Multi-energy complementary power generation system distribution robust chance constraint scheduling method and system

Abstract

The invention relates to the technical field of power dispatching, in particular to a distributed robust chance constraint dispatching method and system for a multi-energy complementary power generation system, and the method comprises the steps: building a target function, taking the maximum new energy power generation power as a target, and comprehensively considering the output of a thermal power generating unit, a pumped storage unit, a wind power unit and a photovoltaic unit; establishing operation constraint conditions of the multi-energy complementary power generation system; the distributed robust chance constraint is introduced, the nonlinear constraint is subjected to linearization processing, the uncertainty of new energy output is represented, and a distributed robust chance constraint peak regulation scheduling model of the multi-energy complementary power generation system is constructed according to an objective function and constraint conditions; and solving the scheduling model by using a mixed integer linear programming method in combination with a solver, obtaining the optimal output distribution of each time period, and obtaining a scheduling method. According to the invention, the influence of the new energy output randomness and volatility on the peak regulation scheduling of the power system is effectively solved, and the new energy consumption capability and the operation stability of the multi-energy complementary power generation system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a method and system for dispatching distributed blue-robust opportunity constraints of a multi-energy complementary power generation system. Background Art

[0002] With the introduction of the "dual carbon" goal, building a clean and low-carbon power system is a necessary condition for achieving sustainable development. The multi-energy complementary power generation system combines new energy with local conditions and has huge energy potential. It is crucial to the clean and low-carbon transformation of the power system. After the large-scale grid connection of new energy sources, mainly wind power and photovoltaics, the randomness and volatility of their own output greatly increase the probability of new energy power abandonment in the system. The joint and coordinated dispatch of thermal power, pumped storage and new energy can effectively promote the grid connection and consumption of new energy.

[0003] Renewable energy output is often difficult to predict accurately and has obvious uncertainty, which in turn affects the system's peak-shaving capacity. Stochastic optimization and robust optimization are two commonly used methods to deal with the uncertainty of renewable energy output. However, stochastic optimization relies on the assumed probability distribution. In fact, the probability distribution of renewable energy output is difficult to obtain accurately. Robust optimization completely ignores probability information and directly uses the deterministic set of renewable energy output uncertainties, which will lead to overly conservative system peak-shaving scheduling decisions. Summary of the invention

[0004] The present invention provides a method and system for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints, thereby effectively solving the problems pointed out in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints, comprising:

[0007] Establish an objective function, taking the maximum power generation of renewable energy as the goal, and comprehensively consider the output of thermal power units, pumped storage units, wind power units, and photovoltaic units;

[0008] Establish the operating constraints of the multi-energy complementary power generation system, including the operating characteristics of thermal power units and pumped storage units, the peak load constraints of the system, and the constraints considering the stability of DC transmission;

[0009] The distributed robustness opportunity constraint is introduced to linearize the nonlinear constraint, characterize the uncertainty of the output of new energy, and build a distributed robustness opportunity constraint peak-shaving scheduling model for the multi-energy complementary power generation system based on the objective function and constraint conditions;

[0010] The mixed integer linear programming method is used in combination with the solver to solve the scheduling model, and the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period is obtained, and the peak-shaving scheduling method with distributed robust opportunity constraints for multi-energy complementary power generation systems is obtained.

[0011] Furthermore, the objective function of establishing the peak load scheduling of the multi-energy complementary power generation system includes:

[0012]

[0013] in, Power generation for renewable energy; is the number of time periods in the scheduling cycle; and Wind power and photovoltaic power generation The power generated during the period.

[0014] Furthermore, the constraint conditions for establishing the operating characteristics of the thermal power unit and the pumped storage unit include:

[0015] Power upper and lower limits of thermal power units:

[0016]

[0017] in, and Respectively The lower and upper limits of power generation of each thermal power unit; For thermal power units exist The power generated during the period; for Thermal power units The operating status of

[0018] Power ramp constraints of thermal power units:

[0019]

[0020] in, and Thermal power units Maximum up and down climbing rate; and They are Thermal power units Start and stop operation status;

[0021] Thermal power unit start and stop constraints:

[0022]

[0023]

[0024] in, , They are Time period The start-up and shutdown status variables of each thermal power unit; , Respectively Minimum start-up and shutdown time of each thermal power unit; Indicates Maximum number of starts and stops per day for each thermal power unit;

[0025] Constraints on the operating status of thermal power units:

[0026]

[0027] in, express Thermal power units The operating status of , Respectively Thermal power units The startup and shutdown states;

[0028] Pumped storage power station storage capacity constraints:

[0029]

[0030] in, for The amount of water stored in the reservoir during the period; and are the minimum and maximum storage capacities of the upper reservoir, respectively; and They are Time period The flow rate of the pumped storage unit under power generation and pumping conditions; It is the water level control target at the end of the dispatching period of the upper reservoir; is the number of pumped storage units;

[0031] Pumped storage unit start and stop constraints:

[0032]

[0033]

[0034]

[0035]

[0036] in, , They are Time period 0-1 state variables of the operating status of the pumped storage unit under power generation and pumping conditions; , They are Time period 0-1 action variable of the start-up and shutdown actions of the pumped storage unit under power generation conditions; , They are Time period 0-1 action variable of the start-up and shutdown actions of the pumped storage unit under pumping conditions; , Respectively The maximum number of starts and stops of a pumped storage unit in a day under power generation and pumping conditions;

[0037] Power constraints of pumped storage units:

[0038]

[0039]

[0040] in, , Respectively The lower and upper power limits of each pumped storage unit under power generation conditions; For the The upper power limit of each pumped storage unit under pumping conditions;

[0041] Power / water conversion efficiency constraints of pumped storage power stations:

[0042]

[0043]

[0044]

[0045] in, is the power generation of the nth pumped storage unit at time t, is the pumping power of the nth pump at time t, , Respectively The power generation and pumping efficiency of the pumped storage units; , They are the maximum and minimum water discharge under power generation state respectively; It is the maximum pumping volume under pumping state.

[0046] Furthermore, the establishment of the constraint conditions for system peak load regulation includes:

[0047]

[0048] in, for System DC transmission power during the period; and for Wind power and photovoltaic power generation power in different time periods; for Power generation capacity of thermal power units during the period; for Power generation capacity of pumped storage units during the time period; for Pumping power of pumped storage units during the period; The upper limit of the DC channel transmission capacity; is the receiving end load curve; and is a slack variable, which indicates the positive and negative deviation of the matching between the transmission curve at the sending end and the load curve at the receiving end; is the matching deviation coefficient.

[0049] Furthermore, the establishment of constraint conditions considering DC transmission stability includes:

[0050] DC constant operation time constraints:

[0051]

[0052]

[0053]

[0054] in, is a 0-1 variable indicating Whether the DC power is adjusted during the time period; Indicates the minimum DC constant operating time;

[0055] Stability constraints of the sending end system:

[0056]

[0057] in: for Time period The operating status of the thermal power units; , They are Time period The operating status of the pumped storage units under power generation and pumping conditions; is the number of thermal power units; is the number of pumped storage units; The minimum number of thermal power units and pumped storage units that need to be started in each period.

[0058] Furthermore, the distributed robust chance constraint is introduced, and the Wasserstein distance is used to describe the probability distribution fuzzy set of renewable energy power prediction. The nonlinear constraints are linearized to characterize the uncertainty of renewable energy output, including:

[0059] Constraints on renewable energy output opportunities:

[0060]

[0061]

[0062] in, is a probability operator; To plan and dispatch wind power output; Power forecasting for wind power; The risk tolerance of wind power output; To plan and dispatch photovoltaic output power; Predict power for photovoltaics; The risk tolerance of photovoltaic power output;

[0063] Rewriting the chance constraint in general form:

[0064]

[0065] in, for and The vector composed of for and The vector composed of , They are and The coefficient vector of ; is the confidence level;

[0066] Further construct the fuzzy set of new energy prediction power probability distribution based on Wasserstein distance :

[0067]

[0068]

[0069]

[0070] in, is the empirical distribution of known samples; is the random variable Samples The Dirac measure of is the number of samples; fuzzy set To be close to the empirical distribution A collection of distributions of ; is a random probability distribution; is a random variable The support set of can be taken as ; is the set of all probability distributions in the support set; is the radius of the Wasserstein sphere; For two probability distributions and The distance between for and About Margin and The joint probability distribution of For Any norm on ;

[0071] Based on fuzzy sets , construct the distributed robust chance constraint form:

[0072]

[0073] The linearization is performed using the conditional value-at-risk approximation and the saddle point theorem:

[0074]

[0075] in, is a random variable; , , is the dual variable generated during the transformation process; is an infinite norm.

[0076] Furthermore, the mixed integer linear programming method is used in combination with the solver to solve the scheduling model, and the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period is obtained, and the peak-shaving scheduling method with distributed robust opportunity constraints of multi-energy complementary power generation system is obtained, including:

[0077] Linearizing the nonlinear constraints involved in the distributed robust opportunity constraints, and transforming the uncertainty constraints of the new energy output from nonlinear to linear constraints;

[0078] Using the solver, input the forecast data of renewable energy output and model parameters, optimize the objective function value, and solve the optimal dispatching plan that meets the constraints;

[0079] Output the optimal power distribution of thermal power units, pumped storage units and new energy units in each time period, as well as the matching optimization results of DC transmission power and receiving end load curve;

[0080] The parameters of the distributed peak-loading opportunity constraints are adjusted, the new energy consumption capacity and scheduling optimization effects under different conditions are analyzed, and finally the distributed peak-loading opportunity constraint peak-loading scheduling method of the multi-energy complementary power generation system is formed.

[0081] Furthermore, Yalmip is used to call the Gurobi solver to solve the scheduling model.

[0082] A multi-energy complementary power generation system distributed blue boom opportunity constraint scheduling system, the system comprising:

[0083] The objective function establishment module establishes the objective function, taking the maximum power generation of new energy as the goal, and comprehensively considering the output of thermal power units, pumped storage units, wind power units, and photovoltaic units;

[0084] Constraint construction module, which establishes the operating constraints of the multi-energy complementary power generation system, including the operating characteristics of thermal power units and pumped storage units, the system peak load constraints and the constraints considering the stability of DC transmission;

[0085] The dispatch model construction module introduces the distributed robust opportunity constraint, linearizes the nonlinear constraint, characterizes the uncertainty of the output of new energy, and constructs the distributed robust opportunity constraint peak-shaving dispatch model of the multi-energy complementary power generation system based on the objective function and constraint conditions;

[0086] The scheduling model solving module uses the mixed integer linear programming method and the solver to solve the scheduling model, obtains the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period, and obtains the peak-shaving scheduling method with distributed robust opportunity constraints for multi-energy complementary power generation systems.

[0087] Furthermore, the scheduling model solving module includes:

[0088] A linear transformation unit is used to linearize the nonlinear constraints involved in the distributed robust opportunity constraints, and transform the uncertainty constraints of the new energy output from nonlinear to linear constraints;

[0089] The solution solving unit uses the solver to input the forecast data of new energy output and model parameters, optimize the objective function value, and solve the optimal scheduling solution that meets the constraints;

[0090] The scheme output unit outputs the optimal power distribution of thermal power units, pumped storage units and new energy units in each time period, as well as the matching optimization results of DC transmission power and receiving end load curve;

[0091] The scheme generation unit adjusts the parameters of the distributed robust opportunity constraints, analyzes the new energy consumption capacity and scheduling optimization effect under different conditions, and finally forms the distributed robust opportunity constrained peak-shaving scheduling method of the multi-energy complementary power generation system.

[0092] The technical solution of the present invention can achieve the following technical effects:

[0093] It effectively solves the impact of the randomness and volatility of renewable energy output on the peak-shaving and dispatching of the power system, and improves the renewable energy absorption capacity and operational stability of the multi-energy complementary power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] 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, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0095] Figure 1 A flowchart of a scheduling method for distributed blue-rod opportunity constraints in a multi-energy complementary power generation system is provided;

[0096] Figure 2 It is a diagram showing the output results of each part of the multi-energy complementary power generation system in an embodiment of the present invention;

[0097] Figure 3 This is a diagram showing the comparison between the system transmission curve and the receiving end load curve in an embodiment of the present invention;

[0098] Figure 4 This is a graph showing the output results of new energy under different parameter values ​​in the distributed robust opportunity constraint in an embodiment of the present invention. DETAILED DESCRIPTION

[0099] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0100] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0101] Embodiment 1

[0102] like Figure 1As shown, the present invention provides a method for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints, the method comprising:

[0103] S1: Establish an objective function, taking the maximum power generation of renewable energy as the goal, and comprehensively consider the output of thermal power units, pumped storage units, wind power units, and photovoltaic units;

[0104] Specifically, by establishing an objective function with maximizing the power generation of renewable energy as the core, the output coordination of various units (thermal power, pumped storage, wind power, photovoltaics) in the multi-energy complementary power generation system is optimized to maximize the utilization of renewable energy power generation and reduce the rate of wind and solar power abandonment. At the same time, the economy and stability of the system are taken into account, providing an optimization target basis for the construction of subsequent scheduling models.

[0105] S2: Establish the operating constraints of the multi-energy complementary power generation system, including the operating characteristics of thermal power units and pumped storage units, the peak load constraints of the system, and the constraints considering the stability of DC transmission;

[0106] Specifically, by setting constraints, the operating boundaries and dispatching rules of thermal power units and pumped storage units are clarified, while taking into account the system's peak-shaving needs and the stability of DC transmission. This ensures the actual operability of the system operation while achieving optimization goals (such as maximizing the power generation of renewable energy) and avoids unrealistic results or operational risks caused by over-optimization.

[0107] S3: Introduce the distributed robustness opportunity constraint, linearize the nonlinear constraint, characterize the uncertainty of renewable energy output, and build the distributed robustness opportunity constraint peak-shaving scheduling model of multi-energy complementary power generation system based on the objective function and constraint conditions;

[0108] Specifically, the purpose of introducing distributed robust chance constraints and linearizing nonlinear constraints is to effectively characterize the uncertainty of renewable energy output when constructing a peak-shaving scheduling model, while ensuring that the model is solvable and efficient. By using distributed robust chance constraints, the randomness and volatility of renewable energy output are incorporated into the optimization model, which not only avoids the shortcomings of random optimization methods that rely entirely on accurate probability distribution assumptions, but also overcomes the problem of traditional robust optimization methods being too conservative, thereby improving the absorption capacity and scheduling flexibility of renewable energy while ensuring the safety of system operation.

[0109] S4: Using the mixed integer linear programming method and the solver to solve the scheduling model, the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period is obtained, and the peak-shaving scheduling method with distributed robust opportunity constraints of multi-energy complementary power generation system is obtained.

[0110] Specifically, based on the established objective function and constraints, the optimal output allocation plan for thermal power units, pumped storage units and new energy units in each time period is obtained, so as to maximize the economy, reliability and dispatching efficiency of system operation. By solving the dispatching model, the theoretical dispatching optimization strategy can be transformed into a practical and operational dispatching plan, and the effectiveness of the distributed robust opportunity constraint model can be verified, and finally an optimized peak-shaving dispatching method for multi-energy complementary power generation systems is formed to improve the consumption of new energy and system stability.

[0111] Through the present invention, the impact of the randomness and volatility of renewable energy output on the peak-shaving scheduling of the power system is effectively resolved, and the renewable energy consumption capacity and operational stability of the multi-energy complementary power generation system are improved.

[0112] The embodiment of the present invention includes 10 thermal power units, 6 pumped storage units, 1 wind farm and 1 photovoltaic power station, which will be described below in conjunction with specific application scenarios.

[0113] As a preferred embodiment of the above, an objective function of peak load scheduling of a multi-energy complementary power generation system is established, including:

[0114]

[0115] in, Power generation for renewable energy; is the number of time periods in the scheduling cycle; and Wind power and photovoltaic power generation The power generated during the period.

[0116] As a preferred embodiment of the above embodiment, establishing the constraint conditions of the operation characteristics of the thermal power unit and the pumped storage unit includes:

[0117] Power upper and lower limits of thermal power units:

[0118]

[0119] in, and Respectively The lower and upper limits of power generation of each thermal power unit; For thermal power units exist The power generated during the period; for Thermal power units The operating status of

[0120] Power ramp constraints of thermal power units:

[0121]

[0122] in, and Thermal power units Maximum up and down climbing rate; and They are Thermal power units Start and stop operation status;

[0123] Thermal power unit start and stop constraints:

[0124]

[0125]

[0126] in, , They are Time period The start-up and shutdown status variables of each thermal power unit; , Respectively Minimum start-up and shutdown time of each thermal power unit; Indicates Maximum number of starts and stops per day for each thermal power unit;

[0127] Constraints on the operating status of thermal power units:

[0128]

[0129] in, express Thermal power units The operating status of , Respectively Thermal power units The startup and shutdown states;

[0130] Pumped storage power station storage capacity constraints:

[0131]

[0132] in, for The amount of water stored in the reservoir during the period; and are the minimum and maximum storage capacities of the upper reservoir, respectively; and They are Time period The flow rate of the pumped storage unit under power generation and pumping conditions; It is the water level control target at the end of the dispatching period of the upper reservoir; is the number of pumped storage units;

[0133] Pumped storage unit start and stop constraints:

[0134]

[0135]

[0136]

[0137]

[0138] in, , They are Time period 0-1 state variables of the operating status of the pumped storage unit under power generation and pumping conditions; , They are Time period 0-1 action variable of the start-up and shutdown actions of the pumped storage unit under power generation conditions; , They are Time period 0-1 action variable of the start-up and shutdown actions of the pumped storage unit under pumping conditions; , Respectively The maximum number of starts and stops of a pumped storage unit in a day under power generation and pumping conditions;

[0139] Power constraints of pumped storage units:

[0140]

[0141]

[0142] in, , Respectively The lower and upper power limits of each pumped storage unit under power generation conditions; For the The upper power limit of each pumped storage unit under pumping conditions;

[0143] Power / water conversion efficiency constraints of pumped storage power stations:

[0144]

[0145]

[0146]

[0147] in, is the power generation of the nth pumped storage unit at time t, is the pumping power of the nth pump at time t, , Respectively The power generation and pumping efficiency of the pumped storage units; , They are the maximum and minimum water discharge under power generation state respectively; It is the maximum pumping volume under pumping state.

[0148] like Figure 2 As shown, the example of the present invention includes the output of 10 thermal power units and 6 pumped storage units. When the power generation power of renewable energy is high, the output of the thermal power units is low and the pumped storage units are used to pump water to maximize the consumption of excess renewable energy output; when the output of renewable energy is low, the output of the thermal power units is increased and the pumped storage units are used to generate electricity, thereby jointly providing support for the system power transmission.

[0149] The parameters of the thermal power unit are shown in Table 1:

[0150] Table 1 Parameters of thermal power units

[0151] Unit No. 1 2 3 4 5 6 7 8 9 10 Output upper limit (MW) 60 60 60 60 60 60 60 60 60 60 Output lower limit (MW) 24 24 24 24 24 24 24 24 24 24

[0152] The parameters of the pumped storage power station are shown in Table 2:

[0153] Table 2 Pumped storage power station parameters

[0154] <![CDATA[Minimum storage capacity of the upper reservoir (m 3 )]]> 0 <![CDATA[Maximum storage capacity of the upper reservoir (m 3 )]]> 2000 <![CDATA[Final reservoir capacity (m 3 ).]]> 1000 <![CDATA[Initial reservoir capacity (m 3 ).]]> 1000 Maximum number of starts and stops of the unit under power generation conditions (times) 4 Maximum number of starts and stops of the unit under pumping conditions (times) 4

[0155] The parameters of the pumped storage unit are shown in Table 3:

[0156] Table 3 Pumped storage unit parameters

[0157] Unit No. 1 2 3 4 5 6 Minimum power generation (MW) 20 20 20 20 20 20 Maximum power generation (MW) 50 50 50 50 50 50 <![CDATA[Minimum water discharge (m 3 ).]]> 30 30 30 30 30 30 <![CDATA[Maximum water discharge (m 3 ).]]> 50 50 50 50 50 50 Maximum pumping power (MW) 50 50 50 50 50 50 <![CDATA[Maximum pumping volume (m 3 )]]> 60 60 60 60 60 60

[0158] As a preferred embodiment of the above, establishing the constraint conditions for system peak load regulation includes:

[0159]

[0160] in, for System DC transmission power during the period; and for Wind power and photovoltaic power generation power in different time periods; for Power generation capacity of thermal power units during the period; for Power generation capacity of pumped storage units during the time period; for Pumping power of pumped storage units during the period; The upper limit of the DC channel transmission capacity; is the receiving end load curve; and is a slack variable, which indicates the positive and negative deviation of the matching between the transmission curve at the sending end and the load curve at the receiving end; is the matching deviation coefficient.

[0161] like Figure 3 As shown, the embodiment of the present invention includes a system transmission curve and a receiving-end load curve. The transmission curve is consistent with the load curve in terms of overall trend, reflecting the peak-shaving capability of the system.

[0162] As a preferred embodiment of the above embodiment, a constraint condition considering the stability of DC transmission is established, including:

[0163] DC constant operation time constraints:

[0164]

[0165]

[0166]

[0167] in, is a 0-1 variable indicating Whether the DC power is adjusted during the time period; Indicates the minimum DC constant operating time;

[0168] Stability constraints of the sending end system:

[0169]

[0170] in: for Time period The operating status of the thermal power units; , They are Time period The operating status of the pumped storage units under power generation and pumping conditions; is the number of thermal power units; is the number of pumped storage units; The minimum number of thermal power units and pumped storage units that need to be started in each period.

[0171] As a preferred embodiment of the above, a distributed robust chance constraint is introduced, and the Wasserstein distance is used to describe the probability distribution fuzzy set of the new energy power prediction, and the nonlinear constraint is linearized to characterize the uncertainty of the new energy output, including:

[0172] Constraints on renewable energy output opportunities:

[0173]

[0174]

[0175] in, is a probability operator; To plan and dispatch wind power output; Power forecasting for wind power; The risk tolerance of wind power output; To plan and dispatch photovoltaic output power; Power prediction for photovoltaics; The risk tolerance of photovoltaic power output;

[0176] Rewriting the chance constraint in general form:

[0177]

[0178] in, for and The vector composed of for and The vector composed of , They are and The coefficient vector of ; is the confidence level;

[0179] Further construct the fuzzy set of new energy prediction power probability distribution based on Wasserstein distance :

[0180]

[0181]

[0182]

[0183] in, is the empirical distribution of known samples; is the random variable Samples The Dirac measure of is the number of samples; fuzzy set To be close to the empirical distribution A collection of distributions of ; is a random probability distribution; is a random variable The support set of can be taken as ; is the set of all probability distributions in the support set; is the radius of the Wasserstein sphere; For two probability distributions and The distance between for and About Margin and The joint probability distribution of For Any norm on ;

[0184] Based on fuzzy sets , construct the distributed robust chance constraint form:

[0185]

[0186] The linearization is performed using the conditional value-at-risk approximation and the saddle point theorem:

[0187]

[0188] in, is a random variable; , , is the dual variable generated during the transformation process; is an infinite norm.

[0189] This model constructs a random variable fuzzy set based on the historical data of renewable energy predicted output, takes renewable energy predicted power generation as the mean and 5% of the mean as the variance, and generates 100 groups of random samples based on normal distribution as the sample set of renewable energy predicted power generation.

[0190] The predicted power generation of new energy is shown in Table 4:

[0191] Table 4 Forecasted power generation of new energy

[0192] time Wind power forecast power (MW) Photovoltaic predicted power generation (MW) 1 560 0 2 640 0 3 720 0 4 640 0 5 560 0 6 320 0 7 160 10 8 80 20 9 80 40 10 160 80 11 120 120 12 80 160 13 120 170 14 160 60 15 240 120 16 320 80 17 400 40 18 480 20 19 520 0 20 600 0 21 480 0 22 560 0 23 640 0 24 720 0

[0193] like Figure 4As shown in the figure, on the one hand, as the confidence level decreases, the chance constraint becomes more relaxed in meeting the requirements for renewable energy power generation, thus the conservatism of the scheduling decision decreases and the renewable energy power generation increases accordingly; on the other hand, as the radius of the Wasserstein sphere decreases, the renewable energy power generation also increases accordingly. This is because the smaller the radius, the smaller the distribution set of random variables, the closer the probability distribution is to the empirical distribution of the sample, and the lower the conservatism of the distributed robust chance constraint model. Therefore, in the actual operation of the multi-energy complementary power generation system, the renewable energy power generation can be increased by setting the appropriate confidence level and the radius of the Wasserstein sphere, and the flexibility of the peak-shaving scheduling decision can be improved.

[0194] As a preferred embodiment of the above, a mixed integer linear programming method is used in combination with a solver to solve the scheduling model, to obtain the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period, and to obtain a peak-shaving scheduling method with distributed robust opportunity constraints for a multi-energy complementary power generation system, including:

[0195] Linearizing the nonlinear constraints involved in the distributed robust opportunity constraints, and transforming the uncertainty constraints of the new energy output from nonlinear to linear constraints;

[0196] Using the solver, input the forecast data of renewable energy output and model parameters, optimize the objective function value, and solve the optimal dispatching plan that meets the constraints;

[0197] Output the optimal power distribution of thermal power units, pumped storage units and new energy units in each time period, as well as the matching optimization results of DC transmission power and receiving end load curve;

[0198] The parameters of the distributed peak-loading opportunity constraints are adjusted, the new energy consumption capacity and scheduling optimization effects under different conditions are analyzed, and finally the distributed peak-loading opportunity constraint peak-loading scheduling method of the multi-energy complementary power generation system is formed.

[0199] Specifically, firstly, the nonlinear constraints involved in the distributed robust opportunity constraints are linearized, and the uncertainty of the renewable energy output is converted from nonlinear constraints to linear constraints to ensure that the model can be efficiently solved using the mixed integer linear programming (MILP) method. The linearization simplifies the uncertainty description through mathematical transformation or duality method, making the model easier to solve while retaining the accuracy of the uncertainty representation. Then, the renewable energy output forecast data and related model parameters are input using a solver (such as Yalmip calling Gurobi), and the objective function and operating constraints are established. During the solution process, the objective function value is optimized to ensure that the renewable energy power generation power is maximized while meeting the operating constraints of thermal power units, pumped storage units and new energy units. Through the efficient algorithm of the solver, the global optimal solution is quickly obtained; then, the optimal power distribution results of thermal power units, pumped storage units and new energy units in each time period are output, and the matching optimization results of DC transmission power and receiving load curve are further calculated to verify the actual feasibility of the scheduling scheme in system operation; finally, by adjusting the key parameters of the distributed robust opportunity constraints (such as confidence level and Wasserstein distance parameters), the new energy consumption capacity and scheduling optimization effect of the system under different conditions are analyzed. Through this process, the sensitivity and robustness of the model are further evaluated, and finally the distributed robust opportunity constraint peak-shaving scheduling method of the multi-energy complementary power generation system is formed, providing reliable technical support for system optimization operation and efficient utilization of new energy.

[0200] As a preferred embodiment of the above embodiment, Yalmip is used to call the Gurobi solver to solve the scheduling model.

[0201] Specifically, Gurobi is a commercial solver with excellent performance, which is particularly suitable for mixed integer linear programming (MILP) problems. It has the advantages of high solution efficiency and strong result reliability, and is very consistent with the solution requirements of the dispatching model of the present invention. After the dispatching model is linearized, Gurobi can quickly and efficiently handle large-scale linear constraint problems, ensure the accuracy of the solution, and meet the real-time requirements of power dispatching problems. Combined with Yalmip, a MATLAB-based modeling tool, it is easy to construct complex objective functions and constraints, and realize efficient solution of the model by calling Gurobi; in addition, Gurobi's parameter tuning function can optimize the solution process according to the model characteristics, improve the convergence speed and result quality, and Yalmip provides flexibility for the sensitivity analysis and expansion of the model. This combination can significantly improve the efficiency and effect of model solution, and provide reliable technical support for new energy consumption and system scheduling optimization.

[0202] Embodiment 2

[0203] Based on the same inventive concept as the distributed robust opportunity constraint scheduling method of a multi-energy complementary power generation system in the aforementioned embodiment, the present invention also provides a distributed robust opportunity constraint scheduling system for a multi-energy complementary power generation system, the system comprising:

[0204] The objective function establishment module establishes the objective function, taking the maximum power generation of new energy as the goal, and comprehensively considering the output of thermal power units, pumped storage units, wind power units, and photovoltaic units;

[0205] Constraint construction module, which establishes the operating constraints of the multi-energy complementary power generation system, including the operating characteristics of thermal power units and pumped storage units, the system peak load constraints and the constraints considering the stability of DC transmission;

[0206] The dispatch model construction module introduces the distributed robust opportunity constraint, linearizes the nonlinear constraint, characterizes the uncertainty of the output of new energy, and constructs the distributed robust opportunity constraint peak-shaving dispatch model of the multi-energy complementary power generation system based on the objective function and constraint conditions;

[0207] The scheduling model solving module uses the mixed integer linear programming method and the solver to solve the scheduling model, obtains the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period, and obtains the peak-shaving scheduling method with distributed robust opportunity constraints for multi-energy complementary power generation systems.

[0208] The above-mentioned dispatching system in the present invention can effectively implement the distributed robust opportunity constraint dispatching method of the multi-energy complementary power generation system. The technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0209] As a preferred embodiment of the above, the scheduling model solving module includes:

[0210] The linear transformation unit linearizes the nonlinear constraints involved in the distributed robust opportunity constraints and transforms the uncertainty constraints of the new energy output from nonlinear to linear constraints;

[0211] The solution solving unit uses the solver to input the forecast data of new energy output and model parameters, optimize the objective function value, and solve the optimal scheduling solution that meets the constraints;

[0212] The scheme output unit outputs the optimal power distribution of thermal power units, pumped storage units and new energy units in each time period, as well as the matching optimization results of DC transmission power and receiving end load curve;

[0213] The scheme generation unit adjusts the parameters of the distributed peak-shaving opportunity constraints, analyzes the new energy consumption capacity and scheduling optimization effects under different conditions, and finally forms a distributed peak-shaving opportunity-constrained peak-shaving scheduling method for a multi-energy complementary power generation system.

[0214] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0215] Although the present application has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined herein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application.

[0216] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these modifications and variations.

Claims

1. A method for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints, characterized in that: include: Establish an objective function, taking the maximum power generation of renewable energy as the goal, and comprehensively consider the output of thermal power units, pumped storage units, wind power units, and photovoltaic units; Establish the operating constraints of the multi-energy complementary power generation system, including the operating characteristics of thermal power units and pumped storage units, the peak load constraints of the system, and the constraints considering the stability of DC transmission; The distributed robustness opportunity constraint is introduced to linearize the nonlinear constraint, characterize the uncertainty of the output of new energy, and build a distributed robustness opportunity constraint peak-shaving scheduling model for the multi-energy complementary power generation system based on the objective function and constraint conditions; The mixed integer linear programming method is used in combination with the solver to solve the scheduling model, and the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period is obtained, and the peak-shaving scheduling method with distributed robust opportunity constraints for multi-energy complementary power generation systems is obtained.

2. The method for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints according to claim 1, characterized in that: The objective function of establishing the peak load dispatching of the multi-energy complementary power generation system includes: ； in, Power generation for renewable energy; is the number of time periods in the scheduling cycle; and Wind power and photovoltaic power generation The power generated during the period.

3. The multi-energy complementary power generation system distributed blue opportunistic constraint scheduling method according to claim 1 is characterized in that: The constraint conditions for establishing the operating characteristics of thermal power units and pumped storage units include: Power upper and lower limits of thermal power units: ； in, and Respectively The lower and upper limits of power generation of each thermal power unit; For thermal power units exist The power generated during the period; for Thermal power units The operating status of Power ramp constraints for thermal power units: ； in, and Thermal power units Maximum up and down climbing rate; and They are Thermal power units Start and stop operation status; Thermal power unit start and stop constraints: ； ； in, , They are Time period Startup and shutdown status variables of thermal power units; , Respectively Minimum start-up and shutdown time of each thermal power unit; Indicates Maximum number of starts and stops per day for each thermal power unit; Constraints on the operating status of thermal power units: ； in, express Thermal power units The operating status of , Respectively Thermal power units The startup and shutdown states; Pumped storage power station storage capacity constraints: ； in, for The amount of water stored in the reservoir during the period; and are the minimum and maximum storage capacities of the upper reservoir, respectively; and They are Time period The flow rate of the pumped storage unit under power generation and pumping conditions; It is the water level control target at the end of the dispatching period of the upper reservoir; is the number of pumped storage units; Pumped storage unit start and stop constraints: ； ； ； ； in, , They are Time period 0-1 state variables of the operating status of the pumped storage unit under power generation and pumping conditions; , They are Time period 0-1 action variable of the start-up and shutdown actions of the pumped storage unit under power generation conditions; , They are Time period 0-1 action variable of the start-up and shutdown actions of the pumped storage unit under pumping conditions; , Respectively The maximum number of starts and stops of a pumped storage unit in a day under power generation and pumping conditions; Power constraints of pumped storage units: ； ； in, , Respectively The lower and upper power limits of each pumped storage unit under power generation conditions; For the The upper power limit of each pumped storage unit under pumping conditions; Power / water conversion efficiency constraints of pumped storage power stations: ； ； ； in, is the power generation of the nth pumped storage unit at time t, is the pumping power of the nth pump at time t, , Respectively The power generation and pumping efficiency of the pumped storage units; , They are the maximum and minimum water discharge under power generation state respectively; It is the maximum pumping volume under pumping state.

4. The method for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints according to claim 1, characterized in that: The constraint conditions for establishing the system peak load regulation include: ； in, for System DC transmission power during the period; and for Wind power and photovoltaic power generation power in different time periods; for Power generation capacity of thermal power units during the period; for Power generation capacity of pumped storage units during the time period; for Pumping power of pumped storage units during the period; The upper limit of the DC channel transmission capacity; is the receiving end load curve; and is a slack variable, which indicates the positive and negative deviation of the matching between the transmission curve at the sending end and the load curve at the receiving end; is the matching deviation coefficient.

5. The method for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints according to claim 1, characterized in that: The constraint conditions for establishing the DC transmission stability considerations include: DC constant operation time constraints: ； ； ； in, is a 0-1 variable indicating Whether the DC power is adjusted during the time period; Indicates the minimum DC constant operating time; Sending end system stability constraints: ； in: for Time period The operating status of the thermal power units; , They are Time period The operating status of the pumped storage units under power generation and pumping conditions; is the number of thermal power units; is the number of pumped storage units; The minimum number of thermal power units and pumped storage units that need to be started in each period.

6. The method for scheduling a multi-energy complementary power generation system with distributed robust opportunity constraints according to claim 1, characterized in that: The distributed robust chance constraint is introduced, and the Wasserstein distance is used to describe the probability distribution fuzzy set of renewable energy power prediction. The nonlinear constraints are linearized to characterize the uncertainty of renewable energy output, including: Constraints on renewable energy output opportunities: ； ； in, is a probability operator; To plan and dispatch wind power output; Power forecasting for wind power; The risk tolerance of wind power output; To plan and dispatch photovoltaic output power; Power prediction for photovoltaics; The risk tolerance of photovoltaic power output; Rewriting the chance constraint into general form: ； in, for and The vector composed of for and The vector composed of , They are and The coefficient vector of ; is the confidence level; Further construct the fuzzy set of new energy prediction power probability distribution based on Wasserstein distance : ； ； ； in, is the empirical distribution of known samples; is the random variable Samples The Dirac measure of is the number of samples; fuzzy set To be close to the empirical distribution A collection of distributions of ; is a random probability distribution; is a random variable The support set of can be taken as ; is the set of all probability distributions in the support set; is the radius of the Wasserstein sphere; For two probability distributions and The distance between for and About Margin and The joint probability distribution of For Any norm on ; Based on fuzzy sets , construct the distributed robust chance constraint form: ； The linearization is performed using the conditional value-at-risk approximation and the saddle point theorem: ； in, is a random variable; , , is the dual variable generated in the transformation process; is an infinite norm.

7. The method for scheduling multi-energy complementary power generation system with distributed robust opportunity constraints according to claim 1, characterized in that: The mixed integer linear programming method is used in combination with the solver to solve the scheduling model, and the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period is obtained, and the peak-shaving scheduling method with distributed robust opportunity constraints of multi-energy complementary power generation system is obtained, including: Linearizing the nonlinear constraints involved in the distributed robust opportunity constraints, and transforming the uncertainty constraints of the new energy output from nonlinear to linear constraints; Using the solver, input the forecast data of renewable energy output and model parameters, optimize the objective function value, and solve the optimal dispatching plan that meets the constraints; Output the optimal power distribution of thermal power units, pumped storage units and new energy units in each time period, as well as the matching optimization results of DC transmission power and receiving end load curve; The parameters of the distributed peak-shaving opportunity constraints are adjusted, the new energy consumption capacity and scheduling optimization effects under different conditions are analyzed, and finally the distributed peak-shaving opportunity constraint peak-shaving scheduling method of the multi-energy complementary power generation system is formed.

8. The method for scheduling multi-energy complementary power generation system with distributed robust opportunity constraints according to claim 7, characterized in that: The scheduling model is solved by using Yalmip to call the Gurobi solver.

9. A multi-energy complementary power generation system distributed blue stick opportunity constraint scheduling system, characterized in that: The system comprises: The objective function establishment module establishes the objective function, taking the maximum power generation of new energy as the goal, and comprehensively considering the output of thermal power units, pumped storage units, wind power units, and photovoltaic units; Constraint construction module, which establishes the operating constraints of the multi-energy complementary power generation system, including the operating characteristics of thermal power units and pumped storage units, the system peak load constraints and the constraints considering the stability of DC transmission; The dispatch model construction module introduces the distributed robust opportunity constraint, linearizes the nonlinear constraint, characterizes the uncertainty of the output of new energy, and constructs the distributed robust opportunity constraint peak-shaving dispatch model of the multi-energy complementary power generation system based on the objective function and constraint conditions; The scheduling model solving module uses the mixed integer linear programming method and the solver to solve the scheduling model, obtains the optimal output distribution of thermal power units, pumped storage units and new energy units in each time period, and obtains the peak-shaving scheduling method with distributed robust opportunity constraints for multi-energy complementary power generation systems.

10. The multi-energy complementary power generation system distributed blue opportunistic constraint scheduling system according to claim 9, characterized in that: The scheduling model solving module includes: A linear transformation unit is used to linearize the nonlinear constraints involved in the distributed robust opportunity constraints, and transform the uncertainty constraints of the new energy output from nonlinear to linear constraints; The solution solving unit uses the solver to input the forecast data of new energy output and model parameters, optimize the objective function value, and solve the optimal scheduling solution that meets the constraints; The scheme output unit outputs the optimal power distribution of thermal power units, pumped storage units and new energy units in each time period, as well as the matching optimization results of DC transmission power and receiving end load curve; The scheme generation unit adjusts the parameters of the distributed robust opportunity constraints, analyzes the new energy consumption capacity and scheduling optimization effects under different conditions, and finally forms the distributed robust opportunity constrained peak-shaving scheduling method of the multi-energy complementary power generation system.

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