A method and device for optimizing the operation of a source-load-storage system cluster

Abstract

The present invention provides an operation optimization method and device for a source-load-storage system cluster, which relates to the technical field of energy system scheduling, wherein the method comprises: obtaining transaction data and operation data of each source-load-storage system; wherein the operation data is obtained by optimizing and adjusting the control unit and the production capacity unit of the source-load-storage system; based on the transaction data and the operation data, calculating the total system cost of each source-load-storage system; based on the total system cost, establishing a cooperative operation model of a source-load-storage system cluster; solving and calculating the cooperative operation model to obtain the optimal benefit value of the source-load-storage system cluster. This solution can effectively reduce the total cost and carbon emissions of the source-load-storage system cluster, and enhance the enthusiasm of each source-load-storage system to participate in cooperative operation.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system scheduling and operation, and in particular to an operation optimization method and device for a source-load-storage system cluster. Background Art

[0002] With the rapid development of information technology, as the core infrastructure of information construction, high-energy-consuming source-load-storage systems form intensive and large-scale source-load-storage system clusters through energy and information interaction, realize the complementary advantages among systems, and form a coordinated and optimized emission reduction path to meet my country's "dual carbon" development goals.

[0003] In related technologies, since each source-load-storage system belongs to a different stakeholder, the uneven distribution of operating benefits will affect the cooperation enthusiasm of each source-load-storage system, thereby causing the operating benefits of the source-load-storage system cluster to continue to decline.

[0004] Based on this, there is an urgent need for an operation optimization method and device for a source-load-storage system cluster to solve the above technical problems. Summary of the invention

[0005] The embodiments of the present invention provide an operation optimization method and device for a source-load-storage system cluster, which can improve the operation benefits of the source-load-storage system cluster.

[0006] In a first aspect, an embodiment of the present invention provides an operation optimization method of a source-load-storage system cluster, comprising:

[0007] Acquire transaction data and operation data of each source-load-storage system; wherein the operation data is obtained by optimizing and adjusting the control unit and the production capacity unit of the source-load-storage system;

[0008] Based on the transaction data and the operation data, the total system cost of each source-load-storage system is calculated;

[0009] Based on the total system cost, a cooperative operation model of the source-load-storage system cluster is established;

[0010] The cooperative operation model is solved and calculated to obtain the optimal profit value of the source-load-storage system cluster.

[0011] In a second aspect, an embodiment of the present invention further provides an operation optimization device for a source-load-storage system cluster, comprising:

[0012] An acquisition module, used to acquire transaction data and operation data of each source-load-storage system; wherein the operation data is obtained by optimizing and adjusting the control unit and the production capacity unit of the source-load-storage system;

[0013] A first calculation module, configured to calculate the total system cost of each of the source-load-storage systems based on the transaction data and the operation data;

[0014] A modeling module, used for establishing a cooperative operation model of a source-load-storage system cluster based on the total system cost;

[0015] The second calculation module is used to solve and calculate the cooperative operation model to obtain the optimal profit value of the source-load-storage system cluster.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any embodiment of this specification.

[0018] The embodiment of the present invention provides an operation optimization method and device for a source-load-storage system cluster, obtains the operation data of each source-load-storage system, calculates the operation cost of each source-load-storage system based on these operation data, establishes a cooperative operation model of the source-load-storage system cluster in combination with the operation cost of each system, and finally optimizes and solves the cooperative operation model to obtain the optimal benefit value of the source-load-storage system cluster. The above method can effectively reduce the total cost and carbon emissions of the source-load-storage system cluster and enhance the enthusiasm of each source-load-storage system to participate in cooperative operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of an operation optimization method of a source-load-storage system cluster provided by an embodiment of the present invention;

[0021] Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0022] Figure 3 It is a structural diagram of an operation optimization device of a source-load-storage system cluster provided by an embodiment of the present invention;

[0023] Figure 4is a data load and renewable energy power generation curve diagram provided by an embodiment of the present invention;

[0024] Figure 5 is a schematic diagram of data load optimization scheduling results provided by an embodiment of the present invention;

[0025] Figure 6 It is a supply and use energy balance curve diagram of a source-load-storage system provided by an embodiment of the present invention;

[0026] Figure 7 It is a curve diagram of electric energy trading information of each source-load-storage system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] As mentioned earlier, the current source-load-storage system clusters often belong to different stakeholders, resulting in uneven distribution of the benefits of collaborative interactions between systems, which greatly affects the cooperation enthusiasm of the entire cluster and leads to reduced benefits for the source-load-storage system clusters.

[0029] Based on this, the idea of ​​the present invention is to establish a cooperative operation model that comprehensively considers the coordinated interaction of multiple resources such as electric power, data load and carbon quota according to the cost of each source-load-storage system in the entire cluster, and distribute the corresponding operation benefits according to the contribution ratio of each system, thereby improving the benefit value of the entire cluster.

[0030] The specific implementation of the above concept is described below.

[0031] Please refer to Figure 1 The embodiment of the present invention provides an operation optimization method of a source-load-storage system cluster, the method comprising:

[0032] Step 100, obtaining transaction data and operation data of each source-load-storage system;

[0033] Step 102, calculating the total system cost of each source-load-storage system based on the transaction data and the operation data;

[0034] Step 104, establishing a cooperative operation model of the source-load-storage system cluster based on the total system cost;

[0035] Step 106, solving and calculating the cooperative operation model to obtain the optimal profit value of the source-load-storage system cluster.

[0036] In the embodiment of the present invention, the operation data of each source-load-storage system is obtained, and the operation cost of each source-load-storage system is calculated based on the operation data. In combination with the operation cost of each system, a cooperative operation model of the source-load-storage system cluster is established, and finally the cooperative operation model is optimized and solved to obtain the optimal benefit value of the source-load-storage system cluster. The above method can effectively reduce the total cost and carbon emissions of the source-load-storage system cluster and enhance the enthusiasm of each source-load-storage system to participate in cooperative operation.

[0037] Described below Figure 1 How the various steps are performed.

[0038] First, with respect to step 100, the transaction data and operation data of each source-load-storage system are obtained.

[0039] The source-load-storage system consists of a capacity unit and a control unit. The capacity unit includes a gas-fired power generation subsystem, a refrigeration subsystem, an energy storage subsystem, renewable energy power generation equipment, and a load subsystem. The gas-fired power generation subsystem includes a gas turbine, carbon capture, and power-to-gas equipment. The refrigeration system includes an absorption chiller and an electric chiller. The load subsystem is divided into the electric load of the server and its supporting equipment, and the cooling load to maintain the normal operation of the server.

[0040] Furthermore, the source-load-storage system guarantees the energy supply and information exchange of the system through the upper-level power grid, carbon trading market and communication network. After receiving the equipment information of the source-load-storage system, the control unit can transmit the relevant operation information to other source-load-storage systems and receive the operation information of other source-load-storage systems. Each source-load-storage system can not only purchase energy from the upper-level power grid and gas source, but also realize the interaction of electric energy, data load and carbon quota within the cluster through cooperation, so as to realize the optimal scheduling of the equipment of each source-load-storage system.

[0041] Specifically, when the electricity produced by source-load-storage system i cannot meet the electricity load demand, electricity trading is required. Source-load-storage system i first purchases electricity from other source-load-storage systems, and then considers purchasing electricity from the upper power grid. The power interaction between source-load-storage systems should meet the consistency constraint, as shown in the following formula:

[0042]

[0043] In the formula, is the power interaction between source-load-storage system i and source-load-storage system j. It means that source-load-storage system i purchases electricity from source-load-storage system j.

[0044] Depending on whether delayed processing is allowed, the data load of the source-load-storage system can be divided into two categories: delay-sensitive and delay-tolerant. The source-load-storage system can interact with delay-sensitive data and delay-tolerant data through the communication network. Assuming that each source-load-storage system can accept and process data from other source-load-storage system servers, the interacting parties jointly decide on the amount of data interaction. Similar to the interaction of electric power, the data interaction between source-load-storage systems should also meet the consistency constraint, as shown in the following formula:

[0045]

[0046] In the formula, is the delay-sensitive data load interacting between source load storage system i and source load storage system j; is the delay-tolerant data load interacting between source load-storage system i and source load-storage system j. It indicates that source load-storage system i transmits data to source load-storage system j.

[0047] Under the carbon trading market mechanism, the carbon emission trading authority will allocate initial carbon quotas to each source-load-storage system, and source-load-storage systems can also exchange carbon quotas. When the carbon emissions of a source-load-storage system are greater than the carbon quota, it will first purchase carbon quotas from other source-load-storage systems, and then consider purchasing carbon quotas through the carbon trading market. When the carbon emissions of a source-load-storage system are less than the carbon quota, it can sell carbon quotas through the carbon trading market. The consistency constraints that must be met for the interaction of carbon quotas between source-load-storage systems are shown in the following formula:

[0048] E i-j,t +E j-i,t =0

[0049] In the formula, E i-j,t 、E j-i,t E is the carbon quota for the interaction between source-load-storage system i and source-load-storage system j. i-j,t >0 means that source-load-storage system i sells carbon quotas to source-load-storage system j.

[0050] In the embodiment of the present invention, the operating data is obtained by optimizing and adjusting the control unit and the production capacity unit of the source-load-storage system.

[0051] Specifically, the first step is to optimize the flexibility of the control unit's data processing process, which is based on the temporal and spatial transferability of delay-sensitive data and delay-tolerant data loads. By changing the temporal and spatial location of data loads, the source-load-storage system can achieve optimal scheduling of power loads.

[0052] The spatial transfer characteristics of data loads can be achieved through communication networks without relying on power transmission lines, which can effectively solve the problem of increased cost of source-load-storage systems caused by grid congestion.

[0053] Considering the data load interaction between the source load storage system, the delay-sensitive data and delay-tolerant data load of the source load storage system i at time t are calculated by the following formula:

[0054]

[0055] In the formula, and are respectively the delay-sensitive data and delay-tolerant data loads of source-load-storage system i at time t; and They are respectively the delay-sensitive data load and delay-tolerant data load of source-load-storage system i at time t when the interactive data between the source-load-storage system is not considered.

[0056] The delay-sensitive data of source-load-storage system i needs to be processed in time, and the delay-tolerant data can be processed with a delay, but must meet the maximum delay time constraint, as shown in the following formula:

[0057]

[0058] In the formula, and are the actual delay-sensitive data and delay-tolerant data loads of source-load-storage system i at time t, respectively; is the maximum delay time.

[0059] To ensure the server processing capacity when the data load fluctuates, the server should reserve spare capacity to reduce the processing delay of the data load. The spare capacity is calculated by the following formula:

[0060]

[0061] Where n i is the number of servers in source-load-storage system i; π is the upper limit of the processing speed of a single server; σ is the server capacity margin.

[0062] The power consumption of the source-load-storage system includes the power of the server and its supporting equipment, which can be calculated based on the energy efficiency level of the source-load-storage system. Assuming that the data load is evenly distributed among the servers, before and after flexible scheduling, the power consumption of the source-load-storage system i at time t is shown in the following formula:

[0063]

[0064] In the formula, are the power consumption of source-load-storage system i before and after flexible scheduling; γ PUE is the energy efficiency level of the source-load-storage system; P i idle is the server no-load power; P i peakFully load the server.

[0065] Furthermore, after completing the optimization of the control unit, the operation mode of the production capacity unit is then flexibly optimized and adjusted. This adjustment can effectively avoid the problem of service quality degradation caused by data load scheduling, which is more feasible and advantageous.

[0066] Specifically, the gas-fired power generation subsystem is regulated first, which includes the gas turbine, carbon capture and power-to-gas equipment.

[0067] The gas turbine first meets the energy demand of carbon capture and power-to-gas, and the remaining power is then provided to the source-load storage system. Carbon capture captures the carbon dioxide produced by the gas turbine and provides it to the power-to-gas equipment. The hydrogen produced by the power-to-gas equipment and the carbon dioxide captured by carbon capture can be synthesized into natural gas and provided to the gas turbine.

[0068] Gas turbines generate electricity by burning natural gas, and the high-temperature waste heat gas can be recovered and cooled by absorption refrigerators. The power characteristics of gas turbines and the constraints of their operation in the heat-to-power mode are shown in the following formula:

[0069]

[0070] Where: is the electrical power of the gas turbine; is the natural gas consumed by the gas turbine; η GT,e is the gas turbine power generation efficiency; HV NG is the calorific value of natural gas; h m is the slope of the gas turbine thermal power; is the cooling power of the absorption refrigerator; η AC is the refrigeration coefficient of the absorption chiller; is the upper limit of gas turbine electrical power; c v is the gas turbine thermal-electric conversion coefficient; ΔP i GT It is the upper limit of gas turbine climbing power.

[0071] The operating characteristics of the carbon capture equipment and its constraints are shown in the following formula:

[0072]

[0073] In the formula, is the electrical power of the carbon capture equipment; η CCS for carbon capture equipment efficiency; The amount of carbon dioxide captured by the carbon capture facility; It is the upper limit of the electrical power of the carbon capture equipment.

[0074] The operating characteristics and constraints of the power-to-gas equipment are shown in the following formula:

[0075]

[0076] Where: is the natural gas output of the power-to-gas equipment; P2G,1 The gas production efficiency of the power-to-gas equipment; The electrical power of the power-to-gas equipment; The carbon dioxide consumption of the power-to-gas equipment should be less than the carbon dioxide captured by the carbon capture equipment; η P2G,2 is the CO2 consumption rate of the power-to-gas equipment; It is the upper limit of the electrical power of the power-to-gas equipment.

[0077] The operating characteristics and constraints of the power-to-gas equipment are shown in the following formula:

[0078]

[0079] In the formula, is the natural gas output of the power-to-gas equipment; P2G,1 The gas production efficiency of the power-to-gas equipment; The electrical power of the power-to-gas equipment; The carbon dioxide consumption of the power-to-gas equipment should be less than the carbon dioxide captured by the carbon capture equipment; η P2G,2 is the CO2 consumption rate of the power-to-gas equipment; It is the upper limit of the electrical power of the power-to-gas equipment.

[0080] The power of carbon capture equipment and power-to-gas equipment should also meet the following constraints:

[0081]

[0082] In the formula, is the net electrical power output of the gas turbine.

[0083] According to the above-mentioned operation regulation method for the gas power generation subsystem, the net output constraint of the gas turbine electric power and the natural gas production constraint of the power-to-gas equipment are adjusted as shown in the following formula:

[0084]

[0085] Then adjust the refrigeration subsystem, which is used to meet the cooling load demand of the source load storage system server. When the absorption refrigeration machine cannot meet the cooling load demand, the electric refrigeration machine is started. The operation characteristics of the absorption refrigeration machine and the electric refrigeration are shown in the following formula:

[0086]

[0087]

[0088] In the formula, is the cooling power of the absorption refrigerator; η AC is the refrigeration coefficient of the absorption chiller; is the cooling power of the electric refrigerator; is the power consumption of the electric refrigerator; η EC is the cooling efficiency of the electric refrigerator; It is the upper limit of the cooling power of the electric refrigerator.

[0089] Considering the thermal inertia during the temperature change of the source-load-storage system, the dynamic thermal balance of the source-load-storage system can be achieved through the optimal scheduling of the refrigeration system to maintain the appropriate indoor temperature, as shown in the following formula:

[0090]

[0091] In the formula, is the indoor temperature of the source-load-storage system; η, σ, ρ are thermodynamic coefficients, which are related to factors such as indoor heat transfer coefficient and indoor area; Δt represents the scheduling time interval; is the outdoor temperature of the source-load-storage system; It is the temperature range for normal operation of the source-load-storage system.

[0092] For the energy storage subsystem, installing electric energy storage helps to improve the power supply flexibility of the source-load-storage system. When the renewable energy generation and gas-fired power generation systems cannot meet the electric load demand of the source-load-storage system, the electric energy storage discharges to maintain the electric power balance of the source-load-storage system. Conversely, the electric energy storage charges. The output characteristics of the electric energy storage and its constraints are shown in the following formula:

[0093]

[0094] Where: represents the remaining energy storage capacity of source-load-storage system i at time t; μ loss Represents the self-loss coefficient of electrical energy storage; η ES,c , η ES,d Respectively represent the charging and discharging efficiency of electrical energy storage; Respectively represent the charging and discharging power of the electric energy storage; Respectively represent the upper and lower limits of the remaining capacity of the electric energy storage; They respectively represent the initial and final values ​​of the remaining capacity of the electric energy storage during the entire dispatching period; are 0-1 variables, representing the charging and discharging states of the energy storage; The upper limit of charging and discharging power for electric energy storage.

[0095] After the above-mentioned optimization and adjustment of the control unit and the production capacity unit, when multiple source-load-storage systems are combined into a cluster, each system can improve the flexible scheduling capability and operation capability of the entire cluster through this optimization method.

[0096] Then, for step 102, the total system cost of each source-load-storage system is calculated based on the transaction data and the operation data.

[0097] When the source-load-storage systems that make up the cluster operate in cooperation, the two source-load-storage systems can directly conduct point-to-point transactions. The transactions include electricity transactions and carbon transactions. The transaction price is agreed upon by the two parties to the transaction. Electricity transactions are conducted once an hour, and carbon transactions are conducted once a day. Therefore, based on the transaction data and the operating data obtained in the above-mentioned optimization and adjustment process, the total system cost of each source-load-storage system can be calculated.

[0098] In an embodiment of the present invention, the total system cost of each source-load-storage system is calculated based on transaction data and operation data, including: calculating the transaction data to obtain the transaction cost of the source-load-storage system; wherein the transaction cost includes the market carbon transaction cost, the inter-system carbon transaction cost and the source-load migration cost; calculating the operation data to obtain the operation cost of the source-load-storage system.

[0099] Specifically, the total system cost is calculated by the following formula:

[0100]

[0101] In the formula, is the total system cost of the i-th source-load-storage system in the cluster; is the operating cost of the source-load-storage system; The cost of participating in the carbon trading market for source-load-storage systems; The cost of electricity-carbon transaction between source, load and storage systems; For the cost of network fees.

[0102] The operating cost of the source-load-storage system is calculated by the following formula:

[0103]

[0104] In the formula, They are, in order, the operation and maintenance costs of gas turbines, carbon capture equipment, power-to-gas equipment, absorption chillers, and electric chillers and energy storage equipment; is the energy purchase cost; T is the total number of time periods in the dispatch cycle; a GT , b GT are the gas turbine operating cost coefficients; c CCS 、c P2G 、c AC 、c ECare the operation and maintenance cost coefficients of carbon capture equipment, power-to-gas equipment, absorption chiller, and electric chiller respectively; c ES The operational degradation cost of charging and discharging the energy storage device; are gas price and purchase quantity respectively; are the purchase price and quantity of electricity from the grid by source-load-storage system i respectively; are the electricity selling price and electricity sales volume of source-load-storage system i to the power grid respectively.

[0105] The transaction cost of the source-load-storage system and the carbon trading market is calculated by the following formula:

[0106]

[0107] In the formula, are the purchase price and sale price of carbon quotas in the carbon trading market; E i,0 is the initial carbon quota of source-load-storage system i; E i,T is the actual carbon emission of source-load-storage system i.

[0108] The initial carbon quota of the source-load-storage system consists of two parts: the carbon quota of the gas turbine and the carbon quota of electricity purchased from the upper power grid, as shown in the following formula:

[0109]

[0110] Where: GT is the carbon quota coefficient of the gas turbine; σ UG It is the carbon quota coefficient for purchasing electricity from the upper-level power grid.

[0111] The carbon capture equipment of the source-load-storage system can capture part of the carbon dioxide emitted by the gas turbine. Therefore, the actual carbon emissions of the source-load-storage system are shown in the following formula:

[0112]

[0113] Where: γ GT is the carbon emission coefficient of the gas turbine; γ UG is the carbon emission coefficient of the regional power grid.

[0114] The carbon trading cost between source, load and storage systems is shown in the following formula:

[0115]

[0116] Where: is the electricity transaction cost between source-load-storage system i and other source-load-storage systems in the cluster; is the carbon transaction cost between source-load-storage system i and other source-load-storage systems in the cluster; are the electricity-carbon trading prices of source-load-storage system i and source-load-storage system respectively.

[0117] The source-load migration cost of the source-load-storage system includes the costs of transmitting electric energy between the source-load-storage systems and migrating data loads, as shown in the following formula:

[0118]

[0119] In the formula, For electricity transmission fees; Data transmission fee; c e is the power transmission cost coefficient; p dyna The power consumption per unit data load transferred by network switching equipment.

[0120] It is worth noting that the source-load-storage system also needs to meet constraints including power balance, gas balance, power trading and carbon trading during cooperative operation, as shown in the following formulas:

[0121]

[0122] In the formula, To provide actual output for wind power; Actual output for photovoltaics; It is the upper limit of the interconnection line power between the source, load and storage system.

[0123] With respect to step 104, a cooperative operation model of the source-load-storage system cluster is established based on the total system cost.

[0124] In the process of cooperative operation, each source-load-storage system wants to reduce its total cost. The present invention builds a cooperative operation model based on Nash bargaining game theory to solve the problem of minimizing the total cost of multi-source-load-storage systems through electricity-carbon trading.

[0125] When the Nash product is the largest, no source-load-storage system can reduce its own total cost without affecting the total cost of the cluster. Therefore, the solution when the Nash product is the largest is the optimal solution for the cooperative operation of multiple source-load-storage systems.

[0126] Specifically, the cooperative operation model is established through the following formula:

[0127]

[0128] Where: The cost of operating the source-load-storage system independently without joining the cluster, which is the negotiation breakdown point of Nash bargaining; The benefits that can be obtained by participating in the cooperation for the source-load-storage system.

[0129] According to the arithmetic-geometric mean inequality, the above model must meet the following conditions to reach the maximum value:

[0130]

[0131] In the formula, It is the minimum total cost of the source-load-storage system participating in cooperative operation.

[0132] Since the electricity-carbon transaction costs between source-load-storage systems offset each other in the accumulation process, the above conditional formula can be rewritten as follows:

[0133]

[0134] In the formula, For cost The optimal value of .

[0135] The cooperative operation model can be rewritten as:

[0136]

[0137] With respect to step 106, the cooperative operation model is solved and calculated to obtain the optimal benefit value of the source-load-storage system cluster.

[0138] In an embodiment of the present invention, the solution calculation process includes: calculating the cooperative operation model based on the improved alternating direction multiplier method to obtain the optimal transaction volume between the source, load and storage systems; calculating the optimal transaction volume to obtain the profit value of the source, load and storage system cluster; allocating the profit value based on a preset contribution ratio value to obtain the optimal profit value; wherein the contribution ratio value includes electric energy contribution and carbon emission contribution.

[0139] In an embodiment of the present invention, the process of calculating the optimal trading volume includes: establishing a solution function model for the trading volume of the source-load-storage system; iteratively updating the solution function model to obtain the optimal trading volume that meets preset conditions; wherein the iterative update includes updating the expected value of the trading volume and updating the model penalty factor.

[0140] Specifically, because the convergence of the standard alternating direction multiplier method is sensitive to the penalty factor of the augmented Lagrangian function, this embodiment proposes a penalty factor update strategy to improve the standard alternating direction multiplier method. 1 and ρ 2 It changes with the number of iterations k, as shown in the following formula:

[0141]

[0142] 0<θ min <1<θ max

[0143] In the formula, γ is a constant greater than 1; θ max and θ min is 1 and ρ2 When the original residual is too large relative to the dual residual, the penalty factor is enlarged; otherwise, the penalty factor is reduced.

[0144] kth iteration, ρ 1 and ρ 2 By adjusting the relative sizes of the primal residual and the dual residual, the slow convergence or failure of the standard alternating direction multiplier method due to inappropriate penalty factor values ​​can be avoided.

[0145] In order to further improve the convergence speed of the alternating direction multiplier method, it is proposed to consider the correction factor μ i The Lagrange multiplier update model of (k) is shown in the following formula:

[0146]

[0147] For the rewritten cooperative operation model, it can be transformed into two sub-problems. Sub-problem 1 is the problem of minimizing the cluster cost of the source-load-storage system, which is a standard convex problem, as shown in the following formula. The improved alternating direction multiplier method can be used to solve the optimal electricity-carbon trading volume between the source-load-storage system.

[0148]

[0149] First, establish the augmented Lagrangian function of subproblem 1, as shown in the following formula:

[0150]

[0151] Where: are the Lagrange multipliers for electricity trading and carbon quota trading respectively; ρ 1 is the penalty factor, and its initial value is 0.2.

[0152] The initial value of the expected electricity-carbon trading volume of each source-load-storage system is set to zero, and the expected electricity-carbon trading volume of each source-load-storage system is updated as shown in the following formula:

[0153]

[0154] Where: k is the number of iterations. In each iteration, the initial data of source-load-storage system i is first updated Update E with the updated electricity trading data and the carbon trading data before the update i-j,t (k). Similarly, When each source-load-storage system completes the update of the electricity-carbon trading volume strategy in the kth iteration, the Lagrange multiplier is updated according to the following equation:

[0155]

[0156] The convergence condition of subproblem 1 is shown in the following formula. If it does not converge, update the number of iterations and set k = k + 1. Then update the above formula until subproblem 1 meets the convergence condition and the iteration ends to obtain the optimal transaction volume.

[0157]

[0158] Where: 1 is the dual residual convergence criterion; ε 1 is the original residual convergence criterion.

[0159] Furthermore, sub-problem 2 is the problem of maximizing the benefits of the source-load-storage system participating in the cooperation. Substituting the optimal solution of sub-problem 1 into the original cooperative operation model can obtain the calculation equation of sub-problem 2, and calculate the cluster benefit value, as shown in the following formula:

[0160]

[0161] In the formula, The cost of subproblem 1 is Optimal solution; is the electricity-carbon transaction cost between the source-load-storage system in sub-question 1; They are respectively the electricity-carbon trading volumes between the source, load and storage systems in sub-question 1.

[0162] In the embodiment of the present invention, in order to ensure that the benefits of cooperation can be reasonably distributed to each source-load-storage system, this embodiment allocates higher benefits to the source-load-storage system with a greater contribution degree according to the different contributions of each source-load-storage system to the electric carbon of the cluster, so as to improve its enthusiasm for participating in cooperation. The contribution ratio value is described by the following formula:

[0163]

[0164] Where: κ i is the carbon contribution rate; α 1 , α 2 are the weight factors for electric energy contribution and carbon emission contribution respectively; They are electricity contribution factor and carbon emission contribution factor respectively.

[0165] The embodiment of the present invention adopts a nonlinear energy sharing mapping method to quantify the degree of contribution of the source-load-storage system to the electric energy of the cluster, the electric energy contribution factor of the source-load-storage system i, and the purchase and sale of electric energy between the source-load-storage system i and other source-load-storage systems during the cooperation process are shown in the following formula:

[0166]

[0167] In the formula, The amount of electricity purchased by source-load-storage system i from other source-load-storage systems; The amount of electricity sold by source-load-storage system i to other source-load-storage systems; They are the maximum values ​​of electricity purchased and sold between each source-load-storage system and other source-load-storage systems.

[0168] The source-load-storage system reduces carbon emissions, which helps protect the environment and reduce costs. The carbon emission contribution factor is shown in the following formula:

[0169]

[0170] Where: E i,T,b 、E i,T,a are the actual carbon emissions of source-load-storage system i before and after it participates in cooperative operation.

[0171] The contribution rate of electricity and carbon is considered in the bargaining process of transaction prices. The source-load-storage system with a high contribution rate of electricity and carbon has strong bargaining power and can be allocated higher benefits. Therefore, in order to reflect the impact of the contribution of the source-load-storage system on its cooperation benefits, the calculation formula of sub-problem 2 is rewritten as follows:

[0172]

[0173] Its logarithmic form is:

[0174]

[0175] Establish the augmented Lagrangian function of subproblem 2 as shown in the following formula:

[0176]

[0177] Where: are the Lagrange multipliers of the electricity-carbon trading price; ρ 2 is the penalty factor, and its initial value is 0.2.

[0178] The initial value of the electricity-carbon trading price expected by each source-load-storage system is set to zero, and the electricity-carbon trading price update equation expected by each source-load-storage system is as shown in the following formula:

[0179]

[0180] In the formula, k is the number of iterations.

[0181] In each iteration, the initial data of source-load-storage system i is first updated Update the electricity trading data after the update and the carbon trading data before the update Similarly, yes When each source-load-storage system completes the update of the electricity-carbon trading price strategy in the kth iteration, the Lagrange multiplier is updated according to the following equation:

[0182]

[0183] The convergence condition of subproblem 2 is shown in the following formula.

[0184]

[0185] Where: 2 is the dual residual convergence criterion; ε 2 is the original residual convergence criterion.

[0186] If it does not converge, update the number of iterations and set k = k + 1. Then update the above equation until subproblem 2 meets the convergence condition and the iteration ends.

[0187] It is worth noting that in the MATALB environment, YALMIP can be used to call the CPLEX solver to solve subproblem 1. YALMIP can be used to call the MOSEK solver to solve subproblem 2.

[0188] By solving sub-problems 1 and 2, the final cost of each source-load-storage system and the benefits obtained through cooperation are shown in the following formula:

[0189]

[0190] In the formula, is the final cost of source-load-storage system i; It is the income obtained by source-load-storage system i through cooperation.

[0191] In order to verify the effectiveness of the operation optimization method proposed in the embodiment of the present invention, the East-West Computing Source-Load-Storage System is taken as an example to analyze and illustrate the advantages of the source-load-storage system cluster cooperation optimization operation method considering multiple flexibility. The cluster consists of three source-load-storage systems. Source-load-storage system 1 and source-load-storage system 2 are equipped with photovoltaic, wind turbine, gas turbine, refrigeration system and energy storage system, and source-load-storage system 3 is additionally equipped with carbon capture equipment and power-to-gas equipment.

[0192] The initial values ​​of IN data load and BA data load of each source-load-storage system are the same, such as Figure 4 (a) shows the wind power and photovoltaic output forecast curves of each source-load-storage system, as shown in Figure 4 (b)~ Figure 4 (d) is shown. Each source-load-storage system is equipped with 100 servers. The power purchase and sale prices of the power grid, gas prices and carbon market transaction prices are shown in Table 1.

[0193] Table 1 Energy and carbon quota market transaction prices

[0194]

[0195] Furthermore, through comparative analysis of five scenarios, the advantages of the method proposed in the embodiment of the present invention in terms of economy, environmental protection and cooperative benefit distribution are verified.

[0196] The factors considered in the five scenarios are shown in Table 2. The total cost and carbon emissions of each source-load-storage system in scenarios 2 to 5 are shown in Tables 3 and 4, respectively.

[0197] Table 2 Description of the factors considered in the five scenarios

[0198] Scenario one two three Four five Each source, load and storage system operates independently × √ √ × × Various sources, loads and storage systems operate in cooperation √ × × √ √ Consider carbon capture and power-to-gas plants √ × √ √ √ Electricity-carbon trading between source, load and storage systems √ × × × √ Considering the contribution rate of carbon √ × × × ×

[0199] Table 3 Total cost of scenarios 2 to 5

[0200] Scenario two three Four five Source load storage system 1 cost / $ 3546.15 3546.15 3647.06 3194.44 Source load storage system 2 cost / $ 4132.49 4132.49 3687.42 3780.43 Source load storage system 3 cost / $ 4466.7 3544.5 3256.6 3194.39 Total cost / $ 12145.34 11223.14 10591.08 10169.26

[0201] Table 4 Carbon emissions of Scheme 2 to Scheme 5

[0202] Scenario two three Four five Carbon emission of source-load-storage system 1 / ton 18.87 18.87 17.6 16.76 Source-load-storage system 2 carbon emissions / ton 15.97 15.97 15.35 14.94 Source-load-storage system 3 carbon emissions / ton 19.47 18.6 18.67 18.72 Total carbon emissions / ton 54.31 53.44 51.62 50.42

[0203] By comparing Scenario 2 and Scenario 3, the necessity of configuring carbon capture and power-to-gas equipment is illustrated. Compared with Scenario 2, the source-load-storage system 3 in Scenario 3 adds carbon capture and power-to-gas equipment. As shown in Tables 3 and 4, the total cost of the source-load-storage system 3 in Scenario 2 and Scenario 3 is $4466.7 and $3544.5 respectively; the carbon emissions are 19.47 tons and 18.6 tons respectively. By comparison, it can be seen that the total cost and carbon emissions of the source-load-storage system 3 in Scenario 3 are reduced by 20.65% and 4.47% respectively compared with Scenario 2. It can be seen that carbon capture and power-to-gas can help reduce the total cost and carbon emissions of the source-load-storage system, making Scenario 3 advantageous in terms of both economy and environmental protection.

[0204] By comparing Scenario 3, Scenario 4 and Scenario 5, the necessity of cooperative operation of each source-load-storage system and electricity-carbon trading between them is illustrated. As shown in Table 3, the total costs of Scenario 3, Scenario 4 and Scenario 5 are $11223.14, $10591.08 and $10169.26 respectively. Compared with Scenario 3, the total costs of Scenario 4 and Scenario 5 are reduced by 5.63% and 9.39% respectively. It shows that compared with the independent operation of each source-load-storage system, the total cost of cooperative operation of multiple source-load-storage systems is significantly reduced. It is worth noting that the total cost of Scenario 5 is 3.98% lower than that of Scenario 4. It shows that after the cooperative operation of multiple source-load-storage systems, considering the electricity trading between source-load-storage systems can help further reduce the total cost of the source-load-storage system.

[0205] As shown in Table 4, the total carbon emissions of scenario 3, scenario 4 and scenario 5 are 53.44 tons, 51.62 tons and 50.42 tons respectively. Compared with scenario 3, the carbon emissions of scenario 4 and scenario 5 are reduced by 3.39% and 5.64% respectively. This shows that compared with the independent operation of each source-load-storage system, the total carbon emissions after the cooperative operation of multiple source-load-storage systems are significantly reduced. It is worth noting that the total carbon emissions of scenario 5 are 2.34% lower than those of scenario 4. This shows that after the cooperative operation of multiple source-load-storage systems, considering the interaction of electricity and carbon quotas between source-load-storage systems can help further reduce the carbon emissions of the source-load-storage system. It can be seen that the cooperative operation of multiple source-load-storage systems and their mutual electricity-carbon interaction can help reduce the total cost and carbon emissions of the source-load-storage system, making scenario 5 have advantages in both economy and environmental protection.

[0206] By comparing Scenario 1 and Scenario 5, the necessity of the source-load-storage system cluster revenue distribution based on the carbon-to-electricity contribution rate is illustrated. The carbon-to-electricity contribution rates of each source-load-storage system in Scenario 1 are shown in Table 5. Among them, source-load-storage system 1 contributes the most to the carbon-to-electricity of the cluster, and source-load-storage system 3 contributes the least to the carbon-to-electricity of the system.

[0207] Table 5 Calculation results of carbon contribution rate of each source-load-storage system

[0208] name Source load storage system 1 Source load storage system 2 Source load storage system 3 Electricity contribution factor 1.9616 1.3675 0.5818 Carbon emission contribution factor 0.6291 0.3056 -0.0348 Electricity carbon contribution rate 0.5278 0.3483 0.1238

[0209] The comparison of the income distribution between scenario 1 and scenario 5 is shown in Table 6. The cooperative income of each source-load-storage system in scenario 5 is $351.71, $352.06, and $350.11 respectively. The income obtained by the three source-load-storage systems is roughly the same, which will affect the enthusiasm of the source-load-storage system with a large contribution to participate in the cooperation. Scenario 1 comprehensively considers the contribution of each source-load-storage system to the cluster from the two dimensions of power contribution and carbon emission contribution.

[0210] Table 6 Comparison of profit distribution between scenario 1 and scenario 5

[0211]

[0212] As shown in Table 6, source-load-storage system 1 has the largest contribution rate to cluster electricity-carbon, and its cooperation income is also the highest, which is $547.29; source-load-storage system 3 has the smallest contribution rate to cluster electricity-carbon, and its cooperation income is also the lowest, which is $138.8. Therefore, the distribution method of source-load-storage system cluster income based on electricity-carbon contribution rate is reasonable, which can strengthen the cooperation willingness of each source-load-storage system and consolidate the cooperation relationship among each source-load-storage system in the cluster.

[0213] In summary, the source-load-storage system cluster cooperative optimization operation method considering multiple flexibility proposed in the embodiment of the present invention has advantages in terms of economy, environmental protection and cooperative profit distribution, which verifies the rationality of the method proposed in the embodiment of the present invention.

[0214] In addition, the effectiveness of the method proposed in the present invention can be verified by comparing and analyzing multiple scenarios. The scheduling results of the source-load-storage system in scenario 1 are described in detail. The data load scheduling results of each source-load-storage system are shown in Figure 5 As shown in the figure, from the perspective of data load time scheduling, part of the data load is delayed from the peak electricity price period to the non-peak electricity price period to reduce the power consumption of the server during the peak electricity price period and reduce the operating cost of the source load storage system. From the perspective of data load space scheduling, the operating cost of source load storage system 3 is lower. In order to reduce the cluster cost, source load storage system 3 undertakes more data load processing tasks compared to other source load storage systems.

[0215] The energy balance curves of each source-load-storage system are as follows: Figure 6 As shown. Figure 6 The power transaction information between each source-load-storage system can be obtained, such as Figure 7 As shown. Combined Figure 6 and Figure 7 (a) It can be seen that the wind turbine power generation of source-load-storage system 1 is relatively large during the time periods of 0:00-10:00 and 19:00-24:00, and the surplus power can be sold to other source-load-storage systems. The server power load of source-load-storage system 2 is relatively low during the time periods of 2:00-5:00 and 11:00-18:00, and the surplus power can be sold to other source-load-storage systems. The power generation of the energy supply equipment of source-load-storage system 3 is relatively low during the time periods of 0:00-18:00 and 22:00-24:00, and it is necessary to purchase electricity from other source-load-storage systems. The P2P electricity transaction price curve between each source-load-storage system is as follows: Figure 7 (b) As shown in the figure, since the price of electric energy trading between various source-load-storage systems is between the purchase and sale prices of the power grid, the electric energy interaction between various source-load-storage systems helps to reduce their total cost.

[0216] The carbon trading results between various source-load-storage systems are shown in Table 7.

[0217] Table 7 P2P carbon trading results of each source-load-storage system

[0218] name Carbon quota trading volume / kg Carbon trading price / $ / kg IDC 1 - IDC 2 1512.77 0.036 IDC 1 - IDC 3 -286.19 0.049 IDC 2 - IDC 3 -1252.5 0.041

[0219] Source-load-storage system 2 purchased 1512.77kg of carbon quota from source-load-storage system 1; source-load-storage system 3 sold 286.19kg of carbon quota to source-load-storage system 1; source-load-storage system 3 sold 1252.5kg of carbon quota to source-load-storage system 2. Therefore, source-load-storage system 3 and source-load-storage system 1 have surplus carbon quotas and can sell 1538.69kg and 1226.58kg of carbon quotas to other source-load-storage systems respectively. Source-load-storage system 2 is short of carbon quotas and needs to purchase 2765.27kg of carbon quotas from other source-load-storage systems. Because the price of carbon quota transactions between source-load-storage systems is between the purchase and sale prices in the carbon trading market, the interaction of carbon quotas between source-load-storage systems helps to reduce their total costs.

[0220] like Figure 2 , Figure 3 As shown, an embodiment of the present invention provides an operation optimization device for a source-load-storage system cluster. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown, it is a hardware architecture diagram of an electronic device where an operation optimization device of a source-load-storage system cluster provided by an embodiment of the present invention is located, except Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, the CPU of the electronic device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides an operation optimization device for a source-load-storage system cluster, including:

[0221] The acquisition module 300 is used to acquire the transaction data and operation data of each source-load-storage system; wherein the operation data is obtained by optimizing and adjusting the control unit and the production capacity unit of the source-load-storage system;

[0222] A first calculation module 302 is used to calculate the total system cost of each source-load-storage system based on the transaction data and the operation data;

[0223] A modeling module 304 is used to establish a cooperative operation model of the source-load-storage system cluster based on the total system cost;

[0224] The second calculation module 306 is used to solve and calculate the cooperative operation model to obtain the optimal profit value of the source-load-storage system cluster.

[0225] In an embodiment of the present invention, when the first calculation module 302 calculates the total system cost of each source-load-storage system based on transaction data and operation data, it is specifically used to perform the following operations: calculating the transaction data to obtain the transaction cost of the source-load-storage system; wherein the transaction cost includes the market carbon transaction cost, the inter-system carbon transaction cost and the source-load migration cost; calculating the operation data to obtain the operation cost of the source-load-storage system.

[0226] In the embodiment of the present invention, the total system cost is calculated by the following formula:

[0227]

[0228] In the formula, is the total system cost of the i-th source-load-storage system in the cluster; is the operating cost of the source-load-storage system; The cost of participating in the carbon trading market for source-load-storage systems; The cost of electricity-carbon transaction between source, load and storage systems; For the cost of Internet access; They are, in order, the operation and maintenance costs of gas turbines, carbon capture equipment, power-to-gas equipment, absorption chillers, and electric chillers and energy storage equipment; The cost of purchasing energy; are the purchase price and sale price of carbon quotas in the carbon trading market; E i,0 is the initial carbon quota of source-load-storage system i; E i,T is the actual carbon emission of source-load-storage system i; E i-j,t 、E j-i,t is the carbon quota for the interaction between source-load-storage system i and source-load-storage system j; is the electricity transaction cost between source-load-storage system i and other source-load-storage systems in the cluster; is the carbon transaction cost between source-load-storage system i and other source-load-storage systems in the cluster; For electricity transmission fees; Data transfer fee.

[0229] In the embodiment of the present invention, the cooperative operation model is established by the following formula:

[0230]

[0231] In the formula, Cost of operating the source-load-storage system independently without joining the cluster; For cost The optimal value of .

[0232] In an embodiment of the present invention, when the second calculation module 306 performs a solution calculation on the cooperative operation model to obtain the optimal profit value of the source-load-storage system cluster, it is specifically used to perform the following operations: calculate the cooperative operation model based on the improved alternating direction multiplier method to obtain the optimal transaction volume between the source-load-storage systems; calculate the optimal transaction volume to obtain the profit value of the source-load-storage system cluster; allocate the profit value based on a preset contribution ratio value to obtain the optimal profit value; wherein the contribution ratio value includes electric energy contribution and carbon emission contribution.

[0233] In an embodiment of the present invention, the optimal transaction volume is obtained through the following process: establishing a solution function model for the transaction volume of the source-load-storage system; iteratively updating the solution function model to obtain the optimal transaction volume that meets preset conditions; wherein the iterative update includes updating the expected value of the transaction volume and updating the model penalty factor.

[0234] In the embodiment of the present invention, the contribution ratio value is calculated by the following formula:

[0235]

[0236] In the formula, κ i is the contribution ratio; α 1 , α 2 The weight factors of electricity contribution and carbon emission contribution respectively; They are electricity contribution factor and carbon emission contribution factor respectively; The amount of electricity purchased by source-load-storage system i from other source-load-storage systems; The amount of electricity sold by source-load-storage system i to other source-load-storage systems; are the maximum values ​​of the purchased and sold electric energy between each source-load-storage system and other source-load-storage systems; E i,T,b 、E i,T,a They are the actual carbon emissions of source-load-storage system i before and after it participates in cooperative operation.

[0237] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the operation optimization device of a source-load-storage system cluster. In other embodiments of the present invention, an operation optimization device of a source-load-storage system cluster may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0238] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0239] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, an operation optimization method of a source-load-storage system cluster in any embodiment of the present invention is implemented.

[0240] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes an operation optimization method for a source-load-storage system cluster in any embodiment of the present invention.

[0241] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0242] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0243] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0244] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the operation of a source-load-storage system cluster, characterized in that: include: Acquire transaction data and operation data of each source-load-storage system; wherein the operation data is obtained by optimizing and adjusting the control unit and the production capacity unit of the source-load-storage system; Based on the transaction data and the operation data, the total system cost of each source-load-storage system is calculated; Based on the total system cost, a cooperative operation model of the source-load-storage system cluster is established; Solving and calculating the cooperative operation model to obtain the optimal benefit value of the source-load-storage system cluster; The control unit processes data flexibly, optimizes the time and space location of delay-sensitive data and delay-tolerant data, and flexibly schedules the power consumption of the source-load-storage system i at time t. for: In the formula, γ PUE is the energy efficiency level of the source-load-storage system; P i idle is the server no-load power; P i peak Fully load the server with power; and are the actual delay-sensitive data and delay-tolerant data loads of source-load-storage system i at time t, respectively; n i is the number of servers in source-load-storage system i; π is the upper limit of the processing speed of a single server; The optimization of the energy generation unit includes the optimization of the gas power generation subsystem, the refrigeration subsystem and the energy storage subsystem; The calculating the total system cost of each source-load-storage system based on the transaction data and the operation data includes: Calculating the transaction data to obtain the transaction cost of the source-load-storage system; wherein the transaction cost includes market carbon transaction cost, inter-system carbon transaction cost and source-load migration cost; Calculating the operating data to obtain the operating cost of the source-load-storage system; The total system cost is calculated by the following formula: In the formula, is the total system cost of the i-th source-load-storage system in the cluster; is the operating cost of the source-load-storage system; The cost of participating in the carbon trading market for source-load-storage systems; The cost of electricity-carbon transaction between source, load and storage systems; For the cost of Internet access; They are, in order, the operation and maintenance costs of gas turbines, carbon capture equipment, power-to-gas equipment, absorption chillers, and electric chillers and energy storage equipment; The cost of purchasing energy; are the purchase price and sale price of carbon quotas in the carbon trading market; E i,0 is the initial carbon quota of source-load-storage system i; E i,T is the actual carbon emission of source-load-storage system i; E i-j,t 、E j-i,t is the carbon quota for the interaction between source-load-storage system i and source-load-storage system j; is the electricity transaction cost between source-load-storage system i and other source-load-storage systems in the cluster; is the carbon transaction cost between source-load-storage system i and other source-load-storage systems in the cluster; For electricity transmission fees; For data transmission charges; The balance constraints of the total system cost are determined based on the optimized control units and production capacity units, including electric power balance constraints, gas balance constraints, electric energy trading constraints and carbon trading constraints.

2. The method according to claim 1, characterized in that The cooperative operation model is established by the following formula: In the formula, Cost of operating the source-load-storage system independently without joining the cluster; For cost The optimal value of .

3. The method according to claim 1, characterized in that The step of solving and calculating the cooperative operation model to obtain the optimal benefit value of the source-load-storage system cluster includes: The cooperative operation model is calculated based on an improved alternating direction multiplier method to obtain an optimal transaction volume between the source, load and storage systems; Calculating the optimal transaction volume to obtain the revenue value of the source-load-storage system cluster; The benefit value is distributed based on a preset contribution ratio value to obtain the optimal benefit value; wherein the contribution ratio value includes electric energy contribution and carbon emission contribution.

4. The method according to claim 3, characterized in that The cooperative operation model is calculated based on the improved alternating direction multiplier method to obtain the optimal transaction volume between the source, load and storage systems, including: Establishing a solution function model for the transaction volume of the source-load-storage system; The solution function model is iteratively updated to obtain an optimal transaction volume that meets preset conditions; wherein the iterative update includes updating the expected value of the transaction volume and updating the model penalty factor.

5. The method according to claim 3, characterized in that: The contribution ratio value is calculated by the following formula: In the formula, κ i is the contribution ratio; α1 and α2 are the weight factors of electric energy contribution and carbon emission contribution respectively; They are electricity contribution factor and carbon emission contribution factor respectively; The amount of electricity purchased by source-load-storage system i from other source-load-storage systems; The amount of electricity sold by source-load-storage system i to other source-load-storage systems; are the maximum values ​​of the purchased and sold electric energy between each source-load-storage system and other source-load-storage systems; E i,T,b 、E i,T,a They are the actual carbon emissions of source-load-storage system i before and after it participates in cooperative operation.

6. An operation optimization device for a source-load-storage system cluster, characterized in that: include: An acquisition module, used to acquire transaction data and operation data of each source-load-storage system; wherein the operation data is obtained by optimizing and adjusting the control unit and the production capacity unit of the source-load-storage system; A first calculation module, configured to calculate the total system cost of each of the source-load-storage systems based on the transaction data and the operation data; A modeling module, used for establishing a cooperative operation model of a source-load-storage system cluster based on the total system cost; The second calculation module is used to solve and calculate the cooperative operation model to obtain the optimal profit value of the source-load-storage system cluster.

7. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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