Layered energy storage optimal configuration method based on day-ahead-day scheduling

Abstract

The invention discloses a day-ahead-day scheduling-based layered energy storage optimal configuration method, which comprises the following steps of: S1, analyzing the influence of electric energy quality on power distribution network loss after distributed energy storage is accessed to a power distribution network, further evaluating the influence degree of the electric energy quality on the power distribution network loss, and taking the influence degree as a power distribution network electric energy quality comprehensive index; s2, constructing a multi-layer energy storage optimal configuration model based on day-ahead-day scheduling, wherein the multi-layer energy storage optimal configuration model comprises an upper planning layer and a lower operation layer; s3, giving an energy storage locating and sizing scheme by taking the lowest total operation cost of the power distribution network as a target through an upper planning layer; and S4, on the basis of a given locating and sizing scheme, determining an energy storage optimal operation scheme in the lower operation layer by day-ahead scheduling and intra-day scheduling in sequence. According to the method, the problem that the influence on the electric energy quality is not considered perfectly in the traditional network loss research is solved, the technical economy of the power distribution network can be quantified according to the actual operation condition of the power grid, and the obtained configuration scheme can provide a theoretical reference basis for system planning investment.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power dispatching, and in particular relates to a hierarchical energy storage optimization configuration method based on day-ahead and intra-day dispatching. Background Art

[0002] As the country continues to promote the "dual carbon" strategic goal, the power industry, as a major energy consumer, has received widespread attention for its energy conservation and loss reduction work. Among the five links of power system generation, transmission, transformation, distribution and use, the power loss in the distribution link accounts for more than 60% of the total power grid loss. It is increasingly important to achieve energy conservation and loss reduction at the distribution level. In recent years, my country has vigorously developed new energy technologies and accelerated the pace of adjusting the energy structure. The penetration rate of multiple new technology elements such as distributed power sources, energy storage systems, and electric vehicles in the distribution network is increasing, making the distribution network evolve from the traditional radial unidirectional power supply mode to the bidirectional power supply mode of multiple power sources. If the reverse transmission of surplus power causes load fluctuations on the transformer on the substation side, it will increase the loss of the transformer and distribution lines. At the same time, with the development of power electronics technology, the proportion of various nonlinear and impact loads in the distribution network is also increasing. The access of a large number of nonlinear loads at the user end has deteriorated the power quality, making the power quality problems such as harmonics, three-phase imbalance, and voltage deviation in the low-voltage distribution network increasingly serious, and increasing the additional power loss of the low-voltage distribution network.

[0003] The loss reduction measures of traditional distribution networks are mainly to replace high-energy-consuming distribution transformers, shorten the power supply radius, balance three-phase loads, and compensate for reactive power, but they face problems such as high investment, difficult construction, and long cycles. With the large-scale access of distributed power sources such as photovoltaics and wind power to the distribution network, their output is uncertain and intermittent. The energy storage system has a flexible load with bidirectional power transmission. When participating in the system optimization operation, it can achieve peak shaving and valley filling, promote the consumption of renewable energy in the distribution network, and optimize the power grid flow. The energy storage system has the ability to operate in four quadrants of active and reactive power. By adjusting the real-time supply and demand relationship of active and reactive power / energy in the grid in time and space, it can improve and optimize the power transmission and power quality of the grid, thereby reducing the power loss of the distribution network, maintaining the safe and stable operation of various equipment in the grid, and avoiding safety and stability problems caused by unstable power generation or unmet load demand.

[0004] However, current research focuses on the support capacity of energy storage systems when disturbances occur in distribution networks, while there are relatively few studies on the impact of energy storage on network losses. The few studies are also multi-objective optimization studies for relevant application scenarios. There is no systematic quantitative analysis of the mechanism and change process of energy storage affecting network losses. There is a lack of consideration of the overall economic operation of energy storage access and distribution network losses, and the ability of energy storage to reduce losses in distribution networks in substations has not been fully utilized. Therefore, in the context of the widespread access of distributed new energy to distribution networks and the increasingly prominent power quality issues, it is urgent to conduct research on the use of new energy storage to reduce network losses in substation distribution networks to ensure efficient and economic operation of distribution networks in substations. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the layered energy storage optimization configuration method based on day-ahead and intraday scheduling provided by the present invention solves the problem that the existing related methods lack consideration of the overall economic operation of energy storage access and distribution network losses, and fail to give full play to the loss reduction capacity of energy storage in the distribution network of the substation.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a hierarchical energy storage optimization configuration method based on day-ahead-intraday scheduling, comprising the following steps:

[0007] S1. Analyze the impact of power quality on distribution network loss after distributed energy storage is connected to the distribution network, and then evaluate the impact of power quality on distribution network loss, and use it as a comprehensive indicator of power quality of the distribution network;

[0008] S2. Construct a multi-layer energy storage optimization configuration model based on day-ahead and intra-day scheduling;

[0009] The multi-layer energy storage optimization configuration model includes an upper planning layer and a lower operation layer;

[0010] S3, through the upper planning layer, a site selection and capacity determination plan for energy storage is given with the goal of minimizing the total cost of distribution network operation;

[0011] S4. Based on the given site selection and capacity determination plan, the optimal energy storage operation plan is determined in the lower operation layer by day-ahead scheduling and intraday scheduling to achieve optimal energy storage configuration;

[0012] The day-ahead dispatching aims to minimize the loss of the distribution network and determines the day-ahead energy storage output plan. The intraday dispatching aims to optimize the comprehensive indicators of the power quality of the distribution network and determines the optimal energy storage operation plan in combination with the day-ahead energy storage output plan.

[0013] Furthermore, in step S1, after the distributed energy storage is connected to the distribution network, the distribution network losses affected by the power quality include voltage deviation loss, frequency offset loss, three-phase unbalanced loss and harmonic loss.

[0014] Furthermore, the step S1 includes the following sub-steps:

[0015] S11. The quotient of the difference in distribution network loss before and after the distributed energy storage is connected to the distribution network and the distribution network loss before the distributed energy storage enters the distribution network is used as an indicator to measure the change in distribution network loss;

[0016] Among them, each type of distribution network loss corresponds to an indicator of distribution network loss change;

[0017] S12. Calculate the subjective weight and objective weight of the indicators using the analytic hierarchy process and standard deviation method respectively, and then determine the comprehensive weight of different indicators;

[0018] S13. Evaluate the impact of power quality on distribution network losses based on different indicators and their corresponding comprehensive weights.

[0019] Furthermore, in step S11, the index ε for measuring the change in distribution network loss is expressed as:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Where P loss2 represents the distribution network loss after the distributed energy storage is connected, P loss1 represents the distribution network loss before the distributed energy storage is connected, p lossl1 represents the distribution network loss before the distributed energy storage is connected to line l, P lossl2 represents the distribution network loss after the distributed energy storage is connected to line l, P j and Q j They represent the active power and reactive power of node j in the distribution network, R l represents the resistance of line l, V j represents the voltage at node j, μ t Represents the control parameters of energy storage / discharge on the user side, P des It represents the power absorbed / generated by the distributed energy storage on the user side, X represents the ratio of the power absorbed / generated by the distributed energy storage to the total active load of the distribution network, L represents the total number of lines, and N represents the total number of nodes.

[0027] Furthermore, in step S3, the upper planning layer aims to minimize the total cost of distribution network operation, which is expressed as:

[0028] minC all

[0029] C all =C inv +C op +C loss +C sag +C others

[0030]

[0031] In the formula, C all represents the total cost of distribution network operation, C inv Represents the investment cost of energy storage device; C op represents the annual operation and maintenance cost of the energy storage device, C loss represents the network loss cost, C sag represents the average economic loss cost of voltage sag for users, C others Represents other costs in the operation of the distribution network, Ω SL ,Ω ess Respectively represent the set of nodes where sensitive loads and energy storage systems are located, Ω b represents the set of all branches of the distribution network, T op Indicates the number of operating hours, r ess represents the discount rate of ESS, c inv represents the unit capacity investment cost, c op represents the annual operation and maintenance cost per unit capacity, c sag represents the average economic loss of a single voltage drop for a user, c loss represents the unit network loss cost, y ess Indicates the service life of ESS, E ess,i represents the total capacity of the ESS configured at node i, I ij,t represents the current flowing through branch ij at time t, r ij represents the resistance of branch ij, P load,i represents the load demand at node i, and K is the number of voltage sag events occurring in one operating cycle.

[0032] Furthermore, the constraints of the upper planning layer are:

[0033]

[0034] In the formula, k i represents a 0-1 variable, indicating whether ESS is configured at node i. and They represent the lower and upper limits of the power of a single energy storage unit, and Respectively represent the lower and upper limits of the capacity of a single energy storage unit, P i ESS Indicates the power of a single energy storage unit, Indicates the capacity of a single energy storage unit.

[0035] Furthermore, in step S4, the day-ahead scheduling aims to minimize the loss of the distribution network, which is expressed as:

[0036] minP loss

[0037]

[0038] Where P loss Represents the distribution network loss, Ω b represents the set of all branches of the distribution network, T op Indicates the number of operating hours, I ij,t represents the current flowing through branch ij at time t, r ij Represents the resistance of branch ij.

[0039] Furthermore, in step S4, the constraint conditions for determining the day-ahead energy storage output plan with the goal of minimizing the distribution network loss include conventional constraints and energy storage operation constraints;

[0040] The conventional constraints are expressed as:

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, Ω G ,Ω ess Respectively represent the set of nodes where the thermal power unit and the energy storage system are located, Ω b Represents the set of all branches of the distribution network, I ij,t represents the current flowing through branch ij at time t, P j,t and P load,t They represent the power generation of the jth unit and the system load demand at time t respectively;

[0046] p j,t and q j,t They represent the injected power of node j at time t, P ij,t and Q ij,t represents the active power flow and reactive power flow of branch ij at time t, P jk,t and Q jk,trepresents the active power flow and reactive power flow of branch kj at time t, X ij,t represents the square of the current amplitude flowing through branch ij at time t, g j , b j They represent the conductance and susceptance of node j to the ground, respectively. δ(j) represents the set of branch end nodes with j as the head node. π(j) represents the set of branch head nodes with j as the end node.

[0047] Y j,t and Y i,t They represent the square of the voltage amplitude of node j and node i at time t, respectively. ij and x ij are the resistance and reactance of branch ij respectively;

[0048] V i,min and V i,max Respectively represent the upper and lower limits of the voltage at node i; I ij,max Indicates the maximum current limit allowed for branch ij, Y i represents the square of the voltage amplitude at node i;

[0049] The energy storage operation constraint is expressed as:

[0050]

[0051]

[0052] In the formula, A represents the auxiliary variable, P i,t represents the discharge power of ESS at node i at time t, M represents a large constant, k i Represents a 0-1 variable, used to indicate whether node i is equipped with energy storage. and They represent the maximum value of ESS charging and discharging power at node i, and e i,t and e i,t+1 They represent the remaining capacity of ESS at node i at time t and t+1 respectively, Δt represents the time difference, and denote the minimum and maximum state of charge of the ESS at node i, respectively. and They represent the charge state of the ESS at node i at the initial time, time t, and time T respectively.

[0053] Furthermore, in step S4, the constraint condition when the goal is to optimize the comprehensive power quality index of the distribution network includes a power quality constraint, which is expressed as:

[0054]

[0055] Wherein, THD, δU and d represent harmonic distortion rate, voltage deviation and voltage fluctuation respectively.

[0056] Furthermore, the step S2 also includes: performing a second-order cone transformation on the nonlinear multi-layer energy storage optimization configuration model.

[0057] The beneficial effects of the present invention are:

[0058] The distribution network loss improvement method taking into account the influence of power quality proposed in the present invention makes up for the problem of incomplete consideration of the influence of power quality in traditional network loss research, and can quantify the technical and economic benefits of the distribution network according to the actual operation of the power grid, and use it as an objective function to guide the optimization process of the subsequent multi-layer energy storage optimization configuration model. The final configuration scheme can provide a theoretical reference for system planning investment. Specifically:

[0059] (1) Based on the impact of power quality events, the impact of large-scale distributed renewable energy access on distribution network losses is fully considered, which is more in line with the scenario under the new power system;

[0060] (2) Using the subjective and objective weighting method, the corresponding weight factors are obtained based on the degree of influence of different power quality events on network losses, and finally a comprehensive evaluation method for distribution network losses is established to provide a quantitative basis for relevant governance strategies;

[0061] (3) A multi-layer energy storage optimization configuration model is adopted. The influence of the energy storage system site selection and capacity determination is considered at the planning level, and the influence of energy storage on the output strategy of the day-ahead and intra-day stages is considered at the operation level. The final energy storage configuration scheme can effectively improve the distribution network loss while ensuring the economic operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flow chart of the layered energy storage optimization configuration method based on day-ahead and intra-day scheduling provided by the present invention.

[0063] Figure 2 This is a schematic diagram of the first line of the distribution network provided by the present invention.

[0064] Figure 3 This is a schematic diagram of the IEEE-33 node power distribution system provided by the present invention. DETAILED DESCRIPTION

[0065] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0066] The embodiment of the present invention provides a hierarchical energy storage optimization configuration method based on day-ahead-intraday scheduling, such as Figure 1 As shown, the following steps are included:

[0067] S1. Analyze the impact of power quality on distribution network loss after distributed energy storage is connected to the distribution network, and then evaluate the impact of power quality on distribution network loss, and use it as a comprehensive indicator of power quality of the distribution network;

[0068] S2. Construct a multi-layer energy storage optimization configuration model based on day-ahead and intra-day scheduling; the multi-layer energy storage optimization configuration model includes an upper planning layer and a lower operation layer;

[0069] S3, through the upper planning layer, a site selection and capacity determination plan for energy storage is given with the goal of minimizing the total cost of distribution network operation;

[0070] S4. Based on the given site selection and capacity determination plan, the optimal energy storage operation plan is determined in the lower operation layer by day-ahead scheduling and intraday scheduling to achieve optimal energy storage configuration;

[0071] Among them, the day-ahead dispatching determines the day-ahead energy storage output plan with the goal of minimizing distribution network losses, and the intraday dispatching determines the optimal energy storage operation plan with the goal of optimizing the comprehensive indicators of power quality in the distribution network, combined with the day-ahead energy storage output plan.

[0072] In step S1 of the embodiment of the present invention, after the distributed energy storage is connected to the distribution network, the distribution network losses affected by the power quality include voltage deviation loss, frequency offset loss, three-phase unbalanced loss and harmonic loss.

[0073] In a specific embodiment, voltage deviation loss refers to the terminal voltage of the high and low voltage distribution lines distributing power to the electrical equipment being too low due to power equipment failure, power transmission and distribution, etc., and its state exceeds the rated range, which will cause the electrical appliances to fail to work normally.

[0074] Specifically, the expression of power loss ΔP during the operation of the distribution network is:

[0075]

[0076] In the formula, ΔP represents the power loss due to low voltage, P represents the active power, and U represents the effective voltage of the power grid. represents the power factor, and R represents the equivalent resistance of the power grid.

[0077] When the power and power factor of the transmission line remain unchanged, the power loss of the line is inversely proportional to the operating voltage, and the expression is:

[0078]

[0079] From the above formula, we can see that the lower the line operating voltage, the greater the power loss. N Reduce to operating voltage U b When , the power loss is:

[0080]

[0081]

[0082] The corresponding power loss ΔP a >ΔP N .

[0083] Set the voltage to be too low and the loss rate is ξ u , the voltage drop is ΔU, and the voltage drop rate is λ u The following formula is obtained:

[0084] ΔP=ΔP a -ΔP N

[0085]

[0086] ΔU=U N -U a

[0087]

[0088] From the above formula, the increase and loss rate expression when the voltage is too low can be obtained as:

[0089] ξ u =(1+λ u ) 2 -1

[0090] From the above formula, it can be obtained that when the voltage deviation reaches 10% of the rated voltage, the power loss of the line will increase by 21% compared to when operating at the rated voltage.

[0091] In a specific embodiment, for frequency offset loss: the relationship between the no-load loss of the transformer and the power supply frequency and voltage is:

[0092]

[0093] Where ΔP N is the no-load loss at rated voltage and rated frequency, f is the grid operating frequency, U re is the transformer tap voltage, U1 is the transformer primary voltage. Assume that the power grid frequency increase loss rate is ξ f , the frequency increase is Δf, and the frequency increase is λ f , the rated frequency of the power grid is f N , the frequency after the shift is fb The no-load loss after the frequency is reduced is ΔP b , we can get the following expression:

[0094]

[0095] From the above formula, we can know that the no-load loss of the power grid is proportional to the operating frequency of the power grid. When the power frequency increases by 1Hz, that is, 2%f N When the power grid is powered by 400MW, the power grid loss increases by 2.5%.

[0096] In a specific embodiment, for three-phase unbalanced loss: when the three-phase power grid system is unbalanced, the line loss is expressed as:

[0097] ΔP0=3I 2 R e

[0098] In the formula, ΔP e is the three-phase smoothing power loss, I is the three-phase balanced circuit current, R e is the line resistance component. When the load power of the power grid remains unchanged, but the operating environment changes, the three-phase voltage and current are unbalanced, and the increase or decrease of the current is ΔI, and the power loss ΔP is obtained. b for:

[0099] ΔP b =R e [(1+ΔI) 2 +(1-ΔI) 2 ]+2R e ΔI 2 =3I 2 R e +4ΔI 2 R e

[0100] Assume that the three-phase unbalanced loss rate is ξ i , the grid current load ratio is λ i , we can get the following calculation formula:

[0101]

[0102] From the above formula, we can know that when the unbalanced current of the power grid system increases by 10%, the network loss will increase by 1.3%. In summary, when the three-phase current of the power grid is unbalanced, it will affect the output efficiency of the transformer and motor, thereby increasing the loss.

[0103] In a specific embodiment, for harmonic loss, in an ideal state, the power grid system should have a single frequency and a sinusoidal waveform of the rated frequency (50Hz), and the voltage is divided into several levels. In reality, due to the access of a large number of power electronic nonlinear devices in the point power grid system, harmonic currents are inevitably injected into the power grid, causing the grid voltage waveform to be distorted, causing pollution to the electrical environment, and the generated harmonic power will increase the loss of the power grid. In recent years, due to the rapid development of science and technology and the improvement of people's living standards, a large number of nonlinear loads have been connected on the low-voltage side (especially 0.4kV) of the power grid, generating very serious harmonic currents. According to measurement and investigation analysis, the average content of phase current harmonics on the low-voltage side is between 10% and 20%, and the serious ones are as high as 42%. The harmonics on the neutral line are more serious, averaging about 70%, and sometimes reaching 150%. This part of the harmonic power is usually consumed directly on the low-voltage side and rarely fed back to the high-voltage side. Therefore, it is particularly important to analyze and calculate this part of the harmonic loss, so as to formulate corresponding energy-saving and loss-reducing measures and take measures to suppress the generation of harmonics.

[0104] The degree to which the waveform in the power grid deviates from the sine wave due to distortion caused by harmonics is usually expressed as the total harmonic distortion (THD). When analyzing and estimating the harmonic loss in the power grid, the total harmonic distortion of the current is THD. I , whose expression is:

[0105]

[0106] Where I1 is the RMS value of the fundamental current, I n is the mean square value of the nth harmonic current, M is the highest harmonic order considered in the analysis and calculation, which is determined by the requirements, usually M≤50. In the analysis and calculation of power lines, when the skin effect caused by alternating current or alternating magnetic field is not considered, the impedance of the line conductor is set to R. When no harmonics are generated, the line loss is I1 2 R, when there are harmonics, the line loss of the system is:

[0107]

[0108] Where I is the sum of the fundamental current and the harmonic current. The line loss increase rate of the power line is the ratio of the harmonic loss to the fundamental loss, expressed as:

[0109]

[0110] From the above formula, we can get that if the total harmonic distortion rate of the current in the power grid system is 40%, the line loss will increase by 16%. Therefore, from the perspective of economic operation or energy utilization, the loss caused by harmonics cannot be ignored.

[0111] In step S1 of the embodiment of the present invention, when analyzing the impact of power quality on distribution network loss after the distributed energy storage is connected to the distribution network, the power flow distribution and voltage distribution of the distribution network will change after the energy storage is connected to the distribution network. Figure 2 As shown, taking the first line of the distribution network as an example, the network loss before and after the user-side distributed energy storage is connected to the distribution network is analyzed. Specifically, step S1 of this embodiment includes the following sub-steps:

[0112] S11. The quotient of the difference in distribution network loss before and after the distributed energy storage is connected to the distribution network and the distribution network loss before the distributed energy storage enters the distribution network is used as an indicator to measure the change in distribution network loss;

[0113] Among them, each type of distribution network loss corresponds to an indicator of distribution network loss change;

[0114] S12. Calculate the subjective weight and objective weight of the indicators using the analytic hierarchy process and standard deviation method respectively, and then determine the comprehensive weight of different indicators;

[0115] S13. Evaluate the impact of power quality on distribution network losses based on different indicators and their corresponding comprehensive weights.

[0116] 4. The hierarchical energy storage optimization configuration method based on day-ahead and intraday scheduling according to claim 3 is characterized in that in step S11, the index ε for measuring the change in distribution network loss is expressed as:

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] Where P loss2 represents the distribution network loss after the distributed energy storage is connected, P loss1 represents the distribution network loss before the distributed energy storage is connected, p lossl1 represents the distribution network loss before the distributed energy storage is connected to line l, P lossl2 represents the distribution network loss after the distributed energy storage is connected to line l, P j and Q j They represent the active power and reactive power of node j in the distribution network, R l represents the resistance of line l, V jrepresents the voltage at node j, μ t Represents the control parameters of energy storage / discharge on the user side, P des represents the power absorbed / generated by the distributed energy storage on the user side, X represents the ratio of the power absorbed / generated by the distributed energy storage to the total active load of the distribution network, L represents the total number of lines, and N represents the total number of nodes; where,

[0124] In this embodiment, the indicator ε is introduced to quantify the degree of change in the distribution network loss before and after the user-side distributed energy storage is connected. The greater the absolute value of the network loss change rate ε, the greater the impact of the access of distributed energy storage on the distribution network loss. When ε>0, the distribution network loss increases; when ε<0, the distribution network loss decreases.

[0125] In step S12 of this embodiment, the method of calculating the subjective weight of the indicator using the hierarchical analysis method is:

[0126] (1) Constructing a judgment matrix

[0127] The importance of each indicator is compared pairwise, and the 1-9 scale method is used to reflect the relative importance of each indicator. The specific meaning of the scale is as follows: 1-equally important, 3-slightly important, 5-obviously important, 7-very important, 9-extremely important, and 2, 4, 6, and 8 are intermediate values ​​between the two;

[0128] Construct judgment matrix A = [a ij ], where a ij Indicates the relative importance of factors i and j;

[0129] (2) Calculate the weight vector

[0130] According to the judgment matrix, the weights between the indicators are calculated. The present invention adopts the eigenvector method:

[0131] First, normalize each column of the judgment matrix to obtain the matrix B = [b ij ],in:

[0132]

[0133] Then sum the normalized matrix B by row and divide it by the number of rows n to get the weight vector W:

[0134]

[0135] (3)Consistency test

[0136] In order to test the rationality of the judgment matrix, a consistency test is required. It is mainly evaluated through the consistency index CI and the consistency ratio CR:

[0137]

[0138]

[0139] Among them, λ max It represents the maximum eigenvalue of the judgment matrix, n represents the order of the matrix, RI is the random consistency index, different matrix orders correspond to different RI values, and the RI value in the present invention is 1.12; generally, if CR<0.1, the consistency of the judgment matrix is ​​considered acceptable.

[0140] In step S12 of this embodiment, the method of calculating the subjective weight of the indicator using the standard deviation method is:

[0141] (1) Data standardization

[0142] For each indicator, the original data must first be standardized. Assuming there are m samples and n indicators, the standardization formula is:

[0143]

[0144] Among them, x ij represents the value of the i-th sample under the j-th index, X ij is the normalized value.

[0145] (2) Calculate the standard deviation of each indicator

[0146] Calculate the standard deviation of each indicator to reflect the degree of dispersion of the indicator. The standard deviation formula is:

[0147]

[0148] in, represents the average value of the jth index, σ j Represents the standard deviation of the j-th indicator.

[0149] (4) Calculate weight

[0150] The weight is calculated based on the standard deviation of each indicator. The larger the standard deviation, the greater the volatility of the indicator and the greater the impact on the final decision. The weight formula is:

[0151]

[0152] Among them, W j represents the weight of the jth indicator.

[0153] In step S12 of this embodiment, based on the calculation formula of the subjective weight and the objective weight, the comprehensive weights of different indicators determined in this embodiment are expressed as:

[0154]

[0155] In the formula, represents the weight obtained by the hierarchical analysis method, Represents the weight obtained by the standard deviation method.

[0156] In step S13 of this embodiment, the evaluation result of the influence of power quality on distribution network loss can be obtained as follows:

[0157] F all =W1F1+W2F2+W3F3+W4F4

[0158] In the formula, F1, F2, F3 and F4 represent voltage deviation, frequency deviation, voltage unbalance and harmonic distortion rate respectively. Therefore, the influence of power quality events in the distribution network on the network loss of the distribution network can be evaluated by calculation, and this can be used as a comprehensive indicator of power quality of the distribution network.

[0159] In step S2 of the embodiment of the present invention, the multi-layer energy storage optimization configuration model based on day-ahead and intra-day is constructed, including an upper planning layer and a lower operation layer; wherein the upper planning layer determines the installation location and installation capacity of the energy storage as a site selection and capacity determination scheme, and the lower operation layer is divided into two stages: day-ahead scheduling and intra-day scheduling, which can determine the optimal operation mode of the energy storage under the premise of a given configuration of the upper model.

[0160] In this embodiment, since the energy storage optimization configuration model of the distribution network is a nonlinear programming model, step S2 also includes performing a second-order cone transformation on it to facilitate solving; wherein the formula for the second-order cone transformation is:

[0161]

[0162] In the formula, X ij,t and Y j,t is a new variable introduced by the second-order cone transformation; I ij,t represents the current flowing through branch ij at time t; V j,t is the voltage amplitude of node j at time t.

[0163] In step S3 of the embodiment of the present invention, the upper planning layer aims to minimize the total cost of distribution network operation, which is expressed as:

[0164] minC all

[0165] C all =C inv +C op +C loss +C sag +C others

[0166]

[0167] In the formula, C all represents the total cost of distribution network operation, C inv Represents the investment cost of energy storage device; C op represents the annual operation and maintenance cost of the energy storage device, C loss represents the network loss cost, C sag represents the average economic loss cost of voltage sag for users, C others Represents other costs in the operation of the distribution network, Ω SL ,Ω ess Respectively represent the set of nodes where sensitive loads and energy storage systems are located, Ω b represents the set of all branches of the distribution network, T op Indicates the number of operating hours, r ess represents the discount rate of ESS, c inv represents the unit capacity investment cost, c op represents the annual operation and maintenance cost per unit capacity, c sag represents the average economic loss of a single voltage drop for a user, c loss represents the unit network loss cost, y ess Indicates the service life of ESS, E ess,i represents the total capacity of the ESS configured at node i, I ij,t represents the current flowing through branch ij at time t, r ij represents the resistance of branch ij, P load,i represents the load demand at node i, and K is the number of voltage sag events occurring in one operating cycle.

[0168] Furthermore, the constraints of the upper planning layer are:

[0169]

[0170] In the formula, k i represents a 0-1 variable, indicating whether ESS is configured at node i. and They represent the lower and upper limits of the power of a single energy storage unit, and Respectively represent the lower and upper limits of the capacity of a single energy storage unit, P i ESS Indicates the power of a single energy storage unit, Indicates the capacity of a single energy storage unit.

[0171] In this embodiment, the above constraints can ensure that the configuration power of a single energy storage unit is within a certain range, and to a certain extent, ensure the rationality of the planning configuration results.

[0172] In step S4 of the embodiment of the present invention, the operation layer determines the optimal operation plan of the energy storage, that is, the power of the energy storage in each time period, under the premise of the given configuration of the planning layer. However, since photovoltaic predictions usually have deviations, the output of various units in the distribution network has multi-time scale characteristics. Therefore, in order to reduce the impact of volatility and uncertainty of new energy output, a multi-layer optimization scheduling model of the day-ahead and intra-day is adopted in the operation layer.

[0173] Specifically, the day-ahead dispatch aims to minimize the loss of the distribution network, which can be expressed as:

[0174] minP loss

[0175]

[0176] Where P loss Represents the distribution network loss, Ω b represents the set of all branches of the distribution network, T op Indicates the number of operating hours, I ij,t represents the current flowing through branch ij at time t, r ij Represents the resistance of branch ij.

[0177] Furthermore, after the general model of the operation layer model based on the branch power flow is transformed by the second-order cone, the constraints for determining the day-ahead energy storage output plan with the goal of minimizing the distribution network loss include conventional constraints and energy storage operation constraints;

[0178] Among them, the general constraints are expressed as:

[0179]

[0180]

[0181]

[0182]

[0183] In the formula, Ω G ,Ω ess Respectively represent the set of nodes where the thermal power unit and the energy storage system are located, Ω b Represents the set of all branches of the distribution network, I ij,t represents the current flowing through branch ij at time t, P j,t and P load,t They represent the power generation of the jth unit and the system load demand at time t respectively;

[0184] p j,t and q j,t They represent the injected power of node j at time t, P ij,t and Q ij,trepresents the active power flow and reactive power flow of branch ij at time t, P jk,t and Q jk,t represents the active power flow and reactive power flow of branch kj at time t, X ij,t represents the square of the current amplitude flowing through branch ij at time t, g j , b j They represent the conductance and susceptance of node j to the ground, respectively. δ(j) represents the set of branch end nodes with j as the head node. π(j) represents the set of branch head nodes with j as the end node.

[0185] Y j,t and Y i,t They represent the square of the voltage amplitude of node j and node i at time t, respectively. ij and x ij are the resistance and reactance of branch ij respectively;

[0186] V i,min and V i,max Respectively represent the upper and lower limits of the voltage at node i; I ij,max Indicates the maximum current limit allowed for branch ij, Y i represents the square of the voltage amplitude at node i;

[0187] The energy storage operation constraints are expressed as:

[0188]

[0189] In the formula, A represents the auxiliary variable, P i,t represents the discharge power of ESS at node i at time t, M represents a large constant, k i Represents a 0-1 variable, used to indicate whether node i is equipped with energy storage. and They represent the maximum value of ESS charging and discharging power at node i, and e i,t and e i,t+1 They represent the remaining capacity of ESS at node i at time t and t+1 respectively, Δt represents the time difference, and denote the minimum and maximum state of charge of the ESS at node i, respectively. and They represent the charge state of the ESS at node i at the initial time, time t, and time T respectively.

[0190] In step S4 of the embodiment of the present invention, the intraday scheduling aims to minimize the evaluation result of the power quality index, that is:

[0191] f all

[0192] The operation constraints of each power station in intraday dispatch are similar to those in day-ahead dispatch. The only difference is that for the safe and stable operation of the distribution network, it is necessary to ensure that the power quality index does not exceed the limit in the distribution network after the energy storage is configured. Therefore, there are power quality index constraints in intraday dispatch, which are expressed as:

[0193]

[0194] Wherein, THD, δU and d represent harmonic distortion rate, voltage deviation and voltage fluctuation respectively.

[0195] In an embodiment of the present invention, an example is provided to verify the effect of the above-mentioned tiered energy storage optimization configuration method on improving distribution network losses.

[0196] In this embodiment, an IEEE 33-node power distribution system is built in Matlab for example analysis. Figure 3 As shown in the figure, the system contains 33 nodes, of which the nodes where distributed photovoltaic users are located are 10, 23 and 31. The typical daily power data with a time interval of 1 hour and a total of 24 hours is used for subsequent simulation analysis.

[0197] The optimization configuration results obtained by the above method are shown in Table 1:

[0198] Table 1: Optimization configuration results

[0199]

[0200] In the above Table 1, scenario 1, scenario 2 and scenario 3 are respectively:

[0201] Scenario 1: For the distribution network in the demonstration area with distributed photovoltaic access, only the optimal distribution network loss is used as the optimization target.

[0202] Scenario 2: Since energy storage devices are expensive, in actual engineering applications, it is necessary to balance the impact of distribution network losses and economic efficiency in the optimization results, that is, the lowest overall cost and lowest network losses are the optimization goals.

[0203] Scenario 3: Based on Scenario 2, the impact of power quality events on distribution network losses is considered by combining the day-ahead and intraday scheduling methods.

[0204] As shown in Table 1, since the configuration of energy storage can reduce the network loss of the distribution network and improve the power quality indicators such as voltage deviation and harmonic distortion rate, the access of the energy storage system will greatly reduce the comprehensive network loss of the system under the premise of considering the impact of power quality events on network loss. However, due to the expensive investment and operation and maintenance costs of energy storage equipment, the economic efficiency of the distribution network is also reduced. Therefore, the total cost of scenario 1 is the highest, and the minimum comprehensive network loss is 106kW·h. Scenario 2 considers the economy of the distribution network on the basis of scenario 1, and scenario 3 considers the impact of power quality events on the basis of scenario 2, and divides the operation layer into two stages: day-ahead dispatch and intraday dispatch. By comparison, it can be seen that under the multi-layer optimization configuration model established in this paper, after considering power quality events, the comprehensive network loss of the distribution network can be further improved without increasing the total daily investment and operation cost. This is because under the optimization strategy of scenario 2, the additional network loss caused by power quality problems will be ignored, making the configuration result not optimal.

[0205] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0206] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling, characterized in that: The following steps are involved: S1. Analyze the impact of power quality on distribution network loss after distributed energy storage is connected to the distribution network, and then evaluate the impact of power quality on distribution network loss, and use it as a comprehensive indicator of power quality of the distribution network; S2. Construct a multi-layer energy storage optimization configuration model based on day-ahead and intra-day scheduling; The multi-layer energy storage optimization configuration model includes an upper planning layer and a lower operation layer; S3, through the upper planning layer, a site selection and capacity determination plan for energy storage is given with the goal of minimizing the total cost of distribution network operation; S4. Based on the given site selection and capacity determination plan, the optimal energy storage operation plan is determined in the lower operation layer by day-ahead scheduling and intraday scheduling to achieve optimal energy storage configuration; The day-ahead dispatching aims to minimize the loss of the distribution network and determines the day-ahead energy storage output plan. The intraday dispatching aims to optimize the comprehensive power quality index of the distribution network and determines the optimal energy storage operation plan in combination with the day-ahead energy storage output plan.

2. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 1 is characterized in that: In step S1, after the distributed energy storage is connected to the distribution network, the distribution network losses affected by the power quality include voltage deviation loss, frequency offset loss, three-phase unbalanced loss and harmonic loss.

3. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 1 is characterized in that: The step S1 comprises the following sub-steps: S11. The quotient of the difference in distribution network loss before and after the distributed energy storage is connected to the distribution network and the distribution network loss before the distributed energy storage enters the distribution network is used as an indicator to measure the change in distribution network loss; Among them, each type of distribution network loss corresponds to an indicator of distribution network loss change; S12. Calculate the subjective weight and objective weight of the indicators using the analytic hierarchy process and standard deviation method respectively, and then determine the comprehensive weight of different indicators; S13. Evaluate the impact of power quality on distribution network losses based on different indicators and their corresponding comprehensive weights.

4. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 3 is characterized in that: In step S11, the index ε for measuring the change in distribution network loss is expressed as: Where P loss2 represents the distribution network loss after the distributed energy storage is connected, P loss1 represents the distribution network loss before the distributed energy storage is connected, p lossl1 represents the distribution network loss before the distributed energy storage is connected to line l, P lossl2 represents the distribution network loss after the distributed energy storage is connected to line l, P j and Q j They represent the active power and reactive power of node j in the distribution network, R l represents the resistance of line l, V j represents the voltage at node j, μ t Represents the control parameters of energy storage / discharge on the user side, P des It represents the power absorbed / generated by the distributed energy storage on the user side, X represents the ratio of the power absorbed / generated by the distributed energy storage to the total active load of the distribution network, L represents the total number of lines, and N represents the total number of nodes.

5. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 1 is characterized in that: In step S3, the upper planning layer aims to minimize the total cost of distribution network operation, which is expressed as: minC all C all =C inv +C op +C loss +C sag +C others In the formula, C all represents the total cost of distribution network operation, C inv Represents the investment cost of energy storage device; C op represents the annual operation and maintenance cost of the energy storage device, C loss represents the network loss cost, C sag represents the average economic loss cost of voltage sag for users, C others Represents other costs in the operation of the distribution network, Ω SL ,Ω ess Respectively represent the set of nodes where sensitive loads and energy storage systems are located, Ω b represents the set of all branches of the distribution network, T op Indicates the number of operating hours, r ess represents the discount rate of ESS, c inv represents the unit capacity investment cost, c op represents the annual operation and maintenance cost per unit capacity, c sag represents the average economic loss of a single voltage drop for a user, c loss represents the unit network loss cost, y ess Indicates the service life of ESS, E ess,i represents the total capacity of the ESS configured at node i, I ij,t represents the current flowing through branch ij at time t, r ij represents the resistance of branch ij, P load,i represents the load demand at node i, and K is the number of voltage sag events occurring in one operating cycle.

6. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 5 is characterized in that: The constraints of the upper planning layer are: In the formula, k i represents a 0-1 variable, indicating whether ESS is configured at node i. and They represent the lower and upper limits of the power of a single energy storage unit, and Respectively represent the lower and upper limits of the capacity of a single energy storage unit, P i ESS Indicates the power of a single energy storage unit, Indicates the capacity of a single energy storage unit.

7. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 1 is characterized in that: In step S4, the day-ahead scheduling aims to minimize the loss of the distribution network, which is expressed as: minP loss Where P loss Represents the distribution network loss, Ω b represents the set of all branches of the distribution network, T op Indicates the number of operating hours, I ij,t represents the current flowing through branch ij at time t, r ij Represents the resistance of branch ij.

8. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 1 is characterized in that: In step S4, the constraints for determining the day-ahead energy storage output plan with the goal of minimizing the distribution network loss include conventional constraints and energy storage operation constraints; The conventional constraints are expressed as: In the formula, Ω G ,Ω ess Respectively represent the set of nodes where the thermal power unit and the energy storage system are located, Ω b Represents the set of all branches of the distribution network, I ij,t represents the current flowing through branch ij at time t, P j,t and P load,t They represent the power generation of the jth unit and the system load demand at time t respectively; p j,t and q j,t They represent the injected power of node j at time t, P ij,t and Q ij,t represents the active power flow and reactive power flow of branch ij at time t, P jk,t and Q jk,t represents the active power flow and reactive power flow of branch kj at time t, X ij,t represents the square of the current amplitude flowing through branch ij at time t, g j 、b j They represent the conductance and susceptance of node j to the ground, respectively. δ(j) represents the set of branch end nodes with j as the head node. π(j) represents the set of branch head nodes with j as the end node. Y j,t and Y i,t They represent the square of the voltage amplitude of node j and node i at time t, respectively. ij and x ij are the resistance and reactance of branch ij respectively; V i,min and V i,max Respectively represent the upper and lower limits of the voltage at node i; I ij,max Indicates the maximum current limit allowed for branch ij, Y i represents the square of the voltage amplitude at node i; The energy storage operation constraint is expressed as: In the formula, A represents the auxiliary variable, P i,t represents the discharge power of ESS at node i at time t, M represents a large constant, k i Represents a 0-1 variable, used to indicate whether node i is equipped with energy storage. and They represent the maximum value of ESS charging and discharging power at node i, and e i,t and e i,t+1 They represent the remaining capacity of ESS at node i at time t and t+1 respectively, Δt represents the time difference, and denote the minimum and maximum state of charge of the ESS at node i, respectively. and They represent the charge state of the ESS at node i at the initial time, time t, and time T respectively.

9. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 1 is characterized in that: In step S4, the constraint condition when the goal is to optimize the comprehensive power quality index of the distribution network includes power quality constraint, which is expressed as: Wherein, THD, δU and d represent harmonic distortion rate, voltage deviation and voltage fluctuation respectively.

10. The hierarchical energy storage optimization configuration method based on day-ahead and intra-day scheduling according to claim 1 is characterized in that: The step S2 also includes: performing a second-order cone transformation on the nonlinear multi-layer energy storage optimization configuration model.

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