Pareto optimization-based power system dispatch method and apparatus, terminal device, and storage medium

Abstract

A Pareto optimization-based power system dispatch method and apparatus, a terminal device, and a storage medium, relating to the field of power system dispatch. The method comprises: acquiring generating unit power data, generating unit cost data, nodal power data, and branch power data of a system to be dispatched; constructing a power system dispatch model and corresponding security constraints according to the generating unit power data, the generating unit cost data, the nodal power data, and the branch power data; with the objective of minimizing a sum of generating unit operating costs, carbon emissions, and peak-to-valley differences, solving, under the security constraints, the power system dispatch model according to the generating unit power data, the generating unit cost data, the nodal power data, and the branch power data to obtain a Pareto solution set; computing a relative closeness of each Pareto solution according to the Pareto solution set; and using the Pareto solution corresponding to the minimum relative closeness as a dispatch strategy parameter to perform dispatching on the system to be dispatched. Implementing the method satisfies multi-objective dispatching requirements of power systems.

Description

A power system dispatching method, apparatus, terminal equipment, and storage medium based on Pareto optimization. Technical Field

[0001] This invention relates to the field of power system dispatching technology, and in particular to a power system dispatching method, apparatus, terminal equipment, and storage medium based on Pareto optimization. Background Technology

[0002] In the current global energy structure, the power industry, as a major consumer of fossil fuels, generates enormous amounts of carbon dioxide emissions during its operation, causing significant impacts on the natural environment. With the proposed goals of carbon peaking and carbon neutrality, achieving economical, low-carbon, and safe power system operation has become one of the core objectives of the power industry. Therefore, traditional single-objective economic dispatch of power systems should shift to multi-objective dispatch. However, current single-objective conversion methods for multi-objective power system dispatch cannot assess the conflicts and constraints between various objectives, making it difficult to find an effective balance point among multiple conflicting objectives. This leads to difficulties in meeting the needs of multi-objective power system dispatch in practical applications, thus limiting the overall performance of the power system. Summary of the Invention

[0003] This invention provides a power system scheduling method, apparatus, terminal equipment, and storage medium based on Pareto optimization, which can effectively solve the problem that existing technologies are unable to find an effective balance point among multiple conflicting objectives and thus cannot meet the multi-objective scheduling requirements of power systems.

[0004] One embodiment of the present invention provides a power system dispatching method based on Pareto optimization, comprising:

[0005] Acquire the unit power data, unit cost data, node power data, and branch power data of the system to be scheduled;

[0006] Based on the unit power data, the unit cost data, the node power data, and the branch power data, a power system dispatch model and the corresponding security constraints of the power system dispatch model are constructed.

[0007] With the goal of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, under the aforementioned safety constraints, the power system dispatch model is solved based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatch model;

[0008] Based on the Pareto solution set, the relative proximity of each Pareto solution in the Pareto solution set is calculated;

[0009] The Pareto solution corresponding to the minimum relative proximity is used as the scheduling strategy parameter of the system to be scheduled.

[0010] The scheduling system to be scheduled is scheduled according to the scheduling strategy parameters.

[0011] Furthermore, the safety constraints include: unit output constraints, unit ramp-up constraints, system reserve demand constraints, new energy output constraints, branch transmission constraints, and system power balance constraints;

[0012] The unit output constraint is:

[0013] Where i is the unit number of the system to be scheduled; p i,t The output of unit i during time period t; Downward reserve is reserved for unit i to cope with power fluctuations during time period t; The upward reserve reserved for unit i to cope with power fluctuations during time period t; P i The lower bound of the output of unit i; The upper limit of the output of unit i;

[0014] The unit's ramp-up constraint is:

[0015] Where, p i,t+1 The output of unit i during the time period t+1; Upward reserve is reserved for unit i to cope with power fluctuations during the t+1 period; For unit i, the downward ramp limit during time period t; Downward reserve is reserved for unit i to cope with power fluctuations during the t+1 period; The upward ramp limit for unit i during time period t; This is the upper limit of the downward ramp for unit i; This is the upper limit of the downward ramp for unit i;

[0016] The system backup requirement constraint is:

[0017] in, The minimum requirement that the total reserve provided to the generating units should meet; The maximum requirement that the total reserve provided to the generating unit should meet;

[0018] The constraint on the output of the new energy source is:

[0019] Among them, w j,t For the actual output of new energy j during time period t; W j,t The available power output of new energy source j during time period t;

[0020] The branch transmission constraint is:

[0021] Among them, P l This is the lower bound of the transmission power of branch l; g is the upper bound of the transmission power of branch l; l,i g is the coefficient representing the influence of the power of unit i on the transmission power of branch l. l,j The coefficient representing the influence of the power of new energy source j on the transmission power of branch l; g l,n D is the coefficient representing the influence of the injected power at node n on the transmission power of branch l. n,t Let n be the load of node n;

[0022] The system power balance constraint is:

[0023] Furthermore, with the objective of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, under the aforementioned safety constraints, the power system dispatch model is solved based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatch model, including:

[0024] With the objectives of minimizing unit operating costs, minimizing carbon emissions, and minimizing peak-valley differences respectively, the power system dispatch model is solved under the security constraints based on the unit power data, unit cost data, node power data, and branch power data to obtain the first objective solution, the second objective solution, and the third objective solution.

[0025] The first objective solution, the second objective solution, and the third objective solution are each normalized to obtain several normalized objective vectors; and these normalized objective vectors are used as the Pareto front.

[0026] Based on the Pareto front and the preset combination coefficients, the set of reference points for the solution of the power system dispatch model is determined.

[0027] Based on the set of reference points, the first objective solution, the second objective solution, and the third objective solution, a parallel axis is generated;

[0028] The solution is obtained by solving the set of reference points and the parallel axes, so that the Pareto solution corresponding to the parallel axis inside the Pareto front boundary falls on the parallel axis, and the Pareto solution corresponding to the parallel axis outside the Pareto front boundary falls on the boundary, thus obtaining the initial Pareto solution.

[0029] The initial Pareto solution is projected onto a reference surface formed by a set of reference points along the parallel axis direction, and then filtered to obtain the Pareto solution set of the power system dispatch model.

[0030] Furthermore, based on the Pareto front and preset combination coefficients, a set of reference points for the solution of the power system dispatching model is determined, including:

[0031] Based on the Pareto front and the preset combination coefficients, the internal reference point of the solution of the power system dispatch model is calculated.

[0032] The difference vector is calculated based on the Pareto front and the preset number of segments;

[0033] Based on the internal reference point and the difference vector, generate the external reference point for the solution of the power system dispatch model;

[0034] The internal reference points and the external reference points are merged to obtain the reference point set of the solution of the power system dispatching model.

[0035] Further, the initial Pareto solution is projected onto a reference plane formed by a set of reference points along the parallel axis direction, and then filtered to obtain the Pareto solution set of the power system dispatch model, including:

[0036] Based on the set of reference points, generate several reference surfaces;

[0037] The initial Pareto solution is projected onto the reference plane to generate several projection points;

[0038] Calculate the projection distance between every two projection points based on the projection points;

[0039] The neighborhood of the reference point is calculated based on the set of reference points, the difference vector, and the preset projection point distance threshold.

[0040] Within the neighborhood of the reference point, the initial Pareto solution corresponding to the projection point closest to the reference point is preserved to obtain the target Pareto solution;

[0041] The Pareto solutions corresponding to projection distances less than a preset projection point distance threshold are deleted to obtain the Pareto solution set of the power system scheduling model.

[0042] Furthermore, based on the Pareto solution set, the relative closeness of each Pareto solution in the Pareto solution set is calculated, including:

[0043] Based on the Pareto solution set, calculate the normalized value of each Pareto solution to obtain the target normalized value;

[0044] The evaluation matrix is ​​constructed based on the normalized target value to obtain the target evaluation matrix;

[0045] The minimum value of the target normalized value is taken as the positive ideal point, and the maximum value of the target normalized value is taken as the negative ideal point;

[0046] The first weight is calculated based on the target evaluation matrix and the number of solutions corresponding to the Pareto solution set;

[0047] The relative proximity is calculated based on the positive ideal point, the negative ideal point, the first weight, and the preset weight.

[0048] Furthermore, the scheduling strategy parameters include: unit output, unit reserved for downward reserve, unit reserved for upward reserve, and new energy output;

[0049] The scheduling of the system to be scheduled is performed according to the scheduling policy parameters, including:

[0050] Adjust the power output allocation of the units to be dispatched in the system according to the power output of the units;

[0051] Based on the unit's reserved downward reserve and the unit's reserved upward reserve, determine the unit's reserved downward reserve and the unit's reserved upward reserve for the system to be dispatched;

[0052] The allocation of new energy output to the system to be dispatched is adjusted according to the new energy output.

[0053] As an improvement to the above solution, another embodiment of the present invention provides a power system dispatching device based on Pareto optimization, comprising:

[0054] The system data acquisition module is used to acquire the unit power data, unit cost data, node power data, and branch power data of the system to be scheduled.

[0055] The model and constraint construction module is used to construct a power system dispatch model and the corresponding security constraints of the power system dispatch model based on the unit power data, the unit cost data, the node power data and the branch power data.

[0056] The scheduling model solving module is used to solve the power system scheduling model under the security constraints, with the goal of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, based on the unit power data, unit cost data, node power data, and branch power data, to obtain the Pareto solution set of the power system scheduling model.

[0057] The relative proximity calculation module is used to calculate the relative proximity of each Pareto solution in the Pareto solution set based on the Pareto solution set.

[0058] The scheduling strategy parameter determination module is used to take the Pareto solution corresponding to the minimum relative proximity as the scheduling strategy parameter of the system to be scheduled.

[0059] The system scheduling module is used to schedule the system to be scheduled according to the scheduling strategy parameters.

[0060] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a Pareto-based power system dispatching method as described in the above embodiments.

[0061] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the Pareto-based power system dispatching method described in the above embodiment.

[0062] By implementing this invention, at least the following beneficial effects are achieved:

[0063] This invention provides a power system dispatching method, apparatus, terminal equipment, and storage medium based on Pareto optimization. The method acquires unit power data, unit cost data, node power data, and branch power data of the system to be dispatched; constructs a power system dispatching model and corresponding security constraints based on the unit power data, unit cost data, node power data, and branch power data; with the objective of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, under the security constraints, solves the power system dispatching model based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatching model; calculates the relative proximity of each Pareto solution in the Pareto solution set; uses the Pareto solution corresponding to the minimum relative proximity as the dispatching strategy parameter of the system to be dispatched; and dispatches the system to be dispatched according to the dispatching strategy parameter. In power system dispatching, three conflicting objectives—cost, carbon emissions, and peak-valley difference—are simultaneously optimized. Using unit power data, unit cost data, node power data, and branch power data, a power system dispatching model is solved under security constraints. The goal is to achieve economical, low-carbon, and safe operation of the power system while minimizing the sum of cost, carbon emissions, and peak-valley difference. The Pareto solution corresponding to the minimum relative proximity is used as the dispatching strategy parameter for the system to be dispatched, covering the Pareto solutions for cost, carbon emissions, and peak-valley difference. The minimum relative proximity represents the optimal achievement of each objective amidst the mutual constraints of cost, carbon emissions, and peak-valley difference. This finds an effective balance point among multiple objectives, meeting the multi-objective dispatching needs of the power system and improving its overall performance. Attached Figure Description

[0064] Figure 1 is a flowchart illustrating a power system dispatching method based on Pareto optimization according to an embodiment of the present invention.

[0065] Figure 2 is a schematic diagram of the equally distributed nearest point method provided in an embodiment of the present invention;

[0066] Figure 3 is a flowchart of the equally distributed nearest point method provided in an embodiment of the present invention;

[0067] Figure 4 is a schematic diagram of another solution process provided by an embodiment of the present invention;

[0068] Figure 5 is a schematic diagram of a power system dispatching device based on Pareto optimization provided in an embodiment of the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Referring to Figure 1, to address the problem that existing technologies struggle to find an effective balance point among multiple conflicting objectives, thus failing to meet the multi-objective scheduling requirements of power systems, an embodiment of the present invention provides a flowchart of a power system scheduling method based on Pareto optimization, comprising:

[0071] S1. Obtain the unit power data, unit cost data, node power data, and branch power data of the system to be dispatched;

[0072] Specifically, the unit power data includes: lower limit of unit output, upper limit of unit output, unit uphill limit, unit downhill limit, minimum total reserve demand, maximum total reserve demand, and available renewable energy output; the unit cost data includes: unit power generation cost, unit reserve cost, renewable energy curtailment penalty cost, and carbon emissions; the node power data includes: node impact coefficient on branch transmission power and node load; the branch power data includes: lower limit of branch transmission power, upper limit of branch transmission power, and unit impact coefficient on branch transmission power.

[0073] S2. Based on the unit power data, the unit cost data, the node power data, and the branch power data, construct a power system dispatch model and the corresponding security constraints of the power system dispatch model;

[0074] Specifically, the safety constraints include: unit output constraints, unit ramp-up constraints, system reserve demand constraints, new energy output constraints, branch transmission constraints, and system power balance constraints.

[0075] The unit output constraint is:

[0076] Where i is the unit number of the system to be scheduled; p i,t The output of unit i during time period t; Downward reserve is reserved for unit i to cope with power fluctuations during time period t; The upward reserve reserved for unit i to cope with power fluctuations during time period t; P i The lower bound of the output of unit i; The upper limit of the output of unit i;

[0077] The unit's ramp-up constraint is:

[0078] Where, p i,t+1 The output of unit i during the time period t+1; Upward reserve is reserved for unit i to cope with power fluctuations during the t+1 period; For unit i, the downward ramp limit during time period t; Downward reserve is reserved for unit i to cope with power fluctuations during the t+1 period; The upward ramp limit for unit i during time period t; This is the upper limit of the downward ramp for unit i; This is the upper limit of the downward ramp for unit i;

[0079] The system backup requirement constraint is:

[0080] in, The minimum requirement that the total reserve provided to the generating units should meet; The maximum requirement that the total reserve provided to the generating unit should meet;

[0081] The constraint on the output of the new energy source is:

[0082] Among them, w j,t For the actual output of new energy j during time period t; W j,t The available power output of new energy source j during time period t;

[0083] The branch transmission constraint is:

[0084] Among them, P l This is the lower bound of the transmission power of branch l; g is the upper bound of the transmission power of branch l; l,i g is the coefficient representing the influence of the power of unit i on the transmission power of branch l. l,j The coefficient representing the influence of the power of new energy source j on the transmission power of branch l; g l,n D is the coefficient representing the influence of the injected power at node n on the transmission power of branch l. n,t Let n be the load of node n. In a system with a fixed topology, the influence coefficient of the injected power of node n on the transmission power of branch l is fixed, that is, the amount of power injected by node n will increase the transmission power of branch l by the same amount.

[0085] The system power balance constraint is:

[0086] Specifically, the unit output constraint limits the upper and lower limits of the output of each generator; the unit ramp constraint means that the reserve provided by the unit cannot exceed the ramp limit; and the system reserve constraint means that the total reserve provided by all units should meet the minimum demand. and maximum demand The renewable energy output constraint represents the actual output w of renewable energy. j,t Cannot exceed available output W j,t Branch transmission constraints represent the limitations imposed by the generating unit on branch transmission and the limitations imposed by the node on branch transmission; system power balance constraints represent that the generating capacity must be able to meet the load demand, and the generating power must be equal to the load demand power.

[0087] S3. With the goal of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, under the aforementioned safety constraints, the power system dispatch model is solved based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatch model.

[0088] Specifically, with the objective of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, under the aforementioned safety constraints, the power system dispatch model is solved based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatch model, including:

[0089] With the objectives of minimizing unit operating costs, minimizing carbon emissions, and minimizing peak-valley differences respectively, the power system dispatch model is solved under the security constraints based on the unit power data, unit cost data, node power data, and branch power data to obtain the first objective solution, the second objective solution, and the third objective solution.

[0090] The first objective solution, the second objective solution, and the third objective solution are each normalized to obtain several normalized objective vectors; and these normalized objective vectors are used as the Pareto front.

[0091] Based on the Pareto front and the preset combination coefficients, the set of reference points for the solution of the power system dispatch model is determined.

[0092] Based on the set of reference points, the first objective solution, the second objective solution, and the third objective solution, a parallel axis is generated;

[0093] The solution is obtained by solving the set of reference points and the parallel axes, so that the Pareto solution corresponding to the parallel axis inside the Pareto front boundary falls on the parallel axis, and the Pareto solution corresponding to the parallel axis outside the Pareto front boundary falls on the boundary, thus obtaining the initial Pareto solution.

[0094] The initial Pareto solution is projected onto a reference surface formed by a set of reference points along the parallel axis direction, and then filtered to obtain the Pareto solution set of the power system dispatch model.

[0095] Preferably, the unit operating cost f1 is: For the unit's power generation cost, a i b i c i These are the power generation cost coefficients for unit i, respectively. For backup costs, and These are the downward reserve cost coefficient and upward reserve cost coefficient for unit i, respectively, ∑ t ∑ j ρ j (W j,t -w j,t ) represents the penalty cost for abandoning new energy sources, ρ j Let f2 be the penalty coefficient for new energy source j. Carbon emissions f2: f2=∑ t ∑ i e i p i,t e i Carbon emissions per unit output of unit i. Peak-to-valley difference f3: f3=∑ t (ΔP t ) 2 ,ΔP t =∑ i p i,t -∑ i p i,t-1 ΔP t This represents the change in the total output of the unit between adjacent time periods, i.e., the total output of unit i in time period t ∑ i p i,t Total output of unit i during time period t-1 ∑ i p i,t-1 The change in quantity. The definition of multi-objective optimization based on Pareto theory is as follows:

[0096] min F={f1,f2,...,f m ,...,f M}

[0097] stG(x)≤0

[0098] Where F is the objective set, namely, minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences; f mThere can be multiple sub-objectives; in this embodiment, the sub-objectives are unit operating cost, carbon emissions, and peak-valley difference. M represents the number of objectives, which is three in this embodiment. x is the decision variable vector, i.e., the scheduling strategy parameters of the system to be scheduled, including the output p of each unit. i,t Reserved for downward backup and up for backup And the output of various new energy sources j,t G represents the constraints, specifically the security constraints of the power system dispatch model in this embodiment. Dominance relationships exist between solutions in Pareto optimization:

[0099] i,j represent the i-th and j-th solutions; m,n represent the m-th and n-th objective function values; f i,m f represents the i-th solution to the m-th objective function value; j,m f represents the j-th solution to the m-th objective function value; i,n f represents the i-th solution to the n-th objective function value; j,n The j-th solution represents the n-th objective function value. If the above condition holds, then the i-th solution dominates the j-th solution. A solution not dominated by other solutions is called a Pareto solution (or non-dominated solution). All solutions within the feasible region constitute the Pareto solution set, and the corresponding objective vectors constitute the Pareto front. Since the Pareto front is continuously distributed, it is impossible to obtain all Pareto solutions. In this case, the Pareto solution set is used to approximate all Pareto solutions. To ensure the approximation, the Pareto front corresponding to the Pareto solution set should not only be non-dominated but also uniformly and widely distributed. In multi-objective problems, it is impossible to find a single solution that minimizes all objective values ​​(the smaller the better), so there will be many solutions. Solution i dominates solution j, meaning that all objective values ​​of solution i are less than or equal to the objective value of solution j, and at least one objective value is less than solution j, indicating that the result of solution i is better than solution j. The solution is the value of the decision variable x. The Pareto solution set is actually an evaluation based on the objective function values ​​corresponding to the solutions, resulting in a set without distinction of superiority or inferiority.

[0100] In a preferred embodiment of the present invention, a method of evenly distributed nearest neighbors is proposed to solve the Pareto solution set of a power system scheduling model. First, the power system scheduling model is solved with the goal of minimizing each objective, yielding an initial solution. This involves individually optimizing each objective to obtain M extreme solutions, namely, the first objective solution, the second objective solution, and the third objective solution for each objective. Then, the first objective solution, the second objective solution, and the third objective solution are normalized to obtain several normalized objective vectors. The normalized objective vector is then used as the Pareto front; based on the Pareto front and preset combination coefficients, a reference point set for the solution of the power system dispatching model is determined. Let c be the i-th reference point. i,m Ω is the preset combination coefficient. R Let S be the set of reference points. Reference points are derived from the set [-S]. E ,…,0,1 / D,…,1+S E Extract the following segments sequentially from [D] (where D is the preset number of segments, S...). E (As an expansion factor), to ensure that the reference points are uniformly distributed and cover the entire Pareto front. Then, based on the set of reference points, the first objective solution, the second objective solution, and the third objective solution, parallel axes are generated. The direction vector e of the parallel axes is also calculated from these endpoints. The solution is obtained by solving the problem based on the set of reference points and the parallel axes, so that the Pareto solution corresponding to the parallel axis within the Pareto front boundary falls on the parallel axis, and the Pareto solution corresponding to the parallel axis outside the Pareto front boundary falls on the boundary, thus obtaining the initial Pareto solution.

[0101] We employ two sequential optimization problems to obtain the initial Pareto solution for each reference point:

[0102] max λ i

[0103] min ∑ m∈M r i,m

[0104] In the formula For max λ i , The optimal value obtained through optimization; r i,m R represents the target normalized value of target m for the i-th Pareto solution; i This represents the i-th Pareto solution. The two sequential optimization problems above guarantee that the Pareto solution corresponding to the parallel axis within the Pareto front boundary falls exactly on the axis, while the Pareto solution corresponding to the parallel axis outside the Pareto front boundary will fall on the boundary. Then, the initial Pareto solution is projected onto the reference surface formed by the set of reference points along the parallel axis direction, and filtered to obtain the Pareto solution set of the power system dispatch model. The reference points, Pareto solutions, parallel axes, and endpoints of the Pareto front are shown in Figure 2. The Pareto solution set obtained by considering unit operating costs, carbon emissions, and peak-valley difference objectives is shown in Figure 2. As shown in the flowchart in Figure 3, the Pareto solution set of the power system dispatch model is obtained by solving for the reference point set and mapping it to a Pareto solution set. Then, sequential optimization is used to solve for the Pareto solution corresponding to each reference point, and redundant Pareto solutions are eliminated. By optimizing unit operating costs and carbon emissions, the overall cost and environmental impact of the power system can be reduced. Simultaneously, by considering the minimum peak-valley difference, the load on the power system can be balanced, improving the stability and efficiency of the power system.

[0105] Specifically, based on the Pareto front and preset combination coefficients, the set of reference points for the solution of the power system dispatching model is determined, including:

[0106] Based on the Pareto front and the preset combination coefficients, the internal reference point of the solution of the power system dispatch model is calculated.

[0107] The difference vector is calculated based on the Pareto front and the preset number of segments;

[0108] Based on the internal reference point and the difference vector, generate the external reference point for the solution of the power system dispatch model;

[0109] The internal reference points and the external reference points are merged to obtain the reference point set of the solution of the power system dispatching model.

[0110] In a preferred embodiment of the present invention, the nearest neighbor method is used to construct the maximum possible set of reference points consisting of internal and external reference points. The internal reference points are determined by the formula... To obtain, the following additional conditions must be met: This is the normalized value of the i-th internal reference point on the m-th target dimension; e is the normalized value of the i-th internal reference point on the n-th target dimension; m e is the m-th target reference value; n This is the reference value for the nth target.

[0111] Based on the Pareto front and the preset number of segments, the difference vector is calculated. Taking a three-objective optimization problem as an example, from... If we select the two shortest difference vectors d1 and d2, then the external reference point is generated as follows:

[0112] As the first external reference point, As the second external reference point, The internal reference points are used as internal reference points. The internal and external reference points are merged, and overlapping points are removed to obtain the reference point set for the solution of the power system dispatch model.

[0113] Specifically, the initial Pareto solution is projected onto a reference plane formed by a set of reference points along the parallel axis direction, and then filtered to obtain the Pareto solution set of the power system dispatch model, including:

[0114] Based on the set of reference points, generate several reference surfaces;

[0115] The initial Pareto solution is projected onto the reference plane to generate several projection points;

[0116] Calculate the projection distance between every two projection points based on the projection points;

[0117] The neighborhood of the reference point is calculated based on the set of reference points, the difference vector, and the preset projection point distance threshold.

[0118] Within the neighborhood of the reference point, the initial Pareto solution corresponding to the projection point closest to the reference point is preserved to obtain the target Pareto solution;

[0119] The Pareto solutions corresponding to projection distances less than a preset projection point distance threshold are deleted to obtain the Pareto solution set of the power system scheduling model.

[0120] In a preferred embodiment of the present invention, firstly, the boundary Pareto solution (initial Pareto solution) is projected onto the reference surface along the direction parallel to the axis, generating a plurality of projection points. These projection points are denoted as... According to the projection point and reference points The distribution of the Pareto solution set is optimized by removing redundant boundary Pareto solutions. To ensure the uniformity of the Pareto solution set, each reference point... Only one Pareto solution is retained in the vicinity; that is, the Pareto solution corresponding to the nearest projection point in its neighborhood is preserved, thus obtaining the target Pareto solution. Each reference point The reference point neighborhood is defined as: |c1|≤0.5,|c2|≤0.5;

[0121] Secondly, Pareto solutions that are close together are removed to further ensure uniformity. If the projection distance between two nearby projection points is less than a preset projection point distance threshold, one of the two target Pareto solutions associated with these two projection points will be removed, resulting in the Pareto solution set of the power system scheduling model. The preset projection point distance threshold is set to: |c1|<0.25, |c2|<0.25.

[0122] S4. Based on the Pareto solution set, calculate the relative proximity of each Pareto solution in the Pareto solution set;

[0123] Preferably, based on the Pareto solution set, the relative closeness of each Pareto solution in the Pareto solution set is calculated, including:

[0124] Based on the Pareto solution set, calculate the normalized value of each Pareto solution to obtain the target normalized value;

[0125] The evaluation matrix is ​​constructed based on the normalized target value to obtain the target evaluation matrix;

[0126] The minimum value of the target normalized value is taken as the positive ideal point, and the maximum value of the target normalized value is taken as the negative ideal point;

[0127] The first weight is calculated based on the target evaluation matrix and the number of solutions corresponding to the Pareto solution set;

[0128] The relative proximity is calculated based on the positive ideal point, the negative ideal point, the first weight, and the preset weight.

[0129] In a preferred embodiment of the present invention, based on the Pareto solution set, a normalized value for each Pareto solution is calculated to obtain a target normalized value. Then, an evaluation matrix is ​​constructed based on the target normalized value to obtain a target evaluation matrix. The elements r in the evaluation matrix... i,m Let m represent the target normalized value of the target m in the i-th Pareto solution. The minimum value of the target normalized value is taken as the positive ideal point, and the maximum value is taken as the negative ideal point. The positive and negative ideal points are respectively... and Based on the target evaluation matrix and the number of solutions corresponding to the Pareto solution set, a first weight is calculated. This first weight represents the objective weight. From entropy weight σ m It is determined that entropy weight represents the amount of information provided by each target, and its calculation formula is as follows:

[0130] N PF ρ is the number of solutions corresponding to the Pareto solution set. i,m For the normalized value of the i-th Pareto solution on the m-th index, use the original normalized value r. i,m Obtained through normalization. Preset weights. This is a subjective weight, which can be customized. Then, the overall weight is calculated based on the first weight and the preset weights. Finally, the relative proximity is calculated based on the positive ideal point, the negative ideal point, the first weight, and the preset weight. The relative proximity of the i-th Pareto solution can be calculated as follows:

[0131] In the formula, the smaller T iTo represent a better solution, the minimum T is... i The corresponding Pareto solution is the optimal compromise solution, which is the scheduling strategy parameter of the system to be scheduled.

[0132] S5. Use the Pareto solution corresponding to the minimum relative proximity as the scheduling strategy parameter of the system to be scheduled.

[0133] In a preferred embodiment of the present invention, the evenly distributed nearest neighbor method obtains the Pareto solution set, providing dispatchers with diverse scheduling decision options. In practical applications, an optimal compromise solution should be selected from the Pareto solution set as the final scheduling decision. The entropy-weighted ideal point method is used to select the optimal compromise solution, and the Pareto solution corresponding to the minimum relative proximity is used as the scheduling strategy parameter of the system to be scheduled. As shown in Figure 4, by establishing a multi-objective low-carbon scheduling model (power system scheduling model) for the power system, and then solving the Pareto solution set of the model, the optimal compromise solution is selected from the Pareto solution set according to the entropy-weighted ideal point method as the scheduling strategy parameter of the system to be scheduled. The proposed evenly distributed nearest neighbor method can solve for a uniformly distributed and widely distributed Pareto solution set, thereby providing decision-makers with more diversified decision options. The proposed entropy-weighted ideal point method fully considers actual scheduling needs and mines the information contained in the distribution of the Pareto solution set, thereby achieving a more scientific selection of compromise solutions.

[0134] S6. Schedule the system to be scheduled according to the scheduling strategy parameters.

[0135] Specifically, the scheduling strategy parameters include: unit output, unit reserve for downward backup, unit reserve for upward backup, and new energy output;

[0136] The scheduling of the system to be scheduled is performed according to the scheduling policy parameters, including:

[0137] Adjust the power output allocation of the units to be dispatched in the system according to the power output of the units;

[0138] Based on the unit's reserved downward reserve and the unit's reserved upward reserve, determine the unit's reserved downward reserve and the unit's reserved upward reserve for the system to be dispatched;

[0139] The allocation of new energy output to the system to be dispatched is adjusted according to the new energy output.

[0140] By implementing this embodiment, the unit power data, unit cost data, node power data, and branch power data of the system to be dispatched are obtained. Based on the unit power data, unit cost data, node power data, and branch power data, a power system dispatch model and the corresponding security constraints are constructed. With the goal of minimizing the sum of unit operating cost, carbon emissions, and peak-valley difference, under the security constraints, the power system dispatch model is solved based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatch model. Based on the Pareto solution set, the relative proximity of each Pareto solution in the Pareto solution set is calculated. The Pareto solution corresponding to the minimum relative proximity is used as the dispatch strategy parameter of the system to be dispatched. The system to be dispatched is then dispatched according to the dispatch strategy parameter. In power system dispatching, three conflicting objectives—cost, carbon emissions, and peak-valley difference—are simultaneously optimized. By using unit power data, unit cost data, node power data, and branch power data, and under security constraints, the power system dispatching model is solved to achieve economical, low-carbon, and safe operation of the power system while minimizing the sum of cost, carbon emissions, and peak-valley difference. An effective balance point is found among multiple objectives, namely the Pareto solution corresponding to the minimum relative proximity, which satisfies the multi-objective dispatching requirements of the power system and improves the overall performance of the power system.

[0141] Referring to Figure 5, it is a schematic diagram of the structure of a power system dispatching device based on Pareto optimization according to an embodiment of the present invention, including:

[0142] The system data acquisition module is used to acquire the unit power data, unit cost data, node power data, and branch power data of the system to be scheduled.

[0143] The model and constraint construction module is used to construct a power system dispatch model and the corresponding security constraints of the power system dispatch model based on the unit power data, the unit cost data, the node power data and the branch power data.

[0144] The scheduling model solving module is used to solve the power system scheduling model under the security constraints, with the goal of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, based on the unit power data, unit cost data, node power data, and branch power data, to obtain the Pareto solution set of the power system scheduling model.

[0145] The relative proximity calculation module is used to calculate the relative proximity of each Pareto solution in the Pareto solution set based on the Pareto solution set.

[0146] The scheduling strategy parameter determination module is used to take the Pareto solution corresponding to the minimum relative proximity as the scheduling strategy parameter of the system to be scheduled.

[0147] The system scheduling module is used to schedule the system to be scheduled according to the scheduling strategy parameters.

[0148] This invention provides a power system dispatching device based on Pareto optimization. The device acquires unit power data, unit cost data, node power data, and branch power data of the system to be dispatched using a system data acquisition module. In a model and constraint construction module, a power system dispatching model and corresponding security constraints are constructed based on the unit power data, unit cost data, node power data, and branch power data. Through a dispatching model solving module, with the objective of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, the power system dispatching model is solved using the unit power data, unit cost data, node power data, and branch power data under the security constraints to obtain the Pareto solution set of the power system dispatching model. Then, in a relative proximity calculation module, the relative proximity of each Pareto solution in the Pareto solution set is calculated. In a dispatching strategy parameter determination module, the Pareto solution corresponding to the minimum relative proximity is used as the dispatching strategy parameter for the system to be dispatched. Finally, in a system dispatching module, the system to be dispatched is dispatched according to the dispatching strategy parameter. In power system dispatching, three conflicting objectives—cost, carbon emissions, and peak-valley difference—are simultaneously optimized. By using unit power data, unit cost data, node power data, and branch power data, and under security constraints, the power system dispatching model is solved to achieve economical, low-carbon, and safe operation of the power system while minimizing the sum of cost, carbon emissions, and peak-valley difference. An effective balance point is found among multiple objectives, namely the Pareto solution corresponding to the minimum relative proximity, which satisfies the multi-objective dispatching requirements of the power system and improves the overall performance of the power system.

[0149] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0150] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0151] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a Pareto-based power system dispatching method as described in the above embodiments. The terminal device may be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The terminal device may include, but is not limited to, a processor and a memory.

[0152] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0153] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0154] Another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the Pareto-based power system dispatching method described in the above embodiment.

[0155] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0156] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power system dispatching method based on Pareto optimization, characterized in that, include: Acquire the unit power data, unit cost data, node power data, and branch power data of the system to be scheduled; Based on the unit power data, the unit cost data, the node power data, and the branch power data, a power system dispatch model and the corresponding security constraints of the power system dispatch model are constructed. With the goal of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, under the aforementioned safety constraints, the power system dispatch model is solved based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatch model; Based on the Pareto solution set, the relative proximity of each Pareto solution in the Pareto solution set is calculated; The Pareto solution corresponding to the minimum relative proximity is used as the scheduling strategy parameter of the system to be scheduled. The scheduling system to be scheduled is scheduled according to the scheduling strategy parameters.

2. The power system dispatching method based on Pareto optimization of claim 1, wherein, The safety constraints include: unit output constraints, unit ramp-up constraints, system reserve demand constraints, new energy output constraints, branch transmission constraints, and system power balance constraints. The unit output constraint is: wherein i is the unit number of the system to be dispatched; p i,t is the output of unit i at time period t; a downward reserve reserved for the unit i to cope with power fluctuation at time period t; the upward reserve reserved for the unit i to cope with power fluctuation at time period t; P i the lower bound of the unit i's output; The upper limit of the output of unit i; The unit climbing constraint is: wherein p i,t+1 is the output of the unit i at the time period t+1; an upward reserve reserved for the unit i to cope with power fluctuation at the t+1 period; a downward ramping limit for the unit i at the time period t; a downward reserve reserved for the unit i to cope with power fluctuation at the t+1 period; an upward ramping limit for the unit i at the time period t; upper bound of downward ramping for the machine group i; This is the upper limit of the downward ramp for unit i; The system reserve requirement constraint is: wherein The minimum requirement to be reached by the sum of the reserves provided for the unit; The maximum requirement that the total reserve provided to the generating unit should meet; The new energy output constraint is: wherein w j,t is the actual output of the new energy j at the t period; W j,t is the available output of the new energy j at the t period; The branch transmission constraint is: Among them, P l This is the lower bound of the transmission power of branch l; is the upper bound of transmission power for branch l; g l,i is the influence coefficient of power of unit i on transmission power of branch l; g l,j is the influence coefficient of power of new energy j on transmission power of branch l; g l,n is the influence coefficient of injection power of node n on transmission power of branch l; D n,t is the load of node n; The system power balance constraint is:

3. The power system dispatching method based on Pareto optimization of claim 1, wherein, With the objective of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, under the aforementioned safety constraints, the power system dispatch model is solved based on the unit power data, unit cost data, node power data, and branch power data to obtain the Pareto solution set of the power system dispatch model, including: With the objectives of minimizing unit operating costs, minimizing carbon emissions, and minimizing peak-valley differences respectively, the power system dispatch model is solved under the security constraints based on the unit power data, unit cost data, node power data, and branch power data to obtain the first objective solution, the second objective solution, and the third objective solution. The first objective solution, the second objective solution, and the third objective solution are each normalized to obtain several normalized objective vectors; and these normalized objective vectors are used as the Pareto front. Based on the Pareto front and the preset combination coefficients, the set of reference points for the solution of the power system dispatch model is determined. Based on the set of reference points, the first objective solution, the second objective solution, and the third objective solution, a parallel axis is generated; The solution is obtained by solving the set of reference points and the parallel axes, so that the Pareto solution corresponding to the parallel axis inside the Pareto front boundary falls on the parallel axis, and the Pareto solution corresponding to the parallel axis outside the Pareto front boundary falls on the boundary, thus obtaining the initial Pareto solution. The initial Pareto solution is projected onto a reference surface formed by a set of reference points along the parallel axis direction, and then filtered to obtain the Pareto solution set of the power system dispatch model.

4. The power system dispatching method based on Pareto optimization of claim 3, wherein, Based on the Pareto front and preset combination coefficients, the reference point set for the solution of the power system dispatching model is determined, including: Based on the Pareto front and the preset combination coefficients, the internal reference point of the solution of the power system dispatch model is calculated. The difference vector is calculated based on the Pareto front and the preset number of segments; Based on the internal reference point and the difference vector, generate the external reference point for the solution of the power system dispatch model; The internal reference points and the external reference points are merged to obtain the reference point set of the solution of the power system dispatching model.

5. A power system dispatching method based on Pareto optimization as claimed in claim 4, wherein, The initial Pareto solution is projected onto a reference plane formed by a set of reference points along the parallel axis direction, and then filtered to obtain the Pareto solution set of the power system dispatch model, including: Based on the set of reference points, generate several reference surfaces; The initial Pareto solution is projected onto the reference plane to generate several projection points; Calculate the projection distance between every two projection points based on the projection points; The neighborhood of the reference point is calculated based on the set of reference points, the difference vector, and the preset projection point distance threshold. Within the neighborhood of the reference point, the initial Pareto solution corresponding to the projection point closest to the reference point is preserved to obtain the target Pareto solution; The Pareto solutions corresponding to projection distances less than a preset projection point distance threshold are deleted to obtain the Pareto solution set of the power system scheduling model.

6. A power system dispatching method based on Pareto optimization as claimed in claim 1, wherein, Based on the Pareto solution set, the relative closeness of each Pareto solution in the Pareto solution set is calculated, including: Based on the Pareto solution set, calculate the normalized value of each Pareto solution to obtain the target normalized value; The evaluation matrix is ​​constructed based on the normalized target value to obtain the target evaluation matrix; The minimum value of the target normalized value is taken as the positive ideal point, and the maximum value of the target normalized value is taken as the negative ideal point; The first weight is calculated based on the target evaluation matrix and the number of solutions corresponding to the Pareto solution set; The relative proximity is calculated based on the positive ideal point, the negative ideal point, the first weight, and the preset weight.

7. The power system dispatching method based on Pareto optimization of claim 1, wherein, The scheduling strategy parameters include: unit output, unit reserve for downward backup, unit reserve for upward backup, and renewable energy output; The scheduling of the system to be scheduled is performed according to the scheduling policy parameters, including: Adjust the power output allocation of the units to be dispatched in the system according to the power output of the units; Based on the unit's reserved downward reserve and the unit's reserved upward reserve, determine the unit's reserved downward reserve and the unit's reserved upward reserve for the system to be dispatched; The allocation of new energy output to the system to be dispatched is adjusted according to the new energy output.

8. A power system dispatching device based on Pareto optimization, characterized by, include: The system data acquisition module is used to acquire the unit power data, unit cost data, node power data, and branch power data of the system to be scheduled. The model and constraint construction module is used to construct a power system dispatch model and the corresponding security constraints of the power system dispatch model based on the unit power data, the unit cost data, the node power data and the branch power data. The scheduling model solving module is used to solve the power system scheduling model under the security constraints, with the goal of minimizing the sum of unit operating costs, carbon emissions, and peak-valley differences, based on the unit power data, unit cost data, node power data, and branch power data, to obtain the Pareto solution set of the power system scheduling model. a relative closeness calculation module configured to calculate relative closeness of each Pareto solution in the Pareto solution set according to the Pareto solution set; a dispatch strategy parameter determination module configured to determine the Pareto solution corresponding to the minimum relative closeness as a dispatch strategy parameter of the system to be dispatched; a system dispatch module configured to dispatch the system to be dispatched according to the dispatch strategy parameter.

9. A terminal device, comprising: A computer readable storage medium comprising a computer program stored therein, wherein the computer program, when executed, controls a device in which the computer readable storage medium is located to perform a power system dispatch method based on Pareto optimization according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a computer program stored therein, wherein the computer program, when executed, controls a device in which the computer readable storage medium is located to perform a power system dispatch method based on Pareto optimization according to any one of claims 1 to 7.

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