthermore,considering the intermittency of wind power generation and the flexibility of load demand,the low carbon economic dispatch problem is modeled as a Markov decision process.The twin delayed deep deterministic policy gradient(TD3)algorithm is used to solve the complex scheduling problem.The effectiveness of the proposed method is verified in the simulation case studies.Compared with TD3,SAC,A3C,DDPG and DQN algorithms,the operating cost is reduced by 8.6%,4.3%,6.1%and 8.0%.展开更多
Between 2018 and 2020, an average of 15 TWh of energy peat was consumed in Finland. Energy peat is used in 260 boilers in Finland, which produce district heat and heat and steam for industry, as well as electricity as...Between 2018 and 2020, an average of 15 TWh of energy peat was consumed in Finland. Energy peat is used in 260 boilers in Finland, which produce district heat and heat and steam for industry, as well as electricity as cogeneration (CHP) in connection with district heating and industrial heat production. Peat accounts for 3% - 5% of the energy sources used in Finland, but its importance has been greater in terms of security of supply. With current use in accordance with the 2018-2020 average, the emissions from peat are almost 6 Mt CO<sub>2</sub> per year in Finland, which is 15% of emissions from the energy sector. In this study, the technical limitations related to peat burning, economic limitations related to the availability of biomass, and socio-economic limitations related to the regional economy are reviewed. By 2040, the technical minimum use of peat will fall to 2 TWh. The techno-economical potential may be even lower, but due to socio-economic objectives, peat production will not be completely ceased. The reduction in the minimum share assumes that old peat boilers are replaced with new biomass boilers or are alternatively replaced by other forms of heat production. Based on the biomass reserves, the current use of peat can be completely replaced by forest chips, but regional challenges may occur along the coast and in southern Finland. It is unlikely that the current demand for all peat will be fully replaced by biomass when part of CHP production is replaced by heat production alone and combustion with waste heat sources.展开更多
光伏储能接入牵引供电系统(Traction Power Supply System,TPSS)为铁路节能减排、节支降耗提供了解决方案,但储能系统的成本及其运行经济性制约了其在铁路牵引供电领域的大规模应用。铁路光伏储能系统的容量配置方案决定其投资成本和经...光伏储能接入牵引供电系统(Traction Power Supply System,TPSS)为铁路节能减排、节支降耗提供了解决方案,但储能系统的成本及其运行经济性制约了其在铁路牵引供电领域的大规模应用。铁路光伏储能系统的容量配置方案决定其投资成本和经济效益,储能能量管理策略影响其最大需量削减效果和再生制动能量利用率,进而影响牵引变电所电费支出。因此,为实现铁路光伏储能系统的经济性提升,首先提出一种自适应多阈值能量管理策略:基于行车运行图将日牵引负荷分成高需量时段和其他时段,不同时段储能系统设定不同的充放电启动阈值优化储能出力,以兼顾牵引负荷最大需量削减和再生制动能量利用率,并采用模糊逻辑控制(Fuzzy Logic Control,FLC)自适应调整储能放电阈值,以延长储能使用寿命。然后,建立光伏储能接入铁路牵引供电系统配置与能量管理协同优化模型,以系统全寿命周期净收益和内部收益率为优化目标,对储能系统进行容量配置。最后,基于某牵引变电所的实测数据采用NSGA-Ⅱ算法进行求解,并与固定阈值能量管理策略进行运行效果对比。结果表明所提方法更能够适应牵引负荷大功率和高能量密度的特点,可以兼顾再生制动能量利用率和最大需量削减,最优配置下的净收益为1364.1万元,内部收益率达14.60%,投资回收期为6年。展开更多
基金supported in part by the Scientific Research Fund of Liaoning Provincial Education Department under Grant LQGD2019005in part by the Doctoral Start-up Foundation of Liaoning Province under Grant 2020-BS-141.
文摘Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy coupling equipment,and renewable energy.An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost,carbon emission and enhance the power supply reliability.Firstly,the lowcarbon mathematical model of combined thermal and power unit,carbon capture system and power to gas unit(CCP)is established.Subsequently,we establish a low carbon multi-objective optimization model considering system operation cost,carbon emissions cost,integrated demand response,wind and photovoltaic curtailment,and load shedding costs.Furthermore,considering the intermittency of wind power generation and the flexibility of load demand,the low carbon economic dispatch problem is modeled as a Markov decision process.The twin delayed deep deterministic policy gradient(TD3)algorithm is used to solve the complex scheduling problem.The effectiveness of the proposed method is verified in the simulation case studies.Compared with TD3,SAC,A3C,DDPG and DQN algorithms,the operating cost is reduced by 8.6%,4.3%,6.1%and 8.0%.
文摘Between 2018 and 2020, an average of 15 TWh of energy peat was consumed in Finland. Energy peat is used in 260 boilers in Finland, which produce district heat and heat and steam for industry, as well as electricity as cogeneration (CHP) in connection with district heating and industrial heat production. Peat accounts for 3% - 5% of the energy sources used in Finland, but its importance has been greater in terms of security of supply. With current use in accordance with the 2018-2020 average, the emissions from peat are almost 6 Mt CO<sub>2</sub> per year in Finland, which is 15% of emissions from the energy sector. In this study, the technical limitations related to peat burning, economic limitations related to the availability of biomass, and socio-economic limitations related to the regional economy are reviewed. By 2040, the technical minimum use of peat will fall to 2 TWh. The techno-economical potential may be even lower, but due to socio-economic objectives, peat production will not be completely ceased. The reduction in the minimum share assumes that old peat boilers are replaced with new biomass boilers or are alternatively replaced by other forms of heat production. Based on the biomass reserves, the current use of peat can be completely replaced by forest chips, but regional challenges may occur along the coast and in southern Finland. It is unlikely that the current demand for all peat will be fully replaced by biomass when part of CHP production is replaced by heat production alone and combustion with waste heat sources.
