A method for decision-making on safety-constrained unit commitment in power system based on load criterion

Abstract

The invention discloses a method for making a decision on a combination of power system safety constraints based on a load criterion, and belongs to the field of power system optimization and dispatching. The power system operation framework is a double-layer structure, the upper layer calculates the user criterion, and the lower layer establishes a load aggregator to optimize the uncertainty of flexible load aggregation, and refers to the user criterion to calculate the load aggregation expectation and deviation, and optimize the benefits of the load aggregator. The method comprises the following steps: S1 obtains the parameters of the entire network, obtains the fixed load in the system, and predicts the output data of new energy; S2 calculates the CDL curve in the upper layer architecture; S3 transmits the CDL line shape to the lower layer, and the LA takes this line shape as the adjustment target and calculates the real operation load curve in combination with its own response characteristics; S4 uploads the real operation load curve to the upper layer architecture in a new round of iteration, repeats the above steps for cyclic iteration, and stops iteration if the calculated load criterion and the real load no longer change; S5 evaluates the effect after the LA responds and determines the response incentive.

Description

Technical Field

[0001] The invention belongs to the field of power system optimization dispatching, and in particular relates to a power system safety constraint unit combination decision method based on load criterion. Background Art

[0002] With the increasing popularity of renewable energy, the share of traditional power generation continues to decline. It is difficult to smooth out the volatility of renewable energy by relying solely on traditional power generation. Therefore, it is necessary to increase the demand for demand-side resources to adapt to the safe integration of uncertain renewable resources.

[0003] The planning and construction of large-scale user pipelines will drive the consumption of renewable energy. The entire process of related projects from investment and construction to production and operation will bring significant benefits to the national economy, energy production and utilization methods, and the environment. It is necessary to design scientific evaluation indicators, methods and standards to conduct a systematic and scientific evaluation of the investment, construction, operation and benefits of the large-scale user pipeline mechanism.

[0004] At present, there are still few demand response theoretical systems applicable to large-scale load aggregation. The existing demand response methods that consider large-scale response do not take into account the actual situation of system operation and the response characteristics of users. With the implementation of various demand response demonstration projects in my country, the demand response mechanism for large-scale load aggregation has certain implementation conditions. It is necessary to establish a scientific and reasonable large-scale demand response method as soon as possible to provide a reference for the investment decision-making of demand response investment entities, thereby promoting the construction process of large-scale demand response. Summary of the invention

[0005] In view of the deficiencies in the prior art, the object of the present invention is to provide a method for making decisions on power system safety constraint unit combination based on load criteria, which can effectively promote the consumption of renewable energy while maintaining the safe operation of the power system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for making decisions on safe constraint unit combination of power system based on load criterion, characterized in that the operation framework of the power system is a double-layer structure, the user criterion calculated by the upper layer is broadcasted in the whole network so that the load aggregator can make reasonable resource allocation; the lower layer establishes the load aggregator to optimize the uncertainty of flexible load aggregation, uses the reference user criterion, calculates the load aggregation expectation and deviation, and optimizes the benefits of the load aggregator; the method specifically comprises the following steps:

[0008] S1: Obtain the parameters of the entire network, obtain the fixed load in the system, and predict the output data of new energy;

[0009] S2: Calculate the CDL curve in the upper architecture;

[0010] S3: The CDL line shape is transmitted to the lower layer, and LA takes this line shape as the adjustment target and calculates the real operating load curve based on its own response characteristics;

[0011] S4: Upload the actual operating load curve to the upper layer architecture in the new iteration, repeat the above steps and iterate cyclically. If the calculated load criterion and the actual load do not change, stop the iteration;

[0012] S5: Evaluate the effect of LA response and determine the response stimulus.

[0013] Furthermore, the upper structure in S2 includes a CDL subproblem and a SCUC subproblem. The CDL subproblem calculates the required load criterion, and the SCUC subproblem ensures that the obtained CDL can still maintain the safety of system operation.

[0014] The objective function of the user's directrix is ​​set as:

[0015]

[0016]

[0017] in, represents the output of the i-th adjustable generator set at time t; represents the available output of new energy at time t; N represents the maximum output of renewable energy predicted at time t; G represents the number of generating units; T represents the total time for response implementation; a i ,b i ,c i is the cost coefficient of the generator set; C R is the wind curtailment cost coefficient; is the CDL curve; P d,fix,t represents a fixed load that cannot be changed at time t; is the total amount of flexible load participating in demand response.

[0018] Furthermore, the constraints of the objective function of the user guideline are as follows:

[0019]

[0020]

[0021]

[0022]

[0023] Among them, formula (3) and formula (4) indicate that the user line only changes the electricity consumption behavior of the participating responding users, and the total electricity consumption does not change; formula (5) is the upper and lower limits of the output of the controllable generator set, where is a 0-1 variable, indicating the start and stop status of generator set i at time t. and They represent the upper and lower limits of the output of generator set i respectively; formula (6) is the output constraint of new energy.

[0024] Furthermore, the objective function of the SCUC is set as:

[0025]

[0026] in, is the actual output of generator set i at time t; is the actual output of renewable energy unit j at time t; SU and SD represent the startup cost and shutdown cost respectively; LS is the load shedding, C L is the load shedding cost coefficient.

[0027] Furthermore, the constraints of the SCUC subproblem are as follows:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] Constraint (8) represents the total power balance of the system; in equation (9), represents the on / off state of generator set i at time t; constraint (10) ensures that the output of new energy cannot exceed Constraint (11) indicates the actual load is equal to the sum of the fixed and flexible parts of all buses at time t; constraint (12) indicates that the power flow should be in PL max range; KP, KR and KD are the correlation matrices of bus generator sets, bus new energy generator sets and bus loads respectively; SF is the displacement factor; constraint (13) is the generator set climbing constraint, where DR i and UR i Represent the upper limit of the downhill and climbing of the generator set i respectively; Constraints (14)-(15) calculate the start and stop cost of the generator set, 0-1 variable and They are the status indicators of unit startup and shutdown, and denote the startup and shutdown costs of unit i at time t respectively; constraints (19)-(22) describe the minimum on / off time limit of the generator set, where and Respectively represent the minimum on and off time of unit i; and They represent the initial start and stop time of generator set i respectively.

[0044] Furthermore, the coupling constraints of the upper architecture are:

[0045]

[0046]

[0047] Furthermore, S3 includes the following two steps:

[0048] S31, calculate the relative average deviation R D :

[0049]

[0050] Among them, D act,k represents the actual polymerization amount, D is the polymerization target amount; K is the total number of polymerizations;

[0051] S32, load aggregation optimization, the specific steps are as follows:

[0052] (1+λ 2 +λ 3 +...+λ24 )*P init =M (26)

[0053] Where M is the total amount of flexible load, P init is the flexible load at time t = 1; therefore, the adjustment amount of the flexible load at time t is the absolute difference between the initial curve and the adjusted curve;

[0054] The optimization objective function of the lower layer LA is:

[0055]

[0056] Among them, X res,t and X ind,t are respectively the residential load regulation and the industrial load regulation; X dev,t is the resident load deviation at time t; C res and C ind are the regulation cost coefficients of residential load and industrial load respectively; C dev is the deviation cost coefficient of the residents’ load regulation.

[0057] Furthermore, the constraints of the optimization objective function of the lower layer LA are:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Among them, constraint (28) indicates that the adjustment amount of flexible load includes residential load and industrial load; t arres,t and t arind,t Represent the target adjustment values ​​of residential load and industrial load respectively; l t is the initial load curve; constraint (29) is related to SCUC. If load shedding occurs during the last system operation, we reduce the load during the current operation to ensure the normal operation of the system; is the elastic load of the system during the mth operation, is the fixed load during the mth system operation; constraints (30)-(35) describe the adjustment characteristics of the resident load after receiving the CDL curve; the percentage of resident response load is a three-segment line graph as the adjustment target increases; Xres,t is the proportion of users participating in DR, β 1,t ,β 2,t and β 3,t is a 0-1 variable, res 1,t ,res 2,t and res 3,t Represents the ordinate values ​​of the three inflection points of the three-segment line, slope 1,t , slope 2,t and slope 3,t is the slope of the three-segment line, and is the horizontal coordinate value of the four endpoints of the three-segment line; z 1,t , z 2,t and z 3,t is the value of the area selected on the three-segment line according to the adjusted target value, where only one has a specific value at the same time and the other two are zero; constraints (36)-(41) are similar to (30)-(35), X dev,t is the residential load response deviation; constraint (42) controls the deviation within the allowable range.

[0074] Further, the incentive includes primary incentive and secondary incentive;

[0075] The first level incentives are:

[0076]

[0077] Among them, S k Representing LA i First-level incentive.

[0078] Furthermore, the algorithm steps of the secondary excitation are:

[0079] 1) Calculate the total load curves of n users respectively, and define an indicator to measure the similarity between the user load curve and the standard load criterion:

[0080]

[0081] Among them, l k (t) is LA k User curve after response; d i is the Euclidean distance between the two lines, d k The larger the value, the higher the l k The lower the similarity of (t);

[0082] 2) Find the average similarity of all users:

[0083]

[0084] 3) Similarity is lower than d ave Users with similarity higher than d should deduct part of the incentive to reward ave For users of

[0085]

[0086]

[0087] Among them, f k Represents linear similarity lower than d ave is the amount deducted from user i in the second response incentive; σ is the constant factor of the control incentive equation formulated by the demand response center; the deducted subsidy will be received by users with a higher degree of linear similarity according to the proportion of the excess amount.

[0088] Beneficial effects of the present invention: Under the premise of ensuring the effective operation of the power grid, the present invention can effectively promote the absorption rate of renewable energy and reduce system costs. At the same time, a reasonable reward system can attract electricity users to participate in demand response, thereby promoting the construction of large-scale demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0090] Figure 1 It is the overall framework diagram of the present invention;

[0091] Figure 2 is a flow chart of the method of the present invention;

[0092] Figure 3 The actual aggregate expected value of the load of the present invention;

[0093] Figure 4 The actual aggregate deviation of the load of the present invention;

[0094] Figure 5 Contribute to the new energy of this invention;

[0095] Figure 6 CDL curve and generator set output;

[0096] Figure 7 The line flow under CDL proposed by the present invention;

[0097] Figure 8 It is the line flow under the traditional CDL;

[0098] Fig. 9 is the load curve after the response of the present invention;

[0099] Fig.10 is the absolute load rate of the line flow under the traditional CDL of the 118-bus system;

[0100] Fig.11 is the absolute load rate of the line flow under the CDL of the 118-node system of the present invention;

[0101] Fig.12 This is the LAs incentive allocation in the 118-node system of the present invention. DETAILED DESCRIPTION

[0102] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0103] like Figure 1 As shown, the present invention constructs a large-scale flexible demand response resource coordination power system operation framework. The power system operation framework is a two-layer framework. The upper layer architecture considers the system level and calculates the ideal load curve that is most suitable for new energy consumption under the premise of maintaining the normal operation of the system; the lower layer architecture considers the user side level, takes the load criterion passed down from the upper layer as the adjustment target, takes the load aggregator (LA) as the response intermediary, comprehensively considers the uncertainty of user response, and calculates the actual load curve after the user responds. This actual load curve is uploaded to the upper layer in a new round of iteration, and the calculation iteration is repeated until it converges to the optimal load criterion;

[0104] In the upper-level architecture, the Customer Directrix Load (CDL) subproblem considers the balance constraint of system power, the constraint of new energy consumption, and the "adjustable" balance and "non-adjustable" constraint, and calculates an ideal load directrix. However, if the flexible load adjusts its behavior according to this ideal curve without considering the actual operation constraints, it may lead to dangerous operation such as line flow exceeding the limit. Therefore, the security constraint unit combination constraint is added to the upper layer to maintain the safe operation of the system. The two subproblems are coupled through two constraints. One is that the security constraint unit combination (SCUC) should be consistent with the start and stop status of the unit in the CDL subproblem. On the other hand, considering the uncertainty of large-scale flexible loads, the required amount of new energy to be absorbed should be very close to the actual total amount of new energy absorbed.

[0105] In the lower-level architecture, the CDL curve is published to all LAs participating in demand response; taking into account the uncertainty of large-scale load response, the response characteristics of LA are modeled; considering that the actual value of load aggregation is still uncertain, the expectation and deviation of load aggregation are simulated, and the response probability of large-scale loads and their aggregation characteristics is established, and the response cost and constraints are proposed to obtain the actual load line with load aggregation uncertainty.

[0106] like Figure 2 As shown, a method for making a decision on a power system safety constraint unit combination based on a load criterion line comprises the following steps:

[0107] S1: Obtain the parameters of the entire network, obtain the fixed load in the system, the predicted output of new energy sources and other data;

[0108] S2: Calculate the CDL curve in the upper architecture;

[0109] The upper structure includes CDL subproblems and SCUC subproblems, and solves the ideal load criterion under the premise of safe operation of the power grid;

[0110] CDL sub-questions:

[0111] Figure 1 The CDL subproblem in minimizes the system operation cost and maximizes the consumption of new energy. Therefore, this subproblem can calculate a user criterion that is most suitable for the consumption of new energy. The objective function of the user criterion is set as:

[0112]

[0113] in, represents the output of the i-th adjustable generator set at time t; represents the available output of new energy at time t; N represents the maximum output of renewable energy predicted at time t; G represents the number of generating units; T represents the total time for response implementation; a i ,b i ,c i is the cost coefficient of the generator set; C R is the wind curtailment cost coefficient; the first half of formula (1) is the cost of the generator set, and the second half is the wind curtailment cost;

[0114]

[0115] in is the CDL curve; P d,fix,t represents a fixed load that cannot be changed at time t; is the total amount of flexible load participating in demand response; the “adjustable amount” on the left side of formula (2) stabilizes the “unadjustable amount” on the right side in real time.

[0116] The remaining constraints are as follows:

[0117]

[0118]

[0119]

[0120]

[0121] Among them, formula (3) and formula (4) indicate that the user line only changes the electricity consumption behavior of the participating responding users, and the total electricity consumption does not change; formula (5) is the upper and lower limits of the output of the controllable generator set, where is a 0-1 variable, indicating the start and stop status of generator set i at time t. and They represent the upper and lower limits of the output of generator set i respectively; formula (6) is the output constraint of new energy.

[0122] SCUC sub-questions:

[0123] The CDL subproblem calculates the required load criterion. However, the obtained load criterion may not be operational in actual power grid operation. Therefore, the SCUC subproblem is introduced to ensure that the obtained CDL can still maintain the safety of system operation;

[0124] The objective function of SCUC is set as:

[0125]

[0126] in, is the actual output of generator set i at time t; is the actual output of renewable energy unit j at time t; SU and SD represent the startup cost and shutdown cost respectively; LS is the load shedding, C L is the load shedding cost coefficient.

[0127] The constraints of the SCUC subproblem are as follows:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] Constraint (8) represents the total power balance of the system; in equation (9), represents the on / off state of generator set i at time t; constraint (10) ensures that the output of new energy cannot exceed Constraint (11) indicates the actual load is equal to the sum of the fixed and flexible parts of all buses at time t; constraint (12) indicates that the power flow should be in PL max range; KP, KR and KD are the correlation matrices of bus generator sets, bus new energy generator sets and bus loads respectively; SF is the displacement factor; constraint (13) is the generator set climbing constraint, where DR i and UR iRepresent the upper limit of the downhill and climbing of the generator set i respectively; Constraints (14)-(15) calculate the start and stop cost of the generator set, 0-1 variable and They are the status indicators of unit startup and shutdown, and denote the startup and shutdown costs of unit i at time t respectively; constraints (19)-(22) describe the minimum on / off time limit of the generator set, where and Respectively represent the minimum on and off time of unit i; and They represent the initial start and stop time of generator set i respectively.

[0144] Coupling constraints of the upper-level architecture: The CDL subproblem adjusts the required load curve and renewable energy consumption for the required operating state; the SCUC subproblem focuses on the feasibility and optimal operation of the actual operation; these two subproblems are collaboratively optimized through two sets of coupling constraints, namely, the unit on / off state and REs consumption, expressed as:

[0145]

[0146]

[0147] S3: The CDL line shape is transmitted to the lower layer, and LA takes this line shape as the adjustment target and calculates the real operating load curve based on its own response characteristics;

[0148] After obtaining the load line, the demand response center publishes this load curve to the LA of the lower-level architecture; once receiving the CDL curve, the LA begins to evaluate the uncertainty of load aggregation behavior and makes the flexible load curve shape close to the CDL line shape; considering the uncertainty of flexible load response, we use the CDL line shape as the adjustment target to simulate the aggregated load expectations and deviations in the lower-level optimization model; for each LA, the flexible load includes industrial, residential and commercial loads; the industrial load response volume is large and the response uncertainty is low, but it cannot respond in real time; residential and commercial loads respond quickly, but have higher uncertainty;

[0149] 1) Load uncertainty

[0150] The specified regulation behavior and the corresponding response characteristics may be different due to the different load compositions of different buses; residential and commercial loads are uncertain considering various factors such as individual demand, weather conditions, communication failures and other emergency situations; therefore, the difference between the target dispatch plan and the actual response is inevitable;

[0151] When the system needs to issue a demand response (DR) event, that is, the demand response implementation agency issues a notification to power users containing information such as power prices, load adjustments or transfers, LA aims to select the optimal subset S from n users. t Issue load adjustment instructions to adjust the total power to be as close to the target value as possible; assume that the adjustment behavior of each user is independent and has probability p i Participation; if user k participates, then X k,t =1, on the contrary, if user k refuses to respond, then X k,t =0; the user selection problem adopts the offline algorithm shown in Algorithm 1. Algorithm 1 is divided into two steps: 1) According to p k Sort the users from largest to smallest. 2) Determine the number k and select the first k users based on the probability distribution p and the target response value D:

[0152]

[0153] Due to the uncertainty of aggregation, there is a deviation between the actual response and the target response; the relative average deviation R introduced in formula (25) D To evaluate the convergence error:

[0154]

[0155] Among them, D act,k represents the actual polymerization amount, D is the polymerization target amount; K is the total number of polymerizations.

[0156] Assume that each LA manages 20,000 user loads, each of which can provide 2.5 kW of power adjustment. In addition, the average response probability is set to 0.6; Figure 3 is the relationship diagram between the target aggregation amount and the expected actual aggregation value, Figure 4 is the relationship between the target aggregation amount and the response deviation;

[0157] Due to the uncertainty of the response, there is a deviation between the adjustment target and the actual response of the user. Assuming the expected value of the response V and the relative deviation η, the actual response of the user is in the interval ((1+η)V, (1-η)V). For simplicity, we assume that the expected value of the response in the lower-level optimization is the actual response of the user. Figure 3 It can be seen that the expected value is linearly related to the target value. In addition, when the target value is small, the deviation is relatively small, but when the target value exceeds a certain critical point, the deviation will increase sharply. Therefore, for the accuracy of the model, the deviation needs to be controlled within a certain range.

[0158] 2) Load aggregation optimization

[0159] The goal of LA is to adjust the flexible load so that its shape is as close to the CDL line shape as possible while keeping the deviation within a reasonable range; the CDL curve passed down from the upper architecture is a definite curve, so the relative proportion of CDL at each moment can be determined, expressed as If the total amount of flexible load is M, and the flexible load is distributed according to the above ratio, the following formula is obtained:

[0160] (1+λ 2 +λ 3 +...+λ 24 )*P init =M (26)

[0161] Among them, P init is the flexible load at time t = 1; therefore, the adjustment amount of the flexible load at time t is the absolute difference between the initial curve and the adjusted curve;

[0162] The total regulation includes residential load and industrial load; therefore, the optimization objective function of the lower LA is:

[0163]

[0164] Among them, X res,t and X ind,t are respectively the residential load regulation and the industrial load regulation; X dev,t is the resident load deviation at time t; C res and C ind are the regulation cost coefficients of residential load and industrial load respectively; C dev is the deviation cost coefficient of the residents’ load regulation.

[0165] The remaining constraints are as follows:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

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[0178]

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[0180]

[0181] Among them, constraint (28) indicates that the adjustment amount of flexible load includes residential load and industrial load; t arres,t and t arind,t Represent the target adjustment values ​​of residential load and industrial load respectively; l t is the initial load curve; constraint (29) is related to SCUC. If load shedding occurs during the last system operation, we reduce the load during the current operation to ensure the normal operation of the system; is the elastic load of the system during the mth operation, is the fixed load during the mth system operation; constraints (30)-(35) describe the adjustment characteristics of the resident load after receiving the CDL curve; the percentage of resident response load is a three-segment line graph as the adjustment target increases; Xres,t is the proportion of users participating in DR, β 1,t ,β 2,t and β 3,t is a 0-1 variable, res 1,t ,res 2,t and res 3,t Represents the ordinate values ​​of the three inflection points of the three-segment line, slope 1,t , slope 2,t and slope 3,t is the slope of the three-segment line, and is the horizontal coordinate value of the four endpoints of the three-segment line; z 1,t , z 2,t and z 3,t is the value of the area selected on the three-segment line according to the adjusted target value, where only one has a specific value at the same time and the other two are zero; constraints (36)-(41) are similar to (30)-(35), X dev,t is the residential load response deviation; constraint (42) controls the deviation within the allowable range.

[0182] S4: Upload the actual operating load curve to the upper layer architecture in the new iteration, repeat the above steps and iterate cyclically. If the calculated load criterion and the actual load do not change, stop the iteration;

[0183] S5: Evaluate the effect of LA response and determine the response incentive;

[0184] The present invention encourages LA to adjust the electricity consumption curve to fit the load criterion, so that more new energy can be absorbed on the load side, while making the output of traditional generators smoother, thereby reducing power generation costs and carbon emissions. To achieve these goals and benefits, appropriate evaluation methods are needed to encourage LA to actively participate. The present invention proposes an incentive evaluation and redistribution method, which consists of a primary incentive and a secondary incentive; the primary allocation is calculated based on the absolute response amount; the secondary allocation is based on the primary allocation, giving higher incentives to users whose load lines are closer to the CDL curve, and imposing appropriate penalties on users with lower line similarity.

[0185] 1) Total incentives and primary incentives

[0186] The cost of power generation is not only related to the load, but also to the selection and use of generator sets; when the load curve has a rapid ramp and more rapid generator sets are needed, the cost may be high; therefore, when the total load remains unchanged and the generator output curve is relatively flat, the cost of power generation is usually lower than when the generator output curve changes greatly. The user standard line strategy can make the power consumption curve of the entire network tend to be stable, which can reduce the cost of power generation and thus reduce the cost of purchasing electricity on the grid side. This cost saving can be used to subsidize responsive users.

[0187] The total incentive calculation formula is:

[0188]

[0189] Among them, Rev cdl and Rev ini Respectively represent the benefits of using CDL and not using it, F cdl and F ini The costs are with and without using CDL.

[0190] The first-level incentive calculation formula is:

[0191] Assume that the load adjustment of n LAs is {x 1 ,x 2 ,...,x n}; First, the first allocation of response subsidies is made according to the proportion of each LA adjustment to the total adjustment:

[0192]

[0193] Among them, Sk Representing LA i First-level incentive.

[0194] 2) Secondary incentives

[0195] Calculate the total load curves of n users respectively, and define an indicator to measure the similarity between the user load curve and the standard load criterion:

[0196]

[0197] Among them, l k (t) is LA k User curve after response; d i is the Euclidean distance between the two lines, d k The larger the value, the higher the l k The lower the similarity of (t);

[0198] Next, find the average similarity of all users:

[0199]

[0200] To ensure the fairness of the reward mechanism, the similarity is lower than d ave Users with similarity higher than d should deduct part of the incentive to reward ave The calculation method of compensation deduction is as follows:

[0201]

[0202]

[0203] Among them, f k Represents linear similarity lower than d ave is the amount deducted from user i in the second response incentive; σ is the constant factor of the control incentive equation formulated by the demand response center; the deducted subsidy will be received by users with a higher degree of linear similarity according to the proportion of the excess amount.

[0204] The following is an example:

[0205] Example:

[0206] In order to verify the rationality of the mechanism proposed in this paper, examples are demonstrated in the IEEE6 bus system and the 118 bus system. The new energy data comes from the PJM website, and the load data comes from Open Energy Information (OpenEI). All examples are run on matlab 2021a on an IntelI7 2.1GHz, 16GB RAM computer.

[0207] 1. 6-node system

[0208] 1) CDL Verification

[0209] Consider two examples; Examples 1 and 2 verify the effectiveness of the present invention in promoting the consumption of new energy and maintaining the safety of system operation under high proportion of new energy penetration:

[0210] Example 1: Comparison of system economics with and without CDL

[0211] Example 2: Comparison of system safety with and without CDL

[0212] Example 1: In this example, we compare the new energy consumption under new energy penetration rates of 30% and 60%.

[0213] from Figure 5 It can be seen that when the CDL algorithm is considered, new energy can be fully absorbed and the effect is better. Figure 5 The CDL curve and generator output at 30% penetration are shown. Figure 6 (a) Comparing the proposed CDL with the traditional CDL, it can be seen that the trends of the two lines are basically the same, but there are still some differences, which are caused by the uncertainty of system operating conditions and load aggregation. Figure 6 (b) is a comparison of generator output. It can be seen that the generator output curve in the CDL scenario is smoother and has smaller fluctuations. In contrast, the output fluctuations of traditional generators without CDL are more severe, and fast ramp generator sets need to be called, which increases costs. The results show that the power generation costs with and without CDL are $43,950 and $36,633 respectively, a cost reduction of 16.6%.

[0214] Example 2: The traditional CDL method only considers adjusting the flexible load after receiving the load guideline. However, when the LA is adjusting the load, the line may exceed the limit. Therefore, the present invention adds SCUC constraints to ensure the safety of the power system. Figure 7 and Figure 8 The effectiveness of the CDL algorithm of the present invention on system security is demonstrated.

[0215] The critical value of the line flow is set to 100MW. It can be seen that the CDL proposed by the present invention makes the line flow within the safe range of the line flow at any time, while some line flows in the traditional CDL exceed the limit. Therefore, the CDL of the present invention can effectively maintain system safety while absorbing new energy.

[0216] 2) This example illustrates the incentive evaluation and allocation mechanism. Figure 8The load curves of each LA after participating in the demand response are shown. During this process, the flexible load accounts for 25% of the total load and only the flexible load participates in the adjustment. Therefore, the difference between the total load curves before and after participating in the demand response is relatively small. However, this phenomenon accurately illustrates the effectiveness of the present invention: it does not require large-scale changes in users' electricity consumption habits to promote the consumption of new energy. The calculation results show that the CDL of the present invention reduces costs by US$7,313. It is assumed that all cost savings are used for user incentives, as shown in Table 1.

[0217] Table 1 Primary and secondary excitation of LA

[0218]

[0219]

[0220] The first-level incentive is determined based on the actual load adjustment of each LA. However, the initial load curve of each LA is different. If the initial load curve of the LA is close to the CDL line, its adjustment amount will not be too large. Therefore, in order to ensure the fairness of the incentive mechanism, we need to provide secondary incentives based on the similarity of the LA load curves.

[0221] The secondary stimulus is adjusted based on the linear similarity based on the primary stimulus. In this example, the average similarity d ave =0.1063. LA 1 To Los Angeles 3 The similarity is lower than average, so some of their incentives are deducted.

[0222]

[0223]

[0224]

[0225] According to formula (48), the LA secondary excitation with higher similarity is: 4 =37,c 5 =13,c 6 =17,c 7 =137,c 8 =102.

[0226] 2. 118-node system

[0227] In order to prove that the proposed CDL is applicable to large power systems, the IEEE 118-bus system is used in this example.

[0228] We set all line capacities to 100MW. Fig.10The flow conditions of the routes of the traditional CDL model are shown, in which 42 out of 186 routes have exceeded the limit, and the worst-case flow reaches 729% of the specified value. Fig.11 It shows that when the CDL in this paper is applied, the power flows of the 186 lines are all within the safe range and still absorb most of the new energy.

[0229] The rationality of the incentive mechanism is also verified for the 118-node system. Fig.12 The incentive distribution of LA is shown. The blue area represents the primary incentive, and the yellow and red areas represent the reward and penalty of the secondary incentive, respectively.

[0230] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0231] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for making decisions on power system safety constraint unit commitment based on load criterion, characterized in that: The operation framework of the power system is a two-layer structure. The user line calculated by the upper layer is broadcasted throughout the network to enable load aggregators to make reasonable resource allocation; the lower layer establishes load aggregators to optimize the uncertainty of flexible load aggregation, uses reference user lines, calculates load aggregation expectations and deviations, and optimizes the benefits of load aggregators; the method specifically includes the following steps: S1: Obtain the parameters of the entire network, obtain the fixed load in the system, and predict the output data of new energy; S2: Calculate the CDL curve in the upper architecture; S3: The CDL line shape is transmitted to the lower layer, and LA takes this line shape as the adjustment target and calculates the real operating load curve based on its own response characteristics; S4: Upload the actual operating load curve to the upper layer architecture in the new iteration, repeat the above steps and iterate cyclically. If the calculated load criterion and the actual load do not change, stop the iteration; S5: Evaluate the effect of LA response and determine the response incentive; The S3 includes the following two steps: S31, calculate the relative average deviation R D : Among them, D act,k represents the actual polymerization amount, D is the polymerization target amount; K is the total number of polymerizations; S32, load aggregation optimization, the specific steps are as follows: (1+λ2+λ3+...+λ 24 )*P init =M (26) Where M is the total amount of flexible load, P init is the flexible load at time t = 1; therefore, the adjustment amount of the flexible load at time t is the absolute difference between the initial curve and the adjusted curve; The optimization objective function of the lower layer LA is: Among them, X res,t and X ind,t are respectively the residential load regulation and the industrial load regulation; X dev,t is the resident load deviation at time t; C res and C ind are the regulation cost coefficients of residential load and industrial load respectively; C dev is the deviation cost coefficient of the residents’ load regulation.

2. The method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 1, characterized in that: The upper structure in S2 includes CDL subproblems and SCUC subproblems. The CDL subproblem calculates the required load criterion, and the SCUC subproblem ensures that the obtained CDL can still maintain the safety of system operation. The objective function of the user's directrix is ​​set as: in, represents the output of the i-th adjustable generator set at time t; represents the available output of new energy at time t; N represents the maximum output of renewable energy predicted at time t; G represents the number of generating units; T represents the total time for response implementation; a i ,b i ,c i is the cost coefficient of the generator set; C R is the wind curtailment cost coefficient; is the CDL curve; P d,fix,t represents a fixed load that cannot be changed at time t; is the total amount of flexible load participating in demand response.

3. The method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 2, characterized in that: The constraints of the objective function of the user guideline are as follows: Among them, formula (3) and formula (4) indicate that the user line only changes the electricity consumption behavior of the participating responding users, and the total electricity consumption does not change; formula (5) is the upper and lower limits of the output of the controllable generator set, where is a 0-1 variable, indicating the start and stop status of generator set i at time t. and They represent the upper and lower limits of the output of generator set i respectively; formula (6) is the output constraint of new energy.

4. The method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 3 is characterized in that: The objective function of the SCUC is set as: in, is the actual output of generator set i at time t; is the actual output of renewable energy unit j at time t; SU and SD represent the startup cost and shutdown cost respectively; LS is the load shedding, C L is the load shedding cost coefficient.

5. The method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 4, characterized in that: The constraints of the SCUC subproblem are as follows: Constraint (8) represents the total power balance of the system; in equation (9), represents the on / off state of generator set i at time t; constraint (10) ensures that the output of new energy cannot exceed Constraint (11) indicates the actual load is equal to the sum of the fixed and flexible parts of all buses at time t; constraint (12) indicates that the power flow should be in PL max Within the range; KP, KR and KD are the correlation matrices of bus generator sets, bus new energy units and bus loads respectively; SF is the displacement factor; constraint (13) is the generator set ramp constraint, where DR i and UR i They represent the downhill and climbing upper limits of generator set i respectively; Constraints (14)-(15) calculate the start-stop cost of the generator set, 0-1 variable and They are the status indicators of unit startup and shutdown, and represent the startup and shutdown costs of unit i at time t respectively; constraints (19)-(22) describe the minimum on / off time limit of the generator set, where T i ON and T i OFF Respectively represent the minimum on and off time of unit i; HT i ON and HT i OFF They represent the initial start and stop time of generator set i respectively.

6. The method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 5, characterized in that: The coupling constraints of the upper-level architecture are:

7. A method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 1, characterized in that: The constraints of the optimization objective function of the lower layer LA are: Among them, constraint (28) indicates that the adjustment amount of flexible load includes residential load and industrial load; t arres,t and t arind,t Represent the target adjustment values ​​of residential load and industrial load respectively; l t is the initial load curve; constraint (29) is related to SCUC. If load shedding occurs during the last system operation, we reduce the load during the current operation to ensure the normal operation of the system; is the elastic load of the system during the mth operation, is the fixed load during the mth system operation; constraints (30)-(35) describe the adjustment characteristics of the resident load after receiving the CDL curve; the percentage of resident response load is a three-segment line graph as the adjustment target increases; X res,t is the proportion of users participating in DR, β 1,t ,β 2,t and β 3,t is a 0-1 variable, res 1,t ,res 2,t and res 3,t Indicates the ordinate values ​​of the three inflection points of the three-segment line, slope 1,t , slope 2,t and slope 3,t is the slope of the three-segment line, and is the horizontal coordinate value of the four endpoints of the three-segment line; z 1,t , z 2,t and z 3,t It is the value of the area selected on the three-segment line according to the adjustment target value, where only one has a specific value at the same time, namely X res,t The distance from the horizontal coordinate of to the left end point of the line segment, and the other two are zero; constraints (36)-(41) are similar to (30)-(35), X dev,t is the residential load response deviation; constraint (42) controls the deviation within the allowable range.

8. The method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 1, characterized in that: The incentives include primary incentives and secondary incentives; The first level incentives are: Among them, S k Representing LA i First-level incentive.

9. The method for making decisions on power system safety constraint unit commitment based on load criterion according to claim 8, characterized in that: The algorithm steps of the secondary excitation are: 1) Calculate the total load curves of n users respectively, and define an indicator to measure the similarity between the user load curve and the standard load criterion: Among them, l k (t) is LA k User curve after response; d i is the Euclidean distance between the two lines, d k The larger the value, the higher the l k The lower the similarity of (t); 2) Find the average similarity of all users: 3) Similarity is lower than d ave Users with similarity higher than d should deduct part of the incentive to reward ave For users of Among them, f k Represents linear similarity lower than d ave is the amount deducted from user i in the second response incentive; σ is the constant factor of the control incentive equation formulated by the demand response center; the deducted subsidy will be received by users with a higher degree of linear similarity according to the proportion of the excess amount.

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