An investment optimization decision-making support method for power generation, grid, load and storage integrated projects

Abstract

The present invention belongs to the technical field of power system analysis and optimization decision-making, and specifically relates to an investment optimization decision-making assistance method for source-grid-load-storage integration projects, including the following steps: S1: using a data processing and analysis center to collect and process power and carbon market data in real time; S2: constructing a comprehensive evaluation model of an economic model and a network topology framework, and comprehensively evaluating and analyzing the economic feasibility of source-grid-load-storage integration projects under the operation mode; S3: constructing an investment optimization decision-making assistance method, proposing and applying an improved index optimization algorithm to give the optimal value of an adjustable indicator of investment capacity, optimizing the investment plan of the source-grid-load-storage integration project, and determining the final investment amount of the source-grid-load-storage integration project. This method focuses on optimizing the decision-making process, ensuring the scientificity and accuracy of investment decisions, thereby improving investment benefits and the economic sustainability of the project.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system analysis and optimization decision-making, and specifically relates to an investment optimization auxiliary decision-making method for a source-grid-load-storage integrated project. Background Art

[0002] With the transformation of the global energy structure and the rapid development of the power industry, new power systems are facing unprecedented challenges and opportunities. In particular, the large-scale access to renewable energy, the increasing openness of the power market, and the changes in power consumption patterns require that the power system must be not only efficient and reliable, but also flexible and sustainable. In this context, the source-grid-load-storage integrated project, as a new type of comprehensive energy utilization solution, has become an important way to achieve power system optimization and energy structure adjustment with its ability to effectively integrate multiple links such as power generation, transmission, distribution and energy storage.

[0003] However, despite the significant theoretical and technical advantages of integrated power generation, grid-load and storage projects, their investment and operational decision-making process is still full of challenges. These challenges mainly come from the complexity of the project itself, the uncertainty of the electricity and related markets, and the impact of environmental policies. Current decision-making methods are often based on static analysis or simplified models, which make it difficult to fully capture the impact of factors such as market dynamics, policy adjustments, and technological advances, resulting in investment decisions that may not be optimal or even have large deviations.

[0004] In particular, for information processing in the power market and carbon market, existing technologies often lack effective data integration and intelligent analysis tools. Factors such as price fluctuations in the power market, the cost of carbon emission rights, and the scale of access to renewable energy have a direct impact on the economic benefits of the project. In existing decision-making methods, the comprehensive analysis and forward-looking evaluation of these factors are insufficient, resulting in a lack of sufficient information support for investment decisions, which in turn affects the long-term sustainability and profitability of the project.

[0005] In addition, with the development of information technology and artificial intelligence technology, how to effectively use these technical means to improve the intelligence level and accuracy of decision-making has become a hot topic in current research. However, applying these advanced technologies to the investment optimization decision-making of source-grid-load-storage integrated projects requires solving a large number of technical details, including data collection and processing, model construction and optimization, system development and application, etc. These are all problems that are difficult to fully solve with existing technologies.

[0006] Therefore, in the face of new demands and challenges for the power system, it is urgent to develop a new type of investment optimization decision-making method and support system for integrated source-grid-load-storage projects. This method and system should be able to comprehensively consider the dynamic information of the power market and the carbon market, and use advanced data analysis and intelligent technology to provide comprehensive, accurate and forward-looking support for project investment and operation decisions, thereby helping to achieve efficient, reliable and sustainable development of the power system. Summary of the invention

[0007] In response to the above-mentioned technical problems, the present invention provides an investment optimization decision-making support method for integrated power generation, grid, load and storage projects, which optimizes the investment decision-making process of integrated power generation, grid, load and storage projects, clearly improves the scientificity and accuracy of investment decisions, enables investors to more effectively respond to market and technological changes, and enhances the economic and technical feasibility of the project.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] An investment optimization auxiliary decision-making method for a source-grid-load-storage integrated project includes the following steps:

[0010] S1. Construct a method for collecting electricity and carbon data in the investment optimization of source-grid-load-storage integrated projects, and collect data related to the electricity market and system operation status data in real time;

[0011] S2. An optimal power flow model for optimizing the configuration of source, grid, load and storage based on a sensitivity analysis model. The voltage and power loss of each node are calculated through the optimal power flow model. At the same time, sensitivity analysis is used to identify key nodes in the system that are sensitive to disturbances, and the configuration of power generation and energy storage equipment is optimized. Secondly, based on the specific configuration of power generation and energy storage capacity obtained above and the real-time data calculated during system operation, the demand for adjusting the online power or purchased power of the integrated source, grid, load and storage system is evaluated. Based on the real-time power market data obtained in step S1, an economic model is established to comprehensively evaluate and analyze the economic feasibility of the integrated source, grid, load and storage project under the operation mode;

[0012] S3. Construct an investment optimization decision-making support method, propose and apply an improved index optimization algorithm to give the optimal value of the adjustable indicator of investment capacity, optimize the investment plan of the integrated source-grid-load-storage project, and determine the final investment amount of the integrated source-grid-load-storage project.

[0013] The electricity and carbon data collection method in the S1 source-grid-load-storage integration project investment optimization is as follows:

[0014] S11. Deploy sensors and power electronic controllers in distributed power generation equipment, energy storage equipment, and power grid topology. The sensors are responsible for monitoring and transmitting operating data information of distributed power generation equipment, energy storage equipment, and power system, including: the active power P output of distributed power generation in network node i Gi and reactive power Q Gi , the active power P of the load in node i Di and the reactive power Q of the load Di , the voltage amplitude of each node in the system is V i With phase angle δ i , the current I of the i-th branch Li , the total resistance R of the i-th branch Li , the total number of branches in the system N L , the number of distributed generation units incorporated into the system N G The second parameter represents the total number of nodes N in the system n ,The power electronic controller controls or changes the operating state of the equipment through remote communication;

[0015] S12. Use the intelligent cloud dispatching platform to obtain real-time data from the website, collect provincial power market, carbon market information data and specific data requirements published by government websites; specifically including: photovoltaic investment unit price p PV , wind power investment unit price p WT , energy storage investment unit price p ES , line unit price p L , substation unit price p y , actual electricity price p, annual photovoltaic utilization hours T PV , wind power annual utilization hours T WT , the compensation unit price of inverter active power and reactive power p P and p Q , enterprise debt-to-asset ratio η oc , the total annual liabilities of the enterprise L ip , income tax rate α, total annual assets of the enterprise Z ip .

[0016] The distributed photovoltaic power generation, distributed wind power equipment, energy storage equipment and electric vehicle charging load in S11 are connected to the grid through power electronic equipment. The power electronic controller is included in the micro control unit under the energy router. One end of the power electronic controller is connected to some nodes of the grid topology structure, and the other end is connected to the equipment. The distributed power generation and energy storage are connected to the grid through AC / DC power electronic conversion devices; the energy router, one end is the interface for controlling other equipment in the grid. The energy router sends operation instructions to the device through this port, and can receive data sent by the device to the energy router at the same time. The other end uploads the data in the grid system to the intelligent cloud scheduling platform.

[0017] The operating state of distributed generation in the grid topology is regulated by a droop control model, and the specific formula is as follows:

[0018]

[0019] Where: P G,i , Q G,i are respectively the active and reactive power output by the inverter in the distributed generator; δ is the output voltage phase angle when the inverter is set to operate stably; E is the amplitude of the inverter output voltage; V is the node voltage amplitude of the distributed generation grid-connected node; Z is the self-impedance of the distributed generation;

[0020] The active power output of the inverter is proportional to the phase of the voltage, and the reactive power output is proportional to the voltage amplitude. The output voltage can be regulated by calculating the active and reactive power output of the inverter in each distributed power generation.

[0021] The inverter provides support for the safe and stable operation of the power grid and provides paid ancillary service benefits in the following ways:

[0022] Get the three-phase voltage V on the inverter side abc , three-phase voltage U on the grid side abc And the three-phase current I abc ; Three-phase voltage V on the inverter side abc , three-phase voltage U on the grid side abc And the three-phase current I abc Perform coordinate conversion; input the three-phase voltage on the inverter side and the three-phase voltage on the grid side after coordinate conversion into a phase-locked loop module, the phase-locked loop module is composed of a PI controller and an integral operation module, the input voltage is phase-adjusted by the PI controller, and the adjusted voltage is input into the integral operation module to obtain the phase angle on the inverter side and the phase angle on the grid side; calculate the power angle difference according to the phase angle on the inverter side and the phase angle on the grid side; calculate the phase compensation angle of the power angle using a PID controller according to the power angle difference; compensate the power angle according to the phase compensation angle, output an inverter control signal according to the compensated signal, and control the inverter to restore the power system fault;

[0023] The output equation of the PI controller is:

[0024]

[0025] Among them: PI v is the output of the inverter side PI controller; K pv is the proportional gain of the voltage loop PI controller; V q is the q-axis component of the inverter side voltage; K iv is the integral gain; Uq is the q-axis component of the grid-side voltage; K p is the scale parameter; K i is the integral parameter; PI u is the output of the grid-side PI controller;

[0026] The formula for calculating the phase compensation angle of the power angle using the PID controller is:

[0027] δ=K θp (θ ref -θ v +θ u )+K θi ∫(θ ref -θ v +θ u )dt

[0028] Where: δ is the output voltage phase angle when the inverter is set to operate stably; K θp is the phase compensation angle proportional gain; θ ref is the reference phase angle; θ v is the phase angle on the inverter side; θ u is the phase angle on the grid side; K θi is the phase compensation angle integral gain.

[0029] The method of the optimal power flow model for source-grid-load-storage integrated optimization configuration based on the sensitivity analysis model of S2 is:

[0030] S2a, collect the power generation and load data obtained in S1 and build a calculation model of the source-grid-load-storage integrated system based on these data information, then perform power flow calculation, and calculate the apparent power and power loss of each node by the following formula:

[0031]

[0032] Where: S m Represents the apparent power of each node; P Gi , Q Gi are respectively the active power and reactive power output by distributed generation in network node i; P Di , Q Di are the active power and reactive power of the load in node i respectively; P loss,Li represents the active power loss of the i-th branch; I Li represents the current flowing through the i-th branch; R Li represents the total resistance of the i-th branch; N n It represents the total number of nodes in the system; m represents the mth node in the system;

[0033] The calculation of the above formula must also satisfy the following equality and inequality constraints:

[0034] Equality constraints:

[0035]

[0036] Where: P S and Q S It represents the active and reactive power supplied by the upper transformer to the pilot project of source-grid-load-storage integration; P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed; P D,j , Q D,j It represents the active and reactive power load required by the jth node; P loss,Li represents the active power loss of the i-th branch; Q loss,Li represents the reactive power loss of the i-th branch; N L It represents the number of distributed generation units incorporated into the system; N G Represents the total number of nodes in the system; N n It represents the total number of branches in the system;

[0037] Inequality constraints:

[0038] The configuration capacity of distributed generation must be controlled within a reasonable range:

[0039]

[0040] P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed; P D,j , Q D,j represents the active and reactive power loads required by the jth node; k G N is the configuration ratio coefficient of distributed generation capacity in the source-grid-load-storage integration project, that is, the ratio of the upper limit of distributed generation capacity that can be installed in the system to the total load in the system; G Represents the total number of nodes in the system; N n It represents the total number of branches in the system;

[0041] P G,k , Q G,k The upper and lower limits shown below must be met:

[0042] P G,k(min) ≤P G,k ≤P G,k(max)

[0043] Q G,k(min) ≤Q G,k ≤Q G,k(max)

[0044] Where: P G,k(min) , P G,k(max) represents the lower and upper limits of the active power of the kth distributed generation deployed; Q G,k(min) , Q G,k(max) represents the lower and upper limits of the active power of the kth distributed generation deployed;

[0045] Node voltage V i The following formula must be satisfied:

[0046] V imin ≤V i ≤V imax

[0047] Where: V i(min) 、V i(max) Indicates the lower and upper limits of the voltage amplitude of each node;

[0048] The phase angle constraint must satisfy the following equation:

[0049] |δ i -δ j |≤|δ i -δ j | max

[0050] Where: i , δ j represents the voltage phase angle of nodes i and j; the voltage phase angle corresponding to the distributed generation access node is the voltage phase angle after inverter compensation;

[0051] S2b. Obtain specific configuration plan and system operation status.

[0052] The method for obtaining the specific configuration scheme and system operation status in S22 is:

[0053] S2b1. Introduce a DG or SC reactive power compensator unit with a very small capacity at a certain node of the source-grid-load-storage integrated system to evaluate its impact on the performance of the entire system;

[0054]

[0055] Where: P loss It represents the cumulative value of active power loss of all branches in the system; P loss,Li represents the active power loss of the i-th branch; N L Indicates the total number of branches; SenDG i 、SenSC i They represent the sensitivity values ​​of the distributed generation and reactive power compensation device connected to the i-th node respectively; P loss(i)represents the active power loss of the system after DG or SC is connected to the i-th node; ΔP DG Indicates the active compensation value of DG; ΔQ SC Indicates the reactive power compensation value of SC;

[0056] S2b2, build a power flow calculation optimization model and determine the optimization objectives, such as minimizing energy costs and maximizing energy efficiency objective functions;

[0057] S2b3, select intelligent optimization algorithm to perform optimal power flow calculation;

[0058] S2b4, obtain the specific configuration plan and system operation status, the specific results include: the capacity of photovoltaic in the system P PV 、Capacity of wind power P WT 、Energy storage capacity P ES , number of substations Y, line length L.

[0059] The evaluation process of the economic model in S2 is:

[0060] S2A. Calculate the cost of the integrated power generation, grid, load and storage project:

[0061] S2A1, total electrical equipment investment cost C TEL : Mainly the investment and construction costs of distributed energy, energy transmission and distribution networks, energy storage, control and protection equipment

[0062] C TEL =p PV *P PV +p WT *P WT +4*p ES *P ES +p L *L+p y *Y

[0063] Where: C TEL is the total electrical equipment investment cost; p PV is the unit price of photovoltaic investment; P PV is the photovoltaic capacity; p WT is the unit price of wind power investment; P WT is the wind power capacity; 4 is the energy storage utilization hours stipulated by the state; p ES is the unit price of energy storage investment; P ES is the energy storage capacity; p L is the line unit price; L is the line length; p y is the unit price of substation; Y is the number of substations;

[0064] S2A2、Project Cost C E: Mainly construction costs, equipment and tools purchase costs and other construction costs. The construction costs include labor costs, material costs, construction machinery usage fees, and enterprise management fees;

[0065] C E =C CI +C TEL +C AE

[0066] Where: C E C is the engineering cost; CI It is the construction engineering fee, which is determined according to the actual situation of the project; C TEL is the total electrical equipment investment cost; C AE Other construction costs for the project, generally 2.5% of the sum of the construction costs and the total electrical equipment investment costs, that is, C AE Equal to C CI , C TEL 2.5% of the sum;

[0067] S2A3, preparation cost C C : Usually refers to a part of the funds reserved in the project budget to deal with unforeseen events or additional expenses that may arise during the implementation of the project. It is prepared in advance to prevent cost overruns caused by unexpected circumstances. Generally, a project budget needs to be prepared, depending on the actual situation of the project.

[0068] C C =k C,E C E

[0069] Where: C C For preparation costs; C E is the engineering cost; k C,E It is a customary value to take the ratio of the reserve fund to the total construction cost in such projects;

[0070] S2A4, original value of fixed assets C OFA :Usually refers to the total initial cost paid by an enterprise to purchase or construct fixed assets; fixed assets usually refer to tangible assets that an enterprise uses to produce goods, provide services or carry out daily business activities and are expected to use for a period of time, such as land, buildings, machinery and equipment, and vehicles;

[0071] C OFA =C E +C C

[0072] Where: C OFA is the original value of fixed assets; C E C is the engineering cost; C To prepare expenses;

[0073] S2A5, annual fixed cost C FC (k) Costs that an enterprise must pay to maintain operations during the year and that do not vary with the volume of production or services provided. They are usually not directly related to the production process and must be paid even if production activities are temporarily stopped.

[0074] C FC (k) = C MO +C MOT +C AI +C AIT

[0075] Where: C FC (k) is the annual fixed cost; C MO is the annual maintenance fee, usually C OFA 0.2%; C MOTC Annual maintenance fee tax, usually C MO 13%; C AI The annual insurance premium is usually C OFA 0.15%; C AIT The annual insurance premium tax is usually C AI 6%;

[0076] S2A6, annual variable cost C VC (k) Costs that vary in proportion to the volume of production, services or business activities, are closely related to the direct production activities of the enterprise and are usually directly related to the raw materials used, labor and energy consumption.

[0077] C VC (k) = C AM +C AMT

[0078] Where: C VC (k) is the annual variable cost; C AM is the annual material cost, which is generally 5 yuan / kW commonly used in the industry; C AMT It is the annual material cost tax, which is generally 17% of AMC;

[0079] S2A7, Annual operating cost C OC (k) refers to all the expenses that a business must pay in order to maintain normal operations within a year, including fixed costs and variable costs

[0080] C OC (k) = C FC (k)+C VC (k)

[0081] Where: C OC (k) is the annual operating cost; C FC(k) is the annual fixed cost; C VC (k) is the annual variable cost;

[0082] S2A8, total investment cost C I

[0083] C I =C OFA +C W

[0084] Where: C I is the total investment cost; C OFA is the original value of fixed assets; C W Working capital, generally 30 yuan / kW, which is commonly used in the industry;

[0085] S2B, calculate the benefits of the source-grid-load-storage integration project:

[0086] S2B1, annual electricity sales revenue R Q (k):

[0087] R Q (k) = p*(P PV *T PV +P WT *T WT )*(0.986-0.006*k),k=1,2,…,n

[0088] Where: R Q (k) is the annual power generation in the kth year; p is the actual electricity price; P PV is the photovoltaic capacity; T PV P is the annual utilization hours of photovoltaic power; WT is the wind power capacity; T WT is the annual utilization hours of wind power; (0.986-0.006*k) is the attenuation rate of photovoltaic modules in k years;

[0089] S2B2, ancillary service revenue provided by the inverter R FZ :

[0090] R FZ =p P P G,k +p Q Q G,k

[0091] Where: R FZ Ancillary service income provided to the inverter; P and p Q are the compensation unit prices of active power and reactive power of distributed generation at the inverter access point; P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed;

[0092] S2C, based on the various cost and benefit data obtained from the above calculations, the net present value (NPV), internal rate of return (IRR) and payback period (SPT) of the project are calculated through net cash flow to comprehensively evaluate and analyze the economic feasibility of the source-grid-load-storage integration project under the operation mode;

[0093] Total investment cost C I , Annual operating cost C OC (k), annual electricity sales revenue R Q (k) Ancillary service revenue R FZ Calculate the cash outflow C of the power grid-load storage integration project OUT (k) Cash inflow C IN (k), net cash flow NCF(k), and then calculate the net present value NPV, internal rate of return IRR and payback period SPT:

[0094] S2C1, cash outflow C OUT (k)

[0095]

[0096] Where: C OUT (k) is the annual cash outflow in the kth year; C I is the total investment cost; C OC (k) is the annual operating cost;

[0097] S2C2, cash inflow C IN (k)

[0098]

[0099] Where: C IN (k) is the annual cash inflow in the kth year; R Q (k) is the annual electricity sales revenue in the kth year; R FZ Ancillary service revenue provided to inverters;

[0100] S2C3, Net Cash Flow NCF(k)

[0101] NCF(k)=C IN (k)-C OUT (k)

[0102] Where: NCF(k) is the annual net cash flow in the kth year; C IN (k) is cash inflow; C OUT (k) cash outflow;

[0103] S2C4, cumulative net cash flow ANCF (i)

[0104]

[0105] Where: ANCF(i) is the annual net cash flow in the i-th year; NCF(k) is the annual net cash flow in the k-th year;

[0106] S2C5. Net Present Value (NPV): refers to the balance after the net cash flow after the project is put into use is converted into present value according to the discount rate set by the enterprise, and then the initial investment is deducted. In other words, it is the balance obtained by deducting the total investment from the algebraic sum of the present value of the project's net cash flow each year.

[0107]

[0108] Where: NPV is the net present value; i c NCF is the net cash flow in the tth year; t is the year; n is the project period;

[0109] S2C6, Internal Rate of Return IRR: Internal rate of return is the discount rate when the net present value is zero, reflecting the efficiency of project investment utilization;

[0110]

[0111] Where: IRR is the internal rate of return; i is the year before the cumulative discount value is first positive; i+1 is the year before the cumulative discount value is first positive; DR(i) is the discount rate in the year before the cumulative discount value is first positive; DR(i+1) is the discount rate in the year before the cumulative discount value is first positive; NPV(i) is the net present value in the year before the cumulative discount value is first positive; NPV(i+1) is the net present value in the year before the cumulative discount value is first positive;

[0112] S2C7. Payback period SPT: The time it takes for the total income to reach the total investment after the investment project is put into normal use, that is, the time required for the accumulated economic benefits to be equal to the original capital investment. When ANCF(i)≤0 and ANCF(i+1)>0,

[0113] SPT=i-ANCF(i)÷NCF(i+1),i=1,2,…,n

[0114] In the formula: SPT is the payback period; i is the year when the last cumulative net cash flow is negative; ANCF is the cumulative net cash flow up to the i-th year; NCF is the net cash flow in the i-th year.

[0115] The investment optimization decision-making assistance method in S3 is:

[0116] S31. Data input: input the net present value (NPV), internal rate of return (IRR), and payback period (PP) calculated above as basic investment demand data;

[0117] S32. Based on the operation and financial data of the power grid enterprise, the investment demand capacity is coordinated and judged. When the investment capacity of the enterprise is greater than the investment demand, it is considered that the investment demand and investment capacity are coordinated, and the analysis ends. Otherwise, it is considered that the investment demand and investment capacity are not coordinated, and the optimization stage is entered;

[0118] S33, establish the objective function F of the minimum difference between investment capacity and investment demand, and apply the improved exponential distribution optimization algorithm to find the optimal solution;

[0119]

[0120] Where: is the total investment capacity after optimization; η oc is the asset-liability ratio of the enterprise; L ip is the total annual liabilities of the enterprise; D is the depreciation expense, which is calculated by the power grid enterprise based on the original value of fixed assets and the pricing depreciation rate approved by the government price regulatory department; α is the income tax rate; Z ip is the total annual assets of the enterprise; NCF(k) is the annual net cash flow; C OUT (k) cash outflow;

[0121] S34, outputting results, including the minimum difference between investment capacity and investment demand, and the values ​​of various adjustment indicators when the objective function is satisfied;

[0122] S35. Determine the final investment scale.

[0123] 1 In the above S33, the exponential distribution optimization algorithm is improved to find the optimal solution. Each individual in the population represents a target value. Through the algorithm's iterative optimization, the target function value corresponding to the optimal individual is found. The specific improvement steps are:

[0124] In the initialization phase, the reverse learning mechanism is used to expand the initialization population

[0125] reverseX i,j =X i,j(max) +X i,j(min) -X i,j

[0126] Where: X represents the individual in the population; X i,j(max) and X i,j(min) Indicates the upper and lower limits of the control variable; reverseX i,j Indicates that it corresponds to X i,j The reverse solution of ;

[0127] Introducing the Tent chaos mapping strategy:

[0128]

[0129] Within the given parameter range, the function behaves as a chaotic map with uniform distribution characteristics and excellent correlation. The system will enter a chaotic state within the definition domain of parameter y, and when y is equal to 0.5, the system will transform into a short-period mode.

[0130] Finally, the cross-learning mechanism is introduced:

[0131] X i,j(k+1) =X r1,j(k) +η×(X r2,j(k) -X r3,j(k) )r1,r2,r3∈[1,N]

[0132] Where: X i,j(k+1) represents the new solution generated after the cross-learning mechanism; r1, r2, and r3 represent three different random numbers between 0 and 1; η represents the step size factor, which is used to control the degree of cross-fusion and is set to 0.5; X r1,j(k) , X r2,j(k) and X r3,j(k) Represents three different solutions taken from the previous iteration.

[0133] Compared with the prior art, the present invention has the following beneficial effects:

[0134] 1. The present invention constructs a comprehensive evaluation model, uses an intelligent evaluation model to conduct an in-depth evaluation of different investment and financing methods and operating models, and develops an investment optimization decision-making support system. The present invention provides all-round support for the investment optimization of source-grid-load-storage integration projects. It provides accurate measurement and evaluation of multi-dimensional factors such as project construction cost, operation and maintenance cost, and system network loss by accurately collecting and processing data from the power market and carbon market, combined with the project's operating data.

[0135] 2. The network flow calculation model adopted by the present invention not only improves the accuracy of the evaluation, but also greatly improves the processing efficiency, and can effectively support the rapid decision-making needs of complex power grid systems. By optimizing investment and operation strategies in real time, the present invention can not only significantly improve the economic benefits of the project, but also enhance the ability to adapt to market changes and ensure the long-term sustainable development of the project.

[0136] 3. The present invention optimizes the investment decision-making process of the integrated power generation, grid, load and storage project, clearly improves the scientificity and accuracy of investment decisions, enables investors to respond to market and technological changes more effectively, and enhances the economic and technical feasibility of the project. In addition, the auxiliary decision-making method greatly reduces the complexity of decision-making, improves decision-making efficiency, and enables non-professionals to quickly master and apply it, greatly expanding its scope of application and influence. In short, the present invention provides an efficient, accurate and easy-to-use investment optimization solution for the integrated power generation, grid, load and storage project, laying a solid foundation for the sustainable development of the power system and the widespread application of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0138] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0139] Figure 1 It is a structural schematic diagram of the data acquisition system and the data processing and analysis center of the present invention.

[0140] Figure 2 This is a typical topological model structure diagram of the source-grid-load-storage system of the present invention.

[0141] Figure 3 It is a cost-effectiveness diagram and estimation method diagram of the initial investment, operating cost, income, etc. of the present invention.

[0142] Figure 4 This is a diagram of the calculation method of the financial evaluation indicators of the project of the present invention.

[0143] Figure 5 This is a flow chart of the investment optimization auxiliary decision-making method based on the source-grid-load-storage integrated project of the present invention.

[0144] Figure 6 This is a flow chart for the coordinated optimization analysis of investment demand and investment capacity for the source-grid-load-storage integration project of the present invention. DETAILED DESCRIPTION

[0145] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. These descriptions are only to further illustrate the features and advantages of the present invention, rather than to limit the claims of the present invention. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0146] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0147] An investment optimization auxiliary decision-making method for source-grid-load-storage integrated projects, such as Figure 5 As shown, follow these steps:

[0148] S1: Construct a method for collecting electricity and carbon data in the investment optimization of integrated source-grid-load-storage projects, and collect data related to the electricity market and system operation status data in real time.

[0149] By building a data collection and control system, real-time power and carbon data related to the power market and system operation status data are collected, and through the energy router model, various devices connected to the grid in the source-grid-load-storage integrated system can be accurately controlled, such as Figure 1 This step is to ensure that there is enough input information to support subsequent analysis and decision-making models, especially for the optimal configuration and economic evaluation of the power system, while ensuring the precise regulation of various operating equipment in the source-grid-load-storage integrated system, making the system operation safer and more stable.

[0150] First, if Figure 2 As shown in the figure, a typical topological model structure diagram of source-grid-load-storage integration is constructed, which includes distributed power generation represented by photovoltaics, distributed wind power equipment, energy storage equipment, electric vehicle charging load and grid topology. The grid connection of various equipment is connected through power electronic equipment. Specifically: In the source-grid-load-storage integration project, according to the load size of the project point, the grid topology is first planned. Select the appropriate line and transformer. Secondly, the distributed wind power, photovoltaics, energy storage, charging stations, etc. are connected to the grid. Sensor equipment is installed in the above equipment to keep track of the operation data at any time.

[0151] Then, data related to the power market and system operation status data are collected in real time through the following two methods:

[0152] S11. Deploy sensors and power electronic controllers in distributed power generation equipment, energy storage equipment, and power grid topology. The sensors are mainly responsible for monitoring and transmitting operating data information of distributed power generation equipment, energy storage equipment, and power systems, including: the active power P output of distributed power generation in network node i Gi and reactive power Q Gi , the active power P of the load in node i Di and the reactive power Q of the load Di , the voltage amplitude of each node in the system is V i With phase angle δ i , the current I of the i-th branch Li , the total resistance R of the i-th branch Li , the total number of branches in the system N L , the number of distributed generation units incorporated into the system N G The second parameter represents the total number of nodes N in the system n , the power electronic controller controls or changes the operating status of the equipment through remote communication.

[0153] Distributed photovoltaic power generation, distributed wind power equipment, energy storage equipment and electric vehicle charging loads are connected to the grid through power electronic equipment. The power electronic controller is included in the micro control unit under the energy router. One end of the power electronic controller is connected to some nodes of the grid topology structure, and the other end is connected to the equipment. The distributed power generation and energy storage are connected to the grid through AC / DC power electronic conversion devices. The energy router, one end is the interface for controlling other devices in the grid. The energy router sends operating instructions to the device through this port, and can also receive data sent by the device to the energy router. The other end uploads the data in the grid system to the intelligent cloud scheduling platform.

[0154] In the past, the regulation of distributed generation ignored the interference of power electronic equipment such as inverters in distributed generation. In order to accurately regulate the operating state of distributed generation in the system, an improved droop control model was proposed. The specific formula before the improvement is:

[0155]

[0156] Where: P G,i , Q G,iare respectively the active and reactive power output by the inverter in the distributed generator; θ is the impedance angle of the total equivalent impedance of the system; δ is the output voltage phase angle when the inverter is set to operate stably; E is the amplitude of the inverter output voltage; V is the node voltage amplitude of the distributed generation grid-connected node; Z is the self-impedance of the distributed generation. Assuming that the total equivalent output impedance of the system is purely inductive, it can be considered that sinθ=1, cosθ=0. Since the output voltage phase angle δ is generally very small when the inverter is operating stably, it can be considered that sinδ≈0, cosδ≈1. The specific formula after improvement is:

[0157] Furthermore, the operating state of distributed generation in the grid topology is regulated by a droop control model, and the specific formula is as follows:

[0158]

[0159] The active power output of the inverter is proportional to the phase of the voltage, and the reactive power output is proportional to the voltage amplitude. By calculating the active and reactive power output of each inverter in distributed generation, the output voltage can be regulated to ensure the stability and operating efficiency of the distribution system.

[0160] Furthermore, the inverter can provide support for the safe and stable operation of the power grid and provide paid ancillary service benefits in the following ways:

[0161] Get the three-phase voltage V on the inverter side abc , three-phase voltage U on the grid side abc And the three-phase current I abc ; Three-phase voltage V on the inverter side abc , three-phase voltage U on the grid side abc And the three-phase current I abc Perform coordinate conversion; input the three-phase voltage on the inverter side and the three-phase voltage on the grid side after coordinate conversion into a phase-locked loop module, the phase-locked loop module consists of a PI controller and an integral operation module, the phase of the input voltage is adjusted by the PI controller, and the adjusted voltage is input into the integral operation module to obtain the phase angle on the inverter side and the phase angle on the grid side; calculate the power angle difference according to the phase angle on the inverter side and the phase angle on the grid side; calculate the phase compensation angle of the power angle by using a PID controller according to the power angle difference; compensate the power angle according to the phase compensation angle, output an inverter control signal according to the compensated signal, and control the inverter to restore the power system fault.

[0162] Among them, the output equation of the PI controller is:

[0163]

[0164] Where: PI v is the output of the inverter side PI controller; Kpv is the proportional gain of the voltage loop PI controller; V q is the q-axis component of the inverter side voltage; K iv is the integral gain; U q is the q-axis component of the grid-side voltage; K p is the scale parameter; K i is the integral parameter; PI u is the output of the grid-side PI controller;

[0165] The formula for calculating the phase compensation angle of the power angle using the PID controller is:

[0166] δ=K θp (θ ref -θ v +θ u )+K θi ∫(θ ref -θ v +θ u )dt

[0167] Where: δ is the output voltage phase angle when the inverter is set to operate stably; K θp is the phase compensation angle proportional gain; θ ref is the reference phase angle; θ v is the phase angle on the inverter side; θ u is the phase angle on the grid side; K θi is the phase compensation angle integral gain.

[0168] S12. Use the intelligent cloud dispatching platform to obtain real-time data from the website, collect provincial power market, carbon market information data and specific data published by government websites, etc.; specifically including: photovoltaic investment unit price p PV , wind power investment unit price p WT , energy storage investment unit price p ES , line unit price p L , substation unit price p y , actual electricity price p, annual photovoltaic utilization hours T PV , wind power annual utilization hours T WT , the compensation unit price of inverter active power and reactive power p P and p Q , enterprise debt-to-asset ratio η oc , the total annual liabilities of the enterprise L ip , income tax rate α, total annual assets of the enterprise Z ip .

[0169] S2: Optimal power flow model for optimizing the configuration of source, grid, load and storage based on sensitivity analysis model. The voltage and power loss of each node are calculated through the optimal power flow model. At the same time, sensitivity analysis is used to identify key nodes in the system that are sensitive to disturbances, and the configuration of power generation and energy storage equipment is optimized. Secondly, based on the specific configuration of power generation and energy storage capacity obtained above and the real-time data calculated during system operation, the demand for adjusting the grid-connected power or purchased power of the integrated source, grid, load and storage system is evaluated. According to the real-time power market data obtained in step S1, an economic model is established to comprehensively evaluate and analyze the economic feasibility of the integrated source, grid, load and storage project under the operation mode.

[0170] Among them, the specific steps of the optimal power flow model for source-grid-load-storage integrated optimization configuration based on the sensitivity analysis model are as follows:

[0171] S2a, collect the power generation and load data obtained in step S1 and build a calculation model of the source-grid-load-storage system based on these data information, then perform power flow calculation, and calculate the apparent power and power loss of each node by the following formula:

[0172]

[0173] Where: S m Represents the apparent power of each node; P Gi , Q Gi are respectively the active power and reactive power output by distributed generation in network node i; P Di , Q Di are the active power and reactive power of the load in node i respectively; P loss,Li represents the active power loss of the i-th branch; I Li represents the current flowing through the i-th branch; R Li represents the total resistance of the i-th branch; N n It represents the total number of nodes in the system; m represents the mth node in the system;

[0174] The calculation of the above formula must also satisfy the following equality and inequality constraints:

[0175] Equality constraints:

[0176]

[0177] Where: P S and Q S It represents the active and reactive power supplied by the upper transformer to the pilot project of source-grid-load-storage integration; P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed; P D,j , Q D,jIt represents the active and reactive power load required by the jth node; P loss,Li represents the active power loss of the i-th branch; Q loss,Li represents the reactive power loss of the i-th branch; N L It represents the number of distributed generation units incorporated into the system; N G Represents the total number of nodes in the system; N n It represents the total number of branches in the system;

[0178] Inequality constraints:

[0179] In order to ensure the operating cost and system safety of the distribution source grid load storage integrated project, the configuration capacity of distributed generation must be controlled within a reasonable range;

[0180]

[0181] Where: P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed; P D,j , Q D,j represents the active and reactive power loads required by the jth node; k G N is the configuration ratio coefficient of distributed generation capacity in the source-grid-load-storage integration project, that is, the ratio of the upper limit of distributed generation capacity that can be installed in the system to the total load in the system; G Represents the total number of nodes in the system; N n It represents the total number of branches in the system;

[0182] P G,k , Q G,k The upper and lower limits shown below must be met:

[0183] P G,k(min) ≤P G,k ≤P G,k(max)

[0184] Q G,k(min) ≤Q G,k ≤Q G,k(max)

[0185] Where: P G,k(min) , P G,k(max) represents the lower and upper limits of the active power of the kth distributed generation deployed; Q G,k(min) , Q G,k(max) represents the lower and upper limits of the active power of the kth distributed generation deployed;

[0186] Node voltage V i The following formula must be satisfied:

[0187] V imin ≤V i ≤V imax

[0188] Where: V i(min) 、V i(max) Indicates the lower and upper limits of the voltage amplitude of each node;

[0189] The phase angle constraint must satisfy the following equation:

[0190] |δ i -δ j |≤|δ i -δ j | max

[0191] Where: i , δ j represents the voltage phase angle of nodes i and j; wherein the voltage phase angle corresponding to the distributed generation access node is the voltage phase angle after the inverter compensation in claim 5;

[0192] S2b, obtain specific configuration scheme and system operation status;

[0193] S2b1. Introduce a DG or SG reactive power compensator unit with a very small capacity at a certain node of the source-grid-load-storage integrated system to evaluate its impact on the performance of the entire system;

[0194]

[0195] Where: P loss It represents the cumulative value of active power loss of all branches in the system; P loss,Li represents the active power loss of the i-th branch; N L Indicates the total number of branches; SenDG i 、SenSC i They represent the sensitivity values ​​of the distributed generation and reactive power compensation device connected to the i-th node respectively; P loss(i) represents the active power loss of the system after DG or SC is connected to the i-th node; ΔP DG Indicates the active compensation value of DG; ΔQ SC Indicates the reactive power compensation value of SC.

[0196] S2b2, build a power flow calculation optimization model and determine the optimization objectives, such as minimizing energy costs and maximizing energy efficiency objective functions;

[0197] S2b3, select intelligent optimization algorithm for optimal power flow calculation. Intelligent optimization algorithm can be ISCSO (improved SCSO single-objective optimization algorithm) / MOISCSO (improved SCSO multi-objective optimization algorithm) algorithm, etc.

[0198] S2b4, obtain the specific configuration plan and system operation status, the specific results include: the overall capacity size P of the power generation equipment in the system G , Real-time power generation Q UP 、Internet power consumption Q OG .

[0199] Then, based on the data obtained above, an economic model is constructed and evaluated, such as Figure 3 , Figure 4 As shown, the process is as follows:

[0200] S2A. Calculate the cost of the integrated power generation, grid, load and storage project:

[0201] S2A1, total electrical equipment investment cost C TEL : Mainly the investment and construction costs of distributed energy, energy transmission and distribution networks, energy storage, control and protection equipment

[0202] C TEL =p PV *P PV +p WT *P WT +4*p ES *P ES +p L *L+p y *Y

[0203] Where: C TEL is the total electrical equipment investment cost; p PV is the unit price of photovoltaic investment; P PV is the photovoltaic capacity; p WT is the unit price of wind power investment; P WT is the wind power capacity; 4 is the energy storage utilization hours stipulated by the state; p ES is the unit price of energy storage investment; P ES is the energy storage capacity; p L is the line unit price; L is the line length; p y is the unit price of substation; Y is the number of substations;

[0204] S2A2、Project Cost C E :Mainly construction costs (including labor costs, material costs, construction machinery usage fees, enterprise management fees, etc.), equipment purchase costs and other construction costs

[0205] C E =C CI +C TEL +C AE

[0206] Where: C E C is the engineering cost; CIIt is the construction engineering fee, which is determined according to the actual situation of the project; C TEL is the total electrical equipment investment cost; C AE Other construction costs for the project, generally 2.5% of the sum of the construction costs and the total electrical equipment investment costs, that is, C AE Equal to C CI , C TEL 2.5% of the sum;

[0207] S2A3, preparation cost C C : Usually refers to a part of the funds reserved in the project budget to deal with unforeseen events or additional expenses that may arise during the implementation of the project. It is prepared in advance to prevent cost overruns caused by unexpected circumstances. Generally, it is necessary to prepare a project budget, depending on the actual situation of the project.

[0208] C C =k C,E C E

[0209] Where: C C For preparation costs; C E is the engineering cost; k C,E It is a customary value to take the ratio of the reserve fund to the total construction cost in such projects;

[0210] S2A4, original value of fixed assets C OFA :Usually refers to the total initial cost paid by an enterprise to purchase or build fixed assets. Fixed assets usually refer to tangible assets that an enterprise uses to produce goods, provide services or carry out daily business activities and are expected to use for a period of time, such as land, buildings, machinery and equipment, vehicles, etc.

[0211] C OFA =C E +C C

[0212] Where: C OFA is the original value of fixed assets; C E C is the engineering cost; C To prepare expenses;

[0213] S2A5, annual fixed cost C FC (k) Costs that an enterprise must pay to maintain operations during the year and that do not vary with the volume of production or services provided. They are usually not directly related to the production process and must be paid even if production activities are temporarily stopped.

[0214] C FC (k) = C MO +C MOT +C AI+C AIT

[0215] Where: C FC (k) is the annual fixed cost; C MO is the annual maintenance fee, usually C OFA 0.2%; C MOTC Annual maintenance fee tax, usually C MO 13%; C AI The annual insurance premium is usually C OFA 0.15%; C AIT The annual insurance premium tax is usually C AI 6%;

[0216] S2A6, annual variable cost C VC (k) Costs that change in proportion to the volume of production, services or business activities, are closely related to the direct production activities of the enterprise and are usually directly related to the raw materials used, labor and energy consumption, etc.

[0217] C VC (k) = C AM +C AMT

[0218] Where: C VC (k) is the annual variable cost; C AM is the annual material cost, which is generally 5 yuan / kW commonly used in the industry; C AMT It is the annual material cost tax, which is generally 17% of AMC;

[0219] S2A7, Annual operating cost C OC (k) refers to all the expenses that a business must pay in order to maintain normal operations within a year, including fixed costs and variable costs

[0220] C OC (k) = C FC (k)+C VC (k)

[0221] Where: C OC (k) is the annual operating cost; C FC (k) is the annual fixed cost; C VC (k) is the annual variable cost;

[0222] S2A8, total investment cost C I

[0223] C I =C OFA +C W

[0224] Where: C Iis the total investment cost; C OFA is the original value of fixed assets; C W Working capital, generally 30 yuan / kW, which is commonly used in the industry;

[0225] S2B, calculate the benefits of the source-grid-load-storage integration project:

[0226] S2B1, annual electricity sales revenue R Q (k):

[0227] R Q (k) = p*(P PV *T PV +P WT *T WT )*(0.986-0.006*k),k=1,2,…,n

[0228] Where: R Q (k) is the annual power generation in the kth year; p is the actual electricity price; P PV is the photovoltaic capacity; T PV P is the annual utilization hours of photovoltaic power; WT is the wind power capacity; T WT is the annual utilization hours of wind power; (0.986-0.006*k) is the attenuation rate of photovoltaic modules in k years;

[0229] S2B2, ancillary service revenue provided by the inverter R FZ :

[0230] R FZ =p P P G,k +p Q Q G,k

[0231] Where: R FZ Ancillary service income provided to the inverter; P and p Q are the compensation unit prices of active power and reactive power of distributed generation at the inverter access point; P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed;

[0232] S2C, based on the various cost and benefit data obtained from the above calculations, the net present value (NPV), internal rate of return (IRR) and payback period (SPT) of the project are calculated through net cash flow to comprehensively evaluate and analyze the economic feasibility of the source-grid-load-storage integration project under the operation mode;

[0233] Total investment cost C I , Annual operating cost C OC (k), annual electricity sales revenue R Q(k) Ancillary service revenue R FZ Calculate the cash outflow C of the power grid-load storage integration project OUT (k) Cash inflow C IN (k), net cash flow NCF(k), and then calculate the net present value NPV, internal rate of return IRR and payback period SPT:

[0234] S2C1, cash outflow C OUT (k)

[0235]

[0236] Where: C OUT (k) is the annual cash outflow in the kth year; C I is the total investment cost; C OC (k) is the annual operating cost;

[0237] S2C2, cash inflow C IN (k)

[0238]

[0239] Where: C IN (k) is the annual cash inflow in the kth year; R Q (k) is the annual electricity sales revenue in the kth year; R FZ Ancillary service revenue provided to inverters;

[0240] S2C3, Net Cash Flow NCF(k)

[0241] NCF(k)=C IN (k)-C OUT (k)

[0242] Where: NCF(k) is the annual net cash flow in the kth year; C IN (k) is cash inflow; C OUT (k) cash outflow;

[0243] S2C4, cumulative net cash flow ANCF (i)

[0244]

[0245] Where: ANCF(i) is the annual net cash flow in the i-th year; NCF(k) is the annual net cash flow in the k-th year;

[0246] S2C5. Net Present Value (NPV): refers to the net cash flow after the project is put into use, converted into present value according to the discount rate set by the enterprise (S2C is also the investment rate of return required by the enterprise, often using the industry benchmark discount rate), and then deducting the balance after the initial investment, that is, the algebraic sum of the present value of the project's annual net cash flow minus the total investment.

[0247]

[0248] Where: NPV is the net present value; i c NCF is the net cash flow in the tth year; t is the year; n is the project period;

[0249] S2C6, Internal Rate of Return IRR: Internal rate of return is the discount rate when the net present value is zero, reflecting the efficiency of project investment utilization;

[0250]

[0251] Where: IRR is the internal rate of return; i is the year before the cumulative discount value is first positive; i+1 is the year before the cumulative discount value is first positive; DR(i) is the discount rate in the year before the cumulative discount value is first positive; DR(i+1) is the discount rate in the year before the cumulative discount value is first positive; NPV(i) is the net present value in the year before the cumulative discount value is first positive; NPV(i+1) is the net present value in the year before the cumulative discount value is first positive;

[0252] S2C7. Payback period SPT: The time it takes for the total income obtained from an investment project to reach the total investment after it is put into normal use, that is, the time required for the accumulated economic benefits to be equal to the original capital investment.

[0253] When ANCF(i)≤0 and ANCF(i+1)>0,

[0254] SPT=i-ANCF(i)÷NCF(i+1),i=1,2,…,n

[0255] In the formula: SPT is the payback period; i is the year when the last cumulative net cash flow is negative; ANCF is the cumulative net cash flow up to the i-th year; NCF is the net cash flow in the i-th year.

[0256] According to the above formula, the total cost and total benefit of the source-grid-load-storage integration project can be calculated, and then the net cash flow of the project can be obtained based on these calculation results. Furthermore, the net present value (NPV), internal rate of return (IRR) and payback period (SPT) of the project can be calculated through the net cash flow. These economic analysis indicators can help decision makers comprehensively evaluate the economic feasibility of the project and make scientific investment decisions.

[0257] S3: Figure 6 As shown, an investment optimization decision-making support method is constructed, and an improved index optimization algorithm is proposed and applied to give the optimized value of the adjustable index of investment capacity, optimize the investment plan of the source-grid-load-storage integration project, and determine the final investment amount of the source-grid-load-storage integration project. The technical and market data obtained from the first two steps and the results of system performance analysis are integrated and used. Through the investment optimization decision-making support method, an improved index optimization algorithm is proposed and applied to give the optimized value of the adjustable index of investment capacity, optimize the investment plan of the source-grid-load-storage integration project, and finally determine the investment amount of the project. This step ensures that the investment decision fully considers the impact of market conditions, technological progress and policy changes, so as to achieve the best economic and technical results. As shown Figure 3 , Figure 4 As shown, the specific implementation steps are as follows:

[0258] S31. Data input: input the net present value (NPV), internal rate of return (IRR), and payback period (SPT) calculated above as basic investment demand data;

[0259] S32. Based on the operation and financial data of the power grid enterprise, the investment demand capacity is coordinated and judged. When the investment capacity of the enterprise is greater than the investment demand, it is considered that the investment demand and investment capacity are coordinated, and the analysis ends. Otherwise, it is considered that the investment demand and investment capacity are not coordinated, and the optimization stage is entered;

[0260] S33, establish the objective function F of the minimum difference between investment capacity and investment demand, and apply the improved exponential distribution optimization algorithm to find the optimal solution;

[0261]

[0262] Where: is the total investment capacity after optimization; η oc is the asset-liability ratio of the enterprise; L ip is the total annual liabilities of the enterprise; D is the depreciation expense, which is calculated by the power grid enterprise based on the original value of fixed assets and the pricing depreciation rate approved by the government price regulatory department; α is the income tax rate; Z ip is the total annual assets of the enterprise; NCF(k) is the annual net cash flow; C OUT (k) cash outflow;

[0263] S34, outputting results, including the minimum difference between investment capacity and investment demand, and the values ​​of various adjustment indicators when the objective function is satisfied;

[0264] S35. Determine the final investment scale.

[0265] Furthermore, in step S33, the exponential distribution optimization algorithm is improved to find the optimal solution. Each individual in the population represents a target value. The target function value corresponding to the optimal individual is found through iterative optimization of the algorithm. The specific improvement steps are as follows:

[0266] In the initialization phase, the reverse learning mechanism is used to expand the initialization population

[0267] reverseX i,j =X i,j(max) +X i,j(min) -X i,j

[0268] Where: X represents the individual in the population; X i,j(max) and X i,j(min) Indicates the upper and lower limits of the control variable; reverseX i,j Indicates that it corresponds to X i,j The reverse solution of ;

[0269] Introducing the Tent chaos mapping strategy:

[0270]

[0271] Within the given parameter range, the function behaves as a chaotic map with uniform distribution characteristics and excellent correlation. The system will enter a chaotic state within the definition domain of parameter y, and when y is equal to 0.5, the system will transform into a short-period mode.

[0272] Finally, the cross-learning mechanism is introduced:

[0273] The purpose of introducing the cross-learning mechanism is to expand the spatial distribution of candidate solutions of the algorithm, improve the search accuracy and prevent the algorithm from falling into the local optimum.

[0274] X i,j(k+1) =X r1,j(k) +η×(X r2,j(k) -X r3,j(k) )r1,r2,r3∈[1,N]

[0275] Where: X i,j(k+1) represents the new solution generated after the cross-learning mechanism; r1, r2, and r3 represent three different random numbers between 0 and 1; η represents the step size factor, which is used to control the degree of cross-fusion and is set to 0.5;

[0276] X r1,j(k) , X r2,j(k) and X r3,j(k) Represents three different solutions taken from the previous iteration.

[0277] Only the preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention, and various changes should be included in the protection scope of the present invention.

Claims

1. An investment optimization decision-making support method for source-grid-load-storage integrated projects, characterized in that: The following steps are involved: S1. Construct a method for collecting electricity and carbon data in the investment optimization of source-grid-load-storage integrated projects, and collect data related to the electricity market and system operation status data in real time; S2. An optimal power flow model for optimizing the configuration of source, grid, load and storage based on a sensitivity analysis model. The voltage and power loss of each node are calculated through the optimal power flow model. At the same time, sensitivity analysis is used to identify key nodes in the system that are sensitive to disturbances, and the configuration of power generation and energy storage equipment is optimized. Secondly, based on the specific configuration of power generation and energy storage capacity obtained above and the real-time data calculated during system operation, the demand for adjusting the online power or purchased power of the integrated source, grid, load and storage system is evaluated. Based on the real-time power market data obtained in step S1, an economic model is established to comprehensively evaluate and analyze the economic feasibility of the integrated source, grid, load and storage project under the operation mode; The method of the optimal power flow model for source-grid-load-storage integrated optimization configuration based on the sensitivity analysis model of S2 is: S2a, collect the power generation and load data obtained in S1 and build a calculation model of the source-grid-load-storage integrated system based on these data information, then perform power flow calculation, and calculate the apparent power and power loss of each node by the following formula: Where: S m Represents the apparent power of each node; P Gi , Q Gi are respectively the active power and reactive power output by distributed generation in network node i; P Di , Q Di are the active power and reactive power of the load in node i respectively; P loss,Li represents the active power loss of the i-th branch; I Li represents the current flowing through the i-th branch; R Li represents the total resistance of the i-th branch; N n It represents the total number of nodes in the system; m represents the mth node in the system; The calculation of the above formula must also satisfy the following equality and inequality constraints: Equality constraints: Where: P S and Q S It represents the active and reactive power supplied by the upper transformer to the pilot project of source-grid-load-storage integration; P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed; P D,j , Q D,j It represents the active and reactive power load required by the jth node; P loss,Li represents the active power loss of the i-th branch; Q loss,Li represents the reactive power loss of the i-th branch; N L It represents the number of distributed generation units incorporated into the system; N G Represents the total number of nodes in the system; N n It represents the total number of branches in the system; Inequality constraints: The configuration capacity of distributed generation must be controlled within a reasonable range: P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed; P D,j , Q D,j represents the active and reactive power loads required by the jth node; k G N is the configuration ratio coefficient of distributed generation capacity in the source-grid-load-storage integration project, that is, the ratio of the upper limit of distributed generation capacity that can be installed in the system to the total load in the system; G Represents the total number of nodes in the system; N n It represents the total number of branches in the system; P G,k , Q G,k The upper and lower limits shown below must be met: P G,k(min) ≤P G,k ≤P G,k(max) Q G,k(min) ≤Q G,k ≤Q G,k(max) Where: P G,k(min) , P G,k(max) represents the lower and upper limits of the active power of the kth distributed generation deployed; Q G,k(min) , Q G,k(max) represents the lower and upper limits of the active power of the kth distributed generation deployed; Node voltage V i The following formula must be satisfied: In imin ≤V i ≤V imax Where: V i(min) 、V i(max) Indicates the lower and upper limits of the voltage amplitude of each node; The phase angle constraint must satisfy the following equation: |d i -d j |≤|δ i -d j | max Where: i , δ j represents the voltage phase angle of nodes i and j; the voltage phase angle corresponding to the distributed generation access node is the voltage phase angle after inverter compensation; S2b, obtain specific configuration scheme and system operation status; S3. Construct an investment optimization decision-making support method, propose and apply an improved index optimization algorithm to give the optimal value of the adjustable indicator of investment capacity, optimize the investment plan of the integrated source-grid-load-storage project, and determine the final investment amount of the integrated source-grid-load-storage project.

2. The investment optimization decision-making support method for source-grid-load-storage integrated projects according to claim 1 is characterized in that: The electricity and carbon data collection method in the S1 source-grid-load-storage integration project investment optimization is as follows: S11. Deploy sensors and power electronic controllers in distributed power generation equipment, energy storage equipment, and power grid topology. The sensors are responsible for monitoring and transmitting operating data information of distributed power generation equipment, energy storage equipment, and power system, including: the active power P output of distributed power generation in network node i Gi and reactive power Q Gi , the active power P of the load in node i Di and the reactive power Q of the load Di , the voltage amplitude of each node in the system is V i With phase angle δ i , the current I of the i-th branch Li , the total resistance R of the i-th branch Li , the total number of branches in the system N L , the number of distributed generation units incorporated into the system N G The second parameter represents the total number of nodes N in the system n ,The power electronic controller controls or changes the operating state of the equipment through remote communication; S12. Use the intelligent cloud dispatching platform to obtain real-time data from the website, collect provincial power market, carbon market information data and specific data requirements published by government websites; specifically including: photovoltaic investment unit price p PV , wind power investment unit price p WT , energy storage investment unit price p ES , line unit price p L , substation unit price p y , actual electricity price p, annual photovoltaic utilization hours T PV , wind power annual utilization hours T WT , the compensation unit price of inverter active power and reactive power p P and p Q , enterprise debt-to-asset ratio η oc , the total annual liabilities of the enterprise L ip , income tax rate α, total annual assets of the enterprise Z ip .

3. The investment optimization decision-making support method for source-grid-load-storage integrated projects according to claim 2 is characterized in that: The distributed photovoltaic power generation, distributed wind power equipment, energy storage equipment and electric vehicle charging load in S11 are connected to the grid through power electronic equipment, and the power electronic controller is included in the micro control unit under the energy router. One end of the power electronic controller is connected to some nodes of the grid topology structure, and the other end is connected to the equipment, and the distributed power generation and energy storage are connected to the grid through AC / DC power electronic conversion devices; The energy router has an interface on one end that controls other devices in the power grid. The energy router sends operating instructions to the device through this interface and can also receive data sent by the device to the energy router. The other end uploads data in the power grid system to the intelligent cloud scheduling platform.

4. The investment optimization decision-making support method for source-grid-load-storage integrated projects according to claim 3 is characterized in that: The operating state of distributed generation in the grid topology is regulated by a droop control model, and the specific formula is as follows: Where: P G,i , Q G,i are respectively the active and reactive power output by the inverter in the distributed generator; δ is the output voltage phase angle when the inverter is set to operate stably; E is the amplitude of the inverter output voltage; V is the node voltage amplitude of the distributed generation grid-connected node; Z is the self-impedance of the distributed generation; The active power output of the inverter is proportional to the phase of the voltage, and the reactive power output is proportional to the voltage amplitude. The output voltage can be regulated by calculating the active and reactive power output of the inverter in each distributed power generation.

5. The investment optimization auxiliary decision-making method for source-grid-load-storage integrated projects according to claim 4 is characterized in that: The inverter provides support for the safe and stable operation of the power grid and provides paid ancillary service benefits in the following ways: Get the three-phase voltage V on the inverter side abc , three-phase voltage U on the grid side abc And the three-phase current I abc ; Three-phase voltage V on the inverter side abc , three-phase voltage U on the grid side abc And the three-phase current I abc Perform coordinate conversion; input the three-phase voltage on the inverter side and the three-phase voltage on the grid side after coordinate conversion into a phase-locked loop module, the phase-locked loop module is composed of a PI controller and an integral operation module, the input voltage is phase-adjusted by the PI controller, and the adjusted voltage is input into the integral operation module to obtain the phase angle on the inverter side and the phase angle on the grid side; calculate the power angle difference according to the phase angle on the inverter side and the phase angle on the grid side; The PID controller is used to calculate the phase compensation angle of the power angle according to the power angle difference; the power angle is compensated according to the phase compensation angle, and the inverter control signal is output according to the compensated signal, and the inverter is controlled to restore the power system fault; The output equation of the PI controller is: Among them: PI v is the output of the inverter side PI controller; K pv is the proportional gain of the voltage loop PI controller; V q is the q-axis component of the inverter side voltage; K iv is the integral gain; U q is the q-axis component of the grid-side voltage; K p is the scale parameter; K i is the integral parameter; PI u is the output of the grid-side PI controller; The formula for calculating the phase compensation angle of the power angle using the PID controller is: δ=K θp (i ref -θ v +θ u )+K θi ∫(θ ref -θ v +θ u )dt Where: δ is the output voltage phase angle when the inverter is set to operate stably; K θp is the phase compensation angle proportional gain; θ ref is the reference phase angle; θ v is the phase angle on the inverter side; θ u is the phase angle on the grid side; K θi is the phase compensation angle integral gain.

6. The investment optimization decision-making support method for source-grid-load-storage integrated projects according to claim 1 is characterized in that: The method for obtaining the specific configuration scheme and system operation status in S22 is: S2b1. Introduce a DG or SC reactive power compensator unit with a very small capacity at a certain node of the source-grid-load-storage integrated system to evaluate its impact on the performance of the entire system; Where: P loss It represents the cumulative value of active power loss of all branches in the system; P loss,Li represents the active power loss of the i-th branch; N L Indicates the total number of branches; SenDG i 、SenSC i They represent the sensitivity values ​​of the distributed generation and reactive power compensation device connected to the i-th node respectively; P loss(i) represents the active power loss of the system after DG or SC is connected to the i-th node; ΔP DG Indicates the active compensation value of DG; ΔQ SC Indicates the reactive power compensation value of SC; S2b2, construct a power flow calculation optimization model and determine the optimization objectives, including minimizing energy cost and maximizing energy efficiency objective functions; S2b3, select intelligent optimization algorithm to perform optimal power flow calculation; S2b4, obtain the specific configuration plan and system operation status, the specific results include: the capacity of photovoltaic in the system P PV 、Capacity of wind power P WT 、Energy storage capacity P ES , number of substations Y, line length L.

7. The investment optimization decision-making support method for source-grid-load-storage integrated projects according to claim 1 is characterized in that: The evaluation process of the economic model in S2 is: S2A. Calculate the cost of the integrated power generation, grid, load and storage project: S2A1, total electrical equipment investment cost C TEL : Including the investment and construction costs of distributed energy, energy transmission and distribution networks, energy storage, control and protection equipment C TEL =p PV *P PV +p WT *P WT +4*p ES *P ES +p L *L+p y *Y Where: C TEL is the total electrical equipment investment cost; PV is the unit price of photovoltaic investment; P PV is the photovoltaic capacity; p WT is the unit price of wind power investment; P WT is the wind power capacity; 4 is the energy storage utilization hours stipulated by the state; p ES is the unit price of energy storage investment; P ES is the energy storage capacity; p L is the line unit price; L is the line length; p y is the unit price of substation; Y is the number of substations; S2A2、Project Cost C E : It includes construction costs, equipment and tools purchase costs and other construction costs. The construction costs include labor costs, material costs, construction machinery usage fees and enterprise management fees; C E =C CI +C TEL +C AE Where: C E C is the engineering cost; CI It is the construction engineering fee, which is determined according to the actual situation of the project; C TEL is the total electrical equipment investment cost; C AE Other construction costs for the project, which is 2.5% of the sum of the construction costs and the total electrical equipment investment costs, that is, C AE Equal to C CI , C TEL 2.5% of the sum; S2A3, preparation cost C C : refers to a part of the funds reserved in the project budget to deal with unforeseen events or additional expenses that may arise during the implementation of the project. It is prepared in advance to prevent cost overruns caused by unexpected circumstances; it is necessary to prepare a project budget and it is determined according to the actual situation of the project. C C =k C,E C E Where: C C For preparation costs; C E is the engineering cost; k C,E It is a customary value to take the ratio of the reserve fund to the total construction cost in such projects; S2A4, original value of fixed assets C OFA : refers to the total initial cost paid by an enterprise to purchase or construct fixed assets; fixed assets refer to tangible assets used by an enterprise to produce goods, provide services or carry out daily business activities and are expected to be used for a period of time, including land, buildings, machinery and equipment, and vehicles; C OFA =C E +C C Where: C OFA is the original value of fixed assets; C E C is the engineering cost; C To prepare expenses; S2A5, annual fixed cost C FC (k) Costs that an enterprise must pay to maintain operations during the year, which do not vary with changes in production or service volume, are not directly related to the production process, and must be paid even if production activities are temporarily stopped. C FC (k)=C MO +C MOT +C AI +C AIT Where: C FC (k) is the annual fixed cost; C MO is the annual maintenance fee, C OFA 0.2%; C MOTC is the annual maintenance fee and tax, C MO 13%; C AI is the annual insurance premium, C OFA 0.15%; C AIT is the annual insurance premium tax, and C AI 6%; S2A6, annual variable cost C VC (k) Costs that vary in proportion to the volume of production, services or business activities, are closely related to the direct production activities of the enterprise and are directly related to the raw materials, labor and energy used. C VC (k)=C AM +C AMT Where: C VC (k) is the annual variable cost; C AM is the annual material cost, which is 5 yuan / kW commonly used in the industry; C AMT The annual material cost tax is 17% of AMC; S2A7, Annual operating cost C OC (k) refers to all the expenses that a business must pay in order to maintain normal operations within a year, including fixed costs and variable costs C OC (k)=C FC (k)+C VC (k) Where: C OC (k) is the annual operating cost; C FC (k) is the annual fixed cost; C VC (k) is the annual variable cost; S2A8, total investment cost C I C I =C OFA +C W Where: C I is the total investment cost; C OFA is the original value of fixed assets; C W For working capital, it is 30 yuan / kW, which is commonly used in the industry; S2B, calculate the benefits of the source-grid-load-storage integration project: S2B1, annual electricity sales revenue R Q (k): R Q (k)=p*(P PV *T PV +P WT *T WT )*(0.986-0.006*k),k=1,2,…,n Where: R Q (k) is the annual power generation in the kth year; p is the actual electricity price; P PV is the photovoltaic capacity; T PV P is the annual utilization hours of photovoltaic power; WT is the wind power capacity; T WT is the annual utilization hours of wind power; (0.986-0.006*k) is the attenuation rate of the photovoltaic module in k years; S2B2, ancillary service revenue provided by the inverter R FZ : R FZ =p P P G,k +p Q Q G,k Where: R FZ Ancillary service income provided to the inverter; P and p Q are the compensation unit prices of active power and reactive power of distributed generation at the inverter access point; P G,k , Q G,k represents the active power and reactive power output of the kth distributed generation deployed; S2C, based on the various cost and benefit data obtained from the above calculations, the net present value (NPV), internal rate of return (IRR) and payback period (SPT) of the project are calculated through net cash flow to comprehensively evaluate and analyze the economic feasibility of the source-grid-load-storage integration project under the operation mode; Total investment cost C I , Annual operating cost C OC (k), annual electricity sales revenue R Q (k) Ancillary service revenue R FZ Calculate the cash outflow C of the power grid-load storage integration project OUT (k) Cash inflow C IN (k), net cash flow NCF(k), and then calculate the net present value NPV, internal rate of return IRR and payback period SPT: S2C1, cash outflow C OUT (k) Where: C OUT (k) is the annual cash outflow in the kth year; C I is the total investment cost; C OC (k) is the annual operating cost; S2C2, cash inflow C IN (k) Where: C IN (k) is the annual cash inflow in the kth year; R Q (k) is the annual electricity sales revenue in the kth year; R FZ Ancillary service revenue provided to inverters; S2C3, Net Cash Flow NCF(k) NCF(k)=C IN (k)-C OUT (k) Where: NCF(k) is the annual net cash flow in the kth year; C IN (k) is cash inflow; C OUT (k) cash outflow; S2C4, cumulative net cash flow ANCF (i) Where: ANCF(i) is the annual net cash flow in the i-th year; NCF(k) is the annual net cash flow in the k-th year; S2C5. Net Present Value (NPV): refers to the balance after the net cash flow after the project is put into use is converted into present value according to the discount rate set by the enterprise, and then the initial investment is deducted. In other words, it is the balance obtained by deducting the total investment from the algebraic sum of the present value of the project's net cash flow each year. Where: NPV is the net present value; i c NCF is the net cash flow in the tth year; t is the year; n is the project period; S2C6, Internal Rate of Return IRR: Internal rate of return is the discount rate when the net present value is zero, reflecting the efficiency of project investment utilization; Where: IRR is the internal rate of return; i is the year before the cumulative discount value is first positive; i+1 is the year before the cumulative discount value is first positive; DR(i) is the discount rate in the year before the cumulative discount value is first positive; DR(i+1) is the discount rate in the year before the cumulative discount value is first positive; NPV(i) is the net present value in the year before the cumulative discount value is first positive; NPV(i+1) is the net present value in the year before the cumulative discount value is first positive; S2C7. Payback period SPT: The time it takes for the total income obtained from an investment project to reach the total investment after it is put into normal use, that is, the time required for the accumulated economic benefits to be equal to the original capital investment. When ANCF(i)≤0 and ANCF(i+1)>0, SPT=i-ANCF(i)÷NCF(i+1),i=1,2,…,n In the formula: SPT is the payback period; i is the year when the last cumulative net cash flow is negative; ANCF is the cumulative net cash flow up to the i-th year; NCF is the net cash flow in the i-th year.

8. The investment optimization decision-making support method for source-grid-load-storage integrated projects according to claim 1 is characterized in that: The investment optimization decision-making assistance method in S3 is: S31. Data input: input the net present value (NPV), internal rate of return (IRR), and payback period (PP) calculated above as basic investment demand data; S32. Based on the operation and financial data of the power grid enterprise, the investment demand capacity is coordinated and judged. When the investment capacity of the enterprise is greater than the investment demand, it is considered that the investment demand and investment capacity are coordinated, and the analysis ends. Otherwise, it is considered that the investment demand and investment capacity are not coordinated, and the optimization stage is entered; S33, establish the objective function F of the minimum difference between investment capacity and investment demand, and apply the improved exponential distribution optimization algorithm to find the optimal solution; Where: The total investment capacity after optimization; η oc is the enterprise's debt-to-asset ratio; L ip is the total annual liabilities of the enterprise; D is the depreciation expense, which is calculated by the power grid enterprise based on the original value of fixed assets and the pricing depreciation rate approved by the government price regulatory department; α is the income tax rate; Z ip is the total annual assets of the enterprise; NCF(k) is the annual net cash flow; C OUT (k) cash outflow; S34, outputting results, including the minimum difference between investment capacity and investment demand, and the values ​​of various adjustment indicators when the objective function is satisfied; S35. Determine the final investment scale.

9. The investment optimization decision-making support method for source-grid-load-storage integrated projects as described in claim 8, characterized in that: In S33, the exponential distribution optimization algorithm is improved to find the optimal solution. Each individual in the population represents a target value. The target function value corresponding to the optimal individual is found through iterative optimization of the algorithm. The specific improvement steps are: In the initialization phase, the reverse learning mechanism is used to expand the initialization population reverseX i,j =X i,j(max) +X i,j(min) -X i,j Where: X represents the individual in the population; X i,j(max) and X i,j(min) Indicates the upper and lower limits of the control variable; reverseX i,j Indicates that it corresponds to X i,j The reverse solution of ; Introducing the Tent chaos mapping strategy: Within the given parameter range, the function behaves as a chaotic map with uniform distribution characteristics and excellent correlation. The system will enter a chaotic state within the definition domain of parameter y, and when y is equal to 0.5, the system will transform into a short-period mode. Finally, the cross-learning mechanism is introduced: X i,j(k+1) =X r1,j(k) +η×(X r2,j(k) -X r3,j(k) )r1、r2、r3∈[1,N] Where: X i,j(k+1) represents the new solution generated after the cross-learning mechanism; r1, r2, and r3 represent three different random numbers between 0 and 1; η represents the step size factor, which is used to control the degree of cross-fusion and is set to 0.5; X r1,j(k) , X r2,j(k) and X r3,j(k) Represents three different solutions taken from the previous iteration.

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