Active distribution network involves a variety of energy forms (such as photovoltaic

wind power

micro gas turbine

battery

etc)

the output characteristics of these energy forms are different

and there is a complex coupling relationship between them. Considering the constraints of power consumption and energy storage

it is difficult to obtain the optimal scheduling scheme of multi-energy complementarity of source load and storage. Therefore

a multi-energy complementary cooperative scheduling method for active distribution network is proposed. The output results of power stations in active distribution network are calculated

and the output characteristics of integrated energy after multi-energy complementary are obtained by combining time factors. Based on the original source-load-storage technology

the concept of multi-energy complementarity is introduced

and the optimization goal is to minimize the operating cost and the minimum network loss. At the same time

the adjustment amount of day-ahead scheduling and scheduling units is considered to minimize

and the absorption amount of new energy is strengthened to the maximum extent. The analytic hierarchy process is adopted to optimize the corresponding weight of the target

and the objective function of cooperative optimal scheduling is constructed. In addition

the cold power constraint

hot power constraint

electric power constraint

energy storage constraint and power flow constraint for the cooperation of various energy sources are set

and the particle swarm optimization algorithm is adopted to optimize the cooperative scheduling. The particle swarm optimization algorithm is improved by adjusting the acceleration factor and dynamically adjusting the iterative strategy. The objective function is solved and the multi-energy complementary cooperative optimal scheduling is realized by constantly updating the particle speed and position. The experimental results show that the proposed method significantly improves the consumption of new energy and voltage stability in intra-day optimization scheduling tests

and exhibits high energy utilization efficiency in different geographical

climatic

and energy structure regions

verifying its wide applicability and optimization effect.