Grid Planning Method Oriented to the Electricity Spot Market Based on Carbon Emissions and Interaction among Power Grid, Power Generation, Load and Energy Storage

Abstract

The present invention relates to a power grid planning method for the electricity spot market based on carbon emissions and the interaction of power sources, the grid, loads, and energy storage. Compared with the prior art, it solves the defect that the power grid planning method cannot meet the actual usage requirements. The present invention includes the following steps: obtaining power grid historical data; analyzing power grid blocked lines; calculating power grid data before upgrading; constructing a power grid planning decision model; and solving the power grid planning decision model. The present invention uses the Lagrange multiplier analysis method for blocked lines to analyze, rank, and locate the blocked situations in the power grid to determine the addresses where transmission lines need to be upgraded and where power sources and energy storage need to be newly built or installed. Combining the planning decision-making objectives with the market simulation operation model and considering multiple indicators such as reliability, environmental friendliness, and economy, the optimal planning result is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and specifically to a power grid planning method for the electricity spot market based on carbon emissions and the interaction of power sources, grids, loads, and energy storage. Background Art

[0002] Traditional power grid planning in China is based on known power load forecasting schemes and power source construction plans to meet the development of power transmission and demand, while ensuring the safety and reliability of power transmission and minimizing the grid construction and operation costs. Under the planned mode, power source planning, transmission grid planning, and distribution grid planning are carried out separately. Under the action of multiple controllable factors, such as power generation and consumption prices, power generation quantities, etc., traditional power grid planning methods can plan the optimal grid structure under the background of planned electricity.

[0003] The boundary conditions of traditional power grid planning in China have changed, and the planning objectives and requirements have increased. The original planning mode and methods face challenges in many aspects, which are mainly reflected in the following parts:

[0004] (1) Under the planned mode, the power generation and consumption prices are determined, and the power generation quantities of power plants are also allocated in advance. Factors such as load forecasting and power source construction can be accurately planned in advance and are mostly controllable factors. The results of load forecasting and power source construction plans for power grid planning are obtained through transactions and generally have high accuracy;

[0005] However, in the electricity spot market and the new power system, with the development of distributed power sources, the uncertainty of power sources and loads has increased. The combined effects of the volatility on the power source side and the randomness of the source-connected loads on the user side have enhanced the uncertainty of power grid planning in the source-load link.

[0006] (2) Traditional power grid planning generally conducts power source planning, transmission grid planning, and distribution grid planning separately;

[0007] However, in the electricity spot market environment and the new power system environment, there are more stakeholders participating in power quantity balance. The power source side, grid side, and load side all have flexible and variable operation strategies. The planning idea that only considers a certain link can no longer adapt to the deep interaction of multiple links of power sources, grids, loads, and energy storage. It is necessary to study multi-link collaborative planning technologies including energy storage.

[0008] (3) In traditional planning methods, power source planning aims at economy on the premise of meeting power balance, while grid planning aims to improve the economy of the scheme under the condition of meeting certain stability or reliability requirements;

[0009] In the context of the new power system and carbon peaking and carbon neutrality, in addition to considering reliability and economy, power grid planning should also take into account indicators such as carbon emissions and new energy development. The optimization goal of planning decisions is the comprehensive optimum of economy, reliability, and environmental protection.

[0010] (4) Traditional planning methods mainly consider investment economy in terms of economy, and aim to minimize the operation costs of grid and power source upgrades and construction under the premise of meeting the reliable operation of the power grid.

[0011] In the spot market environment, market operation information represents the operation of the power grid. Grid planning directly affects market results, thus affecting the interests of all market players. The clearing result of the spot market can reflect the regional distribution and severity of grid congestion, etc., and can effectively guide the direction of grid planning upgrades. Moreover, as a dispatching agency and a power market operation agency, improving the economic benefits of power market operation should also be one of the goals of grid planning. In the spot market environment, the economy of grid planning can additionally consider the economy of power market operation, and the economy of the power market can be characterized by indicators such as the reduction of user-side electricity costs, the reduction of total generation-side costs, and the reduction of carbon emission costs calculated through power market and carbon market data, such as nodal electricity prices, carbon emission prices, and power generation of various types of power sources.

[0012] (5) Since the operation characteristics of the power grid before the market is launched are relatively simple, traditional power grid planning methods usually select typical peak load modes (summer peak and winter peak) in the planning year for safety analysis and calculations such as power flow, stability, and short circuit. The coordination between planning and operation is not tight enough;

[0013] In the power spot market environment, the interaction of multiple links such as power sources, grids, loads, and energy storage, and the impact of the volatility and intermittency of large-scale new energy on multiple links of the power grid have all led to the uncertainty of operation modes and the aggravation of the diversification of power grid power flow changes, and the complexity of the decision-making environment. Therefore, it is more necessary to fully consider the operation requirements during the planning stage, and add operation simulation and evaluation of the power system at the grid planning level to evaluate the operation indicators of the planning scheme under a large number of operation scenarios to adapt to the complex characteristics of power system operation in the power spot market environment.

[0014] Therefore, how to design a new type of grid planning method oriented to the power spot market, considering carbon emissions and the interaction of power sources, grids, loads, and energy storage has become an urgent technical problem to be solved. Summary of the Invention

[0015] The purpose of the present invention is to solve the defect that the existing grid planning methods cannot meet the actual usage needs, and provide a grid planning method oriented to the power spot market based on carbon emissions and the interaction of power sources, grids, loads, and energy storage to solve the above problems.

[0016] To achieve the above purpose, the technical solution of the present invention is as follows:

[0017] A grid planning method oriented to the power spot market based on carbon emissions and the interaction of power sources, grids, loads, and energy storage, comprising the following steps:

[0018] 11) Acquisition of grid historical data: Obtain the congestion Lagrange multipliers of each line per hour in the past year, the investment costs for energy storage, power sources, and transmission and distribution line upgrades, the existing power source, energy storage, grid capacity, and structure information, as well as the load forecast information data for the next 5 years;

[0019] 12) Analysis of grid congestion lines: Based on the grid historical data, calculate the average value of the congestion Lagrange multipliers of each line in the past year, which represents the degree of line congestion; sort the lines from the most congested to the least congested, and select the n most severely congested lines; use the n congested lines as the objects for transmission line upgrades, and determine the siting of new construction and installation of power sources and energy storage at the receiving ends of the power transmission of the n congested lines;

[0020] 13) Calculation of pre-upgrade grid data: Simulate the pre-upgrade electricity spot market based on the grid historical data, and calculate the pre-upgrade grid data, which includes nodal electricity value, power generation, and power purchase data;

[0021] 14) Construction of a grid planning decision model: With carbon emissions and the interaction of power sources, grids, loads, and energy storage as the goals, construct a grid planning decision model;

[0022] 15) Solution of the grid planning decision model: Solve the grid planning decision model to obtain the optimal planning structure.

[0023] The analysis of the grid congestion lines includes the following steps:

[0024] 21) Calculate the average value of the Lagrange multiplier for each line: Input the Lagrange multipliers of the line constraints for 8760 hours of each line's history, and the calculation formula for the historical average value of the Lagrange multiplier is as follows:

[0025]

[0026] Among them, represents the average value of the Lagrange multipliers of the line constraints, P i,j represents the Lagrange multiplier of the line constraint on the i-th route at the j-th moment, j represents the j-th hour in a year, and i represents the i-th route in the system;

[0027] 22) Sorting and selection: Sort the calculated Lagrange multipliers from largest to smallest, and select the n most severely congested lines;

[0028] 23) Determine the siting: Use the n congested lines as the objects for transmission line upgrades, and determine the siting of new construction and installation of power sources and energy storage at the receiving ends of the power transmission of the n congested lines.

[0029] The calculation of the pre-upgrade grid data includes the following steps:

[0030] 31) Build a market simulation operation model:

[0031] 311) Determine the objectives of the market simulation operation model:

[0032] Set that the market simulation operation model includes a security-constrained unit commitment model and a security-constrained economic dispatch model, and their objectives are all to minimize the electricity purchase cost.

[0033] Among them, the functional expression of the security-constrained unit commitment model is as follows:

[0034]

[0035] Among them, N represents the total number of units, T represents the total number of time periods considered, P i,t represents the output of unit i at time period t, C i,t (P i,t ) are respectively the operating cost, start-up cost and shutdown cost of unit i at time period t. Among them, the operating cost C i,t (P i,t ) of the unit is a multi-segment linear function of the unit output;

[0036] Security-constrained economic dispatch model function:

[0037]

[0038] Among them, N represents the total number of units, T represents the total number of time periods considered, P i,t represents the output of unit i at time period t, P i,t represents the output of unit i at time period t, C i,t (P i,t ) is the operating cost of unit i at time period t and is a multi-segment linear function of the unit output;

[0039] 312) Determine the constraints of the market simulation operation model:

[0040] For the security-constrained unit commitment model, set its constraint conditions as: system constraints, unit constraints, network security constraints.

[0041] Among them, system constraints include load balance constraints, system positive reserve capacity constraints and system negative reserve capacity constraints. Unit constraints include unit output upper and lower limit constraints, unit ramp rate constraints and unit minimum continuous on-off time constraints. Network security constraints include line power flow constraints and section power flow constraints;

[0042] For the security-constrained economic dispatch model, set its constraint conditions as: system constraints, unit constraints, network security constraints.

[0043] Among them, system constraints include load balance constraints, unit constraints include upper and lower limits of unit output and unit ramp constraints, and network security constraints include line power flow constraints and section power flow constraints;

[0044] 313) Calculate the nodal marginal electricity price:

[0045] Combined with the security-constrained economic dispatch objective function and constraint conditions, using an optimization algorithm software package, the unit output data and nodal marginal electricity price are obtained. The nodal marginal electricity price calculation model is as follows:

[0046]

[0047] Among them, LMP k,t represents the nodal marginal electricity price of node k at time t, λ t represents the Lagrange multiplier of the system load balance constraint at time t, represents the Lagrange multiplier of the maximum forward power flow constraint of line l, represents the Lagrange multiplier of the maximum reverse power flow constraint of line l, G l-k represents the transfer distribution factor of node k to line l, represents the Lagrange multiplier of the maximum forward power flow of section S, represents the Lagrange multiplier of the maximum reverse power flow of section S, G s-k represents the transfer distribution factor of node k to section s;

[0048] 32) Perform data input: including the capacities of existing power sources, energy storage, and grid frameworks, the regulation rates, start-up and shutdown times, and operating parameters of power sources and energy storage, and the load forecast for the next 5 years;

[0049] 33) Output the results, which include nodal electricity price, power generation, and power purchase data.

[0050] The construction of the power grid planning decision model includes the following steps:

[0051] 41) Determine the planning decision objective: In the power grid planning decision model, comprehensively consider system reliability, carbon emissions, and economy, and take their optimization as the objective of the decision model. The formula is as follows:

[0052] max(α reliability +β environmental +γ financial ),

[0053]

[0054]

[0055]

[0056] Among them, α reliability is the reliability index of power grid planning, β environmental is the environmental protection index of power grid planning, γ financial is the economic benefit index of power grid planning, T lock represents the power shortage duration, T total represents the total working duration, P pre represents the system carbon emissions, which are calculated as the product of the cleared electricity of various power sources and the carbon emissions per unit of electricity obtained from the market simulation operation model embedded in this model, P total represents the carbon emission quota, m represents the number of time periods within the time of planning decision optimization, with one time period per hour. Here, the power grid planning for the next 5 years is optimized, that is, m = 8760 * 5; Q i,b , P i,b are respectively the electricity consumption of user i before the planning upgrade and the node electricity value of the node where the user is located, Q i,l 、P i,l are respectively the electricity consumption of user i after the planning upgrade and the node electricity value of the node where the user is located, which are calculated by the market simulation operation model embedded in this model; Q j,b 、P j,b are respectively the power generation of unit j before the planning upgrade and the node electricity value of the node where the unit is located; Q j,l , P j,l are respectively the power generation of unit j after the planning upgrade and the node electricity value of the node where the unit is located, which are calculated by the market simulation operation model embedded in this model; C grid 、C sor 、C sto are respectively the costs of transmission and distribution upgrade, power source upgrade, and energy storage upgrade, which are calculated as the product of the upgrade plans of various types of equipment calculated by the market simulation operation model embedded in this model and the corresponding unit costs set by the system;

[0057] 42) Determine the constraints in the planning decision: The set constraints are on the planned investment cost, reliability index, and carbon emission index. The specific constraint conditions are as follows:

[0058] 421) Set the planned investment cost constraint, and its expression is as follows:

[0059] C grid +C sor +C sto ≤P investment ,

[0060] Among them, P investment represents the planned investment cost;

[0061] 422) Set the reliability index α reliability not less than the minimum requirement, and its expression is as follows:

[0062] α reliability ≥α min ,

[0063] where α min represents the minimum reliability requirement of the system;

[0064] 42) The carbon emission index β environmental shall not be lower than the minimum requirement, and its expression is as follows:

[0065] β environmental ≥β min ,

[0066] where β min represents the minimum requirement of the carbon emission index;

[0067] 43) Determine the market simulation operation model: Input the line, power source, and energy storage upgrade capacity parameters in steps 41) and 42) into the market simulation operation model as its variables, and then input the node electricity value, power generation quantity, and electricity purchase quantity obtained from the market simulation operation model into the planning decision model to complete the nested processing of the planning decision model and the market simulation operation model.

[0068] The solution of the grid planning decision model includes the following steps:

[0069] 51) Input the data of the solution model into the planning decision model and the market simulation operation model. The data includes the planning decision variables and the load forecast values for the next 5 years. The planning decision variables include: power source, energy storage, and grid upgrade capacity planning decision variables;

[0070] 52) Run the planning decision model and the market simulation operation model;

[0071] 53) Obtain the model solution result: After running the model, obtain the optimal solution that meets its conditions, and output its planning result, which includes the power source, energy storage, transmission and distribution line upgrade capacity, and location.

[0072] Beneficial effects

[0073] The grid planning method for the electricity spot market based on carbon emissions and source-grid-load-storage interaction of the present invention, compared with the prior art, adopts the Lagrange multiplier analysis method for blocked lines to analyze, rank, and locate the blocked situations in the grid, so as to determine the addresses where the transmission lines need to be upgraded and the power sources and energy storages need to be newly built or installed, and combines the planning decision objective with the market simulation operation model, and combines multiple indicators such as reliability, environmental protection, and economy to obtain the optimal planning result.

[0074] The present invention can predict the continuously developing and changing power market to a certain extent, and make plans for grid construction for the next 5 years to cope with possible challenges and changes. At the same time, it combines the concept of source-grid-load-storage coordination, which is different from the traditional planning method of separating source-grid-load-storage in grid planning. The planning method is more scientific and systematic. Under this planning method, each part of the power grid can play its role more easily, effectively and systematically, and generate effective and rapid linkage and response with other parts.

[0075] In addition, from the perspective of the economic benefits of the power market, the present invention quantitatively analyzes the economy of grid planning, and takes into account both the scientific nature and economy of grid planning. While enabling the power grid to operate efficiently and scientifically, it also makes its construction and upgrade have practical significance and economic value, achieving the most scientific and effective construction results at the lowest cost.

[0076] In summary, the present invention takes into account both the economy and science of grid planning. While enabling the power grid to operate efficiently and scientifically, it maximizes its economic benefits, controls costs, uses the planning decision model, and comprehensively considers the benefits and values generated by grid upgrade and construction. At the same time, it also meets the constraints and requirements such as carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is the method sequence diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0078] In order to further understand and recognize the structural characteristics and achieved effects of the present invention, the following is a detailed description with reference to preferred embodiments and drawings:

[0079] As Figure 1 shown, a grid planning method for the electricity spot market based on carbon emissions and source-grid-load-storage interaction includes the following steps:

[0080] The first step is to obtain historical data of the power grid: obtain the congestion Lagrange multipliers (shadow parameters) of each line per hour for the past year, the investment costs for upgrading energy storage, power sources, and transmission and distribution lines, the existing capacities and structures of power sources, energy storage, and grid frameworks, and the load forecast information data for the next 5 years.

[0081] Step 2, Analysis of Grid Blocked Lines: Based on the historical data of the power grid, calculate the average value of the Lagrange multipliers of each line's blockage in the past year, which represents the degree of line blockage; sort the degrees of line blockage from large to small, and select the n most severely blocked lines; use the n blocked lines as the objects for upgrading transmission lines, and determine the siting of new construction and installation of power sources and energy storage at the receiving end of the power transmission of the n blocked lines. The Lagrange multiplier reflects the change in the total generation cost with the change of system load or transmission capacity. Using the Lagrange multiplier to analyze the degree of line blockage can effectively reflect the degree of line blockage and determine the upgrading objects and the siting of new construction and installation.

[0082] The specific steps are as follows:

[0083] (1) Calculate the average value of the Lagrange multiplier for each line: Input the Lagrange multipliers of the line constraints for 8760 hours of each line's history, and the calculation formula for the historical average value of the Lagrange multiplier is as follows:

[0084]

[0085] Among them, represents the average value of the Lagrange multiplier of the line constraint, P i,j represents the Lagrange multiplier of the line constraint on the i-th line at the j-th moment, j represents the j-th hour in a year, and i represents the i-th line in the system.

[0086] (2) Sorting and selection: Sort the calculated Lagrange multipliers from large to small, and select the n most severely blocked lines. In practical applications, the value of n is determined according to the investment amount and experience.

[0087] (3) Determine the siting: Use the n blocked lines as the objects for upgrading transmission lines, and determine the siting of new construction and installation of power sources and energy storage at the receiving end of the power transmission of the n blocked lines.

[0088] Step 3, Calculation of Grid Data before Upgrading: Based on the historical data of the power grid, simulate the power spot market before upgrading, and calculate the grid data before upgrading. The grid data before upgrading includes node electricity value, power generation, and power purchase data. Here, a market simulation operation model is constructed to obtain the grid data before upgrading. The market simulation operation model combines a security-constrained unit commitment model and a security-constrained economic dispatch model, and minimizes the power purchase cost under the constraint conditions. The obtained data (including node electricity value, power generation, and power purchase) can be compared with the grid data after upgrading and used for the calculation of economic indicators.

[0089] The specific steps are as follows:

[0090] (1) Construct a market simulation operation model:

[0091] A1) Determine the objectives of the market simulation operation model:

[0092] It is assumed that the market simulation operation model includes a security-constrained unit commitment model and a security-constrained economic dispatch model, and their objectives are all to minimize the power purchase cost.

[0093] Among them, the functional expression of the security-constrained unit commitment model is as follows:

[0094]

[0095] Among them, N represents the total number of units, T represents the total number of time periods considered, P i,t represents the output of unit i in time period t, and C i,t (P i,t ) and are the operating cost, start-up cost, and shut-down cost of unit i in time period t respectively. Among them, the operating cost C i,t (P i,t ) of the unit is a multi-segment linear function of the unit output;

[0096] Security-constrained economic dispatch model function:

[0097]

[0098] Among them, N represents the total number of units, T represents the total number of time periods considered, P i,t represents the output of unit i in time period t, P i,t represents the output of unit i in time period t, and C i,t (P i,t ) is the operating cost of unit i in time period t and is a multi-segment linear function of the unit output.

[0099] A2) Determine the constraints of the market simulation operation model:

[0100] For the security-constrained unit commitment model, its constraint conditions are set as: system constraints, unit constraints, and network security constraints.

[0101] Among them, system constraints include load balance constraints, system positive reserve capacity constraints, and system negative reserve capacity constraints. Unit constraints include unit output upper and lower limit constraints, unit ramp rate constraints, and unit minimum continuous on / off time constraints. Network security constraints include line power flow constraints and section power flow constraints;

[0102] For the security-constrained economic dispatch model, its constraint conditions are set as: system constraints, unit constraints, and network security constraints.

[0103] Among them, system constraints include load balance constraints. Unit constraints include unit output upper and lower limit constraints, unit ramp rate constraints. Network security constraints include line power flow constraints, section power flow constraints;

[0104] A3) Calculate the nodal marginal electricity value:

[0105] Combined with the security-constrained economic dispatch objective function and constraints, using an optimization algorithm software package, the generator output data and nodal marginal electricity values are obtained. The nodal marginal electricity value calculation model is as follows:

[0106]

[0107] where λ t represents the Lagrange multiplier of the system load balance constraint at time t, represents the Lagrange multiplier of the maximum forward power flow constraint of line l, represents the Lagrange multiplier of the maximum reverse power flow constraint of line l, represents the Lagrange multiplier of the maximum forward power flow of section S, represents the Lagrange multiplier of the maximum reverse power flow of section S.

[0108] (2) Perform data input: including the capacities of existing power sources, energy storage, and grid structures, the regulation rates, start-up and shutdown times, and operating parameters of power sources and energy storage, as well as the load forecasts for the next 5 years;

[0109] (3) Output the results, which include nodal electricity values, power generation, and power purchase data.

[0110] Step 4, construction of the power grid planning decision model: With carbon emissions and the interaction of power sources, the grid, loads, and energy storage as the goals, a power grid planning decision model is constructed.

[0111] Traditional power grid planning methods are no longer applicable to the electricity spot market with multiple uncertainties, and it is impossible to effectively stipulate and allocate electricity prices and quantities through planning methods. The electricity spot market, new power systems, and dual-carbon goals have requirements for power grid planning in terms of economic efficiency, grid operation safety, and environmental protection. The disadvantage of new energy power is its unstable power generation and difficult storage. At the same time, the entry of new energy power into the grid means that the transmission lines need to be upgraded or installed, or new power sources, energy storage, etc. need to be built or installed.

[0112] Here, a power grid planning decision model that combines carbon emissions and the coordination of power sources, the grid, loads, and energy storage conducts unified planning and decision-making for power sources, the grid, and energy storage. At the same time, the planning and decision-making consider the economic efficiency of the operation of the electricity spot market, combined with carbon emission indicators, and the goal is to achieve the overall optimal in terms of economy, reliability, and environmental protection. In addition, a market operation simulation model is embedded in the planning model to consider the reliability of the operation of the power system at each time period. Compared with traditional power grid planning methods, the final planning results are more reasonable, reliable, and economical.

[0113] The specific steps are as follows:

[0114] (1) Determine the planning decision-making objective: In the power grid planning decision-making model, comprehensively consider system reliability, carbon emissions, and economy, and optimize them as the objective of the decision-making model. The formula is as follows:

[0115] max(α reliability +β environmental +γ financial ),

[0116]

[0117]

[0118]

[0119] where α reliability is the reliability index of power grid planning, β environmental is the environmental protection index of power grid planning, γ financial is the economic benefit index of power grid planning, T lock represents the power shortage duration, T total represents the total working duration, P pre represents the system carbon emissions, which are calculated as the product of the cleared electricity of various power sources and the carbon emissions per unit of electricity obtained from the market simulation operation model embedded in this model;

[0120] P total represents the carbon emission quota, m represents the number of time periods within the time of planning decision-making optimization, one hour per time period. Here, the power planning for the next 5 years is optimized, that is, m = 8760 * 5; Q i,b ,P i,b are the electricity consumption of user i and the node electricity value of the node where the user is located before the planning upgrade respectively, Q i,l 、P i,l are the electricity consumption of user i and the node electricity value of the node where the user is located after the planning upgrade respectively, which are calculated by the market simulation operation model embedded in this model; Q j,b 、P j,b are the power generation of unit j and the node electricity value of the node where the unit is located before the planning upgrade respectively; Q j,l ,P j,l are the power generation of unit j and the node electricity value of the node where the unit is located after the planning upgrade respectively, which are calculated by the market simulation operation model embedded in this model; C grid 、C sor 、C sto are the costs of transmission and distribution upgrade, power source upgrade, and energy storage upgrade respectively, which are calculated as the product of the upgrade plans of various types of equipment obtained from the market simulation operation model embedded in this model and the corresponding unit costs set by the system.

[0121] (2) Determine the constraints in the planning decision: The set constraints are the planned investment cost, reliability index, and carbon emission index. The specific constraints are as follows:

[0122] B1) Set the planned investment cost constraint, and its expression is as follows:

[0123] C grid +C sor +C sto ≤P investment ,

[0124] where P investment represents the planned investment cost;

[0125] B2) Set the reliability index α reliability not less than the minimum requirement, and its expression is as follows:

[0126] α reliability ≥α min ,

[0127] where α min represents the minimum reliability requirement of the system;

[0128] B3) Set the carbon emission index β environmental not lower than the minimum requirement, and its expression is as follows:

[0129] β environmental ≥β min ,

[0130] where β min represents the minimum requirement of the carbon emission index;

[0131] Determine the market simulation operation model: Input the line, power source, and energy storage upgrade capacity parameters in steps (1) and (2) into the market simulation operation model as its variables, and then input the node electricity value, power generation, and electricity purchase amount obtained from the market simulation operation model into the planning decision model to complete the nested processing of the planning decision model and the market simulation operation model.

[0132] In the actual solution process of the planning decision model, it is necessary to input the line, power source, and energy storage upgrade capacity parameters in the planning decision model into the market simulation operation model as its variables, and then input the node electricity value, power generation, and electricity purchase amount obtained from the market simulation operation model into the planning decision model as its variables. The parameters obtained from the planning decision model and the parameters obtained from the market simulation operation model are mutually variables and are iterated cyclically in actual operation until the optimal solution of the power grid planning decision model is obtained. In this way, the nesting of the planning decision model and the market simulation operation model is completed.

[0133] Step 5, Solving the power grid planning decision model: Solve the power grid planning decision model to obtain the optimal planning structure. The specific steps are as follows:

[0134] (1) Input the data for solving the model into the planning decision model and the market simulation operation model. The data includes the planning decision variables and the load forecast values for the next 5 years. The planning decision variables include: power source, energy storage, and power grid upgrade capacity planning decision variables.

[0135] (2) Run the planning decision model and the market simulation operation model. In traditional power grid planning, the power generation amount and power generation benefits are allocated or determined in advance. The factors of power source and energy storage construction can be accurately planned in advance and are controllable factors. Therefore, in traditional power grid planning, the power grid facility planning within the investment amount is carried out on the basis of the determination of controllable factors.

[0136] (3) Obtain the model solution result: After running the model, obtain the optimal solution that meets its conditions and output its planning result, which includes the power source, energy storage, upgraded capacity and location of the transmission and distribution lines.

[0137] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A power grid planning method for the electricity spot market based on carbon emissions and the interaction of power sources, grids, loads, and energy storage, characterized in that, it includes the following steps: 11) Obtaining power grid historical data: Obtain the blocking Lagrange multipliers of each line per hour in the past year, the investment costs for the upgrading of energy storage, power sources, and transmission and distribution lines, the existing capacities and structures of power sources, energy storage, and grid frameworks, as well as the load forecast information data for the next 5 years; 12) Analyzing the blocked lines of the power grid: According to the power grid historical data, calculate the average value of the blocking Lagrange multipliers of each line in the past year, which represents the line blocking degree; sort the line blocking degrees from large to small, and select the n most severely blocked lines; take the n blocked lines as the objects for upgrading the transmission lines, and determine the siting for the new construction and installation of power sources and energy storage at the receiving end of the power transmission of the n blocked lines; 13) Calculating the power grid data before upgrading: Based on the power grid historical data, simulate the electricity spot market before upgrading, and calculate the power grid data before upgrading. The power grid data before upgrading includes node electricity values, power generation amounts, and power purchase amounts; The calculation of the power grid data before upgrading includes the following steps: 131) Constructing a market simulation operation model: 1311) Determining the goal of the market simulation operation model: It is set that the market simulation operation model includes a security-constrained unit commitment model and a security-constrained economic dispatch model, and their goals are both to minimize the power purchase cost, wherein, the function expression of the security-constrained unit commitment model is as follows: Among them, N represents the total number of units, T represents the total number of time periods considered, and P i,t represents the output of unit i in time period t, and C i,t (P i,t ) and are the operating cost, start-up cost, and shutdown cost of unit i in time period t respectively. Among them, the operating cost C i,t (P i,t ) is a multi-segment linear function of the unit output; Security-constrained economic dispatch model function: Among them, N represents the total number of units, T represents the total number of time periods considered, and P i,t represents the output of unit i in time period t, and P i,t represents the output of unit i in time period t, and C i,t (P i,t ) is the operating cost of unit i in time period t, which is a multi-segment linear function of the unit output; 1312) Determining the constraints of the market simulation operation model: For the security-constrained unit commitment model, set its constraint conditions to be: system constraints, unit constraints, and network security constraints, wherein, the system constraints include load balance constraints, system positive reserve capacity constraints, and system negative reserve capacity constraints, the unit constraints include unit output upper and lower limit constraints, unit ramp rate constraints, and unit minimum continuous on-off time constraints, and the network security constraints include line power flow constraints and section power flow constraints; For the security-constrained economic dispatch model, set its constraint conditions to be: system constraints, unit constraints, and network security constraints, wherein, the system constraints include load balance constraints, the unit constraints include unit output upper and lower limit constraints, unit ramp rate constraints, and the network security constraints include line power flow constraints and section power flow constraints; 1313) Calculating the nodal marginal electricity value: Combining the security-constrained economic dispatch objective function and the constraint conditions, using an optimization algorithm software package, obtain the unit output data and the nodal marginal electricity value. The nodal marginal electricity value calculation model is as follows: Among them, LMP k,t represents the nodal marginal electricity value of node k at time period t, λ t represents the Lagrange multiplier of the system load balance constraint at time period t, represents the Lagrange multiplier of the maximum forward power flow constraint of line l, represents the Lagrange multiplier of the maximum reverse power flow constraint of line l, G l-k represents the transfer distribution factor of node k to line l, represents the Lagrange multiplier of the maximum forward power flow of section S, represents the Lagrange multiplier of the maximum reverse power flow of section S, G s-k represents the transfer distribution factor of node k to section s; 132) Performing data input: including the capacities of existing power sources, energy storage, and grid frameworks, the regulation rates, start-stop times, and operation parameters of power sources and energy storage, and the load forecast for the next 5 years; 133) Outputting the results, and the results include nodal electricity values, power generation amounts, and power purchase amounts; 14) Constructing a power grid planning decision-making model: With carbon emissions and the interaction of power sources, grids, loads, and energy storage as the goal, construct a power grid planning decision-making model; The construction of the power grid planning decision-making model includes the following steps: 141) Determine the planning decision-making objectives: In the power grid planning decision-making model, comprehensively consider system reliability, carbon emissions, and economy, and take their optimization as the objective of the decision-making model. The formula is as follows: max(α reliability +β environmental +γ financial ), Among them, α reliability is the reliability index of power grid planning, β environmental is the environmental protection index of power grid planning, γ financial is the economic benefit index of power grid planning, T lock represents the power shortage duration, T total represents the total working duration, P pre represents the system carbon emissions, which are calculated as the product of the cleared electricity of various power sources and the carbon emissions per unit of electricity obtained from the market simulation operation model embedded in this model, P total represents the carbon emission quota, m represents the number of time periods within the time of planning decision optimization, with one time period per hour. Here, the power planning for the next 5 years is optimized, that is, m = 8760 * 5; Q i,b ,P i,b are respectively the electricity consumption of user i and the node electricity value of the node where user i is located before the planning upgrade, Q i,l 、P i,l are respectively the electricity consumption of user i and the node electricity value of the node where user i is located after the planning upgrade, which are calculated by the market simulation operation model embedded in this model; Q j,b 、P j,b are respectively the power generation of unit j and the node electricity value of the node where unit j is located before the planning upgrade; Q j,l ,P j,l are respectively the power generation of unit j and the node electricity value of the node where unit j is located after the planning upgrade, which are calculated by the market simulation operation model embedded in this model; C grid 、C sor 、C sto are respectively the costs of transmission and distribution upgrade, power source upgrade and energy storage upgrade, which are calculated as the product of the upgrade plans of various types of equipment obtained from the market simulation operation model embedded in this model and the corresponding unit costs set by the system; 142) Determine the constraints in the planning decision-making: The set constraints are the planned investment cost, reliability index, and carbon emission index. The specific constraints are as follows: 1421) Set the planned investment cost constraint, and its expression is as follows: C grid +C sor +C sto ≤P investment , Among them, P investment represents the investment plan cost; 1422) Set the reliability index α reliability Not less than the minimum requirement, and its expression is as follows: α reliability ≥α min ， Among them, α min represents the minimum reliability requirement of the system; 1423) Carbon emission index β environmental Not less than the minimum requirement, and its expression is as follows: β environmental ≥β min ， Among them, β min represents the minimum requirement for carbon emission indicators; 143) Determine the market simulation operation model: Input the line, power source, and energy storage upgrade capacity parameters in steps 141) and 142) into the market simulation operation model as its variables, and then input the nodal electricity value, power generation, and power purchase amount obtained from the market simulation operation model into the planning decision-making model to complete the nested processing of the planning decision-making model and the market simulation operation model; 15) Solve the power grid planning decision-making model: Solve the power grid planning decision-making model to obtain the optimal planning structure.

2. The power grid planning method for the electricity spot market based on carbon emissions and the interaction of power sources, grids, loads, and energy storage according to claim 1, characterized in that the analysis of the power grid blocked lines includes the following steps: 21) Calculate the mean value of the Lagrange multipliers of each line: Input the Lagrange multipliers of the line constraints for 8760 hours of each line's history, and the calculation formula for the historical mean value of the Lagrange multipliers is as follows: Among them, represents the average value of the Lagrange multipliers representing line constraints, P i,j represents the Lagrange multiplier of the line constraint on the i-th route at the j-th moment, j represents the j-th hour of the year, and i represents the i-th route in the system; 22) Sorting and selection: Sort the calculated Lagrange multipliers from large to small, and select the n lines with the most serious congestion; 23) Determine the location selection: Take the n blocked lines as the objects for upgrading the transmission lines, and determine the new construction and installation locations of the power sources and energy storage at the receiving end of the power transmission of the n blocked lines.

3. The power grid planning method for the electricity spot market based on carbon emissions and the interaction of power sources, grids, loads, and energy storage according to claim 1, characterized in that the solution of the power grid planning decision-making model includes the following steps: 31) Input the data of the solution model into the planning decision-making model and the market simulation operation model. The data includes the planning decision-making variables and the load forecast values for the next 5 years. The planning decision-making variables include: power source, energy storage, and power grid upgrade capacity planning decision-making variables; 32) Run the planning decision-making model and the market simulation operation model; 33) Obtain the model solution result: After running the model, obtain the optimal solution that meets its conditions, and output its planning result, which includes the power source, energy storage, upgrade capacity and location of the transmission and distribution lines.

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