The invention belongs to the technical field of power
system optimization scheduling, and provides a source network load storage intelligent collaborative optimization method, which comprises the following steps of: deploying sensors at four ends of a source network load storage respectively, collecting in real time by utilizing a
cloud data center, enabling data of the four ends to be consistent in
time sequence through a
PTP protocol, constructing a
topological graph according to parameters and data, and establishing a source network load storage intelligent collaborative
optimization system. Selecting a model in a digital twinning environment for
simulation; dividing independent agents at four ends of a source network load storage, setting
observation data, an execution space and excitation feedback, forming an excitation item by economy, stability and
environmental protection, interactively circulating actual data, a prediction instruction and an excitation value, recording into a sequence, inputting the sequence into a strategy network, and calculating and outputting logarithmic probability gradient to update the parameters of the strategy network; and the
intelligent agent completes interactive circulation according to the strategy network, generates a local scheduling instruction, aggregates the instruction to perform weighted calculation, generates a
global scheduling scheme, issues the
global scheduling scheme to execution equipment, updates parameters by using an average deviation calculated by a
deviation vector, resolves the
global scheduling scheme and issues the global scheduling scheme to form a closed-loop mechanism.