Scheduling method for source-load-storage collaborative optimization under consideration of stepped carbon transaction mechanism

Abstract

The invention discloses a scheduling method for source-load-storage collaborative optimization under the consideration of a stepped carbon transaction mechanism, and the method comprises an IESH optimization operation model, wherein the IEHS daily operation cost F comprises the carbon transaction cost FCO2, the distributed power supply operation cost FDG, the power grid electricity purchase cost Fnet, and the gas boiler and gas turbine operation cost FMT and FGB; depreciation costs of a heat storage tank and a storage battery are FHST and FBAT; a flexible load compensation cost FL; the objective function is shown as a formula: minF = FCO2 + FIEHE. According to the scheduling method in the scheme, the operation model is optimized by considering the demand response IEHS under the carbon transaction mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a dispatching method for coordinated optimization of sources, loads and storages under a stepped carbon trading mechanism. Background Art

[0002] As global climate issues become increasingly severe, the power industry, as one of the main sources of carbon emissions, has become an urgent need for low-carbon transformation. To this end, my country is actively promoting the carbon trading market to promote "zero emissions" of carbon dioxide through market means.

[0003] The carbon trading mechanism can optimize the resource allocation of the power system and promote energy conservation and emission reduction. The document "Analysis of the Impact of Carbon Trading Mechanism on the Power Industry" explains the impact of carbon trading on my country's power industry from the power generation side and the power grid side, and puts forward policy recommendations for its participation in the carbon trading market. The document "Low-carbon Economic Dispatch of Wind Power System Based on Carbon Trading" introduces carbon trading and proposes a new low-carbon economic dispatch model that takes into account both the economic efficiency of power production and the low carbon nature of system emissions. The document "Low-carbon Dispatch of Wind Power Power System Considering Uncertainties on Both Source and Load Sides" proposes a low-carbon economic dispatch model that considers the uncertainty of wind power and load, and introduces carbon trading costs into the objective function, aiming to reduce the carbon emissions of the system and increase the wind power consumption of the system. A low-carbon economic operation model based on the carbon trading mechanism is proposed for the integrated energy system. The implementation of the carbon trading mechanism can not only optimize the allocation of system resources, but also effectively promote energy conservation and emission reduction.

[0004] At the same time, the inevitable intermittency and volatility in the process of renewable energy generation affect the safe and stable operation of the power system and increase the difficulty of power system dispatch. In order to effectively utilize renewable energy and ensure the reliability of power system operation, the most direct way to dispatch the power system is to use energy storage systems to smooth power fluctuations. However, this method is costly and has a long construction period. Using load-side demand response to reversely regulate the system is a more economical and effective means of power regulation. The document "Optimal Configuration of Photovoltaic Microgrid Energy Storage Considering Demand Response and Source-Load Uncertainty" established a user dispatch priority evaluation index, formulated corresponding preferential policies for users with different priorities, and then proposed an energy storage planning configuration strategy guided by demand response. The document "Optimal Operation Method of AC / DC Hybrid Microgrid under Real-time Electricity Price Mechanism" divides user loads into easy-to-transfer, easy-to-save and rigid loads, established a response model based on classified loads to real-time electricity prices, and established a microgrid optimization operation model based on this, increasing microgrid revenue while reducing users' average electricity purchase costs. However, existing research mostly focuses on the single aspect of carbon trading mechanism or demand response, lacks in-depth discussion of the synergy between the two, which limits the coordinated development of low-carbon and economic efficiency of the system. Summary of the invention

[0005] The present invention aims to provide a scheduling method for coordinated optimization of sources, loads and storage under a stepped carbon trading mechanism, by taking into account an IEHS optimization operation model of demand response under the carbon trading mechanism.

[0006] The scheduling method of source-load-storage coordinated optimization under the ladder carbon trading mechanism in this scheme includes the IESH optimization operation model. The IEHS daily operation cost F includes the carbon trading cost F CO2 , Distributed power generation operating cost F DG , Power grid purchase cost F net , gas boiler, gas turbine operating costs F MT 、F GB ; Depreciation cost of heat storage tank and battery F HST 、F BAT ; Flexible load compensation cost F L ; The objective function is shown as follows:

[0007] minF=F CO2 +F IEHE (10)

[0008] in:

[0009]

[0010] F L =F shift +F tran +F cut (17)

[0011] In the formula, K net (t) is the time-of-use electricity price; P net (t) is the power exchanged with the power grid; K w , K pv , K MT , K GB , K HST , K BAT , P w (t), P pv (t), P MT (t), P GB (t), P HST (t), P BAT (t) are the operating cost coefficient and output power of wind power, photovoltaic power, gas turbine, gas boiler, battery and heat storage tank, respectively. HST (t), P BAT (t) can be positive or negative. When it is positive, it means absorbing power, and when it is negative, it means releasing power.

[0012] Furthermore, the power balance constraint

[0013]

[0014] Where: P load , Q load They are the electric and heating load requirements respectively.

[0015] Since the system power balance contains uncertain variables, this paper introduces a fuzzy processing method when arranging the day-ahead dispatch plan. Scenery blur parameters Equation 18 is relaxed to establish a power constraint balance with a confidence level not less than α.

[0016]

[0017] Among them, P r (·) indicates the credibility of the event;

[0018] Fuzzy parameters are used to represent the uncertain variables in the system, and the output of conventional units satisfies the inequality containing fuzzy parameters as an event, so that the probability of random events in uncertain environments caused by prediction errors meets the preset confidence level. This is the fuzzy chance constrained programming problem.

[0019] Further, battery constraints,

[0020]

[0021] Where: U is the maximum charge and discharge power allowed by energy storage; s,t It is the charge and discharge status, 1 means discharging, 0 means charging; E s (0) is the energy storage capacity at the initial moment; and The upper and lower limits of the remaining capacity of energy storage during the dispatch process;

[0022] Output upper and lower limit constraints

[0023]

[0024] Where: P w,max , P pv,max , P net,min , P net,max , P MT,max , Q HT,max , Q GB,max , Q HST,min , Q HST,max Output limits for wind power, photovoltaic power, electricity purchase and sales, gas turbines, gas boilers, batteries, and heat storage tanks.

[0025] Furthermore, source-load uncertainty modeling: Establish uncertainty models for wind and solar power output and electric load demand:

[0026]

[0027] In the formula, is the value of wind power integrated into the grid, is the value of photovoltaic integration into the grid, is the load size. W (t) is the wind power forecast value, P pv (t) is the photovoltaic prediction value, P load (t) is the load prediction value.

[0028] Furthermore, the dispatch model based on fuzzy chance constraints: averaging the uncertainty model of wind and solar power output and electric load demand can be obtained:

[0029]

[0030] In the formula, and The average value of wind power and photovoltaic power output connected to the grid and the average value of load size are calculated respectively.

[0031] Furthermore, the clear equivalent class of fuzzy chance constraints:

[0032] If the constraint function g(x,z) has the following form:

[0033] g(x,ζ)=h1(x)ζ1+h2(x)ζ2+…+h t (x)ζ t +h0(x)(27)

[0034] In the formula, and The average value of wind power and photovoltaic power output connected to the grid and the average value of load size are calculated respectively.

[0035] When the confidence level α ≥ 0.5, the clear equivalence class of chance constraints is:

[0036]

[0037] Working principle of this scheme and its beneficial effects: This paper proposes an IEHS optimization operation model that considers demand response under the carbon trading mechanism. First, it not only uses the price response mechanism to guide users to adjust their energy demand and reduce load fluctuations, but also reduces the system operation cost through multi-energy complementarity and collaborative optimization. Secondly, the impact of the carbon trading mechanism on the system wind power consumption is considered. Then, based on the fuzzy opportunity constraint, the prediction error between the power supply side and the load side is considered, and the demand side response IEHS low-carbon economic dispatch model under the fuzzy opportunity constraint is constructed to achieve the role of expanding the wind power consumption space and smoothing the load curve to achieve peak shaving and valley filling. Finally, through example simulation verification, this paper shows that considering demand response under fuzzy opportunity constraints and carbon trading mechanisms can effectively achieve peak shaving and valley filling, and synergistically improve the economy and low carbon of the electric and thermal integrated energy system, which provides an important reference for the low-carbon economic operation of the electric and thermal integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is the IEHS model;

[0039] Figure 2 Provide output for load and new energy forecasting;

[0040] Figure 3 To optimize the front power load curve;

[0041] Figure 4 To optimize the front heat load curve;

[0042] Figure 5 Output of each energy source for electric load;

[0043] Figure 6 Output of each energy source for heat load;

[0044] Figure 7 To optimize the rear electrical load curve;

[0045] Figure 8 To optimize the heat load distribution. DETAILED DESCRIPTION

[0046] The following is a further detailed description through specific implementation methods:

[0047] The embodiment is basically as shown in the attached Figure 1 As shown:

[0048] 1. IEHS model considering DR

[0049] 1.1IEHS model

[0050] IEHS contains multiple energy forms such as electricity, heat and gas. Coordinated planning and optimized operation of various heterogeneous energy subsystems can improve the system's operational flexibility and energy supply reliability. At the same time, it can solve the problem of low efficiency in renewable energy utilization when a single energy system operates independently, resulting in high system operating costs and serious pollution.

[0051] This paper constructs the basic architecture of IEHS and the energy interaction as follows Figure 1 As shown in the figure, the system consumes electricity and natural gas to meet the electricity and heat load requirements, and uses batteries and heat storage tanks to store and release electricity and heat energy, improving the system scheduling flexibility and energy supply reliability. The electricity load demand of the system in this paper is jointly supplied by wind power, photovoltaic power generation equipment, large power grid, gas turbines and batteries; the heat load demand is jointly met by waste heat boilers, gas boilers and heat storage tanks.

[0052] 1.2 Demand response load model

[0053] Demand response load refers to the situation in which power users in the power system actively adjust their electricity consumption behavior according to market price signals or incentives to achieve the purpose of balancing power supply and demand and ensuring stable operation of the system. According to its characteristics, this paper considers price-based demand response. According to the way loads participate in demand response, loads are divided into three types: shiftable load (SL), transferable load (TL), and curtailable load (CL).

[0054] (1)TL

[0055] The power demand can be flexibly adjusted during the shiftable period, but the total load demand remains unchanged before and after the shift. Suppose the shiftable period interval is [t tr- ,t tr+ ] In order to prevent the load from being excessively divided into multiple time periods, resulting in frequent start and stop of the power load, the continuous operation time constraint is increased.

[0056]

[0057] Z τ tran P tran min ≤P τ tran ≤Z τ tran P tran max (2)

[0058] Where, T tran main is the minimum continuous running time; Z τtran is the transfer state variable. When it is 1, it indicates load transfer, and when it is 0, it indicates no load change. τ tran is the transferred power; P tran min , P tran max are the upper and lower limits of the transfer power.

[0059] Post-transfer compensation cost F tran :

[0060]

[0061] Where: T ct To reduce the duration; T cut min , T cut max To continuously reduce the upper and lower limits of the duration; Z τ cut It is a reduction state variable. When it is 1, it indicates reduction, and when it is 0, it indicates no change in load.

[0062] (2)CL

[0063] The system stability and balance can be maintained by quantitatively reducing the load, but considering user satisfaction, it is necessary to increase the upper and lower limits of the reduction and the number of reduction constraints:

[0064]

[0065] Where, T ct To reduce the duration; T cut min , T cut max To continuously reduce the upper and lower limits of the duration; Z τ cut It is a reduction state variable. When it is 1, it indicates reduction, and when it is 0, it indicates no change in load.

[0066] 2. Ladder carbon trading model

[0067] The tiered carbon trading model is a carbon trading mechanism that sets different transaction costs or prices based on different carbon emission levels. As the carbon emission level increases, the corresponding transaction costs rise step by step, which encourages all parties to take more active carbon emission reduction measures. This model encourages enterprises and individuals to reduce carbon emissions through economic incentives, promotes the development of a low-carbon economy, and provides more specific and differentiated carbon trading policies for different industries.

[0068] 2.1IEHS carbon emission quota and actual carbon emissions

[0069] At present, there are two main ways to initially allocate carbon emission quotas in China: free allocation and paid allocation through free auction. Free allocation methods are divided into two types: baseline method and historical method. In combination with my country's actual situation, the carbon emission quota in this paper is determined by the baseline method. The calculation formula for the carbon emission quota allocated to the system free of charge is as follows:

[0070]

[0071] In the formula, E c Represents the total carbon emission quota allocated to the system free of charge; P h GT , P e GT are the electrical and thermal power output by GT respectively; P is the thermal conversion coefficient of electricity; h GB is the thermal power output by GB.

[0072] Actual carbon emissions E ac for:

[0073]

[0074] Where δ GT , δ GB are the carbon emission coefficients of GT and GB respectively.

[0075] Freely traded carbon emission credits E IEHS for:

[0076] E IEHS =E ac -E c (8)

[0077] 2.2 Ladder-type carbon trading cost calculation model

[0078]

[0079] Where: P im (A) represents an imprecise probability interval for event A to occur; P (A) are the upper and lower limits of the probability of event A occurring. CO2 is the total carbon trading cost; d is the length of the carbon emission interval; λ is the price growth rate; c is the carbon trading price;

[0080] 3.IESH Optimization Operation Model

[0081] 3.1 Objective Function

[0082] IEHS daily operating cost F includes carbon trading cost F CO2 , Distributed power generation operating cost FDG , Power grid purchase cost F net , gas boiler, gas turbine operating costs F MT 、F GB ; Depreciation cost of heat storage tank and battery F HST 、F BAT ; Flexible load compensation cost F L ; The objective function is shown in Eq.

[0083] minF=F CO2 +F IEHE (10)

[0084] in:

[0085]

[0086]

[0087] F L =F shift +F tran +F cut (17)

[0088] In the formula, K net (t) is the time-of-use electricity price; P net (t) is the power exchanged with the power grid; K w , K pv , K MT , K GB , K HST , K BAT , P w (t), P pv (t), P MT (t), P GB (t), P HST (t), P BAT (t) are the operating cost coefficient and output power of wind power, photovoltaic power, gas turbine, gas boiler, battery and heat storage tank, respectively. HST (t), P BAT (t) can be positive or negative. When it is positive, it means absorbing power, and when it is negative, it means releasing power.

[0089] 3.2 Constraints

[0090] (1) Power balance constraints

[0091]

[0092] Where: P load , Q load They are the electric and heating load requirements respectively.

[0093] Since the system power balance contains uncertain variables, this paper introduces a fuzzy processing method when arranging the day-ahead dispatch plan. Scenery blur parameters Equation 18 is relaxed to establish a power constraint balance with a confidence level not less than α.

[0094]

[0095] Among them, P r (·) indicates the credibility of the event.

[0096] Fuzzy parameters are used to represent the uncertain variables in the system, and the output of conventional units satisfies the inequality containing fuzzy parameters as an event, so that the probability of random events in uncertain environments caused by prediction errors meets the preset confidence level. This is the fuzzy chance constrained programming problem.

[0097] (2) Battery constraints

[0098]

[0099] Where: U is the maximum charge and discharge power allowed by energy storage; s,t It is the charge and discharge status, 1 means discharging, 0 means charging; E s (0) is the energy storage capacity at the initial moment; and The upper and lower limits of the remaining capacity of energy storage during the dispatch process;

[0100] (3) Output upper and lower limits

[0101]

[0102] Where: P w,max , P pv,max , P net,min , P net,max , P MT,max , Q HT,max , Q GB,max , Q HST,min , Q HST,max Output limits for wind power, photovoltaic power, electricity purchase and sales, gas turbines, gas boilers, batteries, and heat storage tanks.

[0103] 3.3 Source-load uncertainty modeling

[0104] Establish uncertainty model of wind and solar power output and electric load demand:

[0105]

[0106] In the formula, is the value of wind power integrated into the grid, is the value of photovoltaic integration into the grid, is the load size. W (t) is the wind power forecast value, P pv (t) is the photovoltaic prediction value, P load (t) is the load prediction value.

[0107] 3.4 Scheduling Model Based on Fuzzy Chance Constraints

[0108] By averaging the uncertainty model of wind and solar power output and electric load demand, we can get:

[0109]

[0110] In the formula, and The average value of wind power and photovoltaic power output connected to the grid and the average value of load size are calculated respectively.

[0111] 3.5 Clear Equivalence Classes of Fuzzy Chance Constraints

[0112] If the constraint function g(x,z) has the following form:

[0113] g(x,ζ)=h1(x)ζ1+h2(x)ζ2+…+h t (x)ζ t +h0(x)(27)

[0114] In the formula, and The average value of wind power and photovoltaic power output connected to the grid and the average value of load size are calculated respectively.

[0115] When the confidence level α ≥ 0.5, the clear equivalence class of chance constraints is:

[0116]

[0117] 4. Example simulation analysis

[0118] 4.1 System parameter settings

[0119] In order to verify the validity of the model, a regional IEHS was selected for analysis.

[0120] The predicted values ​​of power load, wind power and photovoltaic output are as follows: Figure 2 The fuzzy membership values ​​of the load are shown in Table 1.

[0121] Tab.1Fuzzy parameters

[0122] Fuzzy parameters <![CDATA[ω1]]> <![CDATA[ω2]]> <![CDATA[ω3]]> Wind power output 0.6 1 1.4 Photovoltaic output 0.5 1 1.5 load 0.9 1 1.1

[0123] The energy prices in the example are divided into time-of-use electricity prices and fixed gas prices. The 24 hours of the day are divided into three time periods: valley, flat, and peak. The valley period is: 00:00-07:00; the flat period is: 07:00-10:00, 15:00-18:00, 21:00-24:00; the peak period is: 10:00-15:00, 18:00-21:00. The time-of-use electricity price is shown in Table 2. The natural gas price is 2.5 yuan / m 3 .

[0124] Table 2 Electricity prices for different periods

[0125] Time Valley Electricity Price Flat electricity price Peak electricity price Electricity purchase price 0.25 0.53 0.82 Electricity price 0.22 0.42 0.65

[0126] The distribution of each flexible electrical and thermal load before optimization is as follows Figure 3 and Figure 4 shown.

[0127] 4.2 Analysis of the scheduling results of the example

[0128] In order to verify the rationality of the proposed model, this paper conducts a comparative analysis on the following three scenarios.

[0129] Scenario 1: No consideration of fuzzy opportunity constraints, carbon trading mechanisms, and demand response;

[0130] Scenario 2: Considering demand response under the carbon trading mechanism;

[0131] Scenario 3: Demand response under the carbon trading mechanism with fuzzy opportunity constraints;

[0132] Scenario Total cost / yuan Operating cost / yuan New energy output / kW·h Carbon trading cost / yuan Carbon emissions / t 1 3697.40 3697.40 2286.10 0 2.27 2 3409.80 3262.56 2740.00 147.24 1.83 3 3515.50 3323.14 2550.00 192.36 2.15

[0133] As shown in Table 3, compared with scenario 1, the carbon emissions of scenario 2 decreased by 0.44t, and the carbon emissions of scenario 3 decreased by 0.12t, which shows that the carbon emissions of the system can be reduced after considering the ladder carbon trading. At the same time, compared with scenario 1, the output of new energy in scenario 2 increased by 453.9kW·h, and the output of new energy in scenario 3 increased by 263.9kW·h. This is because after considering the ladder carbon trading and demand response, carbon trading increases the cost of purchasing electricity and the cost of gas turbine power supply, while the demand response load reduces the peak-valley difference by adjusting the energy consumption time, so that the system can choose a more economical way to purchase energy and absorb more new energy. Compared with scenario 2, the output of new energy in scenario 3 decreased by 190kW·h, and the carbon emissions increased by 0.32t. This is because after considering the uncertainty of source and load, scenario 3 leaves a certain margin to deal with the uncertainty of wind and solar output and the prediction error of load through fuzzy opportunity constraints, which makes the system more robust at the expense of certain economy.

[0134] The energy output of scenario 3 and the distribution of electric and thermal loads after optimization are shown in the figure below: Figure 5-Figure 8shown.

[0135] Depend on Figure 5 It can be seen that the power demand of the electric load is mainly met by wind power, gas turbines and power grids, and there is no new energy output from 00:00 to 07:00. This is because it is in the valley period of electricity prices, and the operating cost of new energy is greater than the cost of purchasing electricity, resulting in wind and solar power abandonment. The output of the battery is not completely in accordance with the traditional dispatching mode of storing electricity at valley prices and discharging at peak prices. This is because when the system considers the uncertainty of source and load, the system reserves a margin to deal with the uncertainty of new energy and load through source-load-storage coordination, which proves the effectiveness of the model in this paper. Figure 3 and Figure 7 It can be seen that the shiftable and transferable loads are transferred from the peak power demand period to the valley period, which reduces the peak-valley difference and increases the space for the absorption of new energy. The reducible loads are reduced to a certain extent during the peak power demand period, but are basically unaffected during the valley period, which ensures the users' electricity needs while increasing the space for the absorption of new energy.

[0136] Depend on Figure 6 It can be seen that the heat load demand is mainly provided by the heat recovery system, and compared with Figure 4 and Figure 7 It can be seen that the heat load is transferred from the peak period to the valley period, which greatly reduces the peak-valley difference of the heat load. In addition, the heat recovery system improves the utilization rate of the system energy and reduces carbon emissions.

[0137] in conclusion

[0138] This paper considers the coordinated optimization of sources, loads and storage under the carbon trading mechanism, and compares and analyzes the costs, new energy consumption and carbon emissions of three scenarios. Through scenario comparison, the role of source-load-storage coordinated optimization in improving the utilization of system resources and reducing carbon emissions is verified. At the same time, considering the carbon trading mechanism and demand response load, the peak-to-valley difference is reduced, the load curve is smoothed, the consumption of new energy is improved, and the operating cost and carbon emissions of the system are reduced. Furthermore, by leaving a certain margin through fuzzy opportunity constraints, although it will increase certain operating costs and carbon emissions, the system has better robustness and a better balance between economy and robustness.

[0139] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A dispatching method for coordinated optimization of sources, loads and storage under a stepped carbon trading mechanism is characterized by: Including IESH optimization operation model, IEHS daily operation cost F includes carbon trading cost F CO2 , Distributed power generation operation cost F DG , Power grid purchase cost F net , gas boiler, gas turbine operating costs F MT 、F GB ; Depreciation cost of heat storage tank and battery F HST 、F BAT ; Flexible load compensation cost F L ; The objective function is shown as follows: minF=F CO2 +F IEHE (10) in: F L =F shift +F tran +F cut (17) In the formula, K net (t) is the time-of-use electricity price; P net (t) is the power exchanged with the power grid; K w , K pv , K MT , K GB , K HST , K BAT , P w (t), P pv (t), P MT (t), P GB (t), P HST (t), P BAT (t) are the operating cost coefficient and output power of wind power, photovoltaic power, gas turbine, gas boiler, battery and heat storage tank, respectively. HST (t), P BAT (t) can be positive or negative. When it is positive, it means absorbing power, and when it is negative, it means releasing power.

2. The scheduling method for coordinated optimization of source, load and storage under the ladder carbon trading mechanism according to claim 1 is characterized by: Power balance constraints Where: P load , Q load They are the electric and heating load demands; Since the system power balance contains uncertain variables, this paper introduces a fuzzy processing method when arranging the day-ahead dispatch plan. Scenery blur parameters Relax Equation 18 to establish a power constraint balance with a confidence level not less than α; Among them, P r (·) indicates the credibility of the event; Fuzzy parameters are used to represent the uncertain variables in the system, and the output of conventional units satisfies the inequality containing fuzzy parameters as an event, so that the probability of random events in uncertain environments caused by prediction errors meets the preset confidence level. This is the fuzzy chance constrained programming problem.

3. The scheduling method for coordinated optimization of source, load and storage considering the ladder carbon trading mechanism according to claim 2 is characterized by: Battery restraints, Where: U is the maximum charge and discharge power allowed by energy storage; s,t It is the charge and discharge status, 1 means discharging, 0 means charging; E s (0) is the energy storage capacity at the initial moment; and The upper and lower limits of the remaining capacity of energy storage during the dispatch process; Output upper and lower limit constraints Where: P w,max , P pv,max , P net,min , P net,max , P MT,max , Q HT,max , Q GB,max , Q HST,min , Q HST,max Output limits for wind power, photovoltaic power, electricity purchase and sales, gas turbines, gas boilers, batteries, and heat storage tanks.

4. The scheduling method for coordinated optimization of source, load and storage considering the ladder carbon trading mechanism according to claim 3 is characterized by: Source-load uncertainty modeling: Establish uncertainty models for wind and solar power output and electrical load demand: In the formula, is the value of wind power integrated into the grid, is the value of photovoltaic integration into the grid, is the load size; P W (t) is the wind power forecast value, P pv (t) is the photovoltaic prediction value, P load (t) is the load prediction value.

5. The scheduling method for coordinated optimization of source, load and storage under the ladder carbon trading mechanism according to claim 4 is characterized by: Scheduling model based on fuzzy chance constraints: Taking the average of the uncertainty model of wind and solar power output and electric load demand, we can get: In the formula, and The average value of wind power and photovoltaic power output connected to the grid and the average value of load size are calculated respectively.

6. The scheduling method for coordinated optimization of source, load and storage considering the ladder carbon trading mechanism according to claim 5 is characterized by: Sharp equivalence class of fuzzy chance constraints: If the constraint function g(x,z) has the following form: g(x,ζ)=h1(x)ζ1+h2(x)ζ2+...+h t (x)ζ t +h0(x) (27) In the formula, and Calculate the mean value of wind power and photovoltaic power output connected to the grid and the mean value of load size respectively; When the confidence level α ≥ 0.5, the clear equivalence class of chance constraints is: (2-2α)[P load2 (t)-P W2 (t)-P pv2 (t)]+ (2α-1)[P load3 (t)-P W1 (t)-P pv1 (t)]+ (28)。 P BAT -P net -P MT =0

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