Power grid extension planning optimization method considering flexible resource aggregation

Abstract

The invention relates to a power grid extension planning optimization method considering flexible resource aggregation, belongs to the field of power grid planning, and solves the problem of how to improve the high permeability of renewable energy sources. Comprising the steps of constructing a double-layer expansion planning optimization model; predicting a power grid extension planning scene based on the collected historical load illumination wind speed data; performing population initialization by using an improved IPSO algorithm to generate a plurality of initial individuals; based on the initial individual and the predicted power grid extension planning scene, performing operation layer model optimization by using a PCIP algorithm to obtain an optimal operation scheme of the operation layer; returning the optimal operation scheme of the operation layer to the planning layer model to obtain the fitness value of each individual; performing IPSO algorithm iteration to obtain an optimal fitness value; and comparing the current fitness value of each individual with the optimal fitness value of the previous iteration, if the fitness value is better, updating the position and speed of the current individual to generate a next-generation population, and finally obtaining an optimal power grid expansion planning scheme. The renewable energy consumption rate and the grid-connected proportion are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a power grid expansion planning optimization method taking into account flexibility resource aggregation. Background Art

[0002] Against the backdrop of global energy transformation, the penetration rate of renewable energy such as wind and solar energy in the power grid continues to increase. However, the intermittent, volatile and uncertain nature of renewable energy also poses many challenges to the stable operation of the power grid. For example, the difficulty in matching renewable energy generation with electricity load on a time scale directly leads to the frequent occurrence of wind and solar power abandonment, which seriously hinders the efficient use of renewable energy. At the same time, the rise of integrated energy systems has made the coordinated optimization of multiple energy forms such as electricity and natural gas a key issue. In addition, the establishment of a carbon trading market has forced the energy system to consider carbon emission costs in order to achieve low-carbon development goals. At present, in terms of power grid planning and operation, most existing models are difficult to fully take into account the coordination of multiple energy sources in the power grid and the balance of long-term and short-term goals.

[0003] At present, the main methods for solving the two-layer expansion planning model are particle swarm optimization algorithm, genetic algorithm, simulated annealing algorithm, etc. Among them, particle swarm optimization algorithm is widely used in two-layer expansion planning, but the algorithm may fall into the local optimal solution too early in the search process; genetic algorithm can handle complex nonlinear problems and has good global search capabilities, but the computational complexity is high and the convergence speed is slow, and it is also easy to fall into the local optimal solution too early; simulated annealing algorithm will accept inferior solutions with a certain probability, which helps to jump out of the local optimal solution, but its convergence speed is slow, it is sensitive to initial parameters, and the computational efficiency is not high in large-scale problems, which makes it difficult to meet the rapid decision-making needs of real-time planning and operation optimization of power grids. Summary of the invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a grid expansion planning optimization method taking into account the aggregation of flexible resources, so as to solve the existing methods of how to effectively improve the high penetration rate of renewable energy, consider the coordinated optimization needs of the integrated energy system, and achieve low-carbon development goals under the carbon trading market mechanism, while overcoming the limitations of existing optimization algorithms in solution efficiency and accuracy, so as to achieve the technical problem of effective integration of long-term infrastructure construction decisions and short-term operation optimization of the grid.

[0005] The purpose of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention provides a method for optimizing power grid expansion planning taking into account flexibility resource aggregation, comprising the following steps:

[0007] Step S1: construct a two-layer expansion planning optimization model of the RIES system based on the output of wind turbines, photovoltaic units, gas storage devices, heat storage devices, cogeneration units, gas boilers, electric boilers and power-to-gas devices in the RIES system, including a planning layer model and an operation layer model; consider the uncertainty of renewable energy, and predict multiple grid expansion planning scenarios based on the collected historical load, light, and wind speed data;

[0008] Step S2: using the improved IPSO algorithm to initialize the population of the RIES system, and generating multiple network line construction plans to be expanded as initial individuals;

[0009] Step S3, based on the initial individuals and the predicted grid expansion planning scenario, the PCIP algorithm is used to perform optimization calculation of the operation layer model to obtain the optimal operation plan of the operation layer at the minimum operation cost within the operation scheduling cycle;

[0010] Step S4, return the optimal operation plan of the operation layer to the planning layer model to obtain the fitness value of each individual; perform IPSO algorithm iteration to obtain the optimal fitness value; compare the current fitness value of each individual with the optimal fitness value of previous iterations, if it is better, update the position and speed of the current individual, generate the next generation population and go to step S3 until the maximum number of iterations is reached to obtain the optimal power grid expansion planning plan.

[0011] Furthermore, the planning layer model takes the maximum renewable energy consumption rate and the minimum total cost of the power grid planning scheme as joint objectives, including line flow constraints, grid node voltage constraints and line transmission flow constraints;

[0012] The operation layer model aims to minimize the daily operation cost of the RIES system, including system power balance constraints, natural gas network constraints, controllable equipment output constraints, gas storage device constraints, demand response constraints, load transfer constraints at any time period, and electricity expenditure constraints before and after demand response.

[0013] Furthermore, the objective function of the planning layer model is as follows:

[0014]

[0015] Among them, η R is the renewable energy consumption rate; P wt,e,t , P pv,e,t They are the wind turbine and photovoltaic unit outputs actually absorbed by RIES during period t; are the predicted output values ​​of wind turbines and photovoltaic units in period t; F1 is the total cost of the power grid planning scheme; C con is the annual construction cost of power grid expansion; F 2,d is the operating cost of the RIES system on day d; x fl,iIs line i constructed? Its value is 0, indicating that the line is not constructed; its value is 1, indicating that the line is constructed; S fl,i is the construction cost of grid line i; d is the discount rate; y is the useful life; n is the number of grid expansion planning scenarios; and Z is the planning and scheduling cycle.

[0016] Furthermore, the objective function of the operating layer model is as follows:

[0017] minF2=C E +C G +C cb +C curtail +C OP +C ST +C P2G

[0018] Among them, C E is the electricity purchase cost; C G is the gas purchase cost; C cb is the carbon trading cost; C curtail Penalty cost for wind and solar curtailment; C OP is the operating cost; C ST is the energy storage cost; C P2G is the cost of power-to-gas raw materials;

[0019]

[0020] Among them, c e,t P is the real-time unit electricity price purchased by RIES from the power grid during period t; e,t is the power value purchased by RIES from the power grid during period t; Δt is the optimal scheduling interval; c g,t is the unit price of natural gas per kilowatt during period t; P g,t F is the power value of gas purchased by RIES from the natural gas network during period t; WT Penalty cost for wind curtailment; F PV is the penalty cost for abandoning light; wt , β pv are the penalty cost coefficients for wind and solar abandonment respectively; c i is the maintenance cost of the power of the i-th device; is the unit power-to-gas cost; S is the number of equipment; P i,t is the output of the i-th device in time period t; the device is a wind turbine, a photovoltaic unit, a gas storage device, a heat storage device, a cogeneration unit, a gas boiler, an electric boiler or a power-to-gas device; are the charging and discharging costs of the energy storage device during period t. When discharging is the power value of the energy storage device in the discharge or charging state; the energy storage device is a gas storage device or a heat storage device; ch is the unit energy storage cost of the thermal storage device; c ga G is the unit energy storage cost of the gas storage device; s,t , H s,t are the power values ​​of the gas storage and heat storage devices respectively; P ga is the power value of the power-to-gas device; ω is the conversion efficiency of the power-to-gas device.

[0021] Furthermore, the IPSO population in the IPSO algorithm includes a plurality of individuals, each of which is a specific network line to be expanded; wherein the network line includes a power grid line and a gas grid line;

[0022] Each line is encoded based on integer coding, 0 means that the line is not built, 1 means that the line is built, and the construction probability of each line is preset, and the position and speed of the individual are randomly generated;

[0023] Repeat the process of randomly generating individuals N p times, get N p An initial individual.

[0024] Furthermore, the step S3 comprises:

[0025] The maximum number of iterations of the PCIP algorithm is set, and based on the predicted grid expansion planning scenario and the initial individual, the Gurobi solver is called to solve the operation layer model, so as to obtain the optimal operation plan of the operation layer under each grid expansion planning scenario, satisfying the constraint conditions of the operation layer model and minimizing the objective function F2 of the operation layer model;

[0026] The optimal operation plan of the operation layer is the output of each device in daily operation, the start and stop time of the equipment and the minimum daily operation cost of the REIS system.

[0027] Furthermore, the step S4 comprises:

[0028] Return the optimal operation plan of the operation layer to the planning layer;

[0029] Based on the optimal operation plan of the operation layer, the Gurobi solver is called for iterative calculation;

[0030] Calculate the probability of each individual in the population being selected according to the roulette method Calculate the cumulative probability for each individual Generate a pseudo-random number r in the interval [0,1]. If r≤q1, select the first individual. If q i-1 <r≤q i , then select the i-th individual and continue the random selection process until a sufficient number of individuals are selected for the next generation;

[0031] After each iteration, the fitness value of each individual is calculated;

[0032] Compare the current fitness value of each individual with the optimal fitness value recorded in previous iterations;

[0033] If the current fitness value is better than the optimal fitness value in previous iterations, the position and speed of the current individual are updated to the position and speed corresponding to the optimal fitness value; where better means less than;

[0034] Update the individual's velocity and position as follows:

[0035]

[0036] Among them, ε is the preset inertia weight; are the speeds of the i-th individual in the k-th and k+1-th iterations respectively; c1 and c2 are the preset learning factors, gbest is the optimal position of the i-th individual in the k-th iteration; k is the optimal position of the population at the kth iteration; r1 and r2 are random numbers; is the position of the i-th individual in the k-th and k+1-th iterations;

[0037] Repeat steps S3 to S4 until the maximum number of iterations of the preset PCIP algorithm is reached, and finally determine the optimal grid expansion planning scheme;

[0038] The optimal grid expansion planning scheme includes the output of each device in the RIES system, the equipment start and stop time, and the line construction plan.

[0039] Furthermore, the fitness value of each individual is as follows:

[0040] Fitness=γ1F1+γ2η R

[0041] Among them, γ1 and γ2 are the renewable energy consumption rate η of the objective function respectively. R The weight coefficient of the total cost F1 of the power grid planning scheme is a preset value.

[0042] Furthermore, the controllable device output constraints of the operation layer model are as follows:

[0043]

[0044] in, They are wind turbine output P wt,e,t The upper and lower limits of They are the photovoltaic unit output P pv,e,t The upper and lower limits of The output of the gas storage device Gs,t The upper and lower limits of The output of the heat storage device is H s,t The upper and lower limits of The power output of the combined heat and power generation unit The upper and lower limits of Gas boiler output The upper and lower limits of The heating output of electric boilers The upper and lower limits of The power-to-gas device output P ga,t The upper and lower limits of .

[0045] Furthermore, based on the collected historical load, light, and wind speed data, the predicted data of load, light, and wind speed and their probability distribution are obtained using a time series prediction method;

[0046] Based on the probability distribution, samples are extracted from the predicted load, light, and wind speed data using Monte Carlo simulation and combined together to form a plurality of predicted grid expansion planning scenarios including load, light, and wind speed.

[0047] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0048] 1. The present invention takes into account the uncertainty of renewable energy and predicts multiple grid expansion planning scenarios, effectively reducing the phenomenon of wind and solar power abandonment, increasing the absorption rate of wind power and photovoltaic renewable energy, and improving the utilization rate of renewable energy:

[0049] 2. The present invention divides the two-layer extended planning optimization model into a planning layer and an operation layer. The planning layer optimizes the grid structure with the goal of minimizing the total cost of the planning scheme and maximizing the renewable energy consumption rate. The operation layer minimizes the daily operating cost of the RIES system and considers multiple constraints, and uses PCIP and PSO nested algorithms to solve mixed integer nonlinear programming problems. The present invention improves the reliability of grid operation, promotes the consumption of renewable energy and energy utilization, and contributes to its low-carbon transformation. It improves the rapid decision-making ability of grid planning and operation optimization, and meets the needs of real-time grid planning;

[0050] 3. The present invention constructs a two-layer extended planning optimization model, takes the maximum renewable energy consumption rate and the minimum total cost of the power grid planning scheme as the joint goals at the planning layer, optimizes the power grid structure, and improves the reliability and economy of power grid operation;

[0051] 4. The present invention considers the coordinated optimization of various energy forms such as electricity and natural gas, processes long-term and short-term goals through a two-layer model, realizes efficient coordinated optimization of the RIES integrated energy system, and improves energy utilization efficiency;

[0052] 5. Under the carbon trading market mechanism, the carbon emission costs of coal-fired units, cogeneration units and gas boilers should be taken into consideration, which will encourage the energy system to consider carbon emission costs in planning and operation, and help achieve low-carbon development goals.

[0053] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0055] Figure 1 A flow chart of a method for optimizing power grid expansion planning taking into account flexibility resource aggregation in an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of a natural gas transmission model in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0058] In response to the above technical problems, it is of great significance to construct a grid expansion planning optimization model that takes into account the aggregation of flexibility resources. It can effectively improve the reliability and sustainability of grid operation and promote efficient use of energy and low-carbon transformation.

[0059] The present invention aims to propose a grid expansion planning optimization model that takes into account the aggregation of flexible resources, optimizes the grid structure from an environmental protection perspective and minimizes the grid operation cost, maximizes the renewable energy consumption rate, and the proportion of renewable energy connected to the grid, while coordinating the grid and natural gas network to optimize the grid architecture of the RIES system (Regional Integrated Energy System).

[0060] A specific embodiment of the present invention, as Figure 1 As shown, a method for optimizing power grid expansion planning taking into account flexibility resource aggregation is disclosed, comprising the following steps:

[0061] Step S1: construct a two-layer expansion planning optimization model of the RIES system based on the output of wind turbines, photovoltaic units, gas storage devices, heat storage devices, cogeneration units, gas boilers, electric boilers and power-to-gas devices in the RIES system, including a planning layer model and an operation layer model; consider the uncertainty of renewable energy, and predict multiple grid expansion planning scenarios based on the collected historical load, light, and wind speed data;

[0062] Step S2: using an improved IPSO (Improved Particle Swarm Optimization) algorithm to initialize the population of the RIES system, and generating multiple network line construction plans to be expanded as initial individuals;

[0063] Step S3, based on the initial individuals and the predicted grid expansion planning scenario, the PCIP (Predictor-corrector Primal-dual Interior-point) algorithm is used to perform optimization calculation of the operation layer model to obtain the optimal operation plan of the operation layer at the minimum operation cost within the operation scheduling cycle;

[0064] Step S4, return the optimal operation plan of the operation layer to the planning layer model to obtain the fitness value of each individual; perform IPSO algorithm iteration to obtain the optimal fitness value; compare the current fitness value of each individual with the optimal fitness value of previous iterations, if it is better, update the position and speed of the current individual, generate the next generation population and go to step S3 until the maximum number of iterations is reached to obtain the optimal power grid expansion planning plan.

[0065] Step S1 includes steps S11-S14.

[0066] Construct a two-layer extended planning optimization model, including a planning layer model and an operation layer model.

[0067] The planning layer model takes the maximum renewable energy consumption rate and the minimum total cost of the power grid planning scheme as the joint objectives, including line flow constraints, grid node voltage constraints and line transmission flow constraints;

[0068] The operation layer model aims to minimize the daily operation cost of the RIES system, including system power balance constraints, natural gas network constraints, controllable equipment output constraints, gas storage device constraints and load transfer constraints at any time period.

[0069] Grid planning involves long-term infrastructure construction decisions, which need to ensure long-term power supply capacity and reliability, while the daily operation of the grid focuses on short-term optimization and scheduling.

[0070] The two-layer extended planning optimization model processes long-term goals through the planning layer and focuses on short-term goals through the operation layer, thus achieving effective integration of long-term and short-term planning goals and avoiding the limitations of a single model in processing long-term and short-term needs. At the same time, the present invention incorporates the carbon emission cost derived from the carbon trading cost model into the operation layer model of the two-layer extended planning optimization model, so that the RIES system gives priority to low-carbon emission energy equipment.

[0071] Step S11: construct a planning layer model, including the objective function and constraint conditions of the planning layer model.

[0072] The planning layer model optimizes the grid structure from an environmental perspective, with the maximum renewable energy consumption rate and the minimum total cost of the grid planning scheme as the optimization goals.

[0073] The objective function of the planning layer model is as follows:

[0074]

[0075] Among them, η R is the renewable energy consumption rate; P wt,e,t , P pv,e,t They are the wind turbine and photovoltaic unit outputs actually absorbed by RIES during period t; are the predicted output values ​​of wind turbines and photovoltaic units in period t; F1 is the total cost of the power grid planning scheme; C con is the annual construction cost of power grid expansion; F 2,d is the operating cost of the RIES system on day d; x fl,i Is line i constructed? Its value is 0, indicating that the line is not constructed; its value is 1, indicating that the line is constructed; S fl,i is the construction cost of grid line i; d is the discount rate; y is the useful life; n is the number of grid expansion planning scenarios; and Z is the planning and scheduling cycle.

[0076] Exemplarily, Z is 1 year and can be changed according to actual needs.

[0077] At the same time, the constraints that the planning layer model needs to meet are as follows:

[0078] (1) Line flow constraints

[0079]

[0080] (2) Grid node voltage constraints

[0081] U imin <U i <U imax Formula (3)

[0082] (3) Line transmission flow constraints

[0083] I k ≤I kmax Formula (4)

[0084] Among them, P i is the active power injected into the grid node i; Q i is the reactive power injected into the grid node i; U i is the voltage amplitude of node i; U j is the voltage amplitude of node j; G ij , B ij ,θ ij are the conductance, susceptance and voltage phase difference between nodes i and j respectively; U imin is the minimum voltage of node i; U imax is the maximum voltage of node i; I k is the current value of the kth line; I kmax is the maximum current of the kth line.

[0085] Input the parameters required by the planning layer model, the cost S of the grid construction line i fl,i , discount rate d, service life y, minimum voltage value U at node i imin 、The maximum voltage U of node i imax , the maximum current of the kth line I kmax , and obtain a definite planning layer model.

[0086] Step S12: construct an operation layer model, including the objective function and constraint conditions of the operation layer model.

[0087] The operation layer model takes the minimum daily operation cost of the RIES system as the objective function, and the optimized scheduling period T is 24h, that is, T=24h; illustratively, the optimized scheduling interval Δt is 1h, that is, Δt=1h; in order to further encourage the consumption of renewable energy and reduce the phenomenon of wind and solar power abandonment, the penalty cost of wind and solar power abandonment is introduced.

[0088] The objective function of the operational layer model is as follows:

[0089] minF2=C E +C G +C cb +C curtail +C OP +C ST +C P2G Formula (5)

[0090] Among them, C E is the electricity purchase cost; C G is the gas purchase cost; C cb is the carbon trading cost; C curtail Penalty cost for wind and solar curtailment; COP is the operating cost; C ST is the energy storage cost; C P2G is the cost of power-to-gas raw materials;

[0091]

[0092]

[0093] Among them, c e,t P is the real-time unit electricity price purchased by RIES from the power grid during period t; e,t is the power value purchased by RIES from the grid during period t; c g,t is the unit price of natural gas per kilowatt during period t; P g,t F is the power value of gas purchased by RIES from the natural gas network during period t; WT Penalty cost for wind curtailment; F PV is the penalty cost for abandoning light; wt , β pv are the penalty cost coefficients for wind and solar abandonment respectively; c i is the maintenance cost of the power of the i-th device; is the unit power-to-gas cost; S is the number of equipment; P i,t is the output of the i-th device in time period t; the device is a wind turbine, a photovoltaic unit, a gas storage device, a heat storage device, a cogeneration unit, a gas boiler, an electric boiler or a power-to-gas device; are the charging and discharging costs of the energy storage device during period t, respectively. When discharging is the power value of the electric energy storage in the state of discharge or charge; the energy storage device is a gas storage device or a heat storage device; c h is the unit energy storage cost of the thermal storage device; c ga G is the unit energy storage cost of the gas storage device; s,t , H s,t are the power values ​​of the gas storage and heat storage devices respectively; P ga is the power value of the power-to-gas device; ω is the conversion efficiency of the power-to-gas device; Δt is the optimal scheduling interval.

[0094] At the same time, the constraints satisfied by the operation layer model are as follows:

[0095] (1) System power balance constraints

[0096] The power grid formed by the aggregation of flexible resources needs to maintain a power balance between input, conversion and output.

[0097] (2) Natural gas network constraints

[0098] In order to ensure the safe and stable operation of the natural gas pipeline network, relevant constraints are imposed on the natural gas network. The natural gas transmission model is as follows: Figure 2 shown.

[0099] The natural gas network constraints can be obtained from the natural gas transmission model, which mainly include:

[0100] Node flow balance, pipeline flow limit, node pressure limit and booster station constraint, namely:

[0101]

[0102] in,

[0103]

[0104] in, and are the natural gas output flow of the gas source connected to the k node at time t and the natural gas input flow of the RIES natural gas; is the sum of the natural gas flow transmitted by all branches connected to node k at time t; is the total amount of natural gas transmitted by nodes n~j; p n,t 、p k,t 、p j,t are the air pressure values ​​of node n, node i and node j at time t respectively; M nj is the transmission coefficient of natural gas in pipeline n~j; p min,k and p max,k are the upper and lower limits of the voltage at node k respectively; is the limit of natural gas transmission flow rate of pipeline n~j; is the total amount of natural gas transmitted by nodes k~j; M is the natural gas flow consumed by the booster station; in is the constant coefficient of the booster station, usually 3% to 5% of the natural gas transmission volume; u and ζ1 are the upper and lower limits of the compression ratio of the compression station respectively; P is the natural gas power flow; H GV is the high calorific value of natural gas; L is the natural gas flow value.

[0105] (3) Output constraints of controllable equipment

[0106] The controllable device output constraints of the operation layer model are as follows:

[0107]

[0108] in, They are wind turbine output P wt,e,t The upper and lower limits of They are the photovoltaic unit output P pv,e,t The upper and lower limits of The output of the gas storage device G s,t The upper and lower limits of The output of the heat storage device is H s,t The upper and lower limits of The power output of the combined heat and power generation unit The upper and lower limits of Gas boiler output The upper and lower limits of The heating output of electric boilers The upper and lower limits of The power-to-gas device output P ga,t The upper and lower limits of .

[0109] (4) Gas storage device constraints

[0110] The gas storage equipment in the RIES system should ensure gas storage balance and gas storage capacity constraints, and cannot be charged and discharged at the same time in the same cycle. The constraints are:

[0111]

[0112] Among them, W s,t+1 , W s,t are the gas storage volume in the gas storage device at time t+1 and time t respectively; G store,t G is the gas storage capacity of the gas storage device at time t; release,t W is the amount of gas released from the gas storage device at time t; s,T W is the gas storage capacity in the gas storage device at time T; s,0 is the initial gas storage capacity of the gas storage device; η ch is the charging efficiency of the gas storage device; η dch is the deflation efficiency of the gas storage device; W s min , W s max They are the upper and lower limits of the inflation volume at time t respectively; are the 0-1 state variables for deflation and storage, respectively, and W s,T =W s,0 It means that the gas storage volume at the beginning of an optimization cycle is equal. is the upper limit of the gas release amount of the gas storage device at time t, It is the upper limit of the gas storage capacity of the gas storage device at time t.

[0113] (5) Demand response constraints

[0114] The electricity load before and after demand response is the same, as follows:

[0115]

[0116] Among them, P load,i, P load,i0 are the total loads before and after demand response, respectively.

[0117] (6) Load transfer constraints at any time

[0118] The load transfer amount in any time interval is less than or equal to the maximum load increase in the time interval, as follows:

[0119]

[0120] in, is the maximum load growth rate allowed during period t.

[0121] (7) Electricity expenditure constraints before and after demand response

[0122] The sum of the difference in electricity bills before and after demand response is greater than or equal to zero. Demand response means that the user side adjusts the electricity consumption behavior. Only after the implementation of demand response, the user's electricity bill will not increase, can it be guaranteed that the user is willing to participate in demand response.

[0123] The electricity expenditure constraints before and after demand response are as follows:

[0124]

[0125] Among them, c DR is the electricity price after demand response, and c0 is the electricity price before demand response.

[0126] Input the parameters required by the operation layer model, the real-time unit electricity price c purchased by RIES from the power grid during period t e,t , the unit price of natural gas per kilowatt during period t c g,t , wind abandonment penalty cost F WT , Abandonment penalty cost F PV , the maintenance cost c of the power of the i-th device i , Unit power-to-gas cost Charging cost of energy storage equipment during period t Discharge cost of energy storage equipment during period t Unit energy storage cost of thermal storage device c h 、Unit energy storage cost of gas storage device c ga , the conversion efficiency of power to gas ω, the lower limit value p of node k min,k , the lower limit value p of node k max,k , the limit of natural gas transmission flow rate of pipeline n~j Upper limit of the pressure ratio of the booster station ξ u , the lower limit of the boosting ratio of the boosting station ζ1, the upper and lower limits of the output of each unit, and the charging efficiency of the gas storage device η ch , gas storage device deflation efficiency η dch , the upper limit value of the inflation volume at time t Ws max , the lower limit of the inflation volume at time t W s min , the upper limit of the gas storage device’s gas release volume at time t The upper limit of the gas storage capacity of the gas storage device at time t The maximum load growth rate allowed during period t Get a determined operating layer model.

[0127] Steps S11-S12 construct a two-layer expansion planning model for the power grid. The upper planning layer model provides direction for the long-term planning of the power grid; the lower operation layer model can achieve optimal resource allocation and effective cost control in short-term operation. Through this two-layer structure, comprehensive optimization from long-term planning to short-term operation is achieved.

[0128] Step S13: construct a carbon trading cost model to calculate the carbon trading cost C cb .

[0129] For the RIES system, the carbon emissions generated during the entire natural gas production and transportation process are relatively small, so the carbon emissions generated by the gas grid and gas source are no longer considered. At the same time, the carbon emission costs on the load side and the grid side can be effectively reflected on the power supply side, so the carbon emission costs of this part will no longer be calculated separately.

[0130] The present invention mainly considers the carbon emission costs generated by coal-fired units, combined heat and power (CHP) units and gas boilers.

[0131] (1) Carbon emission cost model for gas boilers and CHP

[0132] 1) Carbon emission cost model of gas boiler

[0133] Carbon emissions from gas boilers, expressed as:

[0134]

[0135] in, is the carbon emission of the gas boiler at time t; gb is the carbon emission coefficient of the gas boiler; is the power output value of the gas boiler at time t.

[0136] Using the baseline method to allocate carbon quotas, the carbon quota for gas boilers is expressed as:

[0137]

[0138] in, is the carbon quota of the gas boiler at time t;h Carbon trading quota per unit of heating supply.

[0139] Carbon trading cost of gas boiler at time t It is expressed as:

[0140]

[0141] in, is the carbon trading price of the gas boiler at time t, and T is the optimal scheduling period.

[0142] 2) CHP carbon emission cost model for combined heat and power units

[0143] The calculation of carbon emission costs for cogeneration units is basically the same as that for gas boilers. The only difference is that the electrical energy needs to be converted into thermal energy for analysis.

[0144] The carbon emissions of CHP are:

[0145]

[0146] in, is the carbon emission of CHP at time t; chp is the CHP carbon emission coefficient; is the thermal power output of CHP at time t; ρ eh is the conversion factor of electrical energy into thermal energy; is the electric power output by CHP at time t.

[0147] Carbon quota of CHP at time t It is expressed as:

[0148]

[0149] in, is the carbon quota of CHP at time t.

[0150] The carbon trading cost of CHP at time t is:

[0151]

[0152] (2) Carbon emission cost model of coal-fired units

[0153] The carbon emissions of the coal-fired unit at time t are:

[0154]

[0155] in, is the carbon emission of the coal-fired unit at time t; is the carbon emission coefficient of the i-th coal-fired unit; N is the total number of coal-fired units; is the electric power output by the i-th coal-fired unit at time t.

[0156] The carbon emission quota of the coal-fired unit at time t is:

[0157]

[0158] in, is the carbon emission quota of the coal-fired unit at time t; μ e It is the carbon trading quota per unit of electricity generation.

[0159] When coal-fired units carbon emission quota Higher than the actual emissions of the unit When the excess quota is sold, the profit can be obtained. At this time, the carbon emission transaction cost of coal-fired units is:

[0160]

[0161] in, is the carbon trading price of the coal-fired unit at time t.

[0162] When coal-fired units carbon emission quota Lower than the actual emissions of the unit When the purchased carbon emission rights are higher than the excess carbon emissions, the excess part only needs to be purchased at the price of carbon trading. At this time, the carbon emission trading cost of the unit is:

[0163]

[0164] When the carbon emission quota of a coal-fired unit is lower than the actual emissions of the unit, and the purchased carbon rights are lower than the excess carbon emissions, in addition to paying the amount of the purchased carbon emission rights, a high fine corresponding to the excess amount must also be paid. At this time, the unit's carbon emission trading cost is:

[0165]

[0166]

[0167] in, is the carbon trading cost of the coal-fired unit at time t; K is the margin of purchased carbon emission rights; is the carbon emission rights purchased by the coal-fired unit at time t; is the unit price of the penalty for the excess quota at time t.

[0168] The carbon trading cost of the RIES system in a scheduling cycle is:

[0169]

[0170] Among them, C cbis the carbon emission cost of the RIES system within a scheduling cycle.

[0171] Gas boiler carbon emission coefficient λ required to enter the carbon trading cost model gb 、Carbon trading quota per unit of heating supplyδ h , the carbon trading price of gas boiler at time t CHP carbon emission coefficient λ chp , the conversion coefficient of electrical energy to thermal energy ρ eh , the carbon emission coefficient of the i-th coal-fired unit The total number of coal-fired units N, the power output of the i-th coal-fired unit at time t Carbon trading quota per unit of electricity generation μ e , the carbon trading price of coal-fired units at time t Carbon emission rights purchased by coal-fired units at time t Unit price of penalty for excess quota at time t Obtain a determined carbon trading cost model.

[0172] After inputting the parameters required by the carbon trading cost model, the carbon emission cost C of the RIES system in a scheduling cycle is obtained. cb Under the carbon trading market mechanism, the carbon emission cost calculation method of the main energy equipment in the RIES system is clarified, laying the foundation for the calculation and optimization of the operation layer model.

[0173] Step S14: Considering the uncertainty of renewable energy, multiple grid expansion planning scenarios are predicted based on the collected historical load, light, and wind speed data.

[0174] Based on the collected historical load, light and wind speed data, the predicted data of load, light and wind speed and their probability distribution are obtained by using the time series prediction method;

[0175] Based on the probability distribution, samples are extracted from the predicted load, light, and wind speed data using Monte Carlo simulation and combined together to form a plurality of predicted grid expansion planning scenarios including load, light, and wind speed.

[0176] Scenario construction considering uncertainty. Collect historical load, light, and wind speed data, use time series prediction methods to obtain load, light, and wind speed forecast data and their probability distribution, and use Monte Carlo simulation based on the obtained probability distribution to extract samples from the forecast load, light, and wind speed data and combine them together to form multiple forecasted grid expansion planning scenarios with specific load levels, light and wind speed conditions.

[0177] The generated multiple expansion planning scenarios are the inputs for the optimization calculation of the operation layer model. The optimal operation plans under different grid expansion planning scenarios will be different and will affect the evaluation of different line construction plans by the planning layer model.

[0178] Generate typical grid expansion planning scenarios based on seasonal changes, load, light, and wind speed factors. For example, select 3 to 5 typical scenarios for subsequent planning analysis. Ensure that these scenarios can cover grid operation conditions in different seasons, loads, light, and wind speeds to ensure the comprehensiveness and adaptability of planning results.

[0179] The role of step S1 is to construct a two-layer expansion planning optimization model for the RIES system, and consider the uncertainty of renewable energy. By predicting multiple grid expansion planning scenarios, it provides decision support for the long-term infrastructure construction and short-term operation optimization of the grid.

[0180] Step S2, specifically.

[0181] In the constructed two-layer expansion optimization model, the decision variables of the planning layer are the grid expansion lines, which is essentially an integer programming problem under discrete variables; while the decision variables of the operation layer are the output and start and stop of each device in the system, which is essentially a non-integer programming problem under continuous variables.

[0182] Based on the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm (IPSO) introduces weight coefficient for optimization, which overcomes the premature convergence problem of the traditional algorithm.

[0183] At the same time, the PCIP algorithm is used to iteratively solve the problem through prediction-correction steps, which can quickly converge to the optimal solution. When dealing with large-scale continuous variable optimization, it can more accurately consider the real-time and dynamic nature of system operation, and avoid solution deviations or long calculation times caused by algorithm inadaptability.

[0184] The nested solution algorithm structure of PCIP and IPSO closely integrates the upper and lower layer optimization processes, avoiding the suboptimal solution problem caused by the disconnection between the upper and lower layers or poor information transmission in the traditional hierarchical solution method. It can more comprehensively consider various factors in the distribution network expansion planning and operation process, and realize the coordinated optimization of the overall system.

[0185] Aiming at the complex mixed integer nonlinear programming problem posed by the two-level extended planning optimization model, a PCIP and IPSO nesting method is proposed to solve the above problem, in which PCIP is applied to the operation layer model and IPSO is applied to the planning layer model.

[0186] The IPSO population in the IPSO algorithm includes multiple individuals, each of which is a specific network line to be expanded; wherein the network line includes a power grid line and a gas grid line;

[0187] Each line is encoded based on integer coding, 0 means that the line is not built, 1 means that the line is built, and the construction probability of each line is preset, and the position and speed of the individual are randomly generated;

[0188] Repeat the process of randomly generating individuals N p times, get N p An initial individual.

[0189] Improved multi-objective particle swarm algorithm population initialization, selected IEEE11-node power grid and 7-node natural gas network coupled RIES for analysis, where nodes refer to the connection points of power supply and load, and there are connected and unconnected network lines (power grid lines, gas grid lines) to be expanded between nodes. This RIES system has 13 network lines to be expanded. Define the IPSO population as network lines, encode based on the integer encoding method, define 0 to indicate that the line is not built, 1 to indicate that the line is built, set the construction probability of each line (such as the construction probability is 0.5), randomly generate individuals (that is, randomly generate line construction plans for 13 lines) and their positions and speeds, and repeat the above process of randomly generating individuals N p times, get N p Initial individuals, this patent sets N p The number of iterations is 100, which means that 100 network line construction plans and 100 initial individuals are generated.

[0190] An individual is a specific network line construction plan, such as [1, 0, 0, 0, 0, 1, 1, 0, 0] (the first line is built, the second line is not built, the third line is not built, ...)

[0191] A population consists of many individuals [individual 1], [individual 2], etc.

[0192] The function of step S2 is to use the improved particle swarm optimization algorithm (IPSO) to initialize the population of the grid extension lines of the regional integrated energy system (RIES) and generate multiple network line construction plans to be extended as initial individuals.

[0193] Step S3, specifically.

[0194] The step S3 comprises:

[0195] The maximum number of iterations of the PCIP algorithm is set, and based on the predicted grid expansion planning scenario and the initial individual, the Gurobi solver is called to solve the operation layer model, so as to obtain the optimal operation plan of the operation layer under each grid expansion planning scenario, satisfying the constraint conditions of the operation layer model and minimizing the objective function F2 of the operation layer model;

[0196] The optimal operation plan of the operation layer is the output of each device in daily operation, the start and stop time of the equipment and the minimum daily operation cost of the REIS system.

[0197] Operation layer optimization calculation. Input the objective function of the operation layer model, constraint formulas (12)-(17), multiple grid expansion planning scenarios obtained in step S1, and 100 initial individuals (network line construction plans) obtained in step S2. Set the maximum number of PCIP iterations to 50, call the Gurobi solver to use the PCIP method to solve the RIES optimization scheduling plan, and obtain the optimal operation plan (the output of each device, the start and stop time of the equipment, and the RIES operation cost F2 within a scheduling cycle) under each grid expansion planning scenario, which satisfies the constraints (2)-(4) and minimizes the operation layer objective function F2.

[0198] In the operation layer Δt time, Δt is 1 hour, and the output is 0, which is the stop time of the stop state; the output is not 0, which is the start time of the operation state. In this way, the start and stop time of each device can be obtained.

[0199] The function of step S3 is to use the predicted grid expansion planning scenario and the initial network line construction plan, set the maximum number of iterations and call the Gurobi solver to use the PCIP algorithm to solve the operation layer model to obtain the optimal operation plan under the constraints, including the output of each device, start and stop time, and the minimum daily operation cost of the RIES system.

[0200] Step S4, specifically.

[0201] The step S4 comprises:

[0202] Return the optimal operation plan of the operation layer to the planning layer;

[0203] Based on the optimal operation plan of the operation layer, the Gurobi solver is called for iterative calculation;

[0204] Calculate the probability of each individual in the population being selected according to the roulette method Calculate the cumulative probability for each individual Generate a pseudo-random number r in the interval [0,1]. If r≤q1, select the first individual. If q i-1 <r≤q i , then select the i-th individual and continue the random selection process until a sufficient number of individuals are selected for the next generation;

[0205] After each iteration, the fitness value of each individual is calculated;

[0206] Compare the current fitness value of each individual with the optimal fitness value recorded in previous iterations;

[0207] If the current fitness value is better than the optimal fitness value in previous iterations, the position and speed of the current individual are updated to the position and speed corresponding to the optimal fitness value; where better means less than;

[0208] Update the individual's velocity and position as follows:

[0209]

[0210] Among them, ε is the preset inertia weight; are the speeds of the i-th individual in the k-th and k+1-th iterations respectively; c1 and c2 are the preset learning factors, gbest is the optimal position of the i-th individual in the k-th iteration; k is the optimal position of the population at the kth iteration; r1 and r2 are random numbers; is the position of the i-th individual in the k-th and k+1-th iterations;

[0211] Repeat steps S3 to S4 until the maximum number of iterations of the preset PCIP algorithm is reached, and finally determine the optimal grid expansion planning scheme;

[0212] The optimal grid expansion planning scheme includes the output of each device in the RIES system, the equipment start and stop time, and the line construction plan.

[0213] The optimal operation plan of the operation layer obtained in step S3 is returned to the planning layer model. The planning layer model uses the Gurobi solver to calculate the annual investment cost C of each individual power grid expansion when constraints 2)-(4) are met based on the optimal operation plan returned by the operation layer. con , and obtain the annual comprehensive cost of the power grid F1 and the renewable energy consumption rate η R .

[0214] Compare the renewable energy consumption rate η of each individual in the population R and the annual comprehensive cost of the power grid F1, such as: individual A's F1 is lower than individual B, and η R Not lower than individual B, then individual A is a non-inferior solution relative to individual B.

[0215] The best non-inferior solution is taken as the individual optimal position, and then the best non-inferior solution is selected again from all individual optimal positions as the optimal position of the population (i.e. the optimal line construction plan).

[0216] Introduce the weight coefficient ω to calculate the fitness value of the individual. For example, the weight coefficient assigned to the objective function F1 is set to ω1 = 0.7, and the weight coefficient assigned to the objective function η R The weight coefficient is ω2=0.3, and ω1+ω2=1 is guaranteed, then the fitness value of the i-th individual after weighted summation.

[0217] The fitness value of each individual is as follows:

[0218] Fitness=γ1F1+γ2η R Formula (33)

[0219] Among them, γ1 and γ2 are the renewable energy consumption rate η of the objective function respectively. R The weight coefficient of the total cost F1 of the power grid planning scheme is a preset value.

[0220] Set the maximum number of iterations of the improved particle swarm algorithm, such as the maximum number of iterations is 50.

[0221] If the current number of iterations is greater than 50, there is no need to continue optimization, and the optimal grid expansion planning scheme is finally obtained; if the number of iterations is less than 50, the convergence condition is not met and further updating and iteration is required.

[0222] Compare the current fitness value of each individual with the optimal fitness value in previous iterations. If the current one is better, update it to the position of the current individual.

[0223] According to the roulette method, calculate the probability of each individual in the population being selected And construct a cumulative probability distribution interval, that is, the cumulative probability of the i-th individual Finally, a pseudo-random number r is randomly generated in [0,1].

[0224] If r≤q1, select the first individual; if q i-1 <r≤q i , then select the i-th individual and determine the optimal position of the population. At the same time, update the speed and position of the individual according to formula (32).

[0225] After the position and speed are updated, the next generation population is generated, and the process goes to step S3 to perform optimization calculations on the operating layer again, and steps S3-S4 are repeated, and the iteration cycle is continued until the maximum number of iterations is met, and finally the optimal grid expansion planning scheme is obtained.

[0226] In summary, a method for optimizing power grid expansion planning taking into account flexibility resource aggregation according to an embodiment of the present invention has the following beneficial effects:

[0227] 1. The present invention takes into account the uncertainty of renewable energy and predicts multiple grid expansion planning scenarios, effectively reducing the phenomenon of wind and solar power abandonment, increasing the absorption rate of wind power and photovoltaic renewable energy, and improving the utilization rate of renewable energy:

[0228] 2. The present invention divides the two-layer extended planning optimization model into a planning layer and an operation layer. The planning layer optimizes the grid structure with the goal of minimizing the total cost of the planning scheme and maximizing the renewable energy consumption rate. The operation layer minimizes the RIES daily operation cost and considers multiple constraints, and uses PCIP and PSO nested algorithms to solve mixed integer nonlinear programming problems. The present invention improves the reliability of grid operation, promotes the consumption of renewable energy and energy utilization, and contributes to its low-carbon transformation. It improves the rapid decision-making ability of grid planning and operation optimization, and meets the needs of real-time grid planning;

[0229] 3. The present invention constructs a two-layer extended planning optimization model, takes the maximum renewable energy consumption rate and the minimum total cost of the power grid planning scheme as the joint goals at the planning layer, optimizes the power grid structure, and improves the reliability and economy of power grid operation;

[0230] 4. The present invention considers the coordinated optimization of various energy forms such as electricity and natural gas, processes long-term and short-term goals through a two-layer model, realizes efficient coordinated optimization of the RIES integrated energy system, and improves energy utilization efficiency;

[0231] 5. Under the carbon trading market mechanism, the carbon emission costs of coal-fired units, cogeneration units and gas boilers should be taken into consideration, which will encourage the energy system to consider carbon emission costs in planning and operation, and help achieve low-carbon development goals.

[0232] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing power grid expansion planning taking into account the aggregation of flexibility resources, characterized in that: The steps include: Step S1: construct a two-layer expansion planning optimization model of the RIES system based on the output of wind turbines, photovoltaic units, gas storage devices, heat storage devices, cogeneration units, gas boilers, electric boilers and power-to-gas devices in the RIES system, including a planning layer model and an operation layer model; consider the uncertainty of renewable energy, and predict multiple grid expansion planning scenarios based on the collected historical load, light, and wind speed data; Step S2: using the improved IPSO algorithm to initialize the population of the RIES system, and generating multiple network line construction plans to be expanded as initial individuals; Step S3, based on the initial individuals and the predicted grid expansion planning scenario, the PCIP algorithm is used to perform optimization calculation of the operation layer model to obtain the optimal operation plan of the operation layer at the minimum operation cost within the operation scheduling cycle; Step S4, return the optimal operation plan of the operation layer to the planning layer model to obtain the fitness value of each individual; perform IPSO algorithm iteration to obtain the optimal fitness value; compare the current fitness value of each individual with the optimal fitness value of previous iterations, if it is better, update the position and speed of the current individual, generate the next generation population and go to step S3 until the maximum number of iterations is reached to obtain the optimal power grid expansion planning plan.

2. The method according to claim 1, characterized in that: The planning layer model takes the maximum renewable energy consumption rate and the minimum total cost of the power grid planning scheme as the joint objectives, including line flow constraints, grid node voltage constraints and line transmission flow constraints; The operation layer model aims to minimize the daily operation cost of the RIES system, including system power balance constraints, natural gas network constraints, controllable equipment output constraints, gas storage device constraints, demand response constraints, load transfer constraints at any time period, and electricity expenditure constraints before and after demand response.

3. The method according to claim 2, characterized in that: The objective function of the planning layer model is as follows: Among them, η R is the renewable energy consumption rate; P wt,e,t , P pv,e,t They are the wind turbine and photovoltaic unit outputs actually absorbed by RIES during period t; are the predicted output values ​​of wind turbines and photovoltaic units in period t; F1 is the total cost of the power grid planning scheme; C con is the annual construction cost of power grid expansion; F 2,d is the operating cost of the RIES system on day d; x fl,i Is line i constructed? Its value is 0, indicating that the line is not constructed; its value is 1, indicating that the line is constructed; S fl,i is the construction cost of grid line i; d is the discount rate; y is the useful life; n is the number of grid expansion planning scenarios; and Z is the planning and scheduling cycle.

4. The method according to claim 2, characterized in that: The objective function of the operational layer model is as follows: minF2=C E +C G +C cb +C curtail +C OP +C ST +C P2G Among them, C E is the electricity purchase cost; C G is the gas purchase cost; C cb is the carbon trading cost; C curtail Penalty cost for wind and solar curtailment; C OP is the operating cost; C ST is the energy storage cost; C P2G is the cost of power-to-gas raw materials; Among them, c e,t P is the real-time unit electricity price purchased by RIES from the power grid during period t; e,t is the power value purchased by RIES from the power grid during period t; Δt is the optimal scheduling interval; c g,t is the unit price of natural gas per kilowatt during period t; P g,t F is the power value of gas purchased by RIES from the natural gas network during period t; WT Penalty cost for wind curtailment; F PV is the penalty cost for abandoning light; wt , β pv are the penalty cost coefficients for wind and solar abandonment respectively; c i is the maintenance cost of the power of the i-th device; C CO2 is the unit power-to-gas cost; S is the number of equipment; P i,t is the output of the i-th device in time period t; the device is a wind turbine, a photovoltaic unit, a gas storage device, a heat storage device, a cogeneration unit, a gas boiler, an electric boiler or a power-to-gas device; are the charging and discharging costs of the energy storage device during period t. When discharging is the power value of the energy storage device in the discharge or charging state; the energy storage device is a gas storage device or a heat storage device; c h is the unit energy storage cost of the thermal storage device; c ga G is the unit energy storage cost of the gas storage device; s,t , H s,t are the power values ​​of the gas storage and heat storage devices respectively; P ga is the power value of the power-to-gas device; ω is the conversion efficiency of the power-to-gas device.

5. The method according to claim 1, characterized in that: The IPSO population in the IPSO algorithm includes multiple individuals, each of which is a specific network line to be expanded; wherein the network line includes a power grid line and a gas grid line; Each line is encoded based on integer coding, 0 means that the line is not built, 1 means that the line is built, and the construction probability of each line is preset, and the position and speed of the individual are randomly generated; Repeat the process of randomly generating individuals N p times, get N p An initial individual.

6. The method according to claim 5, characterized in that: The step S3 comprises: The maximum number of iterations of the PCIP algorithm is set, and based on the predicted grid expansion planning scenario and the initial individual, the Gurobi solver is called to solve the operation layer model, so as to obtain the optimal operation plan of the operation layer under each grid expansion planning scenario, satisfying the constraint conditions of the operation layer model and minimizing the objective function F2 of the operation layer model; The optimal operation plan of the operation layer is the output of each device in daily operation, the start and stop time of the equipment and the minimum daily operation cost of the REIS system.

7. The method according to claim 1, characterized in that: The step S4 comprises: Return the optimal operation plan of the operation layer to the planning layer; Based on the optimal operation plan of the operation layer, the Gurobi solver is called for iterative calculation; Calculate the probability of each individual in the population being selected according to the roulette method Calculate the cumulative probability for each individual Generate a pseudo-random number r in the interval [0,1]. If r≤q1, select the first individual. If q i-1 <r≤q i , then select the i-th individual and continue the random selection process until a sufficient number of individuals are selected for the next generation; After each iteration, the fitness value of each individual is calculated; Compare the current fitness value of each individual with the optimal fitness value recorded in previous iterations; If the current fitness value is better than the optimal fitness value in previous iterations, the position and speed of the current individual are updated to the position and speed corresponding to the optimal fitness value; where better means less than; Update the individual's velocity and position as follows: Among them, ε is the preset inertia weight; are the speeds of the i-th individual in the k-th and k+1-th iterations respectively; c1 and c2 are the preset learning factors, gbest is the optimal position of the i-th individual in the k-th iteration; k is the optimal position of the population at the kth iteration; r1 and r2 are random numbers; is the position of the i-th individual in the k-th and k+1-th iterations; Repeat steps S3 to S4 until the maximum number of iterations of the preset PCIP algorithm is reached, and finally determine the optimal grid expansion planning scheme; The optimal grid expansion planning scheme includes the output of each device in the RIES system, the equipment start and stop time, and the line construction plan.

8. The method according to claim 7, characterized in that: The fitness value of each individual is as follows: Fitness=γ1F1+γ2η R Among them, γ1 and γ2 are the renewable energy consumption rate η of the objective function respectively. R The weight coefficient of the total cost F1 of the power grid planning scheme is a preset value.

9. The method according to claim 1, characterized in that: The controllable device output constraints of the operation layer model are as follows: in, They are wind turbine output P wt,e,t The upper and lower limits of They are the photovoltaic unit output P pv,e,t The upper and lower limits of The output of the gas storage device G s,t The upper and lower limits of The output of the heat storage device is H s,t The upper and lower limits of The power output of the combined heat and power generation unit The upper and lower limits of Gas boiler output The upper and lower limits of The heating output of electric boilers The upper and lower limits of The power-to-gas device output P ga,t The upper and lower limits of .

10. The method according to any one of claims 1 to 9, characterized in that: Based on the collected historical load, light and wind speed data, the predicted data of load, light and wind speed and their probability distribution are obtained by using the time series prediction method; Based on the probability distribution, samples are extracted from the predicted load, light, and wind speed data using Monte Carlo simulation and combined together to form a plurality of predicted grid expansion planning scenarios including load, light, and wind speed.

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