CN112906964A - Distributed energy storage combination method considering aggregation effect - Google Patents
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Abstract
The invention belongs to the technical field of distributed power generation and energy storage, and particularly relates to a distributed energy storage combination method considering aggregation effect. The method comprises the steps of obtaining operating parameters of energy storage of each branch; establishing a distributed energy storage aggregation evaluation index; establishing a distributed energy storage aggregation model based on tabu search; determining the polymerization potential entropy of the optimal polymerization energy storage combination in the obtained optimal table, and determining the output sequence of the polymerization energy storage combination according to the potential entropy; calculating the output external characteristics of the polymerization energy storage according to the output sequence of the optimal polymerization energy storage combination; and aggregating the energy storage into an equivalent centralized model according to the external characteristics of the optimal aggregation energy storage combination for a dispatching center to carry out power distribution and dispatching. The invention can realize the external characteristic of distributed energy storage meeting the application requirement of the power grid and provide technical support for the application of a uniform distributed energy storage aggregation model accessed to the power grid.
Description
Technical Field
The invention belongs to the technical field of distributed power generation and energy storage, particularly relates to a distributed energy storage combination method considering aggregation effect, and particularly relates to a method of an optimal combination mode of distributed energy storage considering aggregation effect based on tabu search.
Background
Compared with a centralized energy storage power station, the distributed energy storage can flexibly realize the installation at different places, thereby reducing the construction loss and investment of newly added lines; distributed energy storage capacity contribution, randomness of access points, unpredictability, however, present challenges to traditional operating modes of large power grids.
At present, for the condition that large-scale distributed energy storage access has no effective scheduling measure temporarily, if distributed energy storage is used as a large batch of random disturbance power sources to be randomly accessed into a power grid, certain influence is generated on the frequency, voltage and power quality of the power grid, and meanwhile, energy storage resources are unreasonably utilized. Therefore, how to perform "aggregation" after optimized combination on the energy storage resources dispersed in a wide area and realize uniform external output characteristics becomes an important research direction. The unified regulation and control of the distributed energy storage with diversification, multiple points, certain idle rate and fragmentation dispersion type becomes the centralized direction of domestic and foreign research.
According to the current research situation at home and abroad, the unified aggregation of distributed energy storage resources for power grids is in the form of the original appearance in the countries such as America and Germany, but the aggregated energy storage resources and the application mode are still single, the aggregation scale is limited, and particularly for the electric power system and the power grid structure in China, although a technical theory for reference exists, the core problem is not yet reached. In addition, a concept of distributed energy storage aggregation and an aggregation combination method are involved, wherein the involved aggregation method and combination mode part have reference values, but particularly, a systematic distributed energy storage aggregation combination technology has a plurality of key problems to be urgently broken through, firstly, the aggregation evaluation index of energy storage resources is selected, and then, the distributed energy storage clusters are preferably combined based on what mode, so that technical support is provided for convergence application of the multipoint distribution type energy storage system.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a distributed energy storage combination method considering the polymerization effect. The invention aims to construct the optimal combination mode of distributed energy storage based on tabu search by considering the aggregation effect index of distributed energy storage, and realize the aim of meeting the external characteristics of distributed energy storage aggregation of the power grid application requirements.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a distributed energy storage combination method considering aggregation effect comprises the following steps:
step 2, establishing a distributed energy storage aggregation evaluation index;
step 3, establishing a distributed energy storage aggregation model based on tabu search;
step 4, determining the aggregation potential entropy of the optimal aggregation energy storage combination in the obtained optimal table based on the distributed energy storage aggregation model of tabu search, and determining the output sequence of the aggregation energy storage combination according to the potential entropy;
step 5, calculating the output external characteristics of the polymerization energy storage according to the output sequence of the optimal polymerization energy storage combination;
and 6, aggregating the stored energy into an equivalent centralized model according to the external characteristics of the optimal aggregated stored energy combination for a dispatching center to carry out power distribution and dispatching.
Further, the operating parameters include: energy storage capacity, charge and discharge rate, state of charge and discharge power.
Further, the establishing of the aggregation evaluation index of the distributed energy storage includes: aggregate capacity contribution, aggregate power contribution, effective aggregate ratio, effective state ratio, and system stability capability.
Further, the polymerization capacity contribution force g1The capacity contribution capacity provided by the aggregated stored energy to the frequency modulation and peak regulation actions of the power grid is calculated according to the following formula:
in the formula: sapkThe total capacity can be adjusted for energy storage; sapThe required capacity during energy storage scheduling; t is a scheduling period; ps(i) The output power of the distributed energy storage at the moment i is obtained;
the polymerization power contribution g2The power contribution capacity provided by the accumulated energy to the frequency modulation and peak regulation actions of the power grid is calculated according to the following formula:
in the formula: pBkOutputting power for the kth energy storage system; ploadThe load output power for point i; pGThe total output power sent out by the unit; pDGjOutputting power for the jth distributed generation unit; n is a radical ofsThe number of energy storage devices; n is a radical ofbusThe number of system nodes; n is a radical ofDGThe number of distributed power supplies; pDRequired power during distributed energy storage scheduling; rhokEnergy storage power conversion efficiency for distributed energy storage;
the effective polymerization ratio g3The energy storage aggregation capability which is effectively called according to the system requirement is calculated according to the following formula:
in the formula QDThe required quantity of the system during the calling of the distributed energy storage; qBkThe total number of schedules that can be provided for distributed energy storage;
the effective state ratio g4The energy storage polymerization capacity in a charge-discharge response state according to the system requirements is calculated according to the following formula:
in the formula: sBkRepresenting the current operating state, S, of the distributed battery energy storage system Bk1 denotes energy storage positive charging, S Bk1 denotes positive energy storage discharge, SBkWhen the stored energy is in a hot standby state, 0 represents that the stored energy is in a hot standby state; sDRepresenting the action state of the distributed energy storage when the current system schedules the distributed energy storage, S D1 indicates that energy storage charging is required, SD-1 represents energy storage discharge is required;
stability capacity g of the system5The method refers to the stability of the energy storage basic unit after polymerization, and is calculated according to the following formula:
g5＝(1-ηk)
in the formula etakThe fault rate of the distributed energy storage basic units in the time period is scheduled.
Further, the establishment of the tabu search-based distributed energy storage aggregation model is to obtain an energy storage output sequence, external characteristics, a unified model and a power distribution mode by taking evaluation indexes as consideration factors of a combination method after a single distributed energy storage passes through an aggregation effect;
the taboo search flow comprises the following steps:
constructing an aggregation characteristic criterion, an aggregation privilege criterion and an aggregation preference criterion based on tabu search;
the aggregation characteristic criterion means that the operating parameters of the stored energy of each subsection have the following formula relationship:
in the formula: SOCtIs an energy storage state of charge; SOC0The initial value of the energy storage charge state; pcAnd PdFor charging of stored energyAnd the discharge power; etacAnd ηdThe charging and discharging rate of the stored energy; Δ t is a duration of charging and discharging; srateIs the energy storage capacity;
the aggregation privilege criterion is used for constructing an evaluation function for judging the advantages and disadvantages of candidate energy storage objects, takes an objective function as the evaluation function, and sets the evaluation function as follows according to the objective function of an article:
in the formula, giIs a polymerization evaluation index; omegaiIs a weight coefficient of the aggregation evaluation index;
the aggregation optimization criterion is that the aggregation potential index lambda of each distributed energy storage is obtained according to calculationi(ii) a The larger the distributed energy storage convergence potential index is, the more optimal the combined mode of the aggregated energy storage is:
in the above formula, λiAggregation potential index, g, for distributed energy storageiIs a polymerization evaluation index;
sequencing the output of each aggregation energy storage combination based on the magnitude of the convergence potential index of the distributed energy storage;
randomly selecting distributed energy storage as an input initial solution, calculating operating parameters of energy storage of each subsection, and judging whether the operating parameters meet the aggregation characteristic criterion; if so, generating a primary energy storage preferred combination mode to enter a taboo; if not, generating a candidate energy storage object;
step (3) calculating the aggregation evaluation index of the generated energy storage preference combination mode, and judging whether the candidate energy storage object meets the aggregation privilege criterion; if yes, the energy storage object enters the privileged table, replaces the object which enters the taboo table at the earliest time with the object, and updates the optimal state; if not, replacing the object which enters the tabu table earliest by the non-tabu object, and repeating the step (3);
and (4) calculating the polymerization potential entropy of the generated energy storage optimization combination mode, judging whether the optimization combination mode meets the polymerization optimization criterion, if so, generating an optimization table, and if not, repeating the step (1) and the step (2).
Further, the energy storage is aggregated into an equivalent centralized model according to the external characteristics of the optimal aggregated energy storage combination for a scheduling center to perform power distribution and scheduling, wherein the aggregated external characteristics need equivalent centralized rated power, rated capacity and charging and discharging efficiency to represent the whole aggregated energy storage system:
in the formula: pall-rate、Sall-rate、ηallThe charge-discharge power, the rated capacity and the equivalent charge-discharge efficiency of the equivalent centralized energy storage are achieved.
Further, the algorithm of tabu search includes the following steps:
(1) giving a tabu search algorithm parameter, randomly generating an initial solution x, and leaving a tabu table empty;
(2) judging whether the algorithm termination condition is met: if yes, finishing the algorithm and outputting an optimization result; otherwise, continuing the following steps;
(3) generating all or a plurality of neighborhood solutions by utilizing the neighborhood function of the current solution, and determining a plurality of candidate solutions from the neighborhood solutions;
(4) judging whether scofflaw criteria are met for the candidate solution: if yes, replacing x with the best state y meeting the scofflaw criterion to become a new current solution, namely x is equal to y, replacing the taboo object which enters the taboo table earliest with the taboo object corresponding to y, and replacing the 'best so far' state with y, and then turning to the step (6); otherwise, continuing the following steps;
(5) judging the taboo attribute of each object corresponding to the candidate solution, selecting the optimal state corresponding to the non-taboo object in the candidate solution set as a new current solution, and simultaneously replacing the taboo object which enters the taboo table earliest by the taboo object corresponding to the current solution;
(6) judging whether the algorithm termination condition is met: if yes, finishing the algorithm and outputting an optimization result: otherwise, go to step (3).
A computer storage medium having a computer program stored thereon, the computer program, when being executed by a processor, implementing the steps of the method for distributed energy storage combining considering aggregation effect.
The invention has the following beneficial effects and advantages:
the distributed energy storage aggregation method can aggregate the operating parameters in a certain range more effectively and reliably, evaluate the distributed energy storage with better indexes, and provide technical support for the application of a unified distributed energy storage aggregation model accessed to a power grid.
The invention can improve the aggregation efficiency of energy storage and reduce distributed energy storage access with weak aggregation characteristic. The polymerization quantity economy of energy storage and the external output technology of energy storage polymerization are improved. Aiming at the problems that the conventional energy storage polymerization mode has a complicated polymerization process and cannot obtain an optimal energy storage combination mode, taboo search is adopted to carry out multiple iterations by using different polymerization criteria to avoid losing the optimal solution in the search process, and the polymerization efficiency of energy storage is improved.
The invention reduces the energy storage access with low polymerization characteristic and low convergence potential by exploring the energy storage preferred combination mode with good polymerization indexes, and improves the stability and reliability of energy storage polymerization output.
The invention is also characterized by easy implementation and convenient commercial development. The invention adds tabu search on the basis of the distributed energy storage combination method, so that the energy storage preferred combination mode with the optimal polymerization index can be explored in a certain range. The method is easy to implement algorithmically; meanwhile, the polymerization effect index is considered, and the practical application of energy storage evaluation is easy to realize. With the increase of the disordered access power grid of the distributed energy storage as a large batch of random disturbance power sources, the development of the distributed energy storage combination method with the aggregation effect has great demands and more remarkable commercial development prospect.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a general flow diagram of the process of the present invention;
FIG. 2 is a diagram illustrating a tabu search flow according to the present invention;
FIG. 3 is a schematic diagram of a preferred combination mode of distributed energy storage based on the aggregation effect according to the present invention;
FIG. 4a is a constraint simulation curve of distributed output and keeping SOC relatively balanced in the scheduling process of the aggregated energy storage in the present invention;
fig. 4b is a simulation curve of the preferred assigned output of the aggregated energy storage in the scheduling process according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The solution of some embodiments of the invention is described below with reference to fig. 1-4.
Example 1
The invention relates to a distributed energy storage combination method considering aggregation effect, which is shown in figure 1, and figure 1 is a general flow chart of the method.
The invention relates to a distributed energy storage combination method considering aggregation effect, which is characterized in that aggregation effect indexes of distributed energy storage are considered, a distributed energy storage object is selected as an initial solution of input in a taboo search-based model, and the optimal combination mode of the distributed energy storage is constructed by considering the aggregation effect indexes, aggregation characteristic criteria, aggregation privilege criteria and aggregation preference criteria of the distributed energy storage, so that the external characteristics of distributed energy storage aggregation meeting the application requirements of a power grid are realized.
The method specifically comprises the following steps:
the operating parameters include: energy storage capacity SapCharge-discharge rate eta, state of charge SOCtAnd charging and discharging power PBkThe parameters of (1);
step 2, establishing a distributed energy storage aggregation evaluation index;
the method comprises the following steps: aggregate capacity contribution, aggregate power contribution, effective aggregate ratio, effective state ratio, and system stability;
the polymerization capacity contribution g1The capacity contribution capacity provided by the aggregated stored energy to the frequency modulation and peak regulation actions of the power grid is calculated according to the following formula:
in the formula: sapkThe total capacity can be adjusted for energy storage; sapThe required capacity during energy storage scheduling; t is a scheduling period; ps(i) And (4) storing the output power of the distributed energy at the moment i.
The polymerization power contribution g2The power contribution capacity provided by the accumulated energy to the frequency modulation and peak regulation actions of the power grid is calculated according to the following formula:
in the formula: pBkOutputting power for the kth energy storage system; ploadThe load output power for point i; pGThe total output power sent out by the unit; pDGjOutputting power for the jth distributed generation unit; n is a radical ofsThe number of energy storage devices; n is a radical ofbusThe number of system nodes; n is a radical ofDGThe number of distributed power supplies; pDRequired power during distributed energy storage scheduling; rhokThe energy storage power conversion efficiency of distributed energy storage is improved.
The effective polymerization ratio g3The energy storage aggregation capability which is effectively called according to the system requirement is calculated according to the following formula:
in the formula QDThe required quantity of the system during the calling of the distributed energy storage; qBkThe total number of schedules that can be provided for distributed energy storage.
The effective state ratio g4The energy storage polymerization capacity in a charge-discharge response state according to the system requirements is calculated according to the following formula:
in the formula: sBkRepresenting the current operating state, S, of the distributed battery energy storage system Bk1 denotes energy storage positive charging, S Bk1 denotes positive energy storage discharge, SBkWhen the stored energy is in a hot standby state, 0 represents that the stored energy is in a hot standby state; sDWhen the distributed energy storage is scheduled by the current system, the distributed energy storage is requiredOperating state of (S)D1 indicates that energy storage charging is required, SD-1 indicates that an energy storage discharge is required.
Stability capacity g of the system5The method refers to the stability of the energy storage basic unit after polymerization, and is calculated according to the following formula:
g5＝(1-ηk)
in the formula etakThe fault rate of the distributed energy storage basic units in the time period is scheduled.
Step 3, establishing a distributed energy storage aggregation model based on tabu search; as shown in fig. 2, fig. 2 is a flow chart of tabu search according to the present invention;
further, after the single distributed energy storage is subjected to aggregation effect, the evaluation index is taken as a consideration factor of a combination method, and an energy storage output sequence, external characteristics, a unified model and a power distribution mode are obtained;
the taboo search process comprises the following steps:
constructing an aggregation characteristic criterion, an aggregation privilege criterion and an aggregation preference criterion based on tabu search;
the aggregation characteristic criterion means that the operating parameters of the stored energy of each subsection have the following formula relationship:
in the formula: SOCtIs an energy storage state of charge; SOC0The initial value of the energy storage charge state; pcAnd PdCharging and discharging power for energy storage; etacAnd ηdThe charging and discharging rate of the stored energy; Δ t is a duration of charging and discharging; srateIs the energy storage capacity.
The aggregation privilege criterion is used for constructing an evaluation function for judging the advantages and disadvantages of candidate energy storage objects, takes an objective function as the evaluation function, and sets the evaluation function as follows according to the objective function of an article:
in the formula, giIs a polymerization evaluation index; omegaiIs a weight coefficient of the aggregation evaluation index.
The aggregation optimization criterion is that the aggregation potential index lambda of each distributed energy storage is obtained according to calculationi(ii) a The larger the distributed energy storage convergence potential index is, the more optimal the combined mode of the aggregated energy storage is:
in the above formula, λiAggregation potential index, g, for distributed energy storageiIs an index of polymerization evaluation.
Further, the output of each aggregated energy storage combination is ranked based on the magnitude of the aggregation potential index of the distributed energy storage.
Randomly selecting distributed energy storage as an input initial solution, calculating operating parameters of energy storage of each subsection, and judging whether the operating parameters meet the aggregation characteristic criterion; if so, generating a primary energy storage preferred combination mode to enter a taboo; if not, generating a candidate energy storage object;
step (3) calculating the aggregation evaluation index of the generated energy storage preference combination mode, and judging whether the candidate energy storage object meets the aggregation privilege criterion; if yes, the energy storage object enters the privileged table, replaces the object which enters the taboo table at the earliest time with the object, and updates the optimal state; if not, replacing the object which enters the tabu table earliest by the non-tabu object, and repeating the step (3);
step (4) calculating the polymerization potential entropy of the generated energy storage optimization combination mode, judging whether the optimization combination mode meets the polymerization optimization criterion, if so, generating an optimization table, and if not, repeating the step (1) and the step (2);
step 4, determining the polymerization potential entropy of the optimal polymerization energy storage combination in the obtained optimal table, and determining the output sequence of the polymerization energy storage combination according to the potential entropy; as shown in fig. 3, fig. 3 is a schematic diagram of a preferred combination mode of distributed energy storage based on aggregation effect according to the present invention.
Step 5, calculating the output external characteristics of the polymerization energy storage according to the output sequence of the optimal polymerization energy storage combination, and meeting the application requirements of the power grid;
and 6, aggregating the stored energy into an equivalent centralized model according to the external characteristics of the optimal aggregated stored energy combination for a dispatching center to carry out power distribution and dispatching. As shown in fig. 4, fig. 4 is a relative equilibrium simulation curve of reasonable power distribution and SOC maintenance during the energy storage scheduling process in the present invention.
The external characteristics of the polymerization can represent the whole polymerization energy storage system by the need of equivalent concentrated rated power, rated capacity and charging and discharging efficiency:
in the formula: pall-rate、Sall-rate、ηallThe charge-discharge power, the rated capacity and the equivalent charge-discharge efficiency of the equivalent centralized energy storage are achieved.
Furthermore, a plurality of energy storages are aggregated into an equivalent centralized energy storage for the dispatching center to dispatch. Only one charging and discharging variable for centralized energy storage is needed during scheduling optimization calculation. In order to ensure that the power distribution of each energy storage is reasonable and the relative balance of the SOC is kept in the scheduling process, the charging and discharging power of each energy storage is determined by adopting the rated power and the SOC value of each energy storage.
Example 2
The invention further provides an embodiment, which is a distributed energy storage combination method considering aggregation effect, as shown in fig. 1, fig. 1 is a general flow chart of the method of the invention.
Consistent with the calculation steps described in example 1, it is worth explaining that, as can be seen from the flow in the figure, the method of this embodiment is based on tabu search, and the aggregation characteristic criterion, the aggregation privilege criterion, and the aggregation preference criterion are added in the distributed energy storage combination method considering the aggregation effect, which is different from other methods in essence.
As shown in fig. 2, fig. 2 is a flow chart of tabu search according to the present invention;
the taboo search flow of the invention is defined as follows:
the basic ideas of the tabu search algorithm are: given a current solution (initial solution) and a neighborhood, several candidate solutions are then determined in the neighborhood of the current solution: if the target value corresponding to the optimal candidate solution is better than the state of the best so far, the taboo characteristic is ignored, the current solution and the state of the best so far are replaced by the target value, the corresponding object is added into the taboo table, and meanwhile the tenure of each object in the taboo table is modified: if the candidate solution does not exist, selecting the non-taboo optimal state as a new current solution in the candidate solution regardless of the advantages and disadvantages of the current solution, adding the corresponding object into the taboo table, and modifying the tenure of each object in the taboo table. The above described alternative search process is repeated in this manner until the stopping criterion is met.
The tabu search algorithm steps may be described as follows:
(1) and giving parameters of a tabu search algorithm, randomly generating an initial solution x, and leaving a tabu table empty.
(2) Judging whether the algorithm termination condition is met: if yes, finishing the algorithm and outputting an optimization result; otherwise, the following steps are continued.
(3) All (or some) of its neighborhood solutions are generated using the neighborhood function of the current solution and several candidate solutions are determined therefrom.
(4) Judging whether scofflaw criteria are met for the candidate solution: if yes, replacing x with the best state y meeting the scofflaw criterion to become a new current solution, namely x is equal to y, replacing the taboo object which enters the taboo table earliest with the taboo object corresponding to y, and replacing the 'best so far' state with y, and then turning to the step (6); otherwise, the following steps are continued.
(5) And judging the taboo attribute of each object corresponding to the candidate solution, selecting the optimal state corresponding to the non-taboo object in the candidate solution set as the new current solution, and replacing the taboo object which enters the taboo table earliest by the taboo object corresponding to the current solution.
(6) Judging whether the algorithm termination condition is met: if yes, finishing the algorithm and outputting an optimization result: otherwise, go to step (3).
As shown in fig. 3, fig. 3 is a schematic diagram of a preferred combination mode of distributed energy storage based on aggregation effect according to the present invention.
As shown in fig. 4a and fig. 4b, fig. 4a is a constraint simulation curve of the distributed output of the aggregated energy storage in the scheduling process and keeping SOC relatively balanced, and fig. 4b is a preferred distributed output simulation curve of the aggregated energy storage in the scheduling process.
TABLE 1 method optimization of energy storage output scheme for optimal combination pattern of distributed energy storage considering polymerization effect
For the energy storage combination 1, the maximum energy storage output force reaches 0.265p.u., and then the output force is smooth at 0.195p.u., so that the SOC has the fastest descending speed and falls from 0.5 to 0.12. The maximum output force of the energy storage combination 2 reaches 0.24p.u., and then the output force is stabilized at 0.18p.u., so that the SOC dropping speed is high and falls from 0.5 to 0.25. The maximum output of the energy storage combination 3 reaches 0.2p.u., and then is stable at 0.17p.u., and the SOC drop speed is moderate and falls from 0.5 to 0.35. The maximum output force of the energy storage combination 4 reaches 0.16p.u., and is stably output at 0.16p.u., the SOC dropping speed is slow and falls from 0.5 to 0.4. It can be seen that four energy storage output schemes are preferably selected by the method of the optimal combination mode of the distributed energy storage considering the aggregation effect based on the tabu search, as shown in table 1, the energy storage output sequence specified according to the potential entropy satisfies the requirement that the actual energy storage output is from high to low, the power distribution of the energy storage is reasonable in the scheduling process, the relative balance of the SOC is kept, and the energy storage output sequence is between 0.1 and 0.5, the cycle service life of the energy storage is prolonged to a certain extent, the over-charging and discharging of the energy storage is avoided, the method of the optimal combination mode of the distributed energy storage considering the aggregation effect based on the tabu search is proved to be feasible, and the method can be used for providing technical support for the application of a uniform distributed energy storage.
Example 3
Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the distributed energy storage combining method considering aggregation effect described in embodiment 1 or 2 are implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (8)
1. A distributed energy storage combination method considering aggregation effect is characterized in that: the method comprises the following steps:
step 1, acquiring operating parameters of energy storage of each branch;
step 2, establishing a distributed energy storage aggregation evaluation index;
step 3, establishing a distributed energy storage aggregation model based on tabu search;
step 4, determining the aggregation potential entropy of the optimal aggregation energy storage combination in the obtained optimal table based on the distributed energy storage aggregation model of tabu search, and determining the output sequence of the aggregation energy storage combination according to the potential entropy;
step 5, calculating the output external characteristics of the polymerization energy storage according to the output sequence of the optimal polymerization energy storage combination;
and 6, aggregating the stored energy into an equivalent centralized model according to the external characteristics of the optimal aggregated stored energy combination for a dispatching center to carry out power distribution and dispatching.
2. The distributed energy storage combination method considering aggregation effect as claimed in claim 1, wherein: the operating parameters include: energy storage capacity, charge and discharge rate, state of charge and discharge power.
3. The distributed energy storage combination method considering aggregation effect as claimed in claim 1, wherein: the establishing of the aggregation evaluation index of the distributed energy storage comprises the following steps: aggregate capacity contribution, aggregate power contribution, effective aggregate ratio, effective state ratio, and system stability capability.
4. A distributed energy storage combining method considering aggregation effect as claimed in claim 3, wherein: the polymerization capacity contribution g1The capacity contribution capacity provided by the aggregated stored energy to the frequency modulation and peak regulation actions of the power grid is calculated according to the following formula:
in the formula: sapkThe total capacity can be adjusted for energy storage; sapThe required capacity during energy storage scheduling; t is a scheduling period; ps(i) The output power of the distributed energy storage at the moment i is obtained;
the polymerization power contribution g2The power contribution capacity provided by the accumulated energy to the frequency modulation and peak regulation actions of the power grid is calculated according to the following formula:
in the formula: pBkOutputting power for the kth energy storage system; ploadThe load output power for point i; pGThe total output power sent out by the unit; pDGjOutputting power for the jth distributed generation unit; n is a radical ofsThe number of energy storage devices; n is a radical ofbusThe number of system nodes; n is a radical ofDGThe number of distributed power supplies; pDRequired power during distributed energy storage scheduling; rhokEnergy storage power conversion efficiency for distributed energy storage;
the effective polymerization ratio g3The energy storage aggregation capability which is effectively called according to the system requirement is calculated according to the following formula:
in the formula QDThe required quantity of the system during the calling of the distributed energy storage; qBkThe total number of schedules that can be provided for distributed energy storage;
the effective state ratio g4The energy storage polymerization capacity in a charge-discharge response state according to the system requirements is calculated according to the following formula:
in the formula: sBkRepresenting the current operating state, S, of the distributed battery energy storage systemBk1 denotes energy storage positive charging, SBk1 denotes positive energy storage discharge, SBkWhen the stored energy is in a hot standby state, 0 represents that the stored energy is in a hot standby state; sDRepresenting the action state of the distributed energy storage when the current system schedules the distributed energy storage, SD1 indicates that energy storage charging is required, SD-1 represents energy storage discharge is required;
stability capacity g of the system5Means that the energy storage after polymerization is substantially singleThe meta-stability is calculated as follows:
g5＝(1-ηk)
in the formula etakThe fault rate of the distributed energy storage basic units in the time period is scheduled.
5. The distributed energy storage combination method considering aggregation effect as claimed in claim 1, wherein: the establishment of the tabu search-based distributed energy storage aggregation model is to obtain an energy storage output sequence, external characteristics, a unified model and a power distribution mode by taking evaluation indexes as consideration factors of a combination method after a single distributed energy storage passes through an aggregation effect;
the taboo search flow comprises the following steps:
constructing an aggregation characteristic criterion, an aggregation privilege criterion and an aggregation preference criterion based on tabu search;
the aggregation characteristic criterion means that the operating parameters of the stored energy of each subsection have the following formula relationship:
in the formula: SOCtIs an energy storage state of charge; SOC0The initial value of the energy storage charge state; pcAnd PdCharging and discharging power for energy storage; etacAnd ηdThe charging and discharging rate of the stored energy; Δ t is a duration of charging and discharging; srateIs the energy storage capacity;
the aggregation privilege criterion is used for constructing an evaluation function for judging the advantages and disadvantages of candidate energy storage objects, takes an objective function as the evaluation function, and sets the evaluation function as follows according to the objective function of an article:
in the formula, giIs a polymerization evaluation index; omegaiIs a polymerizationEvaluating a weight coefficient of the index;
the aggregation optimization criterion is that the aggregation potential index lambda of each distributed energy storage is obtained according to calculationi(ii) a The larger the distributed energy storage convergence potential index is, the more optimal the combined mode of the aggregated energy storage is:
in the above formula, λiAggregation potential index, g, for distributed energy storageiIs a polymerization evaluation index;
sequencing the output of each aggregation energy storage combination based on the magnitude of the convergence potential index of the distributed energy storage;
randomly selecting distributed energy storage as an input initial solution, calculating operating parameters of energy storage of each subsection, and judging whether the operating parameters meet the aggregation characteristic criterion; if so, generating a primary energy storage preferred combination mode to enter a taboo; if not, generating a candidate energy storage object;
step (3) calculating the aggregation evaluation index of the generated energy storage preference combination mode, and judging whether the candidate energy storage object meets the aggregation privilege criterion; if yes, the energy storage object enters the privileged table, replaces the object which enters the taboo table at the earliest time with the object, and updates the optimal state; if not, replacing the object which enters the tabu table earliest by the non-tabu object, and repeating the step (3);
and (4) calculating the polymerization potential entropy of the generated energy storage optimization combination mode, judging whether the optimization combination mode meets the polymerization optimization criterion, if so, generating an optimization table, and if not, repeating the step (1) and the step (2).
6. The distributed energy storage combination method considering aggregation effect as claimed in claim 1, wherein: and aggregating the energy storage into an equivalent centralized model according to the external characteristics of the optimal aggregated energy storage combination for a dispatching center to carry out power distribution and dispatching, wherein the aggregated external characteristics need equivalent centralized rated power, rated capacity and charging and discharging efficiency to represent the whole aggregated energy storage system:
in the formula: pall-rate、Sall-rate、ηallThe charge-discharge power, the rated capacity and the equivalent charge-discharge efficiency of the equivalent centralized energy storage are achieved.
7. The distributed energy storage combination method considering aggregation effect as claimed in claim 1, wherein: the algorithm of tabu search comprises the following steps:
(1) giving a tabu search algorithm parameter, randomly generating an initial solution x, and leaving a tabu table empty;
(2) judging whether the algorithm termination condition is met: if yes, finishing the algorithm and outputting an optimization result; otherwise, continuing the following steps;
(3) generating all or a plurality of neighborhood solutions by utilizing the neighborhood function of the current solution, and determining a plurality of candidate solutions from the neighborhood solutions;
(4) judging whether scofflaw criteria are met for the candidate solution: if yes, replacing x with the best state y meeting the scofflaw criterion to become a new current solution, namely x is equal to y, replacing the taboo object which enters the taboo table earliest with the taboo object corresponding to y, and replacing the 'best so far' state with y, and then turning to the step (6); otherwise, continuing the following steps;
(5) judging the taboo attribute of each object corresponding to the candidate solution, selecting the optimal state corresponding to the non-taboo object in the candidate solution set as a new current solution, and simultaneously replacing the taboo object which enters the taboo table earliest by the taboo object corresponding to the current solution;
(6) judging whether the algorithm termination condition is met: if yes, finishing the algorithm and outputting an optimization result: otherwise, go to step (3).
8. A computer storage medium, characterized by: the computer storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of a method for distributed energy storage combining considering aggregation effect as claimed in claims 1 to 7.
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