Multi-energy coupling planning method and device considering source storage load and economic characteristics

Abstract

The invention relates to the field of operation and control of power systems in various energy forms, in particular to a multi-energy coupling planning method and equipment considering source storage load and economic characteristics. The method aims at achieving economical and efficient planning of the energy system. The method comprises the steps that a multi-energy coupling system architecture is constructed, and the multi-energy coupling system architecture integrates a distributed renewable energy source unit, an energy storage unit, a coupling unit and a load unit. Based on a multi-energy coupling system architecture, a target function is established with the minimum comprehensive cost and the maximum clean energy utilization amount, and the target function is combined with a time-related energy price response coefficient. Establishing a source storage load model, wherein the source storage load model comprises a source side equipment model, a storage side equipment model and a load model; and setting system constraint conditions by taking the objective function as an optimization direction and the source storage load model as a parameter basis, and solving by adopting a multi-objective optimization algorithm to obtain a planning scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system operation and control of various energy forms, and particularly relates to a multi-energy coupling planning method and device considering source storage load and economic characteristics. BACKGROUND

[0002] With the acceleration of global energy transformation, the traditional energy structure dominated by fossil energy has been difficult to adapt to the demand for sustainable development, and building a new energy system dominated by clean energy has become an inevitable direction. Multi-energy coordination and complementation of electricity, heat, gas and biogas is the core path to improve energy utilization efficiency and enhance system flexibility, which can effectively alleviate the challenges brought by the intermittency and uncertainty of clean energy.

[0003] However, the current energy system is mostly in an independent operation state, and there is a lack of efficient coordination mechanism between subsystems, resulting in low energy conversion efficiency and difficulty in playing the advantages of multi-energy complementation, which seriously limits the efficient consumption of clean energy. At the same time, the existing multi-energy coupling system planning mostly stays in the theoretical stage, lacking mature engineering guidance methods, especially not fully considering the influence of market price fluctuations on user energy consumption behavior, and not adequately dealing with the uncertainty of wind and light output, making it difficult to achieve economic and efficient system planning. Therefore, it is urgent to develop a multi-energy coupling planning technology that takes into account the characteristics of source storage load and energy economic law to provide support for energy system transformation and upgrading. SUMMARY

[0004] In order to solve the problems in the background art, the purpose of the present application is to provide a multi-energy coupling planning method and device considering source storage load and economic characteristics.

[0005] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: The present application provides a multi-energy coupling planning method considering source storage load and economic characteristics, comprising: S1: constructing a multi-energy coupling system architecture, which integrates distributed renewable energy units, energy storage units, coupling units and load units, and has independent operation ability and interaction ability with the power grid; S2: based on the multi-energy coupling system architecture, a target function is established with the minimum comprehensive cost and the maximum clean energy utilization, and the target function combines the time-related energy price response coefficient; the energy price response coefficient is a coefficient reflecting the fluctuation of energy market price at different times and the price response ability of the user side; the target function is as follows: min Wherein, is the total cost of the multi-energy coupling system planning and operation stage, is the system planning stage cost, is the system operation stage cost; Wherein, is the total utilization amount of the wind turbine, photovoltaic and biogas, is the output power of the photovoltaic and wind turbine equipment at time t, respectively, is the gas-electricity conversion coefficient.

[0006] S3: A source and load model is established, and the source and load model includes a source side equipment model, a storage side equipment model and a load model; wherein the load model includes an adjustable load and a non-adjustable load, the adjustable load includes a similar energy storage adjustable load model and other adjustable load models, and the source side equipment model uses an interval optimization method to process the uncertainty of renewable energy output; S4: taking the target function as the optimization direction and the source and load model as the parameter basis, setting system constraint conditions and using a multi-objective optimization algorithm to solve, to obtain a planning scheme. The target optimization algorithm is an improved MOEA / D algorithm considering the constraint conditions.

[0007] In step S1, the distributed renewable energy unit includes a wind turbine, a photovoltaic unit and a biogas tank; the energy storage unit includes electric energy storage and gas energy storage; and the coupling unit includes an electric boiler, a biogas boiler and a combined heat and power unit.

[0008] The expression of the system planning stage cost is: wherein, is the investment cost of the related equipment designed for the multi-energy coupling system per unit capacity, is the planned service life of the related equipment, is the interest rate, is the equipment type, is the total time period of system scheduling in a day.

[0009] The expression of the system operation stage cost is: wherein, is the operation and maintenance cost of the related equipment designed for the multi-energy coupling system per unit capacity, is the installed capacity of the equipment; is the planned service life of the related equipment, is the interest rate, is the equipment type, is the system electricity and gas purchase amount at time t, is the price response coefficient of the energy price corresponding coefficient at time t, is the total time period of system scheduling in a day, is the total time period of system scheduling in a year.

[0010] In step S3, the expression of the similar energy storage load model in the adjustable load is: in, Let t be the internal electrical quantity of the energy storage-like adjustable load. For energy storage systems, adjustable load charging efficiency is required. The charging power for the energy storage-type adjustable load is the load of the energy storage-type adjustable load at time t. The total internal electrical capacity of the energy storage-type adjustable load is [missing information]. The maximum charging power for adjustable loads of energy storage systems. For energy storage-type adjustable loads, the minimum and maximum power requirements are... This refers to the amount of electricity stored in an adjustable load when it is disconnected from the charging state. The minimum amount of electricity required to charge an adjustable load of this type of energy storage.

[0011] The expressions for other adjustable load models in adjustable load are: in, For other adjustable load power at time t, This represents the baseline load power of other adjustable loads at time t. For the price elasticity coefficient of other adjustable loads, Let t be the user-side electricity purchase price and benchmark electricity price. For other adjustable loads, these are the minimum and maximum load amounts during operation at time t.

[0012] In step S3, the expression for the non-adjustable load is: in, Let be the unadjustable load power at time t. Let t be the proportion of non-adjustable load power in the total energy load power at time t. This is for the load requirements of a multi-energy coupled system.

[0013] In step S3, the expression for the interval optimization method is: in, Let be the output power of the renewable resource at time t. For the range of changes in the renewable resource load factor, , These are the lower bound and the upper bound of the range fluctuation, respectively. Rated power configured for renewable resources.

[0014] In step S3, the source-side equipment model includes a combined heat and power (CHP) unit model, expressed as: in, Let be the electrical / thermal power of the combined heat and power unit at time t. For the electrical and thermal conversion efficiency of combined heat and power units, Let t be the amount of biogas and natural gas consumed by the cogeneration unit.

[0015] In step S3, the energy storage-side equipment model includes an electrical energy storage model, the expression of which is: in, Let represent the energy storage state at time t. Let be the charging and discharging power of the electrical energy storage at time t. For the charging and discharging efficiency of electrical energy storage, For time intervals.

[0016] The present invention also provides an apparatus comprising a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the above-described multi-energy coupling planning method considering source-storage-load and economic characteristics.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This application provides a multi-energy coupling planning method that considers source-storage-load and economic characteristics. By constructing a multi-energy coupling architecture that integrates distributed renewable energy units (wind turbines, photovoltaic units, biogas digesters), energy storage units (electric energy storage, gas energy storage), and coupling units (electric boilers, biogas furnaces, cogeneration units), it achieves coordinated operation of electricity, heat, gas, and biogas across energy forms. This changes the situation of traditional energy systems operating independently. The cogeneration unit uses biogas as energy to achieve heat-based power generation. When there is excess renewable energy output, the electric boiler converts electricity into heat to supply users or biogas digesters. The biogas furnace realizes the directional conversion of biogas into heat. Multiple devices form a source-storage-coupling closed-loop coordination, reducing energy conversion losses and maximizing the utilization value of various energy sources.

[0018] 2. With the dual optimization objectives of minimizing overall cost and maximizing clean energy utilization, this approach controls the system's life-cycle expenditures by meticulously calculating equipment investment costs during the planning phase and energy purchase and maintenance costs during the operation phase. It also avoids the limitations of a single planning approach that prioritizes either cost over clean energy or vice versa by quantifying the total utilization of wind turbines, photovoltaics, and biogas. The objective function incorporates a time-dependent energy price response coefficient, dynamically linking purchased electricity and gas volumes to energy market prices at different times. Furthermore, an adjustable load price response model quantifies the price sensitivity of user energy consumption behavior.

[0019] 3. To address the volatility of wind and solar power due to natural conditions, an interval optimization method is employed. This method defines the upper and lower bounds of output fluctuations by using load factor variation interval variables, transforming uncertainty into quantifiable interval constraints. This avoids planning deviations caused by traditional deterministic modeling and improves the system's adaptability and operational stability to renewable energy fluctuations. By distinguishing between non-adjustable loads (uninterruptible and without price response) and adjustable loads (flexibly adjusted with price) and establishing dedicated models, the energy consumption characteristics of different loads are accurately matched. Adjustable loads are dynamically adjusted according to the price elasticity coefficient, ensuring a stable supply of core loads such as residential and industrial loads while mitigating energy supply-demand imbalances through load-side peak shaving and valley filling, thus enhancing system operational flexibility. Attached Figure Description

[0020] Figure 1 This is a flowchart of a multi-energy coupling planning method considering source-storage-load and economic characteristics provided in an embodiment of this application; Figure 2 This is a schematic diagram of a multi-energy coupled system architecture provided in an embodiment of this application; Figure 3 This is a flowchart of an improved MOEA / D algorithm provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] This application provides a multi-energy coupling planning method that considers source-storage-load and economic characteristics. For example, such as... Figure 1 As shown, it includes: S1: Construct a multi-energy coupling system architecture. The multi-energy coupling system architecture integrates distributed renewable energy units, energy storage units, coupling units and load units, and has the ability to operate independently and interact with the power grid.

[0023] In step S1, the distributed renewable energy unit includes wind turbines, photovoltaic units, and biogas digesters; the energy storage unit includes electric energy storage and gas energy storage; and the coupling unit includes electric boilers, biogas furnaces, and combined heat and power units.

[0024] For example, a multi-energy coupled system architecture such as Figure 2 As shown. In terms of energy flow, the electrical energy output from wind turbines and photovoltaic systems can directly power loads, charge energy storage systems, or be converted into heat energy to supply heating loads via electric boilers. Biogas produced by biogas digesters, after being stored in gas storage systems, can be converted into heat / electricity through biogas furnaces and combined heat and power (CHP) units to meet heat and electricity loads; natural gas from the natural gas grid is input into CHP units to participate in energy conversion; CHP units can simultaneously generate electricity and heat, collaboratively ensuring electricity and heat demand; the main grid realizes power interaction between the system and the external grid (purchasing or selling electricity), and ultimately, electricity, heat, and gas energy are supplied to the corresponding loads respectively, completing the coordinated supply and conversion of multiple energy sources.

[0025] S2: Based on a multi-energy coupled system architecture, an objective function is established to minimize overall cost and maximize clean energy utilization. This objective function incorporates a time-dependent energy price response coefficient. The energy price response coefficient reflects the volatility of energy market prices at different times and the user-side price responsiveness. The objective function is shown below: min in, The total cost of the planning and operation phase of a multi-energy coupled system. Costs during the system planning phase, Costs incurred during the system's operational phase; in, This refers to the total utilization of wind turbines, photovoltaic power, and biogas. Let be the output power of the photovoltaic and wind turbine equipment at time t, respectively. The gas-to-electricity conversion coefficient.

[0026] For example, the expression for the cost of the system planning phase is as follows: in, The investment cost per unit capacity of related equipment designed for multi-energy coupling systems. The planned service life of the relevant equipment. For interest rates, For equipment types, This represents the total daily scheduling time for the system.

[0027] For example, the expression for the system operation phase cost is as follows: in, The operating and maintenance cost per unit capacity of the related equipment designed for multi-energy coupling systems. The installed capacity of the equipment; The planned service life of the relevant equipment. For interest rates, For equipment types, The system's electricity and gas purchases at time t. This refers to the price response coefficient of energy prices at time t. This represents the total daily scheduling time for the system. This represents the total scheduling time for the system over one year.

[0028] S3: Establish a source-storage-load model, which includes a source-side equipment model, a storage-side equipment model, and a load model.

[0029] The load model includes adjustable and non-adjustable loads. Adjustable loads are further subdivided into adjustable load models for energy storage systems such as electric vehicles, and other adjustable load models. The source-side equipment model employs interval optimization methods to handle the uncertainties in renewable energy output.

[0030] Source-side equipment models correspond to the core units of energy production or energy conversion, exemplified by wind turbine models, photovoltaic unit models, biogas digester models, and combined heat and power (CHP) unit models. Storage-side equipment models include electrical energy storage models and gas energy storage models. By classifying the loads of corresponding energy consumption terminals, models are categorized into adjustable load models that can flexibly adjust with energy prices and include a price response function, and non-adjustable load models that cannot be arbitrarily interrupted or reduced and have no response to market energy prices, such as basic residential and industrial loads.

[0031] In step S3, the expression for the energy storage-like load model in the adjustable load is: in, Let t be the internal electrical quantity of the energy storage-like adjustable load. For energy storage systems, adjustable load charging efficiency is required. The charging power for the energy storage-type adjustable load is the load of the energy storage-type adjustable load at time t. The total internal electrical capacity of the energy storage-type adjustable load is [missing information]. The maximum charging power for adjustable loads of energy storage systems. For energy storage-type adjustable loads, the minimum and maximum power requirements are... This refers to the amount of electricity stored in an adjustable load when it is disconnected from the charging state. The minimum amount of electricity required to charge an adjustable load of this type of energy storage.

[0032] The expressions for other adjustable load models in adjustable load are: in, For other adjustable load power at time t, This represents the baseline load power of other adjustable loads at time t. For the price elasticity coefficient of other adjustable loads, Let t be the user-side electricity purchase price and benchmark electricity price. For other adjustable loads, these are the minimum and maximum load amounts during operation at time t.

[0033] In step S3, the expression for the non-adjustable load model is: in, Let be the unadjustable load power at time t. Let t be the proportion of non-adjustable load power in the total energy load power at time t. This is for the load requirements of a multi-energy coupled system.

[0034] In step S3, the expression for the interval optimization method is: in, Let be the output power of the renewable resource at time t. For the range of changes in the renewable resource load factor, , These are the lower bound and the upper bound of the range fluctuation, respectively. Rated power configured for renewable resources.

[0035] For example, the load model includes a wind turbine model and a photovoltaic (PV) turbine model. The output of the wind turbine is affected by multiple environmental factors, with wind speed being the most significant. Since wind speed is fluctuating and uncertain, and the output power of the wind turbine follows a probability distribution, interval optimization is used to address this. The value is defined as the ratio of the actual power generated by the wind turbine to its rated power at time t, expressed as: in, Let be the output power of the wind turbine at time t. For the range of wind turbine load factor changes, , This is the lower bound of the range fluctuation. This is the upper bound of the range fluctuation. The rated power configured for wind turbine units.

[0036] Solar irradiance has a significant impact on the output power of photovoltaic (PV) generators, and it is difficult to predict accurately. Therefore, similar to the approach used for wind turbine generators, the model for PV generators can be represented as follows: in, Let be the output power of the photovoltaic unit at time t. For the interval variable representing the change in the load factor of the photovoltaic unit, , This is the lower bound of the range fluctuation. This is the upper bound of the range fluctuation. The rated power configured for photovoltaic units.

[0037] In step S3, the source-side equipment model includes a combined heat and power (CHP) model. The CHP unit outputs electrical and thermal energy in a constant-power-thermal manner, and its electrical and thermal output power expressions are as follows: in, Let be the electrical / thermal power of the combined heat and power unit at time t. For the electrical and thermal conversion efficiency of combined heat and power units, Let t be the amount of biogas and natural gas consumed by the cogeneration unit.

[0038] For example, the source-side equipment model includes a biogas digester model. The expression for the biogas production and temperature of the biogas digester is as follows: in, Let be the amount of gas produced by the biogas digester at time t. The temperature coefficient of the biogas digester. The temperature after heating and the optimal fermentation temperature. The biogas production rate at the optimal temperature for the biogas digester is determined. A thermodynamic model based on the building structure is established, and a general model of the biogas digester's thermodynamic network is constructed based on the building model's thermal resistance and heat capacity. in, For the external heat capacity and wall heat capacity of the biogas digester, The heat power input to the biogas digester. The external temperature and wall temperature of the biogas digester. These are the internal thermal resistance of the biogas digester, the thermal resistance of the walls, and the convective thermal resistance to the outside.

[0039] In some embodiments, the multi-energy coupling system framework also includes thermal coupling devices, such as a gas-thermal coupling device (biogas furnace) and an electric-thermal coupling device (electric boiler). The biogas furnace can convert biogas into heat energy, and the electric boiler can convert a portion of the electrical energy into heat energy to directly supply users or power the biogas digester when there is a surplus of renewable energy output. The conversion relationships between the two are as follows: in, Let be the output thermal power of the biogas furnace at time t. For the conversion efficiency of biogas furnace, This represents the amount of biogas consumed by the biogas digester at time t. Let be the heat output power of the electric boiler to the system at time t. For the conversion efficiency of electric boilers, Let be the electrical power input from the system to the electric boiler at time t.

[0040] In step S3, the energy storage-side equipment model includes an electrical energy storage model, the expression of which is: in, Let represent the energy storage state at time t. Let be the charging and discharging power of the electrical energy storage at time t. For the charging and discharging efficiency of electrical energy storage, For time intervals.

[0041] Multi-energy coupling systems involve the coordinated conversion and transmission of multiple energy flows, such as electricity, heat, gas, and biogas. The power balance relationship between these energy sources needs to be clearly defined through constraints. For example, the electrical power balance constraint is: in, Let t be the internal power demand of the system. Let t be the electrical power input to the power grid at time t.

[0042] For example, the heat power balance constraint: In a multi-energy coupled system architecture, heat is mainly provided by cogeneration units, electric boilers, and biogas furnaces. It should meet the heat demand for biogas digester temperature rise and the user's heat load demand, that is: in, Let t be the heat demand for the biogas digester temperature increase. The internal heat load of the multi-energy coupled system at time t.

[0043] For example, the gas power balance constraint is: in, Let t be the heat demand for the biogas digester to increase its temperature.

[0044] For example, the output constraint of a combined heat and power unit is: in, Let be a 0-1 variable representing whether the cogeneration unit is on or off at time t. These are the upper and lower limits of the electrical output power of a combined heat and power (CHP) unit. These are the upper and lower limits of the heat output power of a combined heat and power (CHP) unit.

[0045] For example, the output constraints of electric boilers and biogas furnaces are: in, For the minimum and maximum input values ​​of the electric boiler, These are the minimum and maximum input values ​​for the biogas stove.

[0046] For example, the energy storage constraint is: in, The lower and upper limits for configuring energy storage capacity. The lower and upper limits for configuring gas storage capacity.

[0047] It should be understood that charging / discharging / heating cannot occur simultaneously, and the charging / discharging power constraint of energy storage devices is as follows: in, This refers to the power-capacity ratio of electrical energy storage and thermal energy storage. For the charging and discharging of electrical energy storage, there are 0-1 variables. For the charging and discharging of thermal energy storage, there are 0-1 variables.

[0048] S4: Taking the objective function as the optimization direction and the source-storage-load model as the parameter basis, the system constraints are set and the multi-objective optimization algorithm is used to solve the problem to obtain the planning scheme.

[0049] In step S4, the objective optimization algorithm is an improved MOEA / D algorithm that takes into account the constraints.

[0050] For example, refer to Figure 3The process begins at the starting point, signifying the start of the algorithm's initialization. First, uniformly distributed weight vectors are generated, and the neighborhood of each subproblem is calculated based on these weight vectors. Next, an initial population is randomly generated, and ideal reference points are initialized. Simultaneously, an external population (EP) is created to store non-dominated solutions. Each individual in the population corresponds to a set of planning parameters for the multi-energy coupled system, such as equipment capacity and operating strategies. Before entering the main optimization loop, uncertainties and multi-objective problems need to be addressed. This includes calculating the boundaries of the objective function and constraint intervals, and handling uncertain objective functions and constraints based on interval optimization. Multi-energy coupled systems exhibit uncertainties such as wind / solar power output and energy prices. Intervals characterize the impact of these uncertainties on the objective function (e.g., overall cost, clean energy utilization) and constraints (e.g., power balance, equipment capacity limitations), determining the upper and lower bounds of the intervals. Handling uncertain objective functions and constraints based on interval optimization transforms uncertain interval problems into deterministic multi-objective optimization problems, eliminating the interference of uncertainty on optimization and enabling the problem to be solved using mature algorithms.

[0051] After interval processing, the optimization objective becomes a trade-off between minimizing overall cost and maximizing clean energy utilization, entering the iterative loop of the MOEA / D algorithm. First, new individuals are generated through gene recombination. Then, the ideal reference point is updated based on the new individuals. Next, the neighborhood solutions are updated based on the objective function and constraint violation values ​​(i.e., the new solutions), ensuring that each subproblem approaches the optimal solution. Simultaneously, the external population EP is updated, retaining the currently discovered non-dominated solution set. After all updates are completed, the iteration count is incremented by 1, and it is checked whether the current iteration count has reached the preset maximum iteration count. If not, the next round of optimization continues; if it has, the iteration ends. After convergence, the optimal solution set (i.e., the solutions in the external population EP) is output, providing a decision-making basis for cost-cleanliness and other multi-objective trade-offs in multi-energy coupled system planning.

[0052] In some embodiments, this application also provides a device, such as... Figure 4 As shown, the device includes a processor 1201, a bus 1202, a communication interface 1203, and a memory 1204. The processor 1201, memory 1204, and communication interface 1203 communicate with each other via the bus 1202. It should be understood that this application does not limit the number of processors 1201 and memory 1204 in the device.

[0053] Bus 1202 can be a PCI bus, an Extended Industry Standard Architecture (EISA) bus, or a UB bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus 1202 may be represented by a single line, but this does not mean that there is only one bus or one type of bus. The bus 1202 may include a path for transmitting information between various components of the device (e.g., memory 1204, processor 1201, communication interface 1203).

[0054] Processor 1201 may include any one or more processors such as CPU, graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0055] The memory 1204 may include volatile memory, such as random access memory (RAM). The processor 1201 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0056] The communication interface 1203 uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between different devices or between devices and a communication network.

[0057] The memory 1204 stores executable program code, and the processor 1201 executes the executable program code to implement the multiple steps in the aforementioned method embodiments. That is, the memory 1204 stores the multi-energy coupling planning method that considers source-storage-load and economic characteristics.

[0058] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0059] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention 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 the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-energy coupled planning method considering source-storage-load and economic characteristics, characterized in that, include: S1: Construct a multi-energy coupling system architecture. The multi-energy coupling system architecture integrates distributed renewable energy units, energy storage units, coupling units and load units, and has independent operation capabilities and the ability to interact with the power grid. S2: Based on the multi-energy coupling system architecture, an objective function is established to minimize the overall cost and maximize the utilization of clean energy. The objective function incorporates a time-dependent energy price response coefficient. The energy price response coefficient is a coefficient that reflects the volatility of energy market prices and the user-side price responsiveness at different times. The objective function is as follows: min in, The total cost of the planning and operation phase of a multi-energy coupled system. Costs during the system planning phase, Costs incurred during the system's operational phase; in, This represents the total utilization of wind turbines, photovoltaic power, and biogas. Let be the output power of the photovoltaic and wind turbine equipment at time t, respectively. The gas-to-electricity conversion coefficient; S3: Establish a source-storage-load model, which includes a source-side equipment model, a storage-side equipment model, and a load model. The load model includes adjustable loads and non-adjustable loads. Adjustable loads include energy storage-like adjustable load models and other adjustable load models. The source-side equipment model uses an interval optimization method to handle the uncertainty of renewable energy output. S4: Using the objective function as the optimization direction and the source-storage-load model as the parameter basis, set system constraints and use a multi-objective optimization algorithm to solve the problem and obtain the planning scheme. The objective optimization algorithm is an improved MOEA / D algorithm that considers the constraints.

2. The multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, In step S1, the distributed renewable energy unit includes wind turbines, photovoltaic units, and biogas digesters; the energy storage unit includes electric energy storage and gas energy storage; and the coupling unit includes electric boilers, biogas furnaces, and combined heat and power units.

3. The multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, The expression for the cost in the system planning phase is: in, The investment cost per unit capacity of related equipment designed for multi-energy coupling systems. The planned service life of the relevant equipment. For interest rates, For equipment types, This represents the total daily scheduling time for the system.

4. The multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, The expression for the system operation phase cost is: in, The operating and maintenance cost per unit capacity of the related equipment designed for multi-energy coupling systems. The installed capacity of the equipment; The planned service life of the relevant equipment. For interest rates, For equipment types, The system's electricity and gas purchases at time t. This refers to the price response coefficient of energy prices at time t. This represents the total daily scheduling time for the system. This represents the total scheduling time for the system over one year.

5. The multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, In step S3, the expression for the energy storage-like load model in the adjustable load is: in, Let t be the internal electrical quantity of the energy storage-like adjustable load. For energy storage systems with adjustable load charging efficiency, The charging power for the energy storage-type adjustable load is the load of the energy storage-type adjustable load at time t. The total internal electrical capacity of the energy storage-type adjustable load is [missing information]. The maximum charging power for adjustable loads of energy storage systems. For energy storage-type adjustable loads, the minimum and maximum power requirements are... This refers to the amount of electricity stored in an adjustable load when it is disconnected from the charging state. The minimum amount of electricity required to charge an adjustable load of the energy storage type; The expressions for other adjustable load models in adjustable load are: in, For other adjustable load power at time t, This represents the baseline load power of other adjustable loads at time t. For the price elasticity coefficient of other adjustable loads, Let t be the user-side electricity purchase price and benchmark electricity price. These represent the minimum and maximum load values ​​of other adjustable loads during operation at time t.

6. The multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, In step S3, the expression for the non-adjustable load is: in, Let be the unadjustable load power at time t. Let t be the proportion of non-adjustable load power in the total energy load power at time t. This is for the load requirements of a multi-energy coupled system.

7. The multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, In step S3, the expression for the interval optimization method is: in, Let be the output power of the renewable resource at time t. For the range of changes in the renewable resource load factor, , These are the lower bound and the upper bound of the range fluctuation, respectively. Rated power configured for renewable resources.

8. The multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, In step S3, the source-side equipment model includes a combined heat and power (CHP) unit model, expressed as: in, Let be the electrical / thermal power of the combined heat and power unit at time t. For the electrical and thermal conversion efficiency of combined heat and power units, Let t be the amount of biogas and natural gas consumed by the cogeneration unit.

9. A multi-energy coupling planning method considering source-storage-load and economic characteristics according to claim 1, characterized in that, In step S3, the energy storage-side equipment model includes an electrical energy storage model, the expression of which is: in, Let represent the energy storage state at time t. Let be the charging and discharging power of the electrical energy storage at time t. For the charging and discharging efficiency of electrical energy storage, For time intervals.

10. A device, characterized in that, The device includes a processor and a memory, the memory storing at least one computer program, which is loaded and executed by the processor to implement the multi-energy coupling planning method considering source-storage-load and economic characteristics as described in any one of claims 1-9.

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