Cloud energy storage charging and discharging optimization method and system for cloud energy storage provider side

Abstract

The invention discloses a cloud energy storage charging and discharging optimization method and system for a cloud energy storage provider side. The method comprises the following steps: integrating charging and discharging energy and carbon emission data of all cloud energy storage users; respectively establishing models of a carbon transaction mechanism CET and a green certificate transaction mechanism GCT according to energy charging and discharging and carbon emission data of the cloud energy storage user; constructing an interaction model of a carbon transaction mechanism CET and a green certificate transaction mechanism GCT based on the model of the carbon transaction mechanism CET and the green certificate transaction mechanism GCT; and taking an interaction model of a carbon transaction mechanism CET and a green certificate transaction mechanism GCT as a cloud energy storage charging and discharging decision behavior model of a cloud energy storage provider side, solving by taking the cost minimization of the cloud energy storage provider as an objective function, and issuing and executing a cloud energy storage charging and discharging optimization result obtained by solving. The invention aims to realize cloud energy storage charging and discharging optimization scheduling for a cloud energy storage provider so as to improve the economic benefit of cloud energy storage.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a cloud energy storage charging and discharging optimization method and system for a cloud energy storage provider. Background Art

[0002] At present, all countries are accelerating the development and utilization of renewable energy. However, due to the intermittent and volatile characteristics of renewable energy such as wind power and photovoltaic power, there are problems such as difficulty in power generation and consumption, wind and light abandonment in the operation of the power system. As an important means to solve the high proportion of renewable energy access, energy storage technology plays a key role in power load regulation, power fluctuation suppression and optimization of power resource allocation. As an emerging shared energy storage mode, cloud energy storage technology integrates distributed energy storage resources in the cloud to provide flexible energy storage services for multiple users, with the advantages of resource sharing and efficient scheduling. However, in actual operation, the scheduling and operation of cloud energy storage are restricted and affected by many factors, including market trading mechanism, electricity price fluctuation, carbon emission constraints and policy orientation. Although the current cloud energy storage technology and its scheduling optimization method have made certain progress, there are still many shortcomings. The energy storage scheduling method based on peak-valley electricity price uses the peak-valley electricity price difference for charging and discharging scheduling, but ignores factors such as renewable energy consumption and carbon emission constraints, and cannot adapt to the needs of green and low-carbon development. In addition, existing optimization methods mostly take economic benefits or carbon emission reduction as a single goal, and fail to fully consider the compound benefits of carbon trading and green certificate trading. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, a cloud energy storage charging and discharging optimization method and system for a cloud energy storage provider are provided. The present invention aims to achieve cloud energy storage charging and discharging optimization scheduling for cloud energy storage providers to improve the economic benefits of cloud energy storage.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A cloud energy storage charging and discharging optimization method for a cloud energy storage provider comprises the following steps: S1, comprehensive charging, discharging and carbon emission data of all cloud energy storage users; S2, based on the charging and discharging energy and carbon emission data of cloud energy storage users, establish the models of carbon trading mechanism CET and green certificate trading mechanism GCT respectively; S3, based on the models of carbon trading mechanism CET and green certificate trading mechanism GCT, an interactive model of carbon trading mechanism CET and green certificate trading mechanism GCT is constructed; S4, the interaction model between the carbon trading mechanism CET and the green certificate trading mechanism GCT is used as the cloud energy storage charging and discharging decision-making behavior model on the cloud energy storage provider side, and the objective function is solved with the minimum cost of the cloud energy storage provider; S5, the cloud energy storage charging and discharging optimization results are solved and sent for execution.

[0005] Optionally, in step S2, when the models of the carbon trading mechanism CET and the green certificate trading mechanism GCT are established respectively according to the charging and discharging energy and carbon emission data of the cloud energy storage user, the established carbon trading mechanism CET model includes the system carbon emission quota for the t period shown in the following formula: and the actual carbon emissions of the system : , , in, The carbon emission quota per unit of electricity in the region is allocated. is the total gas production power of the gas turbine GT in each period, is the conversion factor of electricity, is the total power generated by the gas turbine GT in each period, is the sum of the heating power of the gas boiler GB in each period; is the carbon emission coefficient of the gas turbine GT, is the carbon emission coefficient of the gas boiler GB.

[0006] Optionally, in step S2, when the models of the carbon trading mechanism CET and the green certificate trading mechanism GCT are established respectively according to the charging and discharging energy and carbon emission data of the cloud energy storage user, the model of the green certificate trading mechanism GCT established includes the number of green certificates obtained by the new energy power generator as shown in the following formula: And the number of green certificates required for the system to meet the assessment : , , in, is the power prediction influencing parameter, is the scheduling period, For unit scheduling period, The total amount of new energy charging for users in each period, Impact weights for power prediction; is the accuracy of power prediction in the previous period; Forecast accuracy standard value; The electricity demand for new energy quota, is the new energy quota coefficient, The impact coefficient of the historical green certificate assessment completion, The electricity load generated by renewable energy for users, is the electrical load of the gas boiler GB and the gas turbine GT, The weight of the historical green certificate assessment completion rate is affected. is the average value of the historical new energy quota completion rate, It is the system’s historical new energy quota completion coefficient.

[0007] Optionally, the interaction model between the carbon trading mechanism CET and the green certificate trading mechanism GCT constructed in step S3 refers to the cost of participating in the green certificate trading using the system shown in the following formula when the cloud energy storage provider meets the carbon quota assessment: Carbon trading revenue from cloud storage providers : , , in, is the unit green certificate transaction price, The number of green certificates obtained for new energy power generators, The number of green certificates required for the system to meet the assessment: is the unit carbon quota price, is the actual carbon emissions of the system during period t, is the system carbon emission quota for period t, The carbon emission quota per unit of electricity in the region is allocated.

[0008] Optionally, the function expression of the objective function in step S4 is: , in, Represents the function to minimize the cost For the goal, is the equal annual value coefficient, Investment costs for cloud storage providers; The operating cost of the cloud energy storage provider, including the difference between the cost of purchasing energy from the electricity, heat and gas grids and the partial income generated by the surplus energy being fed back to the electricity, heat and gas grids; Fixed costs for cloud storage providers; Carbon trading income for cloud energy storage providers, with: , , in, , and are the unit power investment coefficients of lithium batteries, heat storage tanks, and gas storage tanks respectively; , and are the unit capacity investment coefficients of lithium batteries, heat storage tanks, and gas storage tanks respectively; , and The power of lithium batteries, heat storage tanks, and gas storage tanks actually invested by cloud energy storage providers; , and The capacities of lithium batteries, heat storage tanks, and gas storage tanks actually invested by cloud energy storage providers; is the season number, which includes four seasons: spring, summer, autumn and winter. is the scheduling period, , and Respectively represent the price of purchasing unit power energy between users and electricity, heat and gas grids, The actual power drawn from the grid by the cloud energy storage provider is the heating value coefficient of the heat network converted to unit power, is the actual power obtained by the cloud energy storage provider from the heat network, is the calorific value coefficient of the gas network converted to unit power, is the actual power obtained by the cloud energy storage provider from the gas grid, , and are the prices of unit power energy sent back between users and the electricity, heat and gas grids, respectively. and The operational definition is as follows: , in, for or The operation object.

[0009] Optionally, the integrated charging and discharging energy and carbon emission data of all cloud energy storage users in step S1 includes: Calculate the total energy demand of users in each period according to the following formula : , in, A collection of users participating in cloud energy storage. They are Moment User Cloud energy storage charging and discharging, heat and gas power demand, They represent charge and discharge, heat, and gas respectively; The total amount of new energy charging for users in each period is calculated according to the following formula: , in, It is the sum of new energy charging, heat and gas for users in each period. for Moment User New energy charging, heating and gas power.

[0010] Optionally, the integrated charging and discharging energy and carbon emission data of all cloud energy storage users in step S1 also includes: The actual power obtained by the cloud energy storage provider from the electricity, heat and gas grids is calculated according to the following formula: , , , in, , and are the actual power obtained by the cloud energy storage provider from the electricity, heat and gas grids during period t, , , are the charging powers of electricity, heat and gas physical energy storage determined by the cloud energy storage provider during period t, , and are the energy release powers of electricity, heat and gas physical energy storage determined by the cloud energy storage provider during period t, , and The total demand for discharge, heat and gas energy of users in each period , , and They are the sum of the users’ renewable energy charging, heat and gas energy in each period; The total heat generation power of the gas boiler in each period is calculated according to the following formula: , in, is the total heat generation power of the gas boiler GB during period t, A collection of users participating in cloud energy storage. is the heating power of the gas boiler GB of user i during period t; The total power generation and heat generation of the gas turbine GT in each period is calculated according to the following formula: , , in, and are the total power generation and heat generation of the gas turbine GT during period t, and They are users in period t The sum of the electricity and heat production of the gas turbine GT.

[0011] In addition, the present invention also provides a cloud energy storage charging and discharging optimization system for a cloud energy storage provider, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the cloud energy storage charging and discharging optimization method for the cloud energy storage provider.

[0012] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the cloud energy storage charging and discharging optimization method for the cloud energy storage provider side through a processor.

[0013] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the cloud energy storage charging and discharging optimization method for the cloud energy storage provider side through a processor.

[0014] Compared with the prior art, the present invention mainly has the following advantages: the present invention takes into account the carbon emission trading mechanism (CET) and the green certificate trading mechanism (GCT), and proposes a cloud energy storage provider-side cloud energy storage charging and discharging optimization scheduling method based on CET and GCT. This method constructs an optimization model including multiple market mechanisms, organically combines the carbon trading market, the green certificate market and the electricity market to achieve cloud energy storage provider-side cloud energy storage charging and discharging optimization scheduling, which can improve its economic benefits and promote the realization of clean energy consumption and low-carbon transformation goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, the cloud energy storage charging and discharging optimization method for the cloud energy storage provider side of this embodiment includes the following steps: S1, comprehensive charging, discharging and carbon emission data of all cloud energy storage users; S2, based on the charging and discharging energy and carbon emission data of cloud energy storage users, establish the models of carbon trading mechanism CET and green certificate trading mechanism GCT respectively; S3, based on the models of carbon trading mechanism CET and green certificate trading mechanism GCT, an interactive model of carbon trading mechanism CET and green certificate trading mechanism GCT is constructed; S4, the interaction model between the carbon trading mechanism CET and the green certificate trading mechanism GCT is used as the cloud energy storage charging and discharging decision-making behavior model on the cloud energy storage provider side, and the objective function is solved with the minimum cost of the cloud energy storage provider; S5, the cloud energy storage charging and discharging optimization results are solved and sent for execution.

[0018] In step S1 of this embodiment, the charging and discharging energy and carbon emission data of all cloud energy storage users are integrated to include: Calculate the total energy demand of users in each period according to the following formula : , in, A collection of users participating in cloud energy storage. They are Moment User Cloud energy storage charging and discharging, heat and gas power demand, They represent charge and discharge, heat, and gas respectively; The total amount of new energy charging for users in each period is calculated according to the following formula: , in, It is the sum of new energy charging, heat and gas for users in each period. for Moment User New energy charging, heating and gas power.

[0019] In step S1 of this embodiment, the integrated charging and discharging energy and carbon emission data of all cloud energy storage users also includes: The actual power obtained by the cloud energy storage provider from the electricity, heat and gas grids is calculated according to the following formula: , , , in, , and are the actual power obtained by the cloud energy storage provider from the electricity, heat and gas grids during period t, , , are the charging powers of electricity, heat and gas physical energy storage determined by the cloud energy storage provider during period t, , and are the energy release powers of electricity, heat and gas physical energy storage determined by the cloud energy storage provider during period t, , and The total demand for discharge, heat and gas energy of users in each period , , and They are the sum of the users’ renewable energy charging, heat and gas energy in each period; The total heat generation power of the gas boiler in each period is calculated according to the following formula: , in, is the total heat generation power of the gas boiler GB (gas boiler) during period t, A collection of users participating in cloud energy storage. is the heating power of the gas boiler GB of user i during period t; The total power generation and heat generation of the gas turbine GT (gas turbine) at each time period is calculated according to the following formula: , , in, and are the total power generation and heat generation of the gas turbine GT during period t, and They are users in period t The sum of the electricity and heat production of the gas turbine GT.

[0020] A perfect carbon trading mechanism first needs to determine the carbon emission quota. There are two common ways to allocate carbon emission quotas: free allocation and paid allocation. Free allocation refers to the pre-allocation of free carbon emission quotas to the system to increase the enthusiasm of the system to participate; paid allocation requires the system to pay corresponding fees for its own carbon emissions. According to the current actual situation in my country, free allocation is adopted and based on the baseline method to provide carbon emission quotas for the system. The carbon emission sources are gas turbines GT and gas boilers GB. GT generates electricity and heat, and gas boilers GB only generate heat. Carbon emission quotas are allocated to them according to the total equivalent calorific value. In step S2 of this embodiment, when the models of the carbon trading mechanism CET and the green certificate trading mechanism GCT are established respectively according to the charging and discharging energy and carbon emission data of cloud energy storage users, the established model of the carbon trading mechanism CET includes the system carbon emission quota for the period t shown in the following formula and the actual carbon emissions of the system : , , in, The carbon emission quota per unit of electricity in the region is allocated. is the total gas production power of the gas turbine GT in each period, is the conversion factor of electricity, is the total power generated by the gas turbine GT in each period, is the sum of the heating power of the gas boiler GB in each period; is the carbon emission coefficient of the gas turbine GT, is the carbon emission coefficient of the gas boiler GB. The regional unit electricity carbon emission allocation is a given parameter. This embodiment adopts the weighted average of the system area's electricity margin (operating margin, 0M) emission factor and the capacity margin (build margin, BM) emission factor, taking 0.57t / (MW·h). Actual carbon emissions of the system at this moment is the sum of the gas turbine GT and the gas boiler GB. According to the emission factor method, in this embodiment, it is approximately considered that the actual carbon emissions of the unit are proportional to the unit output, so the actual carbon emissions of the system can be obtained. The function expression of .

[0021] Based on the original green certificate assessment system, this embodiment considers the impact of the accuracy of new energy output on the allocation of green certificates, takes industry standards as a reference, and increases or decreases the number of green certificates as incentives or penalties according to the accuracy of renewable energy prediction, thereby improving the accuracy of renewable energy prediction. At the same time, the green certificate is only valid within the assessment period. Therefore, in step S2 of this embodiment, when the models of the carbon trading mechanism CET and the green certificate trading mechanism GCT are established respectively according to the charging and discharging energy and carbon emission data of the cloud energy storage user, the model of the green certificate trading mechanism GCT established includes the number of green certificates obtained by the new energy power generator as shown in the following formula: And the number of green certificates required for the system to meet the assessment : , , in, is the power prediction influencing parameter, is the scheduling period, For unit scheduling period, The total amount of new energy charging for users in each period, Impact weights for power prediction; is the accuracy of power prediction in the previous period; Forecast accuracy standard value; The electricity demand for new energy quota, is the new energy quota coefficient, The impact coefficient of the historical green certificate assessment completion, The electricity load generated by renewable energy for users, is the electrical load of the gas boiler GB and the gas turbine GT, The weight of the historical green certificate assessment completion rate is affected. is the average value of the historical new energy quota completion rate, is the system's historical new energy quota completion coefficient. In order to accurately calculate the total demand for green certificates for the integrated energy system over the entire life cycle, the above function expression determines the number of green certificates required for the current quota target electricity based on the historical quota completion situation.

[0022] To avoid repeated calculation of information, when the cloud energy storage provider meets the carbon quota assessment, the corresponding green certificate environmental protection attributes should be deducted. At this time, the cloud energy storage provider does not directly participate in carbon trading, but participates in carbon trading by purchasing green certificates. The interaction model between the carbon trading mechanism CET and the green certificate trading mechanism GCT constructed in step S3 of this embodiment refers to the cost of participating in the green certificate transaction when the cloud energy storage provider meets the carbon quota assessment using the system shown in the following formula Carbon trading revenue from cloud storage providers : , , in, is the unit green certificate transaction price, The number of green certificates obtained for new energy power generators, The number of green certificates required for the system to meet the assessment: is the unit carbon quota price, is the actual carbon emissions of the system during period t, is the system carbon emission quota for period t, The carbon emission quota per unit of electricity in the region is allocated.

[0023] After the user sends a request for charging and discharging, the cloud energy storage provider will not respond immediately, but will coordinate the charging and discharging needs of all users and reasonably arrange the physical energy storage for charging and discharging to achieve the goal of minimizing the overall cost. Specifically, the function expression of the objective function in step S4 of this embodiment is: , in, Represents the function to minimize the cost For the goal, is the equal annual value coefficient, Investment costs for cloud storage providers; The operating cost of the cloud energy storage provider, including the difference between the cost of purchasing energy from the electricity, heat and gas grids and the partial income generated by the surplus energy being fed back to the electricity, heat and gas grids; Fixed costs for cloud storage providers; Carbon trading income for cloud energy storage providers, with: , , in, , and are the unit power investment coefficients of lithium batteries, heat storage tanks, and gas storage tanks respectively; , and are the unit capacity investment coefficients of lithium batteries, heat storage tanks, and gas storage tanks respectively; , and The power of lithium batteries, heat storage tanks, and gas storage tanks actually invested by cloud energy storage providers; , and The capacities of lithium batteries, heat storage tanks, and gas storage tanks actually invested by cloud energy storage providers; is the season number, which includes four seasons: spring, summer, autumn and winter. is the scheduling period, , and Respectively represent the price of purchasing unit power energy between users and electricity, heat and gas grids, The actual power drawn from the grid by the cloud energy storage provider is the heating value coefficient of the heat network converted to unit power, is the actual power obtained by the cloud energy storage provider from the heat network, is the calorific value coefficient of the gas network converted to unit power, is the actual power obtained by the cloud energy storage provider from the gas grid, , and are the prices of unit power energy sent back between users and the electricity, heat and gas grids, respectively. and The operational definition is as follows: , in, for or The operation object.

[0024] In step S4 of this embodiment, the interaction model between the carbon trading mechanism CET and the green certificate trading mechanism GCT is used as the cloud energy storage charging and discharging decision-making behavior model on the cloud energy storage provider side. When solving the problem with the minimum cost of the cloud energy storage provider as the objective function, the commercial solver CPLEX is called in MATLAB2020a using the YALMIP toolbox to solve it. In addition, other commercial solvers can also be used for solving as needed.

[0025] In order to verify the effectiveness of the cloud energy storage charging and discharging optimization method used in this embodiment for the cloud energy storage provider side, a large industrial park in the southern region is taken as the research object in this embodiment. The scheduling cycle of each typical day is 24 hours. The scheduling interval in this embodiment is 1 hour. Some parameters in the region are as follows: Carbon emission allocation per unit electricity in the region 0.57; the carbon emission coefficient of gas turbine GT and gas boiler GB Both are 0.6101; the weight of new energy output forecast is 0.2; the accuracy of the forecast of new energy output in the previous period 0.95; Standard value of accuracy of new energy output prediction 0.8; New energy quota coefficient is 0.185; the system's historical new energy quota completion rate is 0.59; fixed cost / 10,000 yuan is 10; investment planning cycle / a is 10; unit green certificate transaction price (US dollars / piece) is 7; unit carbon quota transaction price (USD / t) is 10.5; the average completion rate of new energy quota in history is 0.8; the average completion rate of new energy quota in history is 0.7025. In the experimental part of this embodiment, four different application scenarios are designed, which respectively examine the cost performance of users when using cloud energy storage, considering new energy output forecast, and combining demand response. The four scenarios are specifically described as follows: Scenario 1: Carbon trading mechanism CET and green certificate trading mechanism GCT are not considered; Scenario 2: Green certificate trading mechanism GCT is considered, but carbon trading mechanism CET is not considered; Scenario 3: Carbon trading mechanism CET is considered, but green certificate trading mechanism GCT is not considered; Scenario 4: Carbon trading mechanism CET and green certificate trading mechanism GCT are considered. For the above four different scenarios, the cloud energy storage providers and carbon emissions data are shown in Table 1.

[0026] Table 1 Comparison of experimental results in different scenarios

[0027] As can be seen from Table 1, the overall cost and carbon emissions of cloud energy storage providers vary in different scenarios. In scenario 1, the overall cost and carbon emissions of cloud energy storage providers are both at the highest level. In scenario 2, the overall cost slightly increased, while carbon emissions decreased. This shows that green certificate trading has improved the utilization rate of renewable energy, but increased the cost of purchasing green certificates. In scenario 3, the overall cost and carbon emissions decreased compared to scenario 1, indicating that the carbon trading mechanism has a significant effect on cost control and carbon emission reduction. In scenario 4, the overall cost and carbon emissions were significantly reduced, indicating that the synergy of the two mechanisms not only optimized the economic benefits of cloud energy storage, but also greatly improved environmental performance. It can be seen that the cloud energy storage charging and discharging optimization method for the cloud energy storage provider side of this embodiment combines the carbon trading mechanism CET and the green certificate trading mechanism GCT mechanism to effectively achieve the carbon emission reduction target, while improving the economic benefits of the cloud energy storage provider. The cloud energy storage charging and discharging optimization method for the cloud energy storage provider side of this embodiment takes into account the carbon trading mechanism (carbon emission trading, CET) and the green certificate trading mechanism (green certificate trading, GCT), and proposes a cloud energy storage charging and discharging optimization scheduling method for the cloud energy storage provider side based on CET and GCT. The cloud energy storage charging and discharging optimization method for the cloud energy storage provider side of this embodiment constructs an optimization model including multiple market mechanisms, organically combines the carbon trading market, the green certificate market and the electricity market to achieve cloud energy storage charging and discharging optimization scheduling on the cloud energy storage provider side, which can improve its economic benefits and promote the realization of clean energy consumption and low-carbon transformation goals.

[0028] In addition, this embodiment also provides a cloud energy storage charging and discharging optimization system for a cloud energy storage provider, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the cloud energy storage charging and discharging optimization method for the cloud energy storage provider.

[0029] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the cloud energy storage charging and discharging optimization method for the cloud energy storage provider side through a processor.

[0030] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the cloud energy storage charging and discharging optimization method for the cloud energy storage provider side through a processor.

[0031] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0032] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A cloud energy storage charging and discharging optimization method for a cloud energy storage provider, characterized in that: The steps include: S1, comprehensive charging, discharging and carbon emission data of all cloud energy storage users; S2, based on the charging and discharging energy and carbon emission data of cloud energy storage users, establish the models of carbon trading mechanism CET and green certificate trading mechanism GCT respectively; S3, based on the models of carbon trading mechanism CET and green certificate trading mechanism GCT, an interactive model of carbon trading mechanism CET and green certificate trading mechanism GCT is constructed; S4, the interaction model between the carbon trading mechanism CET and the green certificate trading mechanism GCT is used as the cloud energy storage charging and discharging decision-making behavior model on the cloud energy storage provider side, and the objective function is solved with the minimum cost of the cloud energy storage provider; S5, the obtained cloud energy storage charging and discharging optimization results are sent down for execution.

2. The cloud energy storage charging and discharging optimization method for a cloud energy storage provider according to claim 1, characterized in that: In step S2, when the models of the carbon trading mechanism CET and the green certificate trading mechanism GCT are established respectively according to the charging and discharging energy and carbon emission data of the cloud energy storage user, the established carbon trading mechanism CET model includes the system carbon emission quota for the t period shown in the following formula: and the actual carbon emissions of the system : ， ， in, The carbon emission quota per unit of electricity in the region is allocated. is the total gas production power of the gas turbine GT in each period, is the conversion factor of electricity, is the total power generated by the gas turbine GT in each period, is the sum of the heating power of the gas boiler GB in each period; is the carbon emission coefficient of the gas turbine GT, is the carbon emission coefficient of the gas boiler GB.

3. The cloud energy storage charging and discharging optimization method for a cloud energy storage provider according to claim 2, characterized in that: In step S2, when the models of the carbon trading mechanism CET and the green certificate trading mechanism GCT are established respectively according to the charging and discharging energy and carbon emission data of the cloud energy storage user, the model of the green certificate trading mechanism GCT established includes the number of green certificates obtained by the new energy power generator as shown in the following formula: And the number of green certificates required for the system to meet the assessment : ， ， in, is the power prediction influencing parameter, is the scheduling period, For unit scheduling period, The total amount of new energy charging for users in each period, Impact weights for power prediction; is the accuracy of power prediction in the previous period; Forecast accuracy standard value; The electricity demand for new energy quota, is the new energy quota coefficient, The impact coefficient of the historical green certificate assessment completion, The electricity load generated by renewable energy for users, is the electrical load of the gas boiler GB and the gas turbine GT, The weight of the historical green certificate assessment completion rate is affected. is the average value of historical new energy quota completion, It is the system’s historical new energy quota completion coefficient.

4. The cloud energy storage charging and discharging optimization method for a cloud energy storage provider according to claim 3, characterized in that: The interaction model between the carbon trading mechanism CET and the green certificate trading mechanism GCT constructed in step S3 refers to the cost of participating in the green certificate trading using the system shown in the following formula when the cloud energy storage provider meets the carbon quota assessment: Carbon trading revenue with cloud storage providers : ， ， in, is the unit green certificate transaction price, The number of green certificates obtained for new energy power generators, The number of green certificates required for the system to meet the assessment: is the unit carbon quota price, is the actual carbon emissions of the system during period t, is the system carbon emission quota for period t, The carbon emission quota per unit of electricity in the region is allocated.

5. The cloud energy storage charging and discharging optimization method for a cloud energy storage provider according to claim 4, characterized in that: The functional expression of the objective function in step S4 is: ， in, Represents the function to minimize the cost For the goal, is the equal annual value coefficient, Investment costs for cloud storage providers; The operating cost of the cloud energy storage provider, including the difference between the cost of purchasing energy from the electricity, heat and gas grids and the partial income generated by the surplus energy being fed back to the electricity, heat and gas grids; Fixed costs for cloud storage providers; Carbon trading income for cloud energy storage providers, with: ， ， in, , and are the unit power investment coefficients of lithium batteries, heat storage tanks, and gas storage tanks respectively; , and are the unit capacity investment coefficients of lithium batteries, heat storage tanks, and gas storage tanks respectively; , and The power of lithium batteries, heat storage tanks, and gas storage tanks actually invested by cloud energy storage providers; , and The capacities of lithium batteries, heat storage tanks, and gas storage tanks actually invested by cloud energy storage providers; is the season number, which includes four seasons: spring, summer, autumn and winter. is the scheduling period, , and Respectively represent the price of purchasing unit power energy between users and electricity, heat and gas grids, The actual power drawn from the grid by the cloud energy storage provider is the heating value coefficient of the heat network converted to unit power, is the actual power obtained by the cloud energy storage provider from the heat network, is the calorific value coefficient of the gas network converted to unit power, is the actual power obtained by the cloud energy storage provider from the gas grid, , and are the prices of unit power energy sent back between users and the electricity, heat and gas grids, respectively. and The operational definition is as follows: ， in, for or The operation object.

6. The cloud energy storage charging and discharging optimization method for a cloud energy storage provider according to claim 5, characterized in that: The charging and discharging energy and carbon emission data of all cloud energy storage users in step S1 include: Calculate the total energy demand of users in each period according to the following formula : ， in, A collection of users participating in cloud energy storage. They are Moment User Cloud energy storage charging and discharging, heat and gas power demand, They represent charge and discharge, heat, and gas respectively; The total amount of new energy charging for users in each period is calculated according to the following formula: ， in, It is the sum of new energy charging, heat and gas for users in each period. for Moment User New energy charging, heating and gas power.

7. The cloud energy storage charging and discharging optimization method for a cloud energy storage provider according to claim 6, characterized in that: The integrated charging and discharging energy and carbon emission data of all cloud energy storage users in step S1 also includes: The actual power obtained by the cloud energy storage provider from the electricity, heat and gas grids is calculated according to the following formula: ， ， ， in, , and are the actual power obtained by the cloud energy storage provider from the electricity, heat and gas grids during period t, , , are the charging powers of electricity, heat and gas physical energy storage determined by the cloud energy storage provider during period t, , and are the energy release powers of electricity, heat and gas physical energy storage determined by the cloud energy storage provider during period t, , and The total demand for discharge, heat and gas energy of users in each period , , and They are the sum of the users’ renewable energy charging, heat and gas energy in each period; The total heat generation power of the gas boiler in each period is calculated according to the following formula: ， in, is the total heat generation power of the gas boiler GB during period t, A collection of users participating in cloud energy storage. is the heating power of the gas boiler GB of user i during period t; The total power generation and heat generation of the gas turbine GT in each period is calculated according to the following formula: ， ， in, and are the total power generation and heat generation of the gas turbine GT during period t, and They are users in period t The sum of the electricity and heat production of the gas turbine GT.

8. A cloud energy storage charging and discharging optimization system for a cloud energy storage provider, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the cloud energy storage charging and discharging optimization method for the cloud energy storage provider side as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute, through a processor, the cloud energy storage charging and discharging optimization method for a cloud energy storage provider as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute, through a processor, the cloud energy storage charging and discharging optimization method for a cloud energy storage provider as described in any one of claims 1 to 7.

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