Multi-park integrated energy system collaborative optimization method, system, equipment and medium

Abstract

The invention provides a multi-park integrated energy system collaborative optimization method, system and device and a medium. The method comprises the steps of obtaining electric heating load scheduling data of an integrated energy system of each park; based on the electric heating load scheduling data of the integrated energy system of each park, solving the multi-park cooperative optimization model by using an alternating direction multiplier method to obtain an optimal operation strategy of the integrated energy system of each park; based on the optimal operation strategy, calculating the total income of the multi-park integrated energy system and the contribution factor of the integrated energy system of each park; according to the contribution factor of the integrated energy system of each park, performing income distribution on the total income of the integrated energy system of the multiple parks; according to the method, the cooperative optimization model constructed based on the cooperation cost is solved by adopting the alternating direction multiplier method, the contribution factors of all the integrated energy systems are introduced to realize cooperation income redistribution, the enthusiasm of all contribution subjects can be improved, and the energy utilization efficiency of the multi-park integrated energy systems can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to a collaborative optimization method, system, equipment and medium for a multi-park integrated energy system. Background Art

[0002] Currently, with rapid economic development, total park energy consumption continues to rise, bringing with it environmental challenges such as low energy efficiency and high carbon emissions. Against this backdrop, a park Integrated Energy System (IES) can provide users with clean and economical energy supply by organically coordinating and optimizing energy generation, transmission and distribution, conversion, storage, and consumption. However, due to the limitations of its scale, a park IES cannot integrate regional source, load, and storage resources. Therefore, multiple park IES can be interconnected to form a multi-park integrated energy system, thereby improving the absorption rate of new energy, energy utilization, and reducing the operating costs of each system.

[0003] Multi-park integrated energy systems obtain benefits through collaborative optimization and redistribute the benefits within the alliance. Existing technologies include Nash bargaining to achieve cooperative games among multiple integrated energy service providers, establishing a multi-microgrid cooperative alliance, and conducting power trading games among microgrids to promote the use and absorption of renewable energy and reduce alliance costs. The Shapley method is also used to distribute the emergent benefits of joint operation to each sub-microgrid, achieving fair distribution of alliance benefits. However, the collaborative optimization methods involved in existing technologies do not consider the energy contribution of the main body of each park integrated energy system when distributing benefits, which reduces the cooperation enthusiasm of the high-contributing entities, thereby reducing the energy utilization efficiency of the multi-park integrated energy system. Summary of the Invention

[0004] In order to solve the problem in the prior art of integrating resources by interconnecting multiple park integrated energy systems, in which the profit distribution method does not take into account the energy contribution of each park entity, resulting in a decrease in the cooperation enthusiasm of high-contributing entities and a decrease in the energy utilization efficiency of the multi-park integrated energy system, the present invention proposes a collaborative optimization method for a multi-park integrated energy system, comprising:

[0005] Obtain the electric and thermal load dispatch data of each park's integrated energy system;

[0006] Based on the electric and thermal load scheduling data of the integrated energy systems of the parks, the pre-built multi-park collaborative optimization model is solved using the alternating direction multiplier method to obtain the optimal operation strategy of the integrated energy systems of the parks;

[0007] Based on the optimal operation strategy of the integrated energy system of each park, calculate the total benefit of the multi-park integrated energy system and the contribution factor of the integrated energy system of each park;

[0008] Distributing the total revenue of the multi-park integrated energy system according to the contribution factor of each park integrated energy system;

[0009] Among them, the multi-park integrated energy system includes the integrated energy systems of each park; the multi-park collaborative optimization model is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system.

[0010] Optionally, the electric and thermal load scheduling data of the park integrated energy system includes the following acquisition process:

[0011] Obtaining the output power of each energy device in the park integrated energy system;

[0012] Performing load response on the park integrated energy system according to the output power of each energy device in the park integrated energy system, and outputting load response data of the park integrated energy system;

[0013] Outputting the electric and thermal load dispatching data of the park integrated energy system using a pre-built demand response model based on the load response data of the park integrated energy system;

[0014] The demand response model is constructed with the goal of minimizing the operating cost of the park integrated energy system and with power balance constraints and energy interaction constraints as constraints;

[0015] The operating costs include one or more of the following: the cost of purchasing electricity from the external power grid, the cost of purchasing natural gas, the cost of equipment operation and maintenance, the cost of energy storage operation and loss, the penalty cost for curtailing wind and solar power, the cost of carbon trading, and the cost of P2P trading lines;

[0016] The energy equipment includes one or more of the following: a gas turbine, a gas boiler, an absorption refrigerator, and an energy storage device;

[0017] The electric and thermal load scheduling data includes one or more of the following: electric load demand, thermal load demand, energy storage device charging and discharging power, power purchase and sales with the external power grid, and energy equipment output plan at each moment.

[0018] Optionally, the multi-park collaborative optimization model includes the following construction process:

[0019] The operation costs of the integrated energy systems of the various parks are summed to obtain the cooperation cost of the multi-park integrated energy system;

[0020] An objective function is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system;

[0021] Taking the electrical energy consistency constraint and the thermal energy consistency constraint as the constraint conditions of the objective function;

[0022] Establishing a collaborative optimization function according to the objective function and the constraint conditions;

[0023] Based on the collaborative optimization function, a multi-park collaborative optimization model is constructed.

[0024] Optionally, the collaborative optimization function is expressed as follows:

[0025]

[0026] Where,

[0027] A=P i-j,t +P j-i,t ;

[0028] B=H i-j,t +H j-i,t ;

[0029] in, represents the collaborative optimization function of the park's integrated energy system i; represents the operating cost of the park's integrated energy system i; represents the balance factor of power interaction between the park integrated energy system i and the park integrated energy system j; i≠j; A represents the power consistency constraint; P i-j,t P represents the expected transaction power of the park integrated energy system i to the park integrated energy system j at time t; j-i,t represents the expected traded power of the park integrated energy system j to the park integrated energy system i at time t; t = 1…T; T represents the total number of time; ρ e represents the first penalty factor; represents the balance factor of thermal energy interaction between park integrated energy system i and park integrated energy system j; B represents the thermal energy consistency constraint; H i-j,t H represents the expected heat volume traded from the integrated energy system i to the integrated energy system j at time t; j-i,t represents the expected heat volume traded from the integrated energy system j to the integrated energy system i at time t; ρ h represents the second penalty factor; I represents the total number of integrated energy systems in the park.

[0030] Optionally, based on the electric and thermal load scheduling data of the integrated energy systems of the respective parks, a pre-built multi-park collaborative optimization model is solved using an alternating direction multiplier method to obtain the optimal operation strategy of the integrated energy systems of the respective parks, including:

[0031] Based on the integrated energy system of each park, the expected transaction power and expected transaction heat of the integrated energy system are updated according to the electric and thermal load scheduling data of the integrated energy system of the park;

[0032] updating the balance factor between the park integrated energy system and other park integrated energy systems based on the updated expected transaction power and expected transaction heat of the integrated energy system;

[0033] Calculating a collaborative optimization function value between the park integrated energy system and the remaining park integrated energy systems based on a balance factor between the park integrated energy system and the remaining park integrated energy systems;

[0034] If the collaborative optimization function values ​​between the park integrated energy system and other park integrated energy systems converge, the updated expected trading power, expected trading heat and balance factor of the park integrated energy system will be used as the optimal operation strategy of the park integrated energy system.

[0035] Optionally, the total revenue of the multi-park integrated energy system is calculated as follows:

[0036]

[0037] Where W represents the total benefit of the multi-park integrated energy system; represents the independent operation cost of the park's integrated energy system i; Represents the operating cost of the park's integrated energy system i; i=1…I; I represents the total number of the park's integrated energy systems.

[0038] Optionally, the contribution factor of the park integrated energy system is calculated as follows:

[0039]

[0040] Among them, θ i represents the contribution factor of the park's integrated energy system i; represents the electric energy contribution factor of the park's integrated energy system i; represents the thermal energy contribution factor of the park's integrated energy system i; represents the purchase price of electricity at time t; represents the amount of electricity purchased by the park's integrated energy system i at time t; represents the selling price of electricity at time t; represents the amount of electricity sold by the park's integrated energy system i at time t; represents the purchase price of thermal energy at time t; represents the purchased thermal energy of the park integrated energy system i at time t; represents the selling price of heat energy at time t; It represents the thermal energy sold by the integrated energy system i of the park at time t; T represents the total number of moments.

[0041] Optionally, distributing the total revenue of the multi-park integrated energy system according to the contribution factor of each park integrated energy system includes:

[0042] Calculating the index contribution factor of the comprehensive energy system of each park according to the contribution factor of the comprehensive energy system of each park;

[0043] The total revenue of the multi-park integrated energy system is distributed according to the index contribution factor of each park integrated energy system.

[0044] Based on the same inventive concept, the present invention also provides a multi-park integrated energy system collaborative optimization system, comprising:

[0045] Data acquisition module, used to obtain the electric and thermal load scheduling data of each park's integrated energy system;

[0046] A model solving module is used to solve a pre-built multi-park collaborative optimization model based on the electric and thermal load scheduling data of the integrated energy system of each park using the alternating direction multiplier method to obtain the optimal operation strategy of the integrated energy system of each park;

[0047] A contribution calculation module is used to calculate the total benefit of the multi-park integrated energy system and the contribution factor of each park integrated energy system based on the optimal operation strategy of each park integrated energy system;

[0048] A profit distribution module, configured to distribute the total profit of the multi-park integrated energy system according to the contribution factor of each park integrated energy system;

[0049] Among them, the multi-park integrated energy system includes the integrated energy systems of each park; the multi-park collaborative optimization model is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system.

[0050] Optionally, the data acquisition module includes:

[0051] A power output submodule, used to obtain the output power of each energy device in the park integrated energy system;

[0052] A load response submodule, configured to perform load response on the park integrated energy system according to the output power of each energy device in the park integrated energy system, and output load response data of the park integrated energy system;

[0053] A load scheduling submodule is used to output the electric and thermal load scheduling data of the park integrated energy system based on the load response data of the park integrated energy system and using a pre-built demand response model;

[0054] The demand response model is constructed with the goal of minimizing the operating cost of the park integrated energy system and with power balance constraints and energy interaction constraints as constraints;

[0055] The operating costs include one or more of the following: the cost of purchasing electricity from the external power grid, the cost of purchasing natural gas, the cost of equipment operation and maintenance, the cost of energy storage operation and loss, the penalty cost for curtailing wind and solar power, the cost of carbon trading, and the cost of P2P trading lines;

[0056] The energy equipment includes one or more of the following: a gas turbine, a gas boiler, an absorption refrigerator, and an energy storage device;

[0057] The electric and thermal load scheduling data includes one or more of the following: electric load demand, thermal load demand, energy storage device charging and discharging power, power purchase and sales with the external power grid, and energy equipment output plan at each moment.

[0058] Optionally, the system further includes: a model building module, including:

[0059] A cooperation cost calculation submodule, configured to sum the operating costs of the integrated energy systems of the various parks to obtain the cooperation cost of the multi-park integrated energy system;

[0060] A target setting submodule is used to construct an objective function with the goal of minimizing the cooperation cost of the multi-park integrated energy system;

[0061] A constraint setting submodule, configured to use an electrical energy consistency constraint and a thermal energy consistency constraint as constraint conditions of the objective function;

[0062] A collaborative optimization submodule, configured to establish a collaborative optimization function based on the objective function and the constraint conditions;

[0063] The optimization model construction submodule is used to construct a multi-park collaborative optimization model based on the collaborative optimization function.

[0064] Optionally, the collaborative optimization function is expressed as follows:

[0065]

[0066] Where,

[0067] A=P i-j,t +P j-i,t ;

[0068] B=H i-j,t +H j-i,t ;

[0069] in, represents the collaborative optimization function of the park's integrated energy system i; represents the operating cost of the park's integrated energy system i; represents the balance factor of power interaction between the park integrated energy system i and the park integrated energy system j; i≠j; A represents the power consistency constraint; P i-j,t P represents the expected transaction power of the park integrated energy system i to the park integrated energy system j at time t; j-i,t represents the expected traded power of the park integrated energy system j to the park integrated energy system i at time t; t = 1…T; T represents the total number of time; ρ e represents the first penalty factor; represents the balance factor of thermal energy interaction between park integrated energy system i and park integrated energy system j; B represents the thermal energy consistency constraint; H i-j,t H represents the expected heat volume traded from the integrated energy system i to the integrated energy system j at time t; j-i,t represents the expected heat volume traded from the integrated energy system j to the integrated energy system i at time t; ρ h represents the second penalty factor; I represents the total number of integrated energy systems in the park.

[0070] Optionally, the model solving module includes:

[0071] An expected update submodule is used to update the expected transaction power and expected transaction heat of each integrated energy system based on the electric and thermal load scheduling data of the integrated energy system of each park;

[0072] A balance update submodule, configured to update a balance factor between the park integrated energy system and other park integrated energy systems according to the updated expected transaction power and expected transaction heat of the integrated energy system;

[0073] A balance optimization submodule, configured to calculate a collaborative optimization function value between the park integrated energy system and the remaining park integrated energy systems based on a balance factor between the park integrated energy system and the remaining park integrated energy systems;

[0074] The strategy output submodule is used to use the updated expected trading power, expected trading heat and balance factor of the park integrated energy system as the optimal operation strategy of the park integrated energy system if the collaborative optimization function values ​​between the park integrated energy system and other park integrated energy systems converge.

[0075] Optionally, the total revenue of the multi-park integrated energy system is calculated as follows:

[0076]

[0077] Where W represents the total benefit of the multi-park integrated energy system; represents the independent operation cost of the park's integrated energy system i; Represents the operating cost of the park's integrated energy system i; i=1…I; I represents the total number of the park's integrated energy systems.

[0078] Optionally, the contribution factor of the park integrated energy system is calculated as follows:

[0079]

[0080] Among them, θ i represents the contribution factor of the park's integrated energy system i; represents the electric energy contribution factor of the park's integrated energy system i; represents the thermal energy contribution factor of the park's integrated energy system i; represents the purchase price of electricity at time t; represents the amount of electricity purchased by the park's integrated energy system i at time t; represents the selling price of electricity at time t; represents the amount of electricity sold by the park's integrated energy system i at time t; represents the purchase price of thermal energy at time t; represents the purchased thermal energy of the park integrated energy system i at time t; represents the selling price of heat energy at time t; It represents the thermal energy sold by the integrated energy system i of the park at time t; T represents the total number of moments.

[0081] Optionally, the profit distribution module includes:

[0082] A contribution mapping submodule, configured to calculate an index contribution factor of each park comprehensive energy system according to the contribution factor of each park comprehensive energy system;

[0083] The profit distribution submodule is used to distribute the total profit of the multi-park integrated energy system according to the index contribution factor of each park integrated energy system.

[0084] In another aspect, the present invention further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0085] The memory is used to store one or more programs;

[0086] When the one or more programs are executed by the at least one processor, a multi-park integrated energy system collaborative optimization method as described above is implemented.

[0087] On the other hand, the present invention also provides a computer-readable storage medium having an execution program stored thereon. When the execution program is executed, the collaborative optimization method of a multi-park integrated energy system as described above is implemented.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] The present invention provides a collaborative optimization method, system, equipment and medium for a multi-park integrated energy system, including: obtaining the electric and thermal load scheduling data of each park integrated energy system; based on the electric and thermal load scheduling data of each park integrated energy system, using the alternating direction multiplier method to solve a pre-constructed multi-park collaborative optimization model to obtain the optimal operation strategy of each park integrated energy system; based on the optimal operation strategy of each park integrated energy system, calculating the total revenue of the multi-park integrated energy system and the contribution factor of each park integrated energy system; according to the contribution factor of each park integrated energy system, distributing the total revenue of the multi-park integrated energy system; wherein, the multi-park integrated energy system includes each park integrated energy system; the multi-park collaborative optimization model is constructed with the goal of minimizing the collaborative cost of the multi-park integrated energy system; the present invention uses the alternating direction multiplier method to solve the multi-park collaborative optimization model constructed based on the collaborative cost, and introduces the contribution factor of each park integrated energy system to realize the redistribution of collaborative benefits, which can improve the enthusiasm of each contributing subject and improve the energy utilization efficiency of the multi-park integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 A schematic diagram of a process for collaborative optimization of a multi-park integrated energy system provided by the present invention;

[0091] Figure 2 A schematic diagram of the execution framework of a multi-park integrated energy system collaborative optimization method provided by the present invention;

[0092] Figure 3 A schematic diagram of the overall calculation process of a multi-park integrated energy system collaborative optimization method provided by the present invention;

[0093] Figure 4 A schematic diagram of the operating cost distribution of a park integrated energy system 1 in a multi-park integrated energy system collaborative optimization method provided by a specific embodiment of the present invention;

[0094] Figure 5A schematic diagram of the operating cost distribution of a park integrated energy system 2 in a multi-park integrated energy system collaborative optimization method provided by a specific embodiment of the present invention;

[0095] Figure 6 A schematic diagram of the operating cost distribution of a park integrated energy system 3 in a multi-park integrated energy system collaborative optimization method provided by a specific embodiment of the present invention;

[0096] Figure 7 A schematic diagram of the distribution of alliance operating costs of a multi-park integrated energy system in a multi-park integrated energy system collaborative optimization method provided by a specific embodiment of the present invention;

[0097] Figure 8 A schematic diagram of an alliance electric energy interaction curve composed of a multi-park integrated energy system in a multi-park integrated energy system collaborative optimization method provided by a specific embodiment of the present invention;

[0098] Figure 9 A schematic diagram of a thermal energy interaction curve of an alliance composed of a multi-park integrated energy system in a multi-park integrated energy system collaborative optimization method provided by a specific embodiment of the present invention;

[0099] Figure 10 A schematic diagram of the structure of a multi-park integrated energy system collaborative optimization system provided by the present invention;

[0100] Figure 11 This is a structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0101] The present invention proposes a method, system, device and medium for collaborative optimization of a multi-park integrated energy system. The specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0102] Example 1:

[0103] The present invention provides a collaborative optimization method for a multi-park integrated energy system, the flow diagram of which is as follows: Figure 1 Shown, including:

[0104] Step 1: Obtain the electric and thermal load scheduling data of each park's integrated energy system (IES);

[0105] Step 2: Based on the electric and thermal load dispatch data of each park's integrated energy system, the pre-built multi-park collaborative optimization model is solved using the alternating direction multiplier method to obtain the optimal operation strategy of each park's integrated energy system;

[0106] Step 3: Based on the optimal operation strategy of each park's integrated energy system, calculate the total revenue of the multi-park integrated energy system and the contribution factor of each park's integrated energy system;

[0107] Step 4: Distribute the total benefits of the multi-park integrated energy system based on the contribution factor of each park’s integrated energy system;

[0108] The multi-park integrated energy system includes the integrated energy systems of each park; the multi-park collaborative optimization model is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system;

[0109] Generally, the collaborative optimization of integrated energy systems in multiple parks usually aims to minimize the total cost of the integrated energy systems or maximize their benefits. However, existing solutions fail to fully utilize the electric and thermal load scheduling data within each park, which will lead to a series of technical problems. For example, without accurate load scheduling data, the optimization model will not be able to accurately reflect the actual energy demand and consumption characteristics of each park, resulting in insufficient accuracy of load scheduling, which in turn affects the effectiveness of the optimization results. Therefore, if collaborative optimization can be performed based on the electric and thermal load scheduling data within the integrated energy system of each park, it will not only improve the accuracy of load scheduling, but also optimize the allocation of resources, reduce contradictions and conflicts in the scheduling process, and improve the overall economy and flexibility of the system. Specifically:

[0110] In one implementation, the electric and thermal load scheduling data of the park integrated energy system in step 1 above may include the following acquisition process:

[0111] Obtain the output power of each energy device in the park's integrated energy system;

[0112] According to the output power of each energy device in the park's integrated energy system, the load response of the park's integrated energy system is carried out, and the load response data of the park's integrated energy system is output;

[0113] Based on the load response data of the park's integrated energy system, the pre-built demand response model is used to output the electric and thermal load scheduling data of the park's integrated energy system;

[0114] The demand response model is constructed with the goal of minimizing the operating cost of the integrated energy system of the park and with power balance constraints and energy interaction constraints as constraints;

[0115] Operating costs may include one or more of the following: electricity purchase and sales costs from external power grids, natural gas purchase costs, equipment operation and maintenance costs, energy storage operation and loss costs, wind and solar curtailment penalty costs, carbon trading costs, and P2P trading line costs;

[0116] Energy equipment may include one or more of the following: gas turbines, gas boilers, absorption chillers, and energy storage equipment;

[0117] The electric and thermal load dispatch data may include one or more of the following: electric load demand, thermal load demand, energy storage device charge and discharge power, power purchase and sales with the external power grid, and energy equipment output plan at each moment;

[0118] For example, the above-mentioned gas turbine can generate electricity by burning natural gas. While generating electricity, it also emits a large amount of high-temperature gas. The corresponding output power is calculated as follows:

[0119]

[0120] in, represents the output power of gas turbine j0 at time t; represents the amount of natural gas consumed by the gas turbine j0 at time t; η GT Indicates the value of electric power generated per unit heat of gas turbine; Indicates the calorific value of natural gas consumed by the gas turbine; represents the minimum output power of the gas turbine j0; represents the maximum output power of the gas turbine j0; represents the output power of gas turbine j0 at time t-1; represents the maximum ramp power of the gas turbine j0;

[0121] For example, the above-mentioned gas boiler directly generates heat energy by burning natural gas:

[0122]

[0123] in, represents the output power of gas boiler i0 at time t; represents the amount of natural gas consumed by the gas boiler i0 at time t; η GB Indicates the electric-to-heat conversion efficiency of a gas boiler; Indicates the calorific value of natural gas consumed by gas boilers; Indicates the minimum output power of the gas boiler i0; Indicates the maximum output power of gas boiler i0; Indicates the maximum ramp power of the gas boiler i0; represents the output power of gas boiler i0 at time t-1;

[0124] For example, the absorption chiller absorbs the heat generated by the waste heat recovery device to cool the room. When the cooling capacity of the absorption chiller is insufficient to meet the cooling load demand, the electric chiller consumes electricity to output cooling power to meet the demand:

[0125]

[0126] in, represents the cooling power of absorption refrigerator i1 at time t; η AC is the energy efficiency ratio of the absorption chiller; is the cooling output provided to the absorption chiller i1 at time t; and are the minimum and maximum cooling power of absorption chiller i1 respectively.

[0127] For example, the output energy of the above energy storage devices (which may include electrical, thermal, cold, and other energy storage devices, and whose operating characteristics are similar) can be expressed as:

[0128]

[0129] in, represents the stored energy of type m energy storage device i2 at time t; represents the self-energy loss coefficient of type m energy storage device; represents the stored energy of type m energy storage device i2 at time t-1; represents the charging efficiency of type m energy storage device; represents the charging power of the m-type energy storage device i2 at time t; represents the discharge power of type m energy storage device i2 at time t; represents the discharge efficiency of type m energy storage device; Δt represents the time interval; represents the charging state of the m-type energy storage device i2 at time t; Indicates the upper limit of the charge and discharge ramp rate of the m-type energy storage device i2; represents the discharge state of the m-type energy storage device i2 at time t; represents the upper limit of the discharge power of the type m energy storage device i2; represents the lower limit constraint of energy of type m energy storage device i2; represents the energy upper limit constraint of type m energy storage device i2; represents the energy of type m energy storage device at the initial stage; represents the energy of type m energy storage device at the end;

[0130] The integrated energy system of each park can flexibly adjust the load size through price subsidies, thereby improving the economy and reliability of its own scheduling. The adjustable load is divided into curtailable load and shiftable load. The actual electric load after demand response can be expressed as:

[0131]

[0132] in, represents the actual electric load of the park integrated energy system i at time t after demand response; represents the actual electric load of the park integrated energy system i at time t before demand response; represents the reducible electric load of the park integrated energy system i at time t; represents the transferable electric load of the park integrated energy system i at time t;

[0133] For cooling and heating loads, only the basic cooling and heating loads and the reducible parts are considered, and the following relationship is satisfied:

[0134]

[0135] Where: is the net heat load of the park integrated energy system i after demand response at time t; is the net cooling load of the integrated energy system i in the park after demand response at time t; is the heat load of the integrated energy system i at time t before demand response; is the cooling load of the integrated energy system i at time t before demand response; is the heat load that can be reduced in the park's integrated energy system i at time t; is the cooling load that can be reduced in the park's integrated energy system i at time t.

[0136] For example, the operating cost of the integrated energy system in the above demand response model (the integrated energy system in the park that considers the interaction between electric and thermal power) can be expressed as follows:

[0137]

[0138] in, represents the operating cost of the park's integrated energy system i; represents the cost of purchasing and selling electricity from the external power grid to the park's integrated energy system i; represents the cost of purchasing natural gas for the park's integrated energy system i; represents the operation and maintenance cost of equipment i in the park's integrated energy system; represents the energy storage operation and loss costs of the park's integrated energy system i; represents the penalty cost of wind and solar curtailment in the integrated energy system i of the park; represents the carbon transaction cost of the park's integrated energy system i; represents the P2P transaction line cost of the park integrated energy system i;

[0139] For example, the expression of the above power balance constraint is as follows:

[0140]

[0141] in, represents the wind power generation power of the park integrated energy system i at time t; represents the photovoltaic power generation of the park's integrated energy system i at time t; represents the power generated by the gas turbine of the park's integrated energy system i at time t; represents the discharge power of the energy storage device of the park integrated energy system i at time t; represents the power purchased by the park integrated energy system i from the grid at time t; represents the actual electric load of the park integrated energy system i at time t after demand response; represents the charging power of the energy storage device of the park integrated energy system i at time t; P i-j,t represents the electric power transmitted by the park integrated energy system i to the park integrated energy system j at time t (it can also be expressed as the expected transaction amount of the park integrated energy system i to the park integrated energy system j at time t); represents the thermal power generated by the waste heat boiler of the park's integrated energy system i at time t; represents the thermal power generated by the gas boiler of the park's integrated energy system i at time t; represents the heat release power of the energy storage equipment of the park integrated energy system i at time t; represents the heat load demand response power of the park integrated energy system i at time t; represents the thermal power consumed by the absorption chiller of the park's integrated energy system i at time t; represents the heating power of the energy storage device in the park's integrated energy system i at time t; H i-j,t represents the thermal power transmitted from the park integrated energy system i to the park integrated energy system j at time t; represents the cooling power generated by the absorption chiller of the park's integrated energy system i at time t; represents the cooling power generated by the electric refrigerator of the park's integrated energy system i at time t; represents the cooling power of the energy storage equipment of the park integrated energy system i at time t; represents the cooling load demand response power of the park integrated energy system i at time t; represents the charging and cooling power of the energy storage device of the park integrated energy system i at time t; represents the volume of gas purchased from the gas grid by the integrated energy system i at time t; represents the gas volume consumed by the gas turbine of the park's integrated energy system i at time t; represents the gas volume consumed by the gas boiler of the park's integrated energy system i at time t; represents the gas volume generated by the power-to-gas conversion of the park's integrated energy system i at time t;

[0142] For example, the expression of the above energy interaction constraint is as follows:

[0143]

[0144] in, represents the minimum electric power transmitted from the park integrated energy system i to the park integrated energy system j at time t; P i-j,t represents the electric power transmitted from the park integrated energy system i to the park integrated energy system j at time t; P represents the maximum electric power transmitted from the park integrated energy system i to the park integrated energy system j at time t; i,t represents the electric power of integrated energy system i at time t; i = 1…I; I represents the total number of integrated energy systems in the park; H i-j,t represents the thermal power that the park integrated energy system i sends to the park integrated energy system j at time t (it can also be used to represent the expected heat volume that the park integrated energy system i expects to trade with the park integrated energy system j at time t); represents the minimum thermal power transmitted from the park integrated energy system i to the park integrated energy system j at time t; represents the maximum thermal power transmitted from the park integrated energy system i to the park integrated energy system j at time t; H i,t represents the thermal power of the park's integrated energy system i at time t;

[0145] Attach Figure 2 Taking the collaborative optimization framework of a multi-park integrated energy system as an example, the IESs of each park exchange electricity and heat through power interconnectors and heat network pipelines, forming a multi-park integrated energy system cooperative alliance. To achieve energy sharing and coordinated complementarity on a larger scale, multiple park integrated energy systems (IES1, IES2, and IES3 in the figure) collaborate through the flow of electricity, heat, and natural gas to achieve optimal energy allocation. The figure uses colored arrows to represent the flow of various energy types: yellow arrows represent natural gas flow, black arrows represent electricity flow, and red arrows represent heat flow. These energy interactions between park integrated energy systems are manifested through connections to the gas, power, and heat networks. This promotes energy sharing and optimized scheduling among different park integrated energy systems, ensuring efficient energy flow within the system and improving the energy efficiency of the entire multi-park integrated energy system. Furthermore, the collaborative relationships and resource flows between park integrated energy systems reflect the cooperation and competition among the parks within the multi-park system and rely on corresponding collaborative optimization strategies.

[0146] This implementation maximizes energy efficiency by rationally scheduling the output power of various energy devices and effectively controls the energy costs of each park's integrated energy system. Through a scheduling process based on load response data and a demand response model, the operation of each park's integrated energy system is optimized. These technical features enable the park's integrated energy system to minimize operating costs while ensuring system power balance and energy interaction constraints. Furthermore, the calculation of the operating costs of each park's integrated energy system involves multiple factors, such as external grid electricity purchase costs, natural gas procurement costs, and energy storage losses. This enables parks to find the optimal combination of different energy supply methods and flexibly respond to varying market demands and energy price fluctuations. In particular, the introduction of a demand response model optimizes operating cost calculations, enabling equipment to be dispatched based on varying load conditions, enabling the system load to optimally respond to external demand fluctuations. This reduces reliance on the traditional power grid, energy waste, and system operational uncertainty. Furthermore, the diverse equipment selection in this implementation (such as gas turbines, gas boilers, absorption chillers, and energy storage devices) enhances the resilience and adaptability of the multi-park integrated energy system, ensuring stable operation even under unstable or tight energy supply conditions. Therefore, this implementation, through load response and equipment scheduling methods designed for the actual needs of the park, can enable the park to maintain good economic efficiency and stability under various operational constraints, overcoming the limitations of conventional scheduling methods. In particular, it can ensure optimized system operation during the dynamic adjustment of different energy supplies. Furthermore, this implementation can further enhance the system's intelligence level. For example, a dynamic prediction and decision-making optimization module based on artificial intelligence (AI) or machine learning algorithms can be considered. Through real-time monitoring and data analysis, the AI ​​module can predict future load demand trends, equipment failure probabilities, energy price fluctuations, and other factors. Based on these predictions, it can dynamically adjust load scheduling and equipment operation strategies, thereby ensuring system stability while further reducing the park's overall energy operating costs. Furthermore, the inclusion of blockchain technology for energy trading and data verification can be considered to ensure transparency, security, and immutability of energy transactions. The introduction of such technology can not only enhance the traceability of equipment operations within the park but also provide a more reliable infrastructure for peer-to-peer energy trading within the park, thereby improving the overall efficiency and sustainable development of the park's energy management.

[0147] After acquiring and analyzing the electric and thermal load dispatch data for the integrated energy system of the park, we can consider the operating costs, energy exchange, and constraints of different parks to ensure that the results of collaborative optimization can effectively reduce overall costs while meeting the energy demand and supply balance between the parks, and achieve efficient collaboration and cost minimization between the integrated energy systems of different parks. Specifically:

[0148] In one implementation, the multi-park collaborative optimization model may include the following construction process:

[0149] The operating costs of the integrated energy systems of each park are summed up to obtain the cooperation cost of the multi-park integrated energy system;

[0150] The objective function is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system;

[0151] The power consistency constraint and the thermal consistency constraint are used as the constraint conditions of the objective function;

[0152] Establish collaborative optimization function based on objective function and constraints;

[0153] Based on the collaborative optimization function, a multi-park collaborative optimization model is constructed.

[0154] For example, the objective function above can be expressed as follows:

[0155]

[0156] in, represents the operating cost of the park's integrated energy system i; i = 1…I; I represents the total number of the park's integrated energy systems;

[0157] For example, the expression of the above collaborative optimization function can be as follows:

[0158]

[0159] Where,

[0160] A=P i-j,t +P j-i,t ;

[0161] B=H i-j,t +H j-i,t ;

[0162] in, represents the collaborative optimization function of the park's integrated energy system i; represents the operating cost of the park's integrated energy system i; represents the balance factor of the power interaction between the park integrated energy system i and the park integrated energy system j (the balance factor here can also be expressed as a Lagrange multiplier); i≠j; A represents the power consistency constraint; P i-j,t P represents the expected transaction power of the park integrated energy system i to the park integrated energy system j at time t; j-i,t represents the expected traded power of the park integrated energy system j to the park integrated energy system i at time t; t = 1…T; T represents the total number of time; ρ erepresents the first penalty factor; represents the balance factor of thermal energy interaction between park integrated energy system i and park integrated energy system j (the balance factor here can also be called Lagrange multiplier); B represents the thermal energy consistency constraint; H i-j,t H represents the expected heat volume traded from the integrated energy system i to the integrated energy system j at time t; j-i,t represents the expected heat volume traded from the integrated energy system j to the integrated energy system i at time t; ρ h represents the second penalty factor. In this example, considering that the electric energy interaction power and thermal energy interaction power between different campus integrated energy systems are coupled variables, consistency constraints (electric energy consistency constraint and thermal energy consistency constraint) are introduced for decoupling:

[0163]

[0164] In this implementation method, the operating costs of the integrated energy systems of each park are summed up to form the cooperation cost of the multi-park integrated energy system, and the objective function is constructed based on this goal. The power consistency constraint and thermal consistency constraint in the objective function enable the flow of power and thermal energy between the integrated energy systems of different parks to be strictly coordinated, thereby avoiding energy waste and unreasonable resource allocation, and improving the overall energy efficiency of the system; and by introducing a collaborative optimization function (augmented Lagrangian function), a balance factor is used to adjust the energy transaction between parks, especially by introducing the Lagrangian multiplier in the interaction between power and thermal energy, which not only solves the coordination problem of energy flow between parks, but also can effectively avoid inefficient use of resources and unfair benefit distribution in practical applications, and realize the coordination between parks. Energy supply and demand balance, this method not only enhances the coordination of the integrated energy systems of each park in the exchange of electricity and heat energy, but also enables more equitable and efficient cost allocation among the integrated energy systems of different parks, further improving the overall operation efficiency and economy of the multi-park integrated energy system. In order to further improve the adaptability and flexibility of the system in this implementation method, it is also possible to consider adding a dynamic adjustment mechanism based on real-time data feedback to monitor the energy supply, demand and operation status of each park in real time, and dynamically adjust the energy exchange ratio and cooperation strategy between parks according to actual conditions. Such technical features can not only further improve the response speed and flexibility of the system, but also make rapid adjustments in response to sudden demand fluctuations or unstable energy supply to ensure the stability and efficiency of the system operation.

[0165] The key to building a multi-park collaborative optimization model in the above steps is to accurately consider the operating costs of each park, and on this basis, to achieve efficient coordination and optimization of the energy system through constraints and collaborative optimization functions. Through this optimization process, the multi-park integrated energy system can minimize overall operating costs while ensuring the balance of energy supply and demand in each park, and ensure the rational flow and sharing of energy between parks. To achieve this goal, the model solution process further relies on in-depth analysis of electric and thermal load scheduling data and the implementation of optimization strategies. In this process, the alternating direction multiplier method (ADMM) can be considered as a solution method to ensure that energy transactions and dynamic adjustments of heat can be reasonably optimized. Specifically:

[0166] In one implementation, the process of solving the pre-built multi-park collaborative optimization model based on the electric and thermal load scheduling data of the integrated energy system of each park using the alternating direction multiplier method in step 2 to obtain the optimal operation strategy of the integrated energy system of each park may include:

[0167] Based on the integrated energy system of each park, the expected transaction power and expected transaction heat of the integrated energy system are updated according to the electric and thermal load scheduling data of the integrated energy system of the park;

[0168] Update the balance factor between the park integrated energy system and other park integrated energy systems based on the updated expected transaction power and expected transaction heat of the integrated energy system;

[0169] Calculate the collaborative optimization function value between the park integrated energy system and the rest of the park integrated energy systems based on the balance factor between the park integrated energy system and the rest of the park integrated energy systems;

[0170] If the collaborative optimization function values ​​between the park integrated energy system and the rest of the park integrated energy systems converge, the updated expected transaction power, expected transaction heat, and balance factor of the park integrated energy system are used as the optimal operation strategy of the park integrated energy system;

[0171] In this implementation, the initial values ​​of the IES expected transaction power and expected transaction heat are set to zero. Each IES can locally calculate and update its own expected transaction power and expected transaction heat using the following formula:

[0172]

[0173] in, represents the electric power transmitted from the park integrated energy system i to the park integrated energy system j at time t during the k+1th iteration; represents the thermal power transmitted from the park integrated energy system i to the park integrated energy system j at time t during the k+1th iteration; represents the electric power transmitted from the park integrated energy system j to the park integrated energy system i at time t during the k+1th iteration; represents the thermal power transmitted from the park integrated energy system j to the park integrated energy system i at time t during the k+1th iteration; represents the collaborative optimization function value of the park's integrated energy system i; represents the collaborative optimization function value of the park integrated energy system j; represents the Lagrange multiplier of the electric energy interaction between the park integrated energy system i and the park integrated energy system j at the kth iteration; represents the electric power transmitted from the park integrated energy system i to the park integrated energy system j at time t during the k-th iteration; represents the electric power transmitted from the park integrated energy system j to the park integrated energy system i at time t during the k-th iteration; represents the Lagrange multiplier of thermal energy interaction between park integrated energy system i and park integrated energy system j at the kth iteration; represents the thermal power transmitted from the park integrated energy system i to the park integrated energy system j at time t during the k-th iteration; represents the thermal power transmitted from the park integrated energy system j to the park integrated energy system i at time t during the k-th iteration; represents the Lagrange multiplier of the electric energy interaction between the park integrated energy system j and the park integrated energy system i at the kth iteration; represents the electric power transmitted from the park integrated energy system i to the park integrated energy system j at time t during the k+1th iteration; represents the Lagrange multiplier of thermal energy interaction between park integrated energy system j and park integrated energy system i at the kth iteration; represents the thermal power transmitted from the park integrated energy system i to the park integrated energy system j at time t during the first iteration;

[0174] The Lagrange multiplier (balance factor) can be updated according to the following expression:

[0175]

[0176] Whether the collaborative optimization function values ​​between integrated energy systems in different parks converge can be judged according to the following expression:

[0177]

[0178] Wherein, t = 1…T; T represents the total number of time; i = 1…I; I represents the total number of integrated energy systems in the park; represents the convergence threshold of the original residual with respect to electric energy; represents the dual residual convergence threshold with respect to electric energy; represents the raw residual convergence threshold with respect to thermal energy; represents the dual residual convergence threshold with respect to thermal energy;

[0179] If the convergence condition is met, the iteration is terminated. If not, the operation is repeated until the convergence condition is met.

[0180] For example, the total benefit of the multi-park integrated energy system in step 3 above can be calculated as follows:

[0181]

[0182] Where W represents the total benefit of the multi-park integrated energy system; represents the independent operation cost of the park's integrated energy system i; Represents the operating cost of the park's integrated energy system i; i=1…I; I represents the total number of the park's integrated energy systems.

[0183] For example, the calculation formula for the contribution factor of the above-mentioned park integrated energy system (i.e., the energy contribution function) can be as follows:

[0184]

[0185] Among them, θ i represents the contribution factor of the park's integrated energy system i; represents the electric energy contribution factor of the park's integrated energy system i; represents the thermal energy contribution factor of the park's integrated energy system i; represents the purchase price of electricity at time t; represents the amount of electricity purchased by the park's integrated energy system i at time t; represents the selling price of electricity at time t; represents the amount of electricity sold by the park's integrated energy system i at time t; represents the purchase price of thermal energy at time t; represents the purchased thermal energy of the park integrated energy system i at time t; represents the selling price of heat energy at time t; represents the sold thermal energy of integrated energy system i at time t. In this example, by considering the energy price and energy transaction volume during energy trading, an energy contribution function for each IES is established to evaluate the benefits generated by IES transactions at each moment. This ensures a more reasonable distribution of benefits from energy trading, avoids resource waste and unfair cooperation between parks caused by traditional allocation methods, and enhances the enthusiasm for cooperation among parks.

[0186] In this implementation, based on the electric and thermal load dispatch data of each park's integrated energy system, the updated expected transaction electricity and expected transaction heat are used to adjust the energy exchange and heat exchange between the integrated energy systems of each park, thereby finely optimizing the operation of the integrated energy system of multiple parks. This process ensures that each park can achieve the optimal operating state in energy complementarity and collaboration by accurately calculating and updating the collaborative optimization function and balance factor between parks. Through the dynamic adjustment of the Lagrange multiplier (balance factor), the balance of electric and thermal energy is ensured, and conflicts and waste in system operation are reduced, so that the integrated energy system of each park can not only meet local needs but also interact efficiently with other parks. In addition, this implementation adopts a convergence judgment mechanism to ensure that the optimal solution is reached after multiple iterations, avoiding the situation of dependence or incoordination of a single park, thereby improving the stability and economy of the overall energy system. Specifically, this implementation not only enhances the synergy between different parks but also optimizes inter-park energy transactions by adjusting balancing factors, reducing overall carbon emissions and promoting low-carbon, efficient energy use. The alternating direction multiplier method (ADMM) has been widely used in optimization problems. However, in the context of inter-park electric and thermal load scheduling and collaborative optimization, applying this method to handle the complex interactions and coordination between multiple parks can effectively solve the dynamic balance problem in the exchange of electricity and thermal energy between different parks, a feature that is not fully demonstrated in existing technologies. Secondly, by updating the expected amount of electricity and heat traded in real time and combining it with an inter-park Lagrange multiplier adjustment mechanism, the system can dynamically respond to load fluctuations and energy demands between different parks, avoiding the energy mismatch and over-scheduling problems that may arise in traditional methods. This combined multi-park collaborative optimization solution not only improves the overall system's operating efficiency, but also reduces carbon emissions and improves energy utilization during the collaborative process, resolving the difficulty of existing technologies in simultaneously addressing the complex energy demands of multiple parks. In addition, this implementation method can also consider combining machine learning algorithms to dynamically adjust the park's expected trading electricity and heat based on historical data and real-time monitored energy demand, so that the system can self-regulate and optimize according to actual conditions. This data-driven adaptive adjustment can not only improve the system's response speed, but also quickly adjust the coordination strategy between parks when facing sudden load changes, ensuring that the system is always in the optimal operating state.

[0187] After solving the optimal operation strategy for the integrated energy system of the park, the next key step is to reasonably distribute the benefits of each park's contribution to the multi-park collaboration. This process not only helps to achieve fair and efficient benefit sharing among the parks, but also encourages each park to maintain active participation and cooperation in the subsequent energy system optimization. To ensure the fairness of the benefit distribution, it is possible to consider calculating a reasonable weight based on the contribution factor of each park and distributing the total benefits of the multi-park integrated energy system accordingly. Specifically:

[0188] In one implementation, the process of distributing the total revenue of the multi-park integrated energy system based on the contribution factor of each park integrated energy system in step 4 may include:

[0189] Calculate the index contribution factor of each park's comprehensive energy system based on the contribution factor of each park's comprehensive energy system;

[0190] The total benefits of the multi-park integrated energy system are distributed according to the index contribution factor of each park's integrated energy system;

[0191] For example, the calculation formula for the index contribution factor of the above-mentioned park comprehensive energy system is as follows:

[0192]

[0193] Among them, τ i represents the exponential contribution factor of the park's integrated energy system i; e represents the exponential function; θ i represents the contribution factor of the park's integrated energy system i; i = 1…I; I represents the total number of the park's integrated energy systems;

[0194] The integrated energy systems of each park redistribute benefits according to the index contribution factors:

[0195]

[0196] Among them, W i represents the benefit of the integrated energy system i based on the contribution factor; W represents the total benefit of the multi-park integrated energy system;

[0197] In this implementation, the contribution of each park's integrated energy system is amplified using an exponential contribution factor. This ensures that benefit distribution is not based solely on the absolute value of the contribution but also takes into account the relative influence of different parks in the collaboration. This distribution method ensures that the integrated energy systems of parks with greater contributions receive greater benefits while avoiding the unfairness or inefficiency that may result from a simple linear distribution. By treating the contribution factor using an exponential function, it more accurately reflects the role of the park in overall system optimization and positively incentivizes collaboration among multi-park integrated energy systems. Furthermore, this benefit redistribution based on exponential contribution factors can stimulate the initiative of each park's integrated energy system in collaborative optimization, ensuring that the interests of each park align with the overall system benefits, thereby promoting the coordinated optimization and sustainable development of the entire multi-park integrated energy system. Many existing benefit distribution methods typically use linear or simple weighted average approaches, which often ignore potential nonlinear effects and differences in contributions between parks. By calculating exponential contribution factors, the differences in contributions between parks are amplified, allowing more actively participating parks to receive higher returns, effectively encouraging their investment in collaboration. In addition, this implementation method can also consider introducing blockchain technology to ensure the transparency and immutability of profit distribution. By recording the contribution data and profit distribution of each park through blockchain, it can ensure that all parties recognize and trust the distribution results, thereby further improving the fairness and sustainability of the multi-park integrated energy system.

[0198] In summary, the present invention aims to solve the problem that when multiple park integrated energy systems are interconnected in the existing technology to integrate resources, the benefit distribution method does not take into account the energy contribution of each park entity, which reduces the cooperation enthusiasm of high-contribution entities and leads to a decrease in the energy utilization efficiency of the multi-park integrated energy system. A multi-park integrated energy system collaborative optimization method taking contribution factors into account is proposed, as shown in the attached figure. Figure 3As shown, the overall calculation process of the method of the present invention starts from initialization and gradually forms a closed loop. First, the equipment parameters, load data and renewable energy output data are input, and then initialization is performed, including the interaction between electric energy and thermal energy, Lagrange multipliers and the number of iterations. After entering the cyclic optimization stage, the optimal electric energy and thermal energy interaction of each park's integrated energy system (microgrid) is calculated in turn, the Lagrange multipliers are updated to coordinate the coupling constraints between multiple subjects, and the stability of the optimization results is verified by convergence judgment. If the result does not converge, the number of iterations is increased to continue optimization until the convergence conditions are met, and the optimization result of sub-problem 1 is output. Subsequently, based on the collaborative operation results, the total revenue of the alliance is calculated, the electric energy and thermal energy contribution factors of each park are quantified, and they are integrated into a comprehensive index contribution factor. Finally, the benefits are redistributed according to the contribution factors, and the optimization result of sub-problem 2 is output, completing the "collaborative optimization-convergence" process. In the whole process, the alternating direction multiplier method (ADMM) is used as the core algorithm to realize the distributed collaboration and fair benefit distribution of multi-park energy systems through dynamic iteration and contribution factor evaluation mechanism. Based on this collaborative optimization framework, the present invention further constructs a multi-park collaborative optimization model considering electric and thermal transactions, adopts the Nash negotiation method to realize the interaction of electric energy and thermal energy among multiple parks, and redistributes the cooperative benefits through electric and thermal contribution factors. This method not only effectively reduces the system operation cost, improves energy utilization efficiency and new energy consumption rate, but also solves the optimization problem of electric and thermal interaction power among parks through the ADMM distributed algorithm, which is conducive to protecting the privacy of the main body of the comprehensive energy system of each park. At the same time, the benefits are redistributed based on the energy contribution factor, which enhances the cooperation enthusiasm among parks, further optimizes the economic efficiency of system operation, and promotes the realization of low-carbon environmental protection goals.

[0199] Example 2:

[0200] This embodiment uses a cooperative alliance constructed by three different park integrated energy systems to verify the multi-park integrated energy system collaborative optimization method proposed in the present invention.

[0201] As attached Figure 4 -Attached Figure 7 As shown in the figure, the distributed iterative convergence results of the integrated energy systems of each park in the cooperative alliance are given. As can be seen from the figure, the proposed method reaches convergence after 52 iterations, and the calculation time is 77.48s. The results show that the ADMM distributed algorithm can solve the IES electric and thermal interaction problem of each park. Compared with the centralized algorithm, the ADMM algorithm only needs to provide the interaction variables between IES, which plays a role in protecting the privacy of each IES subject.

[0202] The results of power interaction between IES in each park are shown in the attached Figure 8As shown in the figure, for IES1, wind power is more abundant at night and the electrical load is small, so the excess power is transmitted to other IESs. In IES2, since the electrical load is always at a high level, the output of its own renewable energy and units is insufficient to meet the load demand, so it receives power from other IESs throughout the day. For IES3, it transmits power to other IESs during the day when the photovoltaic output is high and there is surplus power, but needs to receive power from other IESs at night when the load increases and the photovoltaic output is zero.

[0203] The results of thermal energy interaction between IES in each park are shown in the attached Figure 9 As shown in the figure, for IES1, since its power supply is mainly wind power and the output of the gas turbine is relatively low, its heat production is also low, and it needs to receive heat energy from other IES most of the time. For IES2, its daytime electricity load is high and the power supply of renewable energy is insufficient. The gas turbine is always in a high power state. At the same time, the cooling and heating loads are relatively small, generating a large amount of surplus heat power that can be exported. For IES3, when the photovoltaic output is high during the day, renewable energy power supply is prioritized, the gas turbine output is relatively low, and the heat power mainly depends on other IES. At night, when the photovoltaic output is zero, the gas turbine unit increases its output power, and the surplus heat power generated can be exported to other IES.

[0204] The cost and benefit of IES cooperation before and after optimization are shown in Table 1. After cooperation and before the benefits are distributed, the costs of the three IES are 36174.29 yuan, 53287.31 yuan and 81427.79 yuan respectively, which is a significant increase in benefits compared with before cooperation. Figure 8-Figure 9 It can be seen that IES2 receives a large amount of electricity and has the largest cost reduction, while IES3, despite its high electricity and heat contributions, does not receive commensurate benefits. This is clearly unfair and will cause dissatisfaction among microgrids with medium and high contributions, reducing their willingness to cooperate. After redistributing benefits based on contribution factors, the benefits of IES1, 2, and 3 are 41,082.85 yuan, 19,279.59 yuan, and 43,987.93 yuan, respectively. This profit distribution matches the electricity and heat contributions of each IES, strengthening the willingness of each IES to cooperate, consolidating cooperative relationships, and maintaining the stability of the multi-IES system collaborative optimization.

[0205] Table 1 Operating cost and income distribution table

[0206] IES Standalone / Meta Cost after cooperation / yuan Revenue redistribution / yuan Final cost / yuan 1 72378.55 36174.29 41082.85 31295.70 2 105106.09 53287.31 19279.59 85826.50 3 97755.11 81427.79 43987.93 53767.18 IES Alliance 275239.75 - - 170889.38

[0207] This embodiment specifically illustrates the collaborative optimization method for a multi-park integrated energy system provided by the present invention. By employing the ADMM distributed algorithm, inter-park electric and thermal interaction can be successfully completed, and the privacy of each park's IES is effectively protected through distributed processing. Experimental results show that in terms of electric energy interaction, IES1 transmits surplus electricity to other parks at night, IES2 receives electricity throughout the day, and IES3 outputs electricity during the day and receives electricity at night. Regarding thermal energy interaction, IES1 relies on thermal energy from other parks most of the time, IES2 outputs surplus thermal energy, and IES3 relies on photovoltaic power during the day and outputs thermal energy through a gas turbine at night. By redistributing benefits based on contribution factors, the problem of unfair benefit distribution between parks is resolved, ensuring that benefits are more consistent with each park's actual contribution, thereby enhancing each park's willingness to cooperate, stabilizing cooperative relationships, and promoting stable system operation. Overall, the present invention not only optimizes energy utilization efficiency but also strengthens inter-park collaboration through fair benefit distribution, contributing to the achievement of low-carbon and environmentally friendly goals.

[0208] Example 3:

[0209] The present invention based on the same inventive concept also provides a multi-park integrated energy system collaborative optimization system, the structural composition diagram is as follows Figure 10 Shown, including:

[0210] Data acquisition module, used to obtain the electric and thermal load scheduling data of each park's integrated energy system;

[0211] The model solving module is used to solve the pre-built multi-park collaborative optimization model based on the electric and thermal load scheduling data of each park's integrated energy system using the alternating direction multiplier method to obtain the optimal operation strategy of each park's integrated energy system;

[0212] Contribution calculation module, used to calculate the total benefits of the multi-park integrated energy system and the contribution factor of each park's integrated energy system based on the optimal operation strategy of each park's integrated energy system;

[0213] The revenue distribution module is used to distribute the total revenue of the multi-park integrated energy system based on the contribution factor of each park's integrated energy system;

[0214] Among them, the multi-park integrated energy system includes the integrated energy systems of each park; the multi-park collaborative optimization model is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system.

[0215] In one implementation, the data acquisition module may include:

[0216] The power output submodule is used to obtain the output power of each energy device in the park's integrated energy system;

[0217] The load response submodule is used to perform load response on the park's integrated energy system according to the output power of each energy device in the park's integrated energy system, and output load response data of the park's integrated energy system;

[0218] The load scheduling submodule is used to output the electric and thermal load scheduling data of the park's integrated energy system based on the load response data of the park's integrated energy system and using a pre-built demand response model;

[0219] The demand response model is constructed with the goal of minimizing the operating cost of the integrated energy system of the park and with power balance constraints and energy interaction constraints as constraints;

[0220] Operating costs include one or more of the following: electricity purchase and sales costs from external power grids, natural gas purchase costs, equipment operation and maintenance costs, energy storage operation and loss costs, wind and solar curtailment penalty costs, carbon trading costs, and P2P trading line costs;

[0221] Energy equipment includes one or more of the following: gas turbines, gas boilers, absorption chillers, and energy storage equipment;

[0222] The electric and thermal load scheduling data may include one or more of the following: electric load demand at each moment, thermal load demand, energy storage device charging and discharging power, power purchase and sales from the external power grid, and energy equipment output plan.

[0223] In one implementation, the above system may further include: a model building module, which may specifically include:

[0224] The cooperation cost calculation submodule is used to sum up the operating costs of the integrated energy systems of each park to obtain the cooperation cost of the multi-park integrated energy system;

[0225] The target setting submodule is used to construct the objective function with the goal of minimizing the cooperation cost of the multi-park integrated energy system;

[0226] The constraint setting submodule is used to set the power consistency constraint and the thermal consistency constraint as the constraint conditions of the objective function;

[0227] Collaborative optimization submodule, used to establish collaborative optimization functions based on objective functions and constraints;

[0228] The optimization model construction sub-module is used to build a multi-park collaborative optimization model based on the collaborative optimization function.

[0229] For example, the expression of the above collaborative optimization function can be as follows:

[0230]

[0231] Where,

[0232] A=P i-j,t +P j-i,t ;

[0233] B=H i-j,t +H j-i,t ;

[0234] in, represents the collaborative optimization function of the park's integrated energy system i; represents the operating cost of the park's integrated energy system i; represents the balance factor of power interaction between the park integrated energy system i and the park integrated energy system j; i≠j; A represents the power consistency constraint; P j-j,t P represents the expected transaction power of the park integrated energy system i to the park integrated energy system j at time t; j-i,t represents the expected traded power of the park integrated energy system j to the park integrated energy system i at time t; t = 1…T; T represents the total number of time; ρ e represents the first penalty factor; represents the balance factor of thermal energy interaction between park integrated energy system i and park integrated energy system j; B represents the thermal energy consistency constraint; H i-j,t H represents the expected heat volume traded from the integrated energy system i to the integrated energy system j at time t; j-i,t represents the expected heat volume traded from the integrated energy system j to the integrated energy system i at time t; ρ h represents the second penalty factor; I represents the total number of integrated energy systems in the park.

[0235] In one implementation, the model solving module may include:

[0236] The expected update submodule is used to update the expected transaction power and expected transaction heat of the integrated energy system based on the electric and thermal load scheduling data of the integrated energy system of each park;

[0237] A balance update submodule is used to update the balance factor between the park integrated energy system and other park integrated energy systems based on the expected transaction power and expected transaction heat of the updated integrated energy system;

[0238] A balance optimization submodule is used to calculate the collaborative optimization function value between the park integrated energy system and the other park integrated energy systems based on the balance factor between the park integrated energy system and the other park integrated energy systems;

[0239] The strategy output submodule is used to use the expected transaction power, expected transaction heat and balance factor of the updated park integrated energy system as the optimal operation strategy of the park integrated energy system if the collaborative optimization function values ​​between the park integrated energy system and other park integrated energy systems converge.

[0240] For example, the total benefit of the above multi-park integrated energy system can be calculated as follows:

[0241]

[0242] Where W represents the total benefit of the multi-park integrated energy system; represents the independent operation cost of the park's integrated energy system i; Represents the operating cost of the park's integrated energy system i; i=1…I; I represents the total number of the park's integrated energy systems.

[0243] For example, the calculation formula for the contribution factor of the above-mentioned park integrated energy system can be as follows:

[0244]

[0245] Among them, θ i represents the contribution factor of the park's integrated energy system i; represents the electric energy contribution factor of the park's integrated energy system i; represents the thermal energy contribution factor of the park's integrated energy system i; represents the purchase price of electricity at time t; represents the amount of electricity purchased by the park's integrated energy system i at time t; represents the selling price of electricity at time t; represents the amount of electricity sold by the park's integrated energy system i at time t; represents the purchase price of thermal energy at time t; represents the purchased thermal energy of the park integrated energy system i at time t; represents the selling price of heat energy at time t; It represents the thermal energy sold by the integrated energy system i of the park at time t; T represents the total number of moments.

[0246] In one implementation, the above-mentioned profit distribution module may include:

[0247] The contribution mapping submodule is used to calculate the index contribution factor of each park's comprehensive energy system based on the contribution factor of each park's comprehensive energy system;

[0248] The profit distribution submodule is used to distribute the total profit of the multi-park integrated energy system according to the index contribution factor of each park's integrated energy system.

[0249] Example 4:

[0250] like Figure 11 As shown, the present invention also provides an electronic device, which may be a computer, a single-chip microcomputer, a smart mobile device, or the like. The electronic device in this embodiment may include a processor, a memory, a transceiver component, and the like. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor may be used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0251] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of a multi-park integrated energy system collaborative optimization method in the above embodiment.

[0252] Example 5:

[0253] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a multi-park integrated energy system collaborative optimization method in the above embodiment.

[0254] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0255] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0256] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0257] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0258] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims.

Claims

1. A collaborative optimization method for a multi-park integrated energy system, characterized in that: include: Obtain the electric and thermal load dispatch data of each park's integrated energy system; Based on the electric and thermal load scheduling data of the integrated energy systems of the parks, the pre-built multi-park collaborative optimization model is solved using the alternating direction multiplier method to obtain the optimal operation strategy of the integrated energy systems of the parks; Based on the optimal operation strategy of the integrated energy system of each park, calculate the total benefit of the multi-park integrated energy system and the contribution factor of the integrated energy system of each park; Distributing the total revenue of the multi-park integrated energy system according to the contribution factor of each park integrated energy system; Among them, the multi-park integrated energy system includes the integrated energy systems of each park; the multi-park collaborative optimization model is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system.

2. The method according to claim 1, wherein The acquisition of electric and thermal load dispatching data of the integrated energy system of each park includes: Obtaining the output power of each energy device in the park integrated energy system; Performing load response on the park integrated energy system according to the output power of each energy device in the park integrated energy system, and outputting load response data of the park integrated energy system; Outputting the electric and thermal load dispatching data of the park integrated energy system using a pre-built demand response model based on the load response data of the park integrated energy system; The demand response model is constructed with the goal of minimizing the operating cost of the park integrated energy system and with power balance constraints and energy interaction constraints as constraints; The operating costs include one or more of the following: the cost of purchasing electricity from the external power grid, the cost of purchasing natural gas, the cost of equipment operation and maintenance, the cost of energy storage operation and loss, the penalty cost for curtailing wind and solar power, the cost of carbon trading, and the cost of P2P trading lines; The energy equipment includes one or more of the following: a gas turbine, a gas boiler, an absorption refrigerator, and an energy storage device; The electric and thermal load scheduling data includes one or more of the following: electric load demand, thermal load demand, energy storage device charging and discharging power, power purchase and sales with the external power grid, and energy equipment output plan at each moment.

3. The method according to claim 2, wherein The multi-park collaborative optimization model includes the following construction process: The operation costs of the integrated energy systems of the various parks are summed to obtain the cooperation cost of the multi-park integrated energy system; An objective function is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system; Taking the electrical energy consistency constraint and the thermal energy consistency constraint as the constraint conditions of the objective function; Establishing a collaborative optimization function according to the objective function and the constraint conditions; Based on the collaborative optimization function, a multi-park collaborative optimization model is constructed.

4. The method according to claim 3, wherein The expression of the collaborative optimization function is as follows: Where, A=P i-j,t +P j-i,t ; B=H i-j,t +H j-i,t ; in, represents the collaborative optimization function of the park's integrated energy system i; represents the operating cost of the park's integrated energy system i; represents the balance factor of power interaction between the park integrated energy system i and the park integrated energy system j; i≠j; A represents the power consistency constraint; P i-j,t P represents the expected transaction power of the park integrated energy system i to the park integrated energy system j at time t; j-i,t represents the expected traded power of the park integrated energy system j to the park integrated energy system i at time t; t = 1…T; T represents the total number of time; ρ e represents the first penalty factor; represents the balance factor of thermal energy interaction between park integrated energy system i and park integrated energy system j; B represents the thermal energy consistency constraint; H i-j,t H represents the expected heat volume traded from the integrated energy system i to the integrated energy system j at time t; j-i,t represents the expected heat volume traded from the integrated energy system j to the integrated energy system i at time t; ρ h represents the second penalty factor; I represents the total number of integrated energy systems in the park.

5. The method according to claim 4, wherein The method of solving a pre-built multi-park collaborative optimization model based on the electric and thermal load scheduling data of the integrated energy system of each park using the alternating direction multiplier method to obtain the optimal operation strategy of the integrated energy system of each park includes: Based on the integrated energy system of each park, the expected transaction power and expected transaction heat of the integrated energy system are updated according to the electric and thermal load scheduling data of the integrated energy system of the park; updating the balance factor between the park integrated energy system and other park integrated energy systems based on the updated expected transaction power and expected transaction heat of the integrated energy system; Calculating a collaborative optimization function value between the park integrated energy system and the remaining park integrated energy systems based on a balance factor between the park integrated energy system and the remaining park integrated energy systems; If the collaborative optimization function values ​​between the park integrated energy system and other park integrated energy systems converge, the updated expected trading power, expected trading heat and balance factor of the park integrated energy system will be used as the optimal operation strategy of the park integrated energy system.

6. The method according to claim 1, wherein The total benefit of the multi-park integrated energy system is calculated as follows: Where W represents the total benefit of the multi-park integrated energy system; represents the independent operation cost of the park's integrated energy system i; Represents the operating cost of the park's integrated energy system i; i=1…I; I represents the total number of the park's integrated energy systems.

7. The method according to claim 1, wherein The calculation formula of the contribution factor of the park's comprehensive energy system is as follows: Among them, θ i represents the contribution factor of the park's integrated energy system i; represents the electric energy contribution factor of the park's integrated energy system i; represents the thermal energy contribution factor of the park's integrated energy system i; represents the purchase price of electricity at time t; represents the amount of electricity purchased by the park's integrated energy system i at time t; represents the selling price of electricity at time t; represents the amount of electricity sold by the park's integrated energy system i at time t; represents the purchase price of thermal energy at time t; represents the purchased thermal energy of the park integrated energy system i at time t; represents the selling price of heat energy at time t; represents the thermal energy sold by the park's integrated energy system i at time t; T represents the total time.

8. The method according to claim 1, wherein The distributing the total revenue of the multi-park integrated energy system according to the contribution factor of each park integrated energy system includes: Calculating the index contribution factor of the comprehensive energy system of each park according to the contribution factor of the comprehensive energy system of each park; The total revenue of the multi-park integrated energy system is distributed according to the index contribution factor of each park integrated energy system.

9. A multi-park integrated energy system collaborative optimization system, characterized in that: include: Data acquisition module, used to obtain the electric and thermal load scheduling data of each park's integrated energy system; A model solving module is used to solve a pre-built multi-park collaborative optimization model based on the electric and thermal load scheduling data of the integrated energy system of each park using the alternating direction multiplier method to obtain the optimal operation strategy of the integrated energy system of each park; A contribution calculation module is used to calculate the total benefit of the multi-park integrated energy system and the contribution factor of each park integrated energy system based on the optimal operation strategy of each park integrated energy system; A profit distribution module, configured to distribute the total profit of the multi-park integrated energy system according to the contribution factor of each park integrated energy system; Among them, the multi-park integrated energy system includes the integrated energy systems of each park; the multi-park collaborative optimization model is constructed with the goal of minimizing the cooperation cost of the multi-park integrated energy system.

10. The system according to claim 9, wherein: The data acquisition module includes: A power output submodule, used to obtain the output power of each energy device in the park integrated energy system; A load response submodule, configured to perform load response on the park integrated energy system according to the output power of each energy device in the park integrated energy system, and output load response data of the park integrated energy system; A load scheduling submodule is used to output the electric and thermal load scheduling data of the park integrated energy system based on the load response data of the park integrated energy system and using a pre-built demand response model; The demand response model is constructed with the goal of minimizing the operating cost of the park integrated energy system and with power balance constraints and energy interaction constraints as constraints; The operating costs include one or more of the following: the cost of purchasing electricity from the external power grid, the cost of purchasing natural gas, the cost of equipment operation and maintenance, the cost of energy storage operation and loss, the penalty cost for curtailing wind and solar power, the cost of carbon trading, and the cost of P2P trading lines; The energy equipment includes one or more of the following: a gas turbine, a gas boiler, an absorption refrigerator, and an energy storage device; The electric and thermal load scheduling data includes one or more of the following: electric load demand, thermal load demand, energy storage device charging and discharging power, power purchase and sales with the external power grid, and energy equipment output plan at each moment.

11. The system according to claim 10, wherein: The system further includes a model building module, including: A cooperation cost calculation submodule, configured to sum the operating costs of the integrated energy systems of the various parks to obtain the cooperation cost of the multi-park integrated energy system; A target setting submodule is used to construct an objective function with the goal of minimizing the cooperation cost of the multi-park integrated energy system; A constraint setting submodule, configured to use an electrical energy consistency constraint and a thermal energy consistency constraint as constraint conditions of the objective function; A collaborative optimization submodule, configured to establish a collaborative optimization function based on the objective function and the constraint conditions; The optimization model construction submodule is used to construct a multi-park collaborative optimization model based on the collaborative optimization function.

12. The system according to claim 11, wherein The expression of the collaborative optimization function is as follows: Where, A=P i-j,t +P j-i,t ; B=H i-j,t +H j-i,t ; in, represents the collaborative optimization function of the park's integrated energy system i; represents the operating cost of the park's integrated energy system i; represents the balance factor of power interaction between the park integrated energy system i and the park integrated energy system j; i≠j; A represents the power consistency constraint; P i-j,t P represents the expected transaction power of the park integrated energy system i to the park integrated energy system j at time t; j-i,t represents the expected traded power of the park integrated energy system j to the park integrated energy system i at time t; t = 1…T; T represents the total number of time; ρ e represents the first penalty factor; represents the balance factor of thermal energy interaction between park integrated energy system i and park integrated energy system j; B represents the thermal energy consistency constraint; H i-j,t H represents the expected heat volume traded from the integrated energy system i to the integrated energy system j at time t; j-i,t represents the expected heat from the park integrated energy system j to the park integrated energy system i at time t; ρ h represents the second penalty factor; I represents the total number of integrated energy systems in the park.

13. The system according to claim 12, wherein: The model solving module includes: An expected update submodule is used to update the expected transaction power and expected transaction heat of each integrated energy system based on the electric and thermal load scheduling data of the integrated energy system of each park; A balance update submodule, configured to update a balance factor between the park integrated energy system and other park integrated energy systems according to the updated expected transaction power and expected transaction heat of the integrated energy system; A balance optimization submodule, configured to calculate a collaborative optimization function value between the park integrated energy system and the remaining park integrated energy systems based on a balance factor between the park integrated energy system and the remaining park integrated energy systems; The strategy output submodule is used to use the updated expected trading power, expected trading heat and balance factor of the park integrated energy system as the optimal operation strategy of the park integrated energy system if the collaborative optimization function values ​​between the park integrated energy system and other park integrated energy systems converge.

14. The system according to claim 9, wherein: The total benefit of the multi-park integrated energy system is calculated as follows: Where W represents the total benefit of the multi-park integrated energy system; represents the independent operation cost of the park's integrated energy system i; Represents the operating cost of the park's integrated energy system i; i=1…I; I represents the total number of the park's integrated energy systems.

15. The system according to claim 9, wherein: The calculation formula of the contribution factor of the park's comprehensive energy system is as follows: Among them, θ i represents the contribution factor of the park's integrated energy system i; represents the electric energy contribution factor of the park's integrated energy system i; represents the thermal energy contribution factor of the park's integrated energy system i; represents the purchase price of electricity at time t; represents the amount of electricity purchased by the park's integrated energy system i at time t; represents the selling price of electricity at time t; represents the amount of electricity sold by the park's integrated energy system i at time t; represents the purchase price of thermal energy at time t; represents the purchased thermal energy of the park integrated energy system i at time t; represents the selling price of heat energy at time t; It represents the thermal energy sold by the integrated energy system i of the park at time t; T represents the total number of moments.

16. The system of claim 9, wherein: The profit distribution module includes: A contribution mapping submodule, configured to calculate an index contribution factor of each park comprehensive energy system according to the contribution factor of each park comprehensive energy system; The profit distribution submodule is used to distribute the total profit of the multi-park integrated energy system according to the index contribution factor of each park integrated energy system.

17. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a multi-park integrated energy system collaborative optimization method as described in any one of claims 1 to 8 is implemented.

18. A computing device readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a multi-park integrated energy system collaborative optimization method as described in any one of claims 1 to 8 is implemented.

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