Characteristic Analysis Of Power And Energy Balance For Microgrid And Computationally Efficient Planning Method Of Flexible Generation Resources

Flexible generation resources(FGRs),typically including distributed dispatchable generators(DDGs)and storage systems(SSs),are the primary approaches to counter the power and energy uncertainties induced by renewable generation and load demand in microgrids.Currently,massive stochastic scenarios are adopted when handling FGR planning problems for microgrids to represent the uncertain nature of renewable generation and load demand.To a great extent,the integrity of uncertainty characterization highly relies on the abundancy of stochastic scenarios.The existing solution approaches generally consume a great amount of computing time in consideration of massive stochastic scenarios,and even worse,fail to deliver the final results.Additionally,scenario reduction techniques extremely deteriorate solution accuracy,and therefore,undermine the adaptability of the obtained planning results against those uncertainties lied in microgrids.The key issue of striking the balance between solution accuracy and computational efficiency of microgrid planning problems has become a prominent barrier hindering theoretical research and practical application in relevant fields.Aiming at making a better trade-off between solution accuracy and computational efficiency of microgrid planning problems,this paper has proposed computationally efficient FGR planning methods separately for isolated microgrids and grid-connected microgrids,by thoroughly investigating the coupling relationships between the power and energy balancing characteristics of microgrids and the capacities of FGR.The major contributions of this paper can be summarized as follows:1)The computational efficiency of the existing solution methods for FGR planning problems has been analyzed,which reveals that the existing methods have some setbacks in terms of keeping the trade-off between solution accuracy and computational efficiency.It is mostly because these methods have not taken into account the relationships between the distinct operational characteristics of different types of FGRs and the power and energy balancing characteristics of microgrids,which actually is a novel point of view to tackle the accuracy-efficiency trade-off problems.2)The relationships of the power and energy balancing characteristics of microgrids which are majorly represented by stochastic scenarios,and FGRs are investigated in this paper.On this basis,the concept of scenario classification is proposed to divide stochastic scenarios into power-surplus scenarios and power-shortage scenarios.Based on the classified scenarios,this paper has found that the capacity of DDG is highly related to the power-shortage scenarios while the power-surplus scenarios play a significant role on the capacity of BS.3)With respect to isolated microgrids,an energy area method based on the worst-case scenario is proposed to determine the feasible capacity range of DDG and the corresponding minimum capacity of BS in order to maintain the energy balance of microgrids.The power balancing process of microgrids is investigated based on the classified scenarios.Sequentially,a coordination-decomposition-based FGR planning method is developed to determine FGR capacities efficiently.4)With respect to grid-connected microgrids,the power exchanging process of SSs,microgrids and external grids is investigated and quantified based on stochastic scenarios.By analyzing the dynamics of SSs,a forward/backward sweeping-based heuristic method for SS energy optimal management is proposed.On this basis,the two-level SS planning method for grid-connected microgrid is proposed to efficiently optimize the SS capacities.This paper is funded by Natural Science Funding of Guangdong Province and thanks to several projects from the Chinese Engineering Academy and the China Southern Power Grids,the work of this paper has been successfully completed.The methods proposed in this paper can highly efficiently determine the capacities of FGRs without scenario reduction when considering a large number of stochastic scenarios,which outperforms the exiting methods with low computational efficiency.The work of this paper managed to strike the balance of computational efficiency and solution accuracy,providing superior methods and research tools with much better performance for microgrid planning research and engineering practice.