Medium and long term contract electric quantity decomposition and rolling decomposition method considering wind and light uncertainty

Abstract

The invention discloses a medium and long term contract electric quantity decomposition and rolling decomposition method considering wind and light uncertainty, and the method comprises the steps: decomposing the annual contract electric quantity of each unit into a daily contract electric quantity collaborative optimization decomposition mechanism under the condition of known medium and long term contract electric quantity signed by each thermal power generating unit, a wind power plant and a photovoltaic power generation field; after each time of contract electric quantity decomposition is finished, based on the decomposed contract electric quantity needing to be completed in each time scale, according to a wind power prediction value and a load prediction value, a day-ahead power generation plan considering the daily contract electric quantity is constructed, and a contract execution scheme and a unit output scheme of the day are obtained; and after the end of each day, obtaining daily contract electric quantity which is not completed every day, carrying out rolling correction on a subsequent contract daily plan, after the end of each month, obtaining monthly contract electric quantity which is not completed every month, and carrying out rolling correction on a subsequent contract annual contract decomposition plan, so as to ensure that the contract electric quantity can be fairly executed when the contract expires. The method is reasonable in design and has good practical application value.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and specifically to a method for decomposing and rolling mid- and long-term contract electricity quantities taking into account the uncertainty of wind and solar power. Background Art

[0002] In January 2023, the National Energy Administration released the "Blue Book on the Development of New Power System", which describes the connotation, characteristics and development path of the new power system. In recent years, in order to alleviate the energy crisis and the increasingly serious environmental pollution problem, the country has actively promoted the development of new energy, and the total amount of new energy grid-connected installed capacity and consumption in my country has grown rapidly. On the other hand, since the promulgation of Document No. 9 of the Central Development and Reform Commission

[2015] , China's power market reform has been continuously deepened, and the proportion of market-oriented electricity has gradually increased. Exploring and seeking to achieve a high proportion of renewable energy consumption through market-oriented means has become a key point to ensure the safe and stable operation of the new power system. At present, my country is in a period of transition in the power market, and the medium- and long-term contracted electricity has basically reached 90% to 95%. However, the unique volatility and randomness of renewable energy make it difficult to accurately predict the medium- and long-term power generation, which brings great challenges to the effective consumption of new energy and the reasonable decomposition of medium- and long-term contracts. For the power trading center, how to decompose the medium- and long-term contract electricity into the spot settlement period based on the random characteristics of renewable energy is of great positive significance to reducing the complexity of scheduling, orderly connecting medium- and long-term transactions and spot transactions, ensuring the economic operation of the system and maintaining market fairness.

[0003] Many researchers have proposed strategies and methods with their own characteristics for the medium- and long-term decomposition of renewable energy access. At present, the research on the forecast of renewable energy power based on data mining to solve the combination of contract units and power decomposition schemes of power systems containing new energy such as wind, solar and hydropower has been relatively mature. Some domestic scholars have also conducted some exploration in the short-term optimal dispatch considering the monthly wind power quality segmentation.

[0004] In addition to short-term fluctuations in renewable energy output, renewable energy also exhibits medium-term (daily, weekly) and long-term (seasonal) changes. However, the current decomposition of medium- and long-term contract electricity for the random characteristics of renewable energy is mostly based on the flexibility needs of market operation, and the medium- and long-term electricity allocation mechanism and short-term flexible trading mechanism of renewable energy are discussed, or the coordination and correction of contract electricity is considered on a longer time scale such as annual and monthly, but the nested optimization of the connection between medium- and long-term market and spot market transactions is ignored. Summary of the invention

[0005] In view of the problem that the reasonable decomposition of medium- and long-term contract electricity is becoming increasingly difficult due to the accelerated marketization of renewable energy, the purpose of the present invention is to provide a multi-time scale rolling coordinated decomposition method for medium- and long-term contract electricity that takes into account the uncertainty of short-term and medium- and long-term output of renewable energy, which is applied to electricity market price management.

[0006] The present invention is achieved by adopting the following technical solutions:

[0007] A method for decomposing and rolling decomposing medium- and long-term contract electricity considering the uncertainty of wind and solar power. Under the condition that the medium- and long-term contract electricity signed by each thermal power unit, wind farm and photovoltaic power farm is known, the annual contract electricity of each unit is decomposed into a daily contract electricity collaborative optimization decomposition mechanism; considering the uncertainty of wind and solar power and the deviation of the contract completion progress, after each contract electricity decomposition is completed, based on the contract electricity that needs to be completed at each decomposed time scale, according to the wind power forecast value and the load forecast value, a day-ahead power generation plan taking into account the daily contract electricity is constructed, and the contract execution plan and unit output plan for that day are obtained; after the end of each day, the unfinished daily contract electricity is obtained, and the subsequent contract day plan is rolled over, and after the end of each month, the unfinished monthly contract electricity is obtained, and the subsequent contract annual contract decomposition plan is rolled over to ensure that the contract electricity can be fairly executed when the contract expires.

[0008] The present invention takes into account the uncertainty prediction deviation of wind and solar power at different time scales and the deviation of real-time completion of contracts, and constructs a rolling optimization model for the decomposition of medium- and long-term annual contract electricity, short-term monthly contract electricity and day-ahead contract electricity. The model and algorithm consist of four parts: annual contract electricity decomposition, short-term contract collaborative decomposition considering the high-quality utilization of renewable energy, day-ahead power generation plan taking into account daily contract electricity, and rolling correction of contract electricity; in the rolling optimization method, the time period is divided into multiple time scales such as months, days, and hours, and the optimization and solution are performed separately to ensure the full and fair execution of the contract electricity on the expiration date. This method can ensure that the annual contract electricity is executed fairly and reasonably within each time scale, and realize the orderly connection between the medium- and long-term market and the spot market. Taking into account the impact of short-term fluctuations in wind and solar power on the decomposition curve of contract electricity, the Latin hypercube sampling method is used in the short-term contract decomposition model to segment the wind and solar power quality, and a monthly contract decomposition model considering the balance cost of renewable energy is established to ensure the utilization of high-quality renewable energy.

[0009] The technical solution provided by the present invention has the following advantages compared with the prior art:

[0010] The present invention proposes a collaborative optimization model for decomposing short-term contract electricity of wind and solar thermal power units. This model takes into account the risk cost of uncertain wind power and the impact of the actual contract deviation of the unit, and realizes the fair and reasonable decomposition of the contract electricity of wind and solar thermal power units to each day. Based on Latin hypercube sampling, the monthly wind and solar power quality segmentation is carried out, and the randomness and volatility of renewable energy are fully considered to achieve differentiated risk cost recovery of wind and solar power. At the same time, in order to ensure that the medium and long-term contracts can be executed as much as possible when the contract expires, an integrated collaborative optimization model of annual contract electricity decomposition, monthly contract electricity decomposition, and rolling correction is proposed.

[0011] The invention has reasonable design and good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 Represents the decomposition framework of medium and long-term contract electricity.

[0015] Figure 2 It represents the coordinated optimization decomposition process of annual contractual electricity volume of wind, solar and thermal power.

[0016] Figure 3 A schematic diagram showing the monthly wind power quality segmentation.

[0017] Figure 4 Indicates the initial output scenario of Wind Farm No. 1.

[0018] Figure 5 It shows the decomposition results of the contracted electricity volume in April. DETAILED DESCRIPTION

[0019] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all of the embodiments.

[0021] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] The basic idea of ​​the medium- and long-term contract electricity decomposition of the present invention is: under the condition that the medium- and long-term contract electricity signed by each thermal power unit, wind farm and photovoltaic power plant is known, explore the collaborative optimization decomposition mechanism of decomposing the annual contract electricity of each unit into the daily contract electricity. Considering the uncertainty of wind and solar power and the deviation of the contract completion progress, after each contract electricity decomposition is completed, based on the contract electricity that needs to be completed at each decomposed time scale, according to the wind power forecast value and the load forecast value, a day-ahead power generation plan taking into account the daily contract electricity is constructed to obtain the contract execution plan and unit output plan for that day. At the end of each day, the unfinished daily contract electricity is obtained, and the subsequent contract day plan is rolled over. At the end of each month, the unfinished monthly contract electricity is used to roll over the subsequent contract annual contract decomposition plan to ensure that when the contract expires, the contract electricity is executed as fairly as possible.

[0023] A method for decomposing and rolling mid- and long-term contract electricity volume considering the uncertainty of wind and solar power. Its framework structure is as follows: Figure 1 shown.

[0024] Step 1: Rolling decomposition model for annual contract electricity of wind, solar and thermal power

[0025] 1) Optimal decomposition model for medium- and long-term annual contract electricity

[0026] Considering factors such as the completion degree of thermal power units and the contract deviation, a medium- and long-term electricity decomposition mechanism that adapts to multi-type energy systems is constructed. The specific objectives are shown in equations (1) and (4). In order to ensure that the same electricity of the thermal power unit group is executed as fairly as possible in each period, the minimum completion progress deviation f1 of the contract electricity of the conventional energy unit group is introduced to indicate the consistency of the decomposed electricity obtained by each thermal power unit, reflecting the principle of "three public dispatch".

[0027]

[0028] in:

[0029]

[0030] Where, G is the total number of thermal power units under contract; is the planned completion progress of conventional energy unit g in the previous z months; is the average planned completion degree of all conventional energy units in the previous z months; g,n is the decomposed power of conventional energy unit g in month n; W G The total annual medium- and long-term contract volume of thermal power unit g needs to be decomposed, in MW·h.

[0031] In order to ensure that the monthly contract electricity obtained by decomposition does not deviate much from the actual monthly decomposition electricity, the size of the deviation electricity f2 is considered in the decomposition model, and its expression is:

[0032]

[0033] in:

[0034]

[0035] Where: R and V are the total number of wind turbines and photovoltaic generators signed contracts; C M Penalty costs for contract deviations; W are the annual contract decomposition deviation electricity of thermal power generation units, wind power generation units and photovoltaic power generation units respectively; R , W V The total amount of annual medium- and long-term contracts signed for all wind turbines and photovoltaic generators, determined by annual bilateral negotiated transactions; r,n 、w v,n They are the decomposed electricity consumption of wind turbine R and photovoltaic generator V in month n respectively.

[0036] The objective function of the medium- and long-term annual contract electricity optimization decomposition model constructed by the present invention is summarized as follows:

[0037] f=η1f1+η2f2 (8)

[0038] Where: η1, η2 are the weight coefficients of the objective functions f1 and f2 respectively.

[0039] Considering the trading rules of the medium- and long-term market, the constraints that the medium- and long-term annual contract power optimization decomposition model needs to meet include the total balance of contract decomposition and unit output constraints. The specific constraints are:

[0040] (1) Output constraints of photovoltaic and wind turbines

[0041]

[0042] Where: is the upper bound of the predicted power generation of wind turbine r on day d; is the upper bound of the power generation forecast of the photovoltaic generator set v on day d.

[0043] (2) Output constraints of thermal power units

[0044] Since thermal power units may have maintenance plans, they need to meet their own output constraints according to the maintenance plans. The specific expression is as follows:

[0045]

[0046] Where: w g,min is the minimum daily power generation of the thermal power unit; w g,max is the maximum daily power generation of the thermal power unit; I g,d,t is the state variable of whether the thermal power unit g is under maintenance at time t on day d. g,d,t When it is 1, it means that unit g is in maintenance status at time t on day d, and when it is 0, it means that the unit is in normal operation.

[0047] (3) Power balance constraints

[0048]

[0049] Where: L j,d The load forecast for node j on day d.

[0050] 2) Annual contract electricity rolling correction and solution process

[0051] Due to the uncertainty of new energy and load, there are deviations between the actual and predicted values ​​of wind power, photovoltaic power and load each month, and there are also deviations between the actual and planned contractual power of conventional units and new energy stations each month. According to this deviation, it is necessary to make rolling corrections to the monthly contractual power plans of conventional units and new energy stations for the remaining months based on the latest predicted values ​​and the contractual power of the completed months after the end of each month, so as to obtain the latest annual contract decomposition update result with the minimum formula (8). The above rolling correction solution process is as follows: Figure 2 shown.

[0052] The steps to solve the rolling correction are as follows:

[0053] (1) Let i0 = 0, r = 1, 2…, R, v = 1, 2…, V, g = 1, 2…, G, and predict the output of each wind turbine in each month from i0+1 to December in the new year based on historical data. Output forecast of each photovoltaic generator set in each month and load forecast

[0054] (2) Let i0 = i0 + 1, solve the medium- and long-term contract decomposition model, and obtain the monthly decomposition of the annual contract electricity of wind power, photovoltaic power generation and thermal power units from i0 to December w r,i 、w v,i and w g,i ;

[0055] (3) At the end of month i0, the actual monthly contract decomposition power w of month i0 is updated r,i 、w v,i and w g,i and month i0+1 and When \(i\leq i_0\), save the data;

[0056] (4) If \(0 < i_0 < 12\), go to step 2; otherwise, end the algorithm.

[0057] Step 2: Short-term contract power collaborative decomposition model considering the uncertainty of wind and light

[0058] Based on the medium- and long-term contract power decomposition model, the monthly contract decomposition power is obtained. In this step, considering the uncertainty of wind and light, the power quality of wind and light is segmented based on the Latin hypercube sampling method, and a monthly contract power collaborative decomposition model is established.

[0059] 1) Power quality segmentation of wind and light based on the Latin hypercube sampling method

[0060] Segmenting the output quality of renewable energy is a good method to indirectly reduce the balancing cost of renewable energy. Although the renewable energy in different regions and different seasons shows different fluctuation characteristics on spatial and temporal scales, generally, the output of renewable energy can be divided into 3 layers according to the degree of uncertainty, as Figure 3 shown. The uncertainty of wind and light clean energy can be transformed and solved through multi-scenario generation technology. The layering method proposed in the present invention is as follows: Using the Latin hypercube sampling method to stratify and sample historical data, taking the lower limit of the error band of the monthly prediction values of all sampled samples as the dividing line between the first and second segments. The part below the lower limit of the error band is the first segment, and it can be considered that the uncertainty cost \(C\) LQ in this segment is 0, that is Taking a certain percentage of the maximum output of all prediction samples as the dividing line between the second and third layers. Below it is the second layer of wind power, and the uncertainty cost is Above it is the third layer of wind power. This area has violent fluctuations and a short continuous power supply time, and the uncertainty cost is

[0061] 2) Monthly contract power collaborative decomposition model

[0062] For the established monthly contract power decomposition model, first considering the power generation cost, the objective function \(f_3\) is to minimize the power generation cost.

[0063]

[0064] In the formula: \(D\) is the number of days of the short-term contract; \(w\) g,d is the daily contract power of the thermal power unit; \(C\) g (.) is the power generation cost calculation function of the \(g\)-th thermal power unit, where \(a\), \(b\), and \(c\) are the power generation cost coefficients of the thermal power unit.

[0065] In order to ensure the smooth completion of medium- and long-term electricity transactions, the objective function f4 is to minimize the deviation between the contracted electricity and the completion progress of the unit, and the electricity is decomposed according to the size of the load demand to ensure the consistency of the planned progress of various types of energy units, reflecting the principle of fair scheduling.

[0066]

[0067]

[0068] Where: C M is the penalty cost for contract deviation; ΔW g , ΔW v , ΔW r are the combined deviations of thermal power, wind power and photovoltaic power generation respectively; w r,d 、w v,d are the daily contracted electricity of wind turbines and photovoltaic generators respectively; Q g , Q r , Q v The total amount of monthly medium- and long-term contracts signed for all thermal power units, wind power units and photovoltaic power units.

[0069] Considering the need to utilize high-quality wind power as much as possible, the objective function constructed is as follows:

[0070]

[0071] in:

[0072]

[0073] Where: They are the risk costs of utilizing the second and third sections of wind power and photovoltaic power respectively; are the predicted lower bounds of power generation of wind turbine r and photovoltaic generator v on day d, are the dividing lines of the second and third power quality of wind power and photovoltaic power generation units on day d, Where ΔT = 1h.

[0074] Therefore, the objective function of the short-term contract electricity collaborative decomposition model constructed by the present invention is:

[0075] f=f3+f4+f5 (21)

[0076] The constraints that the model needs to satisfy are the same as those in equations (9) to (12).

[0077] 3) Monthly contract electricity rolling correction algorithm

[0078] Due to the uncertainty of new energy and load, there is a deviation between the daily contracted electricity and the actual completion. Based on the deviation, a rolling correction is made to the daily planned contracted electricity of the subsequent days to obtain a reasonable compensation electricity, thereby ensuring that the contracted electricity can be executed as much as possible on the contract expiration date. At the end of each day, the total amount of actual deviation electricity for the day is calculated and a rolling correction is made to the deviation electricity. The specific correction algorithm is the same as above, and the rolling correction solves steps (1) to (4).

[0079] Step 3: Day-ahead electricity decomposition optimization model considering wind and solar uncertainty

[0080] Through the short-term contract electricity collaborative decomposition model in step 2, the daily contract electricity planned to be completed by each thermal power unit, wind power unit and photovoltaic power unit is obtained. Then, based on the day-ahead load and wind power forecast values, a power generation plan is formulated to ensure the safe and economic operation of the system and the completion of the daily contract as much as possible while accommodating renewable energy as much as possible.

[0081] The optimization objective of the model is to minimize the base-state power generation cost and contract deviation cost as well as wind curtailment and load shedding costs, specifically:

[0082]

[0083] Where: C f The total contract cost is to take into account the uncertainty of wind and solar power; P g (t) is the generator output at time t; and are the deviations of the day-ahead contract decomposed electricity and the daily contracted electricity of thermal power unit g, wind power unit r and photovoltaic power unit v respectively; C RS C is the cost of curtailing wind and solar power; LS is the load shedding cost; Δw r,t , Δw v,t are the wind and solar power abandonment of wind turbines and photovoltaic generators at time t; Δw j,t is the load shedding amount of node j at time t.

[0084] Wind, solar and thermal power units participating in market transactions must meet certain constraints, including trading restrictions to ensure fair and orderly market operations, as well as relevant constraints to ensure the safe and stable operation of the power system.

[0085] At the same time, the rolling correction of the deviation power after each period is considered. After each day, the total amount of actual deviation power is calculated and the rolling correction of the deviation power is performed. The specific correction algorithm is the same as above, and the rolling correction solution steps (1) to (4) are used.

[0086] The specific example analysis of the embodiment of the present invention is as follows:

[0087] 1) Parameter settings

[0088] To verify the effectiveness of the model, the present invention uses a modified IEEE39 standard node system to simulate and illustrate the load and wind power forecast data and actual data in a certain regional power market environment, combined with the local power structure. In the modified system, the number of conventional units and renewable energy installed capacity is close to 7:3. Assume that there are 7 thermal power units, 2 wind farms and 1 photovoltaic power plant in the system, and a one-year medium- and long-term contract is signed with the electricity user. The basic data of the contracted thermal power units are shown in Table 1, and the parameters of the renewable energy units are shown in Table 2.

[0089] Table 1 Basic parameters of thermal power units

[0090]

[0091] Table 2 Basic parameters of renewable energy units

[0092]

[0093] According to the 2022 monthly load power forecast data obtained from the system's historical load data, the annual load curve shows that the load is larger in summer and winter and smaller in spring and autumn. The annual maximum load often occurs in July and August in summer or December and January in winter, while April and May in spring and September and October in autumn are usually the troughs of the annual load curve.

[0094] The contracted electricity volume for 2022 signed by thermal power units and renewable energy sites obtained through bilateral negotiations in the system is shown in Table 3.

[0095] Table 3 Annual contract electricity of thermal power units and renewable energy power stations

[0096]

[0097] 2) Validation of the decomposition of medium- and long-term contract electricity and the rolling correction model

[0098] According to the load and unit parameters, taking into account minimizing the completion progress deviation of the contracted electricity of the conventional energy unit and minimizing the deviation of the contract decomposition electricity, a preliminary decomposition of the annual contracted electricity without considering the rolling correction is carried out to obtain the annual contract decomposition curve.

[0099] The monthly wind and solar power forecast values ​​are based on the modified test system load values ​​of a certain wind turbine and photovoltaic generator in April 2022.

[0100] The uncertainty of wind and solar clean energy can be transformed and solved through multi-scenario generation technology. This paper uses the historical output data of wind farm No. 1 in April, assumes that the errors of wind power and photovoltaic power obey the normal distribution of σ=4%μ, and uses random sampling method to generate 1000 scenarios. Figure 4This is the Latin hypercube sampling result for wind farm No. 1.

[0101] The lower limit of the error band of the monthly wind power forecast value obtained by sampling is used as the dividing line between the first and second sections, and the part below the lower limit of the error band is the first section; a certain ratio of the upper limit of the error band of the monthly wind power forecast value obtained by sampling is used as the dividing line between the second and third sections; the part between the dividing line between the second and third sections and the upper limit of the error band is the third layer of uncertain power.

[0102] Taking the modified IEEE 39-bus system as an example, the penalty cost settings of various types are shown in Table 4.

[0103] Table 4 Penalty cost settings for each type

[0104]

[0105] In order to verify the effectiveness of the monthly contract electricity decomposition algorithm based on wind and solar power quality segmentation, the algorithm is set to model 1 and model 2. Model 1 is the mathematical model described in the method of the present invention; Model 2 achieves the optimal decomposition plan of the overall annual contract electricity by coordinating and optimizing the decomposition of the annual contract of renewable energy and the annual contract of conventional units. The total contract amount of each unit in the two methods is the same. The decomposition results calculated by the two models are shown in Table 5.

[0106] Table 5 Comparison of monthly contract electricity decomposition results

[0107]

[0108] As can be seen from Table 5, when the two methods have basically the same annual contract completion progress for thermal power units, the total cost calculated by the method of the present invention is 7.6% lower than the total cost calculated by the comparison method, which significantly improves the economy of contract decomposition. The method of the present invention (model 1) performs power quality segmentation, and the cost of low-quality power is low. At the same time, the contract completion rate of each unit in the method of the present invention (model 1) is closer to 1, which meets the principle of fairness and has a small penalty cost for contract deviation. At the same time, these results all prove the effectiveness and advancement of the power quality segmentation method of the present invention using Latin hypercube sampling to decompose contract electricity.

[0109] According to the monthly contract electricity collaborative decomposition model, the monthly contract electricity of 7 thermal power units, 2 wind farms and 1 photovoltaic power plant is decomposed with the goal of minimizing the power generation cost, contract deviation penalty cost and risk cost, and the daily planned contract electricity of subsequent days is rolled over. The monthly contract electricity decomposition results are as follows: Figure 5 shown.

[0110] After each rolling correction process, the annual contract completion rates of each wind power station, photovoltaic power station, and thermal power unit obtained by the model of the present invention are all above 97%, and the deviation of the contract completion rate is very small.

[0111] Table 6 shows the comparison results of the dispatching and operating costs of wind power stations and photovoltaic power stations before and after the rolling correction of the annual contract completion based on the model of the present invention.

[0112] Table 6 Comparison of scheduling operation costs before and after rolling

[0113]

[0114] The contract decomposition rolling optimization model constructed by the present invention reduces the dispatching and operating costs by 0.95%. At the same time, the contract completion rates of wind farms and thermal power units in the present invention model are both above 97%, which meets the principle of fairness. Therefore, the penalty cost for contract deviation is very small, which can improve the consumption of renewable energy and the economic efficiency of the system to a certain extent, and can provide analytical theories and methods for the connection between the medium- and long-term market and the spot market, and promote the participation of renewable energy in the marketization process.

[0115] The above is only a specific implementation of the present invention, which enables those skilled in the art to understand or implement the present invention. Although detailed descriptions are given with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. A method for decomposing and rolling mid- and long-term contract electricity volume considering the uncertainty of wind and solar power, characterized by: Under the condition of the medium- and long-term contract electricity quantities signed by each thermal power unit, wind farm, and photovoltaic power generation field, a collaborative optimization decomposition mechanism for decomposing the annual contract electricity quantity of each unit into the daily contract electricity quantity; considering the uncertainty of wind and light and the deviation of the contract completion progress, after each contract electricity quantity decomposition, based on the contract electricity quantity to be completed at each decomposed time scale, according to the wind power prediction value and the load prediction value, a day-ahead generation plan considering the daily contract electricity quantity is constructed to obtain the contract execution plan and unit output plan for that day; after each day ends, the uncompleted daily contract electricity quantity for that day is obtained, and the subsequent daily contract plan is corrected by rolling, and after each month ends, the uncompleted monthly contract electricity quantity for that month is obtained, and the subsequent annual contract decomposition plan is corrected by rolling to ensure that the contract electricity quantity can be fairly executed when the contract expires.

2. According to claim 1, a method for decomposing and rolling the mid- and long-term contract electricity quantity considering the uncertainty of wind and solar power, characterized in that: Including the following steps: Step 1. Wind-solar-thermal power annual contract electricity quantity decomposition rolling decomposition model 1) Medium- and long-term annual contract electricity quantity optimization decomposition model Construct a medium- and long-term electricity quantity decomposition mechanism suitable for multi-type energy systems. The specific objectives are as shown in Equation (1) and Equation (4). Introduce the minimization of the progress deviation f1 of the contract electricity quantity of conventional energy unit combinations to represent the consistency of the decomposed electricity quantities obtained by each thermal power unit; Where: Where, G is the total number of thermal power units under contract; is the planned completion progress of conventional energy unit g in the previous z months; is the average planned completion degree of all conventional energy units in the previous z months; g,n is the decomposed power of conventional energy unit g in month n; W G is the total annual medium- and long-term contract volume of thermal power unit g to be decomposed, in MW·h; At the same time, consider the magnitude of the deviation electricity quantity f2 in the decomposition model as follows: Where: Where: R and V are the total number of wind turbines and photovoltaic generators signed contracts; C M Penalty costs for contract deviations; W are the annual contract decomposition deviation electricity of thermal power generation units, wind power generation units and photovoltaic power generation units respectively; R , W V The total amount of annual medium- and long-term contracts signed for all wind turbines and photovoltaic generators, determined by annual bilateral negotiated transactions; r,n 、w v,n are the decomposed electricity of wind turbine R and photovoltaic generator V in month n respectively; That is, the objective function of the constructed medium- and long-term annual contract electricity quantity optimization decomposition model is: f = η1f1 + η2f2 (8) In the formula: η1 and η2 are the weight coefficients of the objective functions f1 and f2 respectively; 2) Annual contract electricity quantity rolling correction and solution After each month ends, according to the latest prediction value and the contract electricity quantity of the completed months, the monthly contract electricity quantity plans of the remaining months for conventional units and new energy power stations are corrected by rolling to obtain the latest updated result of the annual contract decomposition that minimizes Equation (8); the steps of the rolling correction solution are as follows: (1) Let i0 = 0, r = 1, 2…, R, v = 1, 2…, V, g = 1, 2…, G, and predict the output of each wind turbine in each month from i0+1 to December in the new year based on historical data. Output forecast of each photovoltaic generator set in each month and load forecast (2) Let i0 = i0 + 1, solve the medium- and long-term contract decomposition model, and obtain the monthly decomposition of the annual contract electricity of wind power, photovoltaic power generation and thermal power units from i0 to December w r,i 、w v,i and w g,i ; (3) At the end of month i0, the actual monthly contract decomposition power w of month i0 is updated r,i 、w v,i and w g,i and month i0+1 and i≤i0, and save the data; (4) If 0 < i0 < 12, go to Step 2; otherwise, the algorithm ends; Step 2. Short-term contract electricity quantity collaborative decomposition model for wind and light uncertainty 1) Segmenting the wind and light power quality based on the Latin hypercube sampling method The output of renewable energy is divided into three layers according to the degree of uncertainty. The stratification method is as follows: the Latin hypercube sampling method is used to sample the historical data in layers. The lower limit of the error band of the monthly forecast value of all samples obtained by sampling is used as the dividing line between the first and second segments. The part below the lower limit of the error band is the first segment, and the uncertainty cost C of this segment is considered to be LQ is 0, that is Take a certain percentage of the maximum output of all predicted samples as the dividing line between the 2nd and 3rd layers, below which is the second layer of wind power, and the uncertainty cost is Above it is the third layer of wind power, which has severe fluctuations and short continuous power supply time. The uncertainty cost is 2) Monthly contract electricity quantity collaborative decomposition model First, considering the generation cost, the objective function f3 is to minimize the generation cost; Where: D is the number of days of the short-term contract; w g,d is the daily contracted electricity of the thermal power unit; C g (.) is the calculation function of the power generation cost of the g-th thermal power unit, where a, b, and c are the power generation cost coefficients of thermal power units; The objective function f4 is to minimize the deviation between the unit contract electricity quantity and the completion progress, and the electricity quantity is decomposed according to the magnitude of the load demand to ensure the consistency of the planned progress completion of various types of energy units; Where: C M is the penalty cost for contract deviation; ΔW g , ΔW v , ΔW r are the combined deviations of thermal power, wind power and photovoltaic power generation respectively; w r,d 、w v,d are the daily contracted electricity of wind turbines and photovoltaic generators respectively; Q g , Q r , Q v The total amount of monthly medium- and long-term contracts signed for all thermal power units, wind power units and photovoltaic power units; The objective function constructed using high-quality wind power is as follows: Where: Where: They are the risk costs of utilizing wind power and photovoltaic power in the second and third sections respectively; are the predicted lower bounds of power generation of wind turbine r and photovoltaic generator v on day d, are the dividing lines of the second and third power quality of wind power and photovoltaic power generation units on day d, Where ΔT = 1h; Therefore, the objective function of the constructed short-term contract electricity quantity collaborative decomposition model is: f = f3 + f4 + f5 (17) 3) Monthly contract electricity quantity rolling correction algorithm Due to the uncertainty of new energy and load, there is a deviation between the daily contract electricity quantity and the actual completion situation; according to this deviation amount, the subsequent daily planned contract electricity quantity is corrected by rolling to obtain a reasonable compensation electricity quantity, so as to ensure that the contract electricity quantity can be executed when the contract expiration date arrives; after each day ends, calculate the total amount of the actual deviation electricity quantity for that day and perform rolling correction of the deviation electricity quantity; Step 3. Day-ahead electricity quantity decomposition optimization model considering wind and light uncertainty The optimization objective of the model is to minimize the base-state power generation cost and contract deviation cost as well as wind curtailment and load shedding costs, specifically: Where: C f The total contract cost is to take into account the uncertainty of wind and solar power; P g (t) is the generator output at time t; and are the deviations of the day-ahead contract decomposed electricity and the daily contracted electricity of thermal power unit g, wind power unit r and photovoltaic power unit v respectively; C RS C is the cost of curtailing wind and solar power; LS is the load shedding cost; Δw r,t , Δw v,t are the wind and solar power abandonment of wind turbines and photovoltaic generators at time t; Δw j,t is the load shedding amount of node j at time t; Considering the rolling correction of the deviation electricity after the end of each period, at the end of each day, the total amount of actual deviation electricity for that day is calculated and a rolling correction of the deviation electricity is performed.

3. According to claim 2, a method for decomposing and rolling the mid- and long-term contract electricity quantity taking into account the uncertainty of wind and solar power, characterized in that: The constraints satisfied by the medium- and long-term annual contract electricity optimization decomposition model include the total balance of contract decomposition and unit output constraints.

4. According to claim 3, a method for decomposing and rolling mid- and long-term contract electricity quantity considering the uncertainty of wind and solar power, characterized in that: The specific constraints of the medium- and long-term annual contract electricity optimization decomposition model and the short-term contract electricity collaborative decomposition model are: (1) Output constraints of photovoltaic and wind turbines Where: is the upper bound of the predicted power generation of wind turbine r on day d; is the upper bound of the power generation forecast of the photovoltaic generator v on day d; (2) Output constraints of thermal power units The thermal power units meet their own output constraints according to the maintenance plan, as shown in the following formula: Where: w g,min is the minimum daily power generation of the thermal power unit; w g,max is the maximum daily power generation of the thermal power unit; I g,d,t is the state variable of whether the thermal power unit g is under maintenance at time t on day d. g,d,t When it is 1, it means that the unit g is in maintenance status at time t on day d, and when it is 0, it means that the unit is in normal operation; (3) Power balance constraints Where: L j,d The load forecast for node j on day d.

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