CN113919620B - Day-ahead energy scheduling algorithm for wind-solar storage micro-grid - Google Patents
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Abstract
The invention discloses a day-ahead energy scheduling algorithm for a wind-solar energy storage micro-grid, which is characterized in that wind-solar energy prediction power data and load prediction demand data are utilized, an energy throughput planning curve of an energy storage battery is designed through charging and discharging power planning behavior control and grid-connected time period setting of the energy storage battery, the charging and discharging capacity of the energy storage battery is not out of limit in the daily operation process of the energy storage battery, and the micro-grid load reliably supplies power. Meanwhile, the economic benefit of the micro-grid system and the power supply pressure of the public power grid at the peak load period are considered, the energy handling plan is corrected by the algorithm in combination with the predicted battery charge state change, if surplus exists in the total amount of the wind and light power generation capacity in the dispatching cycle, the grid-connected discharge capacity of the energy storage battery is comprehensively considered according to the surplus electric quantity and the minimum value of the charge state change process, and the electric energy is released to the public power grid at the peak power utilization in the noon and the evening. The invention fully considers the utilization of new energy, ensures the normal power supply of the load of the micro-grid on the premise of the healthy work of the energy storage battery, and reduces the power supply pressure of the public power grid.
Description
Technical Field
The invention relates to the technical field of new energy power generation system control, in particular to a day-ahead energy scheduling algorithm for a wind-solar micro-grid.
Background
The utilization of renewable energy sources such as wind and light relieves the increasingly serious energy crisis and environmental deterioration problems to a certain extent. However, the inherent intermittent and uncertain characteristics of wind energy and solar energy bring great influence on the stable operation of the load side. The combination of the energy storage system and the wind-solar hybrid power generation system can improve the utilization rate of new energy, improve the acceptance degree of a public power grid to the new energy and ensure the stable operation of loads. However, the energy storage batteries in the energy storage system are limited in their inherent life time, which increases the operating cost of the microgrid system. In addition, improper charging and discharging behaviors such as overcharge and overdischarge can reduce the service life of the energy storage battery, and indirectly improve the operation cost of the microgrid system. Therefore, in order to reduce the operation cost of the microgrid system and prolong the service life of the energy storage battery, the charging and discharging behaviors of the energy storage battery need to be planned, and the service life of the energy storage battery needs to be prolonged.
The stable operation of the micro-grid load cannot be separated from the reasonable configuration of the capacity of the energy storage battery and the reasonable planning of the energy storage charging and discharging behaviors. In the actual operation process, the initial value setting of the energy storage battery can have great influence on the change curve of the state of charge in the next scheduling period, and the stable operation of the energy storage battery can be influenced when the initial value configuration is too high or too low. Meanwhile, due to the existence of prediction errors, the state of charge curve of the energy storage battery can change along with the prediction errors in the actual operation process, and the out-of-limit condition occurs. In order to prevent the change of the state of charge of the energy storage battery from not exceeding the limit, the prior art starts a grid-connected controller when the state of charge is beyond the limit, and redundant electric energy is merged into a power grid. However, although the service life of the energy storage battery can be prolonged, the grid-connected measures can also increase the operation cost of the system and reduce the benefits of the microgrid system.
Disclosure of Invention
The invention aims to provide a wind-solar-storage micro-grid day-ahead energy scheduling algorithm to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a wind-solar-storage micro-grid day-ahead energy scheduling algorithm comprises the following steps:
step 1, wind power generation historical data, photovoltaic power generation historical data and load demand historical data are counted, power deviation predicted values at each sampling moment of a daily period within a year are counted, time integration processing is carried out on the power deviation historical data, an energy storage state of charge (SOC) safety working interval [0.2,0.8] is set, and energy storage capacity configuration required by micro-grid power deviation compensation is obtained;
step 2, forecasting solar wind power generation, photovoltaic power generation and load demand power data, counting power supply and demand deviation values at each sampling moment between 0 point and 24 points according to the forecasted power data, establishing a supply and demand power deviation forecasting sequence, and initializing a throughput plan exchange power sequence and a grid-connected power exchange plan sequence in the energy storage battery microgrid;
step 3, obtaining an initial change curve of the state of charge of the energy storage battery according to the supply and demand power deviation prediction sequence, and setting the initial state of charge to be 0.5; if the difference between the maximum value and the minimum value of the state of charge is less than 60%, considering the constraint condition of overcharge and overdischarge of the energy storage battery and the operation cost of the microgrid, and adjusting the power exchange plan sequence distribution of the microgrid of the energy storage battery and the grid-connected power exchange plan sequence distribution of the public power grid by combining the time interval distribution characteristics of the supply and demand power deviation prediction sequence to obtain a corrected power exchange corresponding to the state of charge plan change curve of the energy storage battery;
and 4, controlling the grid-connected time period of the energy handling behavior of the energy storage battery according to the power exchange plan sequence in the energy storage battery microgrid and the grid-connected exchange plan sequence of the public power grid, wherein the microgrid is operated in a virtual synchronous control mode and a constant power control mode in the non-grid-connected time period and the grid-connected time period respectively.
Preferably, the following components: the step 3 specifically comprises the following steps:
step 3-1: calculating a charge state change curve of the energy storage battery according to the energy storage power exchange plan sequence, if the difference between the maximum value and the minimum value of the initial charge state is more than 60%, starting the standby energy storage and the online energy storage system to work in a combined mode, and jumping to the step 3-4; if the difference between the maximum value and the minimum value of the initial state of charge is less than 60%, skipping to the step 3-2;
step 3-2: if the lowest value of the state of charge is less than 20%, readjusting and setting the initial capacity, and updating a state of charge change curve; if the charge state is more than 80%, calculating the excess capacity of the energy storage battery and the corresponding time interval, adjusting a grid-connected power exchange plan sequence value, calculating according to the supply and demand power deviation prediction sequence and the grid-connected power exchange plan sequence to obtain an energy storage power exchange daily plan sequence, updating a charge state change curve, and repeating the step 3-2 until the charge state change curve meets the constraint condition of overcharge and overdischarge of the energy storage battery;
step 3-3: the energy storage scheduling algorithm adopts a mode of storing electric energy from a public power grid in a low-power consumption valley period and discharging the electric energy from the public power grid in a high-power consumption peak period to obtain economic benefits, considers the influence of charging and discharging on the change of the state of charge, and introduces a state of charge change threshold value delta SOCthr(ii) a If the minimum value of the change of the state of charge and delta SOCthrIf the sum is less than 0, jumping to the step 3-4, otherwise, further judging whether surplus exists in the energy in the scheduling period, if the surplus exists, carrying out plan adjustment on an energy storage power exchange plan sequence and a grid-connected power exchange sequence in the microgrid according to a charge state change threshold, a charge state minimum value and surplus electric quantity, and jumping to the step 3-2, if the surplus does not exist, judging whether the charge state change maximum value before the peak time of the electricity utilization in the noon is less than delta SOCthrIf the conditions are met, grid-connected charging is carried out at the electricity utilization valley and discharging is carried out at the electricity utilization peak, meanwhile, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence are updated and the step 3-2 is skipped, and if the conditions are not met, the step 3-4 is skipped;
step 3-4: and outputting the modified energy storage charge state change curve, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence according to the supply and demand power deviation prediction sequence, the modified micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence.
Preferably, the initial SOC plan curve is adjusted according to the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence, so that the change of the state of charge of the energy storage battery is not out of limit; meanwhile, the energy storage power exchange plan sequence and the grid-connected power exchange plan sequence of the micro-grid are optimized in the modes of charging at a power utilization valley and discharging at a power utilization peak, so that the economic benefit of the system is improved, and the power supply pressure of the public power grid at the power utilization peak is relieved.
Preferably, in the step 3-2, the initial capacity is readjusted and the state of charge variation curve is updated. The specific calculation formula is shown as formula (1); if the state of charge is more than 80%, calculating the excess capacity of the energy storage battery and the corresponding time interval, and adjusting a grid-connected power exchange plan sequence value, wherein a specific calculation formula is shown as a formula (2); calculating according to the supply and demand power deviation prediction sequence and the grid-connected power exchange plan sequence to obtain an energy storage power exchange daily plan sequence, and updating a charge state change curve, wherein a specific calculation formula is shown as a formula (3); repeating the step 3-2 until the change curve of the charge state meets the constraint condition of overcharge and overdischarge of the energy storage battery;
wherein n is the number of sampling points in a daily scheduling period, SOCi,SOCixRespectively, the energy storage state of charge and the corrected state of charge, SOC at the sampling point imin,SOCmaxMaximum and minimum values of the state of charge curve, SOC0The energy storage capacity value at the initial moment; pb,i,Pb,ix,Pg,iRespectively at the moment of sampling point i, the energy storage plan exchange power, the corrected energy storage plan exchange power and the grid-connected plan exchange power, Pb,i,Pb,ixA value of positive indicates charging of the energy storage battery, a value of negative indicates discharging of the energy storage battery, Pg,iA positive value indicates that the microgrid is absorbing energy from the utility grid and a negative value indicates that the microgrid is discharging energy to the utility grid. STNumber of sampling points within one hourQ is the rated capacity of the energy storage battery, Tk1,Tk2The left sampling point and the right sampling point are respectively the intersection of the energy storage battery state of charge curve and 80%.
Preferably, according to the formula (4), carrying out plan adjustment on the energy storage power exchange plan sequence and the grid-connected power exchange sequence in the microgrid according to the charge state change threshold, the charge state minimum and the surplus electric quantity;
SOCdis＝min{SOC24-SOC0,ΔSOCmin+ΔSOCthr}
wherein, SOC0,SOC24Respectively an initial energy storage capacity value and an energy storage capacity value at the end of a scheduling period, delta SOCminAt minimum change of state of charge, Δ SOCthrFor the state of charge change thresholds, Th1, Th2 sample the left and right endpoint values for the peak hours of midday electricity usage, and Th3, Th4 sample the left and right endpoint values for the peak hours of night electricity usage.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method is based on the initial curve of the energy storage battery charge state change plan, gives consideration to stable load operation and the service life of the battery, effectively utilizes the energy storage capacity, and reduces the operation cost of the micro-grid system.
(2) The invention ensures that the capacity margin is fully utilized under the condition that the energy storage work is not out of limit, the energy storage battery is charged by the electricity consumption valley, the part of energy is released at the electricity consumption peak, the full load is stably operated, the economic benefit of the micro-grid system is improved, and the power supply pressure of the public power grid is reduced.
(3) The method and the system have the advantages that the influence of extreme weather and the prediction deviation of the generated power on the stable operation of the load side is considered, the standby energy storage is added into the system, and the stability of the load operation of the micro-grid is improved.
Drawings
FIG. 1 is a schematic flow chart of a wind-solar-storage micro-grid day-ahead energy scheduling algorithm;
FIG. 2 is a schematic diagram of a specific case one of a wind-solar-storage micro-grid day-ahead energy scheduling algorithm;
FIG. 3 is a schematic diagram of a specific case two of a wind-solar-storage micro-grid day-ahead energy scheduling algorithm;
FIG. 4 is a schematic diagram of a specific case three of a wind-solar-storage micro-grid day-ahead energy scheduling algorithm;
fig. 5 is a diagram of a specific case four of the wind-solar-storage micro-grid day-ahead energy scheduling algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides the following technical solutions: a wind-solar-storage micro-grid day-ahead energy scheduling algorithm comprises the following steps:
step 1, wind power generation historical data, photovoltaic power generation historical data and load demand historical data are counted, power deviation predicted values at each sampling moment of a daily period within a year are counted, time integration processing is carried out on the power deviation historical data, an energy storage state of charge (SOC) safety working interval [0.2,0.8] is set, and energy storage capacity configuration required by micro-grid power deviation compensation is obtained;
and 2, predicting solar wind power generation, photovoltaic power generation and load demand power data. According to the predicted power data, power supply and demand deviation values at each sampling moment from 0 point to 24 points are counted, a supply and demand power deviation prediction sequence is established, and a throughput plan exchange power sequence and a grid-connected power exchange plan sequence in the energy storage battery microgrid are initialized;
step 3, due to the fact that wind and light power generation capacity and load demand conditions cause changes of supply and demand power deviation prediction sequence data conditions, the step 3 is described from the perspective of a specific implementation case I, a specific implementation case II, a specific implementation case III and a specific implementation case IV respectively; the first case, the second case, the third case and the fourth case are different supply and demand situations under the same system, so that the capacity configuration of the energy storage battery is the same, and the initial state of charge is 0.5.
In the invention, the step 3 specifically comprises the following steps:
step 3-1: calculating a charge state change curve of the energy storage battery according to the energy storage power exchange plan sequence, if the difference between the maximum value and the minimum value of the initial charge state is more than 60%, starting the standby energy storage and the online energy storage system to work in a combined mode, and jumping to the step 3-4; if the difference between the maximum value and the minimum value of the initial state of charge is less than 60%, skipping to the step 3-2;
step 3-2: if the lowest value of the state of charge is less than 20%, readjusting and setting the initial capacity, and updating a state of charge change curve; if the charge state is more than 80%, calculating the excess capacity of the energy storage battery and the corresponding time interval, adjusting a grid-connected power exchange plan sequence value, calculating according to the supply and demand power deviation prediction sequence and the grid-connected power exchange plan sequence to obtain an energy storage power exchange daily plan sequence, updating a charge state change curve, and repeating the step 3-2 until the charge state change curve meets the constraint condition of overcharge and overdischarge of the energy storage battery;
step 3-3: the energy storage scheduling algorithm adopts a mode of storing electric energy from a public power grid in a low-power consumption valley period and discharging the electric energy from the public power grid in a high-power consumption peak period to obtain economic benefits, considers the influence of charging and discharging on the change of the state of charge, and introduces a state of charge change threshold value delta SOCthr(ii) a If the minimum value of the change of the state of charge and delta SOCthrAnd if the sum is less than 0, skipping to the step 3-4, otherwise, further judging whether the energy in the scheduling period has surplus, and if the energy in the scheduling period has surplus, carrying out energy storage power exchange plan sequence and the energy storage power exchange plan sequence of the microgrid according to the charge state change threshold, the charge state minimum and the surplus electric quantityCarrying out plan adjustment on the grid-connected power exchange sequence, skipping to the step 3-2, and if no surplus exists, judging whether the maximum value of the change of the charge state before the peak time of the power utilization in the noon is smaller than delta SOCthrIf the conditions are met, grid-connected charging is carried out at the electricity utilization valley and discharging is carried out at the electricity utilization peak, meanwhile, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence are updated and the step 3-2 is skipped, and if the conditions are not met, the step 3-4 is skipped;
step 3-4: and outputting the modified energy storage charge state change curve, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence according to the supply and demand power deviation prediction sequence, the modified micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence.
In the step 3-2, the initial capacity is readjusted and set, and the change curve of the state of charge is updated. The specific calculation formula is shown as formula (1); if the state of charge is more than 80%, calculating the excess capacity of the energy storage battery and the corresponding time interval, and adjusting a grid-connected power exchange plan sequence value, wherein a specific calculation formula is shown as a formula (2); calculating according to the supply and demand power deviation prediction sequence and the grid-connected power exchange plan sequence to obtain an energy storage power exchange daily plan sequence, and updating a charge state change curve, wherein a specific calculation formula is shown as a formula (3); repeating the step 3-2 until the change curve of the charge state meets the constraint condition of overcharge and overdischarge of the energy storage battery;
wherein n is the number of sampling points in a daily scheduling period, SOCi,SOCixRespectively at the sampling pointi-time energy storage state of charge and corrected state of charge, SOCmin,SOCmaxMaximum and minimum values of the state of charge curve, SOC0The energy storage capacity value at the initial moment; pb,i,Pb,ix,Pg,iRespectively at the moment of sampling point i, the energy storage plan exchange power, the corrected energy storage plan exchange power and the grid-connected plan exchange power, Pb,i,Pb,ixA value of positive indicates charging of the energy storage battery, a value of negative indicates discharging of the energy storage battery, Pg,iA positive value indicates that the microgrid is absorbing energy from the utility grid, and a negative value indicates that the microgrid is discharging energy to the utility grid. STThe number of sampling points within one hour, Q is the rated capacity of the energy storage battery, Tk1,Tk2The left sampling point and the right sampling point are respectively the intersection of the energy storage battery state of charge curve and 80%.
According to the formula (4), carrying out plan adjustment on an energy storage power exchange plan sequence and a grid-connected power exchange sequence in the microgrid according to the charge state change threshold, the charge state minimum and the surplus electric quantity;
SOCdis＝min{SOC24-SOC0,ΔSOCmin+ΔSOCthr}
therein, SOC0,SOC24Respectively an initial energy storage capacity value and an energy storage capacity value at the end of a scheduling period, delta SOCminAt minimum change of state of charge, Δ SOCthrFor the state of charge change thresholds, Th1, Th2 sample the left and right endpoint values for the peak hours of midday electricity usage, and Th3, Th4 sample the left and right endpoint values for the peak hours of night electricity usage.
The specific case one is as follows:
step 3-1, calculating according to the formula (3) and the energy storage initial power exchange plan sequence to obtain an initial energy storage state of charge change plan curve, wherein the energy storage initial state of charge change curve is shown in fig. 2(c), the difference between the maximum value and the minimum value of the state of charge in the initial state of charge change curve is less than 60%, and entering the step 3-2;
step 3-2, scanning the whole energy storage initial charge state sequence, finding out the maximum value of the charge state of 88%, correcting the energy storage peak value, calculating by the formula (2) and the formula (3) to obtain a corrected charge state plan curve and an energy storage power exchange plan sequence, and repeating the step 3-2 until the maximum value of the charge state is not more than 80%;
and 3-3, rescanning the corrected charge state planning curve in the step 3-2, and meanwhile, acquiring sampling point time periods corresponding to the power consumption valley and the power consumption peak of the next scheduling period, wherein as can be seen from fig. 2(a), the power consumption valley time period is between 0 point and 5 points, and the power consumption peak time periods are respectively between 11 points and 13 points and between 19 points and 21 points. And calculating according to the corrected charge state curve to obtain that the minimum value of the charge state change is-20%, the charge state threshold value is 23%, and the optimization condition of the energy storage power exchange plan sequence is met. According to the corrected initial and final energy storage state-of-charge points, the system energy is not surplus, so that discharging is not needed. Further judging whether the minimum value of the change of the state of charge before the peak of electricity utilization in the noon is less than 23%, as can be seen from fig. 2(c), the system meets the condition, and the charging period is selected from 0 to 3. Considering the influence of the midday power consumption peak discharge capacity on the subsequent energy storage state of charge change, the midday power consumption peak energy storage discharge capacity and the night power consumption peak energy storage discharge capacity need to be calculated, and the specific calculation formula is shown as a formula (5).
Wherein, Δ SOCmax,th2Is the maximum value of the change in state of charge between sample points 1 to Th2, i.e. the maximum value of the change in state of charge before the peak hours of midday use, SOCmax,th4For the maximum value of the state of charge curve of the energy storage battery between Th2 and Th4, SOCchrCompensating capacity, SOC, for energy storage battery at power utilization valleycAnd the Tl1 and the Tl2 are respectively a left end point value and a right end point value of the sampling in the electricity utilization valley period.
Step 3-4: and according to the supply and demand power deviation prediction sequence, the corrected micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence. And outputting the corrected energy storage charge state change curve, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence.
The specific case two is as follows:
and 3-1, calculating to obtain an initial energy storage state of charge change curve according to the formula (3) and the energy storage initial power exchange plan sequence, wherein the energy storage initial state of charge change curve is shown in a figure 3(c), the difference between the maximum value and the minimum value of the state of charge in the initial state of charge change curve is less than 60%, and entering a step 3-2.
And 3-2, scanning the whole energy storage initial charge state sequence, and entering the step 3-3 when no charge state crossing point exists.
And 3-3, rescanning the state of charge curve corrected in the step 3-2, and meanwhile, acquiring the time periods of sampling points where the power consumption valley and the power consumption peak of the next scheduling period are located. As can be seen from fig. 3(a), the electricity consumption valley period is between 0 and 5, and the electricity consumption peak period is between 11 and 13 and between 19 and 21. And calculating according to the corrected charge state curve to obtain that the minimum value of the charge state change is 1 percent, the charge state threshold value is 23 percent, and the optimization condition of the energy storage power exchange plan sequence is met. Meanwhile, as the initial capacity is 50% smaller than the final capacity is 53%, surplus energy exists in the system, the power consumption peak discharge amount in the noon and the evening can be calculated according to the formula (4), and the energy storage power exchange plan sequence is updated. The energy storage state of charge curve is rescanned, and it can be known from fig. 3(c) that the system satisfies the charging condition. The system charging period is 0 to 3 points, and meanwhile, the electricity consumption valley charging amount and the electricity consumption peak discharging amount are calculated according to the formula (5).
Step 3-4: and according to the supply and demand power deviation prediction sequence, the corrected micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence. And outputting the corrected energy storage charge state change curve, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence.
The concrete case three:
and 3-1, calculating to obtain an initial energy storage state of charge change curve according to the formula (3) and the energy storage initial planned power sequence, wherein the energy storage initial state of charge change curve is shown in fig. 4(c), the difference between the maximum value and the minimum value of the state of charge in the initial state of charge change curve is less than 60%, and entering the step 3-2.
Step 3-2: scanning the whole energy storage initial state-of-charge sequence, finding out the maximum value of the state-of-charge of 85.6%, and therefore, correcting the energy storage peak value, and calculating through the formula (2) and the formula (3) to obtain a corrected state-of-charge curve and an energy storage power exchange plan sequence. Repeating the step 3-2 until the maximum value of the charge state is not more than 80%.
Step 3-3: and (4) rescanning the state of charge curve corrected in the step (3-2), and meanwhile, acquiring the power utilization valley and the sampling point time period where the power utilization peak is located in the next dispatching cycle. As can be seen from fig. 4(a), the electricity consumption valley period is between 0 and 5, and the electricity consumption peak period is between 11 and 13 and between 19 and 21. And calculating according to the corrected charge state curve to obtain that the minimum value of the charge state change is 4.8 percent, the charge state threshold value is 23 percent, and the optimization condition of the energy storage power exchange plan sequence is met. Meanwhile, because the initial capacity is 50% smaller than the final capacity which is 63.6%, surplus exists in energy in the system, the power consumption peak discharge amount in the noon and the evening can be calculated according to the formula (4), and the energy storage power exchange plan sequence is updated. The energy storage state of charge curve is rescanned. As can be seen from fig. 4(c), since the change in the state of charge has reached 30% before the peak of the power consumption in the noon, the condition of charging in the power valley is not provided.
Step 3-4: and according to the supply and demand power deviation prediction sequence, the corrected micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence. And outputting the corrected energy storage charge state change curve, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence.
The concrete case four:
and 3-1, calculating to obtain an initial energy storage state of charge change curve according to the formula (3) and the energy storage initial power exchange plan sequence, wherein the energy storage initial state of charge change curve is shown in a figure 5(c), the initial state of charge value is between 35% and 57%, and entering the step 3-2.
And 3-2, scanning the whole energy storage initial charge state sequence, and entering the step 3-3 when no charge state crossing point exists.
And 3-3, rescanning the state of charge curve corrected in the step 3-2, and meanwhile, acquiring the time periods of sampling points where the power consumption valley and the power consumption peak of the next scheduling period are located. As can be seen from fig. 5(a), the electricity consumption valley period is between 0 and 5, and the electricity consumption peak period is between 11 and 13 and between 19 and 21. And calculating the minimum value of the change of the state of charge of-15% and the threshold value of the state of charge of 23% by the corrected state of charge curve, and meeting the optimization conditions of the energy storage power exchange plan sequence. Because the initial energy storage capacity is 50% smaller than the final energy storage capacity is 51.5%, surplus energy exists in the system, the power consumption peak discharge amount in the middle and at night can be calculated according to the formula (4), and the energy storage power exchange plan sequence is updated. The energy storage state of charge curve is rescanned. Because the power deviation value of the energy storage battery before the power utilization peak in the noon is 6.89%, the system meets the power utilization valley charging condition, the power utilization peak discharge amount in the noon and the evening can be calculated according to the formula (4), and the energy storage power exchange plan sequence is updated. The energy storage state of charge curve is rescanned.
Step 3-4: and according to the supply and demand power deviation prediction sequence, the corrected micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence. And outputting the corrected energy storage charge state change curve, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence.
And 4, controlling the grid connection time interval of the energy handling behavior of the energy storage battery according to the power exchange plan sequence in the energy storage battery microgrid and the grid connection exchange plan sequence of the public power grid. The micro-grid system in the non-grid-connected period and the grid-connected period respectively adopts a virtual synchronous control mode and a constant power control mode to operate. In the actual operation process, the influence of the prediction deviation on the energy storage system scheduling is considered, and the standby energy storage and the online energy storage system are combined to work.
In conclusion, the method is based on the initial curve of the energy storage battery state-of-charge change plan, takes stable load operation and battery service life into consideration, effectively utilizes energy storage capacity, and reduces the operation cost of the micro-grid system; under the condition of ensuring that the energy storage work is not out of limit, the invention fully utilizes the capacity margin, charges the energy storage battery at the power consumption valley, releases the part of energy at the power consumption peak, stably runs at full load, improves the economic benefit of the micro-grid system, and reduces the power supply pressure of a public power grid; the method takes the influence of extreme weather and the prediction deviation of the generated power on the stable operation of the load side into consideration, and the standby energy storage is added into the system, so that the stability of the load operation of the micro-grid is improved
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (4)
1. A wind-solar storage micro-grid day-ahead energy scheduling algorithm is characterized in that: the method comprises the following steps:
step 1, wind power generation historical data, photovoltaic power generation historical data and load demand historical data are counted, power deviation predicted values at each sampling moment of a daily period in a year are counted, time integration processing is carried out on the power deviation historical data, an energy storage charge state SOC safety working interval [0.2,0.8] is set, and energy storage capacity configuration required by micro-grid power deviation compensation is obtained;
step 2, forecasting solar wind power generation, photovoltaic power generation and load demand power data, counting power supply and demand deviation values at each sampling moment between 0 point and 24 points according to the forecasted power data, establishing a supply and demand power deviation forecasting sequence, and initializing a throughput power exchange plan sequence and a public power grid-connected power exchange plan sequence in the energy storage battery microgrid;
step 3, obtaining an initial change curve of the state of charge of the energy storage battery according to the supply and demand power deviation prediction sequence, and setting the initial state of charge to be 0.5; if the difference between the maximum value and the minimum value of the state of charge is less than 60%, considering the constraint condition of overcharge and overdischarge of the energy storage battery and the operation cost of the microgrid, and adjusting the distribution of a throughput power exchange plan sequence in the microgrid of the energy storage battery and the distribution of a grid-connected power exchange plan sequence of the public power grid by combining the time interval distribution characteristics of a supply and demand power deviation prediction sequence to obtain a corrected power exchange corresponding to the change curve of the state of charge plan of the energy storage battery;
step 4, performing grid-connected time interval control on energy throughput behaviors of the energy storage battery according to a throughput power exchange plan sequence in the energy storage battery microgrid and a public power grid-connected power exchange plan sequence, wherein the microgrid systems in non-grid-connected time intervals and grid-connected time intervals respectively adopt virtual synchronous control and constant power control modes to operate;
the step 3 specifically comprises the following steps:
step 3-1: calculating a charge state change curve of the energy storage battery according to the energy storage power exchange plan sequence, if the difference between the maximum value and the minimum value of the initial charge state is more than 60%, starting the standby energy storage and the online energy storage system to work in a combined mode, and jumping to the step 3-4; if the difference between the maximum value and the minimum value of the initial state of charge is less than 60%, skipping to the step 3-2;
step 3-2: if the lowest value of the state of charge is less than 20%, readjusting and setting the initial capacity, and updating a state of charge change curve; if the charge state is more than 80%, calculating the excess capacity and the corresponding time interval of the energy storage battery, adjusting a grid-connected power exchange plan sequence value of a public power grid, calculating according to a supply and demand power deviation prediction sequence and the grid-connected power exchange plan sequence of the public power grid to obtain an energy storage power exchange daily plan sequence, updating a charge state change curve, and repeating the step 3-2 until the charge state change curve meets the overcharge and overdischarge constraint conditions of the energy storage battery;
step 3-3: the energy storage scheduling algorithm adopts a mode of storing electric energy from a public power grid in a low-power consumption valley period and discharging the electric energy from the public power grid in a high-power consumption peak period to obtain economic benefits, considers the influence of charging and discharging on the change of the state of charge, and introduces a state of charge change threshold value delta SOCthr(ii) a If the minimum value of the change of the state of charge and delta SOCthrIf the sum is less than 0, jumping to the step 3-4, otherwise, further judging whether surplus exists in the energy in the scheduling period, if the surplus exists, plan adjustment is carried out on a throughput power exchange plan sequence in the energy storage battery microgrid and a grid-connected power exchange sequence of the public power grid according to a charge state change threshold, a charge state change minimum value and surplus electric quantity, and jumping to the step 3-2, if the surplus does not exist, judging whether the charge state change maximum value before the peak time of the electricity utilization in the noon is less than delta SOCthrIf the conditions are met, grid-connected charging is carried out at the electricity utilization valley, discharging is carried out at the electricity utilization peak, and meanwhile, the throughput power in the energy storage battery microgrid is updatedThe plan changing sequence and the public power grid-connected power exchange plan sequence jump to the step 3-2, and if the conditions are not met, the step 3-4 is jumped to;
step 3-4: and outputting the modified energy storage charge state change curve, the micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence according to the supply and demand power deviation prediction sequence, the modified micro-grid energy storage power exchange plan sequence and the grid-connected power exchange plan sequence.
2. The wind-solar-storage micro-grid day-ahead energy scheduling algorithm of claim 1, characterized in that: adjusting an initial SOC (system on chip) plan curve according to a throughput power exchange plan sequence in the energy storage battery microgrid and a public power grid-connected power exchange plan sequence to ensure that the change of the state of charge of the energy storage battery is not out of limit; meanwhile, the energy storage power exchange plan sequence and the grid-connected power exchange plan sequence of the micro-grid are optimized in the modes of charging at a power utilization valley and discharging at a power utilization peak, so that the economic benefit of the system is improved, and the power supply pressure of the public power grid at the power utilization peak is relieved.
3. The wind-solar-storage micro-grid day-ahead energy scheduling algorithm of claim 1, characterized in that: in the step 3-2, the initial capacity is readjusted and set, and the change curve of the state of charge is updated; the specific calculation formula is shown as formula (1); if the state of charge is more than 80%, calculating the excess capacity of the energy storage battery and the corresponding time interval, and adjusting a grid-connected power exchange plan sequence value, wherein a specific calculation formula is shown as a formula (2); calculating according to the supply and demand power deviation prediction sequence and the grid-connected power exchange plan sequence to obtain an energy storage power exchange daily plan sequence, and updating a charge state change curve, wherein a specific calculation formula is shown as a formula (3); repeating the step 3-2 until the change curve of the charge state meets the constraint condition of overcharge and overdischarge of the energy storage battery;
SOCix＝SOCi+0.2-SOCmin i＝1,...,n (1)
wherein n is the number of sampling points in a daily scheduling period, SOCi,SOCixRespectively, the energy storage state of charge and the corrected state of charge, SOC at the sampling point imin,SOCmaxMaximum and minimum values of the state of charge curve, SOC0The energy storage capacity value at the initial moment; pb,i,Pb,ix,Pg,iRespectively at the moment of sampling point i, the energy storage plan exchange power, the corrected energy storage plan exchange power and the grid-connected plan exchange power, Pb,i,Pb,ixA value of positive indicates charging of the energy storage battery, a value of negative indicates discharging of the energy storage battery, Pg,iA positive value indicates that the microgrid is absorbing energy from the utility grid, a negative value indicates that the microgrid is emitting energy to the utility grid, STThe number of sampling points within one hour, Q is the rated capacity of the energy storage battery, Tk1,Tk2The left sampling point and the right sampling point are respectively the intersection of the energy storage battery state of charge curve and 80%.
4. The wind-solar-storage micro-grid day-ahead energy scheduling algorithm of claim 1, characterized in that: according to the formula (4), carrying out plan adjustment on an energy storage power exchange plan sequence and a grid-connected power exchange sequence in the microgrid according to the charge state change threshold, the charge state change minimum and the surplus electric quantity;
therein, SOC0,SOC24Respectively an initial energy storage capacity value and an energy storage capacity value at the end of a scheduling period, delta SOCminAt minimum change of state of charge, Δ SOCthrFor the state of charge change thresholds, Th1, Th2 sample the left and right endpoint values for the peak hours of midday electricity usage, and Th3, Th4 sample the left and right endpoint values for the peak hours of night electricity usage.
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