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In order to improve the control capability of distributed photovoltaic support, a distributed photovoltaic support consumption method based on energy storage configuration mode and random events is proposed. A networked and constrained parameter analysis model for distributed photovoltaic power supply control was constructed. Based on the direct flexible mode of optical storage, an AC/DC voltage level control model for distributed solar power supply control was constructed. In the operation mode of DC hybrid distribution network, the demand response tracking identification method was used to analyze the uncertain characteristic parameters of distributed solar power supply load, and combined with the planned energy storage capacity parameters, the distributed solar power supply load and photovoltaic output were estimated. By configuring the optimal energy storage capacity, adjusting the power distribution of the microgrid, and integrating the analysis of uncertain factors and random events in the energy storage configuration mode, the design of distributed photovoltaic support consumption has been achieved. The experimental results show that the distributed photovoltaic absorption control using this method has lower load requirements, can effectively reduce the exchange power of the interconnection line, and improve the configuration scale, system reliability, and economy of the photovoltaic energy storage system. [ABSTRACT FROM AUTHOR]

Energy management control (EMC) becomes imperative in hybrid energy systems that involve multiple sources. The main objective of this study is to concentrate on the effective control and utilization of energy in a hybrid system combining photovoltaic solar and wind sources, together with a battery bank. A sophisticated intelligent supervisor, employing a fuzzy logic controller (FLC), adeptly balances the power generated by the proposed system in response to the load demand. Simulation results demonstrate that, despite the initial maximum battery state of charge value being set at 90%, the battery's state of charge does not exceed 60% during charging. Additionally, during discharge, the battery state of charge remains above 27%, surpassing the initially set minimum value of 25%. This achievement is attributed to the exchange of energy flow facilitated by the implemented intelligent management. The notable advantage of this intelligent management lies in its ability to precisely control the state of charge (SOC) of the battery across five distinct states, a feat not achievable with traditional management practices that rely on only two SOC levels (SOCmin–SOCmax). [ABSTRACT FROM AUTHOR]