Unified planning method and system for energy-carbon coupling operation model

Abstract

The invention provides a unified planning method and system for an energy-carbon coupling operation model, and relates to the technical field of energy optimization. The method comprises the following steps: under the constraint condition of a capacity conversion component, constructing a layered energy hub model for an energy component comprising an energy conversion component, an energy storage component and a renewable energy component; constructing an energy-carbon coupling basic model of the IES system according to the energy conversion component and the energy storage component under the capacity constraint condition of the energy component; and selecting a to-be-planned representative scene based on the hierarchical energy hub model, solving the to-be-planned representative scene according to the energy-carbon coupling basic model, obtaining an optimal planning scheme considering the carbon emission cost, and realizing unified planning of the IES system. According to the method, white paper planning is realized under the multi-energy carbon flow constraint, namely, equipment configuration and capacity scale are jointly optimized from zero, energy-carbon coupling is realized, carbon emission is reduced, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization, and in particular to a unified planning method and system for an energy-carbon coupling operation model. Background Art

[0002] In the existing integrated energy system (IES) planning research, most of the studies have taken capacity configuration into consideration, and the optimization of IES capacity size is relatively mature, but there are still some shortcomings:

[0003] (1) A small number of studies have refined the blank-paper planning of IES (i.e., planning from scratch), but overall, there is little consideration of the simultaneous optimization of equipment configuration and capacity size, which does not meet the complexity and flexibility requirements of IES planning in the context of source Internet;

[0004] (2) Most studies focus on the configuration of energy, i.e., the conversion and coupling of multiple energy flows, but do not consider carbon emission flows in the operational models;

[0005] (3) When considering carbon emissions, the issue of carbon lock-in is not fully taken into account, which may lead to increased errors in carbon emission detection and thus increase the overall cost of the system. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a unified planning method and system for the energy-carbon coupling operation model, which realizes blank planning under multiple energy and carbon flow constraints, that is, jointly optimizes equipment configuration and capacity scale from scratch, realizes energy-carbon coupling and reduces carbon emissions.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a unified planning method for an energy-carbon coupling operation model, comprising:

[0009] Under the constraint condition of capacity conversion components, a hierarchical energy hub model is constructed based on energy components; the energy components include energy conversion components, energy storage components and renewable energy components;

[0010] Under the capacity constraints of energy components, the IES system energy-carbon coupling basic model is constructed based on energy conversion components and energy storage components;

[0011] Based on the hierarchical energy hub model, representative scenarios to be planned are selected, and the representative scenarios to be planned are solved according to the basic energy-carbon coupling model to obtain the optimal planning scheme taking into account the carbon emission cost, thereby realizing unified planning of the IES system.

[0012] Preferably, the hierarchical energy hub model is specifically:

[0013]

[0014] Among them, X p×q represents a hierarchical energy hub, which is used to describe the distribution of energy components; p represents the total number of layers, and q represents the total number of energy components; 1≤n≤q;x mn is a binary variable: x mn = 1 means that system component n is located at layer m, x mn = 0 means that system component n is not located in layer m; P t γ,in , P t γ,out They represent the input and output power of different energy types γ of the energy hub at different times t; and denote the input and output powers of component n in layer m of the energy hub at time t for energy type γ, respectively.

[0015] Preferably, the capacity conversion component constraint condition is specifically:

[0016]

[0017] in, and They represent the energy conversion device set Ω for energy type γ at time t. conc The input, output, maximum input and maximum output power of the energy conversion component n in layer m.

[0018] Preferably, the energy conversion components and their conversion relationships are:

[0019]

[0020] in, Indicates the input power of gas combined heat and power CHP; and Respectively represent the electrical output power and thermal output power of CHP; η e,CHP and η h,CHP They represent the electricity and heat conversion coefficients of CHP, respectively; is the electrical input power of the EHP, is the heat output power of EHP, η h,EHP is the coefficient of performance, which reflects the ability of EHP to convert electrical energy into thermal energy; is the gas input power of AB, is the heat output power of AB, η h,AB is the thermal energy conversion coefficient of AB; is the electrical input power of CERG, is the cold output power of CERG, η c,CERG is the cold energy conversion performance coefficient of CERG; is the heat input power of WARG, represents the cooling output power of the WARG, η c,WARG is the cooling energy conversion coefficient of WARG;

[0021] The energy storage component is:

[0022]

[0023] in, Respectively represent the energy storage component set Ω for energy type γ at time t and t-1 es The charge state of the energy storage component n in layer m; and They represent the charging efficiency and discharging efficiency of the energy storage component of energy type γ respectively; and They represent the energy storage component set Ω for energy type γ at time t. es The input and output power of energy storage component n in layer m; is the capacity of the energy storage component of energy type γ, and Δt represents the duration of the time period.

[0024] Preferably, the energy component capacity constraint condition includes energy storage component capacity constraint, energy storage component charge capacity constraint and renewable energy component capacity constraint, specifically including:

[0025] Energy storage component capacity constraints:

[0026]

[0027] in, and is a binary variable; and They represent the energy type γ in the energy storage component set Ω es The minimum input, maximum input, minimum output and maximum output power of the energy storage component n; x mn is a binary variable representing the system component;

[0028] Energy storage component charging capacity constraints:

[0029]

[0030] in, Represents the energy storage component set Ω for energy type γ at time t es The charge state of the energy storage component n in layer m; They represent the energy type γ in the energy storage component set Ωes The minimum state of charge and the maximum state of charge of the energy storage component n in the layer m;

[0031] Renewable energy component capacity constraints:

[0032]

[0033] in, They represent the distributed renewable energy component set Ω for energy type γ at time t. rew Output power and maximum output power of renewable energy component n in layer m.

[0034] Preferably, the energy-carbon coupling basic model includes a carbon emission flow model of an energy conversion component and a carbon emission flow model of an energy storage component;

[0035] The carbon emission flow model of the energy conversion component:

[0036]

[0037] in, and They represent the energy conversion device set Ω for energy type γ at time t. conc The output PCI and input PCI of the energy conversion component n in layer m; and They represent the energy conversion device set Ω for energy type γ at time t. conc The output and input power of the energy conversion component n in layer m; Δt represents the duration of the time period;

[0038] The carbon emission flow model of the energy storage component is:

[0039]

[0040] in, and Respectively represent the energy storage component set Ω for energy type γ at time t and t-1 es Virtual carbon emissions of energy storage component n in layer m; Represents the energy storage component set Ω for energy type γ at time t es The charge state of the energy storage component n in layer m; is the capacity of the energy storage component of energy type γ.

[0041] Preferably, the method of selecting representative scenarios to be planned based on the hierarchical energy hub model, solving the representative scenarios to be planned according to the energy-carbon coupling basic model, and realizing unified planning of the IES system specifically includes:

[0042] The K-means clustering method is used to cluster the operation scenarios of system random variables;

[0043] Latin hypercube sampling is used to select representative scenarios to be planned for the hierarchical energy hub model;

[0044] The representative planning scenarios are simulated and calculated based on the basic energy-carbon coupling model to obtain the optimal operation planning scheme taking into account the carbon emission costs.

[0045] In a second aspect, the present invention provides a unified planning system for an energy-carbon coupling operation model, comprising:

[0046] A hierarchical energy hub modeling module is configured to construct a hierarchical energy hub model based on energy components under the constraint of capacity conversion components; the energy components include energy conversion components, energy storage components and renewable energy components;

[0047] The energy-carbon coupling basic model building module is configured to build the IES system energy-carbon coupling basic model according to the energy conversion component and the energy storage component under the energy component capacity constraint condition;

[0048] The unified planning module is configured to select representative scenarios to be planned based on the hierarchical energy hub model, solve the representative scenarios to be planned according to the energy-carbon coupling basic model, obtain the optimal planning scheme taking into account the carbon emission cost, and realize the unified planning of the IES system.

[0049] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the unified planning method for an energy-carbon coupling operation model described in the first aspect.

[0050] In a fourth aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the unified planning method for an energy-carbon coupling operation model described in the first aspect are implemented.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] (1) The present invention constructs a hierarchical energy hub model, comprehensively considers energy conversion, energy storage and renewable energy components, and can comprehensively analyze and plan the energy system. By constructing a basic energy-carbon coupling model under the capacity constraints of energy components, quantitative consideration of carbon emissions is achieved, which helps to reduce the carbon emissions of the system. Based on the basic energy-carbon coupling model, representative scenarios are selected and solved to obtain the optimal planning scheme, which can achieve unified planning of the IES system while ensuring system performance, improve energy utilization efficiency, and reduce overall costs.

[0053] (2) The present invention realizes white paper planning under multiple energy flow constraints, that is, jointly optimizes equipment configuration and capacity scale from scratch. By constructing a hierarchical energy hub model and an IES system energy-carbon coupling basic model, the configuration and capacity optimization of energy conversion components, energy storage components and renewable energy components are comprehensively considered, which meets the complexity and flexibility requirements of IES planning under the background of source Internet. The hierarchical energy hub model can describe the distribution of energy components in detail, and accurately model the input and output power of the energy hub at different times and different energy types, thereby realizing the simultaneous optimization of equipment configuration and capacity size.

[0054] (3) Under the capacity constraint of energy components, the present invention constructs the IES system energy-carbon coupling basic model based on energy conversion components and energy storage components. The model includes a carbon emission flow model of the energy conversion component and a carbon emission flow model of the energy storage component. Through these models, carbon emissions can be quantified and managed during operation, solving the problem that most studies do not consider carbon emission flows in the operation models. For example, in the carbon emission flow model of the energy conversion component, by calculating the output and input PCI, carbon emissions can be accurately tracked and calculated, thereby achieving effective control of carbon emissions during system operation.

[0055] (4) The energy-carbon coupling basic model and the hierarchical energy hub model described in the present invention fully consider the problem of carbon lock-in when considering carbon emissions. By comprehensively considering energy components, energy storage components and renewable energy components, as well as accurately modeling the input and output power at different times and different energy types, carbon emissions can be calculated more accurately, reducing carbon emission detection errors, and thus reducing the overall cost of the system. For example, in the carbon emission flow model of the energy storage component, the charging and discharging efficiency, capacity size and charging status of the energy storage component at different times are taken into account, thereby avoiding inaccurate carbon emission calculations caused by carbon lock-in problems and reducing the overall cost of the system.

[0056] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention but do not constitute a limitation of the present invention.

[0058] Figure 1 A main flow chart of a unified planning method for an energy-carbon coupling operation model provided by an embodiment of the present invention;

[0059] Figure 2A block diagram of an integrated energy system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0061] Technical term explanation

[0062] CHP: Combined Heat and Power

[0063] EHP: Electric Heat Pump

[0064] AB: Auxiliary boiler

[0065] CERG: Compression Refrigerator Group

[0066] WARG: Water absorption refrigerator group

[0067] Embodiment 1

[0068] like Figure 1 As shown, this embodiment discloses a unified planning method for an energy-carbon coupling operation model, comprising the following steps:

[0069] S1: Under the constraint of capacity conversion components, a hierarchical energy hub model is constructed based on energy components; the energy components include energy conversion components, energy storage components and renewable energy components;

[0070] S2: Under the capacity constraints of energy components, the IES system energy-carbon coupling basic model is constructed based on energy conversion components and energy storage components;

[0071] S3: Based on the hierarchical energy hub model, representative scenarios to be planned are selected, and the representative scenarios to be planned are solved according to the basic model of energy-carbon coupling to obtain the optimal planning scheme taking into account the carbon emission cost, so as to realize the unified planning of the IES system.

[0072] Next, combine Figure 1 , a unified planning method for an energy-carbon coupling operation model disclosed in this embodiment is described in detail.

[0073] In S1, hierarchical energy hub modeling is performed.

[0074] In such Figure 2 Within the integrated energy system shown, energy conversion components can operate at planned rated capacity or be adjusted within an output range; renewable energy components, such as wind turbines (WT) and photovoltaics (PV), provide environmentally friendly electricity with zero carbon emissions and are usually operated using a maximum output strategy to avoid wasting wind and solar resources.

[0075] The hierarchical energy hub modeling:

[0076]

[0077] Where, X p×q represents a hierarchical energy hub, which is used to describe the distribution of energy components; p represents the total number of layers, and q represents the total number of energy components; 1≤n≤q;x mn is a binary variable: x mn = 1 means that system component n is located at layer m, x mn = 0 means that system component n is not located in layer m; P t γ,in , P t γ,out They represent the input and output power of different energy types γ of the energy hub at different times t; and They respectively represent the input and output power of component n in layer m of the energy hub at time t for energy type γ (including electrical energy e, gas chemical energy g, thermal energy h, and cold energy c).

[0078] In this embodiment, the layered energy hub can maximize the use of available energy resources by integrating multiple different types of renewable energy components (such as wind turbines and solar panels). These components usually operate using a maximum output strategy to avoid wasting wind and solar resources, thereby achieving efficient use of energy. At the same time, the design of the layered energy hub allows for flexible construction and expansion according to specific needs and scenarios. By adding or adjusting components at different levels, different energy demands and supply situations can be adapted, providing the possibility for optimization and expansion of the energy system.

[0079] In S2, under the capacity constraints of energy components, the IES system energy-carbon coupling basic model is constructed based on energy conversion components and energy storage components.

[0080] 1. Energy conversion components and conversion relationships of the basic energy-carbon coupling model:

[0081]

[0082]

[0083] In the formula, It indicates the input power (kW) of gas combined heat and power (CHP), that is, the calorific value (kJ / s) of the input gas per unit time. and Respectively represent the electrical output power and thermal output power of CHP (kW). e,CHP and η h,CHP Represent the electricity and heat energy conversion coefficients of CHP respectively.

[0084] is the electrical input power of EHP (kW), is the heat output power of EHP (kW), η h,EHP is the coefficient of performance, which reflects the ability of EHP to convert electrical energy into thermal energy.

[0085] is the gas input power of AB (kW), is the heat output power of AB (kW), η h,AB is the thermal energy conversion coefficient of AB.

[0086] is the electrical input power of CERG (kW), is the cooling output power of CERG (kW), η c,CERG is the cold energy conversion performance coefficient of CERG.

[0087] is the heat input power of WARG (kW), represents the cooling output power of WARG (kW), η c,WARG is the cooling energy conversion coefficient of WARG.

[0088] 2. Constraints and limitations of the capacity conversion components of the energy-carbon coupling basic model:

[0089]

[0090] In the formula, and They represent the energy conversion device set Ω for energy type γ at time t. conc The input, output, maximum input and maximum output power of the energy conversion component n in layer m.

[0091] 3. Energy conversion relationship of energy storage components in the basic model of energy-carbon coupling

[0092] (1) Energy storage components:

[0093]

[0094] In the formula, Respectively represent the energy storage component set Ω for energy type γ at time t and t-1 es The charge state of the energy storage component n in layer m; and They represent the charging efficiency and discharging efficiency of the energy storage component of energy type γ respectively; and They represent the energy storage component set Ω for energy type γ at time t. es The input and output power of energy storage component n in layer m; is the capacity of the energy storage component of energy type γ, and Δt represents the duration of the time period.

[0095] In this embodiment, multi-energy flow modeling can simulate the dynamic process of different energy flows and conversions in the energy system, thereby analyzing the stability and reliability of the energy system. This helps to timely discover and solve potential problems in the energy system and ensure the safe and stable operation of the energy system.

[0096] (2) Energy storage component capacity limit and renewable component capacity limit:

[0097]

[0098] In the formula and is a binary variable that obeys Eq (13) to avoid simultaneous charging and discharging of the energy storage component. and They represent the energy type γ in the energy storage component set Ω es The minimum input, maximum input, minimum output and maximum output power of the energy storage component n.

[0099] (3) The state of charge of the energy storage component has the following constraints:

[0100]

[0101] in, Represents the energy storage component set Ω for energy type γ at time t es The charge state of the energy storage component n in layer m; They represent the energy type γ in the energy storage component set Ω es The minimum and maximum state of charge of energy storage component n in layer m. This means that the charge state of the energy storage device cannot exceed the minimum and maximum limits.

[0102] (4) Distributed renewable energy components have the following constraints:

[0103]

[0104] in, They represent the distributed renewable energy component set Ω for energy type γ at time t. rew The output power and maximum output power of renewable energy component n in layer m. This is the maximum output constraint for renewable energy component n of energy type γ in layer m.

[0105] 4. Carbon emission flow modeling of energy-carbon coupling basic model:

[0106] Energy merging principle:

[0107]

[0108] In the formula represents the set of incoming branches of node n, and E i represents the energy flow into branch i; represents the unit carbon emission intensity of node n, They represent the unit carbon emission intensity flowing into branch i respectively.

[0109] The energy merging principle is designed to deal with the mixing of carbon emissions when different branch energies are injected into a node. Its purpose is to provide a basis for modeling the carbon emission flow of subsequent energy conversion, energy storage, and power grid transmission losses. Through this principle, carbon emissions at nodes can be accurately quantified, and then a unified carbon emission flow model framework can be constructed to facilitate comprehensive evaluation and management of energy systems.

[0110] Energy scheduling principle:

[0111]

[0112] In the formula represents the set of incoming branches of node n, and E i represents the energy flow into branch i, E j,i represents the energy share of the energy flow of the j-th output branch of node n that comes from the i-th input branch, E j represents the total energy flow of the j-th output branch of node n. Represents the set of incoming branches of node n.

[0113] The energy dispatch principle focuses on optimizing the allocation and dispatching of energy flows based on the carbon emission characteristics of different components. This principle can reasonably plan the power transmission path and power distribution according to the carbon emission of each component, reduce the use of high-carbon energy, and increase the utilization efficiency of low-carbon energy, thereby achieving low-carbon operation of the energy system and bringing environmental and economic benefits.

[0114] (1) Modeling of carbon emission flows of energy conversion components:

[0115]

[0116] In the formula, and They represent the energy conversion device set Ω for energy type γ at time t. conc The output PCI (power conversion index) and input PCI of the energy conversion component n in layer m; and They represent the energy conversion device set Ω for energy type γ at time t. concThe output and input powers of the energy conversion component n in layer m; Δt represents the duration of the time period.

[0117] In this embodiment, by modeling the carbon emission flow of the energy conversion component, the carbon emissions generated during the energy conversion process can be accurately calculated. This is crucial for evaluating the environmental impact of the energy system and formulating emission reduction strategies. After understanding the carbon emissions of the energy conversion component, the structure and operation of the energy system can be optimized in a targeted manner to reduce unnecessary carbon emissions and improve energy efficiency. By modeling and analyzing the carbon emissions of the energy conversion component, the carbon emission characteristics of different energy conversion technologies can be more clearly understood, thereby promoting the development and application of clean energy technologies.

[0118] (2) Carbon emission flow modeling of energy storage components:

[0119]

[0120] In the formula, and Respectively represent the energy storage component set Ω for energy type γ at time t and t-1 es Virtual carbon emissions of energy storage component n in layer m; Represents the energy storage component set Ω for energy type γ at time t es The charge state of the energy storage component n in layer m. The output PCI of can be mathematically expressed as Eq(21). This reflects the carbon content of the energy stored in the ES. By entering Eq(20) into Eq(21) through calculation, the corresponding relationship between the output PCI and input PCI of the energy storage component can be derived.

[0121] In this embodiment, the carbon emission modeling of energy storage components can help evaluate the environmental impact of different energy storage technologies, including their carbon emissions during the charging and discharging process. By understanding the carbon emission characteristics of energy storage components, energy storage strategies can be optimized, such as charging during low-emission periods to reduce overall carbon emissions; modeling and analyzing the carbon emissions of energy storage components can reveal the carbon emission bottleneck of energy storage technology, thereby promoting innovation and improvement of energy storage technology to achieve a more environmentally friendly and efficient energy storage method.

[0122] (3) Grid transmission loss carbon emission flow model:

[0123]

[0124] In the formula, represents the power loss of line 1, It represents the unit carbon emission intensity of transmission line l in the power network.

[0125] In this embodiment, by constructing a carbon emission flow model for power grid transmission losses, from an environmental perspective, the carbon emissions generated by transmission losses can be accurately calculated, the impact of the transmission link on the environment can be clarified, and targeted emission reduction strategies can be formulated. In terms of power grid operation, the model can be used to identify transmission lines with high losses and high carbon emissions, and losses can be reduced and energy utilization can be improved through technology upgrades and operation optimization.

[0126] In S3, representative scenarios to be planned are selected based on the hierarchical energy hub model, and the representative scenarios to be planned are solved according to the basic energy-carbon coupling model to obtain the optimal planning scheme taking into account the carbon emission cost, thereby realizing unified planning of the IES system.

[0127] The unified planning described in this embodiment means the optimal planning based on representative scenarios.

[0128] First, select the operation scenario through clustering:

[0129]

[0130] In the formula, B k represents the distance between classes, W k represents the intra-class distance, C l represents the set of classes l, c l is the cluster center of class l, c e is the cluster center of class e, n l represents the number of samples belonging to class l, s CH Represents the Calinski-Harabasz index, which is used to evaluate the effect of clustering.

[0131] Normalize the clustering results for each load:

[0132]

[0133] In the formula, S * is the unit value of the normalized afterload curve, S min and S max are the maximum and minimum load values ​​corresponding to the energy flow respectively.

[0134] Latin hypercube sampling (LHS) is used to select samples from the sample set S H The sample set is a sample set generated based on historical data, etc., for training and testing; it refers to a multidimensional data set composed of multiple different variables (such as normalized curves of power, cooling and heat loads, and output characteristic curves of wind power generation and photovoltaics). This sample set is used to describe and analyze the performance of the integrated energy system under different operating conditions.

[0135] S301: Using the method of equal probability interval stratified sampling, generate the sampling matrix S without considering the autocorrelation coefficient LHS ;

[0136] S302: Replace the sample value with the sample order to generate the same LHS Random sequence matrix F of the same size;

[0137] S303: Calculate F using the Spearman coefficient describing the correlation of non-normal distribution;

[0138] S304: After Cholesky decomposition, the lower triangular matrix Q is obtained, and then according to M=Q -1 F transforms the sequence matrix F.

[0139] S305: Initial sample S H Each row of elements in is reordered according to the corresponding row size in the intermediate matrix M to obtain the reordered sample matrix S M .

[0140] Further, the optimization problem based on IES planning is:

[0141] 1. Objective Function

[0142] F=min(C inv +C ope ) (27)

[0143]

[0144] In the formula, C inv Represents the annualized value of investment cost, C ope represents the annual operating cost, C unit,j represents the investment cost per unit of energy j, I j represents the capacity of the selected energy unit j, β represents the discount rate, and τ j represents the life span of energy unit j. D represents the total number of days in a year, N represents the total number of scenarios, p(s) represents scenario s, e is electricity, g is gas, represents the cost of energy type γ at time t, Represents the input of the first layer energy component n at time t for energy type γ.

[0145] 2. Constraints

[0146] (1) Equipment quantity limit:

[0147]

[0148] Where N C 、N S 、N T、N P Represents a collection of energy conversion components, energy storage components, distributed renewable energy components, and placeholder connection components. mn It represents the state of component n in layer m. p represents the total number of layers, and q represents the total number of energy components.

[0149] In this embodiment, the constraints in IES planning include equipment quantity constraints, operation constraints, and carbon emission constraints. In the optimization process, it is necessary not only to decide which equipment (configuration) to select, but also to determine the capacity of each equipment to ensure that while meeting system requirements, costs are minimized and efficiency is improved. Therefore, the equipment quantity restriction reflects the simultaneous optimization of equipment configuration and capacity.

[0150] (2) EH input constraints:

[0151]

[0152] Represents the input of energy component n for energy type γ in the i-th layer.

[0153] (3) The EH power balance constraint between each layer is:

[0154]

[0155] in, represents the energy output of energy type γ in the pth layer at time t, P t γ,L represents the total load of energy type γ at time t.

[0156] (4) EH carbon emission limits:

[0157]

[0158] In the formula, represents the input PCI of system component n in layer m+1 of energy type γ, represents the input of system component n in layer m+1 of energy type γ, represents the output PCI of system component n in layer m of energy type γ, represents the output of system component n in layer m of energy type γ, Represents the virtual carbon emissions of ES n with energy type γ at time t. The output PCI of the last layer system component n representing the energy type γ, Represents the output of the last layer system component n of energy type γ, L is a positive number, indicating the maximum carbon emission of IES.

[0159] 3. Optimization solution based on IES planning:

[0160]

[0161]

[0162] In the formula, and are the velocity and position of particle i at the kth iteration, respectively; ω represents the inertia weight, which starts from 0.9 and gradually decreases to 0.4, allowing global search at the beginning and subsequently enhancing local search capabilities; c1 and c2 are learning factors, usually set to 2; range is a random number in the range [0, 1]; and They represent the optimal position of particle i and the global optimal position of the entire particle swarm found until the kth iteration, respectively.

[0163] In the integrated energy system planning, this embodiment considers both capacity configuration and equipment planning, which greatly improves the economy of the system. After considering carbon emissions, the carbon trading cost can be reduced, thereby reducing the operating cost of the entire system. The IES white paper planning method proposed in the present invention can solve the joint optimization problem of equipment configuration and capacity scale. The optimization model is based on a unified operation planning model of energy-carbon coupling, and takes into account both economic goals and carbon emission constraints. Using the selected representative scenarios, the optimal planning scheme of IES can meet the energy supply stability of multiple loads in different seasons. The proposed method was evaluated through a case study with actual data, and the results showed that the annual total economic cost and carbon emissions were greatly reduced compared with the traditional method. It is proved that the proposed method has excellent performance in the blank paper planning case of IES coupled multi-energy carbon flow.

[0164] Embodiment 2

[0165] This embodiment provides a unified planning system for an energy-carbon coupling operation model, including:

[0166] A hierarchical energy hub modeling module is configured to construct a hierarchical energy hub model based on energy components under the constraint of capacity conversion components; the energy components include energy conversion components, energy storage components and renewable energy components;

[0167] The energy-carbon coupling basic model building module is configured to build the IES system energy-carbon coupling basic model according to the energy conversion component and the energy storage component under the energy component capacity constraint condition;

[0168] The unified planning module is configured to select representative scenarios to be planned based on the hierarchical energy hub model, solve the representative scenarios to be planned according to the energy-carbon coupling basic model, obtain the optimal planning scheme taking into account the carbon emission cost, and realize the unified planning of the IES system.

[0169] This specific embodiment realizes a comprehensive and systematic planning of the energy system by constructing a hierarchical energy hub model and an IES system energy-carbon coupling basic model, which helps to optimize the structure and operation strategy of the energy system while considering the carbon emission cost. The model can be flexibly constructed according to different energy components (such as energy conversion components, energy storage components, and renewable energy components) to adapt to different scenarios and needs, so that the method can be widely used in various energy systems. By solving the representative scenarios to be planned, it is possible to find a planning scheme that takes into account the optimal carbon emission cost, which helps to reduce the carbon emissions of the energy system and achieve environmentally friendly energy utilization. In addition, this embodiment realizes the unified planning of the IES system, comprehensively considering the two key factors of energy and carbon emissions, and avoiding the one-sidedness and limitations that may occur in traditional planning. This helps to improve the overall performance and sustainability of the energy system.

[0170] Embodiment 3

[0171] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the unified planning method of the energy-carbon coupling operation model described in the first embodiment above are implemented.

[0172] Embodiment 4

[0173] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the unified planning method of an energy-carbon coupling operation model as described in the first embodiment above are implemented.

[0174] The steps or modules involved in the above embodiments 2 to 4 correspond to those in embodiment 1. For the specific implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0175] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A unified planning method for an energy-carbon coupled operation model, characterized in that: include: Under the constraint condition of capacity conversion components, a hierarchical energy hub model is constructed based on energy components; the energy components include energy conversion components, energy storage components and renewable energy components; Under the capacity constraints of energy components, the IES system energy-carbon coupling basic model is constructed based on energy conversion components and energy storage components; Based on the hierarchical energy hub model, representative scenarios to be planned are selected, and the representative scenarios to be planned are solved according to the basic energy-carbon coupling model to obtain the optimal planning scheme taking into account the carbon emission cost, thereby realizing unified planning of the IES system.

2. A unified planning method for an energy-carbon coupling operation model as claimed in claim 1, characterized in that: The hierarchical energy hub model is specifically: Among them, X p×q represents the hierarchical energy hub, which is used to describe the distribution of energy components; p represents the total number of layers, and q represents the total number of energy components; 1≤n≤q;x mn is a binary variable: x mn =1 means that system component n is located at layer m, x mn =0 means that system component n is not located in layer m; They represent the input and output power of different energy types γ of the energy hub at different times t; and denote the input and output powers of component n in layer m of the energy hub at time t for energy type γ, respectively.

3. A unified planning method for an energy-carbon coupling operation model as claimed in claim 2, characterized in that: The capacity conversion component constraint conditions are specifically: in, and They represent the energy conversion device set Ω for energy type γ at time t. conc The input, output, maximum input and maximum output power of the energy conversion component n in layer m.

4. A unified planning method for an energy-carbon coupling operation model as claimed in claim 1, characterized in that: The energy conversion components and their conversion relationships are as follows: in, Indicates the input power of gas combined heat and power CHP; and Respectively represent the electrical output power and thermal output power of CHP; η e,CHP and η h,CHP They represent the electricity and heat conversion coefficients of CHP, respectively; is the electrical input power of the EHP, is the heat output power of EHP, η h,EHP is the coefficient of performance, which reflects the ability of EHP to convert electrical energy into thermal energy; is the gas input power of AB, is the heat output power of AB, η h,AB is the thermal energy conversion coefficient of AB; is the electrical input power of CERG, is the cold output power of CERG, η c,CERG is the cold energy conversion performance coefficient of CERG; is the heat input power of WARG, represents the cooling output power of the WARG, η c,WARG is the cooling energy conversion coefficient of WARG; The energy storage component is: in, Respectively represent the energy storage component set Ω for energy type γ at time t and t-1 es The charge state of the energy storage component n in layer m; and They represent the charging efficiency and discharging efficiency of the energy storage component of energy type γ respectively; and They represent the energy storage component set Ω for energy type γ at time t. es The input and output power of energy storage component n in layer m; is the capacity of the energy storage component of energy type γ, and Δt represents the duration of the time period.

5. The unified planning method of the energy-carbon coupling operation model according to claim 1, characterized in that: The energy component capacity constraints include energy storage component capacity constraints, energy storage component charge capacity constraints and renewable energy component capacity constraints, specifically including: Energy storage component capacity constraints: in, and is a binary variable; and They represent the energy type γ in the energy storage component set Ω es The minimum input, maximum input, minimum output and maximum output power of the energy storage component n; x mn is a binary variable representing the system component; Energy storage component charging capacity constraints: in, Represents the energy storage component set Ω for energy type γ at time t es The charge state of the energy storage component n in layer m; They represent the energy type γ in the energy storage component set Ω es The minimum state of charge and the maximum state of charge of the energy storage component n in layer m; Renewable energy component capacity constraints: in, They represent the distributed renewable energy component set Ω for energy type γ at time t. rew Output power and maximum output power of renewable energy component n in layer m.

6. A unified planning method for an energy-carbon coupling operation model as claimed in claim 1, characterized in that: The energy-carbon coupling basic model includes a carbon emission flow model of an energy conversion component and a carbon emission flow model of an energy storage component; The carbon emission flow model of the energy conversion component: in, and They represent the energy conversion device set Ω for energy type γ at time t. conc The output PCI and input PCI of the energy conversion component n in layer m; and They represent the energy conversion device set Ω for energy type γ at time t. conc The output and input power of the energy conversion component n in layer m; Δt represents the duration of the time period; The carbon emission flow model of the energy storage component is: in, and Respectively represent the energy storage component set Ω for energy type γ at time t and t-1 es Virtual carbon emissions of energy storage component n in layer m; Represents the energy storage component set Ω for energy type γ at time t es The charge state of the energy storage component n in layer m; is the capacity of the energy storage component of energy type γ.

7. The unified planning method of the energy-carbon coupling operation model according to claim 1, characterized in that: The representative scenarios to be planned are selected based on the hierarchical energy hub model, and the representative scenarios to be planned are solved according to the energy-carbon coupling basic model to realize the unified planning of the IES system, which specifically includes: The K-means clustering method is used to cluster the operation scenarios of system random variables; Latin hypercube sampling is used to select representative scenarios to be planned for the hierarchical energy hub model; The representative planning scenarios are simulated and calculated based on the basic energy-carbon coupling model to obtain the optimal operation planning scheme taking into account the carbon emission costs.

8. A unified planning system for an energy-carbon coupled operation model, characterized in that: include: A hierarchical energy hub modeling module is configured to construct a hierarchical energy hub model based on energy components under the constraint of capacity conversion components; the energy components include energy conversion components, energy storage components and renewable energy components; The energy-carbon coupling basic model building module is configured to build the IES system energy-carbon coupling basic model according to the energy conversion component and the energy storage component under the energy component capacity constraint; The unified planning module is configured to select representative scenarios to be planned based on the hierarchical energy hub model, solve the representative scenarios to be planned according to the energy-carbon coupling basic model, obtain the optimal planning scheme taking into account the carbon emission cost, and realize the unified planning of the IES system.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the unified planning method of an energy-carbon coupling operation model as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the unified planning method of the energy-carbon coupling operation model as described in any one of claims 1 to 7 are implemented.

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