In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to im...In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to improve energy efficiency and reliability.This study proposes a novel hybrid optimization algorithm,DE-HHO,combining differential evolution(DE)and Harris Hawks optimization(HHO)to address microgrid scheduling issues.The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts.The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind,solar,micro-gas turbine,and battery models.Comprehensive simulation tests show that DE-HHO converges rapidly within 10 iterations and achieves a 4.5%reduction in total cost compared to PSO and a 5.4%reduction compared to HHO.Specifically,DE-HHO attains an optimal total cost of$20,221.37,outperforming PSO($21,184.45)and HHO($21,372.24).The maximum cost obtained by DE-HHO is$23,420.55,with a mean of$21,615.77,indicating stability and cost control capabilities.These results highlight the effectiveness of DE-HHO in reducing operational costs and enhancing system stability for efficient and sustainable microgrid operation.展开更多
Microgrid is considered an important part of the future zero carbon energy systems.However,the uncertainty caused by renewable energy source brings huge challenges to the scheduling of MG and restricts its ability of ...Microgrid is considered an important part of the future zero carbon energy systems.However,the uncertainty caused by renewable energy source brings huge challenges to the scheduling of MG and restricts its ability of carbon emission reduction.In this paper,a novel improved multi-ellipsoidal uncertainty set modeling method is proposed to better depict the uncertainty of wind power and reduce the conservativeness of traditional robust optimization.Probabilistic information from historical data is utilized to capture the temporal correlation of forecast error of wind power,as well as the conditional correlation of forecast error with forecast value,making the uncertainty set more data-adaptive to variation of forecast results and more accurate for uncertainty description.A two-stage robust optimization model of a grid-connected microgrid is established based on the proposed uncertainty set and solved by column and constraint generation algorithm.Simulation results based on actual data illustrate the average unbalanced power of microgrid between day-ahead trading and real-time power exchange with utility grid is dropped by nearly 11.16%compared with a deterministic optimization method,11.86%with traditional box uncertainty set-based robust optimization method,and 2.89%with stochastic optimization method.展开更多
First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time ...First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.展开更多
This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management ...This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.展开更多
文摘In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to improve energy efficiency and reliability.This study proposes a novel hybrid optimization algorithm,DE-HHO,combining differential evolution(DE)and Harris Hawks optimization(HHO)to address microgrid scheduling issues.The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts.The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind,solar,micro-gas turbine,and battery models.Comprehensive simulation tests show that DE-HHO converges rapidly within 10 iterations and achieves a 4.5%reduction in total cost compared to PSO and a 5.4%reduction compared to HHO.Specifically,DE-HHO attains an optimal total cost of$20,221.37,outperforming PSO($21,184.45)and HHO($21,372.24).The maximum cost obtained by DE-HHO is$23,420.55,with a mean of$21,615.77,indicating stability and cost control capabilities.These results highlight the effectiveness of DE-HHO in reducing operational costs and enhancing system stability for efficient and sustainable microgrid operation.
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1506804the National Nature Science Foundation of China under Grant 51907140.
文摘Microgrid is considered an important part of the future zero carbon energy systems.However,the uncertainty caused by renewable energy source brings huge challenges to the scheduling of MG and restricts its ability of carbon emission reduction.In this paper,a novel improved multi-ellipsoidal uncertainty set modeling method is proposed to better depict the uncertainty of wind power and reduce the conservativeness of traditional robust optimization.Probabilistic information from historical data is utilized to capture the temporal correlation of forecast error of wind power,as well as the conditional correlation of forecast error with forecast value,making the uncertainty set more data-adaptive to variation of forecast results and more accurate for uncertainty description.A two-stage robust optimization model of a grid-connected microgrid is established based on the proposed uncertainty set and solved by column and constraint generation algorithm.Simulation results based on actual data illustrate the average unbalanced power of microgrid between day-ahead trading and real-time power exchange with utility grid is dropped by nearly 11.16%compared with a deterministic optimization method,11.86%with traditional box uncertainty set-based robust optimization method,and 2.89%with stochastic optimization method.
基金supported by Beijing Municipal Science Technology commission research(No.Z171100000317003)
文摘First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.
文摘This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.
