CN116993062A - Two-stage optimal scheduling method for chilled water storage air conditioning system - Google Patents
Two-stage optimal scheduling method for chilled water storage air conditioning system Download PDFInfo
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Abstract
The invention discloses a two-stage optimal scheduling method for a chilled water storage air conditioning system, and belongs to the field of operation optimization of heating ventilation air conditioning systems. Specifically comprises two execution stages: the first stage is a day-ahead scheduling stage, and based on day-ahead load prediction, the lowest day operation cost and the operation stability are used as targets, and a punishment constraint condition for reducing the start-stop times of the system is added to avoid frequent start-stop of a unit, so that a mixed integer nonlinear programming model is finally established; and then, converting the established optimization model into a mixed integer linear programming model by adopting an SOS-2 method, and solving to obtain an acceptable initial solution of the storage energy power, the start number of the water chiller and the load rate of the water energy storage system. The second phase is a daytime optimization adjustment phase, aims at improving the reliability and economy of the system operation based on daytime load prediction, and further optimizes the initial solution by adjusting the time-by-time operation number, the operation state and the load rate of the water chilling unit, so that the final scheduling result meets the actual load and the operation requirement of the site. The invention improves the running stability, reliability and economy of the chilled water storage air conditioning system by a two-stage optimal scheduling method.
Description
Technical field:
the invention belongs to the technical field of operation optimization of air conditioning systems, and particularly relates to a two-stage optimization scheduling model for a chilled water storage air conditioning system.
The background technology is as follows:
the chilled water storage air conditioning system can utilize night off-peak electricity price to prepare and store cold energy, and release the cold energy to meet the cooling demand during daytime peak load, so that the pressure of a power grid is relieved. In addition, through carrying out scientific and reasonable dispatch and management to water cold-storage air conditioning system, can improve water cold-storage air conditioning system's operational scheme's economic nature and reliability, bring economic benefits for the user side. The current research on the operation strategy of the water energy storage air conditioning system focuses on the research on the scheduling economy and the research on the day-ahead energy scheduling plan. However, the scheduling result of the current theory is only that a feasible solution of the energy storage strategy and the number of the water chilling unit opening is obtained, and the connection problem with actual operation is not considered. Firstly, the reliability of the actual system cooling is not considered: because the daily load prediction error is relatively large, if the real-time load is not adopted for correction, the phenomenon of mismatching of supply and demand is easily caused, and particularly when the cold load prediction is smaller, the phenomenon of insufficient supply of cold is caused, so that the indoor comfort is affected. And secondly, the continuity of equipment operation is not considered, and the method is difficult to directly use for guiding the system operation. Therefore, a good operation scheme is required to improve the operation economy and realize the stable operation of the actual system on the premise of meeting the actual load demand. In order to enable the optimization scheme to meet the actual operation requirement of the system, the invention provides a two-stage optimization scheduling strategy, considers the actual operation requirement of the system, ensures the cooling reliability and the stability of the system operation while pursuing economy, and has stronger engineering application value.
The invention comprises the following steps:
the invention aims to overcome the defects of the prior art, provides a two-stage optimal scheduling method of a chilled water storage air conditioning system, and aims to meet the actual operation requirement of optimal scheduling of the chilled water storage air conditioning system, thereby improving the cooling reliability and the operation stability while reducing the daily operation cost of the system.
In order to achieve the purpose of the invention, the two-stage optimal scheduling method of the chilled water storage air conditioning system comprises the following specific steps:
s1: and determining a daily load prediction curve, a daily time-of-use electricity price, a daily optimal scheduling boundary condition such as system equipment performance and the like.
S2: the method for establishing the day-ahead optimal scheduling model specifically comprises the following steps of:
(S201) setting an objective function. The optimal scheduling of the chilled water storage air conditioning system aims at reducing the running cost of the whole day by reasonably planning the running number, the running load rate and the time-by-time energy storage rate of the unit based on the time-sharing electricity price. The optimization objective function is as follows:
wherein Pt Is the electricity price at time t, W t Is the total power consumption of the whole system, including the refrigeration power consumption of all the started water chilling unitsAll energy consumption for starting the water pump>All energy consumption for opening cooling tower fans>
(S202) establishing a device energy consumption model. The equipment energy consumption of the chilled water storage air conditioning system mainly relates to the energy consumption of a motor water chilling unit, a chilled water pump, a cooling water pump and a cooling tower fan.
(1) Energy consumption model of electric water chiller is built
The power consumption of the electric chiller can be calculated according to the refrigeration power and the coefficient of performance (COP) of the chiller, as shown in formula (3), wherein the coefficient of performance COP of the chiller can be calculated by the load factor born by the chiller:
COP EC，i ＝β 1 PLR EC，i 3 +β 2 PLR EC，i 2 +β 3 PLR EC，i +β 4
wherein ,WEC，i Represents the power consumption and COP of the ith cooling machine EC，i Representing the efficiency of the ith chiller, PLR EC，i Representing the load factor of the i-th station,is the rated refrigerating power of the refrigerator. Beta 1 ～β 1 Is the parameter coefficient of the COP performance curve and can be obtained by fitting measured data. W (W) EC 、Q EC Indicating the total of the cold machinePower consumption and total refrigeration, mu EC，i Indicating the on state of the chiller and N indicating the number of chillers.
(2) And establishing a power consumption model of the fan and the water pump.
The frequent start-stop problem of the water chilling unit and the energy distribution problem of the water chilling unit are mainly considered in the day-ahead scheduling stage, and the energy consumption of the fan and the water pump is small, so that the operation of the fan and the water pump according to rated power is simplified.
in the formula , and />Respectively representing rated power of the fan and the water pump.
(S203) setting constraint conditions
(1) Power balance condition
The cooling capacity of the whole system should be greater than or equal to the cooling load demand, and can be expressed as:
wherein , and />Indicating the cold stored or released by the energy storage tank, < >>The cold load demand representing the whole system can be obtained by a day-ahead prediction model.
(2) Energy balance condition of cold accumulation tank
The energy storage system satisfies the following energy balance:
WS represents the energy storage capacity of the energy storage tank, epsilon represents the heat loss of the energy storage tank in unit time, eta represents the efficiency of the energy storage tank,representing the rate of the accumulator tank, superscripts in and out represent stored and released energy, respectively. Lambda (lambda) 1 、λ 1 Respectively indicates whether the state of the energy storage tank is energy storage or energy release.
(3) Upper and lower limits of refrigerating capacity of water chilling unit
The refrigerator operates under an excessively small load rate, the service life of the refrigerator is affected, and the refrigerating power cannot exceed the maximum refrigerating power. Thus, the output of the electric chiller has the following constraints:
wherein , EC Ca， HP Ca，/>representing the lowest and highest output cold of the unit respectively.
(S3) piecewise linearization solution
The constraint condition formula can be used for finding out that the optimization solving problem not only contains nonlinear constraint, but also contains integer variable, and belongs to a mixed integer nonlinear programming (MINLP) model, and as the dispatching stage is used for solving the optimization variable for 24 hours in the next day, the optimization variable is more and linearization is needed.
Specifying a series of non-negative continuous variables t 1 ，t 2 ，......t N Two function variables in the formula for calculating the power consumption of the cold machine are Q EC，i And W is EC，i Here denoted by x, y. x, y may further employ N segmentation points (x 1 ，y 1 )(x 2 ，y 2 )……(x N ，y N ) Expressed as:
where x, y represent independent and dependent variables, respectively, in the nonlinear function (power and cold load factor, respectively, herein). t is t 1 ，t 2 ，......t N The method meets the following conditions:
t 1 +t 2 +......+t N ＝1
to ensure adjacent t i A non-0 number not exceeding the limit of 2, introducing an integer auxiliary variable z of 0-1 1 、z 2 ，......z N The following relationships are satisfied:
the initial solution of optimal scheduling can be obtained through the above solution: the load distribution ratio of the energy storage water tank to the water chilling units is the start-stop state of each water chilling unit. Based on time-of-use electricity price, the energy storage strategy is optimized, and the economical efficiency of system operation is ensured
S4: and determining a daytime optimal scheduling boundary condition comprising an hour load predicted value and a storage energy strategy result obtained by daytime optimal scheduling.
S5: and adjusting the running quantity and the running state of the unit.
(S501) the actual payload to be assumed by the computer group. The energy storage power of the energy storage water tank is already determined in the day-ahead optimal scheduling stage, and when the load value is predicted accurately at the time t, the load quantity actually needed to be born by the cold machine can be calculated:
(S502) adjusting the total running number of the chiller at the current moment. Comparative payloadAnd (3) primarily solving the quantity of the cold machines obtained by optimized scheduling in the future, and adjusting the running quantity of the cold machines so as to meet the matching of supply and demand and ensure the reliability of the cooling. The specific principle is as follows: if the actual payload->The maximum refrigerating power which is higher than the maximum refrigerating power which is determined before the day and used for starting the cold machine is higher than the maximum refrigerating power which is determined before the day and used for indicating that the actual load is larger, and then one cold machine is required to be started; if actually cleanLoad->The minimum refrigeration power or the high-efficiency operation interval of the cold machine is lower than the minimum refrigeration power or the high-efficiency operation interval of the cold machine determined before the day, which indicates that the actual load is smaller and a cold machine is required to be shut down; otherwise, the machine runs according to the number of cold machines determined in the past. And (5) the final starting number of the cooling machine is determined through readjusting the number of the running devices time by time.
(S503) adjusting the running state of each chiller to avoid frequent start-up and stop of the chiller. The method comprises the following specific steps:
(1) Counting the longest continuous start time period delta T of the chiller from the current moment k1 And N is in the total running number of the cold machine to be started;
(2) Calculation of Δt k1 And in the period, the minimum starting quantity of each moment is calculated, and the cold machine is started in sequence.
N min ＝ceil(N÷ΔT k1 )
(3) Repeating the steps 2-3, and calculating the number N-N of the residual equipment to be started min ΔT k1 Counting the longest continuous start time period delta T of the residual chiller from the current moment k2 Determining the starting time and the quantity of the residual equipment;
(4) Repeating the steps (1) - (4) from the current moment to the cooling end moment, and determining the quantity of the cold machines to be started at each moment and the running state mu of each cold machine EC，i . Through readjustment of the running quantity and the running state of the cold machine, the running continuity of the equipment is ensured.
S6: and establishing a daytime optimal scheduling model, and further optimizing the load rate.
(S601) setting an optimization target. The optimization objective of the daytime running phase is to optimize the running power consumption at the next moment:
(S602) electric chiller model
COP EC，i ＝β 1 PLR EC，i 3 +β 2 PLR EC，i 2 +β 3 PLR EC，i +β 4
Wherein mu EC，i Is the adjusted cold machine starting state.
(S603) constraint condition
(S604) optimization solution
Because the number of the units is mu EC，i The method has become a known quantity, and the solving variable only has the unit load rate at the next moment, so that the optimization problem of the stage belongs to a simple nonlinear programming (MNLP) problem, and the method can be used for quickly and accurately solving the problem by directly adopting a Gurobi solver.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the first stage establishes the optimal scheduling model of the chilled water storage system based on daily load prediction, so that a more economical energy storage and release scheduling strategy can be obtained, and the running economy is ensured. And in the second stage, the number and the state of the running equipment and the unit load rate are readjusted based on more accurate daytime load prediction, so that the reliability of cooling and the running continuity of the running equipment can be ensured, and an running scheme which meets the actual requirements more can be obtained. The two-stage scheduling plan can ensure the reliability and stability of the system operation while pursuing economy, and has stronger engineering application value.
Description of the drawings:
FIG. 1 is a flow chart of an implementation of a two-stage optimized scheduling method of a reclaimed water cold storage air conditioning system.
Fig. 2 is a flow chart of the chilled water storage air conditioning system of the present invention.
FIG. 3 is a graph of plant model parameters and efficiency for a chiller.
FIG. 4 is a graph showing the result of the segment linearization of the W-Q curve of 1#2# chiller.
FIG. 5 is a graph showing the result of the step-wise linearization of the W-Q curve of the 3# chiller.
Fig. 6 is a two-stage optimized schedule outcome diagram.
FIG. 7 is a single-stage optimized scheduling result diagram.
FIG. 8 is a diagram showing the result of the preferential cold scheduling
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. An optimized scheduling method for a chilled water storage air conditioning system comprises the following two steps: a day-ahead optimization scheduling phase and a day-ahead optimization readjustment phase, as shown in fig. 1. Taking a certain chilled water storage air conditioning system as an example, the specific steps are as follows:
s1: and determining a day-ahead optimal scheduling boundary condition. Including the load forecast curve before day, the next day time of day electricity price and system equipment performance.
Fig. 2 is a flow chart of the chilled water storage air conditioning system of the invention, and specific parameters of each device are shown in table 1. The predicted daily load curve of the building can be obtained by various load prediction modules, not described as an important point, and the predicted daily load curve is obtained by adding noise to the real load curve, as shown in table 2. The time-of-use electricity prices used in this example are shown in table 3.
Table 1 device parameter table
TABLE 2 predictive load values
| Time of day | Load (kW) | Time of day | Load (kW) | Time of day | Load (kW) |
| 1：00 | 0 | 9：00 | 6666.66 | 17：00 | 5790.99 |
| 2：00 | 0 | 10：00 | 4887.47 | 18：00 | 0 |
| 3：00 | 0 | 11：00 | 6610.71 | 19：00 | 0 |
| 4：00 | 0 | 12：00 | 5350.14 | 20：00 | 0 |
| 5：00 | 0 | 13：00 | 5943.11 | 21：00 | 0 |
| 6：00 | 0 | 14：00 | 7698.06 | 22：00 | 0 |
| 7：00 | 4432.51 | 15：00 | 6480.57 | 23：00 | 0 |
| 8：00 | 6927.91 | 16：00 | 6220.44 | 24：00 | 0 |
Table 3 time-of-use electricity price meter
S2: the invention builds a mixed integer nonlinear programming model for optimal scheduling of the system with the aim of economy. The method specifically comprises the following steps:
(S201) establishing an objective function
The objective function is established with the lowest running cost, and the running cost of the system mainly comprises two parts: equipment running cost and unit start-stop punishment cost. The calculation mode is shown in formula (1).
Wherein P is t The electricity price at time t is shown in Table 3. W (W) t Is the total power consumption of the whole system, including the refrigeration power consumption of all the started water chilling unitsAll energy consumption for starting the water pump>All energy consumption for opening cooling tower fans>
(S202) establishing a device energy consumption model. The equipment energy consumption of the chilled water storage air conditioning system mainly relates to the energy consumption of a motor water chilling unit, a chilled water pump, a cooling water pump and a cooling tower fan.
(1) Energy consumption model of electric water chiller is built
The power consumption of the electric chiller can be calculated according to the refrigeration power and the coefficient of performance (COP) of the chiller, such as formulas (2) and (3), wherein the COP of the chiller can be calculated through the load factor of the chiller:
COP EC，i ＝β 1 PLR EC，i 3 +β 2 PLR EC，i 2 +β 3 PLR EC，i +β 4
wherein W is EC，i Represents the power consumption and COP of the ith cooling machine EC，i Representing the efficiency of the ith chiller, PLR EC，i Representing the load factor of the i-th station,is the rated refrigerating power of the refrigerator. Beta 1 ～β 1 Is the parameter coefficient of the COP performance curve and can be obtained by fitting measured data. W (W) EC 、Q EC Represents the total power consumption and the total refrigerating capacity of the chiller, mu EC，i Indicating the on state of the chiller and N indicating the number of chillers. The plant model parameters and efficiency curves of the chiller are shown in figure 3.
(2) And establishing a power consumption model of the fan and the water pump.
The frequent start-stop problem of the water chilling unit and the energy distribution problem of the water chilling unit are mainly considered in the day-ahead scheduling stage, and the energy consumption of the fan and the water pump is small, so that the operation of the fan and the water pump according to rated power is simplified.
In the method, in the process of the invention,and->The rated powers of the fan and the water pump are shown in table 1.
(S203) setting constraint conditions
(1) Power balancing
The cooling capacity of the whole system should be greater than or equal to the cooling load demand, and can be expressed as:
wherein,and->Indicating the cold stored or released by the energy storage tank, < >>Representing the cold load demand of the whole system, which can be obtained by a day-ahead prediction modelObtaining the product. The predicted values of the daily building cold load are shown in table 2.
(2) Energy balance constraint of cold accumulation tank
The energy storage system satisfies the following energy balance:
WS represents the energy storage capacity of the energy storage tank, epsilon represents the heat loss of the energy storage tank in unit time, eta represents the efficiency of the energy storage tank,representing the rate of the accumulator tank, superscripts in and out represent stored and released energy, respectively. Lambda (lambda) 1 、λ 1 Respectively indicates whether the state of the energy storage tank is energy storage or energy release.
(3) Upper and lower limits of refrigerating capacity of water chilling unit
The refrigerator operates under an excessively small load rate, the service life of the refrigerator is affected, and the refrigerating power cannot exceed the maximum refrigerating power. Thus, the output of the electric chiller has the following constraints:
wherein, EC Ca，representing the lowest and highest output cold of the unit respectively.
The values of the constraint parameters are shown in table 4:
table 4 constraint parameter value table
S3: and converting the established mixed integer nonlinear programming model into a linear programming model by adopting a second special ordered set constraint (SOS-2) method to solve the linear programming model. When the built optimized scheduling model is subjected to linearization treatment, the relation between the power consumption and the refrigerating power of the refrigerating unit is directly built, the power consumption of the water chilling unit can be expressed as the product form of the refrigerating power (W) and the refrigerating capacity (Q), and the power consumption of the water chilling unit is as follows after finishing:
the letters in the above formula have the meanings as described above. The results of the sectional linearization by the SOS-2 method are shown in fig. 4 and 5, and represent linearization processing results of the W-Q curves of the 1#2# chiller and the 3# chiller, respectively. The original cooling water unit refrigeration power consumption-refrigeration capacity curve is represented by a solid line, and the refrigeration power consumption-refrigeration capacity curve after piecewise linearization is represented by a dotted line. The relative error of the piecewise linearization of the W-Q curves of the three coolers is within 0.6% before and after linearization treatment, thereby meeting the precision requirement.
The initial solution of the energy storage and release state and the running number of the refrigerating units can be obtained through the optimal scheduling solution of the first stage, as shown in table 5:
table 5 day ago optimal scheduling initial solution
Note that: 18:00-24:00 building air conditioner is not running, so only 1 is shown in the table: 00-17: the scheduling result was optimized before day 00.
S4: and determining a daytime optimal scheduling boundary condition comprising an hour load predicted value and a storage energy strategy result obtained by daytime optimal scheduling. Often more accurate results are obtained for the hour load predictions, here assuming that a completely accurate prediction is obtained, and therefore replaced with a true value, as shown in table 6:
TABLE 6 predicted daytime load value
| Time of day | Load (kW) | Time of day | Load (kW) | Time of day | Load (kW) |
| 1：00 | 0 | 9：00 | 6951.10 | 17：00 | 4301.73 |
| 2：00 | 0 | 10：00 | 7362.97 | 18：00 | 0 |
| 3：00 | 0 | 11：00 | 7586.92 | 19：00 | 0 |
| 4：00 | 0 | 12：00 | 6106.42 | 20：00 | 0 |
| 5：00 | 0 | 13：00 | 6384.33 | 21：00 | 0 |
| 6：00 | 0 | 14：00 | 7781.47 | 22：00 | 0 |
| 7：00 | 4701.38 | 15：00 | 7170.92 | 23：00 | 0 |
| 8：00 | 7320.04 | 16：00 | 6438.98 | 24：00 | 0 |
S5: adjusting the running quantity and running state of the cooling machine
(S501) calculating an actual payload. When the stored energy power of the energy storage water tank is already determined in the day-ahead optimal scheduling stage, as shown in table 5, and a more accurate predicted load value at the time t is known (as shown in table 6), the load actually needed to be born by the cold machine can be calculated:
(S502) adjusting the total running number of the chiller at the current moment. Comparative payloadAnd (3) primarily solving the quantity of the cold machines obtained by optimized scheduling in the future, and adjusting the running quantity of the cold machines so as to meet the matching of supply and demand and ensure the reliability of the cooling. The specific principle is as follows: if the actual payload->The maximum refrigeration power of the cold machine is higher than the maximum refrigeration power of the cold machine determined before the day, and the difference value is larger than the minimum starting load of the cold machine, so that the actual load is larger, and then one cold machine needs to be started; if the actual payload->Lower than the minimum refrigeration work of the refrigerator determined by the day beforeThe rate or the high-efficiency refrigeration load rate range of the cold machine (obtained by efficiency curve, herein [0.5-1 ]]) And after a certain cooler is closed, the supply and demand balance can be still met, and the fact that the actual load is smaller is indicated, one cooler is closed, and otherwise, the cooler operates according to the number of the cooler determined in the past. And (5) the final starting number of the cooling machine is determined through readjusting the number of the running devices time by time. The adjustment conditions of the actual load that the cooling machine needs to bear and the number of cooling machine running according to tables 5 and 6 are shown in table 7:
TABLE 7 actual payload calculation results
The real load of 8 points in daytime is 7320.04kW, the predicted load is 6927.91kW, the stored energy power of the day-ahead optimal scheduling result is 0kW and 6927.91kW respectively, and the load actually needed to be born by the cooler is 392.13kW. At this time, the number of the cold machine is zero according to the current result, so that the load actually needed to be born by the cold machine is higher than the current planned output and is higher than the lowest output level of the cold machine, and an additional cold machine is needed to be added for operation. Similarly, this is the case at 10 points. At 17 points, the real load is 4301.73kW, the predicted load is 5790.99kW, the day-ahead optimal scheduling result is that the energy release power is 142.53kW, and the load actually needed to be born by the cooler is 4159.20kW. As shown in Table 7, the actual cold load to be borne by the chiller is lower than the efficient operating area in which the chiller was scheduled to be turned on before the day, and the balance of supply and demand can be satisfied after one chiller is turned off.
(S503) adjusting the running state of each chiller to avoid frequent start-up and stop of the chiller. The method comprises the following specific steps:
(1) And adjusting the number of cold machine starting at the current moment according to the daytime predicted load.
(2) Counting the longest continuous start time period delta T of the chiller from the current moment k1 And the total running number N of the cold engines which need to be started;
(3) Calculation of Δt k1 In the time period, the minimum starting quantity at each moment and the cold machine is sequentially started。
N min ＝ceil(N÷ΔT k1 )
(4) Repeating the steps 2-3, and calculating the number N-N of the residual equipment to be started min ΔT k1 Counting the longest continuous start time period delta T of the residual chiller from the current moment k2 Determining the starting time and the quantity of the residual equipment;
(5) Repeating the steps (1) - (4) from the current moment to the cooling end moment, and determining the quantity of the cold machines to be started at each moment and the running state of each cold machineThrough readjustment of the running quantity and the running state of the cold machine, the running continuity of the equipment is ensured.
The time-by-time operation state of each chiller is obtained after the adjustment in step S503, as shown in table 8.
Table 8 running state table for cold machine
| Time of day | 1# Cold machine on state | 2# Cold machine on state | 3# Cold machine on state |
| 1：00 | 0 | 0 | 0 |
| 2：00 | 0 | 0 | 0 |
| 3：00 | 0 | 0 | 1 |
| 4：00 | 1 | 1 | 1 |
| 5：00 | 1 | 1 | 1 |
| 6：00 | 1 | 1 | 1 |
| 7：00 | 0 | 1 | 1 |
| 8：00 | 0 | 0 | 1 |
| 9：00 | 0 | 0 | 0 |
| 10：00 | 0 | 0 | 0 |
| 11：00 | 0 | 1 | 0 |
| 12：00 | 1 | 1 | 0 |
| 13：00 | 1 | 1 | 0 |
| 14：00 | 1 | 1 | 0 |
| 15：00 | 1 | 1 | 0 |
| 16：00 | 1 | 1 | 0 |
| 17：00 | 0 | 1 | 0 |
Note that: 1 indicates on and 0 indicates off.
Comparing tables 5 and 8, it is evident that the number of cold start and stop times is reduced.
S6: and establishing a daytime optimal scheduling model, and further optimizing the load rate.
(1) Setting an optimization target. The optimization objective of the daytime running phase is to optimize the running power consumption at the next moment:
(2) Electric water chiller model
Wherein mu EC，i The adjusted cold machine on state is the on-off state of the cold machine listed in table 8.
(3) Constraint conditions
(4) Optimization solution
Because the number of the units is mu EC，i Has become a known quantity and the solution variable is only the next timeThe unit load rate is carved, so that the optimization problem at the stage belongs to a simple nonlinear programming (MNLP) problem, and the Gurobi solver can be directly adopted to quickly and accurately solve the problem.
The final scheduling result is obtained through two-stage optimization solution, as shown in fig. 6, fig. 7 is a single-stage optimization scheduling result, and fig. 8 is a rule optimization result diagram. Comparison of the three figures shows that: the two-stage optimal scheduling method can further ensure the continuity of the equipment. The results of comparison in terms of load imbalance ratio, optimum running economy, and the like are shown in Table 9.
Table 9 scheduling results vs. table
Analysis shows that compared with the adoption of a regular control strategy (maximum power cold accumulation at night until full accumulation, maximum power cold discharge at peak moment until full discharge) for a water cold accumulation system, the daily operation cost is 14605.43 yuan and 13789.63 yuan respectively, and the cost is saved by 5.6%. Seemingly, single-stage optimized scheduling is relatively lower in operating cost than the two-stage optimized scheduling method of the present invention, but single-stage at the expense of partial supply-demand balance (indoor comfort). The invention reduces the running efficiency of the cold machine to a certain extent due to the arrangement of the start-stop punishment function of the unit under the real load boundary condition, thereby losing part of economy. However, the load prediction cannot reach 100% of accuracy, and when a prediction error exists, the invention can ensure the reliability of cooling, reduce the start-stop times of the cooling machine and better meet the operation requirement of an actual system.
It should be noted that the disclosure and the specific embodiments are intended to demonstrate practical applications of the technical solution provided by the present disclosure, and should not be construed as limiting the scope of the present disclosure. Numerous specific modifications can be made by those skilled in the art without departing from the spirit of the invention and scope of the claims, which are within the scope of the invention.
Claims (5)
1. The two-stage optimal scheduling method of the chilled water storage air conditioning system is characterized by comprising the following steps of:
s1: and determining a daily load prediction curve, a daily time-of-use electricity price, a daily optimal scheduling boundary condition such as system equipment performance and the like.
S2: and (5) establishing a day-ahead optimal scheduling model by taking economy as a target. Including establishing optimization objectives, device models, and constraints
S3: and carrying out piecewise linearization on the established mixed integer nonlinear model based on an SOS-2 method and solving.
S4: and determining a daytime optimal scheduling boundary condition comprising an hour load predicted value and a storage energy strategy result obtained by daytime optimal scheduling.
S5: and adjusting the running quantity and the running state of the unit.
S6: and establishing a daytime optimal scheduling model, further optimizing the load rate, and obtaining a final scheduling result.
2. The two-stage optimal scheduling method of the chilled water storage air conditioning system according to claim 1, wherein the step S2 is specifically: and (5) establishing a day-ahead optimal scheduling model by taking economy as a target.
(1) The optimization objective function is:
wherein P is t Is the electricity price at time t, W t Is the total power consumption of the whole system, including the refrigeration power consumption of all the started water chilling unitsAll energy consumption for starting the water pump>All energy consumption for opening cooling tower fans>
(2) The energy consumption model of the electric water chiller is built as follows:
COP EC，i ＝β 1 PLR EC，i 3 +β 2 PLR EC，i 2 +β 3 PLR EC，i +β 4
wherein W is EC，i Represents the power consumption and COP of the ith cooling machine EC，i Representing the efficiency of the ith chiller, PLR EC，i Representing the load factor of the i-th station,is the rated refrigerating power of the refrigerator. Beta 1 ～β 1 Is the parameter coefficient of the COP performance curve and can be obtained by fitting measured data. W (W) EC 、Q EC Represents the total power consumption and the total refrigerating capacity of the chiller, mu EC，i Indicating the on state of the chiller and N indicating the number of chillers.
(3) The power consumption model of the fan and the water pump is established as follows:
in the method, in the process of the invention,and->Respectively representing rated power of the fan and the water pump.
(4) The power balance constraint conditions are:
wherein,and->Indicating the cold stored or released by the energy storage tank, < >>The cold load demand representing the whole system can be obtained by a day-ahead prediction model.
(5) Energy balance condition of cold accumulation tank
The energy storage system satisfies the following energy balance:
WS represents the energy storage capacity of the energy storage tank, epsilon represents the heat loss of the energy storage tank in unit time, eta represents the efficiency of the energy storage tank,representing the rate of the accumulator tank, superscripts in and out represent stored and released energy, respectively. Lambda (lambda) 1 、λ 1 Respectively indicates whether the state of the energy storage tank is energy storage or energy release.
(6) The upper and lower limits of the refrigerating capacity of the water chilling unit are restricted as follows:
wherein, EC Ca、representing the lowest and highest output cold of the unit respectively.
3. The two-stage optimal scheduling method of the chilled water storage air conditioning system according to claim 1, wherein the step S3 is specifically: and converting the established mixed integer nonlinear optimization scheduling model into a mixed integer linear programming model based on an SOS-2 piecewise linearization method. The relation between the power consumption and the refrigerating power of the refrigerating unit is constructed, the power consumption of the water chilling unit can be expressed as the product form of the refrigerating power (W) and the refrigerating capacity (Q), and the power consumption is as follows after finishing:
it is piecewise linearized using the SOS-2 method.
4. The two-stage optimal scheduling method of the chilled water storage air conditioning system according to claim 1, wherein the step S5 specifically comprises: and according to the more accurate load prediction in the daytime, the running number and the running state of the unit are adjusted.
(1) The actual payload to be assumed by the computer group:
wherein,the energy storage and release power of the energy storage water tank is respectively determined for the day-ahead optimal scheduling stage
(2) And adjusting the total running number of the cooling machines at the current moment. Comparative payloadObtained by day-ahead optimal schedulingAnd (3) primarily solving the number of the cold machines, and adjusting the number of the cold machine operation machines to meet the matching of supply and demand and ensure the reliability of the supply of cold. The specific principle is as follows: if the actual payload->The maximum refrigerating power which is higher than the maximum refrigerating power which is determined before the day and used for starting the cold machine is higher than the maximum refrigerating power which is determined before the day and used for indicating that the actual load is larger, and then one cold machine is required to be started; if the actual payload->The minimum refrigerating power which is lower than the minimum refrigerating power which is determined before the day and is used for starting the cold machine is lower, so that the actual load is smaller, and a cold machine is required to be stopped; otherwise, the machine runs according to the number of cold machines determined in the past. And (5) the final starting number of the cooling machine is determined through readjusting the number of the running devices time by time.
(3) And adjusting the running state of each cold machine to avoid frequent start and stop of the cold machine. The method comprises the following specific steps:
1) Counting the longest continuous start time period delta T of the chiller from the current moment k1 N is in the total running number of the cold machine to be started;
2) Calculation of DeltaT k1 And in the period, the minimum starting quantity of each moment is calculated, and the cold machine is started in sequence.
N min ＝ceil(N÷ΔT k1 )
3) Repeating the steps 2-3, and calculating the number N-N of the residual equipment to be started min ΔT k1 Counting the longest continuous start time period delta T of the residual chiller from the current moment k2 The turn-on time and number of the remaining devices are determined.
4) Repeating steps 1) -3) from the current moment to the cooling end moment, and determining the quantity of the cold machines to be started at each moment and the running state mu of each cold machine EC，i . Through readjustment of the running quantity and the running state of the cold machine, the running continuity of the equipment is ensured.
5. The two-stage optimal scheduling method of the chilled water storage air conditioning system according to claim 1, wherein the step S6 is specifically: and establishing a daytime optimal scheduling model, further optimizing the load rate, and obtaining a final scheduling result.
(1) Setting a daytime optimization target as an optimization target of a daytime operation stage and optimizing the operation power consumption at the next moment:
(2) Establishing an electric water chiller energy consumption model
COP EC，i ＝β 1 PLR EC，i 3 +β 2 PLR EC，i 2 +β 3 PLR EC，i +β 4 .
Wherein mu EC，i Is the adjusted cold machine starting state.
(3) Power constraint
(4) And (3) rapidly and accurately solving by adopting a Gurobi optimization solver to obtain the unit load rate.
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