Multi-mode control and on-site reactive voltage management method for port hybrid energy supply system

Abstract

The present application relates to a kind of port hybrid energy supply system multimode control and reactive voltage on-site management and control method, belong to the application technical field in the voltage control in power system.The method includes steps S1, analysis the power flow relationship of DC microgrid under different operating conditions, according to bus voltage fluctuation range, system is divided into 5 kinds of working mode;Step S2, set voltage recovery control and current adjustment control two modules;Step S3, according to historical statistical data photovoltaic output and load day-ahead forecast, construct the probability model of photovoltaic output and load in each period to determine the day-ahead adjustment plan of load regulation transformer tap, capacitor bank and photovoltaic inverter reactive power output;Step S4, adjust the reactive power adjustment of inverter and the active power of photovoltaic inverter.The present application improves the flexibility of the switching of multiple working modes of port microgrid, reduces voltage fluctuation out of limit and network loss increase, improves the economy of port distribution network.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of voltage control in power systems, and relates to a multimodal control and on-site management and control method for a port hybrid energy supply system. BACKGROUND

[0002] With the rapid development of inland green ports, ports, as the gathering point and hub of the shipping industry and land transportation, play a key role in the development of the national economy. The scale of green energy generation in ports also increases year by year, among which photovoltaic power generation is particularly important in inland ports. Through the combination of energy storage, the operation cost of port microgrids is reduced, and carbon emissions are reduced, which plays an important role. How to realize coordinated control under the complex operation of port microgrids, on-line power flow transfer, and seamless switching of operation modes has attracted the attention of domestic and foreign scholars. At the same time, the increase of network loss caused by large-scale distributed power supply to the power grid, the problem of voltage out-of-limit, and the problem that traditional voltage regulating devices cannot respond to rapid changes in voltage and cannot effectively suppress voltage fluctuations also need to be solved. Optimizing the unified control system framework of the flexible switching of multiple working modes of the port microgrid, considering the on-site management and control strategy of new energy consumption, and the priority sequence of multiple charging loads can effectively improve the operation stability of the port microgrid and the utilization rate of new energy. SUMMARY

[0003] The application is a multimodal control and on-site management and control method for a port hybrid energy supply system, which improves the flexibility of switching of multiple working modes of the port microgrid, reduces voltage fluctuation and network loss, and improves the economy of the port distribution network.

[0004] A multimodal control and on-site management and control method for a port hybrid energy supply system, comprising the following steps:

[0005] Step S1, analyze the power flow relationship of the DC microgrid under different operating conditions, and divide the system into five working modes according to the bus voltage fluctuation range;

[0006] Through step S1, the function of overall coordinated control of each unit in the system is realized, and the control strategies of each unit under each operating condition are given in detail in the following part;

[0007] Step S2, set up two modules of voltage recovery control and current adjustment control;

[0008] Step S3, according to the historical statistical data of photovoltaic output and load day-ahead forecast, construct the probability model of photovoltaic output and load in each period to determine the day-ahead adjustment plan of the on-load voltage regulating transformer tap changer (OLTC), capacitor bank and photovoltaic inverter reactive power output;

[0009] Step S4, using the dynamic reactive power response capability of the photovoltaic inverter, adjusting the reactive power adjustment amount of the inverter and the active power of the photovoltaic inverter, and realizing suppression of voltage out-of-limit and fluctuation.

[0010] Optionally, step S1 is specifically:

[0011] Step S11, analyzing the power flow relationship of the port direct-current micro-grid, including the power relationship under various operating conditions;

[0012] Step S12, based on direct-current bus voltage control, according to the power flow relationship of the port micro-grid under different conditions, different working modes are divided, distributed control of energy storage, photovoltaic system and grid-connected converter is realized, and the stability of system operation is realized.

[0013] Optionally, step S11 is specifically:

[0014] S111, the power balance relationship of the port direct-current micro-grid under grid-connected operation condition is: P S = P PV -P bes -P load , wherein P S is the grid exchange power, P PV is the photovoltaic power, P bes is the energy storage exchange power, and P load is the load power.

[0015] S112, the grid-connected converter (GCC) is in rectification and inversion modes, if the AC system and the PV unit cannot meet the power requirement of the load even at full power or maximum power output, the energy storage system will provide the remaining required power and stabilize the direct-current bus voltage, at this time the system enters a new stable operating state, and the power P Bes provided by the energy storage system is: P Bes = P Smax + P PV -P load , wherein P Smax is the maximum grid exchange power.

[0016] S113, if the load cannot completely consume the power generated by the distributed power supply, the grid-connected converter enters the inversion working state, when the power flowing through the grid-connected converter reaches the limit value, at this time the light is very strong or the remaining amount of the energy storage system is not enough, the photovoltaic and energy storage units need to jointly replace the grid-connected converter as the relaxation terminal of the system to maintain the direct-current bus voltage, at this time the power balance relationship is: P Smax = P PV -P load -P Bes .

[0017] S114, when the port DC micro-grid operates in an island mode, the energy storage system is the only balance point of the system, and the power relationship is: P Bes = P PV -P load .

[0018] Optionally, the step S12 is specifically:

[0019] Working mode 1: U dc ≤0.95U ref , the energy storage unit is responsible for maintaining the bus voltage stability and discharging to the system, the distributed power supply works in the MPPT mode, U dc is the DC bus voltage, and U ref is the rated voltage;

[0020] Working mode 2: 0.95U ref ≤U dc ≤0.98U ref , when the photovoltaic power generation is in the MPPT state and is insufficient to provide the required power of the load, the DC bus voltage is lower than the rated voltage, the grid-connected converter supplies the power shortage of the DC micro-grid with the maximum power from the AC grid through the grid-connected converter, the energy storage unit is a slack terminal and discharges, and the discharging should be stopped when the SOC decreases to the minimum limit;

[0021] Working mode 3: 0.98U ref ≤U dc ≤1.02U ref , at this time, the grid-connected converter and the energy storage unit are slack terminals and adopt voltage droop control, the distributed power supply adopts MPPT control, and the load power is provided by the distributed power supply;

[0022] Working mode 4: 1.02U ref ≤U dc ≤1.05U ref , at this time, the DC bus voltage is higher than the rated voltage, the grid-connected converter is in the current limiting control state, the photovoltaic unit still operates in the MPPT mode, and the energy storage unit controls the DC bus voltage stability as a slack terminal;

[0023] Working mode 5: U dc ≥1.05U ref , the system has too much remaining power, the SOC of the energy storage battery reaches the upper limit, and the photovoltaic unit adopts droop control.

[0024] Optionally, the energy storage, photovoltaic system and grid-connected converter are distributed controlled in the step S12, and the step S12 is specifically:

[0025] Step S121, setting the droop control under grid-connected and island operation, maintaining the stability of the DC bus voltage, when the AC main grid fails and the micro-grid needs to operate in island mode for a short time, the energy storage unit will act as a balancing node to maintain system power balance and maintain the stability of the bus voltage;

[0026] Step S122, photovoltaic power generation unit control strategy, the output voltage of the photovoltaic power generation system is lower than the DC bus voltage, and the photovoltaic interface converter is connected to the DC micro-grid through a BOOST type DC / DC converter;

[0027] Step S123, the grid-connected converter (GCC) is a voltage-type converter, and the grid-connected converter is regulated by a PI regulator. The grid-connected converter is used to provide the DC bus voltage and maintain the power balance of the DC grid. The grid-connected converter adopts direct current control, and the outer ring is a voltage loop for controlling the stability of the DC bus voltage. The inner ring is used to control the current.

[0028] Optionally, the step S2 is specifically:

[0029] Step S21, based on the discrete consensus theory, the distributed collaborative secondary control including voltage recovery control and current correction control is added to the DC micro-grid control system;

[0030] Step S22, a consensus gain function is added for noise reduction.

[0031] Optionally, the step S21 is specifically:

[0032] Step S211, the distributed collaborative secondary control is a double closed loop control, which is a current inner loop control and a voltage outer loop return voltage droop control. The local controller of each micro-grid establishes a communication connection. Each controller only collects its own voltage and output current information, and only exchanges information with the controllers of adjacent micro-grids.

[0033] Step S212, the information of the double closed loop control is obtained through consensus iteration to obtain the average voltage and average current values of each node in the whole system;

[0034] Step S213, the current regulator and the voltage regulator are the difference between the average current value and the voltage value output by the consensus algorithm and the actual current value and voltage value of each node, and then the virtual current and voltage increment are obtained through a PI controller to compensate for the bus voltage drop of the whole system.

[0035] Optionally, the consensus gain function is:

[0036]

[0037] where j is the state variable input disturbed by random communication noise, the random communication noise N j and J i can be represented by independent random variables with uniform bound and mean value 0, i.e., Gaussian white noise, a ij is a gain coefficient, x i (k) is the noise in the sending, transmission and receiving stages at time k, respectively, x j (k) is the noise in the sending, transmission and receiving stages at time k, respectively, x i (k) is the noise in the sending, transmission and receiving stages at time k, respectively, x i (k+1) is the noise in the sending stage at time k+1, the consistency gain function a(k), a(k) takes values of ±1, ±2 or ±3.

[0038] Optionally, step S3 is specifically:

[0039] Step S31, reactive power optimization takes the grid voltage out-of-limit risk, network loss and voltage deviation as the objective function, min F = λ1f1 + λ2f2 + λ3f3, where f1 is the cumulative membership function of the voltage out-of-limit risk of all nodes at each time, f2 is the membership function of the expected network loss index, and f3 is the membership function of the voltage deviation index;

[0040]

[0041] where, S i,t is the voltage out-of-limit index value, V min , V max and V B are the maximum allowable voltage, the minimum allowable voltage and the reference voltage, respectively; f i,t (x) is the voltage probability distribution of the node at time t, t and T are time, i and N are nodes, x and dx are voltage and voltage differential variable;

[0042]

[0043] where, N b is the number of branches; P i,t and Q i,t are the active power and reactive power of the i branch at time t, respectively; R i and U i,t are the resistance and the first end voltage of the i branch at time t, respectively;

[0044]

[0045] where, V i N is the rated voltage of node i; V i,t is the voltage of node i at time t;

[0046] Adopting fuzzy set theory, membership function F is adopted to describe optimization result of objective function, value range thereof is 0~1, the smaller F is, the closer to optimal value, the more ideal optimization result is; analytic hierarchy process is used to determine weights λ1, λ2 and λ3;

[0047] Step S32, a constraint condition is constructed, including: power balance constraint, energy storage processing constraint, transformer constraint, photovoltaic output constraint and photovoltaic inverter reactive power output constraint;

[0048] Step S33, a plurality of scene sets are read, each scene set containing load and new energy output, the scene set can be a microgrid, a day-ahead regulation plan of on-load regulating transformer tap, capacitor bank and photovoltaic inverter reactive power output is output in combination with objective function F and constraint condition;

[0049] Step S4 is specifically:

[0050] Step S41, photovoltaic inverter active power output increment is adjusted according to photovoltaic active power output prediction error;

[0051] Step S42, photovoltaic inverter active power output increment is superimposed with day-ahead plan value of the period to obtain inverter reactive power output;

[0052] Step S43, when it is detected that photovoltaic grid-connected point voltage is out of limit, the inverter reactive power output is adjusted by The inverter reactive power adjustment amount required for photovoltaic inverter voltage regulation is calculated Wherein, is voltage reactive sensitivity of node i at period t, V i,τ is grid-connected point voltage value, is voltage limit value of node i.

[0053] According to one aspect of the present application, a multi-modal control and local control of reactive voltage of a port hybrid energy supply system control system, comprising:

[0054] At least one processor; and,

[0055] The memory is in communication connection with the at least one processor; wherein,

[0056] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to realize the multi-modal control and local control of reactive voltage of the port hybrid energy supply system of any one of the above aspects.

[0057] Advantages:

[0058] The multi-mode control and on-site management and control method of reactive voltage of the port hybrid energy supply system improves the flexibility of switching of various working modes of the port micro-grid, reduces voltage fluctuation out-of-limit and network loss increase, and improves the economy, stability and new energy utilization rate of the port power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 is a port micro-grid power flow relationship diagram of an embodiment of the present application.

[0060] Figure 2 is an improved shuffled frog leaping algorithm (SFLA) process schematic diagram of an embodiment of the present application.

[0061] Figure 3 is a port micro-grid real-time on-site management and control process schematic diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0062] The present application scheme will be further described below in combination with the drawings and specific embodiments.

[0063] Embodiment one

[0064] A multi-mode control and on-site management and control method of reactive voltage of a port hybrid energy supply system, comprising the following steps:

[0065] Step S1, analyzing the power flow relationship of the direct-current micro-grid under different operating conditions, and dividing the system into five working modes according to the bus voltage fluctuation range;

[0066] The function of overall coordinated control of each unit in the system is realized through step S1, and the control strategies of each unit under each operating condition are given in detail in the following part;

[0067] Step S2, setting two modules of voltage recovery control and current adjustment control;

[0068] Step S3, according to the historical statistical data of photovoltaic output and load day-ahead prediction, constructing a probability model of photovoltaic output and load in each period to determine the day-ahead adjustment plan of the on-load voltage regulating transformer tap changer (OLTC), capacitor bank and photovoltaic inverter reactive power output;

[0069] Step S4, adjusting the reactive power adjustment amount of the inverter and the active power of the photovoltaic inverter by using the dynamic reactive power response capability of the photovoltaic inverter, to realize the suppression of voltage out-of-limit and fluctuation.

[0070] Preferably, the power flow relationship of the port direct-current micro-grid under different operating conditions is analyzed as described in step S1, then the system is divided into five working modes according to the bus voltage fluctuation range, the function of overall coordinated control of each unit in the system is realized, and the control strategies of each unit under each operating condition are given in detail, and step S1 is specifically:

[0071] Step S11, analyzing the power flow relationship of the port DC microgrid, including the power relationship under various operating conditions;

[0072] Step S12, based on DC bus voltage control, according to the power flow relationship of the port microgrid under different conditions, different working modes are divided to realize the distributed control of energy storage, photovoltaic system and grid-connected converter, and realize the stability of system operation;

[0073] Preferably, the step S11 is specifically:

[0074] S111, the power balance relationship of the port DC microgrid system under grid-connected operation condition is: P S = P PV -P bes -P load , wherein P S is the grid exchange power, P PV is the photovoltaic power, P bes is the energy storage exchange power, and P load is the load power;

[0075] S112, the AC grid-connected converter (GCC) works in both rectification and inversion modes. If the AC system and the PV unit cannot meet the power requirement of the load even at full power or maximum power output, the energy storage system will provide the remaining required power and stabilize the DC bus voltage. At this time, the system enters a new stable operating state, and the power P Bes provided by the energy storage system is: P Bes = P Smax + P PV -P load , wherein P Smax is the maximum grid exchange power;

[0076] S113, if the load cannot completely consume the power generated by the distributed power supply, the grid-connected converter enters the inversion working state. When the power flowing through the grid-connected converter reaches the limit value, the light is very strong or the remaining amount of the energy storage system is not enough, and the photovoltaic and energy storage units need to jointly replace the grid-connected converter as the relaxation terminal of the system to maintain the DC bus voltage. At this time, the power balance relationship is: P Smax = P PV -P load -P Bes ;

[0077] S114, when the port DC microgrid operates in island mode, the energy storage system acts as the only balancing point of the system, and the power relationship is: P Bes = P PV -P load .

[0078] Optionally, in step S12, the port micro-grid operating mode is divided, and the step S12 is specifically:

[0079] Operating mode 1: U dc ≤ 0.95U ref The energy storage unit is responsible for maintaining the bus voltage stability and discharging to the system, the distributed power supply works in the MPPT mode, U dc is the DC bus voltage, U ref is the rated voltage;

[0080] Operating mode 2: 0.95U ref ≤ U dc ≤ 0.98U ref When the photovoltaic power generation is in the MPPT state and is not sufficient to provide the required power of the load, at this time, the DC bus voltage is lower than the rated voltage, the AC power grid supplies the power shortage to the DC micro-grid through the grid-connected converter with the maximum power, the energy storage unit is a slack terminal and discharges, and when the SOC decreases to the minimum limit value, the discharging should be stopped;

[0081] Operating mode 3: 0.98U ref ≤ U dc ≤ 1.02U ref At this time, the grid-connected converter and the energy storage unit are slack terminals and adopt voltage droop control, the distributed power supply adopts MPPT control, and the load power is provided by the distributed power supply;

[0082] Operating mode 4: 1.02U ref ≤ U dc ≤ 1.05U ref At this time, the DC bus voltage is higher than the rated voltage, the grid-connected converter turns to the current limiting control state, the photovoltaic unit still operates in the MPPT mode, and the energy storage unit controls the DC bus voltage stability as a slack terminal;

[0083] Operating mode 5: U dc ≥ 1.05U ref There is too much power in the system, the SOC of the energy storage battery reaches the upper limit, and the photovoltaic unit adopts droop control.

[0084] The operating mode is divided based on the DC bus voltage control to achieve the following goals: making full use of the electric energy generated by the renewable energy source, cooperating with the energy storage unit, realizing the coordinated control and optimal energy allocation among the micro-sources of the port at different voltage levels, realizing the distributed control of the system, and ensuring the stability of the system operation, and because there are at least two slack terminals in each operating mode to control the DC bus voltage stability according to the voltage droop characteristic, the system is prevented from being unstable due to the failure of one end, and the voltage reference critical value is set to not exceed the maximum voltage deviation range allowed by the DC bus.

[0085] Optionally, the energy storage, photovoltaic system and grid-connected converter are distributed controlled in step S12, which is specifically:

[0086] In step S121, the energy storage unit control strategy is based on the characteristics that the photovoltaic unit output is unstable and the battery output power is relatively stable, and the droop control is set in grid-connected and island operation to maintain the stability of the DC bus voltage. When the AC main grid fails and the microgrid needs to operate in island mode for a short time, the energy storage unit will maintain the system power balance as a balancing node and maintain the stability of the DC bus voltage.

[0087] In order to avoid overcharge and overdischarge of the energy storage unit and improve the service life of the battery, a capacity limit protection strategy is added to limit the rated range of SOC to 40%-90%.

[0088] In step S122, the photovoltaic power generation unit control strategy is that the output voltage of the photovoltaic power generation system is lower than the DC bus voltage, and the photovoltaic interface converter is connected to the DC microgrid through a BOOST type DC / DC converter.

[0089] The photovoltaic interface converter has two control modes. In order to fully utilize distributed energy, the PV unit usually adopts maximum power tracking (MPPT) control. In this example, the perturbation and observation method is used to realize the MPPT of the photovoltaic unit, and the judgment steps are as follows:

[0090] a) Measure the output voltage and current of the photovoltaic cell, and calculate the output power;

[0091] b) Compare the output power with the output power at the last time, and adjust the operating point according to the comparison result.

[0092] c) If the output power of the photovoltaic cell is greater than the previous value, compare the voltage. If the voltage is large, make the voltage larger, and if the voltage is small, make the voltage smaller;

[0093] d) If it is less than the previous power, it means that the control will reduce the output power, so the voltage changes in the opposite direction;

[0094] e) Return to step a) and repeat the process.

[0095] In some special cases, such as PCC point failure, in order to reduce the impact of the failure on the system, the direct current micro-grid needs to be in island operation state for a short time, and the grid-connected converter does not control the direct current bus voltage to be constant, at this time, the direct current bus voltage needs to be stabilized by multiple distributed power supplies. In addition, if the output power of the PV unit is much larger than the consumption of the load in the grid-connected operation state, the power in the direct current micro-grid will be excessive, in order to avoid the direct current bus voltage being too large in the above-mentioned case, the second control mode of the photovoltaic system, i.e. the constant voltage droop control, is provided in the present example.

[0096] In step S123, the alternating current grid-connected converter (GCC) is a voltage type converter, the alternating current grid-connected converter is regulated by a PI regulator, the alternating current grid-connected converter is used to provide the direct current bus voltage and maintain the direct current grid power balance at the same time, the alternating current grid-connected converter adopts direct current control, the outer ring is a voltage loop used to control the stability of the direct current bus voltage, and the inner ring is used to control the current.

[0097] In summary, in step S1, the function of overall coordinated control of units in the system is realized, the system is divided into five working modes according to the fluctuation range of the bus voltage, and the control strategy of each unit under each operating condition is given in detail, so that the units can meet the normal operation of the port micro-grid in different working states, the system can be smoothly switched between different operating modes, and the power real-time balance and voltage stability can be maintained.

[0098] According to one aspect of the present application, the multi-mode control and on-site reactive voltage management method of the port hybrid energy supply system as described in step S2, the discrete consensus theory for sensor networks is used for reference, two modules of voltage recovery control and current adjustment control are designed, secondly, the influence of the non-ideal sparse communication condition on the effect of the quadratic optimization control is effectively reduced by adding a consensus gain function, and the step S2 is specifically as follows:

[0099] In step S21, the distributed collaborative quadratic control including the voltage recovery control and the current correction control is added to the direct current micro-grid control system based on the discrete consensus theory;

[0100] In step S22, a consensus gain function is added for noise reduction.

[0101] Preferably, the step S21 is specifically as follows:

[0102] In step S211, the distributed collaborative quadratic control is a double closed loop control, the double closed loop control is a current inner loop control and a voltage outer loop backflow voltage droop control, the local controllers of each micro-grid establish a communication connection, each controller only collects the voltage and output current information of itself, and only exchanges information with the controllers of adjacent micro-grids.

[0103] Step S212, the information of double closed-loop control is obtained through consistency iteration to get the average voltage and average current value of each node in the whole system;

[0104] Step S213, the current regulator and the voltage regulator are the difference between the average current value and the voltage value output by the consistency algorithm and the actual current value and voltage value of each node, and then the virtual current and voltage increment is obtained through a PI controller to compensate the bus voltage drop of the whole system;

[0105] According to one aspect of the present application, the discrete consistency algorithm used in step S212 is based on the local information transmission between each agent and its neighbor nodes, so that the individual reaches the consistency according to the distributed control rule, and the specific is:

[0106] In the present example with more distributed power sources, the distributed power sources will be affected by communication noise in the sending, transmission and receiving stages of the measurement signal, which reduces the convergence of the consistency algorithm, therefore, the following consistency algorithm is used in this paper:

[0107]

[0108] In the formula, j is the state variable input affected by random communication noise, the random communication noise N j and J i It can be represented by independent random variables with uniform bound and mean value 0, i.e. Gaussian white noise, a ij is the gain coefficient, x i (k), x j (k), v i (k) are the noises in the sending, transmission and receiving stages at k time, respectively, x i (k+1) is the noise in the sending stage at k+1 time.

[0109] The convergence coefficient ε is a constant, which does not have robustness for Gaussian noise, therefore, in the non-ideal communication environment, in order to ensure that the consistency error converges to 0, the paper adopts the decay consistency gain function a(k), which takes values of ±1, ±2 or ±3.

[0110]

[0111] In step S2, the distributed collaborative secondary control including voltage recovery control and current correction control is added to the DC micro-grid control system based on the discrete consensus theory, and a consensus gain function is introduced to eliminate the influence of noise, which solves the serious defects that the control method with fixed droop coefficient cannot meet the two control targets of proportional distribution of current and stable bus voltage in the traditional DC micro-grid system primary control, and the main reasons for the defects are as follows: 1) the load cannot be reasonably distributed due to too large line impedance difference; 2) the droop control is a differential regulation, and the addition of virtual impedance increases the voltage deviation of each distributed unit node, which is easy to exceed the allowed deviation range.

[0112] The secondary optimization control method can quickly track and respond to keep the current output consistent under the conditions of unknown line impedance, load change and distributed power fluctuation in the ideal communication environment, and has strong adaptability. When the sparse communication network is disturbed by noise, the influence of noise on the convergence accuracy of consensus is effectively suppressed by adding a consensus gain function, and the reliability of the secondary optimization control is improved.

[0113] Optionally, step S3 is specifically:

[0114] In step S31, the reactive power optimization takes the risk of grid voltage out-of-limit, network loss and voltage deviation as the objective function, min F = λ1f1 + λ2f2 + λ3f3, wherein f1 is the cumulative membership function of the risk of all node voltage out-of-limit at each moment, f2 is the membership function of the expected network loss index, and f3 is the membership function of the voltage deviation index.

[0115]

[0116] Wherein, S i,t is the voltage out-of-limit index value, V min , V max and V B are the maximum allowed voltage, the minimum allowed voltage and the reference voltage respectively. f i,t (x) is the voltage probability distribution of the node at time t, t and T are the time, i and N are the node, x and dx are the voltage and voltage differential variable;

[0117]

[0118] Wherein, N b is the number of branches; P i,t and Q i,t are the active power and reactive power of i branch at t; R i and U i,t are the resistance and the first end voltage of i branch at t.

[0119]

[0120] wherein, V i N V is the rated voltage of the node i; V i,t V is the voltage of the node i at the moment.

[0121] The membership function F is used to describe the optimization result of the objective function by using the fuzzy set theory, and the value range of F is between 0 and 1, wherein the smaller F is, the closer to the optimal value, and the more ideal the optimization result is; the analytic hierarchy process is used to determine the weights λ1, λ2 and λ3.

[0122] Optionally, λ1+λ2+λ3=1, and it can be understood that min is a minimum value.

[0123] Step S32, constructing a constraint condition, including: power balance constraint, energy storage processing constraint, transformer constraint, photovoltaic output constraint and photovoltaic inverter reactive power output constraint.

[0124] It can be understood that the constraint condition includes a power limit value, an energy storage battery SOC value, a transformer power limit value, a photovoltaic output limit value and a photovoltaic inverter reactive power output limit value.

[0125] Step S33, reading a plurality of scene sets, each scene set containing a load and a new energy output, the scene set can be a microgrid, combining the objective function F and the constraint condition, outputting a day-ahead regulation plan of an on-load tap changer (OLTC), a capacitor bank and a photovoltaic inverter reactive power output;

[0126] Optionally, step S33 is specifically: step S34, relaxing discrete variables into continuous variables, using an improved frog leap algorithm to obtain a continuous value of a 24h optimal regulation plan of each capacitor bank and the on-load tap changer (OLTC) through 24h static optimization, completing time sequence segmentation of each capacitor bank capacity curve and on-load tap changer (OLTC) tap position curve based on a Word system clustering, and calculating the capacity or tap position in each segment.

[0127] Step S35, fixing the discrete device regulation plan, using an improved hybrid frog leap (SFLA) algorithm to solve the photovoltaic inverter reactive power output, and finally completing a day-ahead reactive power scheduling plan;

[0128] Step S36, result analysis and verification, including: using a cross-validation method to evaluate the generalization ability of the model, and obtaining a verification index V;

[0129] Performing a sensitivity analysis to evaluate the influence of key parameters on the result, and obtaining a sensitivity matrix S;

[0130] Robustness index R is obtained by generating random field scenes by using Monte Carlo simulation method and testing model robustness;

[0131] The output verification index V, the sensitivity matrix S and the robustness index R.

[0132] Preferably, step S35 is specifically:

[0133] Step S351, the 24h on-load regulation transformer optimal gear and capacitor bank reactive power sequence obtained in the previous step are subjected to Word system clustering;

[0134] Step S352, the final solution of 24h on-load regulation transformer gear and capacitor bank reactive power is determined;

[0135] Step S353, the on-load regulation transformer gear and capacitor bank reactive power are fixed, and the photovoltaic inverter reactive power is solved by using particle swarm;

[0136] Step S354, the day-ahead reactive power / voltage optimization control result is outputted;

[0137] Optionally, step S353 is specifically:

[0138] Step S3531, first, the 24h action gear of the discrete device is arranged in time sequence, and it is regarded as a set of ordered sample set.

[0139] Step S3532, according to The sum of squares of deviations after merging of two adjacent samples is calculated. In the formula, W is the sum of squares of deviations; n d is the number of new samples composed after merging of adjacent samples; H d is the dth sample in the new sample; H^ is the average value of the new sample, is the transpose of

[0140] Step S3533, the two samples with the smallest sum of squares of deviations are selected for merging, and if there are two or more adjacent samples with the same and smallest sum of squares of deviations, they are merged into a class.

[0141] Step S3534, the multiple samples after merging are regarded as a new sample, and the time period order is kept unchanged, and the sample set is updated.

[0142] Step S3535, return to step S3532 until the number of clusters is the same as the number of discrete device action constraints.

[0143] Step S3536, the average value of each action gear in each class is normalized to obtain the regulation gear of the discrete device.

[0144] Optionally, the improved shuffled frog leaping algorithm (SFLA) used in step S35 is specifically as follows:

[0145] Step S321, M frogs are randomly generated, which form an initial population P = {X1, X2,..., XM}, the i-th frog in the s-dimensional solution set is represented as Xi = (xi1, xi2,..., xis), and the local search number and the global maximum convergence number are set;

[0146] Step S322, the fitness of all individuals is solved, and the fitness values are arranged in descending order, and the minimum value is recorded as Xglo;

[0147] Step S323, then the population is divided into small sub-populations according to the rules, and is evenly divided into m, each sub-population contains n individuals, and their relationship is M = m × n. The distribution rule is: the first frog enters the first sub-population group, the second frog enters the second sub-population group, and the distribution continues, the m-th frog is divided into the m-th sub-population group, the m+1-th frog is re-divided into the first sub-population group, and so on. The minimum value of the fitness of each sub-population is recorded as Xb, and the worst one is recorded as Xw;

[0148] Step S324, according to the divided sub-population group, the position is updated according to the updating strategy, wherein the intermediate factor and the acceleration factor are introduced for improvement;

[0149] a) the average value of the individuals except the worst frog is taken as the intermediate factor Xa, and then the re-mixing is performed, and the two populations mX1 and mX2 are divided, Xavel is the average value of population 1, and Xave2 is the average value of population 2;

[0150] b) if Xavel is compared with the intermediate factor, the fitness value is worse than the fitness value corresponding to the intermediate factor, then the population is eliminated, the worst frog is updated by selecting the uneliminated population, and then the above operation is repeated in the uneliminated population;

[0151] c) wherein the formula for position updating is St = C·rand()×(Xb-Xave), Xnew = Xw+St (Stmin≤St≤Stmax), wherein St is the change step of the worst frog, C is the acceleration factor, rand() function represents generating a uniformly distributed random real number between 0 and 1, Xi is the individual other than Xw, and Stmin and Stmax are the minimum and maximum values of the step respectively;

[0152] Step S325, if the frog Xnew is better than the original frog Xw, the frog in the original sub-population group is replaced; if the performance is not improved, Xglo is used to replace Xb, and the position of the worst frog is updated again; if the fitness value is still worse than the value before updating after this time, a random frog individual in the population is used to replace the original Xw.

[0153] Step S326, when the number of local searches reaches the allowed upper limit, the frogs in all sub-population groups are mixed again, and the above optimization process is repeated;

[0154] Step S327, the cycle is repeated until the global maximum convergence number is reached;

[0155] Optionally, step S4 is specifically:

[0156] Step S41, adjusting the active power output increment of the photovoltaic inverter according to the photovoltaic active power output prediction error;

[0157] Step S42, superimposing the active power output increment of the photovoltaic inverter and the day-ahead planning value of the period to obtain the reactive power output of the inverter;

[0158] Step S43, when it is detected that the voltage of the photovoltaic grid-connected point is out of limit, the reactive power adjustment amount of the inverter required for voltage regulation of the photovoltaic inverter is calculated by wherein, is the voltage reactive sensitivity of node i at period t, V i,τ is the grid-connected point voltage value, is the voltage limit value of node i.

[0159] According to an aspect of the present application, a multi-modal control and local reactive voltage control method of a port hybrid energy supply system control system, characterized in that, comprising:

[0160] At least one processor; and,

[0161] The memory is in communication connection with the at least one processor; wherein,

[0162] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to realize the multi-modal control and local reactive voltage control method of the port hybrid energy supply system of any one of the above aspects.

[0163] ​In this embodiment, the multi-mode control and on-site management of the port hybrid energy supply system and the reactive voltage method includes the following steps: the power flow relationship of the DC microgrid under different operating conditions is analyzed, then the system is divided into several working modes according to the bus voltage fluctuation range, the function of overall coordinated control of each unit in the system is realized, and the control strategy of each unit under each operating condition is given in detail; the discrete consistency algorithm is used in the secondary control strategy of the DC microgrid. On the basis of primary control, two modules of voltage recovery control and current regulation control are added, and two control targets of DC voltage secondary recovery and reasonable distribution of load current are realized. On the day-ahead time scale, according to the day-ahead prediction of photovoltaic output and load and its historical statistical data, the probability model of photovoltaic output and load in each period is constructed, and by solving an uncertain optimization model, the day-ahead adjustment plan of OLTC, capacitor bank and photovoltaic inverter reactive power output is determined; by using the dynamic reactive power response capability of photovoltaic inverter, a real-time autonomous control strategy based on local information is adopted, and by adjusting the reactive power adjustment amount of inverter and the active power of photovoltaic, the suppression of voltage overrun and fluctuation is realized.

Claims

1. A method for multi-modal control and on-site management of reactive voltage of a port hybrid energy supply system, characterized in that, Comprise the following steps: Step S1, analyze the power flow relationship of the direct current micro-grid under different operating conditions, and divide the system into 5 working modes according to the bus voltage fluctuation range; Step S2, set voltage recovery control and current adjustment control two modules; Step S3, according to the historical statistical data of photovoltaic output and load day-ahead forecast, construct the probability model of photovoltaic output and load in each period to determine the day-ahead adjustment plan of OLTC, capacitor bank and photovoltaic inverter reactive power output; Step S4, use the dynamic reactive power response capability of photovoltaic inverter to adjust the reactive power adjustment amount of inverter and the active power of photovoltaic inverter, and realize the suppression of voltage overrun and fluctuation; Step S1 is specifically: Step S11, analyze the power flow relationship of the port direct current micro-grid; Step S12, based on the direct current bus voltage control, according to the power flow relationship of the port micro-grid under different working conditions, different working modes are divided, the distributed control of energy storage, photovoltaic system and grid-connected converter is realized, and the stability of system operation is realized; Step S11 is specifically: S111, the power flow relationship of the DC micro-grid system under the grid-connected operation condition of the port DC micro-grid is: P S =P PV -P bes -P load , wherein the grid exchange power, the photovoltaic power generation power, the energy storage exchange power, P load the load power; S112, the grid-connected converter (GCC) works in both rectification and inversion modes. If the AC system and PV unit cannot meet the power requirement of the load even at full or maximum power output, the energy storage system will provide the remaining required power and stabilize the DC bus voltage. At this time, the system enters a new stable operating state, and the power P Bes provided by the energy storage system is: Bes Smax PV load where P is the maximum grid exchange power.​​​ S113, if the load cannot completely consume the power generated by the distributed power supply, the grid-connected converter enters the inverter working state, when the power flowing through the grid-connected converter reaches the limit value, at this time the illumination is very strong or the remaining power of the energy storage system is not enough, the photovoltaic and energy storage units need to jointly replace the grid-connected converter as the slack terminal of the system to maintain the DC bus voltage, at this time the power flow relationship is: ; In S114, when the port DC micro-grid operates in an island mode, the energy storage system is the only balance point of the system, and the power flow relationship is: .

2. The method of claim 1, wherein, The step S12 is specifically: Working mode 1: U dc ≤0.95 U ref The energy storage unit is responsible for maintaining the bus voltage stable and discharging to the system, and the distributed power works in MPPT mode, U dc is the DC bus voltage, U ref is the rated voltage; Mode 2: 0.95 U ref ≤ U dc ≤0.98 U ref When the photovoltaic power generation is in the MPPT state and is not sufficient to provide the required power of the load, at this time the DC bus voltage is lower than the rated voltage, the power shortage of the DC micro-grid is supplemented by the grid-connected converter from the AC power grid with maximum power, the energy storage unit is a slack terminal and discharges, and when the SOC decreases to the minimum limit value, the discharge should be stopped. Mode 3: 0.98 U ref ≤ U dc ≤1.02 U ref At this time, the grid-connected converter and the energy storage unit adopt voltage droop control as slack terminal, the distributed power supply adopts MPPT control, and the load power is provided by the distributed power supply. Operation mode 4: 1.02 U ref ≤ U dc ≤1.05 U ref At this time, the DC bus voltage is higher than the rated voltage, the grid-connected converter turns to the current-limiting control state, the photovoltaic unit still operates in the MPPT mode, and the energy storage unit controls the DC bus voltage stable as a slack terminal. Working mode 5: U dc ≥ 1.05 U ref When the system has too much remaining power and the energy storage battery SOC reaches the upper limit, the photovoltaic unit adopts droop control.

3. The method of claim 1, wherein, In step S12, the energy storage, photovoltaic system and grid-connected converter are controlled in a distributed manner, and the step S12 is specifically: Step S121, set the droop control under grid-connected and island operation to maintain the stability of the direct current bus voltage, when the AC main grid fails and the micro-grid needs to operate in island mode for a short time, the energy storage unit will act as a balance node to maintain system power balance and maintain the stability of the bus voltage; Step S122, photovoltaic power generation unit control strategy, the output voltage of the photovoltaic power generation system is lower than the direct current bus voltage, and the photovoltaic interface converter is connected to the direct current micro-grid through a BOOST type DC / DC converter; Step S123, the AC grid-connected converter is a voltage type converter, the AC grid-connected converter is adjusted by a PI regulator, and the AC grid-connected converter is used for providing the direct current bus voltage and maintaining the power balance of the direct current grid, the AC grid-connected converter adopts direct current control, the outer ring is a voltage ring, and is used for controlling the stability of the direct current bus voltage;The inner ring is used for controlling the current.

4. The method of claim 1, wherein, The step S2 is specifically: Step S21, based on the discrete consensus theory, the distributed collaborative secondary control containing voltage recovery control and current correction control is added to the direct current micro-grid control system; Step S22, add a consistency gain function to reduce noise.

5. The method of claim 4, wherein, Step S21 is specifically: Step S211, the distributed collaborative secondary control is a double closed loop control, the double closed loop control is a current inner loop control and a voltage outer loop backflow voltage droop control, the local controller of each micro-grid establishes a communication connection, each controller only collects its own voltage and output current information, and only exchanges information with the controllers of adjacent micro-grids; Step S212, the information of the double closed loop control is obtained through consistency iteration to obtain the average voltage and average current values of each node in the whole system; In step S213, the current regulator and the voltage regulator are configured to subtract the average current value and the average voltage value output by the consistency algorithm from the actual current value and the actual voltage value of each node, and then amplify the virtual current and voltage increments through a PI controller to compensate for the bus voltage drop of the whole system.

6. The method of claim 4, wherein, The consistency gain function is: where is the state variable input disturbed by random communication noise, random communication noise N j and J i can be represented by independent random variables with uniform bound and mean value 0, i.e. Gaussian white noise, a ij is the gain coefficient, x i (k), x j (k), v j (k) are the noises in the sending, transmission and receiving stages at time k, respectively, x i (k) and x i (k+1) are the noises in the sending stages at time k and k+1, respectively, the consistency gain function a(k), a(k) takes values ±1, ±2 or ±3.

7. The method of claim 1, wherein, Step S3 is specifically: In step S31, the reactive power optimization takes the grid voltage out-of-limit risk, network loss and voltage deviation as the objective function, min F = λ1f1 + λ2f2 + λ3f3, where f1 is the cumulative membership function of the voltage out-of-limit risk of all nodes at each time, f2 is the membership function of the expected network loss index, and f3 is the membership function of the voltage deviation index. where S i,t is the voltage excursion index value, V min , V max , and V B are the maximum allowed voltage, the minimum allowed voltage, and the reference voltage, respectively; f i,t (x) is the voltage probability distribution of node i at time t, t and T are time, i and N are nodes, x and dx are voltage and differential variable of voltage. where N b is the number of branches; P i,t and Q i,t are the active and reactive power of the i-th branch at time t; R i and U i,t are the resistance and the voltage at the head of the i-th branch at time t. where V i N Vset is the rated voltage of node i; V i,t V is the voltage of node i at time t. The fuzzy set theory is used to describe the optimization result of the objective function by using the membership function F, and the value range of F is 0-1, and the smaller F is, the closer to the optimal value it is. The analytic hierarchy process is used to determine the weights λ1, λ2 and λ3. In step S32, the constraint conditions are constructed, including: power balance constraint, energy storage processing constraint, transformer constraint, photovoltaic output constraint and photovoltaic inverter reactive power output constraint. In step S33, a plurality of scene sets are read, each scene set containing load and new energy output, and the scene set can be a microgrid. The day-ahead regulation plan of the on-load voltage regulation transformer tap, the capacitor bank and the photovoltaic inverter reactive power output is output in combination with the objective function and the constraint conditions. Step S4 is specifically: In step S41, the photovoltaic inverter active power output increment is adjusted according to the photovoltaic active power output prediction error. In step S42, the photovoltaic inverter active power output increment is superimposed with the day-ahead plan value of the period to obtain the inverter reactive power output. Step S43, when detecting photovoltaic grid-connected point voltage out-of-limit, through The inverter reactive power adjustment amount ΔQ needed for photovoltaic inverter voltage regulation is calculated ; wherein, V i,t / Q i,t is the voltage reactive sensitivity of node i at time period t, V i,τ is the grid-connected point voltage value, is the voltage limit value of node i.

8. A method for multi-modal control and on-site management of reactive voltage of a port hybrid energy supply system, characterized in that, It comprises: At least one processor; And The memory is in communication connection with the at least one processor; wherein The memory stores instructions executable by the processor, and the instructions are used to execute the multi-modal control and local reactive voltage control method of the port hybrid energy supply system according to any one of claims 1 to 7 by the processor.

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