CN-121998795-A - Comprehensive energy planning method and related equipment

CN121998795ACN 121998795 ACN121998795 ACN 121998795ACN-121998795-A

Abstract

The embodiment of the application provides a comprehensive energy planning method and related equipment, and belongs to the technical field of energy planning. The method comprises the steps of establishing a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model and a cogeneration unit power generation model, establishing a target planning function according to minimum cost constraint through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model, wherein the target planning function comprises limiting conditions for carbon emission of the gas turbine power generation and the cogeneration unit power generation, splitting the target planning function into an inner circulation problem and an outer circulation problem through a planning optimization algorithm, solving the inner circulation problem, calculating to obtain initial scene data, and calculating to obtain a target planning scheme according to the initial scene data through the outer circulation problem. The embodiment of the application can effectively improve the feasibility and the calculation efficiency of model solving, and enhance the adaptability of the planning scheme to multiple operation scenes.

Inventors

KE YIMING
CHEN HAOYUAN
LIU MIN
CAI HONGMING
QI RUI
Gu Dade

Assignees

暨南大学

Dates

Publication Date: 20260508
Application Date: 20251210

Claims (10)

1. A method for planning an integrated energy source, the method comprising the steps of: Establishing a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model and a cogeneration unit power generation model; establishing a target planning function according to minimum cost constraint through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model, wherein the target planning function comprises limiting conditions for carbon emission generated by the gas turbine power generation and the cogeneration unit power generation; And splitting the target planning function into an inner circulation problem and an outer circulation problem through a planning optimization algorithm, solving the inner circulation problem, calculating to obtain initial scene data, and calculating to obtain a target planning scheme through the outer circulation problem according to the initial scene data.
2. The method of claim 1, wherein the objective planning function further comprises a definition of equipment capacity investment costs, operational maintenance costs, energy purchase costs, capture costs, sequestration costs, and carbon trade costs, wherein the equipment capacity investment costs are used to describe costs of deploying energy equipment.
3. The method of claim 2, wherein the establishing a target planning function from the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the cogeneration unit power generation model according to minimum cost constraints comprises: and constructing the target planning function through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model, wherein the target planning function is as follows: Wherein, the As a first stage decision variable, representing the investment capacity of each device; the variable set is a source-load uncertainty variable set and comprises fluctuation values of wind-light output and electric-heat-cold load; a feasible domain that is an uncertainty variable; for the second-stage decision variables, representing in a given uncertainty scenario And (5) making a system operation decision. As a function of the investment cost of the plant, Is a system operation cost function.
4. The method of claim 1, wherein the splitting the objective planning function into an inner loop problem and an outer loop problem by a planning optimization algorithm comprises: the formula of the outer loop problem divided by the objective planning function through the planning optimization algorithm is as follows: Wherein, the To minimize the introduced auxiliary variables The objective function in the enumerated relaxation set S scene is minimized; Representing a current set The number of scenes in (i.e. the number of times the original problem has been iterated in a loop); The formula of the inner loop problem divided by the objective planning function through the planning optimization algorithm is as follows: Wherein the internal circulation problem comprises a continuous variable Discrete variable 。
5. The method of claim 4, wherein solving the inner loop problem, calculating initial scene data, comprises: Splitting the internal circulation problem to obtain a minimum sub-problem and a maximum sub-problem, and splitting the minimum sub-problem through an optimization planning algorithm to obtain a minimum internal layer management problem and a minimum internal layer support problem; Solving the minimum inner layer support problem to obtain an optimized discrete set, wherein the optimized discrete set comprises a plurality of least favorable scene data; and carrying out enumeration calculation on the optimized discrete set through the minimum inner layer management problem so as to solve the minimum sub-problem.
6. The method of claim 5, wherein after said splitting the inner loop problem to obtain a minimum sub-problem and a maximum sub-problem, the method further comprises: and converting the maximum sub-problem into a minimum dual problem by utilizing Lagrangian dual theory, and directly solving the minimum dual problem to solve the inner loop problem, and calculating to obtain the initial scene data.
7. The method according to any one of claims 1 to 5, wherein before said calculating a target planning scheme from said initial scenario data by said outer loop problem, said method further comprises: if the initial planning scheme is determined to be not in accordance with the operation requirement condition in the target planning function, updating the upper bound and the lower bound of the planning optimization algorithm; If the upper and lower bounds do not satisfy the formula Re-solving to obtain new initial scene data, wherein the initial scene data is obtained by the method For the upper bound, the For the lower bound, the Defining parameters; if the upper and lower bounds satisfy the formula And calculating to obtain a target planning scheme according to the initial scene data through the outer circulation problem.
8. A comprehensive energy planning apparatus, the apparatus comprising: The model building module is used for building a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model and a cogeneration unit power generation model; The function building module is used for building a target planning function according to minimum cost constraint through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model, wherein the target planning function comprises limiting conditions for carbon emission of the gas turbine power generation and the cogeneration unit power generation; And the problem solving module is used for splitting the target planning function into an inner circulation problem and an outer circulation problem through a planning optimization algorithm, solving the inner circulation problem, calculating to obtain initial scene data, and calculating to obtain a target planning scheme through the outer circulation problem according to the initial scene data.
9. A computer device, characterized in that it comprises a memory storing a computer program and a processor implementing the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 7.

Description

Comprehensive energy planning method and related equipment Technical Field The application relates to the technical field of energy planning, in particular to a comprehensive energy planning method and related equipment. Background Under the background of accelerating the global energy structure to clean low-carbon transformation, the comprehensive energy system is used as a key infrastructure for realizing multi-energy coordination and energy efficiency improvement, and becomes a leading research direction of the energy field increasingly. The system breaks through barriers among traditional energy systems by constructing a coupling network of various energy forms such as electricity, heat, cold, gas, hydrogen and the like, and realizes cascade utilization and complementary coordination of energy. The multi-element energy storage facility such as heat storage, electricity storage and hydrogen storage is beneficial to stabilizing the output fluctuation of renewable energy sources, improving the overall absorption capacity of clean energy sources, and thus cooperating with the low carbonization and high efficiency development of a propulsion energy system. . Disclosure of Invention The embodiment of the application mainly aims to provide a comprehensive energy planning method and related equipment, which can effectively improve the feasibility and the calculation efficiency of model solving and enhance the adaptability of a planning scheme to multiple operation scenes. In order to achieve the above object, an aspect of an embodiment of the present application provides a method for planning an integrated energy, including: Establishing a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model and a cogeneration unit power generation model; establishing a target planning function according to minimum cost constraint through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model, wherein the target planning function comprises limiting conditions for carbon emission generated by the gas turbine power generation and the cogeneration unit power generation; And splitting the target planning function into an inner circulation problem and an outer circulation problem through a planning optimization algorithm, solving the inner circulation problem, calculating to obtain initial scene data, and calculating to obtain a target planning scheme through the outer circulation problem according to the initial scene data. In some embodiments, the objective planning function further includes a definition of equipment capacity investment costs, operational maintenance costs, energy purchasing costs, capture costs, sequestration costs, and carbon trade costs, wherein the equipment capacity investment costs are used to describe costs of deploying energy equipment. In some embodiments, the building of the objective planning function from the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the cogeneration unit power generation model according to the minimum cost constraint comprises: and constructing the target planning function through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model, wherein the target planning function is as follows: Wherein, the As a first stage decision variable, representing the investment capacity of each device; the variable set is a source-load uncertainty variable set and comprises fluctuation values of wind-light output and electric-heat-cold load; a feasible domain that is an uncertainty variable; for the second-stage decision variables, representing in a given uncertainty scenario And (5) making a system operation decision.As a function of the investment cost of the plant,Is a system operation cost function. In some embodiments, the splitting the objective planning function into an inner loop problem and an outer loop problem by a planning optimization algorithm includes: the formula of the outer loop problem divided by the objective planning function through the planning optimization algorithm is as follows: Wherein, the To minimize the introduced auxiliary variablesThe objective function in the enumerated relaxation set S scene is minimized; Representing a current set The number of scenes in (i.e. the number of times the original problem has been iterated in a loop); The formula of the inner loop problem divided by the objective planning function through the planning optimization algorithm is as follows: Wherein the internal circulation problem comprises a continuous variable Discrete variable。 In some embodiments, the solving the inner loop problem, calculating to obtain initial scene data, includes: Splitting the internal circulation problem to obtain a minimum sub-