A multi-microgrid integrated energy system planning method and device

Abstract

This application provides a planning method and apparatus for a multi-microgrid integrated energy system, comprising: acquiring basic data of the multi-microgrid integrated energy system; constructing a two-level planning model of the multi-microgrid integrated energy system based on the basic data; the lower-level planning model in the two-level planning model is established with the goal of minimizing annual operating costs; the annual operating costs include: annual electricity purchase cost, annual gas purchase cost, electricity interaction cost between microgrid clusters, and heat interaction cost between microgrid clusters; setting operating constraints; and solving the two-level planning model based on the operating constraints to obtain the optimal planning result of the multi-microgrid integrated energy system. Since the lower-level planning model considers the electricity interaction cost and heat interaction cost between microgrid clusters, that is, the heat and electricity interaction between multiple microgrid clusters is considered during the planning process, the planning result obtained based on the upper-level planning model is closer to the actual situation, improving the reliability and accuracy of the planning result.

Description

Technical Field

[0001] This application relates to the field of integrated energy technology, and in particular to a planning method and apparatus for a multi-microgrid integrated energy system. Background Technology

[0002] An Integrated Energy System (IES), centered on electricity and featuring multi-energy coupling and interconnected networks, achieves horizontal coupling of various energy forms such as cooling, heating, electricity, and gas, and vertical connectivity across multiple stages of energy production, conversion, transmission, and consumption. It breaks away from the existing independent power supply models of electricity, heat, and gas, representing a new energy development and utilization model that improves overall energy efficiency. A multi-microgrid system refers to a system where multiple microgrids coordinate their power output, providing auxiliary services and security support to the power grid in emergency situations.

[0003] To better utilize new energy sources, additional energy forms are incorporated into multi-microgrid systems to reduce resource waste and reliability issues, resulting in multi-microgrid integrated energy systems. The planning and research of multi-microgrid integrated energy systems are crucial for the safe and stable operation of the entire integrated energy system. Current common methods for configuring multi-microgrid integrated energy systems involve constructing models for each individual microgrid cluster within the system, solving each individual microgrid integrated energy system model separately, and obtaining the planning results.

[0004] Therefore, current integrated energy system configuration methods only consider independent microgrid clusters in multi-microgrid integrated energy systems, without considering the impact of energy interaction between microgrid clusters on system planning. As a result, the planning results of multi-microgrid integrated energy systems deviate significantly from actual operation, making them difficult to apply in practical engineering. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a planning method for multi-microgrid integrated energy systems. By fully considering the energy interactions between microgrid clusters within the multi-microgrid integrated energy system, the planning results of the multi-microgrid integrated energy system show a smaller deviation from actual operation, thus improving the reliability and accuracy of the planning results.

[0006] The embodiments of this application disclose the following technical solutions:

[0007] In a first aspect, a planning method for a multi-microgrid integrated energy system is provided, characterized in that the method includes:

[0008] Acquire basic data for multi-microgrid integrated energy systems;

[0009] Based on the fundamental data of the multi-microgrid integrated energy system, a two-level planning model for the multi-microgrid integrated energy system is constructed. The two-level planning model includes an upper-level planning model and a lower-level planning model. The upper-level planning model is established with the objective of minimizing the sum of investment, operation, and maintenance costs over the entire life cycle of the multi-microgrid integrated energy system. The lower-level objective function is established with the objective of minimizing the annual operating cost of the multi-microgrid integrated energy system. The annual operating cost of the multi-microgrid integrated energy system includes: annual electricity purchase cost, annual gas purchase cost, electrical energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system, and thermal energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system.

[0010] Set the operational constraints for the multi-microgrid integrated energy system;

[0011] Based on the aforementioned operational constraints, the two-level programming model is solved to obtain the optimal planning result of the multi-microgrid integrated energy system.

[0012] Optionally, the objective function of the upper-level planning model is the annualized total cost of the multi-microgrid integrated energy system;

[0013] The annualized total cost of the multi-microgrid integrated energy system includes: the annual equivalent investment cost of the equipment in the multi-microgrid integrated energy system, the annual maintenance cost of the equipment in the multi-microgrid integrated energy system, and the annual operating cost of the equipment in the multi-microgrid integrated energy system.

[0014] Optionally, the annual equivalent investment cost of the equipment in the multi-microgrid integrated energy system is specifically expressed by the following formula:

[0015]

[0016] Among them, T inv The annual equivalent investment cost of the equipment in the multi-microgrid integrated energy system is given by , l is the residual value rate of fixed assets, W is the total number of microgrid clusters in the multi-microgrid integrated energy system, m is the equipment number, and φ represents the energy conversion equipment. Indicates energy storage device; y w,φ For the lifecycle of the w-th microgrid and the φ-th energy conversion device, For the wth micro-network The lifecycle of an energy storage device, Q w,φ Let φ be the installed capacity of the energy conversion device in the w-th microgrid; For the wth micro-network The installed capacity of an energy storage device, c w,φ The unit capacity installation cost of the energy conversion device of the w-th microgrid is given. For the wth micro-network The unit capacity installation cost of an energy storage device, l h c represents the total length of heat transfer pipelines that need to be installed between microgrid clusters in a multi-microgrid integrated energy system. h Cost per unit length of heat transfer pipe.

[0017] Optionally, the annual maintenance cost of the equipment in the multi-microgrid integrated energy system is specifically expressed by the following formula:

[0018]

[0019] Where O represents the unit capacity maintenance cost vector for the corresponding equipment, D represents the total number of days in a year, and π(s) represents the probability of a typical day s occurring. Let be the thermal power output of the electric boiler in the w-th microgrid during time period t on the s-th typical day. Let be the thermal power output of the gas-fired boiler in the w-th microgrid during time period t on the s-th typical day. Let be the cooling power output of the electric chiller of the w-th microgrid during time period t on the s-th typical day. Let be the cooling power output of the absorption chiller of the w-th microgrid during time period t on the s-th typical day. Let be the power generation of the wind power system of the w-th microgrid during time period t on the s-th typical day. Let be the power generation of the photovoltaic power generation system of the w-th microgrid during time period t on the s-th typical day. Let be the natural gas input of the cogeneration system of the w-th microgrid during time period t on the s-th typical day. Let be the electrical power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day. Let be the charging power of the energy storage device of the w-th microgrid during time period t on the s-th typical day. Let be the discharge power of the energy storage device of the w-th microgrid during time period t on the s-th typical day. Let be the cooling power of the cooling storage device of the w-th microgrid during time period t on the s-th typical day. Let be the cooling power of the cold storage device of the w-th microgrid during time period t on the s-th typical day. Let be the thermal storage power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day. Let be the heat release power of the thermal storage device of the w-th microgrid during the t-th period on the s-th typical day.

[0020] Optionally, the annual gas purchase cost is specifically expressed by the following formula:

[0021]

[0022] Among them, T gasbuyLet be the annual gas purchase cost; D be the total number of days in a year; S be the total number of typical days; and π(s) be the probability of typical day s occurring. cgasbuy (s) represents the purchase price of natural gas on the s-th typical day, W represents the total number of microgrid clusters in the multi-microgrid integrated energy system, and T represents the total number of time periods in a day. Let η be the output electrical power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day. CHP For the gas-to-electric conversion efficiency of a combined heat and power unit, L Λ The calorific value coefficient of natural gas, Let η be the output thermal power of the gas-fired boiler of the w-th microgrid during time period t on the s-th typical day. GB The heat conversion efficiency of the gas-fired boiler is given by Δt, which is the unit time step.

[0023] Optionally, the annual electricity purchase cost is specifically expressed by the following formula:

[0024]

[0025] Among them, T elebuy Let be the annual electricity purchase cost, D be the total number of days in a year, S be the total number of typical days, π(s) be the probability of typical day s occurring, W be the total number of microgrid clusters in the multi-microgrid integrated energy system, and T be the total number of time periods in a day. Let be the electricity purchase price that the microgrid pays to the main grid at time t on the s-th typical day. Let Δt be the power purchased from the main grid by the energy storage device of the w-th microgrid during time period t on the s-th typical day, where Δt is the unit time step.

[0026] Optionally, the power exchange cost between microgrid clusters in the multi-microgrid integrated energy system is specifically expressed by the following formula:

[0027]

[0028] Among them, T Eex Let D be the total number of days in a year, S be the total number of typical days, π(s) be the probability of typical day s occurring, W be the total number of microgrid clusters in the multi-microgrid integrated energy system, T be the total number of time periods in a day, and c be the total number of microgrid clusters in a day. exbuy For the electricity purchase price of the microgrid clusters in a multi-microgrid integrated energy system, c exsell P represents the electricity price for power exchange between microgrid clusters in a multi-microgrid integrated energy system. w,v,s (t) represents the electrical power transmitted from microgrid w to microgrid v during time period t on the s-th typical day, where Δt is the unit time step.

[0029] Optionally, the thermal energy interaction cost between the multi-microgrid clusters in the multi-microgrid integrated energy system is specifically expressed by the following formula:

[0030]

[0031] Among them, T Hex Let μ be the thermal energy interaction cost between microgrid clusters, D be the total number of days in a year, S be the total number of typical days, π(s) be the probability of typical day s occurring, T be the total number of time periods in a day, and H be the total number of time periods in a day. w,v,s (t) represents the thermal energy power transferred from microgrid w to microgrid v during time period t on the s-th typical day.

[0032] Optionally, the step of solving the two-level programming model based on the operational constraints to obtain the optimal planning result of the multi-microgrid integrated energy system includes:

[0033] Construct the Lagrangian function of the lower-level planning model;

[0034] Based on the constructed Lagrangian function, the operational constraints of the lower-level programming model are transformed into additional constraints of the upper-level programming model through complementary relaxation conditions, resulting in a single-level nonlinear programming model.

[0035] Based on the single-layer nonlinear programming model, the nonlinear operating constraints are linearized to obtain the objective programming model; the objective programming model is a single-layer mixed-integer linear programming model.

[0036] The optimal planning result of the multi-microgrid integrated energy system is obtained by calculating the target planning model.

[0037] Secondly, this application provides a multi-microgrid integrated energy system planning device, characterized in that the device comprises:

[0038] The data acquisition module is used to acquire basic data of the multi-microgrid integrated energy system;

[0039] The model building module is used to construct a two-level planning model for the multi-microgrid integrated energy system based on the basic data of the multi-microgrid integrated energy system. The two-level planning model includes an upper-level planning model and a lower-level planning model. The upper-level planning model is established with the objective of minimizing the sum of investment, operation, and maintenance costs over the entire life cycle of the multi-microgrid integrated energy system. The lower-level objective function is established with the objective of minimizing the annual operating cost of the multi-microgrid integrated energy system. The annual operating cost of the multi-microgrid integrated energy system includes: annual electricity purchase cost, annual gas purchase cost, electrical energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system, and thermal energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system.

[0040] The condition setting module is used to set the operating constraints of the multi-microgrid integrated energy system;

[0041] The planning results module is used to solve the two-level planning model based on the operational constraints to obtain the optimal planning results of the multi-microgrid integrated energy system.

[0042] Compared to existing technologies, this application has the following advantages: The objective function of the lower-level planning model incorporates the electrical and thermal interaction costs between microgrid clusters. That is, when considering the processing problem of a multi-microgrid integrated energy system, it considers not only the configuration of each individual microgrid cluster but also the thermal and electrical interactions between multiple microgrid clusters. Based on the embodiments of this application, the two-level planning model yields a final planning result that is closer to the actual situation, improving the reliability and accuracy of the planning results. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating a multi-microgrid integrated energy system planning method provided in this application embodiment;

[0045] Figure 2 This is a flowchart illustrating a process for solving a bi-level programming model based on runtime constraints to obtain the optimal programming result, as provided in an embodiment of this application.

[0046] Figure 3 This is a schematic diagram of the structure of a multi-microgrid integrated energy system planning device provided in an embodiment of this application. Detailed Implementation

[0047] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0048] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0049] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0050] An Integrated Energy System (IES) is an integrated energy supply system that addresses the diverse energy needs of end users, including electricity, heat, cooling, and gas, by using methods such as combined heat, power, and cooling systems (CHP) based on natural gas. This system achieves multi-energy coordinated supply and comprehensive cascade utilization of energy.

[0051] Electricity storage (ES) is an important component of integrated energy systems. The basic working principle of ES is to store excess electricity when renewable energy generation exceeds demand, and to release the stored electricity when renewable energy generation falls short of load demand.

[0052] Heat storage (HS) equipment works on the principle of storing excess heat when the heat production capacity of the heat-generating equipment exceeds the heat demand, and releasing the stored heat when the heat demand is insufficient.

[0053] Cold storage (CS) equipment works on the principle of storing excess cold energy when the cooling capacity of the cooling equipment exceeds the cooling demand, and releasing the stored cold energy when the supply of cold energy is insufficient.

[0054] Combined heating and power (CHP) is an energy-saving technology that generates electricity and uses heat that would otherwise be wasted to provide useful thermal energy (such as steam or hot water), which can be used for space heating, cooling, industrial processes, etc.

[0055] Gas boilers (GB) generate a large amount of heat energy by burning natural gas, and the generated heat energy supplies electricity to users' heat load, realizing the coupling between gas and heat in an integrated energy system.

[0056] Electric chillers (EB) can convert electrical energy into cooling energy under permissible operating conditions.

[0057] Absorption chillers are key equipment for achieving heat exchange and are also thermal coupling elements, playing an important role in the thermal-electric coupling of the entire system.

[0058] The advantages of the multi-microgrid integrated energy system planning method provided in this application will be briefly introduced below, based on existing multi-microgrid integrated energy system planning methods.

[0059] As described earlier, current multi-microgrid integrated energy system planning methods involve constructing models for each individual microgrid cluster within the system, solving these models separately, and deriving planning results. However, because energy interactions exist between microgrid clusters within a multi-microgrid integrated energy system, these interactions affect the overall system's planning and operation, thus influencing the planning results. For example, in a multi-microgrid integrated energy system, the interaction of electrical and thermal energy between microgrid clusters can impact the planning and operation of the entire system, particularly causing deviations in the planning results for combined heat and power (CHP) units. Therefore, current multi-microgrid integrated energy system planning methods often result in significant discrepancies between the planning results and actual operation, indicating low reliability and accuracy, making them difficult to apply in practical engineering projects.

[0060] Furthermore, in current multi-microgrid integrated energy system planning methods, the differences between different energy flows, supply and demand sides, different structures, different capacities, and different operating states make modeling and optimization of integrated energy systems extremely difficult. Moreover, integrated energy system models are generally nonlinear models, and the process of converting nonlinear models to linear models is also very complex. Therefore, current technologies typically simplify the modeling of integrated energy systems to meet linearization requirements, resulting in overly simplistic integrated energy system models. The planning results obtained based on these models are inaccurate and cannot meet the complex realities of actual situations, making them difficult to apply in practical engineering.

[0061] Based on this, this application provides a planning method for a multi-microgrid integrated energy system. The method includes: acquiring basic data of the multi-microgrid integrated energy system; constructing a two-level planning model for the multi-microgrid integrated energy system based on the acquired basic data; the upper-level planning model is established with the objective of minimizing the sum of investment, operation, and maintenance costs over the entire life cycle of the multi-microgrid integrated energy system; the lower-level planning model is established with the objective of minimizing the annual operating cost of the multi-microgrid integrated energy system; setting operating constraints for the multi-microgrid integrated energy system; and solving the two-level planning model based on the operating constraints to obtain the optimal planning result for the multi-microgrid integrated energy system. In this application, the objective function of the lower-level planning model includes the electrical and thermal interaction costs between microgrid clusters. That is, when considering the processing problem of the multi-microgrid integrated energy system, not only the configuration problem of each individual microgrid cluster is considered, but also the thermal and electrical interaction between multiple microgrid clusters. The two-level planning model constructed based on the embodiments of this application yields a final planning result that is closer to the actual situation, improving the reliability and accuracy of the planning result.

[0062] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0063] Example 1:

[0064] The following is combined Figure 1 This application provides a detailed description of a multi-microgrid integrated energy system planning method based on embodiments thereof. Figure 1 A flowchart of a multi-microgrid integrated energy system planning method provided in this application embodiment.

[0065] S101, the planning device acquires basic data of the multi-microgrid integrated energy system.

[0066] Specifically, the basic data of a multi-microgrid integrated energy system includes: the types of equipment constituting the multi-microgrid integrated energy system, the electrical output of the cogeneration generator set and the heat output of the gas boiler, the power generation efficiency of the cogeneration generator set and the thermal efficiency of the gas boiler, the rated power generation of the cogeneration generator set, the grid interaction power, the output power of renewable energy, the system's heat load, and the system's cooling load.

[0067] Specifically, the core components of a combined heat and power (CHP) generator unit are a bromine-based refrigeration unit and a micro gas turbine, which can generate electricity and heat by burning natural gas.

[0068] Gas boilers (GB) refer to boilers that use natural gas as fuel, such as gas-fired hot water boilers, gas-fired steam boilers, etc. In multi-microgrid integrated energy systems, a large amount of heat energy is generated by burning natural gas, and this heat energy is used to supply heat to users' heat loads, thus achieving coupling between gas and heat in the integrated energy system.

[0069] Renewable energy sources include wind and solar power. Specifically, in a multi-microgrid integrated energy system, renewable energy generation includes wind turbine (WT) and photovoltaic (PV) power generation.

[0070] S102. Based on the acquired basic data of the multi-microgrid integrated energy system, the planning device constructs a two-layer planning model for the multi-microgrid integrated energy system.

[0071] Specifically, the two-level programming model includes an upper-level programming model and a lower-level programming model.

[0072] The upper-level planning model is established with the goal of minimizing the sum of investment, operation, and maintenance costs throughout the entire life cycle of the multi-microgrid integrated energy system. This is to address the coordinated configuration issues of the multi-microgrid integrated energy system, such as the types of equipment required in the system, the capacity of each device, and the quantity of each device.

[0073] Furthermore, the decision variables in the upper-level planning model are the model number and quantity of each piece of equipment. The objective function of the upper-level planning model is the annualized total cost of the multi-microgrid integrated energy system.

[0074] Specifically, the annualized total cost of a multi-microgrid integrated energy system includes: the annual equivalent investment cost of the equipment in the multi-microgrid integrated energy system, the annual maintenance cost of the equipment in the multi-microgrid integrated energy system, and the annual operating cost of the equipment in the multi-microgrid integrated energy system.

[0075] In this embodiment of the application, the initial equipment investment cost is incurred at the beginning of the first year of each planning stage, and the settlement period is at the end of each year. Therefore, the objective function of the upper-level planning model is:

[0076] minT total =T inv +T main +T ope

[0077] Among them, T total The annualized total cost of this multi-microgrid integrated energy system, T inv The annual equivalent investment cost of equipment in this multi-microgrid integrated energy system, Tmain The annual maintenance cost of equipment in this multi-microgrid integrated energy system, T ope This represents the annual operating cost of the multi-microgrid integrated energy system.

[0078] Among them, the annual equivalent investment cost (T) of equipment for multi-microgrid integrated energy systems inv The cost consists of the construction cost of various equipment within the multi-microgrid integrated energy system, and is determined by the capacity of various equipment within the multi-microgrid integrated energy system.

[0079] Specifically, the annual equivalent investment cost (T) of equipment in a multi-microgrid integrated energy system. inv The calculation formula for ) is as follows:

[0080]

[0081] Where l is the residual value rate of fixed assets; W is the total number of microgrid clusters in the multi-microgrid integrated energy system; m is the equipment number; φ represents the energy conversion equipment (e.g., cogeneration system, electric boiler, absorption chiller, electric chiller, gas boiler, etc.). This refers to energy storage devices (e.g., electrical storage devices, thermal storage devices, etc.); y w,φ The lifecycle of the w-th microgrid and the φ-th energy conversion device; For the wth micro-network The lifecycle of an energy storage device; Q w,φ Let φ be the installed capacity of the energy conversion device in the w-th microgrid; For the wth micro-network The installed capacity of each energy storage device; c w,φ The unit capacity installation cost of the energy conversion equipment of the w-th microgrid; For the wth micro-network The unit capacity installation cost of an energy storage device; h The total length of heat transfer pipelines that need to be installed between microgrid clusters in a multi-microgrid integrated energy system, l h Determined by the distance between microgrid clusters; c h Cost per unit length of heat transfer pipe.

[0082] In the embodiments of this application, the cost of heat transmission pipelines between microgrid clusters in the multi-microgrid integrated energy system is considered when calculating the annual equivalent investment cost. Instead of only considering the investment cost of a single microgrid cluster, the cost of the multi-microgrid integrated energy system planning result is made closer to the actual situation, thereby improving the accuracy and reliability of the final planning result.

[0083] Among them, the annual maintenance cost (T) of the equipment in the multi-microgrid integrated energy systemmain The output power is determined by the various devices within each microgrid cluster.

[0084] Specifically, the annual maintenance cost (T) of equipment in a multi-microgrid integrated energy system. main The calculation formula for ) is as follows:

[0085]

[0086] O = [o EB ,o GB ,o EC ,o AC ,o WT ,o PV ,o CHP ,o ES ,o CS ,o HS ]

[0087] Where O represents the corresponding unit capacity maintenance cost vector for the equipment, for example: o EB Let be the unit capacity maintenance cost vector for an electric boiler (EB); D is the total number of days in a year; π(s) is the probability of a typical day s occurring. Let be the thermal power output of the electric boiler of the w-th microgrid during time period t on the s-th typical day; Let be the thermal power output of the gas-fired boiler of the w-th microgrid during time period t on the s-th typical day; Let be the cooling power output of the electric chiller of the w-th microgrid during time period t on the s-th typical day; Let be the cooling power output of the absorption chiller of the w-th microgrid during time period t on the s-th typical day; Let be the power generation of the wind power system of the w-th microgrid during time period t on the s-th typical day; Let be the power generation of the photovoltaic power generation system of the w-th microgrid during time period t on the s-th typical day; Let t be the natural gas input of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; Let be the electrical power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; Let be the charging power of the energy storage device of the w-th microgrid during time period t on the s-th typical day; Let be the discharge power of the energy storage device of the w-th microgrid during time period t on the s-th typical day; Let be the cooling power of the cooling storage device of the w-th microgrid during time period t on the s-th typical day; Let be the cooling power of the cold storage device of the w-th microgrid during time period t on the s-th typical day; Let be the thermal storage power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; Let be the heat release power of the thermal storage device of the w-th microgrid during the t-th period on the s-th typical day.

[0088] Among them, the annual operating cost (T) of a multi-microgrid integrated energy system ope If the objective function of the lower-level planning model is the same, then the objective function of the lower-level planning model will be described in detail.

[0089] The lower-level planning model is established with the goal of minimizing the annual operating cost of the multi-microgrid integrated energy system. This aims to address the optimal output problem of equipment within the multi-microgrid integrated energy system, such as the power generation capacity of the power generation equipment and the heat generation capacity of the heat-generating equipment.

[0090] Furthermore, the decision variables in the lower-level planning model are the hourly output of each device and the natural gas allocation coefficient. The objective function of the lower-level planning model is the annual operating cost (T) of the multi-microgrid integrated energy system. ope ).

[0091] Specifically, the annual operating cost (T) of a multi-microgrid integrated energy system ope This includes: annual electricity purchase cost, annual gas purchase cost, electricity exchange cost between microgrid clusters in a multi-microgrid integrated energy system, and heat exchange cost between microgrid clusters in a multi-microgrid integrated energy system.

[0092] The objective function of the lower-level programming model is:

[0093] minT ope =T gasbuy +T elebuy +T Hex +T Eex

[0094] Among them, T gasbuy Annual gas purchase cost; T elebuy Annual electricity purchase cost; T Hex The cost of thermal energy interaction between microgrid clusters in a multi-microgrid integrated energy system; T Eex This refers to the cost of electrical energy exchange between microgrid clusters in a multi-microgrid integrated energy system.

[0095] Among them, the annual gas purchase cost (T) gasbuy The calculation formula for ) is as follows:

[0096]

[0097] Where D is the total number of days in a year; S is the total number of typical days; π(s) is the probability of typical day s occurring; c gasbuy(s) represents the purchase price of natural gas on the s-th typical day (unit: yuan); W represents the total number of microgrid clusters in the multi-microgrid integrated energy system; T represents the total number of moments in a day; η is the output electrical power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; CHP For the gas-to-electric conversion efficiency of a combined heat and power unit; L Λ The calorific value coefficient of natural gas; Let η be the output thermal power of the gas-fired boiler in the w-th microgrid during time period t on the s-th typical day; GB Δt represents the heat conversion efficiency of the gas-fired boiler; Δt represents the unit time step.

[0098] Among them, annual electricity purchase cost (T elebuy The calculation formula for ) is as follows:

[0099]

[0100] Where D is the total number of days in a year; S is the total number of typical days; π(s) is the probability of typical day s occurring; W is the total number of microgrid clusters in the multi-microgrid integrated energy system; and T is the total number of time periods in a day. The electricity purchase price of the microgrid from the large power grid at time t on the s-th typical day (unit: yuan / kW·h); Δt represents the power purchased from the main grid by the energy storage device of the w-th microgrid during time period t on the s-th typical day (unit: kW); Δt is the unit time step.

[0101] Among them, the thermal energy interaction cost (T) between microgrid clusters in a multi-microgrid integrated energy system Hex The calculation formula for ) is as follows:

[0102]

[0103] Where μ is the cost coefficient for thermal energy interaction between microgrid clusters in a multi-microgrid integrated energy system; D is the total number of days in a year; S is the total number of typical days, which can generally be considered as three typical days in spring, autumn, summer, and winter. The load change throughout the year is simulated based on these three typical days, i.e., S = 3; π(s) is the probability of typical day s occurring; T is the total number of time periods in a day, which is generally assumed to be 24, i.e., T = 24; H w,v,s (t) represents the thermal energy power transferred from microgrid w to microgrid v during time period t on the s-th typical day, which can also be called the thermal energy interaction power between microgrid w and microgrid v during time period t on the s-th typical day.

[0104] To facilitate understanding, the following example uses a multi-microgrid integrated energy system consisting of three microgrid clusters.

[0105]

[0106] Among them, H 1,2,s (t) represents the thermal power transmitted from microgrid 1 to microgrid 2 at time t on the s-th typical day (or, the thermal power received by microgrid 2 from microgrid 1 at time t on the s-th typical day); H 2,1,s (t) represents the thermal power sent by microgrid 2 to microgrid 1 at time t on the s-th typical day (or the thermal power received by microgrid 1 from microgrid 2 at time t on the s-th typical day), and so on.

[0107] Among them, the power interaction cost between microgrid clusters in a multi-microgrid integrated energy system (T) Eex The calculation formula for ) is as follows:

[0108]

[0109] Where D is the total number of days in a year; S is the total number of typical days; π(s) is the probability of typical day s occurring; W is the total number of microgrid clusters in the multi-microgrid integrated energy system; T is the total number of time periods in a day; c exbuy The electricity purchase price for power exchange between microgrid clusters in a multi-microgrid integrated energy system; c exsell The electricity price for power exchange between microgrid clusters in a multi-microgrid integrated energy system; P w,v,s (t) represents the electrical energy exchange power between microgrid w and microgrid v during time period t on the s-th typical day (where w and v belong to the total number of microgrid clusters in the multi-microgrid integrated energy system, and w ≠ v), when P w,v,s When (t) is positive, it indicates that microgrid w purchases electricity from microgrid v; when P... w,v,s When (t) is negative, it indicates that grid w sells electricity to microgrid v; Δt is the unit time step.

[0110] In constructing the two-layer planning model in this application embodiment, the objective function of the upper-layer planning model includes cost calculations related to heat transfer pipelines between microgrid clusters. That is, when considering the configuration problem of a multi-microgrid integrated energy system, not only the configuration of each individual microgrid cluster is considered, but also the thermo-electric interaction between multiple microgrid clusters. In the objective function of the lower-layer planning model, the costs of electrical and thermal energy interaction between microgrid clusters are added. This means that when considering the processing problem of a multi-microgrid integrated energy system, not only the configuration of each individual microgrid cluster is considered, but also the thermo-electric interaction between multiple microgrid clusters. Based on the two-layer planning model constructed in this application embodiment, the final planning result is closer to the actual situation, improving the reliability and accuracy of the planning result.

[0111] S103. Operational constraints of the planned device for a multi-microgrid integrated energy system.

[0112] Specifically, the operational constraints of a multi-microgrid integrated energy system include: electrical power balance constraints, thermal power balance constraints, cooling power balance constraints, gas power balance constraints, energy conversion equipment output constraints, transmission constraints, and energy storage constraints.

[0113] The specific power balance constraint conditions are as follows:

[0114]

[0115] in, The power purchased by the w-th microgrid from the main grid during time period t on the s-th typical day; Let be the power generation of the photovoltaic power generation system of the w-th microgrid during time period t on the s-th typical day; Let be the power generation of the wind power system of the w-th microgrid during time period t on the s-th typical day; Let be the discharge power of the energy storage system of the w-th microgrid during time period t on the s-th typical day; P represents the electrical power of the cogeneration system of the w-th microgrid during time period t on a typical day s; v,w,s (t) represents the electrical power transmitted by microgrid v to microgrid w during time period t on the s-th typical day (the electrical power received by microgrid w from microgrid v during time period t on the s-th typical day). Taking a multi-microgrid integrated energy system consisting of three microgrid clusters as an example, for microgrid 1, P... v,w,s (t) includes: P 2,1,s (t) and P 3,1,s (t), which represents the electrical power transmitted from microgrid 2 to microgrid 1 during time period t on the s-th typical day and the electrical power transmitted from microgrid 3 to microgrid 1 during time period t on the s-th typical day.

[0116] in, The user-side electrical load power that the w-th microgrid needs to meet during time period t on the s-th typical day; The electrical power input to the electric chiller system of the w-th microgrid during time period t on the s-th typical day; Let be the electrical power input to the electric boiler of the w-th microgrid during time period t on the s-th typical day; P represents the charging power of the energy storage system of the w-th microgrid during time period t on a typical day s. w,v,s (t) represents the electrical power transmitted from microgrid w to microgrid v during time period t on the s-th typical day.

[0117] The specific thermal power balance constraint conditions are as follows:

[0118]

[0119] in, Let be the thermal power output of the gas-fired boiler of the w-th microgrid during time period t on the s-th typical day; Let be the output thermal power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; Let be the heating power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; H represents the thermal power output of the electric boiler in the w-th microgrid during time period t on the s-th typical day; v,w,s (t) represents the thermal energy power transferred from microgrid v to microgrid w during time period t on the s-th typical day.

[0120] in, The heat load power that the w-th microgrid needs to meet on the user side during time period t on the s-th typical day; H represents the thermal storage power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; w,v,s (t) represents the thermal energy power transmitted between microgrid w and microgrid v during time period t on the s-th typical day.

[0121] The specific cold power balance constraint conditions are as follows:

[0122]

[0123] in, Let be the cooling power output of the electric chiller of the w-th microgrid during time period t on the s-th typical day; Let be the cooling power output of the absorption chiller of the w-th microgrid during time period t on the s-th typical day; Let w be the user-side cooling load power that the w-th microgrid needs to meet during time period t on the s-th typical day.

[0124] The specific gas power balance constraint conditions are as follows:

[0125]

[0126] in, Let w be the natural gas purchase power of the w-th microgrid during time period t on the s-th typical day; Let be the natural gas input power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; The gas boiler of the wth microgrid is supplied with natural gas power during time period t on the sth typical day.

[0127] The specific output constraints of the energy conversion equipment are as follows:

[0128]

[0129] in, Let be the output thermal power of the electric boiler of the w-th microgrid during time period t on the s-th typical day; This indicates the minimum output thermal power of the electric boiler; This indicates the maximum output heat power of the electric boiler.

[0130]

[0131] in, Let be the output power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; This represents the minimum output electrical power of a combined heat and power system; This indicates the maximum output electrical power of the combined heat and power system.

[0132]

[0133] in, Let be the output thermal power of the gas-fired boiler of the w-th microgrid during time period t on the s-th typical day; This indicates the minimum output thermal power of the gas-fired boiler; This indicates the maximum output thermal power of the gas-fired boiler.

[0134]

[0135] in, Let be the output cooling power of the electric chiller of the w-th microgrid during time period t on the s-th typical day; This indicates the minimum output cooling power of the electric chiller; This indicates the maximum output cooling power of the electric chiller.

[0136]

[0137] in, Let be the output cooling power of the absorption chiller of the w-th microgrid during time period t on the s-th typical day; This indicates the minimum output cooling power of an absorption chiller; This indicates the maximum output cooling power of the absorption chiller.

[0138] Specifically, transmission constraints include: tie-line transmission power constraints, inter-microgrid electrical power transmission power constraints, and inter-microgrid thermal power transmission power constraints.

[0139] The specific constraints on the transmission power of the tie line are as follows:

[0140]

[0141]

[0142] in, The power purchased by the w-th microgrid from the main grid during time period t on the s-th typical day; This represents the minimum power consumption that the w-th microgrid purchases from the main power grid; This represents the maximum power that the w-th microgrid purchases from the main grid.

[0143] in, Let w be the power transmitted from the gas pipeline by the w-th microgrid during time period t on the s-th typical day; This represents the minimum power transmitted from the gas pipeline to the w-th microgrid; This represents the maximum power transmitted from the gas pipeline to the w-th microgrid.

[0144] The specific power constraints for inter-microgrid power transmission are as follows:

[0145]

[0146] Among them, P w,v,s (t) represents the electrical power transmitted from microgrid w to microgrid v during time period t on the s-th typical day; This represents the minimum value of the electrical energy exchange power between microgrids; This represents the maximum value of the electrical power exchanged between microgrids.

[0147] The specific constraints on inter-microgrid heat transfer power are as follows:

[0148]

[0149] Among them, H w,v,s (t) represents the thermal energy power transmitted from microgrid w to microgrid v during time period t on the s-th typical day; This represents the minimum value of the thermal energy exchange power between microgrids; This represents the maximum value of the thermal energy exchange power between microgrids.

[0150] Specifically, energy storage constraints include: electrical storage constraints, thermal storage constraints, and cold storage constraints.

[0151] The specific constraints on energy storage are as follows:

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] in, Let be the charging power of the energy storage device of the w-th microgrid during time period t on the s-th typical day; The maximum charging power of the energy storage devices in w microgrids; Let be the discharge power of the energy storage device of the w-th microgrid during time period t on the s-th typical day; Let w be the maximum discharge power of the energy storage devices in the microgrid; and The variable is 0-1, representing the charge / discharge state of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; ES w,s (t) represents the current energy capacity of the energy storage device in the w-th microgrid during time period t on the s-th typical day; For charging efficiency; For discharge efficiency; This represents the maximum energy storage capacity of the energy storage devices in the w-th microgrid; This represents the minimum energy storage capacity of the energy storage device in the w-th microgrid.

[0159] The specific thermal storage constraints are as follows:

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166] in, Let be the thermal storage power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; This represents the maximum thermal storage power of the thermal storage device in the w-th microgrid; Let be the heat release power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; This represents the maximum heat release power of the thermal storage device in the w-th microgrid; and The variable is 0-1, representing the heat charging and discharging state of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; HS w,s (t) represents the current heat capacity of the energy storage device in the w-th microgrid during time period t on the s-th typical day; For heat storage efficiency; For heat release efficiency; This represents the maximum heat storage capacity of the thermal storage device in the w-th microgrid; This represents the minimum heat storage capacity of the thermal storage device in the w-th microgrid.

[0167] The specific constraints for cold storage are as follows:

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] in, Let be the cooling power of the cooling storage device of the w-th microgrid during time period t on the s-th typical day; This represents the maximum value of the cold storage power of the cold storage device in the w-th microgrid; Let be the cooling power of the cold storage device of the w-th microgrid during time period t on the s-th typical day; This represents the maximum cooling capacity of the cold storage device in the w-th microgrid; and CS is a 0-1 variable representing the charging / discharging state of the cold storage device in the w-th microgrid during time period t on the s-th typical day; w,s (t) represents the current cold storage capacity of the cold storage equipment of the w-th microgrid during time period t on the s-th typical day; For cold storage efficiency; To improve cooling efficiency; This represents the minimum value of the cold storage capacity of the cold storage device in the w-th microgrid; This represents the maximum value of the cold storage capacity of the cold storage device in the w-th microgrid.

[0174] Furthermore, operational constraints also include: renewable energy constraints, equipment selection constraints, etc.

[0175] The specific constraints on renewable energy include: constraints on wind power generation systems, constraints on photovoltaic power generation systems, etc.

[0176] The specific constraints of the wind power generation system are as follows:

[0177]

[0178] in, Let be the power generation of the wind power system of the w-th microgrid during time period t on the s-th typical day; Let w be the installed capacity of the wind power generation system of the w-th microgrid; This represents the per-unit value of the normalized power output curve for wind power generation. Among them, The per-unit value of the normalized output curve for wind power generation is obtained based on historical data to determine the wind force level at the current time t. It is a value in the range [0,1], and is obtained by multiplying the installed capacity by the per-unit value of the normalized output curve. It represents the maximum power limit that wind power generation can operate at time t.

[0179] The per-unit value is a type of relative unit system and is a commonly used numerical notation method in power system analysis and engineering calculations, representing the relative values ​​of various physical quantities and parameters.

[0180] The specific constraints on the photovoltaic power generation system are as follows:

[0181]

[0182] in, Let be the power generation of the photovoltaic power generation system of the w-th microgrid during time period t on the s-th typical day; Let w be the installed capacity of the photovoltaic power generation system of the w-th microgrid; This represents the per-unit value of the normalized output curve for photovoltaic power generation.

[0183] The specific constraints for equipment selection are as follows:

[0184]

[0185] x=[PV,WT,CHP,GB,EB,EC,AC,ES,HS,CS]

[0186] Among them, Q w,x Let x be the installed capacity of the xth device in the wth microgrid; Let x be the minimum installed capacity of the x-th device in the w-th microgrid; Let be the maximum installed capacity of the x-th device in the w-th microgrid; x is the list of devices to be planned.

[0187] S104. Based on the operational constraints, the planning device solves the two-level planning model to obtain the optimal planning result of the multi-microgrid integrated energy system.

[0188] Specifically, the upper-level programming model in the bi-level programming model includes integer and continuous variables and contains nonlinear constraints; the lower-level programming model is a mixed-integer linear programming problem, and there is a coupling relationship between the upper-level and lower-level programming models. Therefore, the bi-level programming model is first processed by converting the lower-level programming model into the constraints of the upper-level programming model. The transformed single-level nonlinear model is then processed to form a single-level mixed-integer linear programming model. The single-level mixed-integer linear programming model is then calculated to obtain the optimal planning result of the multi-microgrid integrated energy system.

[0189] Mixed Integer Linear Programming (MILP) is a method for solving programming problems. An MILP model must have at least the following elements: ① a linear objective function f T x, where f is a column vector of constants and x is a column vector of unknowns; ② There are boundary and linear constraints, but no nonlinear constraints; ③ Certain components of x are restricted to have integer values.

[0190] To make it easier to understand, the following will be combined with... Figure 2 Let me explain the specific process of step 104 in detail.

[0191] S201, the Lagrangian function for constructing the lower-level planning model by the planning device.

[0192] Specifically, the planning device converts the two-level planning model into a single-level planning model by constructing the Lagrangian function of the lower-level planning model.

[0193] The Lagrange function is a function that describes the dynamic state of the entire physical system.

[0194] S202. The planning device, based on the constructed Lagrangian function, transforms the operational constraints of the lower-level planning model into additional constraints of the upper-level planning model through complementary relaxation conditions.

[0195] Specifically, by using complementary relaxation conditions, the constraints of the lower-level programming model are transformed into additional constraints of the upper-level programming model, thus obtaining a single-level nonlinear programming model.

[0196] Among them, the KKT (Karush-Kuhn-Tucker) conditions are necessary conditions for finding the optimal solution in nonlinear programming. The KKT conditions extend the constraint optimization problems involving equality, which are handled by the Lagrange multipliers method, to inequalities.

[0197] The complementary slackness condition is one of the KKT conditions. By introducing this complementary slackness condition, m equality constraints are added, making the number of equations the same as the number of variables.

[0198] S203. The planning device is based on a single-layer nonlinear programming model. The nonlinear operating constraints are linearized to form a target programming model.

[0199] Among them, the goal programming model is a single-level mixed-integer linear programming model.

[0200] Specifically, the planning device is based on a single-layer nonlinear programming model. The nonlinear constraints are linearized using the Big-M method and the McCormick method to obtain mixed-integer linear constraints, thus forming a target programming model of the single-layer mixed-integer linear programming type.

[0201] S204. The planning device calculates the target planning model to obtain the optimal planning result.

[0202] Specifically, the planning device uses the YALMIP software package in the MATLAB environment to model the target planning model, calls the GUROBI solver to solve it, and obtains the optimal planning result of the multi-microgrid integrated energy system.

[0203] Among them, YALMIP is an excellent toolbox of optimization solving software. Its biggest feature is that it integrates a large number of external optimization solvers and uses a unified modeling and solving programming language to facilitate the subsequent use of MATLAB to solve the constructed goal programming model.

[0204] Among them, the GUROBI solver is an excellent tool for solving large-scale mathematical programming problems. Traditional intelligent optimization algorithms such as particle swarm optimization and simulated annealing have long computation times and are prone to getting trapped in local optima. The GUROBI solver has a fast solution speed, good stability, and good convergence, and is suitable for solving large-scale mixed integer linear programming problems.

[0205] In the process of solving the bilevel programming model, the bilevel programming model is transformed into a single-level programming model by constructing the Lagrangian function and complementary relaxation conditions of the lower-level programming model, which simplifies the difficulty of solving the bilevel programming function.

[0206] Furthermore, by using the Big-M method and the McCormick method, the nonlinear constraints of the single-layer nonlinear programming model are transformed into linear constraints, thereby obtaining the objective programming model (single-layer mixed integer linear programming model). Solving the objective programming model no longer involves simplifying the model to achieve linearization requirements, thus improving the accuracy and reliability of the planning results.

[0207] This application provides a planning method for a multi-microgrid integrated energy system. The method includes: acquiring basic data of the multi-microgrid integrated energy system; constructing a two-layer planning model for the multi-microgrid integrated energy system based on the acquired basic data; the upper-layer planning model is established with the objective of minimizing the sum of investment, operation, and maintenance costs over the entire life cycle of the multi-microgrid integrated energy system; the lower-layer planning model is established with the objective of minimizing the annual operating cost of the multi-microgrid integrated energy system; setting operating constraints for the multi-microgrid integrated energy system; and solving the two-layer planning model based on the operating constraints to obtain the optimal planning result for the multi-microgrid integrated energy system. In this application embodiment, the objective function of the lower-layer planning model includes the electrical energy interaction cost and thermal energy interaction cost between microgrid clusters. That is, when considering the processing problem of the multi-microgrid integrated energy system, not only the configuration problem of each individual microgrid cluster is considered, but also the thermal and electrical interaction between multiple microgrid clusters. Based on the two-layer planning model constructed in this application embodiment, the final planning result is closer to the actual situation, improving the reliability and accuracy of the planning result.

[0208] Furthermore, when solving the bilevel programming model, the bilevel programming model is converted into a goal programming model (single-level mixed integer linear programming model). Solving the goal programming model no longer simplifies the modeling to achieve linearization requirements. While simplifying the solution process, it further improves the accuracy and reliability of the planning results.

[0209] Example 2:

[0210] Based on the multi-microgrid integrated energy system planning method provided in Embodiment 1, after obtaining the basic data of the multi-microgrid integrated energy system, the method further includes: the planning device constructing a linearized mathematical model of the multi-microgrid integrated energy system based on the obtained basic data.

[0211] Specifically, based on the acquired basic data, linearized mathematical models are established for the equipment in the multi-microgrid integrated energy system.

[0212] The equipment in a multi-microgrid integrated energy system includes: renewable energy equipment, energy conversion equipment, energy storage equipment, thermal storage equipment, and cold storage equipment.

[0213] Energy conversion equipment includes: combined heat and power systems, electric boilers, absorption chillers, electric chillers, gas boilers, etc.

[0214] The mathematical model for the cogeneration system is as follows:

[0215]

[0216]

[0217]

[0218] in, Let be the natural gas input power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; Let t be the natural gas input of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; Let be the output power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; L represents the output thermal power of the cogeneration system of the w-th microgrid during time period t on the s-th typical day; Λ The calorific value coefficient of natural gas; The gas-to-electricity conversion efficiency of a combined heat and power system; This refers to the gas-heat conversion efficiency of a combined heat and power (CHP) system.

[0219] The mathematical model for the electric boiler is as follows:

[0220]

[0221] in, Let be the output thermal power of the electric boiler of the w-th microgrid during time period t on the s-th typical day; η is the input electrical power of the electric boiler in the w-th microgrid during time period t on the s-th typical day; EB This refers to the electrothermal conversion efficiency of the electric boiler.

[0222] The mathematical model of the absorption chiller is as follows:

[0223]

[0224] in, Let be the cooling power output of the absorption chiller of the w-th microgrid during time period t on the s-th typical day; η is the thermal power input to the absorption chiller of the w-th microgrid during time period t on the s-th typical day; AC This is the coefficient of performance (COP) of an absorption chiller.

[0225] The mathematical model of the electric chiller is as follows:

[0226]

[0227] in, Let be the cooling power output of the electric chiller of the w-th microgrid during time period t on the s-th typical day; η is the electrical power input to the electric chiller of the w-th microgrid during time period t on the s-th typical day; ECThe coefficient of performance (COP) of an electric refrigeration unit is the ratio of cooling power to electrical power.

[0228] The mathematical model for the gas-fired boiler is as follows:

[0229]

[0230]

[0231] in, Let be the thermal power output of the gas-fired boiler of the w-th microgrid during time period t on the s-th typical day; Let t be the natural gas power input to the gas-fired boiler of the w-th microgrid during time period t on the s-th typical day; Let η be the natural gas consumption of the gas-fired boiler in the w-th microgrid during time period t on the s-th typical day; GB For the heat conversion efficiency of gas-fired boilers; L Λ This is the calorific value coefficient of natural gas.

[0232] The mathematical model for the energy storage device is as follows:

[0233] Charging process:

[0234] Discharge process:

[0235] Among them, ES w,s (t) represents the current energy capacity of the energy storage device of the w-th microgrid during time period t on the s-th typical day; Let be the charging power of the energy storage device of the w-th microgrid during time period t on the s-th typical day; Let be the discharge power of the energy storage device of the w-th microgrid during time period t on the s-th typical day; The charging efficiency of energy storage devices; The discharge efficiency of the energy storage device.

[0236] The mathematical model for the established thermal storage equipment is as follows:

[0237] Thermal storage process:

[0238] Exothermic process:

[0239] Among them, HS w,s (t) represents the current heat capacity of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; Let be the thermal storage power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; Let be the heat release power of the thermal storage device of the w-th microgrid during time period t on the s-th typical day; The thermal storage efficiency of thermal storage equipment; The heat release efficiency of the thermal storage equipment.

[0240] The mathematical model for the cold storage equipment is as follows:

[0241] Cold storage process:

[0242] Cooling process:

[0243] Among them, CS w,s (t) represents the current cold storage capacity of the cold storage equipment of the w-th microgrid during time period t on the s-th typical day; Let be the cooling power of the cooling storage device of the w-th microgrid during time period t on the s-th typical day; Let be the cooling power of the cold storage device of the w-th microgrid during time period t on the s-th typical day; To improve the cold storage efficiency of cold storage equipment; This refers to the cooling efficiency of the cold storage equipment.

[0244] Specifically, renewable energy power generation includes: photovoltaic power generation, wind power generation, etc.

[0245] The mathematical model for the photovoltaic power generation system is as follows:

[0246]

[0247] in, Let be the power generation of the photovoltaic power generation system of the w-th microgrid during time period t on the s-th typical day; Let be the maximum value of the photovoltaic power generation of the w-th microgrid's photovoltaic power generation system during time period t on the s-th typical day, predicted based on historical data.

[0248] The mathematical model for the established wind power generation system is as follows:

[0249]

[0250] in, Let be the power generation of the wind power system of the w-th microgrid during time period t on the s-th typical day; Let be the maximum value of photovoltaic power generation predicted by the wind power generation system of the w-th microgrid during time period t on the s-th typical day, based on historical data.

[0251] The multi-microgrid integrated energy system planning method provided in this application pre-establishes linearized mathematical models of each device / system before constructing the two-level planning model. This simplifies the construction process of the two-level planning model and shortens the construction time. Thus, while ensuring the accuracy and reliability of the planning results of the multi-microgrid integrated energy system, it greatly shortens the planning time of the multi-microgrid integrated energy system and improves the efficiency of obtaining the planning results.

[0252] Example 3:

[0253] The following is combined Figure 3 This application provides a detailed description of a multi-microgrid integrated energy system planning device.

[0254] The data acquisition module 301 is used to acquire basic data of the multi-microgrid integrated energy system.

[0255] Model building module 302 is used to build a two-level planning model of the multi-microgrid integrated energy system based on the basic data of the multi-microgrid integrated energy system.

[0256] The two-level programming model includes an upper-level programming model and a lower-level programming model. The upper-level programming model is established with the objective of minimizing the sum of investment, operation, and maintenance costs throughout the entire life cycle of the multi-microgrid integrated energy system. The lower-level objective function is established with the objective of minimizing the annual operating cost of the multi-microgrid integrated energy system. The annual operating cost of the multi-microgrid integrated energy system includes: annual electricity purchase cost, annual gas purchase cost, electrical energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system, and thermal energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system.

[0257] The condition setting module 303 is used to set the operating constraints of the multi-microgrid integrated energy system.

[0258] The planning results module 304 is used to solve the two-level planning model based on the operational constraints to obtain the optimal planning results of the multi-microgrid integrated energy system.

[0259] Furthermore, the model building module 302 is specifically used to build the objective function of the upper-level planning model and the objective function of the lower-level planning model.

[0260] Specifically, the objective function of the upper-level planning model is the annualized total cost of the multi-microgrid integrated energy system; the objective function of the lower-level planning model is the annual operating cost of the multi-microgrid integrated energy system.

[0261] Furthermore, the planning results module 304 specifically includes:

[0262] The function construction module is used to construct the Lagrangian function of the lower-level planning model;

[0263] A single-layer transformation module is used to convert the operational constraints of the lower-level programming model into additional constraints of the upper-level programming model based on the constructed Lagrangian function and through complementary relaxation conditions, thereby obtaining a single-layer nonlinear programming model.

[0264] The linear processing module is used to linearize the nonlinear operating constraints based on a single-layer nonlinear programming model to obtain the target programming model.

[0265] Specifically, the nonlinear operating constraints are linearized using the Big-M method and the McCormick inclusion method.

[0266] The objective solution module is used to calculate the objective planning model and obtain the optimal planning result of the multi-microgrid integrated energy system.

[0267] Furthermore, the planning device also includes: an equipment model construction module.

[0268] The equipment model building module is used to construct a linearized mathematical model of a multi-microgrid integrated energy system based on the acquired basic data.

[0269] This application provides a planning device for a multi-microgrid integrated energy system. The device includes: a data acquisition module 301 for acquiring basic data of the multi-microgrid integrated energy system; a model construction module 302 for constructing a two-layer planning model of the multi-microgrid integrated energy system based on the basic data; a condition setting module 303 for setting operational constraints of the multi-microgrid integrated energy system; and a planning result module 304 for solving the two-layer planning model based on the operational constraints to obtain the optimal planning result of the multi-microgrid integrated energy system. In this application embodiment, the objective function of the lower-layer planning model includes the electrical energy interaction cost and thermal energy interaction cost between microgrid clusters. That is, when considering the processing problem of the multi-microgrid integrated energy system, not only the configuration problem of each individual microgrid cluster is considered, but also the thermal and electrical interaction between multiple microgrid clusters. Based on the two-layer planning model constructed in this application embodiment, the final planning result is closer to the actual situation, improving the reliability and accuracy of the planning result.

[0270] Furthermore, when solving the bilevel programming model, the bilevel programming model is converted into a goal programming model (single-level mixed integer linear programming model). Solving the goal programming model no longer simplifies the modeling to achieve linearization requirements. While simplifying the solution process, it further improves the accuracy and reliability of the planning results.

[0271] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments. The device and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components indicated as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0272] The above description is merely one specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for planning a multi-microgrid integrated energy system, characterized in that, The method includes: Acquire basic data for multi-microgrid integrated energy systems; Based on the fundamental data of the multi-microgrid integrated energy system, a two-level planning model for the multi-microgrid integrated energy system is constructed. The two-level planning model includes an upper-level planning model and a lower-level planning model. The upper-level planning model is established with the objective of minimizing the sum of investment, operation, and maintenance costs throughout the entire life cycle of the multi-microgrid integrated energy system. The lower-level planning model is established with the objective of minimizing the annual operating cost of the multi-microgrid integrated energy system. The annual operating cost of the multi-microgrid integrated energy system includes: annual electricity purchase cost, annual gas purchase cost, electrical energy interaction cost between microgrid clusters within the multi-microgrid integrated energy system, and thermal energy interaction cost between microgrid clusters within the multi-microgrid integrated energy system. The operation constraints of the multi-microgrid integrated energy system are set; wherein, the operation constraints include: electrical power balance constraints, thermal power balance constraints, cooling power balance constraints, gas power balance constraints, energy conversion equipment output constraints, transmission constraints, and energy storage constraints; the transmission constraints include: tie-line transmission power constraints, inter-microgrid electrical power transmission power constraints, and inter-microgrid thermal power transmission power constraints. The power constraint for heat transfer between microgrids is specifically expressed by the following formula: ； wherein, is the w is the v is the s is the t is the is the minimum value of the thermal power exchanged between the microgrids; is the maximum value of the thermal power exchanged between the microgrids; Based on the aforementioned operational constraints, the two-level programming model is solved to obtain the optimal planning result of the multi-microgrid integrated energy system. The thermal energy interaction cost between the multi-microgrid clusters in the multi-microgrid integrated energy system is specifically expressed by the following formula: ； in, T Hex For the thermal energy exchange cost between microgrid clusters, The cost coefficient for thermal energy interaction between microgrid clusters in a multi-microgrid integrated energy system. D The total number of days in a year; S The total number of typical days, For typical days s The probability of occurrence T This represents the total number of time periods in a day.

2. The method according to claim 1, characterized in that, The objective function of the upper-level planning model is the annualized total cost of the multi-microgrid integrated energy system. The annualized total cost of the multi-microgrid integrated energy system includes: the annual equivalent investment cost of the equipment in the multi-microgrid integrated energy system, the annual maintenance cost of the equipment in the multi-microgrid integrated energy system, and the annual operating cost of the equipment in the multi-microgrid integrated energy system.

3. The method according to claim 2, characterized in that, The annual equivalent investment cost of the equipment in the multi-microgrid integrated energy system is specifically expressed by the following formula: in, T inv The annual equivalent investment cost of the equipment in the multi-microgrid integrated energy system is given. l The residual value rate of fixed assets. W This refers to the total number of microgrid clusters in a multi-microgrid integrated energy system. m Number the equipment; Indicates energy conversion equipment, Indicates an energy storage device; For the first w The first micro-network The life cycle of an energy conversion device For the first w The first micro-network The lifecycle of an energy storage device For the first w The first micro-network The installed capacity of each energy conversion device; For the first w The first micro-network The installed capacity of an energy storage device For the first w The first micro-network The unit capacity installation cost of an energy conversion device For the first w The first micro-network The unit capacity installation cost of an energy storage device This refers to the total length of heat transfer pipelines that need to be installed between microgrid clusters in a multi-microgrid integrated energy system. Cost per unit length of heat transfer pipe.

4. The method according to claim 2, characterized in that, The annual maintenance cost of the equipment in the multi-microgrid integrated energy system is specifically expressed by the following formula: ； ； in, This represents the vector of maintenance costs per unit capacity for the corresponding equipment. Let this be the vector of maintenance cost per unit capacity for electric boilers. This represents the unit capacity maintenance cost vector for gas-fired boilers. Let this be the vector of unit capacity maintenance cost for electric chillers. This represents the unit capacity maintenance cost vector for absorption chillers. This represents the unit capacity maintenance cost vector for wind power generation systems. This represents the unit capacity maintenance cost vector for photovoltaic power generation systems. This represents the unit capacity maintenance cost vector for a combined heat and power (CHP) system. This represents the unit capacity maintenance cost vector for energy storage devices. This represents the unit capacity maintenance cost vector for cold storage equipment. Vector of unit capacity maintenance cost for thermal storage equipment; D The total number of days in a year; For typical days s The probability of occurrence For the first w The electric boiler of the microgrid is in the first s A typical day t Thermal power output during the time period For the first w The gas-fired boiler of the microgrid is in the first s A typical day t Thermal power output during the time period For the first w The microgrid's electric chiller in the first s A typical day t Cooling power output during the time period For the first w The absorption chiller of the microgrid is in the first s A typical day t Cooling power output during the time period For the first w The wind power generation system of the microgrid is in the first s A typical day t Power generation during the period For the first w The photovoltaic power generation system of the microgrid in the first s A typical day t Power generation during the period For the first w The microgrid cogeneration system in the first s A typical day t Natural gas input during the period For the first w The microgrid cogeneration system in the first s A typical day t Electric power during a period of time For the first w The energy storage devices of the microgrid in the first s A typical day t Charging power during the period For the first w The energy storage devices of the microgrid in the first s A typical day t Discharge power during the period For the first w The microgrid's cold storage equipment in the first s A typical day t Cooling capacity during the time period For the first w The microgrid's cold storage equipment in the first s A typical day t Cooling power during the period For the first w The thermal storage equipment of the microgrid is in the first s A typical day t Thermal storage capacity during a given time period For the first w The thermal storage equipment of the microgrid is in the first s A typical day t The heat release power during a given time period.

5. The method according to claim 1, characterized in that, The annual gas purchase cost is specifically expressed by the following formula: in, T gasbuy Annual gas purchase cost; D The total number of days in a year. S The total number of typical days, For typical days s The probability of occurrence For the first s The purchase price of natural gas on a typical day. W This refers to the total number of microgrid clusters in a multi-microgrid integrated energy system. T This represents the total number of time periods in a day. For the first w The microgrid cogeneration system in the first s A typical day t Output power during the time period For the gas-to-electricity conversion efficiency of combined heat and power units, The calorific value coefficient of natural gas, For the first w The gas-fired boiler of the microgrid is in the first s A typical day t Output thermal power during the time period For the heat conversion efficiency of gas-fired boilers, The unit time step.

6. The method according to claim 1, characterized in that, The annual electricity purchase cost is specifically expressed by the following formula: in, T elebuy Annual electricity purchase cost, D The total number of days in a year. S The total number of typical days; For typical days s The probability of occurrence W This refers to the total number of microgrid clusters in a multi-microgrid integrated energy system. T This represents the total number of time periods in a day. For microgrids in the first s A typical day t The electricity purchase price from the main power grid at all times. For the first w The energy storage device of the microgrid in the first s A typical day t The amount of electricity purchased from the main power grid during a given time period. The unit time step.

7. The method according to claim 1, characterized in that, The energy exchange cost between microgrid clusters in the multi-microgrid integrated energy system is specifically expressed by the following formula: in, T Eex For the power exchange between microgrid clusters, D The total number of days in a year. S The total number of typical days, For typical days s The probability of occurrence W This refers to the total number of microgrid clusters in a multi-microgrid integrated energy system. T This represents the total number of time periods in a day. The electricity purchase price for power exchange between microgrid clusters in a multi-microgrid integrated energy system. The electricity price for power exchange between microgrid clusters in a multi-microgrid integrated energy system. For the first w The micro-network to the first v The micro-network in the first s A typical day t Power transmitted during a time period The unit time step.

8. The method according to claim 1, characterized in that, The optimal planning result of the multi-microgrid integrated energy system obtained by solving the two-level programming model based on the operational constraints includes: Construct the Lagrangian function of the lower-level planning model; Based on the constructed Lagrangian function, the operational constraints of the lower-level programming model are transformed into additional constraints of the upper-level programming model through complementary relaxation conditions, resulting in a single-level nonlinear programming model. Based on the single-layer nonlinear programming model, the nonlinear operating constraints are linearized to obtain the objective programming model; the objective programming model is a single-layer mixed-integer linear programming model. The optimal planning result of the multi-microgrid integrated energy system is obtained by calculating the target planning model.

9. A multi-microgrid integrated energy system planning device, characterized in that, The device includes: The data acquisition module is used to acquire basic data of the multi-microgrid integrated energy system; The model building module is used to construct a two-layer planning model for the multi-microgrid integrated energy system based on the basic data of the multi-microgrid integrated energy system. The two-layer planning model includes an upper-layer planning model and a lower-layer planning model. The upper-layer planning model is established with the goal of minimizing the sum of investment, operation, and maintenance costs over the entire life cycle of the multi-microgrid integrated energy system. The lower-layer planning model is established with the goal of minimizing the annual operating cost of the multi-microgrid integrated energy system. The annual operating cost of the multi-microgrid integrated energy system includes: annual electricity purchase cost, annual gas purchase cost, electrical energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system, and thermal energy interaction cost between microgrid clusters in the multi-microgrid integrated energy system. The condition setting module is used to set the operating constraints of the multi-microgrid integrated energy system; wherein, the operating constraints include: electrical power balance constraints, thermal power balance constraints, cooling power balance constraints, gas power balance constraints, energy conversion equipment output constraints, transmission constraints, and energy storage constraints; the transmission constraints include: tie-line transmission power constraints, inter-microgrid electrical power transmission power constraints, and inter-microgrid thermal power transmission power constraints. The power constraint for heat transfer between microgrids is specifically expressed by the following formula: ； in, For the first w The micro-network to the first v The micro-network in the first s A typical day t Thermal power transferred over time period This represents the minimum value of the thermal energy exchange power between microgrids; This represents the maximum value of the thermal energy exchange power between microgrids; The planning results module is used to solve the two-level planning model based on the operational constraints to obtain the optimal planning results of the multi-microgrid integrated energy system. The thermal energy interaction cost between the multi-microgrid clusters in the multi-microgrid integrated energy system is specifically expressed by the following formula: ； in, T Hex For the thermal energy exchange cost between microgrid clusters, The cost coefficient for thermal energy interaction between microgrid clusters in a multi-microgrid integrated energy system. D The total number of days in a year; S The total number of typical days, For typical days s The probability of occurrence T This represents the total number of time periods in a day.

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