A risk assessment method for a photovoltaic rail transit self-consistent energy system

Abstract

The application discloses a risk assessment method of a self-consistent energy system of a rail transit containing photovoltaics, and belongs to the technical field of risk assessment of power systems, and comprises the following steps: step A, constructing a photovoltaic output uncertainty set based on a total budget fluctuation amount, generating linear constraints of photovoltaic output by using a ranking truncation method, and giving a photovoltaic output risk cost calculation formula; and step B, based on step A, establishing a solution model of a rail transit self-consistent energy system operation strategy containing photovoltaics, introducing a load test to modify the solution result of the model, and quantitatively evaluating the risk of photovoltaic output. The application can flexibly adjust the risk size of photovoltaics according to the prediction accuracy of photovoltaic output, and realizes risk evaluation of photovoltaic output through risk cost.

Description

Technical Field

[0001] This invention relates to the field of power system risk assessment technology, and in particular to a risk assessment method for a self-consistent energy system for rail transit containing photovoltaics. Background Technology

[0002] With the continuous development of rail transit systems, the load on traction substations is constantly increasing, and the safety and reliability of rail transit system operation are receiving increasing attention. While photovoltaic (PV) integration into self-sufficient energy systems for rail transit improves the energy self-sufficiency rate and green electricity ratio of the traction power supply system, it also increases the uncertainty of power output on the power supply side, posing certain risks to system operation. Current research on self-sufficient energy systems for rail transit with PV mainly focuses on the topology of PV integration, with relatively few studies considering PV in risk assessment of such systems. Therefore, it is urgent to conduct risk assessment research on PV output in self-sufficient energy systems for rail transit with PV. Summary of the Invention

[0003] The purpose of this invention is to propose a risk assessment method for a self-consistent energy system for rail transit incorporating photovoltaics, characterized by the following steps:

[0004] Step A: Construct an uncertain set of photovoltaic output based on the total budget fluctuation, generate linear constraints on photovoltaic output using the sorting and truncation method, and provide a formula for calculating the risk cost of photovoltaic output.

[0005] Step B: Based on Step A, establish a solution model for the operation strategy of a self-consistent energy system for rail transit with photovoltaic power generation. At the same time, introduce load verification to correct the solution results of the model and quantitatively assess the risk of photovoltaic power output.

[0006] Step A specifically includes:

[0007] Step A1: Decompose the output of the photovoltaic power station into the sum of the outputs of multiple photovoltaic units, and set the photovoltaic output constraint as follows.

[0008]

[0009] Where: PV t Let t be the output of the photovoltaic power station, j be the photovoltaic unit number, and PV be the output of the photovoltaic power station. jt Let k be the photovoltaic output of unit j at time t, and k be the total number of units.

[0010] Step A2: Express the photovoltaic unit output as an expected value plus a fluctuation term, and set the total fluctuation amount for different time periods. At this point, the photovoltaic output constraint becomes...

[0011]

[0012] In the formula: The expected photovoltaic output of unit j at time t. Let ζ be the maximum fluctuation range of photovoltaic output of unit j at time t. jt Let Γ be the photovoltaic output fluctuation ratio of unit j at time t. t Let be the total fluctuation at time t;

[0013] Step A3: The equation transformation is achieved through the sorting and truncation method, eliminating uncertain variables and converting the photovoltaic output constraint into a linear constraint.

[0014]

[0015] In the formula: For Γ t Round down to the nearest integer. It is a sequence The elements after sorting in descending order;

[0016] Step A4: Divide the confidence interval of photovoltaic output into low-risk and high-risk zones with the expected output as the boundary.

[0017] The formula for calculating the photovoltaic output risk cost in step A is as follows:

[0018]

[0019]

[0020] In the formula: pv e To contribute to the photovoltaic industry. and p These are the upper and lower bounds of the confidence interval for photovoltaic power output, respectively, ω Tl ω Th These are the low-risk and high-risk weighting coefficients at time T, respectively, and G. T Let γ be the electricity price of the large power grid at time T. T Let b1 and b2 be the slack variables at time T, and both be proportionality coefficients.

[0021] Step B specifically includes:

[0022] Step B1: For the solution model of the operation strategy of the self-consistent energy system of rail transit with photovoltaics, first construct the main problem based on the improved light robust model;

[0023] Step B2: Solve the main problem;

[0024] Step B3: Adjust the energy storage discharge power constraint on a second-level time scale;

[0025] Step B4: Add the energy storage discharge power constraint from Step B3 to the main problem and solve the main problem a second time;

[0026] Step B5: Calculate the wasted power on a second-scale timescale;

[0027] Step B6: Calculate the risk cost of photovoltaic power output based on the risk cost generated after adding slack variables, and quantitatively assess the risk of photovoltaic power output.

[0028] The main problem in step B1 includes:

[0029] The objective function is to minimize the total operating cost.

[0030] minC total =C1+C2+C3+C4

[0031]

[0032] In the formula: C total C1 represents the total operating cost, C2 represents the electricity cost of the power supply system, C3 represents the total operating cost of energy storage, C4 represents the photovoltaic incentive cost, and C5 represents the risk cost incurred after adding slack variables; k represents the number of time periods; ΔT represents the length of the time period; G T P is the electricity price of the main power grid at time T; T W provides power to the power supply system at time T; CT Cost per kilowatt-hour for energy storage; W DT P represents the unit revenue from energy storage at time T. C,T The output of energy storage at time T; W V The cost of incentives for photovoltaic power grid integration; PV T ω represents the output of the photovoltaic system at time T; Tl and ω Th These are the low-risk weighting coefficient and high-risk weighting coefficient at time T, respectively; ΔP is the fluctuation range of the photovoltaic power plant's output.

[0033] Constraints:

[0034] Power balance constraints

[0035] P T +P C,T +PV T =D q,T +D f,T

[0036] In the formula, D f,T Let D be the non-traction load at time T. q,T The non-traction load adjusted at time T;

[0037] Power supply system output constraints

[0038] 0≤P T ≤P T,max ;

[0039] Photovoltaic output constraints

[0040]

[0041] In the formula, γ T For slack variables of photovoltaic power output;

[0042] Energy storage constraints

[0043]

[0044] In the formula, P C,max The maximum charge / discharge power of the energy storage; SOC H SOC L For the maximum and minimum states of charge of energy storage; E M For energy storage capacity, E T Let T be the energy storage capacity at time T; E0 is the initial energy storage capacity.

[0045] Step B2 specifically includes:

[0046] Step B21: Consider only γ T In the case where ≤ΔP, the objective function is transformed into a linear function, and the model under low-risk conditions is solved first using gurobi.

[0047] Step B22: Record The solution results for the slack variables in step B1 are obtained through... Adjusting slack variable constraints C in the objective function 4T Adjusted to Then gurobi is called to solve the problem.

[0048] The adjusted energy storage discharge power constraint in step B3 is:

[0049]

[0050] In the formula: The photovoltaic output results after the first solution of the main problem; P C,max P represents the maximum charge / discharge power of the energy storage. C,t The maximum discharge power constraint for energy storage on a timescale of seconds.

[0051] The formula for calculating the power of abandoned light in step B5 is as follows:

[0052]

[0053] In the formula: Let be the power of light discarded at time t. The photovoltaic power output results after the second solution to the main problem. The energy storage output after the second solution to the main problem.

[0054] The beneficial effects of this invention are as follows:

[0055] This invention can flexibly adjust the risk level of photovoltaic power based on the prediction accuracy of photovoltaic power output, and realize the risk assessment of photovoltaic power output through risk cost. Attached Figure Description

[0056] Figure 1 This is a flowchart of the risk assessment method for the photovoltaic-integrated self-consistent energy system for rail transit according to the present invention.

[0057] Figure 2 This is a diagram showing the output of each unit after photovoltaic decomposition.

[0058] Figure 3 Map showing the risk zones for photovoltaic power generation.

[0059] Figure 4 This is a measured load diagram of a traction substation. Detailed Implementation

[0060] This invention proposes a risk assessment method for a self-consistent energy system for rail transit with photovoltaic components. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0061] like Figure 1 As shown, the risk assessment method for a self-consistent energy system for rail transit with photovoltaic components includes the following steps:

[0062] Step A. Construct an uncertain set of photovoltaic output based on the total budget fluctuation, generate linear constraints on photovoltaic output using the sorting and truncation method, and provide a formula for calculating the risk cost of photovoltaic output.

[0063] Step A1: Decompose the output of the photovoltaic power station into the sum of the outputs of multiple photovoltaic units, such as... Figure 2 As shown, the photovoltaic output constraint at this time is:

[0064] Where: PV t Let t be the output of the photovoltaic power station, j be the photovoltaic unit number, and PV be the output of the photovoltaic power station. jt Let t represent the photovoltaic output of unit j at time t, and k represent the total number of units.

[0065] Step A2: Express the photovoltaic unit output as an expected value plus a fluctuation term, and set the total fluctuation amount for different time periods. The photovoltaic output constraint then becomes:

[0066] In the formula: For the expected photovoltaic output of unit j at time t, Let ζ be the maximum fluctuation range of photovoltaic output of unit j at time t. jt Let Γ be the photovoltaic output fluctuation ratio of unit j at time t.t Let t be the total fluctuation at time t.

[0067] Step A3: Transform the equation using the sorting and truncation method to eliminate uncertainties and convert the photovoltaic output constraint into a linear constraint:

[0068] In the formula: For Γ t Round down to the nearest integer. It is a sequence The elements that are sorted in descending order.

[0069] Step A4: Divide the confidence interval of photovoltaic output into low-risk and high-risk zones based on the expected output, such as... Figure 3 As shown, the formula for calculating risk cost is:

[0070]

[0071]

[0072] In the formula: pv e To contribute to the photovoltaic industry. and p ω represents the upper and lower bounds of the confidence interval for photovoltaic output. Tl ω Th The low and high weight coefficients at time T are respectively, G T Let γ be the electricity price of the large power grid at time T. T Let b1 and b2 be the slack variables at time T, and b1 and b2 be the proportionality coefficients.

[0073] Step B. Based on Step A, establish a solution model for the operation strategy of a self-consistent energy system for rail transit with photovoltaic power generation. At the same time, introduce load verification to correct the solution results of the model and quantitatively assess the risk of photovoltaic power output.

[0074] Step B1: For the solution model of the operation strategy of the self-consistent energy system of rail transit with photovoltaics, first construct the main problem based on the improved light robust model;

[0075] Step B11: Use the minimum total operating cost as the objective function:

[0076] minC total =C1+C2+C3+C4

[0077]

[0078] In the formula: C total C1 represents the total operating cost, C2 represents the electricity cost of the power supply system, C3 represents the total operating cost of energy storage, C4 represents the photovoltaic incentive cost, and C5 represents the risk cost incurred after adding slack variables; k represents the number of time periods; ΔT represents the length of the time period; GT Let P be the grid electricity price at time T; T W provides power to the power supply system at time T; CT Cost per kilowatt-hour for energy storage; W DT P represents the unit revenue from energy storage at time T. C,T The output of energy storage at time T; W V The cost of incentives for photovoltaic power grid integration; PV T ω represents the output of the photovoltaic system at time T; Tl and ω Th These represent the low-risk weighting coefficient and the high-risk slack weighting coefficient at time T, respectively; ΔP represents the fluctuation range of the photovoltaic power plant's output.

[0079] Step B12: The constraints of the main problem include:

[0080] 1) Power balance constraints

[0081] P T +P C,T +PV T =D q,T +D f,T

[0082] In the formula: D f,T Let D be the non-traction load at time T. q,T The non-traction load is adjusted at time T.

[0083] 2) Power supply system output constraints

[0084] 0≤P T ≤P T,max

[0085] 3) Photovoltaic output constraints

[0086]

[0087] In the formula: γ T For photovoltaic power output, slack variables.

[0088] 4) Energy storage constraints

[0089]

[0090] In the formula: P C,max The maximum charge / discharge power of the energy storage; SOC H SOC L For the maximum and minimum states of charge of energy storage; E M For energy storage capacity, E T Let T be the energy storage capacity at time T; E0 is the initial energy storage capacity.

[0091] Step B2: Solving the main problem;

[0092] Step B21: First, solve the model under the low-risk condition, considering only γ. T In the case where ΔP ≤ ΔP, the objective function is transformed into a linear function, and gurobi is used to solve it.

[0093] Step B22: Record The solution results for the slack variables in step B1 are obtained through... Adjusting slack variable constraints: C in the objective function 4T Adjusted to Then gurobi is called to solve the problem.

[0094] Step B3: Adjust the energy storage discharge power constraint on a second-level timescale:

[0095]

[0096] In the formula: The photovoltaic output results after the first solution of the main problem; P C,t The maximum discharge power constraint for energy storage on a timescale of seconds.

[0097] Step B4: Add the energy storage discharge power constraint from Step B2 to the main problem and solve the main problem for the second time.

[0098] Step B5: Calculate the wasted power on a second-scale timescale:

[0099]

[0100] In the formula: Let be the power of light discarded at time t. The photovoltaic power output results after the second solution to the main problem. The energy storage output after the second solution to the main problem.

[0101] Step B6: Calculate the risk costs generated by photovoltaic power output and quantify the risks of photovoltaic power output.

[0102] use Figure 4 An analysis of the measured load of a substation was conducted, and the expected output of the photovoltaic power generation was as follows: Figure 2 As shown, the energy storage power is set at 5MW, and the energy storage capacity is 3MWh. The photovoltaic grid connection incentive cost is set at 50 yuan / MWh, and the energy storage unit revenue is set at 80 yuan / MWh.

[0103] The following is a comparison of risk costs under different proportional coefficients:

[0104] Table 1. Cost Comparison under Different Proportion Coefficients

[0105]

[0106] A higher proportionality coefficient means a greater risk from the uncertainty of photovoltaic output, and the corresponding risk cost is also higher. This invention can flexibly adjust the risk level of photovoltaic based on the accuracy of photovoltaic output prediction. As can be seen from Table 1, a higher proportionality coefficient means a higher risk cost. Generally speaking, the lower the accuracy of photovoltaic output prediction, the higher the proportionality coefficient and the higher the risk cost. Risk assessment of photovoltaic output is achieved through risk cost.

Claims

1. A risk assessment method for a photovoltaic rail transit self-consistent energy system, characterized in that, The method comprises the following steps: Step A, constructing a photovoltaic output uncertainty set based on the total amount of budget fluctuations, generating linear constraints of photovoltaic output by using the sorting truncation method, and giving a photovoltaic output risk cost calculation formula; The step A specifically comprises: Step A1: decomposing the output of the photovoltaic power station into the sum of a plurality of photovoltaic unit outputs, and setting the photovoltaic output constraint as ； In the formula: is the output of the photovoltaic power station at time t, j is the number of the photovoltaic unit, is the photovoltaic output of the jth unit at time t, and k is the total number of units. Step A2: expressing the photovoltaic unit output as the form of an expected value plus a fluctuation term, and setting the total amount of fluctuations in different time periods, at this time, the photovoltaic output constraint becomes ， ； In the formula: is the jth unit photovoltaic expected output at time t, is the jth unit photovoltaic output maximum fluctuation amplitude at time t, is the jth unit photovoltaic output fluctuation proportion at time t, is the total fluctuation at time t; Step A3: realizing the equation transformation by the sorting truncation method, eliminating the uncertain variables, and converting the photovoltaic output constraint into a linear constraint ； In the formula: is rounded down, is the element corresponding to the sequence after descending order. Step A4: dividing the confidence interval of the photovoltaic output into a low-risk area and a high-risk area by taking the expected output as a boundary; Step B, based on step A, establishing a solution model of a self-consistent energy system operation strategy containing photovoltaic rail transit, introducing load inspection to modify the solution results of the model, and quantitatively evaluating the risk of photovoltaic output; The step B specifically comprises: Step B1: for the solution model of the self-consistent energy system operation strategy containing photovoltaic rail transit, first construct a main problem based on an improved light robust model; The main problem in the step B1 comprises: An objective function taking the total operation cost as minimum: ； In the formula: is the total operating cost, is the electricity cost of the power supply system, is the total operating cost of the energy storage, is the photovoltaic reward cost, is the risk cost generated after adding the slack variable; k is the number of time periods; is the time period length; is the large grid electricity price at T time; is the power supply system output at T time; is the unit degree electricity cost of the energy storage; is the unit income of the energy storage at T time; is the output of the energy storage at T time; is the photovoltaic consumption unit reward cost; is the output of the photovoltaic at T time; and are the low-risk weight coefficient and the high-risk weight coefficient at T time, respectively; is the fluctuation amplitude of the photovoltaic power station output; Constraint conditions: Power balance constraint ； In the formula, the non-traction load at time T, the adjusted non-traction load at time T; Power supply system output constraint ； Photovoltaic output constraint ； In the formula, is the slack variable for photovoltaic power output; Energy storage constraint ； wherein, is the maximum charge-discharge power of the energy storage; , is the maximum and minimum state of charge of the energy storage; is the energy storage capacity, is the energy storage capacity at time T; is the initial energy storage capacity; Step B2: solving the main problem; The step B2 specifically comprises: Step B21: only consider the case, the objective function becomes a linear function, and the model in the low-risk state is solved first by calling gurobi. Step B22: Note For the solution of the relaxation variable in Step B1, by adjusting the relaxation variable constraint , the in the objective function to , then call gurobi for solution; Step B3: adjusting the energy storage discharge power constraint on the second time scale; The adjusted energy storage discharge power constraint in the step B3 is: ； In the formula: is the photovoltaic output result after the first solving of the main problem; is the maximum charge-discharge power of the energy storage; is the maximum discharge power constraint of the energy storage on the second time scale. Step B4: adding the energy storage discharge power constraint in step B3 to the main problem, and solving the main problem for the second time; Step B5: calculating the light rejection power on the second time scale; The calculation formula of the light rejection power in the step B5 is: ； In the formula: is the abandoned light power at time t, is the photovoltaic output result after the second solving of the main problem, is the energy storage output after the second solving of the main problem; Step B6: calculating the risk cost of photovoltaic output according to the risk cost generated after adding the relaxation variable, and quantitatively evaluating the risk of photovoltaic output.

2. The risk assessment method of the photovoltaic rail transit self-consistent energy system according to claim 1, wherein, The photovoltaic output risk cost calculation formula in the step A is: ； ； wherein: is the photovoltaic expected power, and are the upper and lower bounds of the photovoltaic power confidence interval, respectively, , are the low and high risk weight coefficients at time T, respectively, is the large grid electricity price at time T, is the relaxation variable at time T, are all proportional coefficients.

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