Low-carbon operation scheduling method and device for distributed source-load cluster, equipment and medium

Abstract

The application relates to a low-carbon operation scheduling method, device and equipment of a distributed source-load cluster and a medium. The method comprises the following steps: constructing a benefit evaluation model of the distributed source-load cluster in a predicted time period according to the electricity sales benefit and operation cost of the distributed source-load cluster in the predicted time period; constructing a carbon emission evaluation model of the distributed source-load cluster in the predicted time period according to the carbon value and target power generation of the distributed source-load cluster in the predicted time period; constructing a cluster scheduling model of the distributed source-load cluster in the predicted time period according to the benefit evaluation model, the carbon emission evaluation model and operation fluctuation information of the distributed source-load cluster in a historical time period; and solving the cluster scheduling model to obtain an operation scheduling scheme of the distributed source-load cluster in the predicted time period. The method can improve the scheduling efficiency of an energy system in a real environment with high wind and light output uncertainty.

Description

Technical Field

[0001] This application relates to the field of power system optimization dispatch and control technology, and in particular to a low-carbon operation and dispatch method, device, equipment and medium for distributed source-load clusters. Background Technology

[0002] With the deepening of energy transition and the development of power system optimization technologies, clean and low-carbon renewable energy sources such as wind power and photovoltaics have been applied on a large scale, effectively alleviating the shortage of fossil fuels and environmental pollution. Against this backdrop, distributed source-load clustering technology, which aims to aggregate and optimize distributed resources, has emerged.

[0003] In traditional technologies, grid-connected scheduling of renewable energy typically employs predictive deterministic methods, and the operation of distributed source-load clusters is mostly aimed at maximizing single economic benefits. Their carbon value assessment generally relies on fixed regional carbon emission coefficients and carbon trading prices.

[0004] However, traditional technologies fail to fully consider the dynamic impact of renewable energy output and load fluctuations on the system's carbon value, and their design for coordinated scheduling mechanisms of diverse resources within distributed source-load clusters (such as carbon capture, electricity-to-gas conversion, and demand response resources) is relatively simple. Therefore, in the current environment of high uncertainty in wind and solar power output, the energy system's scheduling efficiency is low. Summary of the Invention

[0005] Therefore, it is necessary to provide a low-carbon operation scheduling method, device, equipment, and medium for distributed source-load clusters that can improve scheduling efficiency in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a low-carbon operation scheduling method for a distributed source-load cluster, including:

[0007] Based on the electricity sales revenue and operating costs of the distributed power generation cluster during the forecast period, a revenue assessment model for the distributed power generation cluster during the forecast period is constructed.

[0008] Based on the carbon value and target power generation of the distributed source-load cluster during the forecast period, a carbon emission assessment model for the distributed source-load cluster during the forecast period is constructed.

[0009] Based on the revenue assessment model, carbon emission assessment model, and the historical operational fluctuation information of the distributed source-load cluster, a cluster scheduling model for the distributed source-load cluster in the predicted time period is constructed.

[0010] Solve the cluster scheduling model to obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period.

[0011] Secondly, this application also provides a low-carbon operation scheduling device for a distributed source-load cluster, comprising:

[0012] The module is used to build a revenue assessment model for the distributed source-load cluster during the forecast period based on the electricity sales revenue and operating costs of the distributed source-load cluster during the forecast period.

[0013] The building module is also used to build a carbon emission assessment model for the distributed source-load cluster during the prediction period based on the carbon value and target power generation of the distributed source-load cluster during the prediction period.

[0014] The building module is also used to construct a cluster scheduling model for the distributed source-load cluster within the predicted time period based on the revenue assessment model, carbon emission assessment model, and the operational fluctuation information of the distributed source-load cluster over a historical period.

[0015] The scheduling module is used to solve the cluster scheduling model and obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period.

[0016] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described low-carbon operation scheduling method for distributed source-load clusters.

[0017] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described low-carbon operation scheduling method for distributed source-load clusters.

[0018] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the aforementioned low-carbon operation scheduling method for distributed source-load clusters.

[0019] The aforementioned low-carbon operation scheduling methods, devices, computer equipment, computer-readable storage media, and computer program products for distributed source-load clusters, by dynamically incorporating revenue, cost, and carbon value data for the forecast period and integrating historical operational fluctuation patterns to construct and solve scheduling models, improve the robustness and efficiency of scheduling schemes for distributed source-load clusters under scenarios of high uncertainty in renewable energy and load. This alleviates the problems of high wind and solar curtailment rates and increased load shedding risks, and also promotes the efficient collaboration of diverse and heterogeneous resources within distributed source-load clusters, ultimately achieving the synergistic maximization of economic and low-carbon benefits of distributed source-load clusters within the forecast period. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is an application environment diagram of a low-carbon operation scheduling method for a distributed source-load cluster in one embodiment;

[0022] Figure 2 This is a schematic diagram of the distributed source-load cluster system framework for a low-carbon operation scheduling method of a distributed source-load cluster in one embodiment.

[0023] Figure 3 This is a schematic diagram of the distributed source-load cluster joint operation mode of the low-carbon operation scheduling method of the distributed source-load cluster in one embodiment.

[0024] Figure 4 This is a schematic diagram of the distributed source-load cluster control architecture of a low-carbon operation scheduling method for a distributed source-load cluster in one embodiment.

[0025] Figure 5 This is a schematic diagram of the C&CG iterative algorithm control flow for day-ahead scheduling and real-time scheduling in a low-carbon operation scheduling method for a distributed source-load cluster in one embodiment.

[0026] Figure 6 This is a flowchart illustrating a low-carbon operation scheduling method for a distributed source-load cluster in one embodiment.

[0027] Figure 7 This is a structural block diagram of a low-carbon operation scheduling device for a distributed source-load cluster in one embodiment.

[0028] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0030] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0031] The low-carbon operation scheduling method for distributed source-load clusters provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Terminal 102 sends a scheduling request to server 104. Server 104 responds to the scheduling request and constructs a revenue assessment model for the distributed source-load cluster during the predicted time period based on the electricity sales revenue and operating costs of the distributed source-load cluster during the predicted time period; constructs a carbon emission assessment model for the distributed source-load cluster during the predicted time period based on the carbon value and target power generation of the distributed source-load cluster during the predicted time period; constructs a cluster scheduling model for the distributed source-load cluster during the predicted time period based on the revenue assessment model, the carbon emission assessment model, and the historical operational fluctuation information of the distributed source-load cluster; solves the cluster scheduling model to obtain the operational scheduling scheme for the distributed source-load cluster during the predicted time period, and then sends the operational scheduling scheme to terminal 102. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0032] In one exemplary embodiment, such as Figure 2 As shown, a low-carbon operation scheduling method for distributed source-load clusters is provided, which is applied to... Figure 1 Taking the server in the example of this, the explanation includes:

[0033] Step 201: Based on the electricity sales revenue and operating costs of the distributed source-load cluster during the forecast period, construct a revenue assessment model for the distributed source-load cluster during the forecast period.

[0034] The distributed source-load cluster can be composed of, but is not limited to, wind turbines, photovoltaics, gas turbines, loads, and carbon capture equipment; the forecast period can be the target execution period of the dispatch scheme; the electricity sales revenue can be the total revenue obtained by the distributed source-load cluster from selling electricity to the electricity market or distribution network during the forecast period; the operating cost can be the total of various expenditures incurred by the distributed source-load cluster in maintaining normal operation during the forecast period; and the revenue assessment model can be a mathematical model with the core objective of quantifying the economic benefits of the distributed source-load cluster during the forecast period.

[0035] Optionally, by defining the time boundary of the prediction period, and then collecting basic data on electricity sales revenue (such as the predicted output value of available electricity resources and the electricity price) and operating costs (such as relevant parameters such as fuel, energy storage, demand response, electricity purchase and operation and maintenance), the total electricity sales revenue and total operating costs can be obtained by calculating and summarizing the items, and finally a mathematical model with explicit constraints can be constructed with the goal of maximizing total revenue.

[0036] In one embodiment, the operating costs include control costs and electricity purchase costs; the method further includes: determining the expected revenue of the distributed source-load cluster based on the electricity sales volume and electricity price of the distributed source-load cluster during the forecast period; determining the electricity purchase cost based on the electricity purchase volume and electricity purchase price of the distributed source-load cluster during the historical period; and determining the control costs based on the expected revenue and the electricity control strategy of the distributed source-load cluster during the historical period.

[0037] Among them, regulation cost can be the relevant expenditures incurred by the distributed source-load cluster in implementing power regulation strategy; electricity purchase cost can be the average electricity purchase expenditure per unit time or within a single dispatch cycle calculated based on historical electricity purchase data; electricity sales volume can be the total amount of electricity sold by the distributed source-load cluster to the power market or distribution network within the forecast period; electricity sales price can be; expected revenue can be the price at which the distributed source-load cluster sells electricity within the forecast period; electricity purchase volume can be the total amount of electricity purchased by the distributed source-load cluster from the grid within the historical period to meet load demand, supplement power supply gaps, or optimize dispatch; electricity purchase price can be the price at which the distributed source-load cluster purchases electricity from the grid within the historical period.

[0038] Optionally, the expected revenue can be determined by specifying the forecast period and collecting the electricity sales volume and electricity price data for the corresponding period, and summarizing and calculating by period; then, a matching historical period is selected, and the average electricity purchase cost is calculated based on the historical electricity purchase volume and electricity price; finally, the cost patterns and revenue correlations of historical electricity regulation strategies are extracted, and the regulation cost is calculated in combination with the expected revenue or the predicted regulation intensity.

[0039] Step 202: Based on the carbon value and target power generation of the distributed source-load cluster during the forecast period, construct a carbon emission assessment model for the distributed source-load cluster during the forecast period.

[0040] Among them, carbon value can be the carbon trading price of a distributed source-load cluster during the forecast period; target power generation can be the total power generation of green energy such as wind turbines and photovoltaics; and carbon emission assessment model can be a mathematical model with the core of quantifying the carbon emission level, carbon emission reduction benefits and carbon value of the distributed source-load cluster during the forecast period.

[0041] Optionally, by defining the prediction period boundary consistent with the benefit assessment model, and then collecting basic data related to carbon value and target power generation, the carbon emission coefficient and carbon value can be dynamically corrected through green power output and low-carbon equipment operation status. The core indicators such as total carbon emissions and carbon emission reduction can be calculated, and finally a mathematical model with the goal of minimizing net carbon emissions or maximizing carbon value, including resource output and low-carbon equipment operation constraints, can be constructed.

[0042] Step 203: Based on the revenue assessment model, carbon emission assessment model, and the operational fluctuation information of the distributed source-load cluster over historical periods, construct a cluster scheduling model for the distributed source-load cluster within the predicted time period.

[0043] Among them, the historical time period can be; the operational fluctuation information can be the data on the fluctuation patterns of various operating parameters obtained by statistical analysis of the distributed source-load cluster in the past operating cycles before the prediction time period, including but not limited to the fluctuation status of load demand and the fluctuation status of power generation resources; the cluster scheduling model can be the robust optimization scheduling model of the distributed source-load cluster.

[0044] Optionally, by integrating the core data and constraints of the revenue assessment model and the carbon emission assessment model, the historical operational fluctuation information of the distributed source-load cluster is standardized and statistically quantified to construct an uncertainty set to characterize the fluctuation characteristics of wind and solar power, load, and equipment operation. Then, by setting a dual-objective optimization function (including fluctuation correction term) for economic and low-carbon purposes, the basic constraints and fluctuation response constraints are integrated, the input / decision variables are clarified, and finally a robust optimization scheduling model for the distributed source-load cluster is formed.

[0045] In one embodiment, constructing a cluster scheduling model for the distributed source-load cluster within the predicted time period based on the revenue assessment model, the carbon emission assessment model, and the operational fluctuation information of the distributed source-load cluster over a historical time period includes: constructing a first scheduling model for the distributed source-load cluster within the predicted time period based on the revenue assessment model and the carbon emission assessment model, the first scheduling model being used to output an initial scheduling scheme; constructing the range of uncertain variables for the fluctuation of power generation resources and load demand under the historical time period based on the load demand fluctuation status and power generation resource fluctuation status; constructing a second scheduling model for the distributed source-load cluster within the predicted time period based on the range of uncertain variables, the second scheduling model being used to evaluate the balance deviation cost of the distributed source-load cluster under the target operating scenario; and constructing a cluster scheduling model for the distributed source-load cluster within the predicted time period based on the second scheduling model and the first scheduling model.

[0046] The first scheduling model can be a single-stage optimization model that integrates the revenue assessment model and the carbon emission assessment model; the load demand fluctuation state can refer to the deviation pattern between the actual and predicted load demand values ​​of the distributed source-load cluster in the past operating cycles before the prediction period; the power generation resource fluctuation state can be; the range of uncertainty variable fluctuation can refer to the deviation pattern between the actual output and the predicted value of various power generation resources in the distributed source-load cluster in the historical operating cycles; the second scheduling model can be a robust assessment model with the range of uncertainty variable fluctuation as the core input; the balance deviation cost can be the deviation between the actual operating state and the initial scheduling scheme caused by the fluctuation of uncertainty variable.

[0047] Optionally, a first scheduling model is constructed by integrating a revenue assessment model and a carbon emission assessment model, and an initial scheduling scheme based on predicted values ​​is output. Then, by analyzing the fluctuation status of load demand and power generation resources over a historical period, the fluctuation range of uncertain variables is quantified. Based on this fluctuation range, a target operating scenario is generated, and a second scheduling model is constructed to evaluate the balance deviation cost. Finally, the dual optimization objectives of the first scheduling model and the robustness constraints of the second scheduling model are integrated to form a final cluster scheduling model that takes into account both economic and low-carbon optimization and the ability to resist fluctuations.

[0048] Step 204: Solve the cluster scheduling model to obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period.

[0049] Among them, the operation scheduling scheme can be the optimal output of each controllable unit of the distributed source-load cluster at each moment within the predicted time period, such as the power generation of the gas turbine, the charging and discharging power of the energy storage, and the power exchanged with the grid.

[0050] Optionally, a hybrid framework of C&CG algorithm (Column-and-Constraint Generation) and improved particle swarm optimization algorithm can be used. The algorithm parameters and solution conditions are set, the cluster scheduling model is linearized preprocessed to adapt to the solution, and then the optimal solution is found by iteratively solving the main problem and sub-problems. After the feasibility of the solution is ensured by constraint verification, the optimal decision variables are transformed into a structured scheduling scheme that includes the operation arrangements of each resource time period.

[0051] In one embodiment, solving the cluster scheduling model to obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period includes: solving the first scheduling model according to the device operation constraints of the first scheduling model to generate an initial scheduling scheme; solving the second scheduling model according to the initial scheduling scheme to obtain the balance deviation cost and target scenario parameters of the distributed source-load cluster under the target operation scenario; if the difference between the balance deviation cost and the target cost of the first scheduling model is less than a preset tolerance threshold, then the initial scheduling scheme is used as the operation scheduling scheme; if the difference between the balance deviation cost and the target cost is greater than or equal to the preset tolerance threshold, then the device operation constraints are updated based on the target scenario parameters, and the step of solving the first scheduling model according to the device operation constraints of the first scheduling model to generate the initial scheduling scheme is executed again, until the difference between the balance deviation cost and the target cost is less than the preset tolerance threshold.

[0052] Among them, the equipment operation constraints can be the technical boundary conditions that ensure the safe and stable operation of various equipment in the distributed source-load cluster, such as the upper and lower limits of equipment output, charging and discharging constraints, ramp rate constraints, low-carbon equipment operation constraints, and standby capacity constraints; the initial scheduling scheme can be the ideal state operation strategy output after solving the first scheduling model; the target scenario parameters can be the key scenario data output after solving the second scheduling model; and the preset tolerance threshold can be the pre-set critical value for judging whether the scheduling scheme meets the robustness requirements.

[0053] Optionally, the initial scheduling scheme under ideal conditions can be generated by solving the equipment operation constraints of the first scheduling model; then, the initial scheduling scheme is substituted into the second scheduling model to solve for the balance deviation cost and target scenario parameters under the target operation scenario; by comparing the difference between the balance deviation cost and the target cost with the preset tolerance threshold, the robustness of the scheme is judged. If the standard is met, the initial scheduling scheme is used as the final operation scheduling scheme; if the standard is not met, the equipment operation constraints are updated based on the target scenario parameters, and the process of solving the first scheduling model and evaluating the deviation cost is repeated iteratively until the difference meets the tolerance requirements.

[0054] It is worth noting that this embodiment divides the optimized scheduling of distributed source-load clusters, considering the uncertainties of wind turbines, photovoltaics, and load power, into two stages: the pre-scheduling stage and the re-scheduling stage. In the pre-scheduling stage, the dual regulation of the power grid and the natural gas grid is combined to better explore the economic and emission reduction benefits of distributed source-load clusters. Its system framework is as follows: Figure 3 As shown, the joint operation mode architecture is as follows: Figure 4 As shown, a function aimed at maximizing economic and low-carbon benefits is calculated based on the predicted values ​​of wind turbine, photovoltaic, and load power, thereby determining the day-ahead output scheme of the distributed power generation cluster. The control structure of the distributed power generation cluster is as follows: Figure 5 As shown. In the rescheduling phase, based on the decisions made in the previous phase, the distributed source-load cluster utilizes power purchase and sale and energy storage systems to rapidly adjust its output, aiming to minimize the costs of wind and solar curtailment and load shedding, and thus determine the real-time control scheme for the distributed source-load cluster.

[0055] For example, for wind turbines in a distributed source-load cluster, their output power is related to the natural wind speed, which follows a Weibull distribution, as follows:

[0056]

[0057] in, This represents the probability distribution when the wind speed is v; These are the shape and scale parameters of the Weibull function. Wind power is related to the turbine output and wind speed. The distribution function is commonly used to represent the relationship between turbine power and wind speed.

[0058]

[0059] in, For the wind speed cut-in and cut-out of the fan, Let t be the real-time wind speed. Rated wind speed, This is the rated power.

[0060] There is a certain relationship between the output power of photovoltaic power generation and the intensity of solar radiation, and the intensity of solar radiation follows a Beta distribution.

[0061]

[0062] In the formula, It is a gamma function; Solar radiation; For solar radiation The probability distribution at time; a and b are the shape parameters of the Beta function; the output power of the photovoltaic unit is related to the solar irradiance and conversion efficiency:

[0063]

[0064] In the formula, Indicates the photovoltaic power conversion efficiency. For the area of ​​the photovoltaic panel, Let be the light intensity at time t.

[0065] In a distributed load cluster, energy storage batteries are introduced to cope with peak and off-peak loads. The operating formula is as follows:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] In the formula, Let i be the state of charge of energy storage i at time t. This refers to the maximum and minimum states of charge of the energy storage battery. This is the rated power of the energy storage. For energy storage charging and discharging efficiency, The peak charge and discharge power of energy storage battery i.

[0072] Currently, there are two flexible response models: Price-Based Demand Response (PBDR) and Incentive-Based Demand Response (IBDR). Price-Based Demand Response guides users in allocating their electricity load by setting time-of-use pricing, thus providing a controlled load adjustment resource.

[0073]

[0074] in, These represent the changes in load demand and electricity price before and after PBDR, respectively. The implementation cost of PBDR is:

[0075]

[0076] After PBDR, the specific load demand is as follows:

[0077]

[0078] in, These represent the load demand and electricity price before and after PBDR, respectively, with t and h representing time. This represents the price elasticity of electricity demand. Incentive-based demand response guides users to adjust their electricity consumption behavior and reduce peak electricity demand by providing incentives in terms of economic, environmental, or social benefits.

[0079]

[0080]

[0081]

[0082] in: This represents the actual output power of the IBDR at time t. The actual load reduction of DRP in stage j at time t; This represents the minimum demand response required by the DRP in phase j. This represents the maximum demand response available in phase j of the DRP. Let be the amount of available load reduction of DRP in stage j at time t.

[0083] The carbon capture equipment mentioned in this embodiment includes: a carbon capture device, a carbon storage tank, and a P2G device. For the carbon capture device, the carbon capture constraints are:

[0084]

[0085] in, Let be the amount of carbon captured by the distributed source-load cluster i at time t. This represents the maximum operating factor of the carbon capture system. For carbon capture efficiency. This represents the carbon emission factor. In addition to carbon capture equipment, distributed source-load clusters also have carbon storage tanks, whose operating formula is:

[0086]

[0087] in, This is the maximum capacity of the carbon storage tank. For P2G equipment, electrical energy can be converted into chemical energy, and its production... and input electrical energy and The relationship is as follows:

[0088]

[0089]

[0090] In the formula, The amount of methane synthesized by the P2G equipment. for molar mass for molar mass This represents the maximum power of the P2G device. The calorific value of methane. For P2G equipment conversion efficiency.

[0091] Gas turbines primarily generate electricity by burning natural gas. They offer advantages such as rapid start-up and shutdown, quick adjustment, and relatively low carbon emission intensity, thus having an economic impact on the decarbonization of systems. The mathematical model of a gas turbine is as follows:

[0092]

[0093] in, For gas turbine power generation efficiency, Let be the amount of natural gas consumed by the gas turbine at time t. In this embodiment, during the pre-scheduling phase, a function is calculated based on the predicted values ​​from the source and load sides, aiming to maximize economic and carbon trading revenue, thereby determining the day-ahead output scheme of the distributed source-load cluster. For the carbon emission and economic revenue of the day-ahead scheduling of the distributed source-load cluster, the objective function for economic revenue is:

[0094]

[0095]

[0096] Where: T is the scheduling period; N is the number of distributed source-load clusters in the power system; For distributed source-load cluster benefits (i.e., benefit evaluation model): Let t represent the revenue from wind turbines, photovoltaics, energy storage batteries, IBDRs, and gas turbines. The cost of P2G, load, and distributed source-load cluster regulation at time t; This refers to the amount of electricity sold to the electricity market by wind turbines, photovoltaic systems, and gas turbines through distributed source-load clusters at time t. The corresponding electricity prices for wind turbines, photovoltaic systems, and gas turbines at time t; The charging and discharging power of the energy storage battery; Provide electricity and its price to the demand response provider at time t; The amount of electricity to be regulated for both purchasing and selling electricity; For e-commerce platforms participating in the purchase, the unit control cost; P2G and loads purchase electricity from the electricity market through distributed source-load clusters; Let t be the electricity purchase price at time t.

[0097] The objective function of a distributed source-load cluster, in addition to economic benefits, also includes carbon emission benefits (i.e., carbon emission assessment model):

[0098]

[0099] The overall objective function for the daytime scheduling of the distributed source-load cluster is:

[0100]

[0101] in, This represents the coefficients for the economic benefits and carbon emission benefits of distributed source-load clusters. This is the comprehensive objective function for the pre-scheduling phase.

[0102] The constraints included in the day-ahead dispatch phase are: adjustable load response constraints, unit output constraints, and system spinning reserve constraints. Among these, the adjustable load response constraints are:

[0103]

[0104] in: This represents the actual change in the adjustable load at time t. These are the upper and lower limits of the climbing rate, respectively; This is a 0-1 logic value indicating whether the adjustable load is working at time t. The continuous operation and downtime of IBDR at time t-1; The minimum start-up and minimum downtime for IBDRs. IBDRs can be dispatched simultaneously in both the energy market and the reserve market, and their maximum output should meet the following requirements:

[0105]

[0106] in: The output power of IBDR in the energy market; This refers to the output power of the IBDR in the standby market. Let be the maximum and minimum output power of the IBDR at time t. The unit output constraint is:

[0107]

[0108] in, This represents the maximum power output of wind turbines, photovoltaic systems, gas turbines, and P2G equipment at time t. The system spinning reserve constraint is:

[0109]

[0110] in, Let be the upper and lower limits of the output of the distributed source-load cluster i at time t; The actual output of the distributed source-load cluster i at time t; denoted as PBDR, representing the load variation to end users at time t; r represents the spinning reserve factor, with subscripts 1-3 indicating upper spinning reserve and subscripts 4-5 indicating lower spinning reserve.

[0111] This embodiment employs a robust optimization method during the rescheduling phase. First, it identifies the worst-case scenario for uncertain variables. Then, based on this worst-case scenario, it adjusts the amount of wind and solar power curtailment and load shedding within the distributed source-load cluster to minimize the penalty cost for wind and solar power curtailment and load shedding under this scenario. The objective function of the robust optimization model in the rescheduling phase is:

[0112]

[0113]

[0114] Where: u is an uncertain variable; U is an uncertain set; y is a decision variable for which wind and solar power curtailment relaxation variables are introduced during the rescheduling phase; Let $t$ be the total penalty cost for the i-th distributed source-load cluster to forgo wind and solar power and to cut off load at time $t$. Let be the penalty coefficient for wind and solar power curtailment and load shedding risk of the i-th distributed source-load cluster at time t; Let be the amount of wind and solar power curtailment and load shedding for the i-th distributed source-load cluster at time t. The constraints of the robust optimization model during the rescheduling phase include uncertainties and constraints on the amount of wind and solar power curtailment and load shedding. The uncertainties are:

[0115]

[0116] in: This represents the predicted values ​​of wind and solar power output and load power for the i-th time period t; This represents the maximum permissible deviation in wind and solar power output and load power. The constraints for wind and solar curtailment and load shedding are:

[0117]

[0118] It is worth noting that this embodiment, based on the concepts of C&CG and regional carbon emission coefficients, combines the dual regulation of the power grid and natural gas network to propose a low-carbon optimization scheduling and control strategy for a distributed source-load cluster consisting of wind turbines, photovoltaics, energy storage, loads, gas turbines, and carbon capture equipment. Its main steps are threefold: First, it proposes the concept of the clean value of the power system, calculates the impact of the operation of each device within the distributed source-load cluster on the low-carbon scheduling of the power system, and obtains the internal carbon trading price of the power grid; second, considering the economic benefits and carbon emission costs of the power system, it constructs a robust optimization scheduling model for the distributed source-load cluster with a carbon capture system that integrates the operation of wind turbines, photovoltaics, loads, gas turbines, and energy storage; third, for the robust optimization model of the distributed source-load cluster, it uses the C&CG algorithm to transform the distributed source-load cluster scheduling model into two stages: day-ahead scheduling and real-time scheduling, and uses an improved particle swarm optimization algorithm to solve the nonlinear programming problem iteratively.

[0119] For example, based on the comprehensive iterative algorithm, this embodiment first uses the C&CG algorithm to transform the nonlinear optimization problem of the two-stage robust optimization scheduling problem of the distributed source-load cluster into a day-ahead pre-scheduling main problem and a real-time rescheduling subproblem. The real-time rescheduling subproblem is used to find the worst-case scenario and add it to the main problem to achieve better convergence. The control process is as follows: Figure 6 As shown. Meanwhile, the pre-scheduling process is a relatively complex nonlinear programming problem; therefore, an improved particle swarm optimization algorithm is used to solve it.

[0120] Optionally, for the two-stage robust optimization model of the distributed source-load cluster, this embodiment first adds a negative sign to the objective function of the pre-scheduling stage to change it into a minimum value form, which is represented as the following general model:

[0121]

[0122] Where: the first-stage variable is The two-stage deterministic variables are: Two-stage uncertainty variables ; The objective function for the pre-scheduling phase; Let be the objective function for the rescheduling phase. In the formula, c and b are the coefficient column vectors of the objective function; d, e, f, g, and h are the constant column vectors of the constraints; A and B are the coefficient matrices of the constraints corresponding to the first phase; and D, K, F, and G are the coefficient matrices of the constraints corresponding to the second phase.

[0123]

[0124] in, Let these represent the dual variables corresponding to the constraints in the rescheduling phase. The main problem in objective function form is as follows:

[0125]

[0126] In the formula: k is the current iteration number; r is the maximum iteration number; As an auxiliary variable; and These represent the internal decision variables and uncertain variables obtained in the k-th iteration of the subproblem, respectively. The subproblem, given the decision variables derived from the main problem, seeks the worst-case scenario and provides it to the main problem for use in the next iteration. The specific subproblem is as follows:

[0127]

[0128] In the formula, maximizing within a given SP yields a linear deterministic optimization function. Based on duality theory and... The corresponding conditions allow us to transform the minimization problem in the subproblem into a maximization problem, which is then merged with the external maximization model. The merged subproblem SP' is as follows:

[0129]

[0130] According to the conclusions of linear optimization theory and the extreme point method, when the optimal solution is obtained, the value of the corresponding uncertain variable takes a certain extreme point within the uncertain set. That is, when the max function in the above equation obtains the final solution, it should take the boundary value of the fluctuation range of the uncertain variable. Therefore, the uncertain set U can be rewritten as follows:

[0131]

[0132] In the formula, These are the uncertain variables related to wind and solar power at specific times and load output; These represent the predicted values ​​of wind and solar power output and load output during time period t, respectively. This indicates that the wind power output variable for the corresponding time period takes the upper boundary value of the interval. This indicates that the boundary value is taken. express Take the predicted value of wind power output; similarly, the uncertainties for photovoltaic and load handling are also the same. related. For wind power robustness parameters, it can be changed The maximum value is used to control the degree of uncertainty of wind power during the response period, while the integer value between 0 and T represents the total time of an uncertain interval consisting of uncertain variables. When the value is large, the calculation result is more conservative; while the smaller the value, the greater the risk. When all uncertainty control parameters are 0, the uncertain problem is transformed into a deterministic problem.

[0133] At this point, substituting the rewritten expression will result in a product term between binary and continuous variables. Using the Big-M method, continuous auxiliary variables and relevant constraints are introduced to linearize this expression. Specifically as follows:

[0134]

[0135] In the formula: An auxiliary variable introduced for linearization; M is a sufficiently large positive real number.

[0136] For the pre-decomposed day-ahead pre-scheduling master problem and real-time rescheduling subproblem, this embodiment uses the C&CG method for solution. First, a set of worst-case scenarios is initialized, and an upper bound is set for the model. The lower realm Convergence threshold An improved particle swarm optimization (PSO) algorithm is adopted, incorporating the clustering principles of ant colony optimization and improving the traditional PSO algorithm from the perspective of weights, to solve the day-ahead pre-scheduling master problem. The parameters of the improved PSO algorithm are initialized: particle movement speed... Learning factors Maximum number of iterations and inertia weight Particle swarm initialization: Initialize the number of particles n, and randomly initialize the velocity of the particle swarm. The starting point is obtained by randomly generating a set of solutions within the range of values ​​for each optimization variable. and the starting point Substitute the values ​​into the fitness function to calculate the fitness value of the particle.

[0137]

[0138] in, For the fitness function, is the penalty coefficient. The paths of particles in the first iteration are marked as the best paths for individual particles, and the fitness values ​​of these individual particles are marked as their individual best fitness values. The minimum fitness value generated by all particles in the swarm is selected as the global best fitness value, and the path of the particle with the minimum fitness value is taken as the global best path. The particle velocity is updated according to the following formula:

[0139]

[0140] in, The learning factors represent the influence of a particle based on its own experience and the experience of other particles in the population, respectively. Their calculation formulas are shown below:

[0141]

[0142] In the formula: These represent the upper limit and lower limit of the individual learning factor, respectively, and are typically set to 2.5 and 0.1. These represent the upper and lower limits of the population learning factor, respectively, and are typically set to 3.2 and 0.8. This represents the current particle swarm optimization iteration number; This represents the maximum number of iterations in the particle swarm optimization algorithm.

[0143] in, The inertial weight represents the influence of the particle velocity in the previous iteration on the particle in the current iteration, and its calculation formula is shown below. The iteration number is r; r is a random number between 0 and 1 that follows a uniform distribution. The optimal path for a single particle; This is the optimal path for all particles.

[0144]

[0145] In the formula, The value is 1; This is an adjustment factor, and its value range is... The adjustment factor reflects the adjustment speed of the particle swarm. After a certain number of iterations, its value increases continuously and eventually equals 1. This indicates that the particle swarm has found the optimal path. Its calculation formula is shown in (58). for Weights under the influence; For particle aggregation degree, this invention adopts the concept of aggregation degree in ant colony algorithm to integrate and improve the particle swarm algorithm. In ant colony algorithm, ants on each path will aggregate during the optimization process, that is, a certain path will have a large number of ants. This paper introduces the concept of ant aggregation degree to describe the number of ants on a single path, and its calculation formula is as follows; for Weights under the influence.

[0146]

[0147] in, These represent the optimal fitness values ​​for iterations t+1 and t, respectively.

[0148]

[0149] In the formula: 𝑖 represents the particle's starting point; 𝑚 represents the number of particles; 𝑛 represents the number of paths and the destination; 𝑗 represents the number of particles traversed along the path. The particle velocity is calculated based on... The particle's position is updated using the following formula.

[0150]

[0151] In the formula, Particle i represents the first particle in the second... The position at the next iteration; Particle i represents the first particle in the second... The speed at the next iteration. Based on the improved particle swarm optimization algorithm, the optimal solution for the first stage is obtained, and the lower bound is updated. The subproblems are solved through the solution of the first stage. and the objective function value of the second stage And update the upper bound of the model. .when The iteration has ended. Return. and Otherwise, update the worst-case scenario in the main problem, set k=k+1, and return to recalculate.

[0152] The aforementioned low-carbon operation scheduling method for distributed source-load clusters dynamically incorporates revenue, cost, and carbon value data for the forecast period, integrates historical operational fluctuation patterns to construct and solve the scheduling model, thereby improving the robustness and efficiency of the scheduling scheme for distributed source-load clusters under scenarios of high uncertainty in renewable energy and load. This alleviates the problems of high wind and solar curtailment rates and increased load shedding risks, and also promotes the efficient collaboration of diverse and heterogeneous resources within the distributed source-load cluster, ultimately achieving the synergistic maximization of economic and low-carbon benefits of the distributed source-load cluster within the forecast period.

[0153] In an exemplary embodiment, a carbon emission assessment model for a distributed source-load cluster during a predicted time period is constructed based on the carbon value and target power generation of the distributed source-load cluster during the predicted time period. This includes: obtaining the carbon value and target power generation of the distributed source-load cluster during the predicted time period; determining the resource acquisition amount of the distributed source-load cluster during the predicted time period based on the carbon value and target power generation; and determining the carbon emission assessment model for the distributed source-load cluster during the predicted time period based on the resource acquisition amount.

[0154] Among them, the amount of resources acquired can be a comprehensive evaluation indicator under low-carbon rules, which is used to balance and weigh the interests of distributed source-load clusters and power system operators.

[0155] Optionally, carbon value information and target power generation within the prediction period can be collected, and then the resource acquisition amount can be determined by calculating the total carbon emissions and the carbon emission reduction of green power sources and low-carbon equipment. Finally, a mathematical model can be constructed with the goal of maximizing carbon emission benefits, combined with the benefit composition and constraints.

[0156] In one embodiment, determining the resource acquisition amount of a distributed generation and load cluster during a predicted time period based on carbon value information and a target power generation includes: determining a first operating cost of the distributed generation and load cluster during the predicted time period based on electricity consumption and sales information of the distributed generation and load cluster during the predicted time period; determining a second operating cost of the distributed generation and load cluster during the predicted time period based on electricity consumption information, electricity sales information, carbon value information, and the target power generation; calculating the difference between the second operating cost and the first operating cost to obtain an operating cost difference of the distributed generation and load cluster during the predicted time period; and determining the resource acquisition amount of the distributed generation and load cluster during the predicted time period based on the operating cost difference and the first operating cost.

[0157] Among them, electricity consumption information can be electricity consumption-related data of distributed source-load clusters within the predicted time period, including but not limited to the total amount of electricity purchased from the grid and the purchase price of electricity; electricity sales information can be electricity sales-related data of distributed source-load clusters within the predicted time period, including but not limited to the total amount of electricity sold to the electricity market / distribution network and the price of electricity; the first operating cost can be the basic operating cost that only considers the core links of electricity consumption and electricity sales; the second operating cost can be the comprehensive operating cost that is based on the first operating cost, plus carbon emission-related costs and low-carbon operation additional costs.

[0158] Optionally, basic data on electricity consumption, electricity sales, carbon value, and target power generation can be collected within the forecast period. The first operating cost, which includes only the core components, is obtained by calculating the difference between the total electricity consumption cost and the electricity sales revenue. Then, carbon emission-related costs and low-carbon equipment additional costs are added to obtain the second operating cost, which comprehensively reflects the basic operation and low-carbon operation. The absolute difference between the two is calculated to quantify the additional input or benefits related to low-carbon operation. Finally, based on the unit carbon value, the cost difference is converted into the amount of resources obtained related to carbon emission reduction.

[0159] It is worth noting that the clean energy value proposed in this embodiment considers the carbon reduction benefits obtained from the coupled management of energy storage batteries and green energy sources such as wind turbines and photovoltaics within the distributed energy source cluster, which affects the carbon trading price at different times, laying the foundation for establishing a robust optimization scheduling model for the distributed energy source cluster. First, the total carbon emissions of each region within the power system are calculated:

[0160]

[0161] in, For the load power within the distributed source-load cluster i, The carbon emission coefficient of the entire power system. The time interval is defined as follows: When green power sources within a distributed energy source cluster generate electricity, the proportion of green power sources within the cluster increases. Simultaneously, due to the effect of carbon capture devices, the carbon emission coefficient within the cluster decreases, thereby altering the carbon emission coefficient within the cluster.

[0162]

[0163]

[0164]

[0165]

[0166] in, The equivalent green power ratio for distributed source-load clusters, For distributed source-load cluster carbon dioxide emission reduction, Let be the output power of the wind turbine at time t. The charge / discharge power of the energy storage battery at time t. The power consumed by the carbon capture unit at time t. The power consumed by the P2G device at time t. The carbon emission coefficient conversion value of distributed source-load cluster i at time t+1. Let be the change in carbon emission coefficient of distributed source-load cluster i at time t+1. The reduction in carbon emissions for carbon capture equipment.

[0167] The carbon trading prices for different distributed source-load clusters can be obtained from the above formula:

[0168]

[0169]

[0170] in, Let be the carbon trading price of distributed source-load cluster i at time t. Let be the initial carbon trading price of the power system at time t. Based on the obtained distributed source-load cluster carbon trading price, this embodiment proposes an environmental benefit evaluation system for distributed source-load clusters under different scenarios. The cleanliness value can be obtained by calculating the difference in operating costs considering and not considering environmental benefits; the change in environmental benefits can be obtained by calculating the difference in operating costs considering and not considering the impact of distributed source-load cluster green energy power on the regional carbon emission coefficient.

[0171] Optionally, three scenarios are pre-defined: Scenario 1: The objective function considers both energy costs and environmental benefits. Energy costs refer to the energy interaction between the distributed energy source cluster and the power grid, mainly affected by factors such as time-of-use pricing and nodal pricing. Environmental benefits refer to the carbon emission reduction gains of the distributed energy source cluster's green power, mainly affected by time-varying carbon trading prices and regional carbon emission coefficients. Scenario 2: The impact of distributed energy source cluster's green energy on regional carbon emission coefficients is further considered, as different regional carbon emission coefficients lead to different regional carbon trading prices. Scenario 3: Only energy costs are considered, without considering environmental benefits.

[0172] The difference in the objective function under different scenarios is used as the evaluation result of the environmental benefits of the distributed source-load cluster, as follows:

[0173]

[0174]

[0175]

[0176]

[0177] in, The total operating cost for scenarios 1, 2, and 3. Let be the purchase and sale price of electricity for distributed source-load cluster i at time t. Let be the purchase and sale power of distributed source-load cluster i at time t. Let be the green power of distributed source-load cluster i at time t. In this embodiment, to balance the interests of distributed source-load clusters and power system operators, the comprehensive evaluation index under the low-carbon rule is as follows:

[0178]

[0179]

[0180] LCR is a revenue evaluation metric for distributed source-load clusters. For cost evaluation indicators of power system operators, among which, The expected value of environmental compensation costs that power system operators are willing to bear. The weighting coefficients for distributed source-load clusters and power systems.

[0181] In this embodiment, by dynamically acquiring core data on carbon value and power generation during the prediction period, the carbon emission reduction resources corresponding to the low-carbon operation of the distributed source-load cluster are accurately quantified, and a standardized carbon emission benefit accounting model is constructed. This not only alleviates the problem of the difficulty in quantifying traditional low-carbon value, but also establishes a collaborative accounting framework for low-carbon benefits and economic benefits, providing accurate low-carbon quantification support for the multi-objective scheduling of distributed source-load clusters.

[0182] To more comprehensively demonstrate this solution, this embodiment presents a low-carbon operation scheduling method for a distributed source-load cluster, specifically including:

[0183] 1. Based on the electricity sales revenue and operating costs of the distributed power generation cluster during the forecast period, construct a revenue assessment model for the distributed power generation cluster during the forecast period.

[0184] 2. Obtain carbon value information and power generation information of distributed source-load clusters during the prediction period;

[0185] 3. Based on carbon value information and power generation information, determine the resource acquisition amount of the distributed source-load cluster during the forecast period;

[0186] 4. Based on the resource availability, determine the carbon emission assessment model for the distributed source-load cluster within the forecast period;

[0187] 5. Based on the revenue assessment model and carbon emission assessment model, construct the first scheduling model of the distributed source-load cluster within the predicted time period. The first scheduling model is used to output the initial scheduling scheme.

[0188] 6. Based on the fluctuations in load demand and power generation resources over historical periods, construct the range of fluctuations in uncertain variables of power generation resources and load demand over historical periods.

[0189] 7. Based on the fluctuation range of uncertain variables, construct a second scheduling model for the distributed source-load cluster within the predicted time period. The second scheduling model is used to evaluate the balance deviation cost of the distributed source-load cluster under the target operating scenario.

[0190] 8. Based on the second scheduling model and the first scheduling model, construct a cluster scheduling model for the distributed source-load cluster within the predicted time period;

[0191] 9. Solve the cluster scheduling model to obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period.

[0192] The specific process of the above steps can be found in the description of the above method embodiments. The implementation principle and technical effect are similar, and will not be repeated here.

[0193] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0194] Based on the same inventive concept, this application also provides a low-carbon operation scheduling device for a distributed source-load cluster, which implements the low-carbon operation scheduling method for the distributed source-load cluster described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the low-carbon operation scheduling device for a distributed source-load cluster provided below can be found in the limitations of the low-carbon operation scheduling method for the distributed source-load cluster described above, and will not be repeated here.

[0195] In one exemplary embodiment, such as Figure 7 As shown, a low-carbon operation scheduling device for a distributed source-load cluster is provided, comprising: a construction module 71 and a scheduling module 72, wherein:

[0196] Module 71 is used to construct a revenue assessment model for the distributed source-load cluster during the predicted time period based on the electricity sales revenue and operating costs of the distributed source-load cluster during the predicted time period.

[0197] The construction module 71 is also used to construct a carbon emission assessment model for the distributed source-load cluster during the predicted time period based on the carbon value and target power generation of the distributed source-load cluster during the predicted time period.

[0198] The construction module 71 is also used to construct a cluster scheduling model for the distributed source-load cluster within the predicted time period based on the revenue assessment model, the carbon emission assessment model, and the operational fluctuation information of the distributed source-load cluster in historical time periods.

[0199] The scheduling module 72 is used to solve the cluster scheduling model to obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period.

[0200] In one embodiment, the construction module 71 is further configured to:

[0201] Obtain the carbon value information and power generation information of the distributed source-load cluster during the prediction period;

[0202] Based on the carbon value information and the power generation information, the resource acquisition amount of the distributed source-load cluster during the predicted time period is determined;

[0203] Based on the resource availability, a carbon emission assessment model for the distributed source-load cluster during the predicted time period is determined.

[0204] In one embodiment, the construction module 71 is further configured to:

[0205] Based on the electricity consumption and sales information of the distributed source-load cluster during the predicted time period, determine the first operating cost of the distributed source-load cluster during the predicted time period.

[0206] Based on the electricity consumption information, the electricity sales information, the carbon value information, and the power generation information, the second operating cost of the distributed source-load cluster during the predicted time period is determined;

[0207] Calculate the difference between the second operating cost and the first operating cost to obtain the operating cost difference of the distributed source-load cluster during the prediction period;

[0208] Based on the difference in operating costs and the first operating cost, the resource acquisition amount of the distributed source-load cluster during the predicted time period is determined.

[0209] In one embodiment, the construction module 71 is further configured to:

[0210] Based on the revenue assessment model and the carbon emission assessment model, a first scheduling model for the distributed source-load cluster during the predicted time period is constructed. The first scheduling model is used to output an initial scheduling scheme.

[0211] Based on the load demand fluctuation and power generation resource fluctuation status during the historical period, the range of fluctuation of uncertain variables of power generation resources and load demand during the historical period is constructed.

[0212] Based on the fluctuation range of the uncertain variables, a second scheduling model for the distributed source-load cluster is constructed within the predicted time period. The second scheduling model is used to evaluate the balance deviation cost of the distributed source-load cluster under the target operating scenario.

[0213] Based on the second scheduling model and the first scheduling model, a cluster scheduling model for the distributed source-load cluster is constructed within the predicted time period.

[0214] In one embodiment, the scheduling module 72 is further configured to:

[0215] The first scheduling model is solved based on the equipment operation constraints of the first scheduling model to generate an initial scheduling scheme;

[0216] The second scheduling model is solved according to the initial scheduling scheme to obtain the balance deviation cost and target scenario parameters of the distributed source-load cluster under the target operating scenario;

[0217] If the difference between the balance deviation cost and the target cost of the first scheduling model is less than a preset tolerance threshold, then the initial scheduling scheme is used as the running scheduling scheme.

[0218] If the difference between the balance deviation cost and the target cost is greater than or equal to the preset tolerance threshold, then the equipment operation constraints are updated based on the target scenario parameters, and the step of solving the first scheduling model according to the equipment operation constraints of the first scheduling model to generate an initial scheduling scheme is executed again until the difference between the balance deviation cost and the target cost is less than the preset tolerance threshold.

[0219] In one embodiment, the construction module 71 is further configured to:

[0220] The expected revenue of the distributed source-load cluster is determined based on the electricity sales volume and electricity price of the distributed source-load cluster during the predicted time period.

[0221] The electricity purchase cost is determined based on the electricity purchase volume and purchase price of the distributed source-load cluster during the historical time period;

[0222] The control cost is determined based on the expected revenue and the power control strategy of the distributed source-load cluster during the historical period.

[0223] Each module in the aforementioned low-carbon operation scheduling device for distributed source-load clusters can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0224] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a low-carbon operation scheduling method for distributed source-load clusters. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0225] Those skilled in the art will understand that Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0226] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0227] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0228] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0229] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0230] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0231] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0232] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A low-carbon operation scheduling method for a distributed source-load cluster, characterized in that, The method includes: Based on the electricity sales revenue and operating costs of the distributed power generation cluster during the predicted time period, a revenue assessment model for the distributed power generation cluster during the predicted time period is constructed. Based on the carbon value and target power generation of the distributed source-load cluster during the predicted time period, a carbon emission assessment model for the distributed source-load cluster during the predicted time period is constructed. Based on the revenue assessment model, the carbon emission assessment model, and the operational fluctuation information of the distributed source-load cluster over a historical period, a cluster scheduling model for the distributed source-load cluster is constructed for the predicted time period. Solving the cluster scheduling model yields the operation scheduling scheme of the distributed source-load cluster within the predicted time period.

2. The method according to claim 1, characterized in that, The step of constructing a carbon emission assessment model for the distributed source-load cluster during the predicted time period based on the carbon value and target power generation of the distributed source-load cluster during the predicted time period includes: Obtain the carbon value and target power generation of the distributed source-load cluster during the prediction period; Based on the carbon value and the target power generation, determine the resource acquisition amount of the distributed source-load cluster during the predicted time period; Based on the resource availability, a carbon emission assessment model for the distributed source-load cluster during the predicted time period is determined.

3. The method according to claim 2, characterized in that, Determining the resource acquisition amount of the distributed source-load cluster during the predicted time period based on the carbon value and the target power generation includes: Based on the electricity consumption and sales information of the distributed source-load cluster during the predicted time period, determine the first operating cost of the distributed source-load cluster during the predicted time period. Based on the electricity consumption information, the electricity sales information, the carbon value, and the target power generation, the second operating cost of the distributed source-load cluster during the predicted time period is determined; Calculate the difference between the second operating cost and the first operating cost to obtain the operating cost difference of the distributed source-load cluster during the prediction period; Based on the difference in operating costs and the first operating cost, the resource acquisition amount of the distributed source-load cluster during the predicted time period is determined.

4. The method according to claim 1, characterized in that, The operational fluctuation information includes the load demand fluctuation status and power generation resource fluctuation status of the distributed source-load cluster; the step of constructing a cluster scheduling model for the distributed source-load cluster in the predicted time period based on the revenue assessment model, the carbon emission assessment model, and the operational fluctuation information of the distributed source-load cluster in historical time periods includes: Based on the revenue assessment model and the carbon emission assessment model, a first scheduling model for the distributed source-load cluster is constructed within the predicted time period. The first scheduling model is used to output an initial scheduling scheme. Based on the load demand fluctuation and power generation resource fluctuation status during the historical period, the range of fluctuation of uncertain variables of power generation resources and load demand during the historical period is constructed. Based on the fluctuation range of the uncertain variables, a second scheduling model for the distributed source-load cluster is constructed within the predicted time period. The second scheduling model is used to evaluate the balance deviation cost of the distributed source-load cluster under the target operating scenario. Based on the second scheduling model and the first scheduling model, a cluster scheduling model for the distributed source-load cluster is constructed within the predicted time period.

5. The method according to claim 1, characterized in that, The cluster scheduling model includes a first scheduling model and a second scheduling model; solving the cluster scheduling model to obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period includes: The first scheduling model is solved based on the equipment operation constraints of the first scheduling model to generate an initial scheduling scheme; The second scheduling model is solved according to the initial scheduling scheme to obtain the balance deviation cost and target scenario parameters of the distributed source-load cluster under the target operating scenario; If the difference between the balance deviation cost and the target cost of the first scheduling model is less than a preset tolerance threshold, then the initial scheduling scheme is used as the running scheduling scheme. If the difference between the balance deviation cost and the target cost is greater than or equal to the preset tolerance threshold, then the equipment operation constraints are updated based on the target scenario parameters, and the step of solving the first scheduling model according to the equipment operation constraints of the first scheduling model to generate an initial scheduling scheme is executed again until the difference between the balance deviation cost and the target cost is less than the preset tolerance threshold.

6. The method according to any one of claims 1 to 5, characterized in that, The operating costs include regulation costs and electricity purchase costs; the method further includes: The expected revenue of the distributed source-load cluster is determined based on the electricity sales volume and electricity price of the distributed source-load cluster during the predicted time period. The electricity purchase cost is determined based on the electricity purchase volume and purchase price of the distributed source-load cluster during the historical time period; The control cost is determined based on the expected revenue and the power control strategy of the distributed source-load cluster during the historical period.

7. A low-carbon operation scheduling device for a distributed source-load cluster, characterized in that, The device includes: The construction module is used to construct a revenue assessment model for the distributed source-load cluster during the predicted time period based on the electricity sales revenue and operating costs of the distributed source-load cluster during the predicted time period. The construction module is also used to construct a carbon emission assessment model for the distributed source-load cluster during the predicted time period based on the carbon value and target power generation of the distributed source-load cluster during the predicted time period. The construction module is also used to construct a cluster scheduling model for the distributed source-load cluster within the predicted time period based on the revenue assessment model, the carbon emission assessment model, and the operational fluctuation information of the distributed source-load cluster over a historical time period. The scheduling module is used to solve the cluster scheduling model to obtain the operation scheduling scheme of the distributed source-load cluster within the predicted time period.

8. A low-carbon operation and scheduling device for a distributed source-load cluster, characterized in that, The low-carbon operation scheduling device of the distributed power generation cluster includes: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the low-carbon operation scheduling method of the distributed power generation cluster as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the low-carbon operation scheduling method for the distributed source-load cluster as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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