A multi-source joint peak-shaving dispatching transaction method considering inter-provincial tie line access

Abstract

The present invention is a multi-source joint peak-shaving dispatching and trading method considering the access of inter-provincial tie lines, which belongs to the technical field of economic operation of power grids, and is characterized by comprising the following steps: ① obtaining the operating parameters of each unit equipment in the power system; ② obtaining the wind power generation and daily load demand of the next day; ③ establishing a multi-source joint peak-shaving dispatching model based on a three-stage hybrid intelligent solution algorithm; ④ verifying its dispatching results according to the historical data of power grid dispatching; ⑤ obtaining the clearing price of each unit participating in the peak-shaving; ⑥ calculating the peak-shaving cost of each unit according to the peak-shaving dispatching results and clearing prices. The multi-source joint peak-shaving dispatching and trading method considering the access of inter-provincial tie lines adopted by the present invention can conduct dispatching transactions more effectively and reliably, and provide a technical basis and practical method for the coordinated economic operation of the power grid.

Description

Technical Field

[0001] The present invention relates to a multi-source joint peak-shaving dispatching and trading method considering inter-provincial interconnection line access, and in particular to a multi-source joint peak-shaving dispatching and trading method considering inter-provincial interconnection line access based on a three-stage hybrid intelligent solution algorithm, belonging to the technical field of power grid economic operation. Background Art

[0002] Traditional power grids face huge challenges in absorbing new energy. On the one hand, due to the limitations of power grid transmission capacity and unit operation requirements, the power grid's ability to absorb new energy is insufficient; at the same time, the access to new energy has changed the comprehensive load characteristics of the power grid, and the peak-shaving space in this area is subject to certain restrictions. There is currently no effective response strategy. Considering the multi-source joint peak-shaving dispatching transaction with cross-provincial interconnection lines is an effective way to absorb new energy and expand peak-shaving space, and it is also an effective support for realizing flexible dispatching of power grids.

[0003] Considering the multi-source joint peak-shaving dispatching transaction with the access of inter-provincial tie lines, it has a great technical complementary advantage in the peak-shaving operation of the power grid. By dynamically dispatching some units of the tie lines outside the province and some units within the province, it can realize the consumption of wind power and alleviate the peak-shaving pressure of units within the province. At present, the research on multi-source joint peak-shaving dispatching transactions at home and abroad mainly focuses on how to coordinate the dispatching of various units distributed in the power system in the region, minimize the dispatching cost, trading strategy, modeling and simulation, etc., and there are few studies on multi-source joint peak-shaving dispatching transactions considering the access of inter-provincial tie lines. The core technology for the stable operation of the power system with inter-provincial tie lines and the large-scale consumption of new energy lies in how to coordinate the dispatching and trading strategies of units within and outside the province. At present, the strategy adopted by the power grid dispatching is to give priority to the peak-shaving resources within the province, and to conduct peak-shaving assessments on each power plant in a competitive manner to select the final optimization plan. Under the above strategy mode, the operation of the power system in the province is relatively fragile and sensitive to peak-shaving. Under the condition of insufficient peak-shaving space, there is a possibility of supply exceeding demand. The application has great limitations, which is not conducive to the safe and economic operation of the power system in the entire region and has poor practicality.

[0004] The key to solving the problem is how to optimize the dispatch of peak-shaving units within and outside the province so as to trade. The complexity of this dispatch transaction lies in that, on the one hand, the dispatch transaction method must adapt to changes in various operating conditions, such as unit status, inter-provincial interconnection line transmission conditions, etc. Different operating conditions require different outputs from units, and their output characteristics are also related to the operating status of the power system. On the other hand, the power system also needs to respond to the demand for wind power consumption, such as the randomness and volatility of wind power. How to achieve peak-shaving dispatch that adapts to various conditions is a key issue.

[0005] The purpose of the present invention is to solve the above problems existing in the prior art, by considering joining the inter-provincial tie line to participate in the peak-shaving dispatching transaction in the region, based on the minimum total peak-shaving cost, constructing a power grid dispatching model, and realizing the coordination of inter-provincial units and units in the province to participate in the peak-shaving of the power grid. The method adopted by the present invention can be more efficient and reliable in dispatching, and provide a technical basis and practical method for the coordinated economic operation of the power grid. Summary of the invention

[0006] The purpose of the present invention is to solve the above-mentioned problems existing in the prior art and provide a multi-source joint peak-shaving dispatching transaction method considering the access of inter-provincial tie lines. The peak-shaving dispatching transaction method adopted by the present invention takes into account the access of inter-provincial tie lines, and can conduct peak-shaving dispatching transactions more effectively and reliably, providing a technical basis and practical method for multi-source joint peak-shaving dispatching transactions considering the access of inter-provincial tie lines.

[0007] The basic idea of ​​the multi-source joint peak-shaving dispatching and trading method proposed in the present invention that takes into account the access of inter-provincial interconnection lines is: based on the theory of the three-stage hybrid intelligent solution algorithm, the operating conditions of each unit are considered, a dispatching model is established, and dispatching is implemented under the condition of considering the inter-provincial interconnection lines, thereby greatly improving the flexibility and economy of the power system, and can also accommodate a large proportion of wind power access.

[0008] The technical solution adopted by the present invention is:

[0009] A multi-source joint peak-shaving dispatching and trading method considering the access of inter-provincial interconnection lines, in a power system composed of thermal power units of inter-provincial interconnection lines and wind power units, thermal power units, and pumped storage units in the province, the inter-provincial thermal power units and various units in the province are dynamically dispatched to make the optimal peak-shaving dispatching decision, and the multi-source joint peak-shaving dispatching and trading are used to realize the consumption of large-scale wind power and maintain the stable operation of the power system in the province, including the following steps:

[0010] Step 1) obtaining the operating parameters of each unit equipment in the power system;

[0011] Step 2) Obtain the wind power generation and daily load demand for the next day;

[0012] Step 3) Establish a multi-source joint peak-shaving scheduling model based on a three-stage hybrid intelligent solution algorithm;

[0013] Step 4) Verify the dispatch result according to the historical data of power grid dispatch;

[0014] Step 5) Obtaining the clearing price of each unit participating in peak load regulation;

[0015] Step 6) Calculate the peak load cost of each unit based on the peak load dispatch results and clearing price.

[0016] The operating parameters of each unit equipment in the power system obtained in the step 1) refer to: the minimum output, maximum output, upper limit of climbing, lower limit of climbing, shortest operating time, shortest downtime, optimal operating point, plant power consumption rate, power generation coal consumption coefficient of the thermal power unit and the minimum power, maximum power, minimum upper storage capacity, maximum upper storage capacity, pumping power, initial upper storage capacity, and efficiency of the pumped storage unit.

[0017] The wind power generation and daily load demand conditions of the next day obtained in step 2) refer to the wind power generation power curve and daily load power curve for 24 hours of the next day.

[0018] The step 3) multi-source joint peak-shaving scheduling model includes the following steps:

[0019] (1) Taking the minimum total cost of peak load regulation as the optimization objective function;

[0020] (2) Setting the operating range of each unit as the model's constraints;

[0021] (3) Taking the unit operating parameters that affect the objective function as the input of the scheduling model;

[0022] (4) Using the wind power generation and daily load demand data of the next day as the input of the model;

[0023] (5) Taking the minimum total cost of peak load regulation of the unit as the objective function, the cloud evolution particle swarm algorithm is used to solve it;

[0024] (6) Based on the solution results, the objective function decision attribute table is obtained and the weight of the objective function is solved;

[0025] (7) Obtaining the global optimal solution set that takes into account the objective function, and selecting the global optimal solution using the membership function;

[0026] (8) The peak load dispatching model is used to calculate the output of each unit participating in the peak load dispatching;

[0027] In step (1), the objective function for minimizing the total cost of peak load regulation is:

[0028]

[0029] In the formula: the first term represents the peak-shaving cost of the tie line in the peak-shaving dispatch; the second term represents the power generation cost of the thermal power unit in the peak-shaving dispatch; the third term represents the peak-shaving cost of the pumped storage unit in the peak-shaving dispatch. Among them, T represents the power generation dispatch cycle; i represents the number of units in the system; f Linei represents the cost function of the tie-line generation; Indicates the generation capacity of the tie line; f Gi represents the cost function of thermal power generation; Indicates the generating capacity of thermal power units; Indicates the identification parameters for the pumped storage unit to be put into operation; represents the peak load regulation capacity of the pumped storage unit; C Pu Represents the peak-shaving cost coefficient of pumped-storage units.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. This multi-source joint peak-shaving dispatching and trading method considering the access of inter-provincial interconnection lines can improve the dispatching economy of the system. In the traditional dispatching method, the peak-shaving pressure is only shared by the units within the province, resulting in insufficient peak-shaving space and even large-scale wind abandonment. The present invention reduces the peak-shaving pressure and improves the reliability and economy of the system by optimizing the peak-shaving dispatching of units within and outside the province.

[0032] 2. This method is easy to implement. This method is based on the original dispatching, adding the link of mobilizing resources outside the province by considering the tie line, so that the peak-shaving space within the province is expanded. It is easy to implement from the transaction point of view; at the same time, each algorithm function has a ready-made algorithm or software, and the dispatching strategy is also easy to implement.

[0033] 3. This method is convenient for commercial development. With the increase of large-scale renewable energy access to the power grid, there will inevitably be a great demand for the development of this peak-shaving scheduling transaction method, and the present invention has a good commercial development prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is the overall flow chart of the multi-source joint peak-shaving dispatching transaction method considering the access of inter-provincial tie lines;

[0035] Figure 2 It is a schematic diagram of the three-stage hybrid solution algorithm model;

[0036] Figure 3 are the operating parameters of each unit;

[0037] Figure 4 It is the typical daily wind power and daily load curve;

[0038] Figure 5 It is the result of power grid dispatch and transaction. DETAILED DESCRIPTION

[0039] The following is combined with Figure 1-5 The technical solution of the present invention is further described in detail by simulation experiments. A multi-source joint peak-shaving dispatching transaction method considering inter-provincial tie line access comprises the following steps: Step 1) obtaining the operating parameters of each unit equipment in the power system;

[0040] Step 2) Obtain the wind power generation and daily load demand for the next day;

[0041] Step 3) Establish a multi-source joint peak-shaving scheduling model based on a three-stage hybrid intelligent solution algorithm;

[0042] (1) Taking the minimum total cost of peak load regulation as the optimization objective function;

[0043] (2) Setting the operating range of each unit as the model's constraints;

[0044] (3) Taking the unit operating parameters that affect the objective function as the input of the scheduling model;

[0045] (4) Using the wind power generation and daily load demand data of the next day as the input of the model;

[0046] (5) Taking the minimum total cost of peak load regulation of the unit as the objective function, the cloud evolution particle swarm algorithm is used to solve it;

[0047] (6) Based on the solution results, the objective function decision attribute table is obtained and the weight of the objective function is solved;

[0048] (7) Obtaining the global optimal solution set that takes into account the objective function, and selecting the global optimal solution using the membership function;

[0049] (8) The peak load dispatching model is used to calculate the output of each unit participating in the peak load dispatching.

[0050] Step 4) Verify the dispatch result according to the historical data of power grid dispatch;

[0051] Step 5) Obtaining the clearing price of each unit participating in peak load regulation;

[0052] Step 6) Calculate the peak load cost of each unit based on the peak load dispatch results and clearing price.

[0053] Step 1) obtaining the operating parameters of each unit in the power system refers to the minimum output, maximum output, upper limit of climbing, lower limit of climbing, shortest operating time, shortest downtime, optimal operating point, plant power consumption rate, power generation coal consumption coefficient of the thermal power unit and the minimum power, maximum power, minimum upper storage capacity, maximum upper storage capacity, pumping power, initial upper storage capacity, and efficiency of the pumped storage unit.

[0054] Step 2) obtaining the wind power generation and daily load demand conditions for the next day refers to the wind power generation power curve and daily load power curve for 24 hours on the next day.

[0055] Taking the minimum total cost of peak load regulation as the optimization objective function means setting the mathematical function according to this objective:

[0056] The objective function of minimizing the total cost of peak load regulation is:

[0057]

[0058] In the formula: the first term represents the peak-shaving cost of the tie line in the peak-shaving dispatch; the second term represents the power generation cost of the thermal power unit in the peak-shaving dispatch; the third term represents the peak-shaving cost of the pumped storage unit in the peak-shaving dispatch. Among them, T represents the power generation dispatch cycle; i represents the number of units in the system; f Linei represents the cost function of the tie-line generation; Indicates the generation capacity of the tie line; f Gi represents the cost function of thermal power generation; Indicates the generating capacity of thermal power units; Indicates the identification parameters for the pumped storage unit to be put into operation; represents the peak load regulation capacity of the pumped storage unit; C Pu Represents the peak-shaving cost coefficient of pumped-storage units.

[0059] The operating range of each unit is set as the constraint condition of the model, which refers to power balance constraint, tie line operation constraint, system reserve constraint, thermal power unit operation constraint, and pumped storage unit operation constraint, as follows:

[0060] 1) System power balance constraints:

[0061]

[0062] In the formula: the left side is the output power of the wind farm, the output power of the thermal power unit, the output power of the pumped storage unit, and the transmission power of the tie line in period t; is the equivalent load of the system in period t.

[0063] 2) Tie line operation constraints:

[0064] -P Linemax- ≤P Line t ≤P Linemax+ (3)

[0065] Where: P Linemax+ , P Linemax- Indicates the maximum forward and reverse power transmission of the interconnection line, both of which are positive values.

[0066] 3) System backup constraints:

[0067]

[0068] Where: P R t , P R′ t They are the system's backup demands during peak and off-peak periods, respectively, and the backup is borne by thermal power units.

[0069] 4) Operation constraints of conventional thermal power units

[0070] 4.1) Upper and lower limits of unit output:

[0071] P GiMin ≤P Gi t ≤P GiMax (5)

[0072] Where: P Gi t is the output of thermal power unit i in period t, PGiMin , PGiMax are the lower and upper limits of the output of thermal power unit i.

[0073] 4.2) Unit climbing speed constraints:

[0074] -ΔP Gi down ≤P Gi t -P Gi t-1 ≤ΔP Gi up (6)

[0075] Where: ΔP Gi up is the upper limit of the ramp rate of thermal power unit i in period t, ΔP Gi down is the upper limit of the power reduction rate of thermal power unit i in period t.

[0076] 5) Operation constraints of pumped storage units:

[0077] 5.1) Output constraints of pumped storage units:

[0078]

[0079] 5.2) Upper reservoir capacity constraints:

[0080]

[0081] Where: η f and η p are the average water / electricity conversion constants for power generation and pumping, respectively; C 0 is the initial water volume of the upper reservoir, C Max and C Min They are the maximum and minimum water volumes in the reservoir of the pumped storage power station, respectively.

[0082] 5.3) Water balance constraints:

[0083]

[0084] Where: η fand η p are the average water / electricity conversion constants for power generation and pumping, respectively.

[0085] The cloud evolution particle swarm algorithm refers to selecting excellent particles according to the particle fitness value, and obtaining the optimal solution of the model through continuous updating and iteration. The specific solution process is as follows:

[0086] (1) Set the inertia weight ω and acceleration constant c 1 ,c 2 , maximum number of iterations N;

[0087] (2) Input peak load operation data of each unit;

[0088] (3) Substitute the data into the iterative formula, which is:

[0089]

[0090] Where: v t+1 is the updated particle velocity, v t is the current particle speed, r 1 ,r 2 is a random number, x t is the current unit output power, x t+1 is the updated unit output power, x pt is the current local optimal position, x qt is the current global optimal position;

[0091] (4) m particles are selected through data screening to form an initialization particle group;

[0092] (5) Substitute the initialized particle swarm into the cloud model Cloud = [C 1 ; C 2 ;…;Cm] to generate updated particle swarm, the formula is as follows:

[0093]

[0094] Where: E x For expectation, E n is entropy, f max,i is the i-th particle in the initial particle group, f av is the average value of the fitness of each particle in the initialized particle swarm, c is the preset value, n r is the number of new particles generated, k is the dimension of the particles, r is the population size, C r is the sub-particle population;

[0095] (6) If the updated particle swarm can meet the expected value, it will be used as the output result. If not, a new round of particles will be generated through the cloud model to determine whether the conditions are met.

[0096] The objective function decision attribute table refers to solving the objective function through the cloud evolution particle swarm algorithm, and the results of the solution constitute a decision table for single-objective optimization.

[0097] The weight of solving the objective function is:

[0098] (1) According to the decision attribute table, construct the decision matrix [r ij ] k×k (i,j=1,2,...,k);

[0099] (2) Data preprocessing, the formula is as follows:

[0100]

[0101] (3) Obtain the objective function decision matrix [r' ij ] k×k ;

[0102] (4) Calculate the entropy of the attribute objective function, the formula is:

[0103]

[0104] Where: ψ = 1 / ln(n) is a constant related to the number of samples, so that E i ∈[0,1]; r' ij Satisfying 0<r' ij <1 and And when r ij = 0, r ij ln(r' ij )=0.

[0105] (5) Objective function weight calculation, the formula is:

[0106] d i =1-E i ,i=1,2,...,k (14)

[0107]

[0108] Where: d i Denotes the objective function z i The information deviation degree of i Denotes the objective function z i The weight of .

[0109] The use of the membership function to select the global optimal solution refers to:

[0110] The membership function of the objective function i in the Pareto optimization solution r is defined as Its formula is

[0111]

[0112] Where: i represents the objective function; r represents the sequence number of the Pareto solution set; R represents the number of Pareto solution sets. and Respectively represent the objective function in the Pareto optimization solution r The solution with high membership degree is selected as the global optimal solution.

[0113] Verifying the dispatch results according to the historical dispatch data of the power grid means verifying the reliability of the dispatch results based on the historical dispatch data of the dispatch center.

[0114] Obtaining the clearing price of each unit participating in peak load regulation means that each power generation company quotes a price to the dispatching center, and the dispatching center determines the final clearing price.

[0115] Calculating the peak load cost of each unit means

[0116] p i =(P low,i -P av,i )×p price,i (i=1,2,...,n) (18)

[0117] Where: p i is the peak load adjustment cost of unit i; P low,i is the reduced output power of unit i; P av,i is the average operating output power of unit i; p price,i is the clearing price of unit i.

[0118] Figure 1 It is an overall flow chart, which is consistent with the above calculation steps. This method is to solve the dispatching model by establishing a three-stage hybrid algorithm, and add inter-provincial interconnection line units to the dispatching model to realize the peak-shaving dispatching transactions of units within and outside the province.

[0119] Figure 2 It is a schematic diagram of the three-stage hybrid algorithm model. Its basic principle is: first, all particles in the space are assigned initial random positions and initial random speeds, and then the position of each particle is successively advanced according to the speed of each particle, the known optimal global position in the problem space, and the known optimal position of the particle. As the calculation progresses, by exploring and utilizing the known favorable positions in the search space, the particles gather or aggregate around one or more optimal points to form a new particle swarm, which is further screened in the cloud model until the expected value is met to form an updated particle swarm; the updated particle swarm is constructed into a decision attribute table, and the entropy weight calculation formula is used to obtain the weights; the weights are substituted into the objective function, and the solution with a high membership is found as the global optimal solution through the membership function calculation.

[0120] The simulation scenario is set in a province's day-ahead market, with a regional system consisting of a 1,750MW inter-provincial interconnection line unit, a 1,800MW thermal power unit in the province, and a 600MW pumped storage unit, matched with a 1,100MW wind farm. The load situation is obtained by analyzing the wind power and daily load curves of the next day, and the wind power consumption demand within the 1 hour from 02:00 to 03:00 in the morning is used for peak-shaving scheduling transactions.

Claims

1. A multi-source joint peak-shaving dispatching and trading method considering the access of inter-provincial interconnection lines, which refers to the dynamic dispatching of inter-provincial thermal power units and various units in the province in the power system composed of inter-provincial interconnection lines and wind power units, thermal power units, and pumped storage units in the province, so as to make the optimal peak-shaving dispatching decision, and realize the large-scale wind power consumption and maintain the stable operation of the power system in the province through multi-source joint peak-shaving dispatching and trading; It is characterized in that The following steps are involved: Step 1) obtaining the operating parameters of each unit equipment in the power system; Step 2) Obtain the wind power generation and daily load demand for the next day; Step 3) Establish a multi-source joint peak-shaving scheduling model based on a three-stage hybrid intelligent solution algorithm; Step 4) Verify the dispatch result according to the historical data of power grid dispatch; Step 5) Obtaining the clearing price of each unit participating in peak load regulation; Step 6) Calculate the peak load cost of each unit based on the peak load dispatching results and clearing prices; The step 3) multi-source joint peak-shaving scheduling model includes the following steps: (1) Taking the minimum total cost of peak load regulation as the optimization objective function; (2) Setting the operating range of each unit as the model's constraints; (3) Taking the unit operating parameters that affect the objective function as the input of the scheduling model; (4) Using the wind power generation and daily load demand data of the next day as the input of the model; (5) Taking the minimum total cost of peak load regulation of the unit as the objective function, the cloud evolution particle swarm algorithm is used to solve it; (6) Based on the solution results, the objective function decision attribute table is obtained and the weight of the objective function is solved; (7) Obtaining the global optimal solution set that takes into account the objective function, and selecting the global optimal solution using the membership function; (8) The peak load dispatching model is used to calculate the output of each unit participating in the peak load dispatching; In step (1), the objective function for minimizing the total cost of peak load regulation is: Where: the first term represents the peak-shaving cost of the tie line in the peak-shaving dispatch; the second term represents the power generation cost of the thermal power unit in the peak-shaving dispatch; the third term represents the peak-shaving cost of the pumped storage unit in the peak-shaving dispatch; where T represents the power generation dispatch cycle; i represents the number of units in the system; f Linei represents the cost function of the tie-line generation; Indicates the generation capacity of the tie line; f Gi represents the cost function of thermal power generation; Indicates the generating capacity of thermal power units; Indicates the identification parameters for the pumped storage unit to be put into operation; represents the peak load regulation capacity of the pumped storage unit; C Pu Represents the peak-shaving cost coefficient of pumped-storage units.

2. According to claim 1, a multi-source joint peak-shaving scheduling transaction method considering inter-provincial tie line access is characterized in that: The operating parameters of each unit equipment in the power system obtained in step 1) are: the minimum output, maximum output, upper limit of climbing, lower limit of climbing, shortest operating time, shortest downtime, optimal operating point, plant power consumption rate, power generation coal consumption coefficient of thermal power units and the minimum power, maximum power, minimum upper storage capacity, maximum upper storage capacity, pumping power, initial upper storage capacity, and efficiency of pumped storage units.

3. According to claim 1, a multi-source joint peak-shaving scheduling transaction method considering inter-provincial tie line access is characterized in that: The wind power generation and daily load demand conditions of the next day obtained in step 2) refer to the wind power generation power curve and daily load power curve for 24 hours of the next day.

4. According to claim 1, a multi-source joint peak-shaving scheduling transaction method considering inter-provincial tie line access is characterized in that: In step (2), the operating range of each unit is set as the constraint condition of the model, which refers to power balance constraint, tie line operation constraint, system reserve constraint, thermal power unit operation constraint, and pumped storage unit operation constraint. The specific process is as follows: 1) System power balance constraints: In the formula: the left side is the output power of the wind farm, the output power of the thermal power unit, the output power of the pumped storage unit, and the transmission power of the tie line in period t; is the equivalent load of the system in period t; 2) Tie line operation constraints: -P Linemax- ≤P Linet ≤P Linemax+ (3) Where: P Linemax+ , P Linemax- Indicates the maximum forward and reverse transmission power of the tie line, both are positive values; 3) System backup constraints: Where: P R t , P R′ t They are the backup requirements of the system during peak hours and off-peak hours, and the backup is borne by thermal power units; 4) Operation constraints of conventional thermal power units; 4.1) Upper and lower limits of unit output: P GiMin ≤P Gi t ≤P GiMax (5) Where: P Gi t is the output of thermal power unit i in period t, P GiMin , P GiMax is the lower and upper limits of the output of thermal power unit i; 4.2) Unit climbing speed constraints: -ΔP Gi down ≤P Gi t -P Gi t-1 ≤ΔP Gi up (6) Where: ΔP Gi up is the upper limit of the ramp rate of thermal power unit i in period t, ΔP Gi down is the upper limit of the power reduction rate of thermal power unit i in period t; 5) Operation constraints of pumped storage units: 5.1) Output constraints of pumped storage units: 5.2) Upper reservoir capacity constraints: Where: η f and η p are the average water / electricity conversion constants for power generation and pumping respectively; C0 is the initial water volume of the upper reservoir, C Max and C Min are the maximum and minimum water volumes in the upper reservoir of the pumped storage power station, respectively; 5.3) Water balance constraints: Where: η f and η p are the average water / electricity conversion constants for power generation and pumping, respectively.

5. According to claim 1, a multi-source joint peak-shaving scheduling transaction method considering inter-provincial tie line access is characterized in that: The cloud evolution particle swarm algorithm in step (5) refers to selecting excellent particles according to the particle fitness value, and obtaining the optimal solution of the model through continuous updating and iteration. The specific solution process is as follows: (1) Set the inertia weight ω, acceleration constants c1, c2, and maximum number of iterations N; (2) Input peak load operation data of each unit; (3) Substitute the data into the iterative formula, which is: Where: v t+1 is the updated particle velocity, v t is the current particle speed, r1, r2 are random numbers, x t is the current unit output power, x t+1 is the updated unit output power, x pt is the current local optimal position, x qt is the current global optimal position; (4) m particles are selected through data screening to form an initialization particle group; (5) Substitute the initialized particle swarm into the cloud model Cloud = [C1; C2; ...; Cm] to generate the updated particle swarm, the formula is as follows: Where: E x For expectation, E n is entropy, f max,i is the i-th particle in the initial particle group, f av is the average value of the fitness of each particle in the initialized particle swarm, c is the preset value, n r is the number of new particles generated, k is the dimension of the particles, r is the population size, C r is the sub-particle population; (6) If the updated particle swarm can meet the expected value, it will be used as the output result. If not, a new round of particles will be generated through the cloud model to determine whether the conditions are met.

6. A multi-source joint peak-shaving scheduling transaction method considering inter-provincial tie line access according to claim 1, characterized in that: The objective function decision attribute table in step (6) refers to solving the objective function by cloud evolution particle swarm algorithm, and the solution results constitute a decision table for single objective optimization; The weight for solving the objective function in step (6) is: (1) According to the decision attribute table, construct the decision matrix [r ij ] k×k (i,j=1,2,...,k); (2) Data preprocessing, the formula is as follows: (3) Obtain the objective function decision matrix [r ij '] k×k ; (4) Calculate the entropy of the attribute objective function, the formula is: Where: ψ = 1 / ln(n) is a constant related to the number of samples, so that E i ∈[0,1]; r' ij Satisfying 0<r' ij <1 and And when r ij = 0, r ij ln(r' ij )=0; (5) Objective function weight calculation, the formula is: d i =1-E i ,i=1,2,...,k (14) Where: d i Denotes the objective function z i The information deviation degree of i Denotes the objective function z i The weight of .

7. A multi-source joint peak-shaving dispatching transaction method considering inter-provincial tie line access according to claim 1, characterized in that: The step (7) of selecting the global optimal solution using the membership function means: Define the membership function of the objective function i in the Pareto optimization solution r as Its formula is Where: i represents the objective function; r represents the sequence number of the Pareto solution set; R represents the number of Pareto solution sets; and Respectively represent the objective function in the Pareto optimization solution r The solution with high membership degree is selected as the global optimal solution.

8. According to claim 1, a multi-source joint peak-shaving scheduling transaction method considering inter-provincial tie line access is characterized in that: Step 4) verifying the dispatch result according to the historical dispatch data of the power grid refers to verifying the reliability of the dispatch result according to the historical dispatch data of the dispatch center; Step 5) Obtaining the clearing price of each unit participating in peak load regulation means that each power generation company quotes to the dispatching center, and the dispatching center determines the final clearing price; Step 6) Calculate the peak load cost of each unit: p i =(P low,i -P av,i )×p price,i (i=1,2,...,n) (18) Where: p i is the peak load adjustment cost of unit i; P low,i is the reduced output power of unit i; P av,i is the average operating output power of unit i; p price,i is the clearing price of unit i.

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