Power balance method for power system source-grid-load-storage coordination

Abstract

The present invention relates to the technical field of distribution network planning and construction, and discloses a power balancing method for power system source-grid-load-storage coordination, including the following steps: S10: superimpose total load, the total load includes conventional load and electric energy substitution load, perform superimposition calculation on them, adopt a method combining natural growth and special load when predicting electric energy substitution load, and fit the total load curve after the total load is superimposed; S20: net load curve; S30: curve after natural peak shaving; S40: curve after grid side peak regulation; S50, balance curve; S60, power demand. The present invention establishes a clear and highly operable power balancing process for new power system source-grid-load-storage coordination, clarifies the relationship between the source-grid-load-storage interaction after a large number of new energy sources are connected to the power grid, and can improve the utilization efficiency of the power grid on the basis of ensuring the security of the power grid while fully absorbing a large number of new energy sources.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning and construction, and in particular to a power balancing method for power system source-grid-load-storage coordination. Background Art

[0002] With the rapid development of new energy and the widespread access of new energy-consuming equipment, the operating characteristics of the power system will change significantly. The randomness of new energy generation represented by wind power and photovoltaic power generation, and new energy-consuming equipment represented by electric vehicles will further widen the peak-to-valley difference of power load, reduce the efficiency of the power grid, and pose a challenge to the planning of traditional distribution networks. The power balance of source-grid-load-storage coordination is the core of the planning of new power systems. The balance result determines the scale of power grid construction and the benefits of power grid planning. The existing power balance method is not suitable for the planning of new power systems, and cannot consider the role of new balancing resources such as new energy, energy storage, demand response and microgrids, and cannot consider the effect of source-grid-load-storage coordination. The more new balancing resources are considered, the more economical the power grid is, but the greater the risk is, and vice versa, so it is necessary to compromise and optimize the balance.

[0003] Many scholars at home and abroad have actively carried out research on source-grid-load-storage scheduling methods, such as the invention patent application specification CN202011379111.X (An active distribution network scheduling method considering source-storage-load interaction), but there is a lack of systematic research on power balance methods for planning with source-grid-load-storage coordination in power systems. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a power balancing method for power system source-grid-load-storage coordination, which solves the problems that the existing power balancing method is not suitable for new power system planning, cannot consider the role of new balancing resources such as new energy, energy storage, demand response and microgrids, and cannot consider the effect of source-grid-load-storage coordination.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The power balancing method for power system source-grid-load-storage coordination includes the following steps:

[0009] S10: superimpose the total load. The total load includes conventional load and electric energy substitution load. The conventional load and electric energy substitution load are superimposed and calculated. The electric energy substitution load is predicted by combining the natural growth and special load methods. The total load curve is fitted after the total load is superimposed.

[0010] The specific method for calculating the natural growth in S10 is: calculating by qualitative analysis in the form of an average annual growth rate, and the specific method for calculating the special load is: analyzing and calculating the industrial field and special electric energy substitution in combination with the actual terminal energy consumption situation, and when fitting the total load curve in S10, the total load is obtained by superimposing the conventional load and the electric energy substitution load and deducting the 110kV grid load and the 220kV direct supply 35kV load;

[0011] The electric energy replacement load in S10 includes electric vehicle load and port shore power load;

[0012] The specific method for calculating the electric vehicle load forecast is: predicting the number of electric vehicles and establishing a charging load forecast model; measuring the number of electric vehicles of different types, and establishing a charging load forecast model. The electric vehicle charging load is related to the starting charging time and mileage. The behavioral characteristics of four types of electric vehicles, namely buses, taxis, official cars, and private cars, are analyzed, and a charging load forecast model is established. The charging time, daily mileage distribution function and charging mode are analyzed. The starting charging time of buses approximately follows the normal distribution N (14, 0.52), and the daily mileage The mileage is approximately subject to normal distribution N (0.5, 0.12), and the fast charging mode is adopted; the initial charging time of taxis is approximately subject to uniform distribution, and the daily mileage is approximately subject to normal distribution N (0.3, 0.12), and the fast charging mode is adopted; the initial charging time of official cars is approximately subject to uniform distribution U (18, 24), and the daily mileage is approximately subject to normal distribution N (0.4, 0.12), and the slow charging mode is adopted; the initial charging time of private cars is approximately subject to normal distribution N (17.6, 3.4), and the daily mileage is approximately subject to log-normal distribution (3.2, 0.88), using the slow charging mode, dividing 1 day into 1440 minutes, combining the prediction results of the four types of electric vehicles, using the Monte Carlo method to simulate the four types of electric vehicles through MATLAB software, and finally superimposing the 1440-minute load prediction results of the four types of electric vehicles to obtain the near-, medium- and long-term total load prediction results of electric vehicles;

[0013] The charging load of a typical electric vehicle in the i-th period is expressed as:

[0014]

[0015] Where: Li represents the total charging power in different time periods; Pci represents the charging power of the cth private car in the i-th time period; C represents the total number of electric private cars; Pbi represents the charging power of the bth bus in the i-th time period; B represents the total number of electric buses; Pti represents the charging power of the tth taxi in the i-th time period; E represents the total number of electric taxis; Pzi represents the charging power of the zth special-purpose vehicle in the i-th time period; Z represents the total number of special-purpose vehicles;

[0016] The calculation method for calculating the port shore power load forecast is as follows: the port shore power load is determined according to the ship type, tonnage, characteristics of power equipment and power supply distance. The shore power system capacity adopts a series of: 200, 400, 630, 1000, 2000, 8000kVA. The regional shore power system capacity scale forecast is determined in combination with the port special plan and policy changes. The calculation method for the annual power consumption and annual alternative fuel volume of the port shore power is as follows:

[0017] Taking the annual electricity consumption of shore power berth W as the target value, and the annual number of berthing ships a, average berthing time t, average power load p, and actual shore power utilization u as variables, the calculation process of port shore power load prediction is as follows:

[0018]

[0019]

[0020] Where: Q—annual alternative fuel volume, toe;

[0021] β—diesel consumption per unit of power generation, kg / kW·h;

[0022] γ—diesel standard oil coefficient, kgoe / kg;

[0023] S20: Net load curve. When fitting the net load curve, the power plant output curve is superimposed on the total load curve to obtain the net load curve. The power plant output is calculated by comprehensive analysis of distributed conventional power sources, new energy sources and power supply side energy storage, including the distributed photovoltaic construction scale prediction method. The calculation formula is; P = S × P1 × R1 (4)

[0024] Where S is the effective roof area, P1 is the installed capacity per unit area, R1 is the photovoltaic area construction coefficient, and the value range of R1 is 0.3-0.85;

[0025] S30: natural peak shaving curve, user-active peak shaving and valley filling includes price-based demand response and user-side energy storage. The net load curve is superimposed on the user-active peak shaving and valley filling curve to obtain the natural peak shaving curve.

[0026] S40: The post-peak shaving curve on the grid side is obtained by calculating the new grid capacity investment per unit load, calculating the new energy storage capacity investment per unit load, analyzing the break-even point, and superimposing the grid side energy storage peak shaving curve and the natural peak shaving curve to obtain the post-peak shaving curve on the grid side.

[0027] In S40, the investment required for the additional grid capacity per unit load is calculated based on the proportional relationship between the number of substations at all levels of the power grid and the investment cost. The additional capacity per unit load is 2,600 yuan / kW. The investment in the additional energy storage capacity per unit load is calculated in combination with the typical project cost. The unit investment price of the iron-lithium battery energy storage is 1,300 yuan / kWh. When analyzing the break-even point, the typical daily curve after natural peak regulation is used as the basis to analyze and calculate the break-even point between the grid-side energy storage construction and the grid construction. The grid-side energy storage investment calculation formula is:

[0028] The conversion factor calculation formula is:

[0029] The calculation formula for power grid construction investment is:

[0030] The peak clipping intensity calculation formula is:

[0031] Where P max is the peak load of the power grid; H is the reduction percentage; C E P is the unit price for capacity configuration; N The load needs to be reduced at different times when participating in the peak load regulation of the power grid; m is the total number of hours of load to meet the demand response, where m is the total number of hours of load to meet the demand response; is the discount rate; N Z is the operating life, the power grid construction investment is taken as 30 years, and the energy storage system investment is taken as 10 years; T is the conversion factor; is the energy storage investment conversion factor: is the conversion coefficient of power grid investment; D is the investment required for adding new power grid capacity per unit load;

[0032] S50: Balance curve, the balance curve is obtained by superimposing the peak-shaving and valley-filling curve of the demand response based on incentives on the curve after peak regulation on the grid side, and the fitting includes the demand response prediction method based on incentives and the establishment of a demand response library;

[0033] The incentive-based demand-side response of the incentive-based demand response prediction method in S50 is: when the system reliability is affected or the electricity price is high, the institution implementing the demand-side response encourages users to respond in time and reduce the load according to the established feasible incentive mechanism. When calculating, first calculate the investment in new grid capacity per unit load, which is the same as the calculation method of formula 7 in S40, and then calculate the incentive-based demand response grid enterprise cost, only considering the user subsidy fee during the calculation, and finally analyze the break-even point. The analysis is based on the curve after the natural peak regulation at the whole point of the year, and analyzes and calculates the incentive-based demand response grid enterprise cost and the grid construction break-even point. When calculating,

[0034] Incentive-based demand response grid enterprise costs:

[0035] Power grid construction investment:

[0036] Operation cycle factor:

[0037] Peak clipping strength:

[0038] In the formula To meet the demand response, the capacity needs to be reduced at different times; m is the number of hours involved in the demand response; is the peak load of the power grid; D is the investment required to increase the power grid capacity per unit load; Subsidy fee for users; Q is the reduction percentage; is the inflation rate, which is taken as 3%; is the discount rate, which is taken as 6%; is the conversion factor;

[0039] S60: Power demand, taking the maximum load of the balance curve as the calculated load, combined with the capacity-load ratio required by the guidelines, determine the required transformer capacity.

[0040] On the basis of the above scheme, the net load curve fitting in S20 includes energy flow analysis, distributed power output characteristics analysis, total load characteristics analysis, and determination of typical scenarios. Typical scenario factors include: spring, summer, autumn, winter, sunny, cloudy photovoltaic, wind power, distributed power. When analyzing, first determine the typical scenario, and analyze the energy flow when determining. The energy input type focuses on the peak load shaving effect of distributed power during peak load, and the energy output type focuses on the distributed power supply transmission capacity during valley load. Then, analyze the annual output curve characteristics of the mainstream distributed energy in the region and the typical daily curve characteristics of the four seasons of spring, summer, autumn and winter, and preliminarily analyze various typical spring, summer, autumn and winter and sunny and cloudy days. The impact of typical scenarios on the total load characteristic curve is selected, and one or more typical scenarios with large influence are selected as the typical scenarios for calculating the net load curve of this area. Then the distributed power output curve of the typical scenario is calculated. Specifically, the mainstream distributed power output curve is superimposed under each typical scenario to obtain the distributed power output characteristic curve under each typical scenario. Then the net load curve of the typical scenario is calculated. Specifically, the distributed power output characteristic curve under each typical scenario is superimposed on the total load characteristic curve to obtain the net load curve under each typical scenario. Then, the net load curve under each typical scenario is analyzed, and the net load curve under the scenario with the largest absolute value of load is selected as the net load curve of this area.

[0041] Furthermore, the double limit verification principle needs to be used in the fitting in S20, which is specifically divided into limit test one and limit test two. Limit test one: when the load is at peak, the output of new energy is 0. At this time, the power grid sends the largest power, which is an energy input type. Considering the participation of energy storage on the power supply side, the grid margin is checked to at least ensure that the main transformer and the line are not overloaded. Limit test two: when the load is low, the new energy is fully generated. At this time, the power grid sends the largest power, which is an energy output type. Considering the participation of energy storage on the power supply side, energy is stored when the power supply has a large output. The grid margin is checked to ensure that the main transformer and the line are not overloaded when the power grid is reversed.

[0042] On the basis of the above scheme, the price-based demand response prediction method in S30 includes time-of-use electricity prices, real-time electricity prices, and peak electricity prices. The price-based demand response method is peak-shifting electricity consumption, and the influencing factor is the electricity price policy. When external conditions remain unchanged, the price-based demand response remains unchanged. The total load curve already includes the current demand response-based impact part, so the price-based demand response is "0". When the policy changes, the qualitative analysis gives the increase in the form of a curve as the price-based demand response value.

[0043] (III) Beneficial effects

[0044] Compared with the prior art, the present invention provides a power balancing method for power system source-grid-load-storage coordination, which has the following beneficial effects:

[0045] 1. The present invention establishes a clear and highly operable power balancing process for source-grid-load-storage coordination in new power systems, and clarifies the relationship between the source-grid-load-storage interaction after a large number of new energy sources are connected to the power grid.

[0046] 2. In the present invention, the minimum cost evaluation method of the entire life cycle is used to configure grid-side energy storage and incentive-based user demand response, highlighting the economic orientation.

[0047] 3. In the present invention, the main aspects of the main contradiction are grasped, the calculation process is simplified, and the calculation methods of each key process are given by combining quantitative calculation and qualitative analysis. By superimposing electric energy substitution load, traditional load, new energy output, active peak shaving and valley filling on the user side, peak shaving on the grid side, incentive-based demand response, and orderly electricity consumption, the total load curve, net load curve, curve after natural peak shaving, peak shaving curve on the grid side, balance curve fitting and power demand forecast are obtained.

[0048] 4. In the present invention, orderly electricity consumption is a basic means to alleviate the contradiction of tight electricity supply during peak hours and ensure the safe and stable operation of the power grid. The power balance method for the coordination of source, grid, load and storage in the power system can improve the utilization efficiency of the power grid while ensuring the security of the power grid when a large number of new energy sources are fully absorbed. This method can be widely used in power grid planning to guide power grid construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the flow structure of the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0050] Figure 2 A schematic diagram of total load curve fitting for the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0051] Figure 3 A schematic diagram of a net load curve calculation process of a power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0052] Figure 4 A schematic diagram of net load curve fitting for the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0053] Figure 5 A schematic diagram of curve fitting after natural peak shaving of the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0054] Figure 6 A schematic diagram of the grid-side energy storage construction and grid construction profit and loss model for the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0055] Figure 7 A schematic diagram of grid-side energy storage peak shaving and valley filling for the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0056] Figure 8 A schematic diagram of curve fitting after grid-side peak load regulation of the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0057] Fig. 9 A schematic diagram of benefit analysis of each participant in the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0058] Fig.10 A schematic diagram of cost analysis of each participant in the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0059] Fig.11 A schematic diagram of the demand response cost and power grid construction profit and loss model based on incentives for the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0060] Fig.12 A schematic diagram of demand response peak shaving and valley filling based on incentives for the power balancing method for power system source-grid-load-storage coordination proposed by the present invention;

[0061] Fig.13This is a schematic diagram of the balance curve fitting of the power balancing method for power system source-grid-load-storage coordination proposed by the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Reference Figure 1-Figure 13 , a power balancing method for power system source-grid-load-storage coordination, comprising the following steps:

[0064] S10: superimpose the total load. The total load includes conventional load and electric energy substitution load. The conventional load and electric energy substitution load are superimposed and calculated. The electric energy substitution load is predicted by combining the natural growth and special load methods. The total load curve is fitted after the total load is superimposed.

[0065] S20: Net load curve. When fitting the net load curve, the power plant output curve is superimposed on the total load curve to obtain the net load curve. The power plant output is calculated by comprehensive analysis of distributed conventional power sources, new energy sources and power supply side energy storage, including the distributed photovoltaic construction scale prediction method. The calculation formula is; P = S × P1 × R1 (4)

[0066] Where S is the effective roof area, P1 is the installed capacity per unit area, R1 is the photovoltaic area construction coefficient, and the value range of R1 is 0.3-0.85;

[0067] S30: Natural peak shaving post-curve, user-active peak shaving and valley filling includes price-based demand response and user-side energy storage. Users make comparisons based on time-of-use electricity prices, carbon trading benefits and investment management costs. Users decide whether to participate in peak shaving and valley filling based on the benefits obtained from the comparison. The greater the benefits, the more users will participate in peak shaving and valley filling. User-active peak shaving and valley filling behavior is driven by user interests, so users are more inclined to use it frequently every day. It includes price-based demand response prediction methods and user-side energy storage prediction methods. The net load curve is superimposed on the user-active peak shaving and valley filling curve to obtain the natural peak shaving post-curve, establishing a clear and highly operational power balance process for source-grid-load-storage coordination of new power systems, and clarifying the relationship between source-grid-load-storage interaction after a large number of new energy sources are connected to the power grid;

[0068] S40: The post-peak shaving curve on the grid side is obtained by calculating the new grid capacity investment per unit load, calculating the new energy storage capacity investment per unit load, analyzing the break-even point, and superimposing the grid side energy storage peak shaving curve and the natural peak shaving curve to obtain the post-peak shaving curve on the grid side.

[0069] S50: Balance curve. The balance curve is obtained by superimposing the peak-shaving and valley-filling curve of the incentive-based demand response on the grid-side peak-shaving curve. The fitting includes the incentive-based demand response prediction method and the establishment of a demand response library. The grid-side energy storage and incentive-based user demand response configuration are performed using the full life cycle minimum cost evaluation method, highlighting the economic orientation.

[0070] S60: Electricity demand, taking the maximum load of the balance curve as the calculation load, combined with the capacity-load ratio value required by the guidelines, determine the required transformer capacity, grasp the main aspects of the main contradiction, simplify the calculation process, combine quantitative calculation with qualitative analysis to give the calculation methods of each key process, and obtain the total load curve, net load curve, curve after natural peak shaving, peak shaving curve on the grid, balance curve fitting and electricity demand forecast by superimposing electric energy substitution load, traditional load, new energy output, active peak shaving and valley filling on the user side, peak shaving on the grid side, incentive-based demand response, and orderly electricity consumption. The key calculation methods include electric energy substitution load forecasting method, electric vehicle charging load forecasting method, port shore power load forecasting method, distributed photovoltaic construction scale forecasting method, double limit verification principle, price-based demand response forecasting method, user energy storage forecasting method, grid-side energy storage forecasting method, and incentive-based demand response forecasting method.

[0071] The specific method for calculating natural growth in S10 of the present invention is: natural growth refers to the small-scale and wide-area electricity substitution according to the policy situation, and the common types are residential electrification upgrades and electricity-to-gas substitution in the catering industry. Then, it is calculated by qualitative analysis in the form of an average annual growth rate. The specific method for calculating special loads is: special loads refer to the increase of point loads with larger loads or electricity substitution for a special task, and the common types are electric vehicle charging loads, port shore power, large industrial enterprise electricity substitution transformation and special electricity substitution transformation. The industrial field and special electricity substitution need to be analyzed and calculated in combination with the actual terminal energy consumption situation, and one case is calculated. When fitting the total load curve in S10, the conventional load and the electricity substitution load are superimposed to deduct the 110kV grid load and the 220kV direct supply 35 (10) kV load to obtain the total load. The specific method for calculating the electric vehicle load prediction in S10 is: electric vehicle ownership prediction and establishment of a charging load prediction model, wherein the electric vehicle ownership prediction needs to be combined with the national and local new energy vehicle promotion and application related policy requirements, development goals and electric vehicle charging infrastructure special planning materials to calculate the ownership of different types of electric vehicles. When establishing a charging load prediction model, the charging load of electric vehicles is mainly related to the starting charging time and the mileage. In addition, different types of electric vehicles have behavioral characteristics. Generally, electric vehicles are mainly promoted in four major areas, namely buses, taxis, official vehicles, and private cars. The behavioral characteristics of these four types of electric vehicles are analyzed, and a charging load prediction model is established. The charging time, daily mileage distribution function and charging mode of buses, taxis, official vehicles, and private cars are analyzed. The starting charging time of buses approximately follows the normal distribution N (14, 0.52), and the daily mileage distribution function The mileage is approximately subject to normal distribution N (0.5, 0.12), and the fast charging mode is adopted; the initial charging time of taxis is approximately subject to uniform distribution, and the daily mileage is approximately subject to normal distribution N (0.3, 0.12), and the fast charging mode is adopted; the initial charging time of official cars is approximately subject to uniform distribution U (18, 24), and the daily mileage is approximately subject to normal distribution N (0.4, 0.12), and the slow charging mode is adopted; the initial charging time of private cars is approximately subject to normal distribution N (17.6, 3.4), and the daily mileage is approximately subject to log-normal distribution (3.2, 0.88), using the slow charging mode, dividing 1 day into 1440 minutes, combining the prediction results of the four types of electric vehicles, using the Monte Carlo method to simulate the four types of electric vehicles through MATLAB software, and finally superimposing the 1440-minute load prediction results of the four types of electric vehicles to obtain the near-, medium- and long-term total load prediction results of electric vehicles;

[0072] The charging load of a typical electric vehicle in the i-th period can be expressed as:

[0073]

[0074] Where: Li represents the total charging power in different time periods; Pci represents the charging power of the Cth private car in the i-th time period; C represents the total number of electric private cars; Pbi represents the charging power of the bth bus in the i-th time period; B represents the total number of electric buses; Pbi represents the charging power of the tth taxi in the i-th time period; T represents the total number of electric taxis; Pzi represents the charging power of the zth special-purpose vehicle in the i-th time period; Z represents the total number of special-purpose vehicles. The calculation method for calculating the port shore power load forecast is as follows: the port shore power load should be determined according to the ship type, tonnage, characteristics of electrical equipment and power supply distance. The shore power system capacity should adopt the series of: 200, 400, 630, 1000, 2000, 8000kVA. The regional shore power system capacity scale forecast should be determined in combination with the port special plan and policy changes. The annual power consumption and annual alternative fuel volume of the port shore power are calculated as follows:

[0075] Taking the annual electricity consumption of shore power berth W as the target value, and the annual number of berthing ships a, average berthing time t, average power load p, and actual shore power utilization u as variables, the calculation process of port shore power load prediction is as follows:

[0076]

[0077]

[0078] Where: Q—annual alternative fuel volume (toe);

[0079] β—diesel consumption per unit of power generation (kg / kW·h);

[0080] γ—diesel standard oil coefficient (kgoe / kg).

[0081] It should be noted that the net load curve fitting in S20 includes energy flow analysis, distributed power output characteristics analysis, total load characteristics analysis, and determination of typical scenarios. Typical scenario factors include: spring, summer, autumn, winter, sunny, cloudy photovoltaic, wind power, and distributed power. When analyzing, first determine the typical scenario, and analyze the energy flow when determining. The energy input type pays more attention to the peak-shaving effect of distributed power during peak loads, and the energy output type pays more attention to the distributed power supply transmission capacity during valley loads. Then analyze the annual output curve characteristics of the mainstream distributed energy in the region and the typical daily curve characteristics of the four seasons of spring, summer, autumn and winter. Preliminary analysis of the impact of various typical scenarios of spring, summer, autumn, winter and sunny and cloudy days on the total load characteristic curve, select one or more typical scenarios with great influence as the typical scenarios for calculating the net load curve in this area, and then calculate the distributed power output curve of the typical scenario. Specifically, the mainstream distributed power output curve is superimposed under each typical scenario to obtain the distributed power output characteristic curve under each typical scenario, and then calculate the net load of the typical scenario. The curve is specifically the distributed power output characteristic curve under each typical scenario superimposed with the total load characteristic curve to obtain the net load curve under each typical scenario, and then the net load curve is analyzed to obtain the net load curve. Specifically, the net load curve under each typical scenario is analyzed, and the net load curve under the scenario with the largest absolute value of the load is selected as the net load curve of this area. In S20, the double limit verification principle needs to be used during fitting, which is specifically divided into limit test one and limit test two. Limit test one: "When the load is at peak, the output of new energy is 0", that is, "large load, small output". At this time, the power grid sends the largest power, which is mainly for energy input type. When considering the participation of energy storage on the power supply side, the grid margin should at least ensure that the main transformer and the line are not overloaded. Limit test two: "When the load is low, the new energy is fully generated", that is, "small load, large output". At this time, the grid reverses the power to the maximum, which is mainly for energy output type. When considering the participation of energy storage on the power supply side, energy storage is used when the power supply has a large output. The grid margin should at least ensure that the main transformer and the line are not overloaded when the power grid is reversed.

[0082] In particular, the price-based demand response prediction method in S30 allows users to adjust their electricity consumption according to different electricity prices (including time-of-use electricity prices, real-time electricity prices, and peak electricity prices, etc.). Users adjust their electricity consumption time according to their own situation, make full use of low electricity price periods, and reduce electricity bill expenditures, including time-of-use electricity prices, real-time electricity prices, and peak electricity prices. Time-of-use electricity prices are fixed electricity prices that are transformed into different price mechanisms for different time periods. The price of electricity decreases during low-peak hours, and the price of electricity increases during peak hours, such as peak-valley electricity prices and seasonal electricity prices. Real-time electricity prices can have a faster electricity price update cycle of one hour or less. TOU cannot cope with short-term capacity shortages, etc., so RTP is more reasonable. Peak electricity price RTP has high requirements for measurement infrastructure and marketing systems. In the initial stage, it can be combined with TOU and dynamic CPP. The CPP price is pre-set a certain time in advance. Notify users in time, which can help resist sudden peak electricity consumption. It is particularly important to note that the tiered electricity price is not strictly a real demand-side response measure and cannot accurately adjust demand. It can only reduce energy consumption to a certain extent. The price-based demand response method is mainly to stagger peak electricity consumption. The main influencing factor is the electricity price policy. When external conditions remain unchanged, the price-based demand response remains basically unchanged. The total load curve already includes the current demand response-based impact part, so the price-based demand response can be considered as "0". When the policy changes, the qualitative analysis gives the increase in the form of an "S-shaped curve". The user-side energy storage of the user-side energy storage prediction method in S3 realizes profitability through the difference in peak-valley electricity prices in the mode of "charging at low valleys and discharging at peak times". At the same time, user-side energy storage will also bring about more obvious economic benefits such as dynamic capacity expansion, reduction of demand electricity charges, and obtaining demand-side response subsidies. User-side energy storage methods include not only direct charging and discharging methods, but also heat storage (cold) at low electricity prices and heat release (cold) at high electricity prices. The main influencing factors are time-sharing related policies and changes in the unit price of energy storage technology and energy storage investment. When external conditions remain unchanged, the user-side energy storage remains basically unchanged. When policies, energy storage technology, and energy storage investment unit prices remain unchanged, the user-side energy storage will continue to increase. When changes occur, qualitative analysis uses an "S-curve" to give the increase as the added value of user-side energy storage. In S40, the investment required for the new grid capacity per unit load is calculated based on the proportional relationship between the number of substations at all levels of the power grid and the investment cost. The new capacity per unit load is considered to be 2,600 yuan, that is, 2,600 yuan / kW. When calculating the investment in new energy storage capacity per unit load, the typical project cost is combined. The unit investment price of iron-lithium battery energy storage is 1,300 yuan / kWh. When analyzing the break-even point, the typical daily curve after natural peak regulation is used as the basis. The break-even point between grid-side energy storage construction and grid construction is analyzed and calculated. The formula for calculating grid-side energy storage investment is:

[0083] The conversion factor calculation formula is:

[0084] The calculation formula for power grid construction investment is:

[0085] The formula for calculating the peak shaving (valley filling) strength is:

[0086] In the formula, Pmax is the peak load of the power grid, Q is the reduction percentage; CE is the unit price of capacity configuration, PN is the load reduction required at different times when participating in the peak load regulation of the power grid, m is the total number of hours to meet the demand response load, is the discount rate, NZ is the operating life, the power grid construction investment is considered as 30 years, the energy storage system investment is considered as 10 years, T is the conversion factor, energy storage investment conversion factor: power grid investment conversion factor, D is the investment required for the new power grid capacity per unit load, the main influencing factors are the smoothness of the curve after natural peak shaving, unit power grid investment, unit energy storage investment, etc. The greater the fluctuation of the curve after natural peak shaving, the greater the peak shaving (valley filling) effect of grid-side energy storage. With the advancement of energy storage technology and the maturity of technology, the unit price of energy storage is getting lower and lower, while the difficulty of power grid construction is increasing, resulting in an increase in unit capacity investment. The role of grid-side storage in peak shaving and valley filling is increasing.

[0087] In the present invention, the incentive-based demand-side response of the incentive-based demand response prediction method in S50 is: when the system reliability is affected or the electricity price is high, the institution implementing the demand-side response encourages users to respond in time and reduce the load according to the established feasible incentive mechanism. When calculating, first calculate the investment in new grid capacity per unit load, which is the same as the calculation method in S40, and then calculate the incentive-based demand response grid enterprise cost, only considering the user subsidy fee during the calculation, and finally analyze the break-even point. The analysis is based on the curve after natural peak regulation at the whole point of the year, and analyzes and calculates the incentive-based demand response grid enterprise cost and the grid construction break-even point. When calculating,

[0088] Incentive-based demand response grid enterprise costs:

[0089] Power grid construction investment:

[0090] Operation cycle factor:

[0091] Peak cutting (valley filling) intensity:

[0092] Wherein, capacity needs to be reduced at different times to meet demand response; m is the number of hours participating in demand response; is the peak load of the power grid; D is the investment required for adding new power grid capacity per unit load; is the user subsidy fee; Q is the reduction percentage; is the inflation rate, which is considered as 3% here; is the discount rate, which is taken as 6% here; is the conversion rate, that is, the one-time fee converted according to the investment in power grid construction for 30 years. The greater the fluctuation of the curve after peak regulation on the power grid side, the greater the peak shaving (valley filling) effect of the incentive-based demand response. When establishing the demand response library in S50, the marketing department will find users according to this scale to sign an agreement to establish the demand response library. The demand response library requires the following two points to be met at the same time: one is that the total amount of demand response must be greater than the break-even point, and the other is to set a threshold for the number of times each user participates in demand response within a year. , the number of times cannot exceed the threshold. S60 includes the principle of orderly power consumption. Orderly power consumption is the basic guarantee means to alleviate the contradiction of power supply tension during peak hours and ensure the safe and stable operation of the power grid. The principle of orderly power consumption arranges users' orderly power consumption in the order of peak shifting, peak avoidance and power restriction in the regulation method, and controls the impact to a minimum. When the power supply capacity of the power grid changes, the power consumption limit of related users is adjusted evenly so that users can adjust production shifts or take turns to avoid peak power consumption in a planned manner. In an emergency, power supply companies at all levels should strictly implement the power restriction priority table, emergency plans for handling large-scale power outages in the power grid and black start plans, enhance the response and handling capabilities in the event of emergencies, ensure the overall stable operation of the power grid, and then have limited protection, differentiate and give priority to the following power consumption:

[0093] A. Electricity used for residents’ daily life, irrigation and drainage, fertilizer production and other agricultural production;

[0094] B. Emergency command and disposal departments, major party, government and military agencies, radio, television, telecommunications, transportation, prisons and other users related to national security and social order;

[0095] C. Security load of enterprises producing hazardous chemicals, mining, etc. where power outages will cause serious personal injury or equipment damage;

[0096] D. Major social activity venues, hospitals, financial institutions, schools and other users that are related to the safety of people’s lives and property;

[0097] E. Users of infrastructure such as water supply, heating and energy supply;

[0098] F. National key projects and military industrial enterprises.

[0099] The following electricity consumption is restricted:

[0100] A. Projects that have been completed or are under construction in violation of regulations;

[0101] B. Eliminated and restricted enterprises in the industrial structure adjustment catalogue;

[0102] C. Enterprises whose unit product energy consumption exceeds the national or local mandatory energy consumption limit standards;

[0103] D. Landscape lighting and lighting engineering;

[0104] E. Other high energy consumption and high emission enterprises;

[0105] Strengthen technical means to control load. For electricity users that have not implemented orderly electricity consumption measures as required within 30 minutes after the start of the specified time, the power operation management department authorizes the local power supply company to use a variety of technical means to limit power or shut down the entire plant. The power balance method for the coordination of source, grid, load and storage in the power system can improve the utilization efficiency of the power grid while ensuring the security of the power grid when fully absorbing a large number of new energy sources. This method can be widely used in power grid planning to guide power grid construction.

[0106] In the description of this article, it should be noted that relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0107] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power balancing method for power system source-grid-load-storage coordination, characterized in that: The following steps are involved: S10: Superimposed total load: The total load includes conventional load and electric energy replacement load, which are superimposed and calculated. The electric energy replacement load is predicted by combining natural growth and special load, and the total load curve is fitted after the total load is superimposed; The specific method for calculating the natural growth in S10 is: calculating by qualitative analysis in the form of an average annual growth rate, and the specific method for calculating the special load is: analyzing and calculating the industrial field and special electric energy substitution in combination with the actual terminal energy consumption situation, and when fitting the total load curve in S10, the total load is obtained by superimposing the conventional load and the electric energy substitution load and deducting the 110kV grid load and the 220kV direct supply 35kV load; The electric energy replacement load in S10 includes electric vehicle load and port shore power load; The specific method for calculating the electric vehicle load forecast is: predicting the number of electric vehicles and establishing a charging load forecast model; measuring the number of electric vehicles of different types, and establishing a charging load forecast model. The electric vehicle charging load is related to the starting charging time and mileage. The behavioral characteristics of four types of electric vehicles, namely buses, taxis, official cars, and private cars, are analyzed, and a charging load forecast model is established. The charging time, daily mileage distribution function and charging mode are analyzed. The starting charging time of buses approximately follows the normal distribution N (14, 0.52), and the daily mileage The mileage is approximately subject to normal distribution N (0.5, 0.12), and the fast charging mode is adopted; the initial charging time of taxis is approximately subject to uniform distribution, and the daily mileage is approximately subject to normal distribution N (0.3, 0.12), and the fast charging mode is adopted; the initial charging time of official cars is approximately subject to uniform distribution U (18, 24), and the daily mileage is approximately subject to normal distribution N (0.4, 0.12), and the slow charging mode is adopted; the initial charging time of private cars is approximately subject to normal distribution N (17.6, 3.4), and the daily mileage is approximately subject to log-normal distribution (3.2, 0.88), using the slow charging mode, dividing 1 day into 1440 minutes, combining the prediction results of the four types of electric vehicles, using the Monte Carlo method to simulate the four types of electric vehicles through MATLAB software, and finally superimposing the 1440-minute load prediction results of the four types of electric vehicles to obtain the near-, medium- and long-term total load prediction results of electric vehicles; The charging load of a typical electric vehicle in the i-th period is expressed as: Where: Li represents the total charging power at different time periods; P ci represents the charging power of the cth private car in the i-th period; C represents the total number of electric private cars; P bi represents the charging power of the bth bus in the i-th period; B represents the total number of electric buses; P ei represents the charging power of the t-th taxi in the i-th period; E represents the total number of electric taxis; P zi represents the charging power of the z-th special-purpose vehicle in the i-th period; Z represents the total number of special-purpose vehicles; The calculation method for calculating the port shore power load forecast is as follows: the port shore power load is determined according to the ship type, tonnage, characteristics of power equipment and power supply distance. The shore power system capacity adopts a series of: 200, 400, 630, 1000, 2000, 8000kVA. The regional shore power system capacity scale forecast is determined in combination with the port special plan and policy changes. The calculation method for the annual power consumption and annual alternative fuel volume of the port shore power is as follows: Taking the annual electricity consumption of shore power berth W as the target value, and the annual number of berthing ships a, average berthing time t, average power load p, and actual shore power utilization u as variables, the calculation process of port shore power load prediction is as follows: Where: Q—annual alternative fuel volume, toe; β—diesel consumption per unit of power generation, kg / kW·h; γ—diesel standard oil coefficient, kgoe / kg; S20: Net load curve: When fitting the net load curve, the power plant output curve is superimposed on the total load curve to obtain the net load curve. The power plant output is calculated by comprehensive analysis of distributed conventional power sources, new energy sources and power supply side energy storage, including the distributed photovoltaic construction scale prediction method. The calculation formula is; P = S × P1 × R1 (4) Where S is the effective roof area, P1 is the installed capacity per unit area, R1 is the photovoltaic area construction coefficient, and the value range of R1 is 0.3-0.85; S30: Natural peak shaving curve: User-initiated peak shaving and valley filling includes price-based demand response and user-side energy storage. The net load curve is superimposed on the user-initiated peak shaving and valley filling curve to obtain the natural peak shaving curve. S40: Grid-side peak-shaving curve: By calculating the new grid capacity investment per unit load, calculating the new energy storage capacity investment per unit load, analyzing the break-even point, the grid-side energy storage peak-shaving curve is superimposed on the natural peak-shaving curve to obtain the grid-side peak-shaving curve; In S40, the investment required for the additional grid capacity per unit load is calculated based on the proportional relationship between the number of substations at all levels of the power grid and the investment cost. The additional capacity per unit load is 2,600 yuan / kW. The investment in the additional energy storage capacity per unit load is calculated in combination with the typical project cost. The unit investment price of the iron-lithium battery energy storage is 1,300 yuan / kWh. When analyzing the break-even point, the typical daily curve after natural peak regulation is used as the basis to analyze and calculate the break-even point between the grid-side energy storage construction and the grid construction. The grid-side energy storage investment calculation formula is: The conversion factor calculation formula is: The calculation formula for power grid construction investment is: The peak clipping intensity calculation formula is: Where P max is the peak load of the power grid; H is the reduction percentage; C E P is the unit price for capacity configuration; N The load needs to be reduced at different times when participating in the peak load regulation of the power grid; m is the total number of hours of load to meet the demand response, is the discount rate; N Z is the operating life, the power grid construction investment is taken as 30 years, and the energy storage system investment is taken as 10 years; T is the conversion factor; is the energy storage investment conversion factor; is the conversion coefficient of power grid investment; D is the investment required for adding new power grid capacity per unit load; S50: Balance curve: The balance curve is obtained by superimposing the peak-shaving and valley-filling curve of the demand response based on incentives on the curve after peak regulation on the grid side. The fitting includes the demand response prediction method based on incentives and the establishment of a demand response library; The incentive-based demand-side response of the incentive-based demand response prediction method in S50 is: when the system reliability is affected or the electricity price is high, the institution implementing the demand-side response encourages users to respond in time and reduce the load according to the established feasible incentive mechanism. When calculating, first calculate the investment in new grid capacity per unit load, then calculate the incentive-based demand response grid enterprise cost, and only consider the user subsidy fee when calculating, and finally analyze the break-even point. When analyzing, the curve after natural peak regulation at the whole point of the year is used as the basis, and the incentive-based demand response grid enterprise cost and the grid construction break-even point are analyzed and calculated. When calculating, Incentive-based demand response grid enterprise costs: Incentive-based demand response grid construction investment: Operation cycle factor: Incentive-based demand response peak shaving intensity: In the formula To meet the demand response, the capacity needs to be reduced at different times; m is the total number of hours to meet the demand response load; is the peak load of the power grid; D is the investment required to increase the power grid capacity per unit load; Subsidize the user fees; 激 is the percentage of reduction; is the inflation rate, which is taken as 3%; is the discount rate, which is taken as 6%; S60: Power demand: Take the maximum load of the balance curve as the calculated load, and determine the required transformer capacity in combination with the capacity-load ratio required by the guidelines.

2. The power balancing method for power system source-grid-load-storage coordination according to claim 1 is characterized in that: The net load curve fitting in S20 includes energy flow analysis, distributed power output characteristics analysis, total load characteristics analysis, and determination of typical scenarios. Typical scenario factors include: spring, summer, autumn, winter, sunny days, cloudy days, photovoltaic, wind power, and distributed power sources. When analyzing, first determine the typical scenario, and analyze the energy flow when determining. The energy input type focuses on the peak load shaving effect of distributed power sources during peak loads, and the energy output type focuses on the distributed power supply capacity during valley loads. Then, analyze the annual output curve characteristics of the mainstream distributed energy in the region and the typical daily curve characteristics of the four seasons of spring, summer, autumn, and winter, and preliminarily analyze the effects of various typical scenarios of spring, summer, autumn, winter, and sunny and cloudy days on the total Influence of load characteristic curve, select one or more typical scenarios with great influence as typical scenarios for calculating the net load curve of this area, and then calculate the distributed power output curve of the typical scenario. Specifically, superimpose the mainstream distributed power output curve under each typical scenario to obtain the distributed power output characteristic curve under each typical scenario, and then calculate the net load curve of the typical scenario. Specifically, superimpose the distributed power output characteristic curve under each typical scenario on the total load characteristic curve to obtain the net load curve under each typical scenario, and then analyze the net load curve under each typical scenario, and select the net load curve under the scenario with the largest absolute value of load as the net load curve of this area.

3. The power balancing method for power system source-grid-load-storage coordination according to claim 2 is characterized in that: The double limit verification principle needs to be used in the fitting in S20, which is specifically divided into limit test one and limit test two. Limit test one: when the load is at peak, the output of new energy is 0. At this time, the power grid sends the largest power, which is an energy input type. When considering the participation of energy storage on the power supply side, the grid margin must be checked to at least ensure that the main transformer and the line are not overloaded. Limit test two: when the load is low, the new energy is fully generated. At this time, the power grid sends the largest power, which is an energy output type. When considering the participation of energy storage on the power supply side, energy is stored when the power supply has a large output. The grid margin must be checked to ensure that the main transformer and the line are not overloaded when the power grid is reversed.

4. The power balancing method for power system source-grid-load-storage coordination according to claim 1 is characterized in that: The price-based demand response prediction method in S30 includes time-of-use electricity prices, real-time electricity prices, and peak electricity prices. The price-based demand response method is peak-shifting electricity consumption, and the influencing factor is the electricity price policy. When external conditions remain unchanged, the price-based demand response remains unchanged. The total load curve already includes the current demand response-based impact part, so the price-based demand response is "0". When the policy changes, the qualitative analysis gives the increase in the form of a curve as the price-based demand response value.

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