CN113792974A - Distributed generalized energy storage convergence coordination method - Google Patents
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Abstract
The invention discloses a distributed generalized energy storage convergence coordination method which is suitable for converging scattered generalized energy storage resources in space-time distribution and assisting power grid operation. It includes: 1) acquiring relevant operation parameters of distributed generalized energy storage; 2) evaluating the schedulable potential of the distributed generalized energy storage participating in convergence by utilizing an analytic hierarchy process based on the obtained parameters; 3) designing the schedulable potential of distributed generalized energy storage according to the relative importance degree of the rule layer factors of the analytic hierarchy process, and establishing a aggregator-distributed generalized energy storage shunting step convergence utilization framework; 4) on the basis of a shunting cascade convergence utilization architecture, a convergence cooperative interaction model is established by using a Stackelberg game, so that convergence multiplexing is realized. The distributed generalized energy storage convergence collaborative modeling method can realize the schedulable potential evaluation of the distributed generalized energy storage, the multiplexing scene shunting and individual cascade output sequencing of the distributed generalized energy storage with different characteristics, and the distributed generalized energy storage convergence collaborative modeling, and finally realize the distributed generalized energy storage convergence multiplexing.
Description
Technical Field
The invention relates to the technical field of power grid decentralized resource assessment and convergence utilization, in particular to a distributed generalized energy storage convergence coordination method.
Background
In recent years, the proportion of various types of distributed generalized energy storage resources such as small-scale renewable energy power generation side distributed energy storage, power distribution network side distributed energy storage, user side distributed energy storage, gas storage tank resources, controllable loads and the like in a power distribution network is continuously increased. And the resources have certain idle time and idle capacity, and have the potential of participating in power grid convergence and assisting the power grid operation on the basis of meeting the requirements of the user on the job. Meanwhile, with the gradual development and improvement of technologies such as the Internet of things, edge calculation, communication, measurement and control and the like, a hardware basis is provided for energy storage convergence work; with the opening of the electric power market and the emergence of terminal energy service providers, a software foundation is provided for energy storage convergence; with consumer arousal of the active nature, participant possibilities are provided for energy storage pooling.
After the generalized energy storage resources distributed in a scattered manner in time and space are gathered, ordered, large-scale and schedulable energy storage resources are formed, and the energy storage resources can participate in the operation of an auxiliary power grid, such as consuming local renewable energy power generation, system peak clipping and valley filling, primary frequency modulation and secondary frequency modulation, providing reactive support, serving as an emergency power supply and the like. The distributed generalized energy storage resources after aggregation can provide capacity support, power support, fast climbing support and the like in a specific scene.
At present, a complete system is not formed aiming at the convergence work of distributed generalized energy storage, especially a scheme for evaluating schedulable potential of energy storage resources is lacked, a reasonable convergence framework capable of fully playing energy storage with different characteristics is lacked, and a feasible distributed energy storage convergence collaborative modeling method is lacked. The distributed generalized energy storage convergence coordination method provided by the patent can assist in convergence multiplexing of distributed generalized energy storage.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, provides a distributed generalized energy storage convergence collaborative method, can effectively solve the problems of how to realize schedulable potential evaluation of distributed generalized energy storage convergence, division of energy storage use scenes with different characteristics and distributed generalized energy storage convergence collaborative modeling, can realize schedulable potential evaluation of distributed generalized energy storage, multiplexing scene shunting and individual step output sequencing of distributed generalized energy storage with different characteristics and distributed generalized energy storage convergence collaborative modeling, and finally realizes distributed generalized energy storage convergence multiplexing.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a distributed generalized energy storage convergence coordination method comprises the following steps:
1) acquiring relevant operation parameters of distributed generalized energy storage;
2) evaluating the schedulable potential of the distributed generalized energy storage participating in convergence by utilizing an analytic hierarchy process based on the obtained parameters;
3) designing the schedulable potential of distributed generalized energy storage according to the relative importance degree of the rule layer factors of the analytic hierarchy process, and establishing a aggregator-distributed generalized energy storage shunting step convergence utilization framework;
4) on the basis of a shunting cascade convergence utilization architecture, a convergence cooperative interaction model is established by using a Stackelberg game, so that convergence multiplexing is realized.
Further, in step 1), distributed generalized energy storage refers to energy storage on a generalized level: the application angle comprises small-scale renewable energy power generation side energy storage, power distribution side energy storage and user side energy storage; formally including electrochemical, mechanical, and electromagnetic energy storage; adjustable loads, both load shedding and load shifting, are also included, including electrical, thermal, and gas loads in specific types; if the resources have space regulation and convergence potential, the resources are collectively referred to as distributed generalized energy storage and belong to a converged resource rank.
Further, in the step 1), the related operation parameters of the distributed generalized energy storage include an energy storage equivalent available capacity, an energy storage equivalent available power, an energy storage equivalent climbing capacity, an energy storage estimated initial available time and an energy storage estimated end available time; the listed operation parameters are all parameters at the current evaluation moment and are all converted into electrical parameters, and the parameter sets of specific energy storage types have differences.
Further, in step 2), the criterion layer factors of the analytic hierarchy process include a capacity support factor, a power support factor, a fast ramp-up factor, an available time matching factor, and a resource reliability factor, which are specifically as follows:
wherein, c1Representing a capacity support factor; qnRepresenting the equivalent available capacity which can be provided by the energy storage individual from the current evaluation time to the convergence ending time; qdRepresenting the capacity provided by the energy storage individuals required by the convergent call;
c2representing a power support factor; pnRepresenting the equivalent available power which can be provided by the energy storage individual at the current evaluation moment; pdRepresenting the power provided by the energy storage individuals required by the convergent call;
c3representing a fast ramp factor; vppRepresenting the equivalent climbing capacity which can be provided by the energy storage individual at the current evaluation moment; vdRepresenting the rapid climbing capability provided by the energy storage individuals required for convergent calling;
c4represents an available time matching factor; t isinRepresenting the estimated initial available time; t isoutRepresenting the estimated available time of ending; t isd2Indicating the aggregate scheduling initial time；Td1Representing the convergence scheduling end time; n represents taking intersection;
c5representing a resource reliability factor; n is a radical ofhRepresenting the total times of the energy storage individual history participation aggregation; n is a radical ofwRepresenting the number of times of the accumulated default of the history participation of the energy storage individuals;
giving the relative importance degree grade of the criterion layer factors of the analytic hierarchy process according to the multiplexing scene requirement, constructing a pair comparison matrix A between the criterion layer factors according to the relative importance degree grade of the factors, and calculating a normalized eigenvector w corresponding to the maximum eigenvalue of the matrix A; respectively constructing a pair comparison matrix { B) of each factor of all distributed generalized energy storage individuals relative to the criterion layer according to the calculated size of the factor of the criterion layer of the distributed generalized energy storage individuals1，B2，B3，B4，B5And calculating the normalized eigenvector { w) corresponding to the maximum eigenvalue of the corresponding matrix1，w2，w3，w4，w5}, and then construct a weight matrix W ═ W1 w2 w3 w4 w5](ii) a Finally, the schedulable potential evaluation coefficient vector delta of the distributed generalized energy storage individual relative to the target layer is obtained through calculationT，wTIs the transpose of the feature vector w.
Further, in the step 3), the aggregator undertakes the distributed generalized energy storage convergence work downwards and provides various types of auxiliary services for the power grid upwards; the established shunting cascade convergence utilizes an architecture to present two functions of shunting and cascading, wherein shunting means that an aggregator establishes a criterion layer pair comparison matrix based on an analytic hierarchy process and divides distributed generalized energy storage with different characteristics into the most appropriate multiplexing scene; the step is to sort the output sequence of the distributed generalized energy storage individuals based on the schedulable potential of each distributed generalized energy storage individual relative to the target layer, and preferentially schedule the distributed generalized energy storage individuals with high potential.
Further, in the step 4), the aggregator is used as a leader, and the distributed generalized energy storage is used as a follower; the aggregator guides the distributed generalized energy storage to participate in the aggregation process based on an electricity price means or an excitation induction means, meanwhile, the distributed generalized energy storage individuals autonomously decide an electricity utilization behavior in a local control unit based on received induction information, the decided planned electricity utilization behavior is sent to the aggregator, and the aggregator optimizes an induction strategy; the interaction of the aggregators and the distributed generalized energy storage forms a two-stage game process, the interaction process can be captured by using the Stackelberg game, and the balance of the game process is realized through iteration.
Further, in the step 4), an objective function of the aggregator is established as a multi-objective function, the self profitability, the aggregation multiplexing effect and the system stability are considered, and the constraint conditions comprise induced information constraint and power grid operation constraint; the distributed generalized energy storage objective function is established as a multi-objective function, energy use economy, comfort and greenness are considered, and constraint conditions comprise capacity constraint, power constraint, charge state constraint, climbing constraint, available time constraint and power demand constraint.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention forms a complete distributed generalized energy storage convergence multiplexing process, which comprises distributed generalized energy storage scheduling potential evaluation, convergence framework establishment and multiplexing scene modeling.
2. The invention is suitable for the convergence of various distributed generalized energy storage and is not limited to the traditional battery energy storage.
3. The invention fully respects the individual initiative of the distributed generalized energy storage and protects the individual privacy of the distributed generalized energy storage.
4. The method can realize the most applicable scene division of the distributed generalized energy storage with different characteristics, fully exert the calling potential of the distributed generalized energy storage and assist the operation of the power grid.
5. The invention can realize the prior utilization of distributed generalized energy storage with large scheduling potential, and is beneficial to the improvement of convergence multiplexing effect.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a diagram of a shunting step convergence utilization architecture.
FIG. 3 is a hierarchical structure diagram of a distributed generalized energy storage schedulable potential evaluation based on an analytic hierarchy process.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Referring to fig. 1, the distributed generalized energy storage convergence coordination method provided in this embodiment includes the following steps:
s1: and acquiring related operating parameters of the distributed generalized energy storage.
Specifically, distributed generalized energy storage refers to energy storage on a generalized level: the application angle comprises small-scale renewable energy power generation side energy storage, power distribution side energy storage and user side energy storage; the forms include electrochemical energy storage, mechanical energy storage, electromagnetic energy storage and the like; some adjustable loads, including electrical, thermal, and gas loads, are also included, including reducible and translatable loads. If the resources have space regulation and convergence potential, the resources are collectively referred to as distributed generalized energy storage and belong to a converged resource rank.
Specifically, the related operating parameters of the distributed generalized energy storage include an energy storage equivalent available capacity, an energy storage equivalent available power, an energy storage equivalent climbing capacity, an energy storage estimated initial available time and an energy storage estimated end available time. The listed operation parameters are all parameters at the current evaluation moment and are all converted into electrical parameters, and the parameter sets of specific energy storage types have differences.
S2: and based on the obtained parameters, evaluating the schedulable potential of the distributed generalized energy storage participating in convergence by utilizing an analytic hierarchy process.
Specifically, according to the hierarchy of fig. 3: the bottom layer is a scheme layer and corresponds to different types of distributed generalized energy storage; the middle layer is a criterion layer and corresponds to a criterion factor; the top layer is a target layer and corresponds to the schedulable latent coefficient of different types of distributed generalized energy storage. The criterion layer factors comprise a capacity support factor, a power support factor, a fast ramp factor, an available time matching factor and a resource reliability factor. Specifically, the method comprises the following steps:
wherein, c1Representing a capacity support factor; qnRepresenting the equivalent available capacity which can be provided by the energy storage individual from the current evaluation time to the convergence ending time; qdRepresenting the capacity provided by the energy storage individuals required by the convergent call;
c2representing a power support factor; pnRepresenting the equivalent available power which can be provided by the energy storage individual at the current evaluation moment; pdRepresenting the power provided by the energy storage individuals required by the convergent call;
c3representing a fast ramp factor; vppRepresenting the equivalent climbing capacity which can be provided by the energy storage individual at the current evaluation moment; vdRepresenting the rapid climbing capability provided by the energy storage individuals required for convergent calling;
c4represents an available time matching factor; t isinRepresenting the estimated initial available time; t isoutRepresenting the estimated available time of ending; t isd2Representing the initial time of the convergence scheduling; t isd1Representing the convergence scheduling end time; n represents taking intersection;
c5representing a resource reliability factor; n is a radical ofhIndividual calendar for representing stored energyThe history participates in the total times of aggregation; n is a radical ofwRepresenting the number of times of the accumulated default participated in by the energy storage individual history.
Further, giving the relative importance degree grade of the standard layer factors in the hierarchical structure of fig. 3 according to the multiplexing scene requirements (system peak regulation, primary frequency modulation, secondary frequency modulation, etc.), constructing a pair comparison matrix a between the standard layer factors according to the relative importance degree grade of the factors, and calculating a normalized eigenvector w corresponding to the maximum eigenvalue of the matrix a; respectively constructing a pair comparison matrix { B) of each factor of all distributed generalized energy storage individuals relative to the criterion layer according to the calculated size of the factor of the criterion layer of the distributed generalized energy storage individuals1，B2，B3，B4，B5And calculating the normalized eigenvector { w) corresponding to the maximum eigenvalue of the corresponding matrix1，w2，w3，w4，w5}, and then construct a weight matrix W ═ W1 w2 w3 w4 w5](ii) a Finally, the schedulable potential evaluation coefficient vector delta of the distributed generalized energy storage individual relative to the target layer is obtained through calculationT，wTIs the transpose of the feature vector w.
S3: designing the schedulable potential of the distributed generalized energy storage according to the relative importance degree of the rule layer factors of the analytic hierarchy process, and establishing a aggregator-distributed generalized energy storage shunting step convergence utilization framework.
Specifically, fig. 2 is a diagram of a shunting cascade aggregation utilization architecture, where aggregators downward undertake distributed generalized energy storage aggregation work and upward provide multiple types of auxiliary services for a power grid. The built structure has two functions of ' shunting ' and ' gradient ', wherein the shunting ' means that a aggregator builds a criterion layer-to-layer comparison matrix based on an analytic hierarchy process, and divides distributed generalized energy storage with different characteristics into different energy storage clusters, such as an energy storage cluster 1 participating in system peak regulation, an energy storage cluster 2 participating in primary frequency modulation, an energy storage cluster 3 participating in secondary frequency modulation and the like; the step is to sort the output sequence of the distributed generalized energy storage individuals based on the schedulable potential of each distributed generalized energy storage individual relative to a target layer, and preferentially schedule the distributed generalized energy storage individuals with high potential in different energy storage clusters.
S4: on the basis of a shunting cascade convergence utilization architecture, a convergence cooperative interaction model is established by using a Stackelberg game, so that convergence multiplexing is realized.
Specifically, the aggregator serves as a leader, and the distributed generalized energy storage serves as a follower. The aggregator guides the distributed generalized energy storage to participate in the aggregation process based on an electricity price means or an incentive inducing means such as a coupon, meanwhile, the distributed generalized energy storage individuals autonomously decide the electricity utilization behavior in the local control unit based on the received inducing information, the decided planned electricity utilization behavior is sent to the aggregator, and the aggregator optimizes the inducing strategy. The interaction of the aggregators and the distributed generalized energy storage forms a two-stage game process, the interaction process can be just captured by using the Stackelberg game, and the balance is realized through iteration in the game process.
Furthermore, an objective function of the aggregator is generally established as a multi-objective function, the profitability, the convergence multiplexing effect and the system stability of the aggregator are considered, and constraint conditions generally comprise induced information constraint and power grid operation constraint; the objective function of the distributed generalized energy storage is generally established as a multi-objective function, energy use economy, comfort and greenness are considered, and constraint conditions generally comprise capacity constraint, power constraint, state of charge constraint, climbing constraint, available time constraint and power demand constraint.
In summary, the method of the present invention can realize: carrying out schedulable potential evaluation on the distributed generalized energy storage resources, and screening out distributed generalized energy storage individuals with convergence potential; constructing a distributed generalized energy storage shunting cascade convergence utilization framework, and realizing shunting of distributed generalized energy storage most suitable multiplexing scenes with different characteristics and cascade output sequencing of distributed generalized energy storage individuals; and establishing a general interaction model between the aggregator and the distributed generalized energy storage individuals by using a Stackelberg game, wherein the aggregator preferentially interacts with the distributed generalized energy storage individuals with high schedulable potential. Finally, the improvement of the operating characteristics of the power grid, the meeting of the energy consumption requirements of distributed generalized energy storage individuals, the cost reduction and the benefit of aggregators can be realized.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (7)
1. A distributed generalized energy storage convergence coordination method is characterized by comprising the following steps:
1) acquiring relevant operation parameters of distributed generalized energy storage;
2) evaluating the schedulable potential of the distributed generalized energy storage participating in convergence by utilizing an analytic hierarchy process based on the obtained parameters;
3) designing the schedulable potential of distributed generalized energy storage according to the relative importance degree of the rule layer factors of the analytic hierarchy process, and establishing a aggregator-distributed generalized energy storage shunting step convergence utilization framework;
4) on the basis of a shunting cascade convergence utilization architecture, a convergence cooperative interaction model is established by using a Stackelberg game, so that convergence multiplexing is realized.
2. The distributed generalized energy storage convergence coordination method according to claim 1, wherein: in step 1), distributed generalized energy storage refers to energy storage on a generalized level: the application angle comprises small-scale renewable energy power generation side energy storage, power distribution side energy storage and user side energy storage; formally including electrochemical, mechanical, and electromagnetic energy storage; adjustable loads, both load shedding and load shifting, are also included, including electrical, thermal, and gas loads in specific types; if the resources have space regulation and convergence potential, the resources are collectively referred to as distributed generalized energy storage and belong to a converged resource rank.
3. The distributed generalized energy storage convergence coordination method according to claim 1, wherein: in the step 1), relevant operation parameters of distributed generalized energy storage comprise energy storage equivalent available capacity, energy storage equivalent available power, energy storage equivalent climbing capacity, energy storage estimated initial available time and energy storage estimated end available time; the listed operation parameters are all parameters at the current evaluation moment and are all converted into electrical parameters, and the parameter sets of specific energy storage types have differences.
4. The distributed generalized energy storage convergence coordination method according to claim 1, wherein: in step 2), the criterion layer factors of the analytic hierarchy process include a capacity support factor, a power support factor, a fast ramp-up factor, an available time matching factor and a resource reliability factor, and specifically are as follows:
wherein, c1Representing a capacity support factor; qnRepresenting the equivalent available capacity which can be provided by the energy storage individual from the current evaluation time to the convergence ending time; qdRepresenting the capacity provided by the energy storage individuals required by the convergent call;
c2representing a power support factor; pnRepresenting what the energy storage individual can provide at the current evaluation momentThe equivalent available power of; pdRepresenting the power provided by the energy storage individuals required by the convergent call;
c3representing a fast ramp factor; vppRepresenting the equivalent climbing capacity which can be provided by the energy storage individual at the current evaluation moment; vdRepresenting the rapid climbing capability provided by the energy storage individuals required for convergent calling;
c4represents an available time matching factor; t isinRepresenting the estimated initial available time; t isoutRepresenting the estimated available time of ending; t isd2Representing the initial time of the convergence scheduling; t isd1Representing the convergence scheduling end time; n represents taking intersection;
c5representing a resource reliability factor; n is a radical ofhRepresenting the total times of the energy storage individual history participation aggregation; n is a radical ofwRepresenting the number of times of the accumulated default of the history participation of the energy storage individuals;
giving the relative importance degree grade of the criterion layer factors of the analytic hierarchy process according to the multiplexing scene requirement, constructing a pair comparison matrix A between the criterion layer factors according to the relative importance degree grade of the factors, and calculating a normalized eigenvector w corresponding to the maximum eigenvalue of the matrix A; respectively constructing a pair comparison matrix { B) of each factor of all distributed generalized energy storage individuals relative to the criterion layer according to the calculated size of the factor of the criterion layer of the distributed generalized energy storage individuals1，B2，B3，B4，B5And calculating the normalized eigenvector { w) corresponding to the maximum eigenvalue of the corresponding matrix1，w2，w3，w4，w5}, and then construct a weight matrix W ═ W1w2 w3 w4 w5](ii) a Finally, the schedulable potential evaluation coefficient vector delta of the distributed generalized energy storage individual relative to the target layer is obtained through calculationT，wTIs the transpose of the feature vector w.
5. The distributed generalized energy storage convergence coordination method according to claim 1, wherein: in the step 3), the aggregator undertakes distributed generalized energy storage convergence work downwards and provides multi-type auxiliary services for the power grid upwards; the established shunting cascade convergence utilizes an architecture to present two functions of shunting and cascading, wherein shunting means that an aggregator establishes a criterion layer pair comparison matrix based on an analytic hierarchy process and divides distributed generalized energy storage with different characteristics into the most appropriate multiplexing scene; the step is to sort the output sequence of the distributed generalized energy storage individuals based on the schedulable potential of each distributed generalized energy storage individual relative to the target layer, and preferentially schedule the distributed generalized energy storage individuals with high potential.
6. The distributed generalized energy storage convergence coordination method according to claim 1, wherein: in the step 4), the aggregator is used as a leader, and the distributed generalized energy storage is used as a follower; the aggregator guides the distributed generalized energy storage to participate in the aggregation process based on an electricity price means or an excitation induction means, meanwhile, the distributed generalized energy storage individuals autonomously decide an electricity utilization behavior in a local control unit based on received induction information, the decided planned electricity utilization behavior is sent to the aggregator, and the aggregator optimizes an induction strategy; the interaction of the aggregators and the distributed generalized energy storage forms a two-stage game process, the interaction process can be captured by using the Stackelberg game, and the balance of the game process is realized through iteration.
7. The distributed generalized energy storage convergence coordination method according to claim 1, wherein: in the step 4), the objective function of the aggregator is established as a multi-objective function, the self profitability, the convergence multiplexing effect and the system stability are considered, and the constraint conditions comprise induced information constraint and power grid operation constraint; the distributed generalized energy storage objective function is established as a multi-objective function, energy use economy, comfort and greenness are considered, and constraint conditions comprise capacity constraint, power constraint, charge state constraint, climbing constraint, available time constraint and power demand constraint.
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| CN113112062A (en) * | 2021-04-07 | 2021-07-13 | 国家电网有限公司 | Regional energy aggregator gaming method considering new energy consumption |
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| CN115566740A (en) * | 2022-12-05 | 2023-01-03 | 广东电网有限责任公司江门供电局 | Distributed renewable energy cluster aggregation regulation and control potential evaluation method and device |
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