An improved power exponent type SOC coordinated control method based on model predictive control

Abstract

The application discloses an improved power exponent type SOC coordination control method based on model prediction control, which comprises the following steps: signal measurement, calculation of droop coefficient prediction value, multi-target optimization solution of optimal value, and determination of duty cycle through voltage and current double-loop control. The application adopts model prediction control to optimize the power exponent in the traditional power distribution strategy based on SOC, solves the problem of unstable system control performance under the fixed power exponent, realizes the distribution of the output power of each energy storage according to the SOC, and has a good promoting effect on the stable operation of the direct-current microgrid.

Description

TECHNICAL FIELD

[0001] The application relates to a multi-energy storage SOC coordination control method, in particular to an improved power index type SOC coordination control method based on model predictive control, and belongs to the technical field of power supply control. BACKGROUND

[0002] As an effective form of connecting distributed power to a large power grid, a DC microgrid has been widely studied due to its simple structure, no need to consider phase synchronization and reactive power compensation problems. However, the intermittency and randomness of distributed energy can cause power fluctuation problems in the DC microgrid. Reasonable use of energy storage units in the microgrid can effectively smooth the fluctuation of new energy output and solve the problem of new energy grid connection and consumption.

[0003] In a DC microgrid containing multiple energy storage units, a large difference in the state of charge of different units may cause individual energy storage units to exit operation prematurely, increase the output of other energy storage units, and thus affect the safety and stability of the system. In addition, SOC balancing control can also avoid overcharging and over-discharging of some batteries, and improve the service life of the batteries. Therefore, many scholars have studied power distribution methods based on the goal of balancing the SOC of each energy storage unit. In the Chinese Journal of Electrical Engineering, 2013, 33(16): 37-46+20. "Load power dynamic allocation method with bus voltage drop compensation function in DC microgrid energy storage system", Lun Xiaonan discloses a power distribution scheme that obtains an improved droop coefficient by taking the quotient of the initial droop coefficient and the nth power of the state of charge of the energy storage unit. However, the method does not provide a clear method for determining the value of the power index n, and does not consider the case where the initial droop coefficients of the battery control units are different. In addition, when the power index is large, the SOC converges quickly, but the system power response is slow. When the power index is small, the power response is fast, but the SOC converges slowly. That is, using a fixed power index for SOC coordination will result in the problem of not being able to simultaneously optimize the power response speed and the SOC convergence speed.

[0004] Therefore, for the power coordination control problem of batteries in a multi-energy storage DC microgrid, a variable droop coefficient control strategy based on a model predictive control algorithm is studied. By optimizing the power index, the influence of different initial droop coefficients on the improved droop coefficient is reduced, and the problem of not being able to simultaneously optimize the power response speed and the SOC convergence speed when the power index is constant is solved, which has certain practical value. SUMMARY

[0005] The technical problem to be solved by the present application is to improve the traditional power index type SOC coordination control method, and solve the problem that the method has poor effect under the condition that the initial droop coefficient is different and the fixed power index affects the control performance.

[0006] To solve the above technical problems, the present application adopts the following technical solutions:

[0007] An improved power index type SOC coordination control method based on model predictive control, a direct current micro-grid system is connected by a plurality of energy units and a large grid unit to form, the energy unit contains a distributed power generation unit, an energy storage unit and a load unit; the distributed power generation unit, the energy storage unit, the large grid unit and the load unit are connected with the direct current bus through the corresponding voltage source converter or DC-DC converter respectively; the distributed power generation unit, the energy storage unit, the large grid unit and the load unit contained in the direct current micro-grid system all include a control system, a measuring element and a converter; the input end of the control system of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit contained in the direct current micro-grid system is connected with the output end of the corresponding measuring element, and the output end is connected with the input end of the corresponding converter; the direct current micro-grid system further contains a direct current measuring element and an alternating current measuring element, the direct current measuring element includes a direct current bus side voltage sensor and a current sensor of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit, and a distributed power source side, an energy storage element side, an alternating current grid side and a load side voltage sensor and a current sensor of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit; the energy storage unit includes a battery;

[0008] It is characterized by comprising the following steps:

[0009] Step 1: signal measurement: measure the bus voltage v busi and the battery output current i bati of each energy unit in the direct current micro-grid through the voltage sensor, collect the state of charge of each battery and the initial droop coefficient information of the control unit;

[0010] Step 2: calculate the droop coefficient prediction value: put the collected local information into the prediction model to calculate the prediction value, formula (1) is the established droop coefficient prediction model, the power index ηi is taken as the prediction coefficient, and the droop coefficient prediction value k i * (k) is calculated.

[0011]

[0012] In the formula: k oi is the initial droop coefficient of the i th battery control unit, soc i (k) is the state of charge of the i th battery at the current moment.

[0013] Step 3: Multi-objective optimization solution optimal value: the predicted value is substituted into the objective function, formula (2) is the objective function expression, and the optimal prediction coefficient minimizing the objective function is obtained by an optimizer;

[0014]

[0015] In the formula, w k is a weight coefficient; N is the number of energy units in the microgrid system; a ij is a communication coefficient, when the i th energy unit and the j th energy unit have a direct communication channel, a ij = 1, otherwise 0; v ref is a bus voltage reference value; i di (k) represents the predicted output current of the droop control; v busi (k) is the measured value of the bus voltage in the i th energy unit at the current time; Δ vsi (k) is a quadratic voltage compensation amount, a voltage observer based on a consensus algorithm is used in the voltage control link to perform average voltage control, and formula (3) is the voltage compensation amount expression;

[0016]

[0017] In the formula, v avgi (k) is the average observed value of the bus voltage of the i th energy unit at the current time; k PV and k IV are proportional and integral coefficients respectively;

[0018] Step 4: Duty cycle is obtained through voltage-current double-loop control: the optimal prediction coefficient is substituted back into the prediction model to obtain the droop coefficient compensation amount at the current time, which is added to the improved droop control of the voltage outer loop together with the quadratic voltage compensation amount, and formula (4) is the target value v busi (k+1) of the bus voltage in the i th energy unit at the next time under the action of the droop control;

[0019]

[0020] The current reference value output by the voltage outer loop control is input into the current inner loop control, and the PI control is used in the inner loop to make the actual current of the battery track the current reference value; the duty cycle is calculated according to the voltage average equation in one switching period, the switching signal is obtained through PWM modulation, and is sent into the switching tube to realize the control of the output power of the battery.

[0021] The technical effects achieved by the above technical scheme are as follows:

[0022] The application adopts a self-adaptive power exponent type SOC coordination control method, adopts model predictive control to optimize and solve the power exponent, and effectively solves the problem that the power response speed and the SOC convergence speed cannot be simultaneously optimized under the traditional power exponent type control method, and the problem that the traditional method is not good in the case of different initial droop coefficients of each energy storage control unit. BRIEF DESCRIPTION OF DRAWINGS

[0023] The application will be further described in detail below in combination with the drawings and specific embodiments.

[0024] Figure 1 is a flowchart of the application;

[0025] Figure 2 is a structure schematic diagram of a direct current micro-grid;

[0026] Figure 3 is a DC-DC converter control principle diagram in the application. DETAILED DESCRIPTION

[0027] The application will be further described in detail below in combination with the drawings and specific embodiments.

[0028] REFERENCE Figure 1 I Figure 3 An improved power exponent type SOC coordination control method based on model predictive control, a direct current micro-grid system is connected by a plurality of energy units and a large grid unit to form, the energy unit contains a distributed power generation unit, an energy storage unit and a load unit; the distributed power generation unit, the energy storage unit, the large grid unit and the load unit are connected with the direct current bus through the corresponding voltage source converter or DC-DC converter respectively; the distributed power generation unit, the energy storage unit, the large grid unit and the load unit contained in the direct current micro-grid system all include a control system, a measuring element and a converter; the input end of the control system of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit contained in the direct current micro-grid system is connected with the output end of the corresponding measuring element respectively, and the output end is connected with the input end of the corresponding converter; the direct current micro-grid system further contains a direct current measuring element and an alternating current measuring element, the direct current measuring element includes the direct current bus side voltage sensor and current sensor of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit, and the distributed power source side, the energy storage element side, the alternating current grid side and the load side voltage sensor and current sensor of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit; the energy storage unit includes a battery;

[0029] It is characterized by comprising the following steps:

[0030] Step 1: Signal measurement: Measure the bus voltage vbusi and battery output current i of each energy unit in the DC micro-grid by voltage sensor bati , Collect the initial droop coefficient information of each battery state of charge and control unit;

[0031] Step 2: Calculate the predicted value of the droop coefficient: Substitute the collected local information into the prediction model to obtain the predicted value, formula (1) is the established droop coefficient prediction model, the power index ηi is taken as the prediction coefficient, and the predicted value k of the droop coefficient is calculated i * (k);

[0032]

[0033] In the formula: k oi is the initial droop coefficient of the i-th battery control unit, soc i (k) is the state of charge of the i-th battery at the current time;

[0034] Step 3: Multi-objective optimization to solve the optimal value: Substitute the predicted value into the objective function, formula (2) is the expression of the objective function, and the optimal prediction coefficient that minimizes the objective function is obtained by the optimizer;

[0035]

[0036] In the formula, w k is the weight coefficient; N is the number of energy units in the micro-grid system; a ij is the communication coefficient, a ij =1 when there is a direct communication channel between the i-th energy unit and the j-th energy unit, otherwise 0; V ref is the bus voltage reference value; i di (k) represents the predicted output current of droop control; v busi (k) is the measured value of the bus voltage of the i-th energy unit at the current time, Δv si (k) is the quadratic voltage compensation, and the voltage observer based on the consensus algorithm is used in the voltage control link to perform average voltage control, formula (3) is the expression of the voltage compensation;

[0037]

[0038] In the formula, V avgi (k) is the average observed value of the bus voltage of the i-th energy unit at the current time; k PV and k IV are the proportional coefficient and integral coefficient respectively;

[0039] Step 4: Obtain the duty ratio by voltage-current double loop control: put the optimal prediction coefficient back into the prediction model to obtain the droop coefficient compensation at the current time, and add it to the improved droop control of the voltage outer loop together with the quadratic voltage compensation, and formula (4) is the target value of the bus voltage in the i-th energy unit at the next time under the action of the droop control busi (k+1);

[0040]

[0041] The current reference value output by the voltage outer loop control is input into the current inner loop control, and the inner loop adopts PI control to make the actual current of the battery track the current reference value; the duty ratio is calculated according to the voltage average equation in one switching period, the switching signal is obtained through PWM modulation, and is sent into the switching tube to realize the control of the output power of the battery.

Claims

1. An improved power exponential type SOC coordinated control method based on model predictive control, a direct current micro-grid system is connected by a plurality of energy units and a large grid unit to form, the energy unit contains a distributed power generation unit, an energy storage unit and a load unit; the distributed power generation unit, the energy storage unit, the large grid unit and the load unit are connected with the direct current bus through the corresponding voltage source converter or DC-DC converter respectively; the distributed power generation unit, the energy storage unit, the large grid unit and the load unit contained in the direct current micro-grid system all include control system, measuring element and converter; the input end of the control system of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit contained in the direct current micro-grid system is connected with the output end of the corresponding measuring element respectively, and the output end is connected with the input end of the corresponding converter; the direct current micro-grid system further contains direct current measuring element and alternating current measuring element, the direct current measuring element includes direct current bus side voltage sensor and current sensor of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit and voltage sensor and current sensor of the distributed power generation unit, the energy storage unit, the large grid unit and the load unit on the side of the distributed power source, the side of the energy storage element, the side of the alternating current grid and the side of the load; the energy storage unit includes a battery; characterized in that The method comprises the following steps: Step 1: Signal measurement: measure the bus voltage v of each energy unit in the direct current micro-grid by a voltage sensor busi and the battery output current i bati , collect the initial droop coefficient information of each battery state of charge and control unit; Step 2: Calculate the predicted value of droop coefficient: put the collected local information into the prediction model to get the predicted value, formula (1) is the established prediction model of droop coefficient, take the power index ηi as the prediction coefficient, and calculate the predicted value of droop coefficient k i * (k); wherein: k oi is the initial droop coefficient of the i-th battery control unit, SOC i (k) is the state of charge of the i-th battery at the current time Step 3: multi-objective optimization is used to solve the optimal value: the predicted value is substituted into the objective function, formula (2) is the expression of the objective function, and the optimal prediction coefficient that minimizes the objective function is obtained by solving through the optimizer; where w k is the weight coefficient; N is the number of energy units in the microgrid system; a ij is the communication coefficient, a ij = 1 when there is a direct communication channel between the i-th energy unit and the j-th energy unit, otherwise 0; v ref is the bus voltage reference value; i di (k) represents the predicted output current of the droop control; v busi (k) is the measured value of the bus voltage in the i-th energy unit at the current time; Δv si (k) is the quadratic voltage compensation, a voltage observer based on the consensus algorithm is used in the voltage control link to control the average voltage, and formula (3) is the voltage compensation expression. In the formula, v avgi (k) is the average observation value of the bus voltage of the i th energy unit at the current time; k PV and k IV are the proportional coefficient and the integral coefficient, respectively; Step 4: Obtain the duty ratio by voltage-current double-loop control: Put the optimal prediction coefficient back into the prediction model to obtain the droop coefficient compensation at the current time, and add it to the improved droop control of the voltage outer loop together with the quadratic voltage compensation. Equation (4) is the target value of the bus voltage in the i-th energy unit at the next time under the action of the droop control busi (k+1); The current reference value output by the voltage outer loop control is input into the current inner loop control, the PI control is adopted in the inner loop to make the actual current of the battery track the current reference value; the duty cycle is calculated according to the voltage average equation in one switching period, the switching signal is obtained through PWM modulation, and the switching signal is sent into the switching tube to realize the control on the output power of the battery.

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