Source network load storage day-ahead economic dispatching method based on CVaR in extreme weather

Abstract

The invention discloses a CVaR-based source network load storage day-ahead economic dispatching method in extreme weather, relates to the technical field of source network load storage, and solves the technical problem that the influence of flexible resources such as energy storage and demand side response on probabilistic power and electric quantity balance is not comprehensively considered in the extreme weather in the prior art. The net load of the power grid is analyzed; establishing a CVaR and VaR uncertainty model, analyzing the total risk of the power system, establishing a grading demand side response day-ahead economic dispatching model, and evaluating the total cost of the power system; solving the uncertainty of the new energy output and load; according to the method, the VaR index is used for planning the reserve capacity provided by the flexible resources, the CVaR is used for establishing an optimal scheduling model for the value-at-risk, and the system risk and economy are considered.

Description

Technical Field

[0001] The present invention belongs to the field of power grid load storage, and specifically is a day-ahead economic dispatching method of power grid load storage based on CVaR under extreme weather conditions. Background Art

[0002] In order to cope with extreme weather conditions, measures can be taken to ensure power supply security in all links of "source, grid, load and storage". On the power supply side, this can be achieved by increasing the spare capacity of adjustable generator sets; on the grid side, cross-regional power transmission can be achieved through the "big grid"; on the demand side, demand response (DR) is one of the measures to increase power supply security in extreme weather.

[0003] There have been a lot of studies on demand-side response strategies, and loads are usually divided into primary and secondary loads, transferable loads, and interruptible loads; among them, primary and secondary loads usually refer to loads that will cause major or large casualties and economic losses if the power supply is cut off, corresponding to extremely high or high load shedding costs, and have rigid characteristics; loads other than primary and secondary loads are tertiary loads; among them, transferable loads and interruptible loads are flexible resources on the demand side. The existing technology uses CVaR to characterize the uncertainty in the power system, but none of them comprehensively considers the impact of flexible resources such as energy storage and demand-side response on the probabilistic power balance under extreme weather conditions. Therefore, the present invention provides a source-grid-load-storage day-ahead economic dispatch method based on CVaR under extreme weather conditions. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a day-ahead economic dispatch method for source, grid, load and storage based on CVaR under extreme weather conditions, which is used to solve the technical problem that the prior art does not comprehensively consider the impact of flexible resources such as energy storage and demand-side response on the probabilistic power and electricity balance under extreme weather conditions.

[0005] To achieve the above object, a first aspect of the present invention provides a method for day-ahead economic dispatch of source, grid, load and storage based on CVaR under extreme weather conditions, comprising the following steps:

[0006] Step 1: Analyze the net load of the power grid;

[0007] Obtain the load forecast of the power grid and the predicted output of new energy; the load forecast includes non-interruptible load, interruptible load of different levels and transferable load; the predicted output of new energy includes wind power output and photovoltaic output, and the two are independent random variables;

[0008] Calculate the difference between the load forecast and the new energy forecast to get the net load;

[0009] Step 2: Establish CVaR and VaR uncertainty models, analyze the total risk of the power system, and establish a hierarchical demand-side response day-ahead economic dispatch model to evaluate the total cost of the power system; the total risk includes the risk of wind and solar power abandonment and the risk of load shedding; the total cost includes the power generation cost and start-up and shutdown cost of adjustable units, the penalty cost of wind and solar power abandonment and load shedding, and the time transfer cost of load;

[0010] Step 3: Solve the uncertainty of renewable energy output and load.

[0011] Preferably, the establishment of CVaR and VaR uncertainty models includes:

[0012] The maximum loss VaR of random variable X β (X):

[0013] VaR β (X) = inf[X:F(X)≥β];

[0014] Where F(X) is the cumulative probability density function of the random variable X; β is the confidence level of VaR, β∈(0,1];

[0015] Building CVaR based on VaR provides more detailed information about the tail of the loss distribution, focusing on the average loss under extreme loss conditions, and is defined as follows:

[0016] QUR β (X) = E{X|X≥VaR β (X)};

[0017] Among them, VaR represents the maximum loss under a certain confidence level β, and CvaR represents the expected value of the loss exceeding VaR.

[0018] Preferably, the analyzing the overall risk of the power system includes:

[0019] Under the confidence level β, the CvaR of wind power curtailment and load shedding caused by net load fluctuation at each time t is as follows:

[0020]

[0021]

[0022] Among them, C and S are the total number of samples collected for wind and solar curtailment and load shedding respectively; a and b are the samples corresponding to C and S respectively; and They represent the power of wind and solar power abandonment and load shedding caused by the sum of net load fluctuations of all nodes, i is a node in the transmission network, i∈B, B is a set of nodes; and They represent the VaR values ​​of wind and solar curtailment and load curtailment caused by the sum of net load fluctuations of all nodes,

[0023]

[0024] R is a real number;

[0025] Total risk to the power system:

[0026] Preferably, the step of establishing a hierarchical demand-side response day-ahead economic dispatch model includes:

[0027] Hierarchical demand-side response day-ahead economic dispatch model: C = c g +c u +c c +c d +c s +ξ CVaR CvR t ;

[0028] Among them, c g is the power generation cost in the period t∈[1,D], c g =∑ t c T p t , c∈R N is the power generation cost parameter, P t is the output decision variable of the adjustable unit, p t =[p 1t , p 2t , ..., p Nt ] T , N is the number of adjustable generator sets; c u is the start-up and shutdown cost of the traditional adjustable unit; c c The penalty cost for abandoning wind and solar power generation units; c d is the load shedding cost, is the cost per unit load shedding; c s is the load transfer cost; CVaR Penalty cost coefficient for wind and solar curtailment and load shedding risks.

[0029] Preferably, the start-up and shutdown costs of the conventional adjustable units are: Among them, v is the traditional adjustable unit v, v∈W, W is the set of traditional adjustable units; For period t, unit v is in the startup state. The unit v is in the stopped state during the period t; and are the starting and stopping costs of unit v respectively.

[0030] Preferably, the penalty cost for abandoning wind and solar power of the new energy generator set is: Among them, j is a new energy unit, j∈Q, Q is the set of new energy units; is the penalty cost coefficient for abandoned wind and solar power generation of new energy unit j, is the abandoned wind and solar power of renewable energy unit j at time t.

[0031] Preferably, the load transfer cost is: Among them, η i is the load transfer cost coefficient of node i; t and t' are the time periods before and after the transfer, respectively, t≠t'; Represents the amount of load transferred from time period t to time period t'.

[0032] Preferably, the constraints of the hierarchical demand-side response day-ahead economic dispatch model include demand-side response constraints, power balance constraints, spinning reserve constraints, adjustable generator set state and technical constraints, and energy storage state constraints;

[0033] Load shedding Ratio of load shelving The constraint relationship is: in, Total load;

[0034] Transfer amount of transferable load Constraints: in, is the proportion of transferable load in the total load;

[0035] The power flow of a node in an ultra-high voltage network during period t:

[0036] Among them, θ it and θ jt They represent the voltage phase angles of node i and node j during period t respectively; ij is the reactance of the branch between nodes i and j; is the maximum transmission power of the branch;

[0037] The node power balance constraint is:

[0038] in, represents the expectation of a random variable; is the charging and discharging power of energy storage unit k, k∈S i , S i is the set of energy storage devices connected to node i, when When is discharge, When is charging; is the power generation of adjustable generator set i at time t;

[0039] The start-up state of the adjustable generator set is represented by a binary decision variable, 0 for shutdown and 1 for startup; the start-stop operation indicator variable and for:

[0040]

[0041] in, and are the initial states, indicating the on state and the off state respectively;

[0042] The technical constraints of adjustable generator sets include maximum / minimum output constraints and ramp constraints;

[0043] Maximum / minimum output constraints of the unit: in, are the lower and upper output limits of unit i respectively;

[0044] Upward and downward climbing constraints of the unit:

[0045]

[0046] in, are the positive and negative ramp limits of unit i respectively;

[0047] Energy storage state constraints include energy storage capacity constraints and maximum charge and discharge power constraints;

[0048] Energy storage charging power of energy storage unit k during period t and discharge power are decision variables, and

[0049] Energy storage capacity:

[0050]

[0051] Among them, E kt is the energy storage capacity; are the charge and discharge efficiency, Respectively represent the minimum and maximum values ​​of energy storage capacity;

[0052] Charging and discharging constraints of energy storage equipment:

[0053]

[0054] in, They are the upper limits of the speed of charging and discharging respectively;

[0055] Spinning reserve constraints in power systems:

[0056]

[0057]

[0058] in, is the net load.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. The present invention fully considers the interrelationships among power generation, transmission, load and energy storage, and introduces the CVaR method to quantify and optimize potential risks to achieve more robust scheduling decisions; and uses the VaR indicator to plan the spare capacity provided by flexible resources, and uses CVaR to establish an optimized scheduling model for risk value, taking into account both system risk and economy;

[0061] 2. The present invention obtains the load forecast of the power grid and the predicted output of new energy, and analyzes the net load of the power grid; establishes CVaR and VaR uncertainty models to analyze the total risk of the power system, and establishes a hierarchical demand-side response day-ahead economic dispatch model to evaluate the total cost of the power system; solves the uncertainty of new energy output and load; the present invention comprehensively considers the impact of flexible resources such as energy storage and demand-side response on the probabilistic power balance under extreme weather conditions, and analyzes the new energy output and load characteristics under extreme weather conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 It is a schematic diagram of the process of the present invention;

[0064] Figure 2 The figure is a flow chart of the overall risk assessment of the power system of the present invention. DETAILED DESCRIPTION

[0065] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] See also Figure 1The first embodiment of the present invention provides a method for day-ahead economic dispatch of source, grid, load and storage based on CVaR under extreme weather conditions, comprising the following steps:

[0067] Step 1: Analyze the net load of the power grid;

[0068] Extract the load forecast of the power grid and the predicted output of new energy; the load forecast includes non-interruptible load, interruptible load of different levels and transferable load; the predicted output of new energy includes wind power output and photovoltaic output, and the two are independent random variables;

[0069] Calculate the difference between the load forecast and the new energy forecast to get the net load;

[0070] Step 2: Establish CVaR and VaR uncertainty models to analyze the total risk of the power system, and establish a hierarchical demand-side response day-ahead economic dispatch model to evaluate the total cost of the power system;

[0071] Among them, the total risk includes the risk of wind and solar power abandonment and the risk of load shedding; the total cost includes the power generation cost and start-up and shutdown cost of adjustable units, the penalty cost of wind and solar power abandonment and load shedding, and the time transfer cost of load;

[0072] See also Figure 2 , specifically, the CVaR and VaR uncertainty models are established as follows:

[0073] VaR (Value at Risk) refers to the maximum possible loss of a random variable X in a specific future period under a certain confidence level. It is defined as:

[0074] VaR β (X) = inf[X:F(X)≥β];

[0075] Where F(X) is the cumulative probability density function of the random variable X; β is the confidence level of VaR, β∈(0,1]; when the confidence level of VaR exceeds β, it ensures that the value of the random variable X of the objective function is less than VaR β (X).

[0076] It should be noted that in power systems, random variables can usually represent uncertain factors such as load, photovoltaic or wind power output.

[0077] CVaR (Conditional Value-at-Risk) is developed based on VaR. It provides more detailed information about the tail of the loss distribution, focusing on the average loss under extreme loss conditions, and thus can more comprehensively assess risk. It is defined as:

[0078] QUR β(X) = E{X|X≥VaR β (X)};

[0079] The physical meaning of CVaR is the average loss value of the investment strategy when the random variable exceeds a given VaR value. It can be seen that VaR only gives the maximum loss under a certain confidence level, while CVaR gives the expected value of the loss that exceeds VaR.

[0080] Under the confidence level β, the CvaR of wind power curtailment and load shedding caused by net load fluctuation at each time t is as follows:

[0081]

[0082] Among them, C and S are the total number of samples collected for wind and solar curtailment and load shedding respectively; a and b are the samples corresponding to C and S respectively; and They represent the power of wind and solar power abandonment and load shedding caused by the sum of net load fluctuations of all nodes, i is a node in the transmission network, i∈B, B is a set of nodes;

[0083] and They represent the VaR values ​​of wind and solar curtailment and load curtailment caused by the sum of net load fluctuations of all nodes, R is a real number;

[0084] Total risk to the power system:

[0085] Establish a hierarchical demand-side response day-ahead economic dispatch model:

[0086] C=c g +c u +c c +c d +c s +ξ CVaR CvR t ;

[0087] Among them, c g is the power generation cost in the period t∈[1,D], c g =∑ t c T p t , c∈R N is the power generation cost parameter, P t is the output decision variable of the adjustable unit, p t =[p 1t ,p 2t ,…,p Nt ] T , N is the number of adjustable generator sets;

[0088] c u is the start-up and shutdown cost of the traditional adjustable unit; c c The penalty cost for abandoning wind and solar power generation units; c d is the load shedding cost, is the cost per unit load shedding; c s is the load transfer cost; CVaR Penalty cost coefficient for wind and solar curtailment and load shedding risks.

[0089] The start-up and shutdown costs of traditional adjustable units:

[0090] The start-up and shutdown costs of traditional adjustable units:

[0091] Among them, v is the traditional adjustable unit v, v∈W, W is the set of traditional adjustable units; For period t, unit v is in the startup state. The unit v is in the stopped state during the period t; and are the starting and stopping costs of unit v respectively.

[0092] Penalty costs for abandoning wind and solar power of new energy generators:

[0093]

[0094] Among them, j is a new energy unit, j∈Q, Q is the set of new energy units; is the penalty cost coefficient for abandoned wind and solar power generation of new energy unit j, is the abandoned wind and solar power of renewable energy unit j at time t.

[0095] Since load shifting requires compensation to users, the shifting costs need to be modeled:

[0096]

[0097] Among them, η i is the load transfer cost coefficient of node i. Usually, load transfer can be achieved by signing contracts with large customers or controllable terminals. To simplify the model, the cost coefficient is set to be related only to the node;

[0098] t and t' are the time periods before and after the transfer, respectively, t≠t'; Represents the amount of load transferred from time period t to time period t'.

[0099] The constraints of the hierarchical demand-side response day-ahead economic dispatch model include demand-side response constraints, power balance constraints, spinning reserve constraints, adjustable generator state and technical constraints, and energy storage state constraints;

[0100] Load shedding Ratio of load shelving The constraint relationship is: in, Total load; that is, the amount of load shedding shall not exceed the proportion of the load shedding in the total load.

[0101] Transfer amount of transferable load Constraints: in, is the proportion of transferable load in the total load;

[0102] The power flow of nodes in the UHV network during period t is as follows:

[0103] The power flow calculation can be approximated by using the DC power flow equation, which has the advantages of linear expression and fast calculation. In the ultra-high voltage transmission line, the resistance is much smaller than the reactance, so the power flow of the node in the ultra-high voltage network in the t period can be approximated by the injection of active power;

[0104]

[0105] Among them, θ it and θ jt They represent the voltage phase angles of node i and node j during period t respectively; ij is the reactance of the branch between nodes i and j; is the maximum transmission power of the branch.

[0106] The node power balance constraint is:

[0107]

[0108] in, represents the expectation of a random variable; is the charging and discharging power of energy storage unit k, k∈S i ,S i is the set of energy storage devices connected to node i, when When is discharge, When is charging; is the power generation of adjustable generator set i at time t;

[0109] The startup state of the adjustable generator set is represented by a binary decision variable, namely u it ∈{0, 1} represents the switch state of generator set i at time t, where 0 represents shutdown and 1 represents startup;

[0110] Start and stop operation indicator variable and for:

[0111]

[0112] in, and are the initial states, indicating the on state and the off state respectively.

[0113] The technical constraints of adjustable generator sets include maximum / minimum output constraints and ramp constraints;

[0114] Maximum / minimum output constraints of the unit:

[0115]

[0116] in, are the lower and upper output limits of unit i respectively;

[0117] Upward and downward climbing constraints of the unit:

[0118]

[0119] in, are the positive and negative ramp limits of unit i respectively;

[0120] Energy storage state constraints include energy storage capacity constraints and maximum charge and discharge power constraints;

[0121] Energy storage charging power of energy storage unit k during period t and discharge power are decision variables, and

[0122] Capacity of energy storage unit k:

[0123]

[0124] Among them, E kt is the energy storage capacity; are the charge and discharge efficiency, Respectively represent the minimum and maximum values ​​of energy storage capacity;

[0125] Charging and discharging constraints of energy storage equipment:

[0126]

[0127] in, They are the upper limits of the speed of charging and discharging respectively;

[0128] Spinning reserve constraints in power systems:

[0129]

[0130]

[0131] in, is the net load.

[0132] Step 3: Solve the uncertainty of renewable energy output and load.

[0133] The uncertainty of new energy output and load is solved by using the CVaR method. The solution method adopts Monte Carlo sampling to approximate the solution. After VaR and CVaR are obtained, they are solved as optimization scheduling problems respectively. The present invention adopts CPLEX solver for solving.

[0134] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0135] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for day-ahead economic dispatch of source, grid, load and storage based on CVaR under extreme weather conditions, characterized in that: The following steps are involved: Step 1: Analyze the net load of the power grid; Obtain the load forecast of the power grid and the predicted output of new energy; the load forecast includes non-interruptible load, interruptible load of different levels and transferable load; the predicted output of new energy includes wind power output and photovoltaic output, and the two are independent random variables; Calculate the difference between the load forecast and the new energy forecast to get the net load; Step 2: Establish CVaR and VaR uncertainty models, analyze the total risk of the power system, and establish a hierarchical demand-side response day-ahead economic dispatch model to evaluate the total cost of the power system; the total risk includes the risk of wind and solar power abandonment and the risk of load shedding; the total cost includes the power generation cost and start-up and shutdown cost of adjustable units, the penalty cost of wind and solar power abandonment and load shedding, and the time transfer cost of load; Step 3: Solve the uncertainty of renewable energy output and load.

2. The method for day-ahead economic dispatch of source, grid, load and storage based on CVaR under extreme weather conditions according to claim 1, characterized in that: The establishment of CVaR and VaR uncertainty models includes: The maximum loss VaR of random variable X β (X): VaR β (X)=inf[X:F(X)≥β]; Where F(X) is the cumulative probability density function of the random variable X; β is the confidence level of VaR, β∈(0,1]; Building CVaR based on VaR provides more detailed information about the tail of the loss distribution, focusing on the average loss under extreme loss conditions, and is defined as follows: CVaR β (X)=E{X|X≥VaR β (X)}; Among them, VaR represents the maximum loss under a certain confidence level β, and CvaR represents the expected value of the loss exceeding VaR.

3. The method for day-ahead economic dispatch of source, grid, load and storage based on CVaR under extreme weather conditions according to claim 2, characterized in that: The above analysis analyzes the overall risk of the power system, including: Under the confidence level β, the CvaR of wind power curtailment and load shedding caused by net load fluctuation at each time t is as follows: Among them, C and S are the total number of samples collected for wind and solar curtailment and load shedding respectively; a and b are the samples corresponding to C and S respectively; and They represent the power of wind and solar power abandonment and load shedding caused by the sum of net load fluctuations of all nodes, i is a node in the transmission network, i∈B, B is a set of nodes; and They represent the VaR values ​​of wind and solar curtailment and load curtailment caused by the sum of net load fluctuations of all nodes, R is a real number; Total risk to the power system:

4. The method for day-ahead economic dispatch of source, grid, load and storage based on CVaR in extreme weather according to claim 1, characterized in that: The step of establishing a hierarchical demand-side response day-ahead economic dispatch model comprises: Hierarchical demand-side response day-ahead economic dispatch model: C = c g +c u +c c +c d +c s +ξ CVaR CvR t ; Among them, c g is the power generation cost in the period t∈[1,D], c g =∑ t c T p t , c∈R N is the power generation cost parameter, P t is the output decision variable of the adjustable unit, p t =[p 1t ,p 2t ,…,p Nt ] T , N is the number of adjustable generator sets; c u is the start-up and shutdown cost of the traditional adjustable unit; c c The penalty cost for abandoning wind and solar power generation units; c d is the load shedding cost, is the cost per unit load shedding; c s is the load transfer cost; CVaR Penalty cost coefficient for wind and solar curtailment and load shedding risks.

5. The method for day-ahead economic dispatch of source, grid, load and storage based on CVaR in extreme weather according to claim 4, characterized in that: The start-up and shutdown costs of the traditional adjustable units are: Among them, v is the traditional adjustable unit v, v∈W, W is the set of traditional adjustable units; For period t, unit v is in the startup state, The unit v is in the stopped state during the period t; and are the starting and stopping costs of unit v respectively.

6. The method for day-ahead economic dispatch of source, grid, load and storage based on CVaR in extreme weather according to claim 4, characterized in that: The penalty cost for abandoning wind and solar power of the new energy generator set is: Among them, j is a new energy unit, j∈Q, Q is the set of new energy units; is the penalty cost coefficient for abandoned wind and solar power generation of new energy unit j, is the abandoned wind and solar power of renewable energy unit j at time t.

7. The method for day-ahead economic dispatch of source, grid, load and storage based on CVaR in extreme weather according to claim 4, characterized in that: The load transfer cost is: Among them, η i is the load transfer cost coefficient of node i; t and t' are the time periods before and after the transfer, respectively, t≠t'; Represents the amount of load transferred from time period t to time period t'.

8. The method for day-ahead economic dispatch of source, grid, load and storage based on CVaR in extreme weather according to claim 4, characterized in that: The constraints of the hierarchical demand-side response day-ahead economic dispatch model include demand-side response constraints, power balance constraints, spinning reserve constraints, adjustable generator state and technical constraints, and energy storage state constraints; Load shedding Ratio of load shelving The constraint relationship is: in, Total load; Transfer amount of transferable load Constraints: in, is the proportion of transferable load in the total load; The power flow of a node in an ultra-high voltage network during period t: Among them, θ it and θ jt They represent the voltage phase angles of node i and node j during period t respectively; x ij is the reactance of the branch between nodes i and j; is the maximum transmission power of the branch; The node power balance constraint is: in, represents the expectation of a random variable; is the charging and discharging power of energy storage unit k, k∈S i ,S i is the set of energy storage devices connected to node i, when When is discharge, When is charging; is the power generation of adjustable generator set i at time t; The start-up state of the adjustable generator set is represented by a binary decision variable, 0 for shutdown and 1 for startup; the start-stop operation indicator variable and for: in, and are the initial states, indicating the on state and the off state respectively; The technical constraints of adjustable generator sets include maximum / minimum output constraints and ramp constraints; Maximum / minimum output constraints of the unit: in, are the lower and upper output limits of unit i respectively; Upward and downward climbing constraints of the unit: in, are the positive and negative ramp limits of unit i respectively; Energy storage state constraints include energy storage capacity constraints and maximum charge and discharge power constraints; Energy storage charging power of energy storage unit k during period t and discharge power are decision variables, and Energy storage capacity: Among them, E kt is the energy storage capacity; are the charge and discharge efficiency, Respectively represent the minimum and maximum values ​​of energy storage capacity; Charging and discharging constraints of energy storage equipment: in, They are the upper limits of the speed of charging and discharging respectively; Spinning reserve constraints in power systems: in, is the net load.

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