A distributed electric-thermal integrated energy system configuration optimization method considering source-load matching benefit

Abstract

The application discloses a kind of distributed electric heating integrated energy system configuration optimization method considering source load matching benefit, belong to integrated energy system planning technical field.The method steps of the present application are as follows: step S1, establish the source load matching benefit quantification index based on net load fluctuation, for assessing the source load matching degree of electric, heat energy flow in distributed electric heating integrated energy system;Step S2, establish the economic index of system full life cycle cost including annualized investment, operation and maintenance cost;Step S3, construct the multi-objective double-layer optimization planning model considering economy and source load matching degree, upper layer optimization equipment capacity, lower layer optimization operation scheduling;Step S4, obtain pareto optimal solution set by using NSGA-II algorithm to solve, obtain the optimal configuration scheme of system.The present application quantifies source load matching benefit, reduces system net load fluctuation effectively while ensuring economy, improves the accommodation capacity of system to renewable energy and the friendliness to power grid.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system planning technology, specifically relating to a method for optimizing the configuration of a distributed electric and thermal integrated energy system that considers source-load matching benefits. Background Technology

[0002] The construction of a new type of power system based on new energy sources has become a development trend. Distributed integrated energy systems, by integrating various energy resources such as electricity and heat within a region, can achieve cascaded utilization of energy and source-load matching. In traditional system planning, economic efficiency (such as the lowest life-cycle cost) is often the sole objective. However, this planning approach ignores the source-load matching characteristics between multiple energy flows (especially electricity and heat) within the integrated energy system. "Source-load matching degree" is a key characteristic of integrated energy systems, referring to the smoothing of fluctuations in renewable energy and load through the coordination of different energy suppliers and energy forms. If the source-load matching benefits are not considered in the planning stage, it may lead to excessive dependence on the external power grid, impacting grid stability, or resulting in redundant equipment configuration, failing to fully utilize the regulation potential of coupled equipment such as gas turbines and heat pumps. Therefore, how to quantitatively evaluate the source-load matching effect of the system and optimize it as an important indicator in the planning stage is an urgent problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a method for optimizing the configuration of a distributed electric and thermal integrated energy system that considers the source-load matching benefits, in order to solve the problem that existing planning methods fail to quantitatively consider the source-load matching benefits of the system, resulting in an unreasonable system configuration structure.

[0004] This invention is achieved through the following technical solution: A method for optimizing the configuration of a distributed integrated electric and thermal energy system, considering source-load matching benefits, includes the following steps: Step S1: Establish a source-load matching benefit quantification index based on net load fluctuation to evaluate the source-load matching degree of electric and heat energy flow in the distributed electric and heat integrated energy system; the source-load matching benefit quantification index is defined as the standard deviation of the net load sequence; the net load is defined as the difference between load demand and internal system output. Step S2: Establish economic indicators that include the system's total lifecycle cost, which includes annualized investment cost, annual operating cost, and annual maintenance cost. Step S3: Construct a multi-objective, two-layer optimization planning model that considers economic efficiency and source-load matching degree. The multi-objective, two-layer optimization planning model includes an upper-layer capacity planning model and a lower-layer operation scheduling model. The upper-layer capacity planning model aims to minimize the economic efficiency index and optimize the source-load matching benefit quantification index to optimize the capacity configuration of each device. The lower-layer operation scheduling model is used to calculate the optimal operation plan for a typical day, thereby obtaining the annual operating cost in the upper-layer capacity planning model. Step S4: The multi-objective optimization algorithm is used to solve the multi-objective bi-level optimization planning model to obtain the Pareto optimal solution set and the optimal configuration scheme of the distributed electric and thermal integrated energy system.

[0005] In the above technical solution, further, in step S1, the method for constructing the source-load matching benefit quantification index based on net load fluctuation is as follows: The net load of the system is defined as the difference between load demand and system output, and the standard deviation of the net load sequence is used to describe the volatility of the net load. The source-load matching benefit quantification index is defined as the standard deviation of the net load sequence. The calculation formula is: in, It is a collection of energy types, including electrical energy and thermal energy; Represents the standard deviation function; for Time of the first Net load value of this energy source; For the first The average value of the net energy load series; The total number of time steps in the scheduling cycle; for Time zone for the first The load demand for this type of energy; It is the collection of all power supply devices within the system; for Time device The output of the first The power of a type of energy source.

[0006] The source-load matching benefit quantification index The smaller the value, the more the system output matches the user's energy consumption pattern, and the stronger the source-load matching effect of the system.

[0007] In step S2, the comprehensive cost method is used to reflect the economic efficiency of the integrated energy system capacity configuration. The economic efficiency indicators include annualized investment cost, annual operating cost, and maintenance cost. The calculation formulas for the economic efficiency indicators are shown below: In the formula, As an economic indicator, , , These are the annualized investment cost, annual maintenance cost, and annual operating cost of the integrated energy system; among which, the annualized investment cost... Includes the annual value of the initial investment cost of the energy unit and energy storage unit; annual maintenance cost. It is calculated using the annual value of the initial investment cost and the maintenance factor.

[0008] Step S3 specifically includes the following steps: Step S31: Construct the upper-level capacity planning model The objective functions are economic cost and source-load matching degree, respectively. The objective function for source-load matching degree is: The economic cost objective function is Annual operating costs The optimal operation plan is determined by the lower-level operation scheduling model; The optimization variables of the upper-level capacity planning model are the rated capacity of the energy units and the energy storage units; Step S32: Construct the lower-level runtime scheduling model The lower-level operation scheduling model is used to calculate the optimal operation plan for a typical day, thus forming the annual operating cost in the upper-level capacity planning model. Given the equipment capacity, the objective is to minimize the system operating cost within a typical day. The objective function is: in, , These are the purchase price and the retail price of electricity, respectively. , These refer to the power purchased and the power sold, respectively. For natural gas prices; This represents gas consumption; the lower-level operation and scheduling model feeds back the optimized equipment output sequence and operating costs to the upper-level capacity planning model.

[0009] In step S4, a non-dominated sorting genetic algorithm with an elitist strategy (NSGA-II) is used to perform multi-objective optimization on the upper-level capacity planning model. The upper-level capacity planning model has two objective functions: economic cost and source-load matching degree. The specific process of multi-objective optimization includes: initializing the population and randomly generating a set of equipment capacity configuration schemes; calling the lower-level operation scheduling model to calculate the fitness of each individual (including economic indicators and source-load matching benefit quantification indicators), performing fast non-dominated sorting and crowding distance calculation on the population; generating offspring populations through selection, crossover, and mutation operations; merging the parent and offspring populations, and selecting individuals on the Pareto front as the optimal solution set.

[0010] Finally, by weighing the economic indicators corresponding to different solutions in the optimal solution set against the quantitative indicators of source-load matching benefits, the optimal capacity ratio of the equipment is determined.

[0011] The beneficial effects of this invention are as follows: Compared to existing planning techniques that primarily focus on a single economic objective, this invention significantly improves the overall operational efficiency of distributed power and thermal integrated energy systems by constructing a multi-objective, two-layer optimization planning architecture. Traditional planning methods often prioritize minimizing lifecycle costs, easily leading to system configurations that prioritize generation over storage or insufficient equipment regulation capabilities. This invention innovatively establishes a two-layer coupled model comprising upper-layer capacity planning and lower-layer operation scheduling, elevating source-load matching from a traditional constraint to an optimization objective of equal importance to economics. This structure not only ensures the feasibility of the planning scheme in terms of static investment but also ensures the system possesses the ability to actively interact with sources and loads during actual operation through dynamic operation strategies fed back from the lower-layer model, overcoming the shortcomings of traditional single-layer models that struggle to consider dynamic regulation characteristics.

[0012] At the theoretical level, this invention fills the technical gap in the quantification and evaluation of source-load matching characteristics. It introduces a quantitative index for source-load matching benefits based on the standard deviation of net load fluctuations, theoretically defining precisely the temporal matching degree between system output and load demand. By minimizing this index, the optimal configuration for utilizing energy storage devices to mitigate the randomness of renewable energy can be automatically selected, effectively reducing the volatility of power interaction between the system and the external grid, thereby significantly improving the system's grid friendliness and its internal renewable energy absorption capacity.

[0013] Furthermore, this invention demonstrates its significant advantages in decision support through specific experimental data. Analysis based on the Pareto optimal solution set shows that this invention can clearly quantify the trade-off between "economic cost" and "system stability." Example data indicates that, compared to solutions that only pursue economic optimization, this invention achieves a significant improvement in source-load matching accuracy of approximately 41.5% with an approximately 16% increase in cost. This quantitative analysis provides planners with a cost-effective decision-making basis, enabling them to select the configuration scheme with optimal system stability within budgetary constraints, avoiding the risk of sacrificing long-term system operational safety and independence due to blindly pursuing low costs. Attached Figure Description

[0014] Figure 1 This invention provides a method for optimizing the configuration of a distributed electric-thermal integrated energy system that considers source-load matching benefits. Figure 2 Framework diagram of a multi-objective two-stage planning method for optimizing the configuration of distributed electric and thermal integrated energy systems, taking into account source-load matching benefits; Figure 3 A graph showing the relationship between the economic indicators and the quantitative indicators of source-load matching benefits for each planning scheme in the Pareto solution set. Detailed Implementation

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This description is intended to explain the invention and not limit it. This invention discloses a method for optimizing the configuration of a distributed electric and thermal integrated energy system, considering the benefits of source-load matching, such as... Figure 1 This includes the following steps: Step S1: Establish a source-load matching benefit quantification index based on net load fluctuation to evaluate the source-load matching degree of electric and heat energy flow in the distributed electric and heat integrated energy system; the source-load matching benefit quantification index is defined as the standard deviation of the net load sequence; the net load is defined as the difference between load demand and internal system output. Step S2: Establish economic indicators that include the system's total lifecycle cost, which includes annualized investment cost, annual operating cost, and annual maintenance cost. Step S3: Construct a multi-objective, two-layer optimization planning model that considers economic efficiency and source-load matching degree. The multi-objective, two-layer optimization planning model includes an upper-layer capacity planning model and a lower-layer operation scheduling model. The upper-layer capacity planning model aims to minimize the economic efficiency index and optimize the source-load matching benefit quantification index to optimize the capacity configuration of each device. The lower-layer operation scheduling model is used to calculate the optimal operation plan for a typical day, thereby obtaining the annual operating cost in the upper-layer capacity planning model. Step S4: The multi-objective optimization algorithm is used to solve the multi-objective bi-level optimization planning model to obtain the Pareto optimal solution set and the optimal configuration scheme of the distributed electric and thermal integrated energy system.

[0016] This embodiment uses a distributed electric and thermal integrated energy system in a certain region as an example. The system's design life is 20 years. The region's average annual electrical load is 2,007.43 kW, with a peak load of 2,678.50 kW; the average annual thermal load is 584.97 kW, with a peak load of 1,241.26 kW. A system model containing six candidate energy devices needs to be constructed (as shown in Table 1), including photovoltaic units, wind turbines, gas turbines, electric heat pump units, electric energy storage units, and thermal storage tanks. Among them, photovoltaic units and wind turbines serve as renewable energy inputs; gas turbines consume natural gas and generate electricity and heat simultaneously; electric heat pumps consume electricity and generate heat; and energy storage devices are used to smooth out fluctuations. The purchased electricity price is calculated using a two-part tariff annual settlement model. The capacity fee is €114.29 / (kW·a), with the capacity size determined based on the maximum annual demand; the electricity price is €0.0025 / kWh. The natural gas price is set at a constant €0.0286 / kWh. The system does not subsidize energy sales (i.e., revenue is low or zero). The discount rate is set at 8%.

[0017] Table 1 Technical and economic parameters of energy equipment In step S1, the method for constructing the source-load matching benefit quantification index based on net load fluctuation is as follows: The net load of the system is defined as the difference between load demand and system output, and the standard deviation of the net load sequence is used to describe the volatility of the net load. The source-load matching benefit quantification index is defined as the standard deviation of the net load sequence. The calculation formula is: in, It is a collection of energy types, including electrical energy and thermal energy; Represents the standard deviation function; for Time of the first Net load value of this energy source; For the first The average value of the net energy load series; The total number of time steps in the scheduling cycle; for Time zone for the first The load demand for this type of energy; It is the collection of all power supply devices within the system; for Time device The output of the first The power of a type of energy source.

[0018] The source-load matching benefit quantification index The smaller the value, the more the system output matches the user's energy consumption pattern, and the stronger the source-load matching effect of the system.

[0019] In step S2, the comprehensive cost method is used to reflect the economic efficiency of the integrated energy system capacity configuration. The economic efficiency indicators include annualized investment cost, annual operating cost, and maintenance cost. The calculation formulas for the economic efficiency indicators are shown below: In the formula, As an economic indicator, , , These are the annualized investment cost, annual maintenance cost, and annual operating cost of the integrated energy system; among which, the annualized investment cost... Includes the annual value of the initial investment cost of the energy unit and energy storage unit; annual maintenance cost. It is calculated using the annual value of the initial investment cost and the maintenance factor.

[0020] Step S3 specifically includes the following steps: Step S31: Construct the upper-level capacity planning model The objective functions are economic cost and source-load matching degree, respectively. The objective function for source-load matching degree is: The economic cost objective function is Annual operating costs The optimal operation plan is determined by the lower-level operation scheduling model; The optimization variables of the upper-level capacity planning model are the rated capacity of the energy units and the energy storage units; Step S32: Construct the lower-level runtime scheduling model The lower-level operation scheduling model is used to calculate the optimal operation plan for a typical day, thus forming the annual operating cost in the upper-level capacity planning model. Given the equipment capacity, the objective is to minimize the system operating cost within a typical day. The objective function is: in, , These are the purchase price and the retail price of electricity, respectively. , These refer to the power purchased and the power sold, respectively. For natural gas prices; This represents gas consumption; the lower-level operation and scheduling model feeds back the optimized equipment output sequence and operating costs to the upper-level capacity planning model.

[0021] In step S4, as Figure 2 As shown, a non-dominated sorting genetic algorithm with an elitist strategy (NSGA-II) is used to perform multi-objective optimization on the upper-level capacity planning model. The upper-level capacity planning model has two objective functions: economic cost and source-load matching degree. The specific process of multi-objective optimization includes: initializing the population and randomly generating a set of equipment capacity configuration schemes; calling the lower-level operation scheduling model to calculate the fitness of each individual (including economic indicators and source-load matching benefit quantification indicators), performing fast non-dominated sorting and crowding distance calculation on the population; generating offspring populations through selection, crossover, and mutation operations; merging the parent and offspring populations, and selecting individuals on the Pareto front as the optimal solution set.

[0022] Finally, by weighing the economic indicators corresponding to different solutions in the optimal solution set against the quantitative indicators of source-load matching benefits, the optimal capacity ratio of the equipment is determined.

[0023] like Figure 3 As shown, the economic objective function value (annualized total cost) of the planning scheme is distributed in the range of [670, 480.82 € / a, 778, 120.41 € / a]; the quantitative index value of source-load matching benefit is distributed in the range of [319.23 kW, 545.41 kW].

[0024] The most economically efficient option has the lowest annualized total cost (€670,480.82 / year), but the highest quantitative indicator of source-load matching benefits (545.41 kW), indicating that this option has the largest net load fluctuation and the highest demand for grid regulation. This option typically uses smaller capacity gas turbines and energy storage devices.

[0025] The optimal source-load matching scheme has the lowest quantitative index of source-load matching benefit (319.23 kW), indicating the strongest system self-balancing capability and the smallest net load fluctuation. However, this scheme has the highest annualized total cost (€778,120.41 / a), approximately 1.16 times that of the most economically optimal scheme. This is usually because this scheme is equipped with larger capacity gas turbines, electric heat pumps, and energy storage devices to mitigate fluctuations.

[0026] Planners can choose the appropriate scheme based on actual needs. If the region has strict restrictions on grid dependence or seeks independence, a scheme with a lower source-load matching degree (i.e., a smaller index value) can be selected; if funds are limited, a more economical scheme can be chosen. Meanwhile, the results show that by increasing costs by approximately 16%, the system's source-load matching degree can be improved by about 41.5%, significantly enhancing system stability.

Claims

1. A method for optimizing the configuration of a distributed electric-thermal integrated energy system considering source-load matching benefits, characterized in that, Includes the following steps: Step S1: Establish a source-load matching benefit quantification index based on net load fluctuation to evaluate the source-load matching degree of electric and heat energy flow in the distributed electric and heat integrated energy system; the source-load matching benefit quantification index is defined as the standard deviation of the net load sequence; the net load is defined as the difference between load demand and internal system output. Step S2: Establish economic indicators that include the system's total lifecycle cost, which includes annualized investment cost, annual operating cost, and annual maintenance cost. Step S3: Construct a multi-objective bi-layer optimization planning model that considers economic efficiency and source-load matching degree; the multi-objective bi-layer optimization planning model includes an upper-layer capacity planning model and a lower-layer operation scheduling model; the upper-layer capacity planning model optimizes the capacity configuration of each device with the goal of minimizing the economic efficiency index and optimizing the source-load matching benefit quantification index. The lower-level operation scheduling model is used to calculate the optimal operation plan for a typical day, thereby obtaining the annual operating cost in the upper-level capacity planning model; Step S4: The multi-objective optimization algorithm is used to solve the multi-objective bi-level optimization planning model to obtain the Pareto optimal solution set and the optimal configuration scheme of the distributed electric and thermal integrated energy system.

2. The method for optimizing the configuration of a distributed electric and thermal integrated energy system considering source-load matching benefits according to claim 1, characterized in that, In step S1, the source-load matching benefit quantification index based on net load fluctuation is used. Specifically, it is expressed as follows: ； ； in, It is a collection of energy types, including electrical energy and thermal energy; Represents the standard deviation function; for Time of the first Net load value of this energy source; For the first The average value of the net energy load series; The total number of time steps in the scheduling cycle; for Time zone for the first The load demand for this type of energy; It is the collection of all power supply devices within the system; for Time device The output of the first The power of a type of energy source.

3. The method for optimizing the configuration of a distributed electric and thermal integrated energy system considering source-load matching benefits according to claim 2, characterized in that, In step S2: The formula for calculating the economic indicators is as follows: ； In the formula, As an economic indicator, , , These are the annualized investment cost, annual maintenance cost, and annual operating cost of the integrated energy system; among which, the annualized investment cost... Includes the annual value of the initial investment cost of the energy unit and energy storage unit; annual maintenance cost. It is calculated using the annual value of the initial investment cost and the maintenance factor.

4. The method for optimizing the configuration of a distributed electric and thermal integrated energy system considering source-load matching benefits according to claim 3, characterized in that, Step S3 is as follows: Step S31: Construct the upper-level capacity planning model The objective functions are economic cost and source-load matching degree, respectively. The objective function for source-load matching degree is: The economic cost objective function is Annual operating costs The optimal operation plan is determined by the lower-level operation scheduling model; The optimization variables of the upper-level capacity planning model are the rated capacity of the energy units and the energy storage units; Step S32: Construct the lower-level runtime scheduling model Given the available equipment capacity, the objective is to minimize the typical intraday system operating cost; the objective function is: in, , These are the purchase price and the retail price of electricity, respectively. , These refer to the power purchased and the power sold, respectively. For natural gas prices; This refers to gas consumption; The lower-level operation scheduling model feeds back the optimized equipment output sequence and operating costs to the upper-level capacity planning model.

5. The method for optimizing the configuration of a distributed electric and thermal integrated energy system considering source-load matching benefits according to claim 1, characterized in that, In step S4: A non-dominated sorting genetic algorithm with an elitist strategy is used to perform multi-objective optimization on the upper-level capacity planning model. The specific process of multi-objective optimization includes: initializing the population and randomly generating a set of equipment capacity configuration schemes; calling the lower-level operation scheduling model to calculate the fitness of each individual, and performing fast non-dominated sorting and crowding distance calculation on the population; generating offspring population through selection, crossover, and mutation operations; merging the parent and offspring populations, and selecting individuals on the Pareto front as the optimal solution set. Finally, by weighing the economic indicators corresponding to different solutions in the optimal solution set against the quantitative indicators of source-load matching benefits, the optimal capacity ratio of the equipment is determined.

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