This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and e...This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and energy storage system (ESS). The reliability of the MG system is modeled based on the loss of power supply probability (SPSP). For optimization, an enhanced Genetic Algorithm (GA) is used to minimize the total cost of the system over a 20-year period, while satisfying some reliability and operation constraints. A case study addressing optimal sizing of an off-grid hybrid microgrid in Nigeria is discussed. The result is compared with results obtained from the Brute Force and standard GA methods.展开更多
With the rapid development of clean energy,photovoltaic(PV)power plants have gained increasing attention.However,the inherent intermittency of PV generation requires the integration of energy storage system(ESS)to smo...With the rapid development of clean energy,photovoltaic(PV)power plants have gained increasing attention.However,the inherent intermittency of PV generation requires the integration of energy storage system(ESS)to smooth power output and enhance grid stability.Coordinating multiple PV-ESS plants is essential to maintain system reliability,balance stochastic renewable outputs with real-time load demands,and leverage time-varying electricity prices for economic benefits.In this paper,a learning-based joint bidding framework is proposed to maximise the aggregated profit of PV–ESS plants.First,a multi-PV-ESS model is built to emulate the coordinated operation of PV and ESS units in the power grid,aiming to maximise PV power revenues while considering penalty payments for power shortages,real-time load demands and dynamic power prices.Then,the joint bidding operations of PV-ESS plants are formulated as a Markov decision process,and a deep reinforcement learning algorithm is developed to learn optimal bidding strategies that adapt to load dynamics and price fluctuations.Extensive case studies on distribution systems of different scales,including the IEEE 33-bus and 69-bus systems,are conducted to demonstrate the effectiveness of the proposed method.展开更多
文摘为了提高光伏并网系统的低压穿越(Low Voltage Ride Through,LVRT)能力,将储能系统(Energy Storage System,ESS)和静止同步补偿器(Static Synchronous Compensator,STATCOM)组成新型功率补偿装置STATCOM/ESS引入光伏并网发电系统。当电网侧发生电压跌落时,STATCOM/ESS不但提供的无功功率可以支撑并网点电压,同时吸收多余的有功功率避免对光伏发电系统的危害,电压恢复后将储存的能量返送回电网,高效利用能源。为便于功率双向流动,STATCOM与ESS之间采用双有源主动桥(Dual Active Bridge,DAB)直流变换器连接。针对DAB变换器,提出一种改进的双移相控制策略,来减小DAB变换器的回流功率。仿真结果表明,提出的控制策略在一定范围内将回流功率限制为零,显著提升光伏并网系统的低压穿越能力,提高光伏并网系统的稳定性,具有良好的灵活性和优越性。
文摘This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and energy storage system (ESS). The reliability of the MG system is modeled based on the loss of power supply probability (SPSP). For optimization, an enhanced Genetic Algorithm (GA) is used to minimize the total cost of the system over a 20-year period, while satisfying some reliability and operation constraints. A case study addressing optimal sizing of an off-grid hybrid microgrid in Nigeria is discussed. The result is compared with results obtained from the Brute Force and standard GA methods.
文摘With the rapid development of clean energy,photovoltaic(PV)power plants have gained increasing attention.However,the inherent intermittency of PV generation requires the integration of energy storage system(ESS)to smooth power output and enhance grid stability.Coordinating multiple PV-ESS plants is essential to maintain system reliability,balance stochastic renewable outputs with real-time load demands,and leverage time-varying electricity prices for economic benefits.In this paper,a learning-based joint bidding framework is proposed to maximise the aggregated profit of PV–ESS plants.First,a multi-PV-ESS model is built to emulate the coordinated operation of PV and ESS units in the power grid,aiming to maximise PV power revenues while considering penalty payments for power shortages,real-time load demands and dynamic power prices.Then,the joint bidding operations of PV-ESS plants are formulated as a Markov decision process,and a deep reinforcement learning algorithm is developed to learn optimal bidding strategies that adapt to load dynamics and price fluctuations.Extensive case studies on distribution systems of different scales,including the IEEE 33-bus and 69-bus systems,are conducted to demonstrate the effectiveness of the proposed method.
