Cone optimization modeling method for allowing distributed stored energy to participate in running adjustment of active power distribution network

Abstract

A cone optimization modeling method for allowing distributed stored energy to participate in running adjustment of an active power distribution network comprises the steps that (1), element parameters of the active power distribution network to be adjusted in an optimization mode are read; (2), according to the parameters provided by the step (1), a time sequence optimization model is established for allowing the distributed stored energy to participate in running adjustment of the active power distribution network, wherein an objective function with the minimum active loss is set, and the running constraint of the power distribution network and the running constraint of an energy storage system are each considered; (3), according to the standard form min {cTx/Ax=b, x<K} for second-order cone optimization, cone model conversion is performed on the time sequence optimization model in the step (2), wherein linearization is performed on the running constrain of the objective function and the running constraint of the active power distribution network, cone conversion is performed on the capacity constraint of stored energy inverters, and a non-linear rotating cone constraint is introduced. The complexity degree of an optimization model function relation is greatly lowered, and meanwhile the requirements for rapid convergence and optimal solving are met.

Description

Technical field [0001] The invention relates to a modeling method for active distribution network operation scheduling. In particular, it relates to a cone optimization modeling method for distributed energy storage to participate in the operation and regulation of active distribution networks. Background technique [0002] The production, transmission, distribution and consumption of electric energy in the traditional power system are highly simultaneous. Because large-scale electric energy storage cannot be realized, the power grid is always in a ready-to-use state, and the generator relies on its own inertia to provide the grid The adjustment ability is also extremely limited, which results in a large peak-to-valley difference caused by the load characteristics. During the peak hours of power consumption, the heavier load flow is transmitted from the generators to the users, and the lines, transformers and other equipment in the system are at a higher load rate, which makes t...

AI Technical Summary

Problems solved by technology

[0004] The essence of the problem of distributed energy storage participating in the operation regulation of active distribution network is a mathematical optimization problem. Although the optimization objectives of existing models are different for this problem, the constraints considered mainly include power flow constraints, branch current constraints, The operating constraints of the system itself such as voltage level constraints and the operating constraints of distributed energy storage, and the solution of such problems often takes a lot of computing time, because: First, for the optimization of the distribution network, its objective function It is often a non-convex function, and the optimization model involves a large number of equality and inequality constraints on control variables and state variables. At the same time, there are complex functional relationships between variables, so the operation regulation of the distribution network is a complex large-scale Non-convex nonlinear optimization problem; secondly, for the optimization of distributed energy storage, it has obvious timing characteristics, and its operation optimization is no longer limited to a single time section, but extends to multiple time sections with timing characteristics, As a result, the dimension of its decision variables increases rapidly with the number of series sections, which further increases the calculation scale of the operation optimization problem of active distribution network

The above two factors together lead to the complexity and scale of distributed energy storage participating in the operation regulation of active distribution network

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