CN115470998B - Port cold box load group power consumption consistency layering optimization scheduling method and system - Google Patents
Port cold box load group power consumption consistency layering optimization scheduling method and system Download PDFInfo
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Abstract
The utility model provides a uniformity layering optimization scheduling method and a system for port cold box load group power consumption, which carry out thermoelectric coupling on cold boxes and establish the method and the system comprise the following steps: a single cold box model of a cold box thermodynamic dynamic model, a cold box electric power model and a cold box temperature model; and then dividing the goods into a plurality of cold box clusters according to the goods types by adopting a cluster equivalent modeling method, and then adopting a cold box load cluster layered scheduling architecture to perform cold box consistency layered optimization scheduling by adopting a dynamic electricity price and cluster pre-scheduling power iterative optimization method and a refrigeration efficiency master-slave consistency algorithm (LREC) so as to realize the dynamic distribution of the cold box multi-intelligent consistency power. The invention sequentially guides the electricity consumption behavior of the cold box, integrates the advantages of centralized control and distributed control, adopts layered control based on terminal load group dispersion autonomy, reduces the control dimension and the information interaction magnitude of the cold box, and simultaneously ensures the optimization effect.
Description
Technical Field
The invention relates to a technology in the field of port energy regulation and control and management, in particular to a port cold box load group power consumption consistency hierarchical optimization scheduling method and system.
Background
With the rapid development of global sea transportation, harbors become large power consumption and emission households, and a refrigerated container (cold box) converts electric energy into cold energy through a self-contained refrigeration compressor, keeps the temperature of the cold box within an allowable range, and is one of the loads with the largest electric quantity of the harbors. At present, three main methods for optimizing the electricity consumption of the cold box are as follows: group modeling optimization method, monomer modeling centralized optimization and monomer modeling distributed optimization. From the current research situation of the current port cold box load electricity optimization scheduling, the serious difficulty and similar technical defects are mainly concentrated in the following aspects:
1) The group modeling optimization method models all cold boxes in the port as a whole, the method does not consider the optimization and adjustment flexibility caused by individual differences of the cold boxes, and meanwhile, the temperature of each cold box is difficult to ensure not to exceed the limit.
2) The port cold box is large in scale, centralized optimization taking a single cold box as a unit not only depends on a central node of centralized scheduling, but also has the problems of large calculation dimension and difficulty in quick solution, and the limited calculation speed is difficult to ensure the real-time scheduling requirement.
3) The port cold box load distribution is relatively centralized, information interconnection can be realized through a smaller local area communication network, so that the collaborative autonomy of the cold box group is realized, and the port cold box load distribution method is suitable for applying a distributed scheduling method. The port load distributed optimization scheduling model and the solving algorithm based on the multi-agent system are provided in the prior art, and each agent independently makes a decision by solving the local optimization problem. A large-scale optimization problem is divided into a plurality of small-scale local optimization problems to solve, the calculation dimension is reduced, the calculation speed is limited in improvement effect, the requirement of accurate control of the temperature of the cold box cannot be met, meanwhile, the integral optimization effect is difficult to ensure, and the limitation still exists in the actual application of the port.
Disclosure of Invention
Aiming at the problems of optimizing effect and calculating efficiency faced by optimizing and dispatching a large number of cold box load groups in a port, the invention provides a layering optimizing and dispatching method and system for power utilization consistency of the cold box load groups in the port. The power dynamic distribution algorithm of the master-slave consistency of the pre-dispatching model and the refrigeration efficiency of the iterative optimization of the dynamic electricity price of the cold box and the cluster electricity power is provided, and the cold box individual actively responds to the pre-dispatching strategy according to the top-level regulation and control signal, the temperature and the refrigeration limit value, so that the self-optimizing operation and the orderly transfer of the load power of the large-scale cold box are realized, and the optimization effect and the calculation efficiency are achieved.
The invention is realized by the following technical scheme:
the invention relates to a port cold box load group power consumption consistency layering optimization scheduling method, which carries out thermoelectric coupling on a cold box and establishes the cold box and comprises the following steps: a single cold box model of a cold box thermodynamic dynamic model, a cold box electric power model and a cold box temperature model; and then dividing a plurality of cold box clusters according to the cargo types, adopting a cold box load cluster hierarchical scheduling architecture, and carrying out cold box consistency hierarchical optimal scheduling by a dynamic electricity price and cluster pre-scheduling power iterative optimization method and a refrigeration efficiency master-slave consistency algorithm (LREC) to realize the dynamic distribution of the cold box multi-intelligent body consistency power.
Drawings
FIG. 1 is a schematic diagram of the working principle of a cold box;
in the figure: (a) cold box structure and cold cycle, (b) electrical characteristics;
FIG. 2 is a schematic diagram of a port large-scale cold box load hierarchical clustered scheduling architecture;
FIG. 3 is a LREC algorithm flow chart;
FIG. 4 is a schematic overall view of the present invention;
FIG. 5 is a schematic diagram of power limits for a single cold box at different temperature settings;
FIG. 6 is a diagram of an optimized scheduling result;
FIG. 7 is a schematic diagram of an electricity price iteration process and a load factor;
FIG. 8 is a graph showing the comparison of the time-sharing power of the cold box in different methods;
fig. 9 is a schematic diagram of a convergence procedure of LREC algorithm;
in the figure: (a) A refrigeration efficiency factor changing process, (b) a power demand changing process of each cold box;
FIG. 10 is a schematic diagram of the ambient temperature and the internal temperature of the cold box;
FIG. 11 is a graph showing the LREC algorithm and the temperature fluctuation of the cold box in CR1 under average distribution;
FIG. 12 is a graph showing the uniform convergence rate of CR1 at different scales;
FIG. 13 is a graph of the method and global optimum cold box time-sharing power;
FIG. 14 is a schematic diagram of a system according to the present invention.
Detailed Description
As shown in fig. 14, a port cold box load group power consumption consistency hierarchical optimization scheduling system according to the present embodiment includes: the system comprises a data acquisition module, a load processing module, a decision optimization module and a consistency control module, wherein: the data acquisition module acquires port basic load, basic electricity price signals and parameters of port cold boxes and outputs the parameters to the load processing module and the decision optimization module, the load processing module is responsible for establishing a cold box thermoelectric coupling model and dividing cold box clusters, the optimization module calculates the cold box cluster pre-dispatching power at all moments in a dispatching cycle by taking the minimum cold box electricity cost as a target and taking the cold box temperature constraint, the power constraint and the energy conversion constraint as conditions, and the consistency control module dynamically distributes the consistent power of the cold boxes in the clusters according to the cold box cluster pre-dispatching power so as to realize the power requirements of the cold boxes.
As shown in fig. 4, the method for consistent hierarchical optimization scheduling of port cold box load group power consumption based on the system according to the present embodiment includes:
step 1) carrying out thermoelectric coupling on the cold box, and establishing the cold box comprises the following steps: a cold box thermodynamic dynamic model, a cold box electric power model and a cold box temperature model.
The thermodynamic dynamic model of the cold box is Wherein: Δt is a unit scheduling period(s); t is the internal temperature (DEG C) of the cold box; t (T) amb Is the external ambient temperature (DEG C); sigma is a correction factor introduced to account for solar radiation effects; a is the external surface area (m) 2 )；k t Is the heat transfer coefficient (W/m) 2 K); m and c are the mass (kg) and specific heat capacity (kJ/kg.K) of the contents of the tank, respectively; p (P) R Is the refrigerating capacity (kW).
As shown in fig. 1 (a), the cold box includes: the built-in refrigeration compressor is arranged at the front end of the cold box, and the temperature in the box is controlled by power supply of the cold chain plug. When the cold box is in a refrigerating state, the refrigerating compressor works, cold air is blown out from the ventilation pipe in the box, flows through the ventilation guide rail at the bottom of the box and surrounds the goods, returns to the refrigerating device for carrying out loop through the cold air suction inlet on the front end wall, and returns to the container through the refrigerating device again. To improve the cooling effect, the inside of the tank wall, the tank top and the tank bottom are paved with heat insulating materials.
As shown in FIG. 1 (b), for the unordered power utilization of the cold box load power utilization model, the operation characteristics of a single cold box are shown, wherein t is as follows on For the time when the cold box is in a refrigerating state, t off For the time when the cold box is in the uncooled state.
The actual power consumption of the cold box is related to the refrigerating capacity, and the power consumption at each moment cannot exceed the limit. Generally, the higher the set temperature inside the cold box, the higher the available refrigeration capacity and the higher the maximum power usage.
The electric power model for the cold box is as followsWherein: the StR (t) is the running state of the cold box, stR (t) =1 is refrigeration, and StR (t) =0 is non-refrigeration; p (P) eR The electric power is used for the cold box; />And->The upper limit and the lower limit of the electric power used by the cold box are respectively; ERR is the refrigeration energy efficiency ratio, and ERR of the cold box is different at different set temperatures.
The temperature model of the cold box is T min ≤T(t)≤T max Wherein: t (T) max And T min The upper limit and the lower limit of the temperature in the box are respectively, namely, the temperature in the box is required to be kept within a certain range without damaging goods.
And 2) dividing the model into a plurality of cold box clusters (Cluster of Reefers, CR) according to the type of goods by adopting a cluster equivalent modeling method, wherein each cold box which is programmed into the same cluster has the same specific heat capacity, temperature set value and temperature allowable range of the goods, and the power consumption model of each CR can adopt a frame of a single cold box power consumption model by equivalent of all the cold boxes in the cluster into a large-capacity cold box set, and the mass and the size of the frame take the sum of the mass and the size of all the cold boxes in the cluster.
And 3) adopting a cold box load group hierarchical scheduling architecture shown in fig. 2, performing cold box consistency hierarchical optimization scheduling by a refrigerating efficiency master-slave consistency method (LREC), and avoiding the conventional management mode of forming an acquisition-processing-control loop through a scheduling center, thereby achieving the aim of good decentralized autonomous-centralized coordination from bottom to top.
The hierarchical scheduling strategy preferably further realizes high-speed communication by means of a 5G network and an optical fiber network.
The hierarchical scheduling architecture includes a port scheduling center (Port Dispatching Center, PDC), a cold box load aggregator (Reefer Aggregator, RFA), and a cold box (Reefer, RF) load group.
The top port dispatching center supplies power for the base load and the cold box in the jurisdiction range, and the cold box power demand is guided to respond by issuing the time-of-use electricity price signal, so that the load power fluctuation is stabilized. The PDC receives the basic electricity price, the basic load information and the aggregated cold box load demand of the optimization period, calculates new electricity price according to the total load and the elasticity of the electricity price on the port power demand in the port area, and transmits the new electricity price to the load aggregator. The cold box can be powered up in the load low valley period as much as possible through the guidance of the electricity price signal.
And the middle layer load aggregator is connected with the dispatching center and delivers the cold box load group. After receiving the electricity price signal, the RFA takes the lowest cost as an optimization calculation target, calculates the optimal power curve (i.e. a pre-scheduling plan) of each CR according to the electricity price and the CR model parameters, and delivers the optimal power curve to the corresponding cluster. Meanwhile, RFA collects and integrates cold box load information, aggregates each CR load demand curve into a total demand curve, and reports the total demand curve to PDC.
The bottom layer cold box load group groups aggregate a certain number of cold boxes with similar electricity utilization characteristics into a group, and a communication network is added among the cold boxes in the group. And distributing an intelligent agent for each cold box in the cluster, and in each scheduling period, each cold box intelligent agent only communicates with the adjacent intelligent agent and receives a pre-scheduling plan instruction of the CR issued by an upper layer through a leading cold box intelligent agent (Leader) as a target of consistency control. After executing a certain protocol, each cold box can obtain the own power consumption requirement by combining with the actual constraint of the cold box. The CR then uploads its own actual demand curve to the upper layer.
As shown in fig. 3, the refrigeration efficiency master-slave consistency method includes:
3.1 Updating PDC regulation signals): the regulation target of the PDC is to reduce the peak-valley difference of the system load. And by utilizing a reasonable virtual electricity price regulation signal, the cold box load is guided to be transferred from the operation peak to the valley of the port power grid, so that the peak-valley difference of the system load can be reduced, the operation risk of the port power grid can be reduced, and the operation cost of the cold box can be reduced. The predicted electricity price of the optimization period and the elasticity of the electricity price on the electric power demand of the cold box are known, and then the virtual electricity price regulating signal is updated to be at the nth iterationWherein: i=1, 2, …, M, j=1, 2, …, N i M is the number of clusters, N i Is CR (CR) i The number of cold boxes; EP (t, n) is the electricity price at the time t obtained by PDC calculation at the nth iteration; />To predict electricity price; a is an electricity price elastic factor, and is the change of unit electricity price when the power demand is changed by 1 kW; TSL is the load factor.
3.2 Increasing the load factor when the port total load power exceeds the high load threshold, thereby transferring the cold box load from peak to other periods, specifically:
wherein: p (P) total The total load power in the harbor area; p (P) load Is the base load in the harbor district except the cold box; p (P) thres Is a harbor area high load threshold; ρ is the overload penalty factor.
3.3 According to the electricity price information issued by the PDC and the electricity behavior characteristics of each CR, RA establishes a pre-scheduling strategy of each CR, wherein each iteration is performed, the RA aims to reduce the running cost as much as possible under the condition of meeting the charging requirement, and the pre-scheduling strategy comprises the following specific steps:wherein: p (P) eR,i CR formulated for RA i Is provided.
The electrical characteristics of each CR are described by a single cold box model. In addition, in order to perform optimal scheduling in a continuous period, the equivalent temperature at the end of the scheduling period of each CR is made to be the same as the initial temperature, specifically: t (T) i (t 0 )＝T i (t f ) Wherein: t (T) i (t 0 ) And T i (t f ) CR respectively i Equivalent temperature at the beginning and end of the scheduling period.
Step 4) dynamic distribution of cold box multi-intelligent body consistency power, which comprises the following steps:
4.1 Calculating refrigerating efficiency factor of cold box to make power distribution so as to make cold box in cluster fully respond to scheduling instruction of RA and CR i Refrigeration efficiency factor of jth cold boxWherein: the numerator is the cooling rate, and the denominator is the cooling margin of the cold box at the moment t. And the cooling efficiency factors of the cold boxes in the same cluster after total power distribution at the same moment are consistent and serve as the standard of correct distribution, and the larger the cooling margin is, the higher the cooling speed of the cold boxes is, and the larger the corresponding power requirement is.
4.2 Through a multi-intelligent refrigeration efficiency master-slave consistency method, the problem of centralized control is solved in a distributed mode, each CR is regarded as a multi-agent system network, and each cold box is distributed with an agent, specifically: by a laplace matrix l= [ L jv ]Reflecting the topology of the multi-agent network,wherein: b= [ B ] jv ]Adjacency matrix for multi-agent network, b jv >0 is the connection weight between agent j and agent v; the refrigeration efficiency factor is selected as the consistent variable of each cold box in CR, and a discrete time first order consistency algorithm framework is used, CR i Refrigeration efficiency factor +.1 of jth following cold box intelligent agent in kth+1 iteration>Wherein: n (N) i Is CR (CR) i The number of the cold boxes; row random matrix->At the kth iteration [ j, v ]]Item, i.e.)>Re-calculating the time t CR i Power command difference of->Make CR i The actual power requirement of (2) is kept as consistent as possible with the pre-scheduling strategy, and CR is finally obtained i The refrigeration efficiency factor updating rule of the leading type agent is as follows:wherein: mu (mu) i Is CR (CR) i Is a positive scalar that controls the convergence rate of the LREC algorithm.
Preferably, when LREC algorithm is employed between cold boxes, safety constraints are further added to prevent cold box temperature or power violations. When at a certain moment CR i The j-th cold box of the temperature reaches the lower limit, the cold box stops refrigerating, stR ij (t) =0, corresponding power P eR,ij And also zero. At this time, the connection weight with the cold box intelligent body j becomes zero, specifically b jv ＝0,v＝1,2,…,N i The method comprises the steps of carrying out a first treatment on the surface of the When CR is i When the power consumption of the j-th cold box reaches the limit, the safety inspection and correction are required, specifically:similarly, the connection weight with the cold box agent j becomes zero at this time.
As can be seen in fig. 3, each iteration, the leading cold box agent needs to perform the entire flow, while the following agent only needs to perform the steps of the basic master-slave consistency algorithm and security constraint check within the small box shown in fig. 3. When the safety constraint of a certain cold box is over the limit, the cold box is immediately withdrawn from the multi-agent network, and the corresponding network information connection weight also needs to be modified. When CR is i Actual power demand and prescheduling strategy difference Δp error,i (t) is less than the maximum allowable deviation ε i When the algorithm iteration terminates.
The convergence rate of the LREC algorithm may be adjusted by adjusting the power error adjustment factor μ i To control. The dominant type intelligent agent will respond to the power command difference delta P error,i (t) increasing or decreasing the refrigeration efficiency factor, the update amplitude of which is affected by the adjustment factor. When the adjustment factor is too large, the amplitude of the update consistency state variable of the leading agent is too large, which may cause the algorithm to not converge. When the adjustment factor is too small, the amplitude of the update consistency state variable of the leading type intelligent agent is smaller, and the corresponding convergence speed is slower. Therefore, proper adjustment factors are required to be selected for protectionThe LREC algorithm has better stability and faster convergence rate.
For N containing i CR of table cold box i When the refrigeration efficiency factor of the leading cold box intelligent body is increased by mu i ΔP error,i (t) average increase in refrigeration efficiency factor per cold box agent mu i ΔP error,i (t)/N i ，CR i Is the total power demand increment of (1)In the LREC algorithm, the power command difference |DeltaP is used error,i (t)|＜ε i As a convergence criterion, the algorithm converges to a sufficient condition of |delta P error,i (t)-ΔP eR,i (t)|＜ε i The method comprises the following steps:
i.e. mu i The choice of (c) is mainly dependent on CR i Number of medium cooling boxes N i Characteristics and internal temperature of each cold box and maximum allowable deviation epsilon i . Mu when power instructions of different scheduling periods are re-allocated in different clusters i The value of (c) can be updated according to the value range.
4.3 As shown in fig. 4, PDC updates electricity prices according to the new power demand curve, specifically: under a layered scheduling architecture, PDC updates electricity price information and then sends the electricity price information to RFA, RFA decides a CR pre-scheduling strategy according to the electricity price information, and each cold box in the cluster calculates own power according to LREC algorithm, so that the actual power requirement of CR is kept consistent with the pre-scheduling plan as much as possible in a constraint range. The resulting load power curve is then aggregated by RFA and uploaded to PDC.
Through specific practical experiments, taking Shandong sunshine harbor as an example, a harbor yard comprises 3000 cold boxes. The parameters of the cold box model used in the present invention are shown in table 1. Consider 20 different temperature setpoints (-23 ℃ C. To +14 ℃ C.) and cargo allowing a range of temperature variation (specific heat capacity of cargo from 1.46 to 4.06 kJ/kg.K). The load of the cold box filled with the similar goods is subjected to log normal distribution. The hysteresis width of the upper limit and the lower limit of the internal temperature of the cold box is 1 ℃, and the initial temperature is equal to the set temperature. The upper power limit and refrigeration capacity of a single cold box at different temperature settings are shown in fig. 5. ERR at different set temperatures can be derived from the ratio of the refrigeration capacity to the upper power limit. The upper/lower power limit ratio of the cold box is 9/1.
The port high load threshold is 80MW and the overload penalty factor is 0.5. The electricity price elastic factor a takes 5 multiplied by 10 < -4 > yuan
/MW. Each time interval Δt was 0.5 hours with a scheduling period of 24 hours.
Table 1 refrigerated container parameters
The embodiment further sets unordered electric field scene of cold box as contrast. In the unordered electric field scene, the electricity price and the port base load of the cold box are not considered. When the internal temperature exceeds the upper temperature limit, the cold box starts the cooling compressor and refrigerates with the maximum power; when the internal temperature is lower than the lower temperature limit, the cold box stops refrigerating. As shown in FIG. 6, the port area total load curve comparison and the base load curve under the field scene of hierarchical optimal scheduling and unordered use are shown. The evolution of each electricity price and the load factor TSL are shown in fig. 7. Compared with unordered electricity consumption, the method transfers a large amount of cold box load demands to valley time periods through electricity price guidance, effectively balances peak-valley differences of load in a harbor area under the condition of reducing electricity consumption cost of the cold box, and well improves the running condition of the system. The whole optimization process can be converged only by three iterations, and it is noted that electricity price prediction is adopted in the first round, and then appropriate adjustment is performed according to the method.
The cold box optimized by the method has higher electricity economy, and compared with unordered electricity, the peak-valley difference of the port load is reduced by 34.14 percent.
The problem of optimizing and scheduling the large-scale cold box load group is a constrained mixed integer linear programming problem. In this embodiment, 288000 decision variables and 432000 constraints are involved if a single cold box is used as an independent scheduling object, and it is practically impossible to solve this problem using a centralized optimization algorithm because it requires a very long calculation time. There is a prior art proposal for an optimization method based on Multi-Agent Systems (MAS) [10]. The MAS method can reduce computational pressure by dividing this large-scale optimization problem into 3000 small-scale optimization problems. The effectiveness of the method in improving the optimization efficiency is proved by comparing the method with a MAS method.
The optimal power requirements for the port cold box load obtained by the method and the MAS method are shown in figure 8. Table 2 compares the optimization results of the cold box and the required optimization time for both methods. It can be seen that the results obtained by the method are very similar to those obtained by the MAS method, and only differ by 0.02%, but the optimization time required by the method is reduced by 79.12% compared with the MAS method, and the calculation efficiency is improved by about 4 times. This is because the improvement of the computation efficiency by the MAS method depends on a high-performance distributed parallel computing unit, and thus the method is advantageous in terms of computation time when the same computing device is used.
Table 2 optimization effects of two methods
As shown in FIG. 1, the consistency topology of the information state of each CR cold box is calculated by using the connection weight b with information exchange ij Let 1 be the value. In this section CR 1 As a subject, other CR simulation analyses were similar. Randomly extracting CR at a certain moment 1 The process of consistency convergence of the refrigeration efficiency factor of the inner cooling box is shown in fig. 9. As can be seen from fig. 9 (a), the refrigeration efficiency factor of the cold box gradually reaches uniformity after continuous information interaction. As can be seen from fig. 9 (b), when a prescheduled instruction is received, the dominant cold box agent will respond first, and then the other agents will follow the dominant agent to respond to the prescheduled instruction.
The temperature change in each cooling box after optimization is shown in fig. 10. In the figure, the cold boxes with initial internal temperatures from low to high respectively belong to CR 1 ～CR 20 . It can be seen that the internal temperature of the cold box is stable and remains within the allowable variation range throughout the scheduling period, despite the ambient temperature variations. In order to further verify that the LREC power redistribution algorithm can take into account the actual constraint of each specific cold box and reasonably distribute the capability of the cluster pre-scheduling strategy, an average distribution method is selected for simulation comparison analysis. FIG. 11 shows the respective moments CR in two ways 1 Maximum and minimum values of all cold box temperatures. It can be seen that the method of power average distribution does not consider different characteristics and practical constraints of each cold box, which results in out-of-limit internal temperature of part of the cold boxes, and the LREC algorithm can make each cold box always within the safe operation constraint range.
Generally, LREC algorithms require more iterations to agree upon as the number of cold boxes in the same cluster increases. The effect of different numbers of cold boxes on the speed of consistency convergence is discussed herein. Based on the above calculation example, CR is set 1 The cold box scale of (2) is (20, 40,60, …, 200). CR at different scales 1 The intra-consistency convergence speed is shown in fig. 12. It can be seen that the median value of the number of iteration steps increases only linearly. If a centralized optimization mode is adopted, the solving time increases exponentially with the number of cold boxes. Thus, the LREC algorithm is able to handle relatively many cold boxes in a reasonable time.
The method and the global optimization algorithm are utilized to simulate the calculation example of one 1000 cold boxes. The implementation process of the global optimization algorithm is as follows: after the PDC issues the electricity price, a single cold box is used as a scheduling object, the RA solves the optimal power demand curve of all the cold boxes, and then the optimal power demand curve is uploaded to the PDC, and the same process is repeated until the electricity price converges. The optimal power requirements of the cold box obtained by the method and the centralized method are shown in fig. 13. It can be seen that the results obtained with this method are very similar to those obtained with the centralized method. In this embodiment, the running cost obtained by global optimization is 0.5% lower than that of the method, and the small difference is almost negligible, so that the whole optimization target can be close to the optimal by adopting the method. Both methods were simulated on a personal computer, and the method converged to a solution after 3 iterations, requiring 156s, while the centralized algorithm requires 8720s. In practical applications, the number of cold boxes stored in ports is often thousands or even tens of thousands, and the solution is almost impossible to solve by using a global optimization method on conventional computing equipment.
In summary, the consistency hierarchical optimization scheduling method can give consideration to the optimization effect and the solving speed, has strong adaptability, and can realize the efficient optimization scheduling of the cold box load groups with different scales. The method can effectively reduce the electricity cost of the cold boxes and stabilize the peak-valley difference of port loads while meeting various operation constraint conditions of each cold box.
Compared with the prior art, the invention adopts a hierarchical scheduling strategy and establishes a plurality of mutually coordinated small communication networks in the cold box load group through the pre-scheduling model of the cold box dynamic electricity price and the cluster electricity power iterative optimization, the aim of good scattered autonomy-centralized coordination is achieved from bottom to top by avoiding the conventional management mode of forming an acquisition-processing-control loop through a scheduling center, the solving speed is greatly improved through a cold box refrigerating efficiency consistency algorithm, the solving efficiency and the solving accuracy are not influenced by the quantity of the cold boxes, and the invention has good robustness for a large-scale cold box optimizing scene, so that the cold box load demand is transferred from a peak period to a valley period, the cold box electricity cost can be effectively reduced, the port load peak-valley difference can be stabilized, and the system operation efficiency can be improved.
In a specific implementation, the application also provides a computer storage medium for port cold box load group power consumption consistency hierarchical optimization scheduling, wherein the computer storage medium can store a program, and the program can comprise part or all of the steps in the port energy system embodiment provided by the invention when being executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like. It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in essence or what contributes to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
The foregoing embodiments may be partially modified in numerous ways by those skilled in the art without departing from the principles and spirit of the invention, the scope of which is defined in the claims and not by the foregoing embodiments, and all such implementations are within the scope of the invention.
Claims (5)
1. The method for uniform hierarchical optimization scheduling of port cold box load group power consumption is characterized by comprising the steps of performing thermoelectric coupling on the cold boxes and establishing the cold boxes: a single cold box model of a cold box thermodynamic dynamic model, a cold box electric power model and a cold box temperature model; then, dividing the cargo type into a plurality of cold box clusters by adopting a cluster equivalent modeling method, and then adopting a cold box load cluster layered scheduling architecture to perform cold box consistency layered optimization scheduling by adopting a refrigeration efficiency master-slave consistency algorithm (LREC) so as to realize the dynamic distribution of cold box multi-intelligent body consistency power;
the thermodynamic dynamic model of the cold box is Wherein: Δt is a unit scheduling period(s); t is the internal temperature (DEG C) of the cold box; t (T) amb Is the external ambient temperature (DEG C); sigma is a correction factor introduced to account for solar radiation effects; a is the external surface area (m) 2 )；k t Is the heat transfer coefficient (W/m) 2 K); m and c are the mass (kg) and specific heat capacity (kJ/kg.K) of the contents of the tank, respectively; p (P) R Is the refrigerating capacity (kW);
the electric power model for the cold box is as followsWherein: the StR (t) is the running state of the cold box, stR (t) =1 is refrigeration, and StR (t) =0 is non-refrigeration; p (P) eR The electric power is used for the cold box; />And->The upper limit and the lower limit of the electric power used by the cold box are respectively; ERR is the refrigeration energy efficiency ratio, and ERR of the cold box is different at different set temperatures;
the temperature model of the cold box is T min ≤T(t)≤T max Wherein: t (T) max And T min The upper limit and the lower limit of the temperature in the box are respectively;
the consistency hierarchical optimization scheduling specifically comprises the following steps:
step 1) carrying out thermoelectric coupling on the cold box, and establishing the cold box comprises the following steps: a single cold box model of a cold box thermodynamic dynamic model, a cold box electric power model and a cold box temperature model;
step 2) dividing the power utilization model into a plurality of cold box clusters according to the type of goods by adopting a cluster equivalent modeling method, wherein each cold box which is coded into the same cluster has the same specific heat capacity, temperature set value and temperature allowable range of the goods, and the power utilization model of each CR adopts a frame of a single cold box power utilization model by effectively forming all the cold boxes in the cluster into a large-capacity cold box set, and the mass and the size of the frame take the sum of the mass and the size of all the cold boxes in the cluster;
step 3) establishing a cold box load group hierarchical dispatching framework, carrying out cold box consistency hierarchical optimization dispatching by a refrigeration efficiency master-slave consistency method, avoiding the conventional management mode that a 'acquisition-processing-control' loop is formed by a dispatching center, and achieving the purpose of good scattered autonomy-centralized coordination from bottom to top;
the layered dispatching architecture comprises a port dispatching center, a cold box load aggregator and a cold box load group;
the port dispatching center supplies power for the base load and the cold box in the jurisdiction range, and guides the cold box power requirement to respond by issuing a time-of-use electricity price signal so as to stabilize load power fluctuation; the PDC receives the basic electricity price, basic load information and aggregated cold box load demand of the optimized period, calculates new electricity price according to total load and electricity price in the harbor area and elasticity of the electricity price on the harbor power demand, and transmits the new electricity price to a load aggregator; the cold box can be powered up in the load low valley period as much as possible through the guidance of electricity price signals;
the cold box load aggregator is connected with the dispatching center and delivers the cold box load group; after receiving the electricity price signal, the RFA takes the lowest cost as an optimization calculation target, and calculates the optimal power curve of each CR according to the electricity price and the CR model parameters, namely, a pre-scheduling plan is adopted and the optimal power curve is issued to a corresponding cluster; meanwhile, RFA collects and integrates cold box load information, aggregates each CR load demand curve into a total demand curve, and reports the total demand curve to PDC;
the cold box load group is used for aggregating a certain number of cold boxes with similar electricity utilization characteristics into a cluster, and a communication network is added among the cold boxes in the cluster; distributing an intelligent agent for each cold box in the cluster, and in each scheduling period, each cold box intelligent agent only communicates with the adjacent intelligent agent and receives a pre-scheduling plan instruction of the CR issued by an upper layer through the leading cold box intelligent agent as a target of consistency control; after executing a certain protocol, combining with the actual constraint of the cold box, each cold box can obtain the power requirement of the cold box; then the CR uploads the actual demand curve of the CR to an upper layer;
the cluster layering optimization scheduling method comprises the following steps:
3.1 Updating PDC regulation signals): the PDC is regulated and controlled to reduce the peak-valley difference of the system load; the reasonable virtual electricity price regulation signals are utilized to guide the cold box load to be transferred from the operation peak to the valley of the port power grid, so that the peak-valley difference of the system load can be reduced, the operation risk of the port power grid can be reduced, and the operation cost of the cold box can be reduced; the predicted electricity price of the optimization period and the elasticity of the electricity price on the electric power demand of the cold box are known, and then the virtual electricity price regulating signal is updated to be at the nth iterationWherein: i=1, 2, …, M, j=1, 2, …, N i M is the number of clusters, N i Is CR (CR) i The number of cold boxes; EP (t, n) is the electricity price at the time t obtained by PDC calculation at the nth iteration; />To predict electricity price; a is an electricity price elastic factor, and is the change of unit electricity price when the power demand is changed by 1 kW; TSL is the load factor;
3.2 Increasing the load factor when the port total load power exceeds the high load threshold, thereby transferring the cold box load from peak to other periods, specifically: wherein: p (P) total The total load power in the harbor area; p (P) load Is the base load in the harbor district except the cold box; p (P) thres Is a harbor area high load threshold; ρ is an overload penalty factor;
3.3 According to the electricity price information issued by the PDC and the electricity behavior characteristics of each CR, RA establishes a pre-scheduling strategy of each CR, wherein each iteration is performed, the RA aims to reduce the running cost as much as possible under the condition of meeting the charging requirement, and the pre-scheduling strategy comprises the following specific steps:wherein: p (P) eR,i CR formulated for RA i Is scheduled in advance;
the electricity utilization characteristics of each CR are described by a single cold box model; in addition, in order to perform optimal scheduling in a continuous period, the effective temperature at the end of the scheduling period of each CR is made to be the same as the initial temperature, specifically: t (T) i (t 0 )＝T i (t f ) Wherein: t (T) i (t 0 ) And T i (t f ) CR respectively i The effective temperature at the beginning and the end of the scheduling period;
step 4) dynamic distribution of cold box multi-intelligent body consistency power, which comprises the following steps:
4.1 Calculating refrigerating efficiency factor of cold box to make power distribution so as to make cold box in cluster fully respond to scheduling instruction of RA and CR i Refrigeration efficiency factor of jth cold boxWherein: the numerator is the cooling rate, and the denominator is the cooling margin of the cold box at the moment t; the refrigeration efficiency factors of the cold boxes in the same cluster after total power distribution at the same moment are consistent and serve as the standard of correct distribution, and the larger the cooling margin is, the higher the cooling speed of the cold boxes is, and the larger the corresponding power requirement is;
4.2 Through a multi-intelligent refrigeration efficiency master-slave consistency method, the problem of centralized control is solved in a distributed mode, each CR is regarded as a multi-agent system network, and each cold box is distributed with an agent, specifically: by a laplace matrix l= [ L jv ]Reflecting the topology of the multi-agent network,wherein: b= [ B ] jv ]Adjacency matrix for multi-agent network, b jv >0 is the connection weight between agent j and agent v; the refrigeration efficiency factor is selected as the consistent variable of each cold box in CR, and a discrete time first order consistency algorithm framework is used, CR i The (j) th following cold box intelligent agent (k+1) th stackRefrigeration efficiency factor at generation>Wherein: n (N) i Is CR (CR) i The number of the cold boxes; row random matrix->At the kth iteration [ j, v ]]Item, i.e.)>Re-calculating the time t CR i Power command difference of->Make CR i The actual power requirement of (2) is kept as consistent as possible with the pre-scheduling strategy, and CR is finally obtained i The refrigeration efficiency factor updating rule of the leading type agent is as follows:wherein: mu (mu) i Is CR (CR) i Is a positive scalar, controls the convergence rate of the LREC algorithm;
4.3 PDC updates electricity prices according to the new power demand curve, specifically: under a layered scheduling architecture, PDC updates electricity price information and then sends the electricity price information to RFA, the RFA determines a CR pre-scheduling strategy according to the electricity price information, and each cold box in the cluster calculates and obtains own power according to LREC algorithm, so that the actual power requirement of CR is kept consistent with the pre-scheduling plan as much as possible in a constraint range; the resulting load power curve is then aggregated by RFA and uploaded to PDC.
2. The method for consistent hierarchical optimization scheduling of port cold box load group power consumption according to claim 1, wherein when LREC algorithm is adopted between cold boxes, safety constraint is further added to prevent cold box temperature or power from exceeding limit: when at a certain moment CR i When the temperature of the j-th cold box reaches the lower limitStop cooling, stR ij (t) =0, corresponding power P eR,ij Is also zero; at this time, the connection weight with the cold box intelligent body j becomes zero, specifically b jv ＝0,v＝1,2,…,N i The method comprises the steps of carrying out a first treatment on the surface of the When CR is i When the power consumption of the j-th cold box reaches the limit, the safety inspection and correction are required, specifically:similarly, the connection weight with the cold box agent j becomes zero at this time.
3. The method for uniform hierarchical optimization scheduling of port cold box load group power consumption according to claim 1, wherein when the safety constraint of a certain cold box is over-limited, the cold box is immediately withdrawn from the multi-agent network, the corresponding network information connection weight also needs to be modified, and when the CR is implemented i Actual power demand and prescheduling strategy difference Δp error,i (t) is less than the maximum allowable deviation ε i When the algorithm iteration terminates.
4. The method for uniform hierarchical optimization scheduling of port cold box load group power consumption according to claim 1, wherein the convergence rate of the LREC algorithm can be adjusted by adjusting the power error adjustment factor μ i The control is performed by selecting a proper adjustment factor to ensure that the LREC algorithm has better stability and faster convergence rate, and the method specifically comprises the following steps: for N containing i CR of table cold box i When the refrigeration efficiency factor of the leading cold box intelligent body is increased by mu i ΔP error,i (t) average increase in refrigeration efficiency factor per cold box agent mu i ΔP error,i (t)/N i ，CR i Is the total power demand increment of (1)In the LREC algorithm, the power command difference |DeltaP is used error,i (t)|＜ε i As a convergence criterion, the algorithm converges to a sufficient condition of |delta P error,i (t)-ΔP eR,i (t)|＜ε i The method comprises the following steps: />I.e. mu i The choice of (c) depends on CR i Number of medium cooling boxes N i Characteristics and internal temperature of each cold box and maximum allowable deviation epsilon i Mu when power instructions of different scheduling periods are allocated in different clusters i The value of (c) can be updated according to the value range.
5. A system for implementing the method for consistent hierarchical optimization scheduling of port cold box load group electricity consumption according to any one of claims 1-4, comprising: the system comprises a data acquisition module, an optimization module and a consistency control module, wherein: the data acquisition module acquires port basic load, basic electricity price signals and parameters of port cold boxes and outputs the parameters to the optimization module, the optimization module calculates cold box cluster pre-dispatching power at all moments of a dispatching cycle by taking cold box temperature constraint, power constraint and energy conversion constraint as conditions, and the consistency control module dynamically distributes the consistent power of the cold boxes in the clusters according to the cold box cluster pre-dispatching power to achieve power requirements of the cold boxes.
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