| Configuring energy storage systems in distribution networks can reduce the volatility of renewable energy output power and accordingly improve power quality,and effectively increase the grid connection ratio and utilization rate of wind power,photovoltaic and other renewable energy sources.Optimizing the configuration of the energy storage system can improve the voltage stability of distribution network,increase the utilization of energy storage resources,and reduce the investment cost of the energy storage system.However,the uncertainty of wind power output,photovoltaic output,and power load demand generally affects the effect of energy storage planning for distribution networks.How to reduce the impact of uncertain factors is a problem that needs to be solved in the current configuration research of energy storage in distribution networks.At present,domestic and foreign scholars have studied how to deal with the influence of uncertain factors in the optimal allocation of energy storage.Existing researches apply robust optimization,multi-scenario analysis and other methods to deal with uncertain variables,but these two methods have some limitations.Multi-scenario analysis methods usually require a large amount of data to solve the model,which leads to inefficient calculation of the model,and the risk evaluation of decision-making programs cannot be performed.Robust optimization methods usually improve the robustness of decision-making by sacrificing the economics of decision-making,which makes solutions too conservative.In view of the above analysis,this paper intends to use information gap decision theory（IGDT）to solve the uncertainty of wind power output,photovoltaic output and load demand.Information gap decision theory is a non-probabilistic and non-fuzzy method,which is more efficient than probability-based methods such as multi-scenario analysis.The overall idea of its robust model is to minimize the influence of uncertain factors on the decision-making plan to the greatest extent by maximizing the fluctuation range of the uncertain variables on the premise that the objective function value is not inferior to the expected target value[1-2].However,the IGDT model is not precise enough to describe uncertain variables.It is prone to have symmetrical fluctuation range out of the actual value range,and its subjectivity is strong,and the risk assessment of decision results is lacking.In view of the above-mentioned shortcomings,this paper proposes to combine the IGDT model and the chance constraint based on probability,and the model is introduced with confidence level to evaluate the decision risk and remove the bias factor in the IGDT model to overcome the lack of strong subjectivity.Considering that processing according to the unified probability distribution cannot accurately reflect the impact of the three different uncertain variables of wind power output,photovoltaic output and load demand on the energy storage configuration results in distribution network,this paper further proposes a robust chance constraint IGDT model based on classified probability.Then based on the uncertainty theory,the opportunity constraints that are difficult to directly solve are transformed into equivalent deterministic constraints.Finally,combined with the characteristics of the model in this paper,a non-dominated sorting compound differential evolution algorithm is used to solve.In summary,this paper proposes a robust optimal configuration method of energy storage in distribution network based on classified probability chance constraint IGDT（CPCC-IGDT）to comprehensively achieve the goal of minimizing the annual investment cost of energy storage and maximizing the improvement indicators of distribution network voltage fluctuation.An example analysis using IEEE 33-node power distribution system shows that the method proposed in this paper has better robustness,economy and voltage fluctuation improvement effect,and it also performs better in terms of flexibility and efficiency. |
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