Multi-target active advanced scheduling optimization method and system based on user preference

Abstract

The invention discloses a multi-target active advanced scheduling optimization method and system based on user preferences, and relates to the technical field of power system scheduling, and the method comprises the steps: carrying out the preprocessing of prediction data of a power system, and building a multi-target active advanced scheduling optimization model containing the user preferences; carrying out global search on the simplified scheduling model by adopting a multi-target particle swarm optimization algorithm to obtain a preliminary scheduling scheme, correcting the scheduling scheme, and generating an accurate scheduling solution meeting actual constraint conditions; and outputting a result according to the optimized scheduling model, and verifying the feasibility and superiority of the result in combination with the preference set by the user. According to the method, a multi-target active advanced scheduling optimization model containing user preferences is established, the precision and adaptability of a scheduling scheme are effectively improved by setting a user decision list and adding preference constraints, and by outputting an optimization result and verifying the user preferences, under a multi-target optimization framework, the optimization efficiency is improved. And the applicability and the flexibility of the scheme are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a multi-objective active power advance dispatching optimization method and system based on user preference. Background Art

[0002] With the development of smart grid technology and the improvement of power market operation, multi-objective optimization problems in power system dispatching have received widespread attention. Traditional economic dispatching mainly focuses on minimizing operating costs, but with the increasing demand for environmental protection and renewable energy utilization, multi-objective optimization models have begun to be introduced into power system dispatching. These models not only need to comprehensively consider economic benefits, but also need to take into account environmental benefits, power quality, and system reliability and stability. Especially in the context of a high proportion of renewable energy access, how to effectively dispatch uncertain resources such as wind power and photovoltaics has become a research hotspot. In addition, with the improvement of computing power and the advancement of optimization algorithms, the application of intelligent optimization methods based on multi-objective evolutionary algorithms (such as MOPSO and NSGA-II) in power system dispatching has gradually increased. These methods have shown significant advantages in high-dimensional nonlinear complex systems due to their strong global search capabilities and diverse solution characteristics.

[0003] However, existing multi-objective optimization techniques have shortcomings when dealing with complex nonlinear constraints. Although traditional optimization methods can quickly solve problems under simplified models, they ignore network transmission capacity and dynamic adjustment requirements, which makes the obtained scheduling solutions unable to fully meet the requirements of system security and stability in actual operation. In addition, these methods lack flexibility when dealing with user preferences, and it is difficult to dynamically adjust optimization objectives and constraints, which makes it difficult to simultaneously meet user needs and system constraints. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing power system dispatching method has the problems of insufficient ability to handle complex nonlinear constraints, inability to dynamically adjust optimization objectives and constraints, and difficulty in simultaneously meeting user needs and system safety requirements, as well as how to improve the system regulation capability and operating efficiency when a high proportion of renewable energy is connected.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a multi-objective active advance scheduling optimization method based on user preferences, comprising preprocessing the predicted data of the power system, establishing a multi-objective active advance scheduling optimization model including user preferences; using a multi-objective particle swarm optimization algorithm to perform a global search on the simplified scheduling model, obtain a preliminary scheduling plan, modify the scheduling plan, and generate an accurate scheduling solution that meets the actual constraints; according to the output results of the optimized scheduling model, combined with the preferences set by the user, verify the feasibility and superiority of the results.

[0007] As a preferred scheme of the multi-objective active advance scheduling optimization method based on user preferences described in the present invention, the preprocessing includes collecting load forecast data of the power system, forecast data of renewable energy and operating conditions, processing missing values, abnormal values ​​and inconsistent data in the data, and using cleaning rules such as filling, mean replacement and deletion according to different data types.

[0008] As a preferred solution of the multi-objective active advance scheduling optimization method based on user preferences described in the present invention, wherein: the establishment of a multi-objective active advance scheduling optimization model including user preferences includes constructing an objective function of a maximum acceptance scheduling model based on minimum abandoned wind volume; the conventional unit operation cost objective function minF1(P a ,P na ,P w ,α) is expressed as:

[0009]

[0010] Among them, N a 、N na are the number of AGC units and non-AGC units, the total number is N g , It is the sum of the actual output of AGC units and non-AGC units. is the output base value of AGC unit i at time t, α is the power allocation factor of the unit, T is the dispatch period, and the target function of pollutant emission of conventional units minF2(P a ,P na ,α) is expressed as:

[0011]

[0012] in, is the actual output of the AGC unit and the non-AGC unit at time t, d i 、e i , γ i is a constant coefficient; the operating cost objective function of conventional units minF1(P a ,P na ,Pw ,α) The fuel cost of the planned power generation Adjusting costs with AGC The sum of the wind power acceptance ratio is taken as the operating cost, and the wind power acceptance ratio objective function minF3(P w ) is expressed as:

[0013]

[0014] in, is the actual power of wind farm k connected to the system during period t, is the predicted output power of wind farm k in period t, N w is the number of wind farms; by setting the user decision list Add preference constraints, expressed as:

[0015]

[0016] Among them, F i (u,x) represents the i-th objective function, and the preference interval is is the lower boundary value of the i-th objective function, indicating the minimum standard accepted by the user. It is the upper boundary value of the i-th objective function, indicating the highest standard accepted by the user. The model simultaneously optimizes the output base value and power allocation factor of the AGC units to coordinate with each other.

[0017] As a preferred solution of the multi-objective active advance scheduling optimization method based on user preference described in the present invention, the global search includes using a consensus-based multi-objective particle swarm algorithm to solve and preliminarily select representative particles evenly distributed in the target space.

[0018] As a preferred solution of the multi-objective active power leading dispatch optimization method based on user preference described in the present invention, wherein: the constraint condition includes replacing the original constraint with the DC power flow equation and the thermal limit constraint, solving the simplified dispatch model, and taking the obtained optimal dispatch solution as the simplified dispatch solution, the simplified dispatch model is expressed as:

[0019] min[F1(P a ,P na ,P w ,α),F2(P a ,P na ,P w ,α),F3(P a ,P na ,P w ,α)]

[0020] The DC power flow equation is expressed as:

[0021] B bus θ+P bus,shift +P d +G sh -C g P g -C w P w =0

[0022] Among them, B bus is the admittance matrix of the bus in the power system, θ is the phase angle vector of the bus, P bus,shift is the bus power correction, P d is the system load power vector, G sh is the power injected from the branch to the bus, C g P g Inject power into the generator, C w P w Injecting power into the wind farm, the thermal limit constraint is expressed as:

[0023] h f (θ) = B f θ+P f,shift ≤F max

[0024] h t (θ) = B f θ-P f,shift ≤F max

[0025] Among them, h f (θ), h t (θ) represents the thermal limit constraint function of the forward and reverse power flows, respectively, B f is the admittance matrix of the branch, F max is the thermal limit value of the branch, and the system spinning reserve capacity constraint is expressed as:

[0026]

[0027] Among them, R ui,t is the spinning reserve capacity of the i-th unit in period t, R * is the minimum spinning reserve capacity required by the system, and the AGC unit output and ramp constraints are expressed as:

[0028]

[0029] in, is the minimum output of the i-th AGC unit, The maximum output of the i-th AGC unit, ΔP t w is the total deviation between the predicted value and the actual value of wind power output, is the actual output of the i-th AGC unit at time period t-1, is the down-regulation rate of the i-th AGC unit, ΔT is the time interval, is the upward adjustment rate of the i-th AGC unit, Q a , are the lower and upper limits of reactive power output of the i-th AGC unit, is the reactive power output of the i-th AGC unit; the output and ramp constraints of non-AGC units are expressed as:

[0030]

[0031] in, is the minimum output of the jth non-AGC unit, The maximum output of the jth non-AGC unit, is the actual output of the jth non-AGC unit at time period t-1, is the turn-down rate of the jth non-AGC unit, is the increase rate of the jth non-AGC unit, Q na , are the lower and upper limits of reactive power output of the jth non-AGC unit, is the reactive power output of the jth non-AGC unit; the AGC power balance regulation is expressed as:

[0032]

[0033] Among them, k To regulate power, the wind farm dispatch power constraint is expressed as:

[0034]

[0035] in, is the maximum allowable power of the wind farm, Forecast output power for wind farms, is the initial predicted power of the kth wind farm in time period t, and the wind power output uncertainty set is expressed as:

[0036]

[0037] Among them, S R It is an uncertain set of renewable energy output; the UPE method is embedded in the evolutionary process of the intelligent optimization method to perform constraint correction on individuals that are not in the feasible area.

[0038] As a preferred solution of the multi-objective active advance scheduling optimization method based on user preferences described in the present invention, the accurate scheduling solution includes: the operator selecting a solution from the Pareto optimal solution set obtained in the first stage as the initial point x0, and setting the user decision list based on the objective function value of x0; adding the preference constraint to the model to construct the LAPD problem based on user preferences; for each scenario in the scenario set, checking whether the initial solution x0 satisfies the constraint set of the accurate scheduling model, and screening out the scenarios with constraints exceeding the limit as the new scenario set At the given initial point x0 = [P a ,P na ,P w ,α], the UPE method is applied to calculate the preference feasible solution of the UP-LAPD problem for the updated uncertainty set; the UP-LAPD problem is transformed into a vector H(x) containing only equality constraints, expressed as:

[0039]

[0040] Among them, V i,t ,θ i,t is the node voltage amplitude and phase angle of node i in time period t, V j,t is the node voltage amplitude of node j in time period t, G ij is the equivalent conductance of the line from node i to node j, B ij is the equivalent susceptance of the line from node i to node j, V i max 、V i min are the upper and lower limits of the voltage at node i, is the load demand of node i in period t, S l,t is the branch power flow of line l in time period t, is the thermal limit of line l, Q Gi,t is the reactive power output of the ith unit in period t, s is the penalty factor, is the rate of change of x(t) with time, Q H (x) is the dynamic behavior function related to state x, D is a diagonal matrix used to adjust the change rate of different state variables of the system. Starting from the initial solution x0, the ode15s integral solver is used for calculation and converges to the solution Using the solution as the starting point, solve it again using Newton's method Optimize the accuracy of the scheduling solution.

[0041] As a preferred solution of the multi-objective active advance scheduling optimization method based on user preferences described in the present invention, the accurate scheduling solution also includes when the user's preference for the problem itself is unclear, resulting in the inability to set a decision list, based on a feasible solution, reducing each objective function value by 5% as a target decision list.

[0042] Another object of the present invention is to provide a multi-objective active advance dispatch optimization system based on user preferences, which can perform a global search on a simplified dispatch model by adopting a multi-objective particle swarm optimization algorithm, obtain a preliminary dispatch plan, modify the dispatch plan, and generate an accurate dispatch solution that meets actual constraints, thereby solving the problem of low efficiency of current power system dispatch technology.

[0043] As a preferred solution of the multi-objective active power advance scheduling optimization system based on user preferences described in the present invention, it includes: a data processing module, a scheme adjustment module, and a verification module; the data processing module is used to pre-process the predicted data of the power system and establish a multi-objective active power advance scheduling optimization model including user preferences; the scheme adjustment module is used to use a multi-objective particle swarm optimization algorithm to perform a global search on the simplified scheduling model, obtain a preliminary scheduling scheme, modify the scheduling scheme, and generate an accurate scheduling solution that meets the actual constraints; the verification module is used to output the results according to the optimized scheduling model, and verify the feasibility and superiority of the results in combination with the preferences set by the user.

[0044] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a multi-objective active power advance scheduling optimization method based on user preferences.

[0045] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a multi-objective active power advance scheduling optimization method based on user preferences.

[0046] Beneficial effects of the present invention: The multi-objective active advance scheduling optimization method based on user preferences provided by the present invention establishes a multi-objective active advance scheduling optimization model including user preferences, ensures that the model can comprehensively consider user preferences and actual scheduling needs during the optimization process, and takes into account economy, environmental protection and system stability. By setting a user decision list and adding preference constraints, the objective function can be flexibly adjusted, so that the optimization result is more targeted and practical, and the accuracy and adaptability of the scheduling scheme are effectively improved. Through the global search of the multi-objective particle swarm optimization algorithm and the processing of the simplified scheduling model, the effect of quickly generating a feasible scheduling scheme is achieved. At the same time, through the modification of the scheme, the accuracy and practicality of the scheduling solution under complex constraints are achieved, and the efficiency and applicability of the scheduling model are improved. Through the output of the optimization results and the verification of the user preferences, the effect of double optimization is achieved, so that the scheduling scheme is not only feasible, but also under the framework of multi-objective optimization, the actual value and user satisfaction of the scheme are further improved, thereby enhancing the applicability and flexibility of the scheme. The present invention achieves better results in terms of stability, reliability and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 An overall flow chart of a multi-objective active power advance scheduling optimization method based on user preference provided in the first embodiment of the present invention.

[0049] Figure 2 An overall flow chart of a multi-objective active power advance dispatch optimization system based on user preferences provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a multi-objective active power advance scheduling optimization method based on user preference, comprising:

[0052] S1: Preprocess the forecast data of the power system and establish a multi-objective active advance dispatch optimization model that includes user preferences.

[0053] Furthermore, preprocessing includes collecting load forecast data of the power system, forecast data of renewable energy, and operating conditions, processing missing values, outliers, and inconsistent data in the data, and using cleaning rules such as filling, mean replacement, and deletion according to different data types.

[0054] It should be noted that the establishment of a multi-objective active power advance dispatch optimization model including user preferences includes constructing the objective function of the maximum acceptance dispatch model based on the minimum wind abandonment volume; the conventional unit operation cost objective function min F1(P a ,P na ,P w ,α) is expressed as:

[0055]

[0056] Among them, N a 、N na are the number of AGC units and non-AGC units, the total number is N g , It is the sum of the actual output of AGC units and non-AGC units. is the output base value of AGC unit i at time t, α is the power allocation factor of the unit, T is the dispatch period, and the target function of pollutant emission of conventional units minF2(P a ,P na ,α) is expressed as:

[0057]

[0058] in, is the actual output of the AGC unit and the non-AGC unit at time t, d i 、e i , γ i is a constant coefficient; the operating cost objective function of conventional units minF1(P a ,P na ,P w ,α) The fuel cost of the planned power generation Adjusting costs with AGC The sum of the wind power acceptance ratio is taken as the operating cost, and the wind power acceptance ratio objective function minF3(P w ) is expressed as:

[0059]

[0060] in, is the actual power of wind farm k connected to the system during period t, is the predicted output power of wind farm k in period t, N w is the number of wind farms; by setting the user decision list Add preference constraints, expressed as:

[0061]

[0062] Among them, F i (u,x) represents the i-th objective function, and the preference interval is is the lower boundary value of the i-th objective function, indicating the minimum standard accepted by the user. It is the upper boundary value of the i-th objective function, indicating the highest standard accepted by the user. The model simultaneously optimizes the output base value and power allocation factor of the AGC units to coordinate with each other.

[0063] S2: Use the multi-objective particle swarm optimization algorithm to perform a global search on the simplified scheduling model, obtain a preliminary scheduling solution, modify the scheduling solution, and generate an accurate scheduling solution that meets the actual constraints.

[0064] Furthermore, the global search includes solving the problem using a consensus-based multi-objective particle swarm algorithm to preliminarily select representative particles that are evenly distributed in the target space.

[0065] It should be noted that the constraints include replacing the original constraints with the DC power flow equation and the thermal limit constraint, solving the simplified dispatch model, and taking the obtained optimal dispatch solution as the simplified dispatch solution. The simplified dispatch model is expressed as:

[0066] min[F1(P a ,P na ,P w ,α),F2(P a ,P na ,P w ,α),F3(P a ,P na ,P w ,α)]

[0067] The DC power flow equation is expressed as:

[0068] B bus θ+P bus,shift +P d +G sh -C g P g -C w P w =0

[0069] Among them, B busis the admittance matrix of the bus in the power system, θ is the phase angle vector of the bus, P bus,shift is the bus power correction, P d is the system load power vector, G sh is the power injected from the branch to the bus, C g P g Inject power into the generator, C w P w Injecting power into the wind farm, the thermal limit constraint is expressed as:

[0070] h f (θ) = B f θ+P f,shift ≤F max

[0071] h t (θ) = B f θ-P f,shift ≤F max

[0072] Among them, h f (θ), h t (θ) represents the thermal limit constraint function of the forward and reverse power flows, respectively, B f is the admittance matrix of the branch, F max is the thermal limit value of the branch, and the system spinning reserve capacity constraint is expressed as:

[0073]

[0074] Among them, R ui,t is the spinning reserve capacity of the i-th unit in period t, R * is the minimum spinning reserve capacity required by the system, and the AGC unit output and ramp constraints are expressed as:

[0075]

[0076] in, is the minimum output of the i-th AGC unit, The maximum output of the i-th AGC unit, ΔP t w is the total deviation between the predicted value and the actual value of wind power output, is the actual output of the i-th AGC unit at time period t-1, is the down-regulation rate of the i-th AGC unit, ΔT is the time interval, is the upward adjustment rate of the i-th AGC unit, Q a , are the lower and upper limits of reactive power output of the i-th AGC unit, is the reactive power output of the i-th AGC unit; the output and ramp constraints of non-AGC units are expressed as:

[0077]

[0078] in, is the minimum output of the jth non-AGC unit, The maximum output of the jth non-AGC unit, is the actual output of the jth non-AGC unit at time period t-1, is the turn-down rate of the jth non-AGC unit, is the increase rate of the jth non-AGC unit, Q na , are the lower and upper limits of reactive power output of the jth non-AGC unit, is the reactive power output of the jth non-AGC unit; the AGC power balance regulation is expressed as:

[0079]

[0080] Among them, k To regulate power, the wind farm dispatch power constraint is expressed as:

[0081]

[0082] in, is the maximum allowable power of the wind farm, Forecast output power for wind farms, is the initial predicted power of the kth wind farm in time period t, and the wind power output uncertainty set is expressed as:

[0083]

[0084] Among them, S R It is an uncertain set of renewable energy output; the UPE method is embedded in the evolutionary process of the intelligent optimization method to perform constraint correction on individuals that are not in the feasible area.

[0085] S3: Based on the output results of the optimized scheduling model and the preferences set by the user, verify the feasibility and superiority of the results.

[0086] Furthermore, the accurate scheduling solution includes the operator selecting a solution from the Pareto optimal solution set obtained in the first stage as the initial point x0, and setting the user decision list based on the objective function value of x0; adding the preference constraint to the model to construct the LAPD problem based on user preferences; for each scenario in the scenario set, checking whether the initial solution x0 satisfies the constraint set of the accurate scheduling model, and screening out the scenarios with constraints exceeding the limit as the new scenario set At the given initial point x0 = [P a ,P na ,P w ,α], the UPE method is applied to calculate the preference feasible solution of the UP-LAPD problem for the updated uncertainty set; the UP-LAPD problem is transformed into a vector H(x) containing only equality constraints, expressed as:

[0087]

[0088] Among them, V i,t ,θ i,t is the node voltage amplitude and phase angle of node i in time period t, V j,t is the node voltage amplitude of node j in time period t, G ij is the equivalent conductance of the line from node i to node j, B ij is the equivalent susceptance of the line from node i to node j, V i max 、V i min are the upper and lower limits of the voltage at node i, is the load demand of node i in period t, S l,t is the branch power flow of line l in time period t, is the thermal limit of line l, Q Gi,t is the reactive power output of the ith unit in period t, s is the penalty factor, is the rate of change of x(t) with time, Q H (x) is the dynamic behavior function related to state x, D is a diagonal matrix used to adjust the change rate of different state variables of the system. Starting from the initial solution x0, the ode15s integral solver is used for calculation and converges to the solution Using the solution as the starting point, solve it again using Newton's method Optimize the accuracy of the scheduling solution.

[0089] It should be noted that when the UPE method based on the trajectory of the dynamic system solves the preferred feasible solution, the system trajectory is continuously changing and converges to the first point on the stable equilibrium manifold encountered. Therefore, the UPE method will calculate a preferred feasible solution that is closer to the initial point. Since the initial solution x0 is the Pareto optimal solution under the same objective function, the preferred feasible solution is still near the Pareto optimal solution of the accurate model, and is even still the global Pareto optimal solution under the accurate model. The scheduling solution Under the premise of meeting complex constraints, the actual needs of operators are fully considered. Therefore, local optimization calculations are no longer performed and it is used as the final scheduling plan of the system. The UPE method has no restrictions on the defined user decision list. When the decision list set by the user is "unreasonable", the preference feasible domain is an empty set, but the method does not diverge, but converges to a degenerate stable equilibrium manifold. If the system trajectory converges to a degenerate stable equilibrium manifold starting from any initial point, it means that a solution that satisfies the user's preference does not exist. In this case, the obtained degenerate stable equilibrium manifold can be analyzed, and the user decision list can be adjusted according to the convergence results. When the user's preference for the problem itself is unclear and the decision list cannot be set, the following method can be used. For example, other multi-objective optimization solvers are used to obtain a non-dominated solution or a feasible solution to the problem. Then, based on this feasible solution, each objective function value is reduced by 5% as a target decision list. It can not only be used to calculate the preferred feasible solution, but also can be used as a supplement to the existing multi-objective optimization method to improve the calculation results of the existing method.

[0090] Embodiment 2, an embodiment of the present invention, provides a multi-objective active power advance scheduling optimization method based on user preference. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0091] Firstly, the load forecast data of the regional power grid and the output forecast data of renewable energy sources such as wind power and photovoltaic power are preprocessed. The preprocessing process includes filling missing values ​​in the data, replacing outliers with mean values, and deleting inconsistent data to ensure the accuracy and consistency of the input data. Then, a multi-objective active power advance dispatch optimization model including user preferences is established. The objective function of the model includes three parts: minimizing the operating cost of conventional units, minimizing pollutant emissions, and maximizing the wind power acceptance ratio. By setting a user decision list and adding preference constraints to the objective function, the optimization process can comprehensively consider the user's personalized needs and system operation requirements.

[0092] After the model is established, a consensus-based multi-objective particle swarm optimization algorithm (MOPSO) is used to perform a global search on the simplified scheduling model. At this stage, the simplified scheduling model uses DC power flow equations and thermal limit constraints instead of more complex AC power flow equations, thereby speeding up the calculation speed and obtaining representative particles evenly distributed in the target space through search. According to the distribution of these particles, a preliminary scheduling plan is generated and corrected. In the correction process, more complex actual constraints are taken into account, such as the nonlinear power flow constraints of the system and the climbing ability of the unit, to generate an accurate scheduling solution that meets the actual constraints.

[0093] After obtaining an accurate dispatch solution that meets the actual constraints, the optimization results are verified in combination with the preferences set by the user. The verification process not only ensures the feasibility of the dispatch plan, but also further verifies the superiority of the plan. By setting the user decision list, the weight and constraint range of the objective function can be dynamically adjusted, and finally an optimal dispatch solution that meets the safety requirements of power grid operation and meets the user's preference requirements is output. In actual operation, this solution can significantly improve the utilization rate of renewable energy such as wind power and photovoltaics, while reducing pollutant emissions from conventional thermal power units and reducing overall operating costs.

[0094] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a multi-objective active power advance scheduling optimization system based on user preferences, including a data processing module, a scheme adjustment module, and a verification module.

[0095] The data processing module is used to pre-process the predicted data of the power system and establish a multi-objective active advance dispatch optimization model that includes user preferences; the scheme adjustment module is used to use a multi-objective particle swarm optimization algorithm to conduct a global search of the simplified dispatch model, obtain a preliminary dispatch plan, modify the dispatch plan, and generate an accurate dispatch solution that meets the actual constraints; the verification module is used to output the results according to the optimized dispatch model, and combine the preferences set by the user to verify the feasibility and superiority of the results.

[0096] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0098] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0099] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-objective active power advance scheduling optimization method based on user preference, characterized in that: include: Preprocess the forecast data of the power system and establish a multi-objective active power advance dispatch optimization model that includes user preferences; The multi-objective particle swarm optimization algorithm is used to perform a global search on the simplified scheduling model, obtain a preliminary scheduling solution, modify the scheduling solution, and generate an accurate scheduling solution that meets the actual constraints; Based on the output results of the optimized scheduling model and the preferences set by the user, the feasibility and superiority of the results are verified.

2. The multi-objective active power advance scheduling optimization method based on user preference according to claim 1, characterized in that: The preprocessing includes collecting load forecast data of the power system, forecast data of renewable energy and operating conditions, processing missing values, abnormal values ​​and inconsistent data in the data, and using cleaning rules such as filling, mean replacement and deletion according to different data types.

3. The multi-objective active power advance scheduling optimization method based on user preference according to claim 2, characterized in that: The establishment of a multi-objective active power advance dispatch optimization model including user preferences includes constructing an objective function of a maximum acceptance dispatch model based on minimum wind abandonment; Conventional unit operation cost objective function minF1(P a ,P na ,P w ,α) is expressed as: Among them, N a 、N na are the number of AGC units and non-AGC units, the total number is N g , It is the sum of the actual output of AGC units and non-AGC units. is the output base value of AGC unit i at time t, α is the power allocation factor of the unit, T is the dispatch period, and the target function of pollutant emission of conventional units minF2(P a ,P na ,α) is expressed as: in, is the actual output of the AGC unit and the non-AGC unit at time t, d i 、e i , γ i is a constant coefficient; Conventional unit operation cost objective function minF1(P a ,P na ,P w ,α) The fuel cost of the planned power generation Adjusting costs with AGC The sum of the wind power acceptance ratio is taken as the operating cost, and the wind power acceptance ratio objective function minF3(P w ) is expressed as: in, is the actual power of wind farm k connected to the system during period t, is the predicted output power of wind farm k in period t, N w is the number of wind farms; By setting a user decision list Add preference constraints, expressed as: Among them, F i (u,x) represents the i-th objective function, and the preference interval is is the lower boundary value of the i-th objective function, indicating the minimum standard accepted by the user. It is the upper boundary value of the i-th objective function, indicating the highest standard accepted by the user. The model simultaneously optimizes the output base value and power allocation factor of the AGC units to coordinate with each other.

4. The multi-objective active power advance scheduling optimization method based on user preference according to claim 3, characterized in that: The global search includes solving the problem using a consensus-based multi-objective particle swarm algorithm to preliminarily select representative particles that are evenly distributed in the target space.

5. The multi-objective active power advance scheduling optimization method based on user preference according to claim 4, characterized in that: The constraint conditions include replacing the original constraints with the DC power flow equation and the thermal limit constraint, solving the simplified scheduling model, and taking the obtained optimal scheduling solution as the simplified scheduling solution. The simplified scheduling model is expressed as: min [ F1 ( P a ,P na ,P w ,α),F2(P a ,P na ,P w ,α),F3(P a ,P na ,P w ,α)] The DC power flow equation is expressed as: B bus θ+P bus,shift +P d +G sh -C g P.S g -C w P.S w 0 Among them, B bus is the admittance matrix of the bus in the power system, θ is the phase angle vector of the bus, P bus,shift is the bus power correction, P d is the system load power vector, G sh is the power injected from the branch to the bus, C g P g Inject power into the generator, C w P w Injecting power into the wind farm, the thermal limit constraint is expressed as: h f (θ)=B f θ+P f,shift ≤F max h t (θ)=B f θ-P f,shift ≤F max Among them, h f (θ), h t (θ) represents the thermal limit constraint function of the forward and reverse power flows, respectively, B f is the admittance matrix of the branch, F max is the thermal limit value of the branch, and the system spinning reserve capacity constraint is expressed as: Among them, R ui,t is the spinning reserve capacity of the i-th unit in period t, R * is the minimum spinning reserve capacity required by the system, and the AGC unit output and ramp constraints are expressed as: in, is the minimum output of the i-th AGC unit, The maximum output of the i-th AGC unit, ΔP t w is the total deviation between the predicted value and the actual value of wind power output, is the actual output of the i-th AGC unit at time period t-1, is the down-regulation rate of the i-th AGC unit, ΔT is the time interval, is the upward adjustment rate of the i-th AGC unit, Q a , are the lower and upper limits of reactive power output of the i-th AGC unit, is the reactive power output of the i-th AGC unit; The output and climbing constraints of non-AGC units are expressed as: in, is the minimum output of the jth non-AGC unit, The maximum output of the jth non-AGC unit, is the actual output of the jth non-AGC unit at time period t-1, is the turn-down rate of the jth non-AGC unit, is the increase rate of the jth non-AGC unit, Q na , are the lower and upper limits of reactive power output of the jth non-AGC unit, is the reactive power output of the jth non-AGC unit; AGC power balance adjustment is expressed as: Among them, k To regulate power, the wind farm dispatch power constraint is expressed as: in, is the maximum allowable power of the wind farm, Forecast output power for wind farms, is the initial predicted power of the kth wind farm in time period t, and the wind power output uncertainty set is expressed as: Among them, S R is the uncertain set of renewable energy outputs; The UPE method is embedded in the evolutionary process of the intelligent optimization method to perform constraint correction on individuals that are not in the feasible region.

6. The multi-objective active power advance scheduling optimization method based on user preference according to claim 5, characterized in that: The accurate scheduling solution includes the operator selecting a solution from the Pareto optimal solution set obtained in the first stage as the initial point x0, and setting a user decision list based on the objective function value of x0; Add preference constraints to the model and construct the LAPD problem based on user preferences; For each scene in the scene set, check whether the initial solution x0 satisfies the constraint set of the accurate scheduling model, and select the scenes that exceed the constraint as the new scene set. At the given initial point x0 = [P a ,P na ,P w ,α], the UPE method is applied to calculate the preference feasible solution of the UP-LAPD problem for the updated uncertainty set; The UP-LAPD problem is transformed into a vector H(x) containing only equality constraints, expressed as: Among them, V i,t ,θ i,t is the node voltage amplitude and phase angle of node i in time period t, V j,t is the node voltage amplitude of node j in time period t, G ij is the equivalent conductance of the line from node i to node j, B ij is the equivalent susceptance of the line from node i to node j, V i max 、V i min are the upper and lower limits of the voltage at node i, is the load demand of node i in period t, S l,t is the branch power flow of line l in time period t, is the thermal limit of line l, Q Gi,t is the reactive power output of the ith unit in period t, s is the penalty factor, is the rate of change of x(t) with time, Q H (x) is the dynamic behavior function related to state x, D is a diagonal matrix used to adjust the change rate of different state variables of the system. Starting from the initial solution x0, the ode15s integral solver is used for calculation and converges to the solution Using the solution as the starting point, solve it again using Newton's method Optimize the accuracy of the scheduling solution.

7. The multi-objective active power advance scheduling optimization method based on user preference according to claim 6, characterized in that: The accurate scheduling solution also includes reducing each objective function value by 5% as a target decision list based on a feasible solution when the user's preference for the problem itself is unclear and a decision list cannot be set.

8. A system using the multi-objective active power advance scheduling optimization method based on user preference as claimed in any one of claims 1 to 7, characterized in that: Including data processing module, scheme adjustment module and verification module; The data processing module is used to pre-process the forecast data of the power system and establish a multi-objective active power advance dispatch optimization model including user preferences; The scheme adjustment module is used to use a multi-objective particle swarm optimization algorithm to perform a global search on the simplified scheduling model, obtain a preliminary scheduling scheme, modify the scheduling scheme, and generate an accurate scheduling solution that meets the actual constraints; The verification module is used to output the results according to the optimized scheduling model, and verify the feasibility and superiority of the results in combination with the preferences set by the user.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-objective active power advance scheduling optimization method based on user preference described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-objective active power advance scheduling optimization method based on user preference described in any one of claims 1 to 7 are implemented.

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