CN115470998A - Layered optimization scheduling method and system for power utilization consistency of port cold box load group - Google Patents
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Abstract
A method and a system for consistent layered optimization scheduling of power utilization of a port cold box load group are disclosed, wherein the thermoelectric coupling and establishment of the cold box comprise the following steps: the system comprises a cold box thermodynamic dynamic model, a cold box electric power model and a single cold box model of a cold box temperature model; then, a cluster equivalent modeling method is adopted, a plurality of cold box clusters are divided according to cargo types, then a cold box load group layered scheduling framework is adopted, cold box consistency layered optimization scheduling is carried out through a dynamic electricity price and cluster pre-scheduling power iterative optimization method and a refrigeration efficiency master-slave consistency algorithm (LREC), and dynamic allocation of cold box multi-intelligence consistency power is realized. The invention guides the power utilization behavior of the cold box in order, integrates the advantages of centralized control and distributed control, adopts the hierarchical control based on the dispersed autonomy of the terminal load groups, 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 management, in particular to a method and a system for layered optimization scheduling of power utilization consistency of a port cold box load group.
Background
With the rapid development of global marine transportation, seaports have become energy-consuming and large-scale households, and refrigerated containers (cold boxes) are one of the loads with the largest electricity consumption in the ports, wherein the refrigerated containers are used for converting electric energy into cold energy by self-contained refrigeration compressors and keeping the temperature of the cold boxes within an allowable range. At present, the electricity utilization optimization method of the cold box mainly comprises three methods: a group modeling optimization method, monomer modeling centralized optimization and monomer modeling distributed optimization. From the current research situation of optimal scheduling of load electricity of cold boxes of a port, the important points and similar technical defects are mainly focused on the following aspects:
1) The group modeling optimization method models all cold boxes in the port into a whole, does not consider the optimization and adjustment flexibility brought by the individual difference of the cold boxes, and simultaneously hardly ensures that the temperature of each cold box is not out of limit.
2) The scale of the port cold box is huge, the centralized optimization taking a single cold box as a unit not only depends on the central node of centralized scheduling, but also has the problems of large calculation dimension and difficulty in fast solving, and the requirement of real-time scheduling is difficult to guarantee due to limited calculation speed.
3) The load distribution of the cold boxes of the port is relatively centralized, information interconnection can be realized through a small local area communication network, so that the cooperative autonomy of the cold box groups is realized, and the method is suitable for applying a distributed scheduling method. The technology provides a port load distributed optimization scheduling model and a solving algorithm based on a multi-agent system, and each agent independently makes a decision by solving a local optimization problem. A large-scale optimization problem is divided into a plurality of small-scale local optimization problems to be solved, the calculation dimension is reduced, the calculation speed improvement effect is limited, the requirement for accurate control of the temperature of the cold box cannot be met, the overall optimization effect is difficult to guarantee, and the limitation still exists when the method is actually applied to ports.
Disclosure of Invention
The invention provides a method and a system for optimizing and scheduling power consumption consistency of a port cold box load group in a layered mode aiming at the difficult problems of optimization effect and calculation efficiency of optimization and scheduling of a large number of cold box load groups of the port. A pre-scheduling model for iterative optimization of cold box dynamic electricity price and cluster electricity power and a dynamic power distribution algorithm for master-slave consistency of refrigeration efficiency are provided, and cold box individuals actively respond to a pre-scheduling strategy according to a top-layer regulation and control signal, temperature and refrigeration limit values, so that self-optimization-seeking operation and ordered load power transfer of large-scale cold boxes 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 layered optimization scheduling method for power utilization consistency of a port cold box load group, which comprises the following steps of performing thermoelectric coupling on a cold box and establishing the cold box: the system comprises a cold box thermodynamic dynamic model, a cold box electric power model and a single cold box model of a cold box temperature model; then, a plurality of cold box clusters are divided according to the types of goods, then a cold box load group layered scheduling framework is adopted, cold box consistency layered optimization scheduling is carried out through a dynamic electricity price and cluster pre-scheduling power iterative optimization method and a refrigeration efficiency master-slave consistency algorithm (LREC), and dynamic allocation of cold box multi-intelligence consistency power is achieved.
Drawings
FIG. 1 is a schematic view of the working principle of a cold box;
in the figure: (a) cold box structure and cold cycle, (b) electricity usage characteristics;
FIG. 2 is a schematic diagram of a layered group scheduling architecture for loads of a large-scale cold box of a port;
FIG. 3 is a flow chart of the LREC algorithm;
FIG. 4 is an overall schematic of the present invention;
FIG. 5 is a schematic diagram of the power limits of a single cold box at different temperature settings;
FIG. 6 is a diagram illustrating an optimized scheduling result;
FIG. 7 is a schematic view of an electricity price iteration process and load factors;
FIG. 8 is a comparison of time-sharing power of cold box in different ways;
FIG. 9 is a diagram illustrating the convergence process of the LREC algorithm;
in the figure: (a) A refrigeration efficiency factor change process, (b) a power demand change process of each cold box;
FIG. 10 is a schematic diagram of ambient temperature and the temperature inside the cold box;
FIG. 11 is a schematic diagram of the LREC algorithm and the temperature fluctuation of the cold box in CR1 under the average distribution;
FIG. 12 is a graph showing the convergence rate of CR1 at different scales;
FIG. 13 is a schematic diagram of the method and globally optimized cold box time sharing power;
FIG. 14 is a schematic diagram of the system of the present invention.
Detailed Description
As shown in fig. 14, the system for hierarchical optimization and scheduling of power utilization consistency of a load group of cold boxes in a port according to this 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 to obtain cold box cluster pre-scheduling power at all moments of a scheduling period by taking cold box temperature constraint, power constraint and energy conversion constraint as conditions and taking cold box power consumption cost minimum as a target, and the consistency control module dynamically distributes the consistency power to the cold boxes in the clusters according to the cold box cluster pre-scheduling power to meet the power requirements of the cold boxes.
As shown in fig. 4, the method for consistent, hierarchical and optimized scheduling of power consumption of the load groups of the cold boxes in the port based on the system of the present embodiment includes:
step 1) performing thermoelectric coupling on the cold box, wherein the establishment comprises the following steps: the system comprises a cold box thermodynamic dynamic model, a cold box electric power model and a single cold box model of a cold box temperature model.
The thermodynamic dynamic model of the cold box is
As shown in fig. 1 (a), the cold box includes: the built-in refrigeration compressor arranged at the front end of the cold box is used for controlling the temperature in the cold box through 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 ventilating pipe in the cold box, flows through the ventilating guide rail at the bottom of the cold box, surrounds the goods, returns to the refrigerating device through the cold air suction inlet on the front end wall for circling, and the return air is blown back to the container through the refrigerating device again. In order to improve the cooling effect, heat insulating materials are paved on the inner parts of the box wall, the box top and the box bottom.
As shown in FIG. 1 (b), t is the running characteristic of a single cold box when the electricity model for cold box load is used for disordered electricity utilization on For the time of the cold box in a cooling state, t off The time that the cold box is in a non-refrigeration state is shown.
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 temperature set inside the cold box, the higher the available cooling capacity and the higher the maximum electric power.
The electric power model for the cold box is
Wherein: stR (t) is the running state of the cold boxStR (t) =1 for cooling, stR (t) =0 for not cooling; p is 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 respectively; ERR is the refrigeration energy efficiency ratio, and the ERR of the cold box is different under different set temperatures.
The cold box temperature model is T min ≤T(t)≤T max Wherein: t is a unit of 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 needs to be kept in a certain range without damaging goods.
And 2) adopting a Cluster equivalent modeling method, dividing the Cluster into a plurality of cold box Clusters (CR) according to the cargo types, wherein each cold box which is coded into the same Cluster has the same cargo specific heat capacity, temperature set value and temperature allowable range, all cold boxes in the Cluster are equivalent to a large-capacity cold box set, the power utilization model of each CR can adopt a frame of a single cold box power utilization model, and the mass and the size of the power utilization model are the sum of the mass and the size of all cold boxes in the Cluster.
And step 3) adopting a cold box load group hierarchical scheduling framework shown in fig. 2, performing cold box consistency hierarchical optimization scheduling by a refrigeration efficiency master-slave consistency method (LREC), avoiding a conventional management mode that an acquisition-processing-control loop is formed by a scheduling center, and achieving the purpose of good decentralized autonomous-centralized coordination from bottom to top.
The hierarchical scheduling strategy preferably further realizes high-speed communication through a 5G network and an optical fiber network.
The hierarchical scheduling architecture includes a Port scheduling Center (PDC), a cold box aggregation Aggregator (RFA), and a cold box (Reefer, RF) load group.
The top port dispatching center supplies power to the basic load and the cold box in the jurisdiction range, and guides the power demand of the cold box to respond by issuing a time-of-use electricity price signal, so that the fluctuation of the load power is stabilized. The PDC receives the basic electricity price, basic load information and aggregated cold box load requirements in the optimization time period, and then calculates new electricity price according to the elasticity of the total load and the electricity price in the port area on port power requirements and sends the new electricity price to a load aggregator. Through the guidance of the electricity price signal, the cold box can be powered as far as possible in the load valley period.
The middle-layer load aggregator is connected with the dispatching center and issues a 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 (namely a pre-dispatching plan) of each CR according to the electricity price and the CR model parameters and sends the optimal power curve to a corresponding cluster. Meanwhile, RFA collects and integrates the load information of the cold box, and each CR load demand curve is aggregated into a total demand curve and reported to PDC.
The load group of the bottom layer cold boxes integrates 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. And distributing an intelligent agent for each cold box in the cluster, wherein in each scheduling period, each cold box intelligent agent only communicates with an 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) to serve as a target of consistency control. After a certain protocol is executed, each cold box can obtain the own power consumption requirement by combining the actual constraint of the cold box. CR then uploads its actual demand curve to the upper layers.
As shown in fig. 3, the refrigeration efficiency master-slave consistency method includes:
3.1 Update PDC regulation signals: the control objective of PDC is to reduce the system load peak-to-valley difference. The reasonable virtual electricity price regulating and controlling signal is utilized to guide the load of the cold box to transfer from the peak to the valley of the port power grid operation, so that the peak-valley difference of the system load can be reduced, the port power grid operation risk can be reduced, and the cold box operation cost can be reduced. The predicted electricity price of the optimization time interval and the elasticity of the electricity price on the power demand of the cold box are known, and the virtual electricity price regulation and control signal is updated to be
Wherein: i =1,2, \8230, M, j =1,2, \8230, N i M is the number of clusters, N i Is CR i The number of cold boxes; EP (t, n) is the electricity price at the time t calculated by the PDC at the nth iteration;
to predict electricity prices; a is an electricity price elastic factor, which is the change of unit electricity price when the power demand changes by 1 kW; TSL is the load factor.
3.2 When the total load power of the port area exceeds a high load threshold value, increasing the load coefficient so as to shift the load of the cold box from the peak time to other time periods, specifically:
3.3 According to the electricity price information issued by the PDC and the electricity utilization behavior characteristics of the CRs, the RA formulates a pre-scheduling strategy of each CR, wherein during each iteration, the RA aims to reduce the operation cost as much as possible under the condition of meeting the charging requirement, and specifically comprises the following steps:
wherein: p eR,i CR enacted for RA i To the pre-dispatch plan.
The power usage characteristics of each CR are described by a single cold box model. In addition, for performing optimal scheduling in a continuous period, making the equivalent temperature at the end of the scheduling period of each CR the same as the initial temperature specifically includes: t is a unit of i (t 0 )＝T i (t f ) Wherein: t is i (t 0 ) And T i (t f ) Are respectively CR i Equivalent temperature at the beginning and end of the scheduling period.
Step 4) multi-wisdom consistency power dynamic allocation of the cold box comprises the following steps:
4.1 To calculate a cold box refrigeration efficiency factor for power allocation such that the cold boxes within the cluster are fully responsive to the scheduling instructions, CR, of RA i Cooling efficiency factor of the j-th cooling box
Wherein: 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 at the same moment after the total power distribution are consistent are taken as the standard of correct distribution, and the cooling speed of the cold box is higher when the cooling margin is larger, so that the corresponding power requirement is higher.
4.2 By a master-slave consistency method of multi-intelligent refrigeration efficiency, the centralized control problem is solved in a distributed mode, each CR is regarded as a multi-intelligent system network, and each cold box is distributed with an intelligent agent, specifically: by laplace matrix L = [ L = jv ]Reflects the topology of the multi-agent network,
wherein: b = [ B ] jv ]Being a contiguous matrix of a multi-agent network, b jv >0 is the connection weight between agent j and agent v; selecting a refrigeration efficiency factor as a consistent variable of each cold box in the CR, and using a discrete time first-order consistency algorithm framework, the CR i The cooling efficiency factor of the jth following type cold box intelligent agent at the k +1 iteration
Wherein: n is a radical of i Is CR i The number of cold boxes; row random matrix
At [ j, v ] th iteration of k]Item, i.e.
Recalculate time-t CR i Power command margin of
Make CR i The actual power requirement is kept consistent with the pre-scheduling strategy as much as possible, and finally CR is obtained i The refrigeration efficiency factor updating rule of the leading intelligent agent is as follows:
wherein: mu.s i Is CR i The power error adjustment factor of (2) is a positive scalar quantity, which controls the convergence speed of the LREC algorithm.
Preferably, when the LREC algorithm is employed between cold boxes, safety constraints are further added to prevent cold box temperature or power violations. When a certain time CR i When the temperature of the jth cold box reaches the lower limit, the cold box stops refrigerating, and StR ij (t) =0, corresponding electrical power P eR,ij Is also zero. At this time, the connection weight with the cold box agent j becomes zero, specifically, b jv ＝0,v＝1,2,…,N i (ii) a When CR is reached i When the power consumption of the jth cold box reaches the limit, the safety inspection and correction need to be carried out on the jth cold box, and the method specifically comprises the following steps:
likewise, the connection weights to cold box agent j all become zero at this time.
As can be seen in fig. 3, each iteration, the leading cold-box agent needs to perform the entire process, while the follower agent only needs to perform the basic master-slave consistency algorithm and security constraint checking steps within the small box shown in fig. 3. When the safety constraint of a certain cold box is out of 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 reached i Actual power demand and pre-scheduling policy difference Δ P error,i (t) less than the maximum permissible deviation ε i The algorithm iteration terminates.
The convergence speed of the LREC algorithm can be adjusted by adjusting the power error adjustment factor mu i To control. The leading type intelligent agent can obtain the difference delta P according to the power instruction error,i (t) increasing or decreasing the refrigeration efficiency factor, the magnitude of the update being affected by the adjustment factor. When the adjustment factor is overWhen the state variable is large, the amplitude of the state variable of the leading agent updating consistency is too large, and the algorithm may not converge. When the adjustment factor is too small, the amplitude of the state variable of the leading type intelligent agent for updating the consistency state is small, and the corresponding convergence speed is slow. Therefore, a proper adjustment factor needs to be selected to ensure that the LREC algorithm has better stability and faster convergence speed.
To contain N i CR of platform cold box i When the refrigeration efficiency factor of the leading cold box agent is increased mu i ΔP error,i (t), the intelligent refrigeration efficiency factor of each refrigeration box increases mu on average i ΔP error,i (t)/N i ，CR i Is increased by a total power demand of
In the LREC algorithm, the difference | Δ P is commanded as power error,i (t)|＜ε i For the convergence criterion, the sufficient condition for the convergence of the algorithm is | Δ P error,i (t)-ΔP eR,i (t)|＜ε i Namely:
i.e. mu i Is mainly determined by CR i Number of cold boxes N i Characteristics and internal temperature of each cold box and maximum allowable deviation epsilon i . Redistributing power instructions for different scheduling periods within different clusters, mu i The value of (b) can be updated according to the value range.
4.3 As shown in fig. 4, the PDC updates the electricity price according to the new power demand curve, specifically: under a layered scheduling framework, the PDC updates the electricity price information and then sends the electricity price information to the RFA, the RFA determines a pre-scheduling strategy of the CR according to the electricity price information, and each cold box in the cluster calculates the electricity utilization power of the cold box according to the LREC algorithm, so that the actual power requirement of the CR is kept as consistent as possible with a pre-scheduling plan in a constraint range. The resulting load power curves are then aggregated by RFA and uploaded to PDC.
Through specific practical experiments, taking a Shandong sunshine port as an example, a port yard comprises 3000 cold boxes. The cold box model parameters used in the present invention are shown in table 1. Consider 20 different temperature setpoints (-23 ℃ to +14 ℃) and cargo (cargo specific heat capacity from 1.46 to 4.06kJ/kg. K) within the allowable temperature variation range. The load of the cold box filled with the same kind of goods is distributed according to the logarithmic normal. The hysteresis width of the upper and lower temperature limits in 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 set points are shown in fig. 5. ERR at different set temperatures can be derived from the ratio of refrigeration capacity to the upper power limit. The upper/lower power limit ratio of the cold box is 9/1.
The high load threshold of the port is 80MW, and the overload penalty factor is 0.5. The electrovalence elastic factor a is 5 x 10-4 yuan
and/MW. Each time interval Δ t is 0.5 hours with a 24 hour scheduling period.
Table 1 refrigerated container parameters
The embodiment further sets the unordered power utilization scene of the cold box as a comparison. In the unordered electricity utilization scene, the cold box power utilization does not consider the electricity price and the port base load. When the internal temperature exceeds the upper temperature limit, the cold box starts a cooling compressor and performs refrigeration with the maximum power; and when the internal temperature is lower than the lower temperature limit, the cold box stops refrigerating. As shown in fig. 6, the comparison of the total load curve of the port area and the base load curve in the scenarios of hierarchical optimal scheduling and unordered power utilization are performed. The power rate evolution and the load factor TSL for each round are shown in fig. 7. Compared with disordered power utilization, the method has the advantages that a large amount of load demands of the cold box are transferred to the valley period through electricity price guide, the load peak-valley difference of the harbor area is effectively stabilized under the condition that the power utilization cost of the cold box is reduced, and the running condition of the system is well improved. The whole optimization process can be converged only by iterating three times, and it is noted that the electricity price prediction is adopted in the first round and then is properly adjusted according to the method.
The cold box optimized by the method has higher electricity economy, and compared with disordered electricity utilization, the difference of the load peak and valley of the port is reduced by 34.14%.
The optimized scheduling problem of the large-scale cold box load group is a constrained mixed integer linear programming problem. If a single cold box is used as an independent scheduling object in the present embodiment, 288000 decision variables and 432000 constraints are involved, and it is practically impossible to solve this problem by using a centralized optimization algorithm because it requires a very long calculation time. There is a technology proposed an optimization method based on Multi-Agent Systems (MAS) [10]. The MAS process can reduce the computational pressure by breaking this large scale optimization problem into 3000 small scale optimization problems. The effectiveness of the method in the aspect of improving the optimization efficiency is proved by comparing the method with the MAS method.
The optimal power requirements for the port cold box loads obtained by the method and the MAS method are shown in FIG. 8. Table 2 compares the optimization results of the cold box and the required optimization time for the two methods. It can be seen that the results obtained by the present process are very similar to those obtained by the MAS process, only by 0.02%, but the optimization time required by the present process is reduced by 79.12% compared to the MAS process, and the calculation efficiency is improved by about 4 times. This is because the MAS method relies on a high-performance distributed parallel computing unit for improving the computational efficiency, and thus the method is advantageous in terms of computation time when the same computing device is used.
TABLE 2 two methods optimization Effect
The topology of consistency of the information state of the boxes in each CR is shown in FIG. 1, and the connection weight b with information exchange is calculated in the example ij Is set to 1. In this section by CR 1 Other CR simulation analyses are similar as the study object. Randomly extracting a certain time CR 1 The process of convergence of the consistency of the cooling efficiency factor of the inner cold box is shown in fig. 9. As can be seen from fig. 9 (a), the refrigeration efficiency factor of the cold box gradually reaches the same value after continuous information interaction. As can be seen from FIG. 9 (b), when the prescheduling is receivedDuring instruction, the leading type cold box intelligent body can respond first, and then other intelligent bodies can respond to the pre-scheduling instruction along with the leading type intelligent body.
The internal temperature change of each cold box after optimization is shown in figure 10. The cold boxes with initial internal temperature from low to high in the figure belong to CR respectively 1 ～CR 20 . It can be seen that the internal temperature of the cold box is stable and remains within the allowed variation range throughout the scheduling period, despite the ambient temperature being changed. In order to further verify that the LREC power redistribution algorithm can take actual constraints of each specific cold box into consideration and reasonably distribute the capacity of the cluster pre-scheduling strategy, an average distribution method is selected for simulation comparison analysis. FIG. 11 shows the respective times CR in two ways 1 Maximum and minimum of all cold box temperatures. It can be seen that the internal temperature of part of the cold boxes is out of limit due to the fact that different characteristics and actual constraints of all the cold boxes are not considered by adopting the method of power average distribution, and the LREC algorithm can enable all the cold boxes to be always in the safe operation constraint range.
In general, as the number of cold boxes in the same cluster increases, the LREC algorithm needs to iterate more steps to reach agreement. The effect of different cold box numbers on the rate of convergence of consistency is discussed herein. Based on the above calculation example, let CR 1 The cold box size of (20, 40,60, \8230;, 200) in this order. CR at different scales 1 The convergence rate of the inner agreement is shown in fig. 12. It can be seen that the median number of iteration steps increases only linearly. If a centralized optimization mode is adopted, the solving time is exponentially increased along with the number of the 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 a 1000-cold-box calculation example. The implementation process of the global optimization algorithm is as follows: after the PDC releases the electricity price, the single cold box is taken as a scheduling object, the optimal power demand curves of all the cold boxes are solved by the RA, then the optimal power demand curves are uploaded to the PDC, and the same process is repeated until the electricity price is converged. The optimal power requirements for 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 the embodiment, the operation cost obtained by global optimization is 0.5% lower than that of the method, and the small difference can be almost ignored, so that the overall optimization target can be close to the optimum by adopting the method. Both methods were simulated on a personal computer, and the method converged to a solution over 3 iterations for 156s, whereas the centralized algorithm took 8720s. In practical application, the number of cold boxes stored in a port is often thousands or even tens of thousands, and the solution is almost impossible on a conventional computing device by using a global optimization method.
In conclusion, the consistent layered optimization scheduling method can give consideration to both optimization effect and solving speed, has strong adaptability, and can realize efficient optimization scheduling of cold box load groups of different scales. The method can effectively reduce the electricity consumption cost of the cold box and stabilize the load peak-valley difference of the port while meeting all the operation constraint conditions of the cold boxes.
Compared with the prior art, the invention adopts a hierarchical scheduling strategy and establishes a plurality of small communication networks which are coordinated with each other in the cold box load group by a pre-scheduling model of the cold box dynamic electricity price and the cluster electricity consumption power iterative optimization, avoids the conventional management mode of forming an acquisition-processing-control loop by a scheduling center, achieves the good dispersed autonomy-centralized coordination purpose from bottom to top, greatly improves the solving speed by a cold box refrigeration efficiency consistency algorithm, has good robustness for large-scale cold box optimization scenes to transfer the cold box load demand from the peak time period to the valley time period, can effectively reduce the cold box electricity consumption cost, stabilize the peak-valley difference of port load and improve the system operation efficiency.
In a specific implementation, the present application further provides a computer storage medium for port cold box load group power utilization consistency hierarchical optimization scheduling, where the computer storage medium may store a program, and the program may include some or all of the steps in the port energy system embodiment provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented using software plus any required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
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 modified in many different ways by one skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and not by the preceding embodiments, and all embodiments within their scope are intended to be limited by the scope of the invention.
Claims (6)
1. A method for consistent layered optimization scheduling of power utilization of a port cold box load group is characterized in that thermoelectric coupling and building of cold boxes comprise the following steps: the system comprises a cold box thermodynamic dynamic model, a cold box electric power model and a single cold box model of a cold box temperature model; then, a cluster equivalent modeling method is adopted, a plurality of cold box clusters are divided according to the cargo types, then a cold box load group layered scheduling framework is adopted, cold box consistency layered optimization scheduling is carried out through a refrigeration efficiency master-slave consistency algorithm (LREC), and dynamic allocation of multiple-intelligence consistency power of cold boxes is realized;
the thermodynamic dynamic model of the cold box is
Wherein: Δ t is a unit scheduling period(s); t is the internal temperature (DEG C) of the cold box; t is amb External ambient temperature (deg.C); σ is a correction coefficient introduced in consideration of the influence of solar radiation; a is the external surface area (m) of the cold box 2 )；k t Is the heat transfer coefficient (W/m) 2 K); m and c are respectively the mass (kg) and specific heat capacity (kJ/kg. K) of the goods in the box; p R Refrigeration capacity (kW);
the electric power model for the cold box is
Wherein: the StR (t) is the running state of the cold box, stR (t) =1 is refrigeration, stR (t) =0 is non-refrigeration; p is 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 set; ERR is refrigeration energy efficiency ratio, and ERR of the cold box is different at different set temperatures;
the cold box temperature model is T min ≤T(t)≤T max Wherein: t is max And T min Respectively an upper limit and a lower limit of the temperature in the box.
2. The port cold box load group power utilization consistency hierarchical optimization scheduling method according to claim 1, is characterized by comprising the following steps:
step 1) performing thermoelectric coupling on the cold box, wherein the establishment comprises the following steps: the system comprises a cold box thermodynamic dynamic model, a cold box electric power model and a single cold box model of a cold box temperature model;
step 2) a cluster effect modeling method is adopted, a plurality of cold box clusters are divided according to cargo types, each cold box which is coded into the same cluster has the same cargo specific heat capacity, temperature set value and temperature allowable range, all cold boxes in the cluster are made into a large-capacity cold box set, each CR power utilization model adopts a frame of a single cold box power utilization model, and the mass and the size of the CR power utilization model are the sum of the mass and the size of all cold boxes in the cluster;
step 3) a cold box load group hierarchical scheduling architecture is established, cold box consistency hierarchical optimization scheduling is carried out through a refrigeration efficiency master-slave consistency method, a conventional management mode that an acquisition-processing-control loop is formed by a scheduling center is avoided, and the purpose of good decentralized autonomous-centralized coordination is achieved from bottom to top;
the hierarchical scheduling architecture comprises a port scheduling center, a cold box load aggregator and a cold box load group;
the top port dispatching center supplies power to the basic load and the cold box in the jurisdiction range, and guides the cold box power demand 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, basic load information and aggregated cold box load requirements in the optimized time period, and then calculates new electricity price according to the elasticity of the total load and the electricity price in the harbor area on the port power requirement and sends the new electricity price to a load aggregator; the cold box can be powered as far as possible in the load valley period through the guidance of the electricity price signal;
the middle-layer load aggregator is connected with a dispatching center and issues a cold box load group; after receiving the electricity price signal, the RFA takes the lowest cost as an optimized calculation target, calculates the optimal power curve of each CR according to the electricity price and the CR model parameters, namely, a pre-dispatching plan and sends the optimal power curve to a corresponding cluster; meanwhile, RFA collects and integrates the load information of the cold box, and the CR load demand curves are aggregated into a total demand curve and reported to PDC;
the bottom layer cold box load group aggregates 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, wherein in each scheduling period, each cold box intelligent agent only communicates with adjacent intelligent agents, and receives a pre-scheduling plan instruction of the CR issued by an upper layer through a leading cold box intelligent agent to serve as a target of consistency control; after a certain protocol is executed, each cold box can obtain the power consumption requirement of the cold box by combining the actual constraint of the cold box; then the CR uploads the actual demand curve of the CR to the upper layer;
the cluster hierarchical optimization scheduling method comprises the following steps:
3.1 Update PDC regulation signals: the control aim of the PDC is to reduce the load peak-valley difference of the system; the reasonable virtual electricity price regulation and control signal is utilized to guide the load of the cold box to transfer from the peak to the valley of the port power grid operation, so that the peak-valley difference of the system load can be reduced, the port power grid operation risk can be reduced, and the cold box operation cost can be reduced; the predicted electricity price in the optimization time period and the elasticity of the electricity price on the electricity demand of the cold box are known, and the virtual electricity price regulation and control signal is updated to be
Wherein: i =1,2, \8230;, M, j =1,2, \8230;, N i M is the number of clusters, N i Is CR i The number of cold boxes; EP (t, n) is the electricity price at the t moment calculated by the PDC during the nth iteration;
to predict electricity prices; a is an electricity price elastic factor which is the change of unit electricity price when the power demand changes by 1 kW; TSL is the load factor;
3.2 When the total load power of the port area exceeds a high load threshold value, increasing the load coefficient so as to shift the load of the cold box from the peak time to other time periods, specifically:
wherein: p is total Is the total load in the port areaPower; p load The basic load except the cold box in the port area is used; p thres A port area high load threshold; rho is an overload penalty factor;
3.3 According to the electricity price information issued by the PDC and the electricity utilization behavior characteristics of the CRs, the RA formulates a pre-scheduling strategy of each CR, wherein during each iteration, the RA aims to reduce the operation cost as much as possible under the condition of meeting the charging requirement, and specifically comprises the following steps:
wherein: p eR,i CR established for RA i The pre-scheduling plan of (2);
the power utilization characteristics of each CR are described by a single cold box model; in addition, 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 is i (t 0 )＝T i (t f ) Wherein: t is a unit of i (t 0 ) And T i (t f ) Are respectively CR i Effective temperature at the beginning and end of the scheduling period;
step 4) multi-wisdom consistency power dynamic allocation of the cold box comprises the following steps:
4.1 ) calculating a cold box refrigeration efficiency factor for power allocation such that the cold boxes within the cluster substantially respond to the scheduling instructions, CR, of RA i Cooling efficiency factor of the j-th cooling box
Wherein: the numerator is the cooling rate, and the denominator is the cooling margin of the cooling box at the moment t; the refrigeration efficiency factors of the cold boxes in the same cluster at the same moment after the total power distribution are consistent are taken as the standard of correct distribution, and the larger the cooling margin is, the higher the cooling speed of the cold box is, the larger the corresponding power requirement is;
4.2 Through a master-slave consistency method of multi-intelligent refrigeration efficiency, the centralized control problem is solved in a distributed mode, each CR is regarded as a multi-intelligent-agent system network, and each cold box is distributed with an intelligent agent, specifically: by laplace matrix L = [ L = jv ]Reflects the topology of the multi-agent network,
wherein: b = [ B ] jv ]Being a contiguous matrix of a multi-agent network, b jv >0 is the connection weight between agent j and agent v; selecting a refrigeration efficiency factor as a consistent variable of each cold box in the CR, and using a discrete time first-order consistency algorithm framework, the CR i The cooling efficiency factor of the jth following type cold box intelligent agent at the k +1 iteration
Wherein: n is a radical of i Is CR i The number of cold boxes; row random matrix
At [ j, v ] th iteration of k]Item, i.e.
Recalculate time t CR i Power command margin of
Make CR i The actual power requirement is kept consistent with the pre-scheduling strategy as much as possible, and finally CR is obtained i The refrigeration efficiency factor updating rule of the leading intelligent agent is as follows:
wherein: mu.s i Is CR i The power error adjustment factor of (2) is a positive scalar quantity and controls the convergence speed of the LREC algorithm;
4.3 ) the PDC updating the electricity prices according to the new power demand curve, specifically: under a layered scheduling framework, the PDC updates the electricity price information and then sends the electricity price information to the RFA, the RFA determines a pre-scheduling strategy of the CR according to the electricity price information, and each cold box in the cluster calculates the electricity consumption power of the cold box according to the LREC algorithm, so that the actual power requirement of the CR is kept as consistent as possible with a pre-scheduling plan within a constraint range; the resulting load power curves are then aggregated by RFA and uploaded to PDC.
3. The method for consistent, layered and optimized dispatching of power consumption of cold box load groups in ports according to claim 2, characterized in that when an LREC algorithm is adopted among cold boxes, safety constraints are further added to prevent the cold box temperature or power from exceeding the limits: when a certain time CR i When the temperature of the jth cold box reaches the lower limit, the cold box stops refrigerating, and StR ij (t) =0, corresponding electrical power P eR,ij Is also zero; at this time, the connection weight with the cold box agent j becomes zero, specifically, b jv ＝0,v＝1,2,…,N i (ii) a When CR is reached i When the power consumption of the jth cold box reaches the limit, the safety inspection and correction need to be carried out on the jth cold box, and the method specifically comprises the following steps:
likewise, the connection weights to cold box agent j all become zero at this time.
4. The method as claimed in claim 2, wherein when the security constraint of a particular cold box is exceeded, the cold box is immediately dropped out of the multi-agent network, and the corresponding network information connection weight is modified, when the CR is satisfied i Actual power demand and pre-scheduling policy difference Δ P error,i (t) less than the maximum permissible deviation ε i The algorithm iteration terminates.
5. The method as claimed in claim 2, wherein the convergence rate of the LREC algorithm is adjusted by adjusting the power error adjustment factor μ i The method comprises the following steps of controlling, selecting a proper adjustment factor to ensure that the LREC algorithm has better stability and faster convergence speed, and specifically: to contain N i CR of platform cold box i When the refrigeration efficiency factor of the leading cold box agent is increased mu i ΔP error,i (t), the intelligent refrigeration efficiency factor of each refrigeration box increases mu on average i ΔP error,i (t)/N i ，CR i Is increased by the total power requirement of
In the LREC algorithm, the difference | Δ P is commanded as power error,i (t)|＜ε i For the convergence criterion, the sufficient condition for the convergence of the algorithm is | Δ P error,i (t)-ΔP eR,i (t)|＜ε i Namely:
i.e. mu i Is selected depending on CR i Number of cold boxes N i Characteristics and internal temperature of each cold box and maximum allowable deviation epsilon i When power instructions of different scheduling periods are redistributed in different clusters, mu i The value of (a) can be updated according to the value range.
6. A system for realizing the power utilization consistency hierarchical optimization scheduling method of the port cold box load group as claimed in any one of claims 1 to 5, is characterized by comprising the following steps: data acquisition module, optimization module and uniformity 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 to obtain cold box cluster pre-scheduling power at all moments of a scheduling period by taking the minimum electricity consumption cost of the cold boxes as a target and taking cold box temperature constraint, power constraint and energy conversion constraint as conditions, and the consistency control module dynamically distributes the consistency power to the cold boxes in the cluster according to the cold box cluster pre-scheduling power to meet the power requirements of the cold boxes.
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