This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid.It considers the uncertainties in solar photovoltaic(PV)generation,load demand,and electric...This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid.It considers the uncertainties in solar photovoltaic(PV)generation,load demand,and electricity price.Furthermore,the opportunity of flexible load demand,i.e.,the effect of demand response(DR),on the test system is studied.The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability.In general,microgrid scheduling is implemented by using substation(source node)price as a reference,but that reference price is not the same at all nodes.Therefore,this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy.The stochastic energy management framework is formulated as a mixed integer non-linear programming(MINLP).Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system(BESS)to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.展开更多
In this paper,the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles(PHEVs)is presented and the influence of different charging and discharging modes on mi...In this paper,the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles(PHEVs)is presented and the influence of different charging and discharging modes on microgrid economic operation is analyzed.The generic algorithm is used to find an economically optimal solution for the microgrid and PHEV owners.The scheduling of PHEVs and the microgrid are optimized to reduce daily electricity cost and the potential benefits of controlled charging/discharging are explored systematically.Constraints caused by vehicle utilization as well as technical limitations of distributed generation and energy storage system are taken into account.The proposed economic scheduling is evaluated through a simulation by using a typical grid-connected microgrid model.展开更多
In this paper,the bi-layer scheduling method for microgrids,based on deep reinforcement learning,is proposed to achieve economic and environmentally friendly operations.First,considering the uncertainty of renewable e...In this paper,the bi-layer scheduling method for microgrids,based on deep reinforcement learning,is proposed to achieve economic and environmentally friendly operations.First,considering the uncertainty of renewable energy,the framework of day-ahead and intra-day scheduling is established,and the implementation scheme for both price-based and incentive-based demand response(DR)for the flexible load is determined.Then,comprehensively considering the operating characteristics of the microgrid in the day-ahead and intra-day time scales,a bi-layer scheduling model of the microgrid is established.In terms of algorithms,since day-ahead scheduling has no strict requirement for dispatching time,the particle swarm optimization(PSO)algorithm is used to optimize the time-of-use electricity price and distributed power output for the next day.Considering the environmental fluctuations and requirements for rapidity of intra-day online scheduling,the deep reinforcement learning(DRL)algorithm is adopted for optimization.Finally,based on the data from the actual microgrid,the rationality and effectiveness of the proposed scheduling method is verified.The results show that the proposed bi-layer scheduling based on the PSO and DRL algorithms achieves the optimization of scheduling cost and calculation speed,and is suitable for microgrid online scheduling.展开更多
This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaie （PV） systems, battery storage, and electric vehicles. The elect...This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaie （PV） systems, battery storage, and electric vehicles. The electrical vehicles （EVs） play two different roles： as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid （MG）. Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search （hybrid DE-HS） algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.展开更多
This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian Uni...This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian University (GJU) microgrid system is used for illustration. We present analyses for islanded and grid-connected MG with no storage. The results show a feasible islanded MG with a substantial operational cost reduction. We obtain an average of $1 k daily cost savings when operating an islanded compared to a grid-connected MG with capped grid energy prices. This cost saving is 10 times higher when considering varying grid energy prices during the day. Although the PV power is intermittent during the day, the MG continues to operate with a voltage variation that does not 10%. The results imply that MGs of GJU similar topology can optimally and safely operate with no energy storage requirements but considerable renewable generation capacity.展开更多
As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncert...As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling,especially for small-scale microgrids.Instead of deploying stochastic models to cope with such challenges,this paper presents a retroactive approach to real-time energy scheduling,which is prediction-independent and computationally efficient.Extensive case studies were conducted using 3-year-long real-life system data,and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11%on average,which suggests better performance than model predictive control with the cost difference at 30%compared to the perfect dispatch.展开更多
针对局部配电网中互联微电网系统的调度问题,提出了一种优化调度模型。首先,配电网调度层通过协调各微电网与配电网之间的交互功率来改善配电网的运行状况;然后,微电网层根据配电网层的优化调度结果制定生产计划,并建立基于储能系统荷...针对局部配电网中互联微电网系统的调度问题,提出了一种优化调度模型。首先,配电网调度层通过协调各微电网与配电网之间的交互功率来改善配电网的运行状况;然后,微电网层根据配电网层的优化调度结果制定生产计划,并建立基于储能系统荷电状态(state of charge,SOC)的阶梯式功率修正策略,以保证储能系统的可调度能力。选用全局搜索能力强的改进花朵授粉算法(flower pollination algorithm,FPA)求解配电网层优化调度模型,并以IEEE-14节点系统为算例进行仿真分析。结果表明:改进FPA算法的初始种群质量与收敛速度均有所提高;配电网的网损和微电网交互功率的波动性下降;各储能系统的SOC保持在0.25~0.6区间内。研究结果证明了优化调度模型的电网友好性和改进FPA算法的有效性,且阶梯式功率修正策略可以保证各储能系统的持续可调度能力。展开更多
基金supported by the Science and Engineering Research Board(SERB)a statutory body of Department of Science and Technology(DST)Government of India(Go I)(No.EMR/2016/002037).
文摘This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid.It considers the uncertainties in solar photovoltaic(PV)generation,load demand,and electricity price.Furthermore,the opportunity of flexible load demand,i.e.,the effect of demand response(DR),on the test system is studied.The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability.In general,microgrid scheduling is implemented by using substation(source node)price as a reference,but that reference price is not the same at all nodes.Therefore,this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy.The stochastic energy management framework is formulated as a mixed integer non-linear programming(MINLP).Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system(BESS)to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.
基金This work was supported in part by the National Natural Science Foundation of China(No.51477067)in part by the China-UK Joint Project of the National Natural Science Foundation of China(No.51361130150)in part by the Fundamental Research Funds for the Central Universities(No.2014QN219).
文摘In this paper,the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles(PHEVs)is presented and the influence of different charging and discharging modes on microgrid economic operation is analyzed.The generic algorithm is used to find an economically optimal solution for the microgrid and PHEV owners.The scheduling of PHEVs and the microgrid are optimized to reduce daily electricity cost and the potential benefits of controlled charging/discharging are explored systematically.Constraints caused by vehicle utilization as well as technical limitations of distributed generation and energy storage system are taken into account.The proposed economic scheduling is evaluated through a simulation by using a typical grid-connected microgrid model.
基金supported in part by National Key R&D Program of China under Grant 2021YFB3800200.
文摘In this paper,the bi-layer scheduling method for microgrids,based on deep reinforcement learning,is proposed to achieve economic and environmentally friendly operations.First,considering the uncertainty of renewable energy,the framework of day-ahead and intra-day scheduling is established,and the implementation scheme for both price-based and incentive-based demand response(DR)for the flexible load is determined.Then,comprehensively considering the operating characteristics of the microgrid in the day-ahead and intra-day time scales,a bi-layer scheduling model of the microgrid is established.In terms of algorithms,since day-ahead scheduling has no strict requirement for dispatching time,the particle swarm optimization(PSO)algorithm is used to optimize the time-of-use electricity price and distributed power output for the next day.Considering the environmental fluctuations and requirements for rapidity of intra-day online scheduling,the deep reinforcement learning(DRL)algorithm is adopted for optimization.Finally,based on the data from the actual microgrid,the rationality and effectiveness of the proposed scheduling method is verified.The results show that the proposed bi-layer scheduling based on the PSO and DRL algorithms achieves the optimization of scheduling cost and calculation speed,and is suitable for microgrid online scheduling.
文摘This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaie （PV） systems, battery storage, and electric vehicles. The electrical vehicles （EVs） play two different roles： as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid （MG）. Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search （hybrid DE-HS） algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.
文摘This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian University (GJU) microgrid system is used for illustration. We present analyses for islanded and grid-connected MG with no storage. The results show a feasible islanded MG with a substantial operational cost reduction. We obtain an average of $1 k daily cost savings when operating an islanded compared to a grid-connected MG with capped grid energy prices. This cost saving is 10 times higher when considering varying grid energy prices during the day. Although the PV power is intermittent during the day, the MG continues to operate with a voltage variation that does not 10%. The results imply that MGs of GJU similar topology can optimally and safely operate with no energy storage requirements but considerable renewable generation capacity.
基金partially supported by Hong Kong RGC Theme-based Research Scheme(No.T23-407/13N and No.T23-701/14N)SUSTech Faculty Startup Funding(No.Y01236135 and No.Y01236235).
文摘As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling,especially for small-scale microgrids.Instead of deploying stochastic models to cope with such challenges,this paper presents a retroactive approach to real-time energy scheduling,which is prediction-independent and computationally efficient.Extensive case studies were conducted using 3-year-long real-life system data,and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11%on average,which suggests better performance than model predictive control with the cost difference at 30%compared to the perfect dispatch.
文摘针对局部配电网中互联微电网系统的调度问题,提出了一种优化调度模型。首先,配电网调度层通过协调各微电网与配电网之间的交互功率来改善配电网的运行状况;然后,微电网层根据配电网层的优化调度结果制定生产计划,并建立基于储能系统荷电状态(state of charge,SOC)的阶梯式功率修正策略,以保证储能系统的可调度能力。选用全局搜索能力强的改进花朵授粉算法(flower pollination algorithm,FPA)求解配电网层优化调度模型,并以IEEE-14节点系统为算例进行仿真分析。结果表明:改进FPA算法的初始种群质量与收敛速度均有所提高;配电网的网损和微电网交互功率的波动性下降;各储能系统的SOC保持在0.25~0.6区间内。研究结果证明了优化调度模型的电网友好性和改进FPA算法的有效性,且阶梯式功率修正策略可以保证各储能系统的持续可调度能力。
