Power system cross-regional scheduling method considering wind and light output uncertainty

Abstract

The invention discloses an electric power system cross-regional scheduling method considering wind and light output uncertainty, and relates to the technical field of new energy, according to the scheme, thermal power generation, transmission network loss, demand response and wind and light abandoning cost are considered, new energy electric power system cross-regional scheduling is implemented, and the target is that the cross-regional scheduling operation cost is the lowest; the unit self-constraint and the power grid line node operation constraint are comprehensively considered to realize the cross-regional optimization scheduling of the power system; considering the uncertainty of wind and light resources, combining probability distribution of wind power and photovoltaic output prediction errors fitted by kernel density estimation with a confidence gap decision model, constructing a confidence gap decision power system cross-regional optimization scheduling model, and solving an optimization scheduling result; in order to solve the problem of line heavy load caused by too high power transmission requirements among the regions, the heavy load lines among the regions are analyzed based on the optimization scheduling result, a power system line capacity expansion scheme is provided accordingly, and after line capacity expansion, the scheduling result is solved again to verify the optimization effect.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a method for cross-regional dispatching of power systems taking into account the uncertainty of wind and solar power output. Background Art

[0002] As the global energy mix accelerates its transition toward cleaner energy, the installed capacity of renewable energy sources, represented by wind and photovoltaic power, continues to climb. However, wind and solar power generation exhibits significant volatility and randomness, and their large-scale grid integration exposes power system operations to the combined impact of uncertainties on both the source and load sides. Traditional dispatch methods based on deterministic models are no longer adaptable to the operational demands of these new power systems.

[0003] Existing dispatching technologies have problems such as insufficient consideration of the uncertainty of wind and solar power processing and high pressure on power transmission of heavy-loaded lines between regions. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a cross-regional dispatching method for power systems that takes into account the uncertainty of wind and solar power output, reduce the operating cost of cross-regional dispatching of power systems, and alleviate the problem of high power transmission pressure on heavy-load lines between regions.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] A method for inter-regional dispatching of a power system considering the uncertainty of wind and solar power output proposed in the present invention includes:

[0007] Step 1: For the regional power system containing renewable energy, considering thermal power generation, transmission network losses, demand response, and wind and solar power curtailment costs, implement cross-regional dispatch of the power system containing renewable energy, with the dispatching goal of minimizing cross-regional dispatching operating costs;

[0008] Step 2: Comprehensively consider the unit's own constraints and the operational constraints of the grid line nodes to achieve cross-regional optimal dispatch of the power system. The operational constraints include node power balance, demand response, transmission channels, unit output, unit ramp rate, and new energy station output constraints.

[0009] Step 3: Considering the uncertainty of wind and solar power output, kernel density estimation is used to fit the probability distribution of wind power and photovoltaic output forecast errors. This probability distribution is combined with the confidence gap decision model, and a confidence gap decision-making power system cross-regional optimal scheduling model is constructed based on the scheduling objectives of step 1 and the constraints of step 2 to solve the optimal scheduling results.

[0010] As a further optimization scheme of the power system inter-regional dispatching method considering the uncertainty of wind and solar power output according to the present invention, step 4 is further included after step 3, wherein:

[0011] Step 4: Analyze the inter-regional overloaded lines based on the optimized dispatch results, and expand the power system lines accordingly; after the power system lines are expanded, re-solve the dispatch results according to steps 1-3 to verify the optimization effect of the optimized dispatch results.

[0012] As a further optimization scheme of the cross-regional dispatching method of the power system considering the uncertainty of wind and solar power output according to the present invention, in step 1, the cross-regional dispatching operation cost of the power system includes the coal consumption cost of the thermal power unit, the transmission network loss cost, the local load demand response dispatching cost, the curtailment penalty cost of solar power and the curtailment penalty cost of wind power, wherein,

[0013] The calculation formula for the coal consumption cost of thermal power units is as follows:

[0014]

[0015] in, is the coal consumption cost of thermal power units during period t, N TH is the number of thermal power units participating in the dispatch in the regional power grid, For a given k TH Output coal consumption cost coefficient of each thermal power unit; The kth time in period t TH The output of each thermal power unit;

[0016] The calculation formula for transmission network loss cost is as follows:

[0017]

[0018] in, is the transmission network loss cost during period t; N L is the total number of system lines; For the kth l The power delivered by a transmission line in time period t; For the kth l The resistance of the transmission line; For the kth l Unit loss cost of each transmission line;

[0019] The calculation formula for local load demand response dispatch cost is as follows:

[0020]

[0021] in, is the local load demand response dispatch cost during period t, N B is the total number of system nodes; For the kth DR The demand response amount of the local load of each node in period t; For the kth DRDemand response cost coefficient for each node;

[0022] The calculation formula for the abandoned light penalty cost is as follows:

[0023]

[0024] in, is the penalty cost of photovoltaic abandonment in period t; N PV is the number of PV panels participating in the dispatch in the regional power grid; PV is the unit cost of photovoltaic curtailment; For the kth PV The output forecast value of a photovoltaic in period t, For the kth PV The dispatch plan value of a photovoltaic in period t;

[0025] The calculation formula for wind curtailment penalty cost is as follows:

[0026]

[0027] in, is the penalty cost of wind farm curtailment in period t, N WF is the number of wind farms participating in the dispatch in the regional power grid, λ WF is the unit curtailment cost of the wind farm, For the kth WF The output forecast value of a wind farm in period t, For the kth WF The dispatch plan value of a wind farm in period t.

[0028] As a further optimization scheme of the cross-regional dispatching method of the power system considering the uncertainty of wind and solar power output according to the present invention, the dispatching goal is to minimize the cross-regional dispatching operation cost, and the objective function f is:

[0029]

[0030] Where T is the scheduling time period.

[0031] As a further optimization scheme for the inter-regional dispatching method of a power system considering the uncertainty of wind and solar power output according to the present invention, in step 2, the unit's own constraints and the operation constraints of the grid line nodes include:

[0032] Node power balance constraints:

[0033]

[0034] in, is the output of the thermal power unit at the i-th node in period t; is the photovoltaic output of the i-th node in period t; is the wind farm output of the i-th node in period t; is the demand response amount of the i-th node in period t; is the load of the i-th node in period t; Ω i is the set of all lines connected to the i-th node; is the number of nodes connected to the i-th node in period t Power on the line;

[0035] Demand response constraints:

[0036]

[0037] Among them, γ t is the proportion of flexible load of local load; is the local load demand during period t;

[0038] Conveyor channel constraints:

[0039]

[0040] in, For the kth l The lower and upper limits of the transmission line power; For the kth l The lower and upper limits of the power ramp of the transmission line, t p For the kth l Minimum maintenance time of power of a transmission line; For the kth l The power transmission capacity of the transmission line in the t-1 period, For the kth l The power transmission capacity of each transmission line in the t+p period;

[0041] Unit output constraints:

[0042]

[0043] in, and For the kth TH The lower and upper limits of the output of each thermal power unit;

[0044] Unit ramp rate constraint:

[0045]

[0046] in, and For the kth TH Maximum upward and downward climbing power of each unit; is the kth time in period t-1 THThe output of each thermal power unit;

[0047] Output constraints of new energy stations:

[0048]

[0049] Among them, the actual output values ​​of photovoltaic and wind power cannot be higher than the output forecast values.

[0050] As a further optimization scheme of the cross-regional dispatching method of the power system considering the uncertainty of wind and solar power output described in the present invention, in step 3, the kernel density estimation is used to fit the probability distribution of wind power and photovoltaic output prediction errors.

[0051] include:

[0052] The kernel density estimate is defined as:

[0053]

[0054] in, is the density estimate at point x, where x is the probability density location to be estimated; n is the sample size; h is the bandwidth, which controls the smoothness; K(*) is the kernel function, which determines the shape of the local weight distribution, x y

[0055] is the y-th sample data point;

[0056] The kernel function K(*) selects the Gaussian kernel for probability fitting, and its calculation formula is:

[0057]

[0058] Among them, K gauss (u) is the Gaussian kernel function, u=(xx y ) / h, u is a standardized distance, u represents

[0059] The probability density position x and x to be estimated are shown y The relative distance between them is scaled by the bandwidth h, and e is the natural base;

[0060] The bandwidth h is calculated using the Silverman criterion:

[0061] h=1.06σn -1 / 5 (15)

[0062] Where σ is the sample standard deviation;

[0063] Based on historical measured photovoltaic and wind power output and forecast data and combined with kernel function estimation, the spatial confidence interval of the actual wind and solar power output is calculated:

[0064]

[0065] in, are the confidence intervals of the actual output space of wind power and photovoltaic power at the 1-α confidence level, are the upper and lower limits of the confidence interval of wind power output prediction error, are the upper and lower limits of the confidence interval of photovoltaic output prediction error, and α is the confidence level parameter.

[0066] As a further optimization scheme of the power system inter-regional dispatching method considering the uncertainty of wind and solar power output according to the present invention, in step 3, the confidence gap decision model includes:

[0067] The inter-regional dispatching problem of power systems considering the uncertainty of wind and solar power output can be summarized as the following optimization problem:

[0068]

[0069] Where F(*) is the objective function; X is the decision variable matrix; h(X,U) and g(X,U) are the equality and inequality constraints established by the unit's own constraints and the grid line node operation constraints; U is the confidence interval of the actual wind and solar power output space;

[0070] The confidence gap decision model is expressed as follows:

[0071]

[0072] Where f0 is the minimum cost of the deterministic model (18), Pr{*} is the probability; β is the target significance level, and Γ is the confidence level function;

[0073] The uncertain chance constraint Pr{F(X,U)≥f0}≤β in formula (19) is converted into a deterministic constraint as follows:

[0074] set up is the uncertainty distribution function of F(X,U). According to the definition of uncertainty distribution, we have

[0075]

[0076] have to

[0077]

[0078] According to the uncertainty variable operation rule,

[0079]

[0080] in, is the inverse cumulative distribution function of the new energy output forecast error;

[0081] Based on this, the uncertain opportunity constraint Pr{F(X,U)≥f0}≤β is transformed into a deterministic constraint:

[0082]

[0083] As a further optimization scheme for the inter-regional dispatching method of the power system considering the uncertainty of wind and solar power output according to the present invention, in step 4, the dispatching result is re-solved based on the line expansion plan, including:

[0084] After the heavy-load line is expanded, the transmission channel constraint becomes:

[0085]

[0086] Among them, N HL is the number of overloaded line channels; For the kth l The power limit after the expansion of the transmission line; For the kth l After the capacity expansion of each transmission line, the lower and upper limits of power ramping are: For the kth l The power transmission amount of a transmission line in time period t, t p For the kth l Minimum maintenance time of power of a transmission line; For the kth l The power transmission capacity of the transmission line in the t-1 period, For the kth l The power transmission capacity of a transmission line in the t+p period, For the kth l The lower power limit of each transmission line;

[0087] After optimizing and solving the line expansion plan, the channel load factor and the cross-regional dispatching and operating costs of the power system are compared.

[0088] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0089] (1) The present invention first considers the cross-regional dispatching operation cost of the partitioned power system containing new energy, and then comprehensively considers the unit's own constraints and the grid line node operation constraints. Furthermore, considering the uncertainty of wind and solar resources, a confidence gap decision optimization dispatching model is constructed to optimize and solve the dispatching results. Finally, based on the optimized dispatching results, a power system line expansion plan is proposed and the dispatching results are re-solved to verify its optimization effect, thereby realizing cross-regional dispatching of the power system for new energy stations, making the dispatching results more reliable and comprehensive.

[0090] (2) The final scheduling results of the present invention are presented in the form of numerical values ​​and charts, which are more accurate and intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a schematic diagram of the steps of the cross-region dispatching control method of the power system in the present invention;

[0092] Figure 2 This is the 38-node power system used in the embodiment of the present invention;

[0093] Figure 3 The power of overloaded transmission lines in the cross-region dispatching results of the embodiment of the present invention is as follows: (a) is the power of transmission lines 7-8, and (b) is the power of transmission lines 13-25;

[0094] Figure 4 This is the power situation of the overloaded transmission lines in the cross-region scheduling results after the line capacity expansion in the embodiment of the present invention: (a) is the power of transmission lines 7-8, and (b) is the power of transmission lines 13-25. DETAILED DESCRIPTION

[0095] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0096] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0097] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0098] In the context of the background technology of the present invention, it is particularly important to study the inter-regional dispatching method of the power system that takes into account the uncertainty of wind and solar power output. This method aims to optimize the operation cost of inter-regional dispatching of the power system based on the confidence gap decision by comprehensively considering the uncertainty of renewable energy generation, the demand response of power load and the physical constraints of the power grid, thereby reducing the system operation cost and improving the overall economic benefits. At the same time, this method analyzes the power transmission of heavy-load lines between regions and can alleviate the pressure of power transmission between regions.

[0099] Therefore, developing a new energy power system cross-regional dispatching method that can not only reduce the cross-regional dispatching operating costs of the power system, but also alleviate the heavy power transmission pressure of heavy-load lines between regions is of great significance for promoting the transformation of the energy structure and promoting the sustainable development of the power industry.

[0100] Reference Figures 1 to 4 , is an embodiment of the present invention, such as Figure 1 , and for Figure 2 The embodiment of the 38-node power system shown provides a power system cross-regional scheduling method considering the uncertainty of wind and solar power output, including:

[0101] S101: For regional power systems containing renewable energy, cross-regional dispatching of the power system containing renewable energy will be implemented, taking into account thermal power generation, transmission network losses, demand response, and wind and solar power curtailment costs. The dispatching goal is to minimize cross-regional dispatching operating costs, including:

[0102] The calculation formula for the coal consumption cost of the thermal power unit is as follows:

[0103]

[0104] Where, is the coal consumption cost of thermal power units during period t; N TH The number of thermal power units participating in the dispatch in the regional power grid; For a given thermal power unit k TH Output coal consumption cost coefficient; is the output of the thermal power unit during period t.

[0105] The calculation formula for the transmission network loss cost is as follows:

[0106]

[0107] in, is the transmission network loss cost during period t; N L is the total number of system lines; For the kth l The power delivered by a transmission line in time period t; For the kth l The resistance of the transmission line; For the kth l The unit loss cost of a transmission line.

[0108] The calculation formula of the local load demand response dispatch cost is as follows:

[0109]

[0110] in, is the local load demand response dispatch cost during period t, N B is the total number of system nodes; For the kth DR The demand response amount of the local load of each node in period t; For the kth DR The demand response cost coefficient of each node.

[0111] The calculation formula for the abandoned light penalty cost is as follows:

[0112]

[0113] in, is the penalty cost of photovoltaic abandonment in period t; N PV is the number of PV panels participating in the dispatch in the regional power grid; PV is the unit cost of photovoltaic curtailment; For the kth PV The output forecast value of a photovoltaic in period t, For the kth PV The dispatch plan value of a photovoltaic in period t.

[0114] The calculation formula for the wind curtailment penalty cost is as follows:

[0115]

[0116] in, is the penalty cost of wind farm curtailment in period t, N WF is the number of wind farms participating in the dispatch in the regional power grid, λ WF is the unit curtailment cost of the wind farm, For the kth WF The output forecast value of a wind farm in period t, For the kth WF The dispatch plan value of a wind farm in period t.

[0117] For new power systems containing renewable energy, considering thermal power generation, transmission network losses, demand response, and wind and solar power curtailment costs, a high proportion of renewable energy inter-regional dispatch is implemented. The dispatch goal is to minimize the inter-regional dispatch operation cost. The specific formula of the objective function is as follows:

[0118]

[0119] Where: T is the scheduling time period.

[0120] S102: Comprehensively consider the unit's own constraints and the operational constraints of the grid line nodes to achieve cross-regional optimal dispatch of the power system. The operational constraints include node power balance, demand response, transmission channels, unit output, unit ramp rate, and new energy station output constraints; including:

[0121] Node power balance constraints:

[0122]

[0123] in, is the output of the thermal power unit at the i-th node in period t; is the photovoltaic output of the i-th node in period t; is the wind farm output of the i-th node in period t; is the demand response amount of the i-th node in period t; is the load of the i-th node in period t; Ω i is the set of all lines connected to the i-th node; is the number of nodes connected to the i-th node in period t Power on the line.

[0124] Demand response constraints:

[0125]

[0126] Where, γ t is the proportion of flexible load of local load; is the local load demand during period t.

[0127] Conveyor channel constraints:

[0128]

[0129] in, For the kth l The lower and upper limits of the transmission line power; For the kth l The lower and upper limits of the power ramp of the transmission line, t p For the kth l Minimum maintenance time of power of a transmission line; For the kth l The power transmission capacity of the transmission line in the t-1 period, For the kth l The power transmission capacity of a transmission line in the t+p period.

[0130] Unit output constraints:

[0131]

[0132] Where, and For the kth TH The lower and upper limits of the output of each thermal power unit.

[0133] Unit ramp rate constraint:

[0134]

[0135] Where, and For the kth TH Maximum upward and downward climbing power of each unit; is the kth time in period t-1 TH The output of each thermal power unit.

[0136] Output constraints of new energy stations:

[0137]

[0138] In the formula, the actual output values ​​of photovoltaic and wind power cannot be higher than the predicted output values.

[0139] S103: Considering the uncertainty of wind and solar power output, kernel density estimation is used to fit the probability distribution of wind power and photovoltaic power output forecast errors. This probability distribution is combined with the confidence gap decision model. Based on the dispatch objectives and constraints, a confidence gap decision-making power system inter-regional optimal dispatch model is constructed to solve the optimal dispatch results; including:

[0140] The kernel density estimate is defined as:

[0141]

[0142] in, is the density estimate at point x, where x is the probability density location to be estimated; n is the sample size; h is the bandwidth, which controls the smoothness; K(*) is the kernel function, which determines the shape of the local weight distribution, x y

[0143] is the yth sample data point

[0144] The kernel function K(*) is a key component in kernel density estimation. Its main function is to define the influence range and shape around each data point and select the Gaussian kernel for probability fitting. Its calculation formula is:

[0145]

[0146] Among them, K gauss (u) is the Gaussian kernel function, u=(xx y ) / h, u is a standardized distance, u represents

[0147] The probability density position x and x to be estimated are shown y The relative distance between them is scaled by the bandwidth h, and e is the natural bottom

[0148] number

[0149] The bandwidth h determines the width of the kernel function, which affects the smoothness of the density estimation. Silverman

[0150] Guidelines for calculating bandwidth:

[0151] h=1.06σn -1 / 5 (15)

[0152] Where σ is the sample standard deviation.

[0153] Based on historical measured photovoltaic and wind power output and forecast data and combined with kernel function estimation, the spatial confidence interval of the actual wind and solar power output is calculated:

[0154]

[0155] in, are the confidence intervals of the actual output space of wind power and photovoltaic power at the 1-α confidence level, are the upper and lower limits of the confidence interval of wind power output prediction error, are the upper and lower limits of the confidence interval of photovoltaic output prediction error, and α is the confidence level parameter.

[0156] The above scheduling problem can be summarized as the following optimization problem:

[0157]

[0158] Where F(*) is the objective function; X is the decision variable matrix; h(X,U) and g(X,U) are the equality and inequality constraints established by the unit's own constraints and the grid line node operation constraints; U is the confidence interval of the actual wind and solar power output space.

[0159] This embodiment performs optimization and solution in a deterministic model, and calculates the overall optimal solution f0=2.0618×10 8 .

[0160] In an uncertain environment, decision makers usually maximize the adverse perturbations of uncertain parameters to ensure the achievement of preset goals, thereby maximizing the robustness of the objective function's ability to avoid the influence of uncertain parameters. The confidence gap decision model can be expressed as follows:

[0161]

[0162] Where f0 is the minimum cost of the deterministic model (Equation (18)); Pr(.) is the probability; β is the target significance level, and Γ is the confidence level function.

[0163] The uncertain chance constraint Pr{F(X,U)≥f0}≤β in equation (19) needs to be converted into a deterministic constraint. The process is as follows:

[0164] set up is the uncertainty distribution function of F(X,U). According to the definition of uncertainty distribution, we have

[0165]

[0166] According to the cumulative distribution function of wind power and photovoltaic output forecast errors, it can be expressed as And F(X,U) is a real-valued measurable function, we can get

[0167]

[0168] According to the uncertainty variable operation rule, we can get

[0169]

[0170] Where, is the inverse cumulative distribution function of the renewable energy output forecast error.

[0171] Based on this, the uncertain opportunity constraint Pr{F(X,U)≥f0}≤β can be transformed into a deterministic constraint

[0172]

[0173] The target significance level was set to 0.15 for optimization and the overall optimal solution was obtained based on the confidence gap decision model: f0 = 2.0203 × 10 8 According to the cross-region dispatch results, the heavy-load line channel is analyzed. The power situation of the heavy-load line is as follows: Figure 3 As shown, Figure 3 (a) is the power of transmission line 7-8, Figure 3 (b) in the figure is the power of transmission line 13-25.

[0174] S104: To address the overloaded lines caused by excessive inter-regional power transmission demand, analyze the overloaded inter-regional lines based on the optimized dispatch results and propose a power system line expansion plan accordingly. After the line expansion, re-solve the dispatch results to verify the optimization effect, including:

[0175] The optimization solution based on the line expansion plan specifically includes:

[0176] After the heavy-load line is expanded, the transmission channel constraint becomes:

[0177]

[0178] Where N HL is the number of overloaded line channels; is the transport channel k l Power ceiling after expansion;

[0179] is the transport channel k l The lower and upper limits of power ramp after capacity expansion.

[0180] After optimizing and solving the line expansion plan, the target significance level was set to 0.15 for optimization and solving, and the overall optimal solution f0 = 2.0128 × 10 8 .

[0181] The power situation of heavy-load line channels in cross-region dispatch results after line expansion is as follows: Figure 4 As shown, Figure 4 (a) is the power of transmission line 7-8, Figure 4 (b) in the figure is the power of transmission line 13-25.

[0182] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for inter-regional dispatching of power systems considering the uncertainty of wind and solar power output, characterized in that: include: Step 1: For the regional power system containing renewable energy, considering thermal power generation, transmission network losses, demand response, and wind and solar power curtailment costs, implement cross-regional dispatch of the power system containing renewable energy, with the dispatching goal of minimizing cross-regional dispatching operating costs; Step 2: Comprehensively consider the unit's own constraints and the operational constraints of the grid line nodes to achieve cross-regional optimal dispatch of the power system. The operational constraints include node power balance, demand response, transmission channels, unit output, unit ramp rate, and new energy station output constraints. Step 3: Considering the uncertainty of wind and solar power output, kernel density estimation is used to fit the probability distribution of wind power and photovoltaic output forecast errors. This probability distribution is combined with the confidence gap decision model, and a confidence gap decision-making power system cross-regional optimal scheduling model is constructed based on the scheduling objectives of step 1 and the constraints of step 2 to solve the optimal scheduling results.

2. The method for cross-regional dispatching of a power system considering the uncertainty of wind and solar power output according to claim 1, characterized in that: Step 3 is followed by step 4, wherein: Step 4: Analyze the inter-regional overloaded lines based on the optimized dispatch results, and expand the power system lines accordingly; after the power system lines are expanded, re-solve the dispatch results according to steps 1-3 to verify the optimization effect of the optimized dispatch results.

3. The method for cross-regional dispatching of a power system considering the uncertainty of wind and solar power output according to claim 1, characterized in that: In step 1, the cross-regional dispatching operation cost of the power system includes the coal consumption cost of thermal power units, transmission network loss cost, local load demand response dispatch cost, curtailment penalty cost and wind curtailment penalty cost, among which: The calculation formula for the coal consumption cost of thermal power units is as follows: in, is the coal consumption cost of thermal power units during period t, N TH is the number of thermal power units participating in the dispatch in the regional power grid, For a given k TH Output coal consumption cost coefficient of each thermal power unit; The kth time in period t TH The output of each thermal power unit; The calculation formula for transmission network loss cost is as follows: in, is the transmission network loss cost during period t; N L is the total number of system lines; For the kth l The power delivered by a transmission line in time period t; For the kth l The resistance of the transmission line; For the kth l Unit loss cost of each transmission line; The calculation formula for local load demand response dispatch cost is as follows: in, is the local load demand response dispatch cost during period t, N B is the total number of system nodes; For the kth DR The demand response amount of the local load of each node in period t; For the kth DR Demand response cost coefficient for each node; The calculation formula for the abandoned light penalty cost is as follows: in, is the penalty cost of photovoltaic abandonment in period t; N PV is the number of PV panels participating in the dispatch in the regional power grid; PV is the unit cost of photovoltaic curtailment; For the kth PV The output forecast value of a photovoltaic in period t, For the kth PV The dispatch plan value of a photovoltaic in period t; The calculation formula for wind curtailment penalty cost is as follows: in, is the penalty cost of wind farm curtailment in period t, N WF is the number of wind farms participating in the dispatch in the regional power grid, λ WF is the unit curtailment cost of the wind farm, For the kth WF The output forecast value of a wind farm in period t, For the kth WF The dispatch plan value of a wind farm in period t.

4. The method for cross-regional dispatching of a power system considering the uncertainty of wind and solar power output according to claim 3, characterized in that: The scheduling goal is to minimize the cross-region scheduling operation cost, and the objective function f is: Where T is the scheduling time period.

5. The method for inter-regional dispatching of a power system considering the uncertainty of wind and solar power output according to claim 3, characterized in that: In step 2, the unit's own constraints and the grid line node's operating constraints include: Node power balance constraints: in, is the output of the thermal power unit at the i-th node in period t; is the photovoltaic output of the i-th node in period t; is the wind farm output of the i-th node in period t; is the demand response amount of the i-th node in period t; is the load of the i-th node in period t; Ω i is the set of all lines connected to the i-th node; is the number of nodes connected to the i-th node in period t Power on the line; Demand response constraints: Among them, γ t is the proportion of flexible load of local load; is the local load demand during period t; Conveyor channel constraints: in, For the kth l The lower and upper limits of the transmission line power; For the kth l The lower and upper limits of the power ramp of the transmission line, t p For the kth l Minimum maintenance time of power of a transmission line; For the kth l The power transmission capacity of the transmission line in the t-1 period, For the kth l The power transmission capacity of each transmission line in the t+p period; Unit output constraints: in, and For the kth TH The lower and upper limits of the output of each thermal power unit; Unit ramp rate constraint: in, and For the kth TH Maximum upward and downward climbing power of each unit; is the kth time in period t-1 TH The output of each thermal power unit; Output constraints of new energy stations: Among them, the actual output values ​​of photovoltaic and wind power cannot be higher than the output forecast values.

6. The method for inter-regional dispatching of a power system considering the uncertainty of wind and solar power output according to claim 3, characterized in that: In step 3, the proposed kernel density estimation fits the probability distribution of wind power and photovoltaic output forecast errors including: The kernel density estimate is defined as: in, is the density estimate at point x, where x is the probability density location to be estimated; n is the sample size; h is the bandwidth, which controls the smoothness; K(*) is the kernel function, which determines the shape of the local weight distribution, x y is the y-th sample data point; The kernel function K(*) selects the Gaussian kernel for probability fitting, and its calculation formula is: Among them, K gauss (u) is the Gaussian kernel function, u=(xx y ) / h, u is a standardized distance, u represents the distance between the probability density position x and x to be estimated y The relative distance between them is scaled by the bandwidth h, and e is the natural base; The bandwidth h is calculated using the Silverman criterion: h=1.06σn -1 / 5 (15)Where, σ is the sample standard deviation; Based on historical measured photovoltaic and wind power output and forecast data and combined with kernel function estimation, the spatial confidence interval of the actual wind and solar power output is calculated: in, are the confidence intervals of wind power and photovoltaic actual output space at the 1-α confidence level, are the upper and lower limits of the confidence interval of wind power output prediction error, are the upper and lower limits of the confidence interval of photovoltaic output prediction error, and α is the confidence level parameter.

7. The method for inter-regional dispatching of a power system considering the uncertainty of wind and solar power output according to claim 6, characterized in that: In step 3, the confidence gap decision model includes: The inter-regional dispatching problem of power systems considering the uncertainty of wind and solar power output can be summarized as the following optimization problem: Where F(*) is the objective function; X is the decision variable matrix; h(X,U) and g(X,U) are the equality and inequality constraints established by the unit's own constraints and the grid line node operation constraints; U is the confidence interval of the actual wind and solar power output space; The confidence gap decision model is expressed as follows: Where f0 is the minimum cost of the deterministic model (18), Pr{*} is the probability; β is the target significance level, and Γ is the confidence level function; The uncertain chance constraint Pr{F(X,U)≥f0}≤β in formula (19) is converted into a deterministic constraint as follows: set up is the uncertainty distribution function of F(X,U). According to the definition of uncertainty distribution, we have have to According to the uncertainty variable operation rule, in, is the inverse cumulative distribution function of the new energy output forecast error; Based on this, the uncertain opportunity constraint Pr{F(X,U)≥f0}≤β is transformed into a deterministic constraint:

8. The method for inter-regional dispatching of a power system considering the uncertainty of wind and solar power output according to claim 7, characterized in that: In step 4, the scheduling results are re-solved based on the line expansion plan, including: After the heavy-load line is expanded, the transmission channel constraint becomes: Among them, N HL is the number of overloaded line channels; For the kth l The upper limit of power after the expansion of the transmission line; For the kth l After the capacity expansion of each transmission line, the lower and upper limits of power ramping are: For the kth l The power transmission amount of a transmission line in time period t, t p For the kth l Minimum maintenance time of power of a transmission line; For the kth l The power transmission capacity of the transmission line in the t-1 period, For the kth l The power transmission capacity of the transmission line in the t+p period, For the kth l The lower power limit of each transmission line; After optimizing and solving the line expansion plan, the channel load factor and the cross-regional dispatching and operating costs of the power system are compared.

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