Comprehensive energy system multi-target extension planning method considering local power capability difference

Abstract

The invention discloses an integrated energy system multi-target extension planning method considering local power capability difference, which comprises the following steps: S1, defining that the power capability of an integrated energy system refers to the sum of all loads when the integrated energy system meets a safety criterion, and establishing an annual power capability model of the integrated energy system; and S2, establishing a local power capability balance degree model of the integrated energy system to measure the power capability difference between any two nodes. S3, model selection of a power line, a natural gas pipeline and a heat distribution pipeline, and capacity and hourly output of energy equipment are taken as decision variables; minimizing the equivalent annual total cost, maximizing the annual power capability and minimizing the local power capability balance degree as optimization objective functions; the transmission upper and lower limits of each pipeline, the input and output of energy equipment and the balance of the system are used as constraint conditions; and establishing a multi-target extension planning model of the integrated energy system. And S4, solving the extended planning model by adopting an NSGA-II algorithm to obtain a group of optimal system planning scheme sets.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system planning technology, and relates to the fields of energy planning, integrated energy systems, power systems, natural gas systems, and thermal systems. In particular, it relates to a multi-objective extended planning method for integrated energy systems that takes into account the differences in local work capacity. Background Technology

[0002] As crucial technologies supporting the high-proportion consumption of new energy sources, emerging technologies such as natural gas blending with hydrogen, electric heating, and green electricity hydrogen production have been widely applied, further enriching the types of energy included in integrated energy systems (IES). The deep coupling of electricity, natural gas, hydrogen, heat, and cold energy further highlights the differences in energy quality within IES. The laws of thermodynamics explain the distinction between the quantity and quality of energy. The essence of energy utilization is to obtain the work converted from energy. Converting electricity, natural gas, hydrogen, heat, and cold energy of the same "quantity" (power) into mechanical work and doing work on the same object results in different displacement distances. The essence of this phenomenon is the difference in the ability of energy to do work, that is, the difference in energy "quality." 㶲 (exergy) is a physical parameter that uniformly quantifies this ability. Therefore, research on IES from the perspective of energy quality has attracted the attention of researchers.

[0003] Traditional energy flow balance-based IES planning methods aim to determine the optimal system configuration and structure that meets the load's energy "quantity" requirements within the target planning period, often neglecting the load's demand for energy "quality." Ignoring energy "quality" in research can lead to a mismatch between "high quantity and low quality"—where users actually receive less work capacity—which energy flow balance masks, creating the illusion of high energy utilization. Furthermore, it can lower energy utilization levels, leading to unnecessary investment and waste as users compensate for the lack of work capacity through incremental increases and other means. Summary of the Invention

[0004] The purpose of this invention is to fill a gap in the existing technology and address the issue that current integrated energy system planning does not consider the differences in overall energy capacity and localized overall energy capacity. This invention provides a multi-objective extended planning method for integrated energy systems that takes into account differences in localized overall energy capacity, thereby obtaining a planning scheme for an integrated energy system that promotes the coordinated development of energy quantity and energy quality. This method proposes to consider overall energy capacity during the planning stage to find a more rational integrated energy system planning scheme.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A multi-objective extended planning method for a comprehensive energy system that takes into account differences in local work capacity includes:

[0007] S1. Define the working capacity of an integrated energy system as the sum of the energy supplied to all loads when the integrated energy system meets the safety criteria, and construct an annual working capacity model for the integrated energy system.

[0008] S2. Establish a local work capacity equilibrium model for the integrated energy system to measure the difference in work capacity between any two nodes;

[0009] S3. Using the selection of power lines, natural gas pipelines, and heating pipelines, the capacity and hourly output of energy equipment as decision variables; minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance as optimization objective functions; and using the upper and lower limits of transmission of each pipeline, the input and output of energy equipment, and the overall balance of the integrated energy system as constraints; a multi-objective extended programming model for the integrated energy system is established.

[0010] S4. Use the NSGA-II algorithm to solve the above multi-objective extended programming model to obtain the optimal integrated energy system planning scheme set.

[0011] Furthermore, in step S1,

[0012] TWC refers to the sum of the energy supplied to all loads when the integrated energy system meets safety criteria. It characterizes the work capacity obtained by all users within the integrated energy system (IES). The TWC at time t is expressed as:

[0013] (1)

[0014] in, Let be the i-th electrical load (active power) at time t, in kW; Let be the load of the j-th natural gas at time t, in kW; Let k be the kth thermal load at time t, in kW; , and These are the number of load nodes in the power system, natural gas system, and heating system, respectively. The annual energy work capacity of a comprehensive energy system, expressed in kWh; This indicates the type of typical day, including transitional seasons, summer, and winter; This represents the number of days corresponding to a typical day in a year.

[0015] Furthermore, in step S2,

[0016] For any two nodes with the same energy demand, differences in pipeline type, node coordinates, and ambient temperature can lead to different work capacities. This phenomenon is defined as the local work capacity difference in the integrated energy system. The local work capacity balance is constructed to characterize the difference between any two nodes. The work capacity balance of any two nodes in the power system, natural gas system, and heating system is expressed as:

[0017] (2)

[0018] in, The local functional balance of nodes i and n in the power system is represented; t represents the current time, and the three typical days total 72 hours. This represents the local work capacity equilibrium of nodes j and o in the natural gas system. This represents the local work-force equilibrium degree at nodes k and m in a thermal system; , Let be the work density, i.e., the power factor, of nodes i and n in the power system at time t, respectively. , Let be the work density of nodes j and o in the natural gas system at time t; , Let be the work density of nodes k and m of the thermodynamic system at time t; for local work-capacity equilibrium calculations for more than or equal to 3 nodes, equation (2) can be expressed in variance form;

[0019] Work density reflects the proportion of work capacity available to the load (i.e., the user) at the current moment, also known as the energy quality coefficient, and is calculated as follows:

[0020] (3)

[0021] in, , and Let be the apparent power of node i in the power system, the power of node j in the natural gas system, and the thermal power of node k in the thermal system at time t, respectively. Let be the i-th electrical load (active power) at time t, in kW; Let be the load of the j-th natural gas at time t, in kW; Let k be the kth thermal load at time t, in kW.

[0022] Furthermore, in step S3, the optimization objective functions, namely minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance, are expressed as follows:

[0023] (4)

[0024] In the formula, Represents the equivalent annual total cost. For the annual work capacity of the integrated energy system, x = e, g, or h. This indicates the local functional balance of the power system. This indicates the local functional balance of the natural gas system. It represents the local work capacity equilibrium of a thermal system.

[0025] The present invention also provides a multi-objective extended planning device for a comprehensive energy system that takes into account differences in local work capacity, comprising:

[0026] The annual working capacity module is used to define the working capacity of an integrated energy system as the sum of the energy supplied to all loads when the integrated energy system meets safety criteria, and to construct the annual working capacity model of the integrated energy system.

[0027] The Local Work Capacity Balance Module is used to establish a Local Work Capacity Balance Model for an integrated energy system, which measures the difference in work capacity between any two nodes.

[0028] The multi-objective extended programming module is used to establish a multi-objective extended programming model for the integrated energy system, taking the selection of power lines, natural gas pipelines, and heating pipelines, the capacity and hourly output of energy equipment as decision variables; minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance as optimization objective functions; and using the upper and lower limits of transmission of each pipeline, the input and output of energy equipment, and the balance of the integrated energy system as constraints.

[0029] The solver module is used to solve the multi-objective extended programming model using the NSGA-II algorithm to obtain the optimal set of integrated energy system planning schemes.

[0030] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the multi-objective extended planning method for a comprehensive energy system that takes into account the differences in local working capacity.

[0031] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-objective extended planning method for a comprehensive energy system that takes into account differences in local work capacity.

[0032] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows:

[0033] 1. The energy quality requirements of users are considered in the planning stage: The essence of energy use is utilizing energy quality, that is, utilizing energy's work capacity. In the planning model of traditional integrated energy systems, the optimal system configuration and structure that can meet the load's energy "quantity" requirements within the target planning period is sought, often neglecting users' requirements for energy "quality". Therefore, this invention introduces the annual work capacity (TWC) index in the planning stage to measure the total amount of energy quality (TWC) provided by the integrated energy system to users; using it as the objective function can ensure that energy quality is fully utilized while meeting the load quantity requirements, avoiding the illusion and waste of "high quantity, low quality".

[0034] 2. The planning phase considers the differences in work capacity among users: Energy inevitably suffers losses during transmission, including not only losses in quantity but also losses in quality, also known as work capacity losses. Traditional integrated energy system planning models have not yet accounted for this factor. Therefore, this invention provides an index to measure the differences in work capacity among users in an integrated energy system, namely, local work capacity balance, which can be used as one of the objective functions of the integrated energy system planning model. By quantifying the work capacity differences between any two nodes through the local work capacity balance model and incorporating the minimization of these differences into the planning objective, it can effectively reduce the imbalance in quality losses caused by pipeline selection, network layout, etc., ensuring that users receive a relatively fair level of work capacity.

[0035] 3. Supporting the efficient and high-quality development of integrated energy systems: This invention constructs a multi-objective extended planning model that includes cost, work capacity, and equilibrium, and uses the NSGA-II algorithm to obtain the Pareto optimal solution set, providing alternative options for different decision preferences. Against the backdrop of the development and utilization of a large amount of renewable energy such as green electricity, green hydrogen, and geothermal energy, the integrated energy system planning approach provided by this invention can eliminate the unfairness of work capacity as much as possible during the planning stage, achieving a balance between cost and quality, and providing a reliable topological foundation for the operation, scheduling, and trading of integrated energy systems with a high proportion of new energy access.

[0036] In summary, the multi-objective extended planning method for integrated energy systems that takes into account the differences in local work capacity provided by this invention not only ensures that the integrated energy system planning scheme has both energy quantity and energy quality, but also guarantees the fairness of users' access to work capacity. It provides important technical support for the future development of energy systems and has significant economic and social benefits. Attached Figure Description

[0037] Figure 1 A flowchart of a multi-objective extended planning method for a comprehensive energy system that takes into account the differences in local functional capabilities.

[0038] Figure 2This is a comprehensive energy system topology diagram for an example.

[0039] Figure 3 The multi-energy load curves for a typical day in a base year of the integrated energy system used as an example.

[0040] Figure 4 The Pareto solution set of the planning results for the example.

[0041] Figure 5 A work capacity density curve is plotted for the integrated energy system in the example. Detailed Implementation

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0043] Example 1

[0044] This embodiment provides a multi-objective extended planning method for a comprehensive energy system that takes into account differences in local work capacity. The calculation process is as follows: Figure 1 As shown, it includes:

[0045] S1. Define the working capacity of an integrated energy system as the sum of the energy supplied to all loads when the integrated energy system meets safety criteria, and establish an annual working capacity model for the integrated energy system.

[0046] Total work capability (TWC) refers to the sum of the work capacity supplied to all loads when the integrated energy system meets safety criteria. It characterizes the work capacity available to all users within the IES. The TWC at time t can be expressed as:

[0047] (1);

[0048] in, Let be the i-th electrical load (active power) at time t, in kW; Let J be the j-th natural gas load at time t, in kW; Let k be the kth thermal load at time t, in kW; , and These are the number of load nodes for the power, natural gas, and heating systems, respectively. The annual energy work capacity of a comprehensive energy system, expressed in kWh; This indicates the type of typical day, including transitional seasons, summer, and winter; This represents the number of days corresponding to a typical day in a year.

[0049] S2. Establish a local work capacity balance model for the integrated energy system to measure the difference in work capacity between any two nodes.

[0050] For any two nodes with the same energy demand, differences in pipeline type, node coordinates, ambient temperature, etc., may lead to different work capacities. This phenomenon is defined as the local work capacity difference in an integrated energy system. However, in reality, the energy demands of any two nodes are not necessarily the same; therefore, simple difference calculations cannot measure the work capacity difference between two nodes. A local work capacity balance degree is proposed to characterize these differences. The work capacity balance degree of any two nodes in an electricity, natural gas, and heat system can be expressed as:

[0051] (2);

[0052] in, The local functional balance of nodes i and n in the power system is represented; t represents the current time, and the three typical days total 72 hours. This represents the local work capacity equilibrium of nodes j and o in the natural gas system. This represents the local work-force equilibrium degree at nodes k and m in a thermal system; , Let be the work density, i.e., the power factor, of nodes i and n in the power system at time t, respectively. , Let be the work density of nodes j and o in the natural gas system at time t; , Let t be the work density of nodes k and m of the thermodynamic system at time t; for multi-node (greater than or equal to 3 nodes) local work capacity balance calculation, equation (2) can be expressed in variance form.

[0053] Work density reflects the proportion of work capacity available to the load (user) at the current moment, also known as the energy quality coefficient, and is calculated as follows:

[0054] (3);

[0055] in, , and Let be the apparent power of node i in the power system, the power of node j in the natural gas system, and the thermal power of node k in the thermal system at time t, respectively.

[0056] S3. Using the selection of power lines, natural gas pipelines, and heating pipelines, the capacity and hourly output of energy equipment as decision variables; minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance as optimization objective functions; and using the upper and lower limits of transmission of each pipeline, the input and output of energy equipment, and the system balance as constraints; a multi-objective extended programming model for a comprehensive energy system is established.

[0057] 301. The optimization objective functions are minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance, which are expressed as follows:

[0058] (4);

[0059] In the formula, Represents the equivalent annual total cost. To determine the annual work capacity of the integrated energy system, the objective function in this embodiment is... Taking the local functional equilibrium of a thermal system as an example, the corresponding values ​​for the electrical, gas, and thermal systems can be selected according to the needs of the decision-maker.

[0060] 302. Constraints include pipeline selection constraints; the selection of power lines, natural gas pipelines, and heating pipeline extensions is subject to the following limitations:

[0061] (5);

[0062] In the formula, u, v, and w are the pipeline numbers for the power, natural gas, and heating systems, respectively; The model for expanding the capacity of the uth power line; The model number for the expansion of the vth natural gas pipeline; The model for expanding the capacity of the wth heating pipeline; This is the initial model number for the u-th power line; This is the initial model number for the v-th natural gas pipeline; This is the initial model number for the w-th heating pipe; , and These represent the number of optional power lines, natural gas pipelines, and heating pipelines.

[0063] Based on current advancements in hydrogen doping technology and real-world engineering cases, the hydrogen doping ratio is subject to the following constraints:

[0064] (5);

[0065] In the formula, and These represent the maximum and minimum hydrogen doping ratios, respectively. It's important to note that the hydrogen doping ratio refers to the volume ratio of the gas.

[0066] In addition, constraints include energy pipeline capacity constraints, energy equipment constraints, current balance constraints, and operating parameter constraints, which will not be elaborated here.

[0067] S4. Use the NSGA-II algorithm to solve the above extended programming model and obtain a set of optimal system planning schemes.

[0068] Example 2

[0069] This embodiment supplements the above-mentioned multi-objective extended planning method for integrated energy systems that takes into account the differences in local work capacity, using specific applications and data, as follows:

[0070] The multi-objective extended planning method for integrated energy systems that takes into account the differences in local work capacity proposed in this embodiment is applied to an embodiment of an integrated energy system with 4 nodes for electricity, 5 nodes for natural gas, and 3 nodes for heat, to carry out extended planning of the integrated energy system and verify the effectiveness of the invention.

[0071] The integrated energy system topology used in this embodiment is as follows: Figure 2 As shown, the system includes a 4-node power system, a 5-node natural gas system, and a 3-node heating system. The 4-node radial power distribution system has a 10kV voltage at power source node E1 and a power factor of 0.95 at all nodes. The 5-node annular low-pressure gas distribution system has a gas pressure of 75mbar at gas source node G1. The 3-node annular heating (heat distribution) system has a maximum supply / return water temperature of 100 / 50℃. Figure 2 As shown, the equipment within the energy station includes CHP, GB, and EB, and a circulating water pump CP is installed at node H3 (heat source). Pipeline parameters for the baseline year are shown in Tables 1-3. It is planned to connect distributed photovoltaic (PV) at node E3, employing a hydrogen blending process without auxiliary mixing devices, directly supplying distributed hydrogen blending (DHI) from node G5 to the natural gas pipeline network.

[0072] Table 1 Power System Line Parameters (Base Year)

[0073]

[0074] Table 2 Natural Gas Pipeline Parameters (Base Year)

[0075]

[0076] Table 3 Parameters of Thermal Pipelines (Base Year)

[0077]

[0078] Three typical day types were set: transition season, summer, and winter, with 145, 100, and 120 days respectively. Load data for each node on these three typical days in the base year are as follows: Figure 3As shown, the annual growth rates for electricity / gas / heat loads are set at 6%, 9.2%, and 8.8%, respectively. The models and parameters of the candidate pipelines are shown in Tables 4-6, the equipment parameters for the to-be-capacitated equipment are shown in Table 7, and the prices of electricity and natural gas, carbon tax, equipment discount rates, and standard coal equivalent coefficients are shown in Table 8. The hydrogen injection ratio is set between 5% and 40%, and low-carbon hydrogen is selected, with a unit carbon emission coefficient of 1.2955 kg·CO2 / m³. 3 The price of hydrogen is $0.2432 / m³. 3 The calorific values ​​of natural gas and hydrogen are 45.574 MJ / m³. 3 and 12.750 MJ / m 3 The relative densities are 0.6048 and 0.0696, respectively.

[0079] Table 4 Parameters of the Selected Power Lines

[0080]

[0081] Table 5 Parameters of the Selected Natural Gas Pipelines

[0082]

[0083] Table 6 Parameters of the Selected Thermal Pipelines

[0084]

[0085] Table 7 Parameters of the device to be expanded

[0086]

[0087] Table 8 Planning Parameters

[0088]

[0089] For the aforementioned multi-objective programming model, the NSGA-II algorithm is used to obtain the Pareto front solution set, and the fuzzy VIKOR method is employed as a multi-objective decision-making approach to obtain a reasonable IES extended programming scheme. Taking nodes H1 and H2 of a thermal system as an example, the necessity of studying the differences in local work capacity and methods for improvement are first discussed, such as... Figure 4 As shown, we will discuss the Pareto solution set of the selected planning scheme.

[0090] by Figure 4 Taking the 9th moment of winter in Scheme A as an example, the load power of nodes H1 and H2 in the target year is 4648.60kW, and their work capacity densities are respectively and The power density difference is 0.01029, and the difference in work capacity between the two nodes is approximately 0.48 kW. Based on this calculation, the difference in work capacity between nodes H1 and H2 throughout the year will reach 4204.8 kWh, which is unfair to users of node H2. (It is worth noting that the load in this example only represents ordinary residential users; if large industrial users were considered, the difference in work capacity throughout the year would be even more significant.)

[0091] To address the above issues, the following discussion will cover how to reasonably reduce the difference in the performance capabilities of nodes H1 and H2. Based on... Can Figure 4 The Pareto solution set is divided into 7 categories, each corresponding to a thermal pipeline selection scheme, as shown in Table 9. Different pipeline selection schemes lead to different differences in work capacity. When the pipeline between nodes H1 and H2 is selected with the DN-150-2 type pipeline with the smallest heat transfer coefficient, the difference in work capacity between the two is the smallest. This is because the heat transfer coefficient determines the degree of temperature drop. The DN-150-2 type pipeline has good thermal insulation performance, which greatly reduces the temperature difference between nodes H1 and H2, and thus reduces the difference in work capacity between the two.

[0092] Therefore, by minimizing the local work capacity balance as the optimization objective, the difference in work capacity between nodes can be significantly reduced, making the process more equitable.

[0093] Table 9 Selection Scheme for Thermal Pipelines

[0094]

[0095] Select Figure 4 The schemes maximizing annual work capacity (Scheme A), minimizing local work capacity equilibrium of the thermodynamic system (Scheme B), and the optimal scheme (Scheme C) are compared. The work capacity density curves for the 72-hour intervals between nodes H1 and H2 in the three schemes are shown below. Figure 5 As shown, scheme A satisfies the maximum However, the mean difference in work capacity density between nodes H1 and H2 reached 0.0288; in scheme B, the mean difference in work capacity density reached the smallest 0.0033, but its This reduces the energy consumption by 6975.31 kWh compared to Option A. Option C, however, ensures... At the same time, the difference in performance between nodes H1 and H2 was minimized as much as possible, ensuring both the absolute value of performance obtained by users and the fairness of energy use for users.

[0096] The overall work capacity and the difference in work capacity of a thermal system are mutually exclusive. Two nodes may have high overall work capacity but large differences in work capacity; conversely, two nodes may have low overall work capacity but small differences in work capacity. Considering only the work capacity equilibrium will cause a decrease in the overall work capacity of H1 and H2; therefore, both work capacity and the difference in work capacity must be considered simultaneously. And to achieve functional balance .

[0097] In summary, for users with the same functional requirements, the planning method of this invention can avoid differences in functional capacity caused by improper pipeline selection during the planning stage, and can reduce the investment in compensating equipment such as boilers and hot water pumps required to improve functional capacity during the operation stage.

[0098] Example 3

[0099] Preferably, embodiments of this application also provide a multi-objective extended planning device for a comprehensive energy system that takes into account differences in local work capacity, comprising:

[0100] The annual working capacity module is used to define the working capacity of an integrated energy system as the sum of the energy supplied to all loads when the integrated energy system meets safety criteria, and to construct the annual working capacity model of the integrated energy system.

[0101] The Local Work Capacity Balance Module is used to establish a Local Work Capacity Balance Model for an integrated energy system, which measures the difference in work capacity between any two nodes.

[0102] The multi-objective extended programming module is used to establish a multi-objective extended programming model for the integrated energy system, taking the selection of power lines, natural gas pipelines, and heating pipelines, the capacity and hourly output of energy equipment as decision variables; minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance as optimization objective functions; and using the upper and lower limits of transmission of each pipeline, the input and output of energy equipment, and the balance of the integrated energy system as constraints.

[0103] The solver module is used to solve the multi-objective extended programming model using the NSGA-II algorithm to obtain the optimal set of integrated energy system planning schemes.

[0104] Example 4

[0105] Preferably, embodiments of this application also provide a specific implementation of an electronic device capable of implementing all steps in the multi-objective extended planning method for a comprehensive energy system that takes into account differences in local work capacity as described in the above embodiments. The electronic device specifically includes the following:

[0106] Processor, memory, communications interface, and bus;

[0107] The processor, memory, and communication interface communicate with each other via a bus; the communication interface is used to realize information transmission between server-side devices, metering devices, and user-side devices.

[0108] The processor is used to call the computer program in the memory. When the processor executes the computer program, it implements all the steps in the multi-objective extended planning method for integrated energy systems that takes into account the differences in local work capacity in the above embodiments.

[0109] Example 5

[0110] Preferably, embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the multi-objective extended planning method for integrated energy systems that takes into account differences in local work capacity as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the multi-objective extended planning method for integrated energy systems that takes into account differences in local work capacity as described in the above embodiments.

[0111] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0112] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0113] While this application provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the method can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0117] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0118] This invention is not limited to the embodiments described above. The above description of specific embodiments is intended to illustrate and explain the technical solutions of this invention. The specific embodiments described above are merely illustrative and not restrictive. Without departing from the spirit and scope of the claims, those skilled in the art can make many specific modifications based on the teachings of this invention, and these modifications all fall within the scope of protection of this invention.

Claims

1. A multi-objective extended planning method for a comprehensive energy system that considers differences in local work capacity, characterized in that, include: S1. Define the working capacity of an integrated energy system as the sum of the energy supplied to all loads when the integrated energy system meets the safety criteria, and construct an annual working capacity model for the integrated energy system. S2. Establish a local work capacity equilibrium model for the integrated energy system to measure the difference in work capacity between any two nodes; S3. Using the selection of power lines, natural gas pipelines, and heating pipelines, the capacity and hourly output of energy equipment as decision variables; minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance as optimization objective functions; and using the transmission upper and lower limits of each pipeline, the input and output of energy equipment, and the overall energy system balance as constraints; a multi-objective extended programming model for the integrated energy system is established. S4. The NSGA-II algorithm is used to solve the above multi-objective extended programming model to obtain the optimal integrated energy system planning scheme set.

2. The multi-objective extended planning method for a comprehensive energy system considering local differences in work capacity as described in claim 1, characterized in that, In step S1, TWC refers to the sum of the energy supplied to all loads when the integrated energy system meets safety criteria. It characterizes the work capacity obtained by all users within the integrated energy system (IES). The TWC at time t is expressed as: ; (1) in, Let be the i-th electrical load 㶲 at time t. In the power system, load 㶲 is equivalent to active power, with the unit being kW. Let J be the j-th natural gas load at time t, in kW; Let k be the kth thermal load at time t, in kW; , and These are the number of load nodes in the power system, natural gas system, and heating system, respectively. The annual energy work capacity of a comprehensive energy system, expressed in kWh; This indicates the type of typical day, including transitional seasons, summer, and winter; This represents the number of days corresponding to a typical day in a year.

3. The multi-objective extended planning method for a comprehensive energy system considering local differences in work capacity as described in claim 1, characterized in that, In step S2, For any two nodes with the same energy demand, differences in pipeline type, node coordinates, and ambient temperature can lead to different work capacities. This phenomenon is defined as the local work capacity difference in the integrated energy system. The local work capacity balance is constructed to characterize the difference between any two nodes. The work capacity balance of any two nodes in the power system, natural gas system, and heating system is expressed as: ; (2) in, The local functional balance of nodes i and n in the power system is represented; t represents the current time, and the three typical days total 72 hours. This represents the local work capacity equilibrium of nodes j and o in the natural gas system. This represents the local work-force equilibrium degree at nodes k and m in a thermal system; , Let be the work density, i.e., the power factor, of nodes i and n in the power system at time t, respectively. , Let be the work density of nodes j and o in the natural gas system at time t; , Let be the work density of nodes k and m of the thermodynamic system at time t; for local work-capacity equilibrium calculations for more than or equal to 3 nodes, equation (2) can be expressed in variance form; Work density reflects the proportion of work capacity available to the load (i.e., the user) at the current moment, also known as the energy quality coefficient, and is calculated as follows: ; (3) in, , and Let be the apparent power of node i in the power system, the power of node j in the natural gas system, and the thermal power of node k in the thermal system at time t, respectively. Let be the i-th electrical load at time t, which is also the active power, in kW; Let be the load of the j-th natural gas at time t, in kW; Let k be the kth thermal load at time t, in kW.

4. The multi-objective extended planning method for a comprehensive energy system considering local differences in work capacity as described in claim 1, characterized in that, In step S3, the optimization objective functions, namely minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance, are expressed as follows: ; (4) In the formula, Represents the equivalent annual total cost. For the annual work capacity of the integrated energy system, x = e, g, or h. This indicates the local functional balance of the power system. This indicates the local functional balance of the natural gas system. It represents the local work capacity equilibrium of a thermal system.

5. A multi-objective extended planning device for a comprehensive energy system that takes into account differences in local work capacity, characterized in that, include: The annual working capacity module is used to define the working capacity of an integrated energy system as the sum of the energy supplied to all loads when the integrated energy system meets safety criteria, and to construct the annual working capacity model of the integrated energy system. The Local Work Capacity Balance Module is used to establish a Local Work Capacity Balance Model for an integrated energy system, which measures the difference in work capacity between any two nodes. The multi-objective extended programming module is used to establish a multi-objective extended programming model for the integrated energy system, taking the selection of power lines, natural gas pipelines, and heating pipelines, the capacity and hourly output of energy equipment as decision variables; minimizing the equivalent annual total cost, maximizing the annual work capacity, and minimizing the local work capacity balance as optimization objective functions; and using the upper and lower limits of transmission of each pipeline, the input and output of energy equipment, and the balance of the integrated energy system as constraints. The solution module is used to solve the multi-objective extended programming model using the NSGA-II algorithm to obtain the optimal set of integrated energy system planning schemes.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the multi-objective extended planning method for a comprehensive energy system that takes into account the differences in local work capacity as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-objective extended planning method for a comprehensive energy system that takes into account the differences in local work capacity as described in any one of claims 1 to 4.

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