A method and terminal for managing carbon emissions of distributed energy in power grid

Abstract

The present invention discloses a method and terminal for managing carbon emissions of distributed energy in a power grid, which establishes a description model for load information and distributed energy information in the power grid; calculates a cost formula corresponding to the power grid based on the description model; establishes an objective function and constraints corresponding to the objective function based on the cost formula and a preset carbon emission optimization target; solves the objective function to obtain optimization parameters, and optimizes the operation of the power grid based on the optimization parameters; when controlling carbon emissions, the present invention increases the dimension of the considered data and enhances the feasibility of the final carbon emission optimization measures; the final established target model is solved under constraints to obtain optimization parameters, and the power grid is low-carbon optimized based on the optimization parameters, thereby achieving optimization of the operation of the power grid under low-carbon requirements.

Description

Technical Field

[0001] The present invention relates to the field of carbon emissions, and in particular to a method and terminal for managing carbon emissions of distributed energy in a power grid. Background Art

[0002] Approximately two-thirds of global CO2 emissions come from power generation and heating. Therefore, given the need to reduce greenhouse gas emissions, particular attention needs to be paid to emissions from the power supply sector to control emissions from high-carbon industries and ensure a low-carbon global future. However, as economies evolve, striking an appropriate balance between new power supply capacity and carbon emissions is crucial. Power operating costs are relatively high in the context of the "dual carbon control" initiative due to high transmission losses and reliance on primary energy sources as the sole backup source. On the other hand, using only distributed generation as a backup source carries environmental impacts due to greenhouse gas emissions. However, leveraging on-site generation systems and existing renewable energy sources, such as wind and solar, can be a cost-effective approach. Furthermore, incorporating battery storage into the system can mitigate the intermittent nature of renewable energy. To minimize the total operating cost of such systems, the design of energy management systems that optimally dispatch diverse energy sources is crucial for future power generation expansion planning in developing countries.

[0003] However, the uncertainties associated with renewable energy and demand power forecasts make energy management of such systems more challenging. Therefore, there is a need to investigate robust energy management techniques that take all system uncertainties into account in the optimization problem. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and terminal for managing carbon emissions of distributed energy in a power grid, so as to optimize the operation of the power grid under low-carbon requirements.

[0005] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0006] A method for managing carbon emissions of distributed energy in a power grid, comprising the steps of:

[0007] Establish a description model for load information and distributed energy information in the power grid;

[0008] Calculate the cost formula corresponding to the power grid according to the description model;

[0009] Establishing an objective function and constraints corresponding to the objective function according to the cost formula and the preset carbon emission optimization target;

[0010] The objective function is solved to obtain optimization parameters, and the operation of the power grid is optimized according to the optimization parameters.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A power grid distributed energy carbon emission management terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0013] Establish a description model for load information and distributed energy information in the power grid;

[0014] Calculate the cost formula corresponding to the power grid according to the description model;

[0015] Establishing an objective function and constraints corresponding to the objective function according to the cost formula and the preset carbon emission optimization target;

[0016] The objective function is solved to obtain optimization parameters, and the operation of the power grid is optimized according to the optimization parameters.

[0017] The beneficial effects of the present invention are: first, a descriptive model corresponding to the load information and distributed energy information in the power grid is established, then the cost formula corresponding to the power grid is obtained according to the descriptive model, and finally an objective function is established according to the cost formula and the preset carbon emission optimization target. When controlling carbon emissions, the dimension of the considered data is increased, and the feasibility of the final carbon emission optimization measures is enhanced. The final established target model is solved under constraints to obtain optimization parameters, and the power grid is low-carbon optimized according to the optimization parameters, so as to achieve optimization of the operation of the power grid under low-carbon requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flowchart of the steps of a method for managing carbon emissions of distributed energy in a power grid according to an embodiment of the present invention;

[0019] Figure 2 This is a flowchart of another step of a method for managing carbon emissions of distributed energy in a power grid according to an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of a typical configuration of a distributed power source and power supply in a power grid according to an embodiment of the present invention;

[0021] Figure 4 A schematic diagram comparing daily carbon emission changes between an application of a method for managing carbon emissions of distributed energy resources in a power grid and a non-application of an embodiment of the present invention;

[0022] Figure 5 This is a schematic structural diagram of a grid distributed energy carbon emission management terminal according to an embodiment of the present invention;

[0023] Description of labels:

[0024] 1. A power grid distributed energy carbon emission management terminal; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0025] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0026] Please refer to Figure 1 , a method for managing carbon emissions of distributed energy in a power grid, comprising the steps of:

[0027] Establish a description model for load information and distributed energy information in the power grid;

[0028] Calculate the cost formula corresponding to the power grid according to the description model;

[0029] Establishing an objective function and constraints corresponding to the objective function according to the cost formula and the preset carbon emission optimization target;

[0030] The objective function is solved to obtain optimization parameters, and the operation of the power grid is optimized according to the optimization parameters.

[0031] From the above description, it can be seen that the beneficial effects of the present invention are: first, a descriptive model corresponding to the load information and distributed energy information in the power grid is established, and then the cost formula corresponding to the power grid is obtained according to the descriptive model. Finally, an objective function is established according to the cost formula and the preset carbon emission optimization target. When controlling carbon emissions, the dimension of the data considered is increased, and the feasibility of the final carbon emission optimization measures is enhanced. The final established target model is solved under constraints to obtain optimization parameters, and the power grid is low-carbon optimized according to the optimization parameters, so as to achieve optimization of the operation of the power grid under low-carbon requirements.

[0032] Furthermore, establishing a description model for distributed energy in the power grid includes:

[0033] The total fuel consumption model of the primary energy generator and the discrete time state variable model of the battery are established respectively.

[0034] From the above description, it can be seen that modeling the total fuel consumption of primary energy generators in the power grid and the state variables of the battery in discrete time provides data support for the subsequent calculation of the cost formula.

[0035] Furthermore, the establishment of the total fuel consumption model of the primary energy generator includes:

[0036]

[0037] Where F represents the total fuel consumption of primary energy generators, T s is the sampling time, t kis the sampling moment, N is the total number of the sampling moments; P DG At time t k is a function of the output power of the primary energy generator when δ DG is a binary number that takes the value 1 when the primary energy generator is turned on and takes the value 0 when it is turned off. N is the total prediction step size. f ,b f ,c f is the fuel consumption coefficient of the energy generator;

[0038] The establishment of a state variable model of the battery in discrete time includes:

[0039]

[0040] Where, SOC BS is the state of charge of the battery; η ch ,η dis is the charge and discharge efficiency of the battery, P ch ,P dis are the charge and discharge power of the battery, C BS,r is the rated capacity of the battery.

[0041] From the above description, it can be seen that sampling of the corresponding sampling duration is performed when the sampling time is reached, the sampling time is dispersed, and the correlation between the sampling sample changes and the actual data changes is improved, thereby ensuring the accuracy of subsequent calculation results.

[0042] Furthermore, the cost formula for calculating the power grid according to the description model includes:

[0043] Establish a formula for grid operating costs;

[0044] Establishing a primary energy generator cost formula based on the primary energy generator total fuel consumption model;

[0045] Establishing a battery cost formula in the power grid based on the battery state variable model in discrete time;

[0046] Develop a formula for load shedding costs.

[0047] From the above description, it can be seen that by expressing various costs in the power grid, the cost of power grid operation is also taken into consideration during the process of carbon emission optimization, ensuring the feasibility of the final overall carbon emission optimization plan.

[0048] Furthermore, the cost formula for calculating the power grid according to the description model includes:

[0049] Establish the grid operating cost formula:

[0050]

[0051] represents the grid operation cost; C pur (t k ) indicates that at t k The electricity price of energy purchased by the grid at the moment, C sold (t k ) indicates that at t k The electricity price of energy sold by the power grid at that moment; P pur (t k ) indicates that at t k The power of the source grid purchased by the grid at that moment; P sold (t k ) indicates that at t k The power at which the grid sells energy at that moment;

[0052] The cost formula of primary energy generator is established based on the total fuel consumption model of primary energy generator:

[0053]

[0054]

[0055]

[0056]

[0057] in, represents the primary energy generator cost; represents the fuel consumption cost; represents the startup cost; represents the maintenance cost; γ f represents the price of energy per liter; γ SU represents the startup cost per startup; Indicates that at t k Number of starts at a certain moment; δ PE (t k ) indicates that at t k The starting state of the primary energy generator at time γ M represents the hourly maintenance cost of the primary energy generator;

[0058] The battery cost formula in the power grid is established based on the battery state variable model in discrete time:

[0059]

[0060]

[0061]

[0062] in, Indicates the battery cost; represents the cycle cost; Indicates the operating cost; A, B are constants that characterize battery characteristics, SOC min represents the minimum state of charge, M represents the total prediction step size; DNC represents the number of daily cycles; CC BS represents the capital construction cost; P ch Indicates the battery charging power; P dis Indicates battery discharge efficiency; C BS,r Indicates the rated capacity of the battery;

[0063] The formula for establishing the load shedding cost includes:

[0064]

[0065] in, represents the load shedding cost; P dl (t k ) indicates that at t k Load shedding power at the moment.

[0066] From the above description, it can be seen that a method for calculating various costs is provided to objectively represent various costs, which facilitates the subsequent generation of objective functions and optimization.

[0067] Furthermore, establishing an objective function and constraints corresponding to the objective function according to the cost formula and the preset carbon emission optimization target includes:

[0068] Establish the objective function:

[0069]

[0070] in, refers to the objective function; represents the total amount of carbon emissions; Indicates the emission-related factors of CO2 gas produced per liter of primary energy combustion; Indicates the emission-related factors of CH4 gas produced per liter of primary energy combustion; Represents the emission-related factor of N2O gas produced per liter of primary energy combustion; γ emission represents the penalty cost per kilogram of carbon emissions; γ dl Represents the load shedding energy cost per kilowatt-hour.

[0071] From the above description, it can be seen that on the basis of quantifying various costs to generate the objective function, the total carbon emissions are also directly included as a part of the optimization function. In the process of solving the objective function, it is possible to take into account both various costs and total carbon emissions, thereby ensuring the feasibility of the final solution.

[0072] Furthermore, establishing an objective function and constraints corresponding to the objective function according to the cost formula and the preset carbon emission optimization target includes:

[0073] Set energy balance constraints, grid power constraints, battery constraints, primary energy generator constraints, and deferrable load constraints.

[0074] From the above description, it can be seen that by establishing corresponding constraints based on the security requirements and actual operating conditions of the power grid, the actual power grid scenario can be simulated during the calculation process, thereby ensuring the feasibility of the final solution.

[0075] Furthermore, the setting of energy balance constraints includes:

[0076] P WT (t k )+P PV (t k )+P dis (t k )+P PE (t k )+P pur (t k )=P L (t k )+P dl (t k )+P ch (t k )+P Ldef (t k )+P sold (t k ).

[0077] From the above description, it can be seen that performing energy balance constraints at every moment can ensure the stable operation of the power system.

[0078] Furthermore, the grid power constraint includes:

[0079] P grid (t k+1 )-P grid (t k )|≤ΔP gmax ;

[0080] P pur (t k )·P sold (tk )=0;

[0081] P grid Indicates the grid power, ΔP gmax Indicates the maximum allowable range of variation.

[0082] As can be seen from the above description, grid stability is affected by the rate of change of grid power exchange at the common coupling point. This is particularly true for weak grids, where the impact is greater. Therefore, the grid power conversion rate is limited to reduce this impact. In addition, to prevent the simultaneous purchase and sale of energy in the same direction, that is, buying and selling energy to another grid at the same time, a limit on the purchase and sale of power at the same time is added to avoid transmission waste.

[0083] Please refer to Figure 5 A grid distributed energy carbon emission management terminal includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, each step in the above-mentioned grid distributed energy carbon emission management method is implemented.

[0084] The above-mentioned method and terminal for managing carbon emissions of distributed energy in a power grid of the present invention can be applied to scenarios where carbon emissions in the power grid need to be optimized, and the present invention uses MPC (Model Predictive Control) to present the optimization problem in objective numbers, so that the optimization process and results can be visualized; the following is an explanation through a specific implementation method.

[0085] Please refer to Figures 1-4 , embodiment 1 of the present invention is:

[0086] A method for managing carbon emissions of distributed energy resources in a power grid includes the following steps:

[0087] S1. Establish a description model for the load information and distributed energy information in the power grid, including: establishing a total fuel consumption model for primary energy generators, a state variable model for batteries in discrete time, a photovoltaic output power model, a wind power output power model, a load rated power model, and a power grid uncertainty factor model;

[0088] S11, establishing a total fuel consumption model of a primary energy generator includes:

[0089]

[0090] Where F represents the total fuel consumption of primary energy generators, T s is the sampling time, t k is the sampling moment, N is the total number of the sampling moments; P DG At time tk is a function of the output power of the primary energy generator when δ DG is a binary number that takes the value 1 when the primary energy generator is turned on and takes the value 0 when it is turned off. N is the total prediction step size. f ,b f ,c f is the fuel consumption coefficient of the energy generator;

[0091] In an optional embodiment, a f The unit is L / kW 2 h, b f The unit is L / kWh, c f The unit is L / h;

[0092] S12, the state variable model of the battery in discrete time is established to simulate the dynamic behavior of the battery. Since the charging efficiency and the discharging efficiency also differ in power flow, the charging efficiency and the discharging efficiency are also separated to include:

[0093]

[0094] Where, SOC BS is the state of charge of the battery; η ch ,η dis is the charge and discharge efficiency of the battery, P ch ,P dis are the charging and discharging power of the battery, C BS,r is the rated capacity of the battery;

[0095] S13, establishing a photovoltaic output power model includes:

[0096]

[0097] Where, P PV Indicates the actual photovoltaic output power; G IN represents the incident irradiance; T C Indicates battery temperature; P STC Indicates the photovoltaic power under standard test conditions; G STC Indicates solar radiation under standard test conditions; T r represents the battery reference temperature, and α represents the temperature coefficient.

[0098] S14, establishing a wind power output power model includes:

[0099]

[0100] Where, P WT represents the actual output power of wind power generation; a, b and c represent preset constants; V wIndicates wind speed, P WT,r Indicates rated output power; V ci represents the wind turbine cut-in speed, V co represents the wind turbine cut-out speed; V r Indicates the rated speed of the wind turbine;

[0101] S15, establishing the load rated power model includes:

[0102] P Ldef (t k )=P Ldef,r

[0103]

[0104] Where, P Ldef,r Indicates the rated power of the delayed load; P Ldef represents the actual power of the delayable load; indicates the preset lower limit, and indicates the preset upper limit; Indicates that the delayable load is at t1-t N Total energy consumption at the time;

[0105] Load demand should always meet power requirements;

[0106] S16, establishing a power grid uncertainty factor model includes:

[0107] U X ={x:x l ≤x≤x u ,x u =x+Δx,x l =x-Δx};

[0108] In the formula, x represents the uncertain parameter; x u represents the upper limit of the uncertain parameter, x l represents the lower limit of the uncertain parameter; Δx represents the limit of the uncertain parameter;

[0109] The uncertainty of the energy management problem mainly comes from wind speed, accident radiation and load power forecast; it is assumed that the uncertainty parameters will vary around their mean values ​​and these variations will be limited by upper and lower bounds;

[0110] The above steps S11-S16 can be performed separately or in any order. The numbers are only for the convenience of distinction and cannot be used to limit the scope of protection of this application.

[0111] S2. Calculate the cost formula corresponding to the power grid based on the descriptive model, establish an accurate nonlinear discrete model and constraints, and simulate the dynamic behavior of each unit in the rolling time domain by considering the simplified discrete model of each participating unit. This allows for a more predictive and forward-looking approach to solving the optimal problem of the system over a long time domain. It is necessary to derive the total cost function associated with each energy source and use an economic model to find the optimal scheduling of each component of the system, including:

[0112] S21. Establish a formula for grid operation costs. Given the price forecasts for energy purchased from the grid and energy sold to the grid, the total cost function of the grid can be expressed as the difference between the cost of energy purchased from the grid and the revenue from energy sold to the grid.

[0113] represents the grid operation cost; C pur (t k ) indicates that at t k The electricity price of energy purchased by the grid at the moment, C sold (t k ) indicates that at t k The electricity price of energy sold by the power grid at that moment; P pur (t k ) indicates that at t k The power of the source grid purchased by the grid at that moment; P sold (t k ) indicates that at t k The power at which the grid sells energy at that moment;

[0114] S22. Establishing a primary energy generator cost formula based on the primary energy generator total fuel consumption model;

[0115]

[0116]

[0117]

[0118]

[0119] in,

[0120] in, represents the primary energy generator cost; represents the fuel consumption cost; represents the startup cost; represents the maintenance cost; γ f represents the price of energy per liter; γ SU represents the startup cost per startup; Indicates that at t k Number of starts at a certain moment; δ PE (t k ) indicates that at t k The starting state of the primary energy generator at time γ M represents the hourly maintenance cost of the primary energy generator;

[0121] S23. Establishing a cost formula for batteries in the power grid based on the discrete-time state variable model of the battery, wherein the operating cost function of the battery storage unit forces the energy management system to optimize the charging and discharging cycles of the battery storage unit according to the electricity price; the total cost of the battery storage unit is affected by the minimum state of charge and the number of daily cycles, and the total cost increases with an increase in the number of daily cycles and a decrease in the percentage of the minimum state of charge;

[0122]

[0123]

[0124]

[0125] in, Indicates the battery cost; represents the cycle cost; Indicates the operating cost; A, B are constants that characterize battery characteristics, SOC min represents the minimum state of charge, M represents the total prediction step size; DNC represents the number of daily cycles; CC BS represents the capital construction cost; P ch Indicates the battery charging power; P dis Indicates battery discharge efficiency; C BS,r Indicates the rated capacity of the battery;

[0126] S24. Establish a load shedding cost formula. The load shedding cost function is one of the optimization goals of the energy management system, namely, minimizing the total shedding energy wasted in load shedding, especially in isolation mode.

[0127]

[0128] in, represents the load shedding cost; P dl (t k ) indicates that at t k Load shedding power at the moment;

[0129] The above steps S21-S24 can be performed separately or in any order. The numbers are only for the convenience of distinction and cannot limit the scope of protection of this application. Use the economic model to find the optimal scheduling of each component of the system;

[0130] S3. Establish an energy carbon emission management system optimization problem with the goals of minimizing the total system operating cost, reducing carbon emissions, and minimizing the wasted energy in load shedding, and perform rolling optimization on the problem using a model predictive control method; establish an objective function and constraints corresponding to the objective function based on the cost formula and the preset carbon emission optimization goal;

[0131] S31. Establish the objective function:

[0132] in,

[0133] in, refers to the objective function; represents the total amount of carbon emissions; Indicates the emission-related factors of CO2 gas produced per liter of primary energy combustion; Indicates the emission-related factors of CH4 gas produced per liter of primary energy combustion; Represents the emission-related factor of N2O gas produced per liter of primary energy combustion; γ emission represents the penalty cost per kilogram of carbon emissions; γ dl represents the energy cost per kilowatt-hour of load shedding;

[0134] Since there are multiple sub-optimization objectives, namely minimizing the total operating cost, minimizing polluting gas emissions, and minimizing load shedding energy, all objective functions are converted so that each of them has the same dimension; thereby, a unified optimization can be performed; therefore, the objective function is the sum of the following: the total cost function related to the power grid, the fuel engine, and the battery storage unit, the carbon emission cost function, and the dump energy cost function; expressed as the above objective function formula;

[0135] S32 sets energy balance constraints, grid power constraints, battery constraints, primary energy generator constraints, and deferrable load constraints. To better reflect actual conditions, all practical limitations associated with each energy source are considered, enabling the maximum simulation of the actual environment, including:

[0136] S321. Setting energy balance constraints includes:

[0137] P WT (t k )+P PV (t k )+P dis (tk )+P PE (t k )+P pur (t k )=P L (t k )+P dl (t k )+P ch (t k )+P Ldef (t k )+P sold (t k );

[0138] P WT (t k ) represents the actual output power of wind power generation at the time; P PV (t k ) represents the actual output power of photovoltaic at the time; P dis (t k ) indicates that the battery is k Discharge efficiency at the moment; P PE (t k ) represents the primary energy generator at t k The actual output power at the moment; P pur (t k ) indicates that at t k The power of the source grid purchased by the grid at that moment; P L (t k ) indicates that at t k The grid load power at the moment; P dl (t k ) indicates that at t k Load shedding power at the moment; P ch (t k ) indicates that the battery is k Charging power at the moment; P Ldef (t k ) indicates that the load can be delayed at t k The actual power at the moment; P sold (t k ) indicates that at t k The power at which the grid sells energy at that moment;

[0139] S322, setting the grid power constraint includes:

[0140] P grid (t k+1 )-P grid (t k )|≤ΔP gmax ;

[0141] Ppur (t k )·P sold (t k )=0;

[0142] P grid Indicates the grid power, ΔP gmax Indicates the maximum allowable range of variation;

[0143] S323, setting battery constraints includes:

[0144]

[0145]

[0146] SOC(t N )≥SOC(t0);

[0147] P ch (t k )·P dis (t k )=0;

[0148] Among them, P BS (t k )=P dis (t k )-P ch (t k );

[0149] Where, SOC max Indicates the maximum discharge state; SOC(t k ) indicates that at t k The discharge status of the battery at all times; DNC max Indicates the maximum number of daily cycles; P BS Indicates the battery storage power; SOC(t0) indicates the discharge state at the initial moment, at which the iteration process has not yet begun;

[0150] And, set P ch (t k )·P dis (t k )=0 can prevent the battery from being charged and discharged at the same time, ensuring charging efficiency and extending the service life of the battery;

[0151] S324. Set primary energy generator constraints:

[0152]

[0153]

[0154] in,

[0155] Where, Indicates the maximum output power of the primary energy generator; Represents the minimum output power of the primary energy generator. The efficiency of the primary energy generator depends on its actual output power during operation. For correct mechanical operation and high operating efficiency of the generator, the manufacturer sets the output power of the primary energy generator between its minimum and maximum limits, usually between 30% and 100% of the rated output power, so it is simulated through constraints.

[0156] Indicates the number of times the primary energy generator is switched on and off; Indicates the maximum number of times the primary energy generator can be switched on and off. This limit is not determined by the operator, i.e. it cannot be changed by the operator.

[0157] S325. Establishing a delayable load constraint, including:

[0158]

[0159] δ Ldef A binary number representing a deferrable load. When a deferrable load exists, δ Ldef 1 if there is no deferrable load, 0 if there is no deferrable load. Indicates the minimum power that can delay the load; represents the maximum power of the delayable load; Indicates the lower limit of the delayable load energy; Indicates the upper limit of the load energy that can be delayed; P Ldef (t k ) indicates that the load can be delayed at t k Energy consumption at all times;

[0160] The above steps S321-S325 can be performed separately or in any order. The numbers are only for the convenience of distinction and cannot be used to limit the scope of protection of this application.

[0161] S4. Solving the objective function to obtain optimization parameters, and optimizing the operation of the power grid according to the optimization parameters, including:

[0162] definition

[0163] The set of constraints is:

[0164]

[0165] (2) P WT (t k )+P PV (t k )+Pdis (t k )+P PE (t k )+P pur (t k )=

[0166] P L (t k )+P dl (t k )+P ch (t k )+P Ldef (t k )+P sold (t k )

[0167] (3) P pur (t k )·P sold (t k )=0

[0168] (4) SOC(t N )≥SOC(t0)

[0169] (5) P ch (t k )·P dis (t k )=0

[0170] (6) SOC min ≤SOC(t k )≤SOC max

[0171] (7) DNC≤DNC max

[0172] (8)

[0173] (9)

[0174] (10)

[0175] (11)

[0176] (12) U X ={x:x l ≤x≤x u ,x u =x+Δx,x l =x-Δx}

[0177] (1) Forecasted hourly weather data, such as wind speed, solar radiation, and ambient temperature, as well as hourly power demand, are input into the energy management system;

[0178] (2) Calculate photovoltaic power generation and wind power generation through the descriptive model;

[0179] (3) Input the maximum and minimum limits of uncertainty of wind speed, solar radiation and load power in real time to form constraints;

[0180] (4) Solve the optimal energy management problem over the entire time horizon considering all system constraints;

[0181] (5) Only apply the above optimal energy management problem at t k The decision variables at time t, and repeat steps (1) to (5).

[0182] (6) Obtain the optimal decision variables that minimize the objective function for the entire time range;

[0183] (7) Optimize the operation of the power grid based on the optimal decision variables;

[0184] Please refer to Figure 4 ,It can be seen that after optimizing the operation of the power grid by solving the objective function, the effect of reducing carbon emissions can be significantly achieved.

[0185] Please refer to Figure 5 , the second embodiment of the present invention is:

[0186] A grid distributed energy carbon emission management terminal 1 includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, each step in Example 1 is implemented.

[0187] In summary, the present invention provides a method and terminal for managing carbon emissions from distributed energy resources in a power grid. This method considers demand-side management by incorporating deferrable loads and load shedding into the optimization problem, formulating the optimization problem as a multi-objective mixed-integer nonlinear programming framework to minimize total system operating costs. The method is applicable to optimized operation in both grid-connected and isolated modes, and accounts for uncertainties associated with renewable energy and load demand forecasts. The energy carbon emission management system includes: considering precise nonlinear models and constraints for distributed energy resources in a power grid; considering demand-side management by incorporating deferrable loads and load shedding into the optimization problem; formulating the optimization problem as a multi-objective mixed-integer nonlinear programming framework. Furthermore, the energy carbon emission management system determines the decision variables in the battery cost function: the initial state of charge, the number of daily cycles, and the minimum state of charge of the battery energy storage system, to minimize total operating costs. The objectives of the energy carbon emission management system include minimizing total system operating costs, reducing carbon emissions, and minimizing the energy wasted in load shedding.

[0188] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for managing carbon emissions of distributed energy in a power grid, characterized in that: Including steps: Establish a description model for load information and distributed energy information in the power grid; Calculate the cost formula corresponding to the power grid according to the description model; Establishing an objective function and constraints corresponding to the objective function according to the cost formula and the preset carbon emission optimization target; Solving the objective function to obtain optimization parameters, and optimizing the operation of the power grid according to the optimization parameters; The establishment of a description model for distributed energy in the power grid includes: Establish the total fuel consumption model of primary energy generator and the state variable model of battery in discrete time respectively; The establishment of the total fuel consumption model of the primary energy generator includes: ； Where F represents the total fuel consumption of primary energy generators, is the sampling duration, tk is the sampling moment, and N is the total number of sampling moments; It is at the moment is a function of the output power of the primary energy generator; is a binary number that takes the value 1 when the primary energy generator is on and takes the value 0 when it is off. N is the total prediction step size. is the fuel consumption coefficient of the energy generator; The establishment of a state variable model of the battery in discrete time includes: ； Where, is the battery charge status; is the charge and discharge efficiency of the battery, are the charge and discharge power of the battery, is the rated capacity of the battery; The cost formula for calculating the power grid according to the description model includes: Establish a formula for grid operating costs; Establishing a primary energy generator cost formula based on the primary energy generator total fuel consumption model; Establishing a battery cost formula in the power grid based on the battery state variable model in discrete time; Establish a load shedding energy formula; The establishing of the objective function and the constraints corresponding to the objective function according to the cost formula and the preset carbon emission optimization target includes: Establish the objective function: ; ； in, refers to the objective function; represents the grid operation cost; represents the primary energy generator cost; Indicates the battery cost; represents the total amount of carbon emissions; Indicates load shedding energy; Indicates the emission-related factors of CO2 gas produced per liter of primary energy combustion; Indicates the emission-related factors of CH4 gas produced per liter of primary energy combustion; Indicates the emission-related factors of N2O gas produced per liter of primary energy combustion; represents the penalty cost per kilogram of carbon emissions; represents the energy cost per kilowatt-hour of load shedding; Set energy balance constraints, grid power constraints, battery constraints, primary energy generator constraints, and deferrable load constraints.

2. A method for managing carbon emissions of distributed energy in a power grid according to claim 1, characterized in that: The cost formula for calculating the power grid according to the description model includes: Establish the grid operating cost formula: ； Indicates The electricity price of energy purchased by the grid at that moment, Indicates The price of energy sold by the grid at that moment; Indicates The power of the source grid purchased by the grid at that moment; Indicates The power at which the grid sells energy at that moment; The cost formula of primary energy generator is established based on the total fuel consumption model of primary energy generator: ； ； ； ； in, represents the fuel consumption cost; represents the startup cost; represents the maintenance cost; Indicates the price per liter of primary energy; represents the startup cost per startup; Indicates Number of starts at the moment; Indicates The startup status of the primary energy generator at all times; represents the hourly maintenance cost of the primary energy generator; The battery cost formula in the power grid is established based on the battery state variable model in discrete time: ； ； ； in, represents the cycle cost; Indicates operating cost; represents a constant that characterizes the battery characteristics, represents the minimum state of charge, M represents the total prediction step size; DNC represents the number of daily cycles; represents the capital construction cost; Indicates the battery charging power; Indicates battery discharge efficiency; Indicates the rated capacity of the battery; The formula for establishing load shedding energy includes: ； in, Indicates Load shedding power at the moment.

3. A method for managing carbon emissions of distributed energy in a power grid according to claim 1, characterized in that: The setting of energy balance constraints includes: ； in, Indicates the actual output power of wind power generation at the moment; Indicates the actual output power of the photovoltaic at the moment; Indicates that the battery is Discharge efficiency at each moment; Indicates that the primary energy generator is The actual output power at the moment; Indicates The power of the source grid purchased by the grid at that moment; Indicates The grid load power at the moment; Indicates Load shedding power at the moment; Indicates that the battery is Charging power at the moment; Indicates that the load can be delayed in Actual power at the moment; Indicates The power at which the grid sells energy at that moment.

4. A method for managing carbon emissions of distributed energy in a power grid according to claim 1, characterized in that: The grid power constraints include: ； ； represents the grid power, Indicates the maximum allowable range of variation.

5. A grid distributed energy carbon emission management terminal, 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 computer program, each step of the method for managing carbon emissions of distributed energy in a power grid according to any one of claims 1 to 4 is implemented.

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