Regional power grid investment optimization method based on multi-objective programming model

Abstract

This invention belongs to the field of power grid investment optimization technology, and relates to a regional power grid investment optimization method based on a multi-objective programming model. It includes the following steps: Step S1: Considering the decoupling requirements between regional power grid investment and electricity generation, a multi-dimensional parameter system covering investment, electricity generation, safety, and green / low-carbon parameters is constructed; Step S2: Based on the multi-dimensional parameter system, a multi-objective programming model is constructed to maximize the net present value of investment, maximize the power grid safety margin, and maximize the investment-electricity decoupling degree; Step S3: An improved multi-objective particle swarm optimization algorithm is used to solve the model, and the Pareto optimal solution set is output to obtain the optimal investment scheme. This invention quantifies the decoupling relationship between investment and electricity generation through a multi-dimensional parameter system, and optimizes the balance between economic efficiency, power supply safety, and resource allocation efficiency using a multi-objective model, solving the problems of excessive coupling between investment and electricity generation and insufficient functional value mining in existing planning.

Description

Technical Field

[0001] This invention belongs to the field of power grid investment optimization technology, and relates to a regional power grid investment optimization method based on a multi-objective programming model. Background Technology

[0002] In current regional power grid planning practices, investment decisions are often deeply tied to power (load) forecasts, exhibiting a singular logic of "power-driven investment." However, as a crucial component of the power system, regional power grid investments not only serve power transmission needs but also fulfill multiple functions, including ensuring the safe and stable operation of the grid, promoting the consumption of clean energy, and supporting the construction of new power systems. Existing technologies often focus on matching investment costs with power growth, neglecting the independent value of investment in the grid's functional dimensions. This leads to extreme situations such as "over-investment to meet power demand" or "under-investment due to insufficient power," failing to achieve optimal investment allocation. Furthermore, traditional planning models often have simplistic constraints, typically considering only basic constraints such as power supply and demand balance and investment budgets. They lack comprehensive consideration of factors such as grid safety margins, the proportion of clean energy consumption, and policy control requirements, making planning schemes ill-suited to the complex needs of multiple scenarios and objectives under new power systems.

[0003] Therefore, there is an urgent need for a regional power grid investment optimization method that can take into account the decoupling relationship between investment and power generation and integrate a multi-objective programming model for regional power grid investment with multiple constraints, so as to provide a scientific and comprehensive quantitative basis for investment decisions. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a regional power grid investment optimization method based on a multi-objective programming model. By quantifying the decoupled relationship between investment and power generation through a multi-dimensional parameter system, and by balancing economic efficiency, power supply security, and resource allocation efficiency through multi-objective optimization, this method solves the problems of excessive coupling between investment and power generation and insufficient functional value mining in existing planning. Under the premise of ensuring the safe and stable operation of the power grid and policy compliance, this method promotes the rational allocation of investment resources and the coordinated development of power grid investment and power generation.

[0005] The technical solution adopted in this invention is a regional power grid investment optimization method based on a multi-objective programming model, comprising the following steps: Step S1: Considering the need to decouple regional power grid investment from electricity generation, construct a multi-dimensional parameter system covering investment, electricity generation, safety, and green and low-carbon parameters; Step S2: Construct a multi-objective programming model based on a multi-dimensional parameter system to maximize the net present value of investment, the power grid safety margin, and the investment-electricity decoupling degree; Step S3: Use the improved multi-objective particle swarm optimization algorithm to solve the model and output the Pareto optimal solution set to obtain the optimal investment plan.

[0006] Furthermore, the multi-dimensional parameter system in step S1 includes: Investment parameters: Unit cost of functional investment The unit is 10,000 yuan / megawatt, representing the unit cost of electricity-generating investments. The unit is 10,000 yuan / kVA, and the investment payback period is... Benchmark yield ; Electrical parameters: Base load Average annual growth rate of load Elastic load coefficient ; Safety parameters: Minimum power supply reliability Maximum transmission power of the line System security margin ; Green and low-carbon parameters: Minimum clean energy consumption ratio Renewable energy subsidy coefficient .

[0007] Furthermore, the multi-objective programming model in step S2 includes 3 objective functions and 5 constraints; Objective function 1: Maximize the net present value of investment, expressed as follows: ; in, Net present value of investment For the first Annual electricity consumption For electricity sales price, For the first Annual increase in the proportion of clean energy consumption For clean energy consumption, For functional investment scale, For electricity-related investment scale; Objective function 2: Maximize the power grid security margin, expressed as follows: ; in, To ensure a comprehensive safety margin, Assign power supply reliability weights to nodes. For the actual power supply reliability of the i-th node, For the first Maximum transmission power of the line For the first Actual transmission power of the line; Objective function 3: Maximize the investment-electricity decoupling degree, expressed as follows: ; in, For investment-electricity decoupling, The ideal proportion of functional investment is set according to the regional power grid development positioning, with a value range of [0.3, 0.6].

[0008] Furthermore, the five constraints in step S2 are specifically as follows: Investment budget constraints: ; in, For functional investment special budget, For electricity-related investment budgets, This represents the maximum value of the total investment budget. ; in, Redundancy coefficient, value , This refers to the increase in electricity consumption during the planning period; Electricity supply and demand constraints: ; in, This refers to the improvement in node power supply capacity solely resulting from electricity-based investments; ; in, Power supply efficiency and value for electricity-type investments ; Safe operation constraints: ; in, To improve the reliability of functional investments, For line transmission power safety redundancy, value selection ; Green and low-carbon constraints: ; in, The utilization rate enhancement factor for functional investments, expressed as % / MW. Carbon emission reduction factor for functional investments, in tons / megawatts per year The carbon emission reduction target for the regional power grid during the planning period, in tons. ; Variable nonnegation constraint: ; Ensure that the physical meaning of the core variables is reasonable and that the economic logic is sound.

[0009] Furthermore, step S3 specifically includes: Step S31: Initialize the population, set the number of particles to 100-150, and the particle positions correspond to... The value is randomly generated, and the speed is determined by the number of values. Step S32: Fitness evaluation, calculate the three objective function values ​​for each particle, and construct an external archive set to store non-dominated solutions; Step S33: Global optimal update, select the particle with the optimal crowding from the external archive set as the global optimal solution. According to the individual optimal solution and The formula for updating particle velocity and position is: ; ; in, For inertial weights, The learning factor has a value of 2.0. A random number in the range [0,1]. Step S34: Constraint handling, using the penalty function method to reduce the fitness of particles that violate the constraints; Step S35: Terminate the decision. When the number of iterations reaches 200-300 or the external archive set converges, output the Pareto optimal solution set.

[0010] The beneficial effects of this invention are as follows: This invention quantifies the decoupling relationship between investment and electricity generation through a multi-dimensional parameter system. By employing a multi-objective programming model that considers the decoupling of investment and electricity generation, it optimizes the balance between economic efficiency, power supply security, and resource allocation efficiency. This solves the technical problems in existing regional power grid investment planning, such as excessive coupling between investment and electricity generation, and the single constraint condition, which leads to insufficient exploitation of investment functional value and inefficient resource allocation. Under the premise of ensuring the safe and stable operation of the power grid and policy compliance, it promotes the rational allocation of investment resources and the coordinated development of power grid investment and electricity generation. Furthermore, the model has high solution accuracy, and the output investment scheme and load matching results are more reasonable. It effectively solves the problem of excessive reliance on electricity generation constraints in traditional planning, providing a scientific and multi-dimensional quantitative basis for regional power grid investment decisions. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of the method of the present invention; Figure 2This is a schematic diagram illustrating the decoupling relationship between investment and electricity consumption in this invention; Figure 3 The flowchart shows the solution process for the improved multi-objective particle swarm optimization algorithm of this invention. Detailed Implementation

[0013] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0014] like Figure 1 As shown, this invention provides a regional power grid investment optimization method based on a multi-objective programming model, comprising the following steps: Step S1: Considering the need to decouple regional power grid investment from electricity generation, construct a multi-dimensional parameter system covering investment, electricity generation, safety, and green and low-carbon parameters; By proposing three core assumptions, the decoupling boundary between investment and electricity generation is clarified, and a multi-dimensional parameter system covering investment, electricity generation, safety, and green low-carbon development is established: Assumption 1: Figure 2 As shown, regional power grid investment is divided into functional investment, used for power grid security upgrades and the construction of clean energy consumption facilities; and power consumption investment, used for transmission and transformation capacity expansion to meet power consumption growth. The benefits of functional investment are enhanced through the power grid security margin improvement coefficient. and the coefficient for increasing the proportion of clean energy consumption Quantitative analysis shows no direct linear correlation with electricity consumption growth. Assumption 2: Electricity (load) growth follows the "base load + flexible load" model. Flexible load is affected by factors such as electricity price and user-side response, and is decoupled from functional investment. The benefits of functional investment are reflected through independent safety and absorption indicators. Assumption 3: The regional power grid must meet the requirements of the "Guidelines for the Safety and Stability of Power Systems" and the provincial clean energy consumption policy, and investment decisions must simultaneously meet the multi-dimensional constraints of technology, economy, and green and low-carbon development.

[0015] The multi-dimensional parameter system includes: Investment parameters: Unit cost of functional investment The unit is 10,000 yuan / megawatt; the unit cost of electricity-type investment. The unit is RMB 10,000 / kVA; the investment payback period T and the benchmark rate of return r are also mentioned. Electrical parameters: Base load Average annual growth rate of load Elastic load coefficient It is used to characterize the sensitivity of load to non-electrical factors; Safety parameters: Minimum power supply reliability Maximum transmission power of the line System security margin ; Green and low-carbon parameters: Minimum clean energy consumption ratio Renewable energy subsidy coefficient .

[0016] Step S2: Construct a multi-objective programming model based on a multi-dimensional parameter system to maximize the net present value of investment, the power grid safety margin, and the investment-electricity decoupling degree; It should be noted that, based on a multi-dimensional parameter system and combined with the functional attributes and decoupling requirements of regional power grid investment, a three-in-one multi-objective programming model of "efficiency-safety-decoupling" is constructed. Each objective function achieves decoupling design with electricity consumption, as detailed below: Objective function 1: Maximize the net present value of investment Breaking away from the traditional single logic that "electricity consumption determines investment returns," this paper breaks down investment returns into two parts: "electricity consumption-related returns" and "decoupling function returns." The decoupling function returns are independent of electricity consumption growth and are quantified through the value of grid functions, expressed as follows: ; in, Net present value of investment For the first Annual electricity consumption (load) For electricity sales price, For the first Annual increase in the proportion of clean energy consumption For clean energy consumption, For functional investment scale, This refers to the scale of investment in electricity-related projects.

[0017] This design introduces decoupled functional benefit items, so that investment returns are no longer entirely dependent on electricity growth. Even if the growth rate of electricity consumption slows down, functional investments can still achieve increased returns through clean energy consumption subsidies and reduced safety losses, reflecting the decoupling logic in the economic dimension.

[0018] Objective function 2: Maximize the power grid security margin Safety margin, as the core output of functional investment, is completely independent of the scale of electricity (load). It is constructed through a two-dimensional framework of "node reliability + line margin" to quantify the effect of functional investment on grid security. The functional expression is as follows: ; in, To ensure a comprehensive safety margin, Assign power supply reliability weights to nodes. For the first Actual power supply reliability of nodes For the first Maximum transmission power of the line For the first Actual transmission power of the line.

[0019] This objective function enables the benefits of functional investments to be directly quantified through independent safety indicators, without needing to be indirectly reflected through electricity consumption, thus achieving complete decoupling between safety dimensions and electricity consumption.

[0020] Objective function 3: Maximize the investment-electricity decoupling degree From the perspective of investment structure, the degree of decoupling is quantitatively determined to ensure that the proportion of functional investments aligns with the functional positioning of the power grid and to avoid imbalances in the investment structure due to power fluctuations. The functional expression is as follows: ; in, For investment-electricity decoupling, The ideal proportion of functional investment is set according to the regional power grid development positioning, with a value range of [0.3, 0.6].

[0021] This objective assesses the decoupling effect through a dual-dimensional evaluation of "investment structure + revenue structure," avoiding both safety shortcomings caused by insufficient functional investment and resource waste caused by over-investment, thereby achieving a decoupling balance between investment structure and electricity generation.

[0022] Based on the actual operation of the regional power grid and the decoupling objectives, a four-dimensional constraint system of "technology-economy-policy-environment" is constructed to ensure the feasibility of the model and the achievement of the decoupling objectives. The specific constraints are as follows: Investment budget constraints – economic feasibility frontier: Considering the differences in the sources of investment funds, whether government subsidies or corporate self-financing, the total budget is divided into a special budget for functional investments and a budget for electricity-related investments to avoid the crowding out of functional investment funds due to electricity fluctuations. ; in, A special budget for functional investments shall be allocated, comprising no less than 30% of the total budget, in accordance with the requirements of the "Regional Power Grid Planning Guidelines". This is a budget for electricity-related investments, linked to electricity growth forecasts.

[0023] ; in, The redundancy coefficient has a value of [value missing]. , This refers to the increase in electricity consumption during the planning period.

[0024] This constraint locks in the scale of functional investments through a dedicated budget, ensuring that the decoupling objective is not affected by the diversion of electricity budget.

[0025] Electricity supply and demand constraints – basic guarantee boundaries: Under the decoupling framework, electricity-related investments must meet basic electricity demand, but functional investments do not participate in the electricity supply and demand balance, thus avoiding functional investments being "hijacked" by electricity demand. ; in, This refers to the improvement in node power supply capacity solely resulting from electricity-based investments; ; in, For power supply efficiency of energy-type investments, the value is... ; Enhanced power supply capacity resulting from functional investments

[0026] ; in, For functional investment power supply efficiency, the value is... It is not included in the electricity supply and demand balance calculation, and is only used to improve the safety margin.

[0027] This constraint clearly distinguishes the functional boundaries of the two types of investment, ensuring that functional investments focus on safety and consumption, and decouple them from electricity supply and demand.

[0028] Safe operation constraints – technical reliability boundaries: Based on the "Guidelines for the Safety and Stability of Power Systems," safety indicator constraints independent of electricity quantity are set to ensure that functional investments meet safety requirements. ; in, This represents the reliability enhancement factor for functional investments; each megawatt of functional investment can improve node reliability. ; To ensure power redundancy in line transmission, the value is set to... This avoids the safety risks caused by the line operating at full load.

[0029] This constraint directly links safety indicators to functional investments, independent of power fluctuations, thus strengthening the decoupling characteristics of the safety dimension.

[0030] Green and low-carbon constraints – compliance boundaries: In line with national policies on clean energy consumption and carbon emission reduction, mandatory constraints are set for functional investments to ensure that the decoupling direction aligns with policy guidance. ; in, This is the coefficient for increasing the utilization rate of functional investments; each megawatt of functional investment can increase the utilization rate of clean energy. ; The carbon emission reduction factor for functional investments, representing the annual emission reduction per megawatt of functional investment. tons of CO2; The carbon emission reduction targets for the regional power grid during the planning period are set according to local policies.

[0031] This constraint mandates functional investments through policy indicators, ensuring the feasibility of decoupling techniques.

[0032] Variable nonnegation constraint—logical rationality boundary: Ensure that the physical meaning and economic logic of the core variables are reasonable: ; The aforementioned multi-factor constraint system not only ensures the economic feasibility and technical reliability of regional power grid investment, but also provides a clear optimization boundary for decoupling investment and electricity generation, avoiding the drawbacks of "electricity generation dominating everything" in traditional constraints, and ensuring that the decoupling objectives of the multi-objective programming model are feasible and verifiable.

[0033] Step S3: Use the improved multi-objective particle swarm optimization algorithm to solve the model and output the Pareto optimal solution set to obtain the optimal investment plan.

[0034] It should be noted that, as Figure 3 As shown, the specific process is as follows: Initialize the population: Set the number of particles to... Particle position corresponding The value is randomly generated, and the speed is determined by the number of values. Fitness evaluation: Calculate the three objective function values ​​for each particle and construct an external archive set to store non-dominated solutions; Global optimal update: Select the particle with the optimal crowding from the external archive as the global optimal solution. ; Particle Update: Based on Individual Optimal Solutions and The formula for updating particle velocity and position is: ; ; in, The inertial weights decrease linearly from 0.9 to 0.4, c1 and c2 are learning factors (taken as 2.0), and r1 and r2 are random numbers in the range [0,1]. Constraint handling: For particles that violate constraints, the fitness is reduced using the inverse function method; Termination criteria: Output the Pareto optimal solution set when the number of iterations reaches 200-300 or the external archive set converges.

[0035] The following examples provide further details.

[0036] Taking a provincial power grid as an example, including 10 power supply nodes and 15 transmission lines, the implementation steps are as follows: Step 1: Construct a multi-dimensional parameter system Investment parameters: 10,000 yuan / megawatt 10,000 yuan / kVA Year, ; Battery parameters: 10,000 kilowatts , ; Security parameters: , (Line average) = 2 million kilowatts ; Green and low-carbon parameters: , Yuan / kWh.

[0037] Step 2: Construct a multi-objective programming model Maximizing net present value of investment: ; 10,000 kilowatt-hours.

[0038] Maximizing power grid safety margin: ; Maximizing investment-power decoupling: ; Investment budget constraints: (10,000 yuan) Electricity supply and demand constraints: ; Safe operation constraints: , , ; Green and low-carbon constraints: ; Variable nonnegation constraint: ; Step 3: Solve using an improved particle swarm optimization algorithm Parameter settings: number of particles 120, number of iterations 250. It decreases linearly from 0.9 to 0.4. ; Solution results: In the Pareto optimal solution set, the optimal solution is... megawatts 1000 kVA, with a total investment of 4.08 billion yuan; Indicator verification: 100 million yuan , .

[0039] In this scheme, functional investments account for 45.7% and power generation investments account for 54.3%, with a deviation of only 5.7% from the ideal proportion of 0.45, demonstrating a significant decoupling effect. The grid safety margin has increased by 82% compared to before the plan, and the clean energy consumption ratio has reached 38%, exceeding the policy requirements. The net present value of investment has increased by 12% compared to the traditional coupled model scheme, achieving synergistic optimization of investment benefits, safety margin, and decoupling objectives.

[0040] In summary, the technical solution of this invention effectively solves the problem of excessive reliance on electricity constraints in traditional planning, and provides a scientific and diversified quantitative basis for regional power grid investment decisions.

[0041] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present technical solution are within the protection scope of the present invention.

[0042] To facilitate understanding by those skilled in the art of the improvements of this invention over the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted from this application. Those skilled in the art should realize that these omitted elements may also constitute the content of this invention.

Claims

1. A regional power grid investment optimization method based on a multi-objective programming model, characterized in that, Includes the following steps: Step S1: Considering the need to decouple regional power grid investment from electricity generation, construct a multi-dimensional parameter system covering investment, electricity generation, safety, and green and low-carbon parameters; Step S2: Construct a multi-objective programming model based on a multi-dimensional parameter system to maximize the net present value of investment, the power grid safety margin, and the investment-electricity decoupling degree; Step S3: Use the improved multi-objective particle swarm optimization algorithm to solve the model and output the Pareto optimal solution set to obtain the optimal investment plan.

2. The regional power grid investment optimization method based on a multi-objective programming model as described in claim 1, characterized in that, The multi-dimensional parameter system in step S1 includes: Investment parameters: Unit cost of functional investment The unit is 10,000 yuan / megawatt, representing the unit cost of electricity-generating investments. The unit is 10,000 yuan / kVA, and the investment payback period is... Benchmark yield ; Electrical parameters: Base load Average annual growth rate of load Elastic load coefficient ; Safety parameters: Minimum power supply reliability Maximum transmission power of the line System security margin ; Green and low-carbon parameters: Minimum clean energy consumption ratio Renewable energy subsidy coefficient .

3. The regional power grid investment optimization method based on a multi-objective programming model as described in claim 2, characterized in that, The multi-objective programming model in step S2 includes 3 objective functions and 5 constraints; Objective function 1: Maximize the net present value of investment, expressed as follows: ； in, Net present value of investment For the first Annual electricity consumption For electricity sales price, For the first Annual increase in the proportion of clean energy consumption For clean energy consumption, For functional investment scale, For electricity-related investment scale; Objective function 2: Maximize the power grid security margin, expressed as follows: ； in, To ensure a comprehensive safety margin, Assign power supply reliability weights to nodes. For the actual power supply reliability of the i-th node, For the first Maximum transmission power of the line For the first Actual transmission power of the line; Objective function 3: Maximize the investment-electricity decoupling degree, expressed as follows: ； in, For investment-electricity decoupling, The ideal proportion of functional investment is set according to the regional power grid development positioning, with a value range of [0.3, 0.6].

4. The regional power grid investment optimization method based on a multi-objective programming model as described in claim 3, characterized in that, The five constraints in step S2 are as follows: Investment budget constraints: ； in, For functional investment special budget, For electricity-related investment budgets, This represents the maximum value of the total investment budget. ； in, Redundancy coefficient, value , This refers to the increase in electricity consumption during the planning period; Electricity supply and demand constraints: ； in, This refers to the improvement in node power supply capacity solely resulting from electricity-based investments; ； in, Power supply efficiency and value for electricity-type investments ; Safe operation constraints: ； in, To improve the reliability of functional investments, For line transmission power safety redundancy, value selection ; Green and low-carbon constraints: ； in, The utilization rate enhancement factor for functional investments, expressed as % / MW. Carbon emission reduction factor for functional investments, in tons / megawatts per year The carbon emission reduction target for the regional power grid during the planning period, in tons. ; Variable nonnegation constraint: ； Ensure that the physical meaning of the core variables is reasonable and that the economic logic is sound.

5. The regional power grid investment optimization method based on a multi-objective programming model as described in claim 4, characterized in that, Step S3 specifically involves: Step S31: Initialize the population, set the number of particles to 100-150, and the particle positions correspond to... The value is randomly generated, and the speed is determined by the number of values. Step S32: Fitness evaluation, calculate the three objective function values ​​for each particle, and construct an external archive set to store non-dominated solutions; Step S33: Global optimal update, select the particle with the optimal crowding from the external archive set as the global optimal solution. According to the individual optimal solution and The formula for updating particle velocity and position is: ； ； in, For inertial weights, The learning factor has a value of 2.

0. A random number in the range [0,1]. Step S34: Constraint handling, using the penalty function method to reduce the fitness of particles that violate the constraints; Step S35: Termination judgment, iteration count reached. When the secondary or external archive set converges, output the Pareto optimal solution set.

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