A self-organizing consensus, incentive and selection method for distributed power aggregators

Abstract

The present invention discloses a self-organized consensus, incentive and selection method for distributed power aggregators, which relates to the technical field of distributed power management. First, the distributed power aggregator provides a self-organized consensus protocol for the distributed power in the aggregator by constructing a dual credibility function, and then calculates the probability of the distributed power becoming a consensus winning node in a certain aggregator according to the dual credibility. Secondly, the distributed power aggregator constructs a triple incentive function including power supply incentive, consensus winning incentive and point-to-point data transmission incentive. Finally, after each round of consensus and selection begins, the distributed power selects the aggregator that maximizes the incentive effect of the distributed power according to the dual credibility proof protocol and triple incentive function provided by each aggregator self-organization, based on the evolutionary game equilibrium model under replication dynamics. It helps to realize the aggregation management of distributed power and ensure the flexibility of the new power system.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed power management, and in particular to a distributed power aggregator self-organizing consensus, incentive and selection method. Background Art

[0002] The development of new power systems involves the aggregation of distributed power sources, including electric vehicles, energy storage devices, temperature control loads, distributed photovoltaics, distributed wind power and other distributed power equipment. Compared with traditional grid-side generators, these distributed power sources have smaller capacity, more dispersed geographical locations, and more complex scheduling and management due to various uncertainties. Therefore, the form of distributed power aggregators is widely used as an intermediary between power systems and distributed power sources.

[0003] While alleviating the pressure of power system dispatch, distributed power aggregators must find an effective way to manage a large number of distributed power individuals. In traditional centralized or hierarchical partitioned organizational forms, there must be one or more centralized control units, and each distributed power source must establish interactive communication with such centralized control units, which puts tremendous pressure on the scalability of the aggregator. In addition, due to the uncertainty of the output of distributed power sources and the reliability of communication data, the network security issues of aggregators cannot be ignored. At the same time, with the continuous opening of the power market environment, the number of distributed power aggregators is increasing, and it is difficult for distributed power sources to reasonably choose the aggregators they join.

[0004] In view of the weaknesses of distributed power aggregators adopting traditional centralized or hierarchical and partitioned organizational forms, some other organizational forms have been reported.

[0005] For example, the patent application with application number 202310359162.3 discloses an adaptive virtual power plant distributed architecture and its economic dispatch method, which improves the dynamic coordination of distributed power sources and energy storage systems through the basic resource layer and the system layer. However, the patent application focuses on the system architecture design and does not involve the consensus method between distributed power sources and aggregators. It only considers a single virtual power plant or distributed resource aggregator, and does not involve options for multiple aggregators.

[0006] For example, the patent application with application number CN202310308038.4 discloses a distributed flexible resource self-organizing aggregation method, system, device and medium. Through the self-organizing aggregation of flexible resources on a social networking platform, a self-organizing aggregate state set is obtained, and the self-organizing aggregate state in the obtained self-organizing aggregate state set is guided. However, this patent application focuses on self-organizing aggregation based on a social networking platform, and does not involve the specific consensus mechanism and incentive method of distributed power sources, nor does it involve the selection of multiple aggregators.

[0007] For example, the patent application with application number CN202110419560.0 discloses a real-time optimization scheduling method, system, terminal and medium for distributed electric vehicles; the patent application with application number CN201811235123.8 discloses a distributed economic scheduling method for power systems based on ADMM; the patent application with application number CN201810811605.7 discloses a distributed control method for heterogeneous air-conditioning clusters based on frequency response. All of them involve distributed structures and distributed power sources, but all focus on the design or solution of related scheduling strategies, and do not involve the consensus and incentive mechanism of distributed power sources and their selection balance among multiple distributed power aggregators.

[0008] Therefore, it is necessary to find a method that can aggregate distributed resources more flexibly and provide reasonable consensus, incentive and selection methods to provide a new model for the self-organization of distributed resources. Summary of the invention

[0009] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a self-organizing consensus, incentive and selection method for distributed power aggregators, optimize the aggregation organization form of distributed power sources, and provide a more flexible and efficient aggregation mode.

[0010] The present invention adopts the following technical solutions to solve the above technical problems:

[0011] A distributed power aggregator self-organizing consensus, incentive and selection method proposed in the present invention includes the following steps:

[0012] Step A: A distributed power aggregator with self-organizing capabilities constructs a dual credibility function and provides a dual credibility proof consensus protocol, wherein the dual credibility includes the error credit and deviation credit of each distributed power source, and the error credit and deviation credit of each distributed power source are combined with different weights, so as to provide a protocol for the distributed power sources in the aggregator to reach a consensus;

[0013] Step B: Distributed power aggregators with self-organizing capabilities build a triple incentive function, where the triple incentives include power supply incentives, consensus winning incentives, and peer-to-peer data transmission incentives;

[0014] Step C: After each round of consensus and selection begins, the distributed power source selects the aggregator that maximizes the incentive effect of the distributed power source based on the dual-credibility proof consensus protocol and triple incentive function provided by each aggregator self-organization and the evolutionary game equilibrium model under replication dynamics.

[0015] As a further optimization scheme for the distributed power aggregator self-organizing consensus, incentive and selection method described in the present invention, step A also includes further calculating the probability of any distributed power source becoming a consensus winning node in a certain aggregator based on dual credibility.

[0016] As a further optimization scheme of the distributed power aggregator self-organization consensus, incentive and selection method described in the present invention, step A is as follows:

[0017] Step A1, the total number of distributed power sources is I, and the dual credibility of any distributed power source includes error credit and deviation credit, wherein the error credit includes prediction error sub-credit and communication failure error sub-credit, and the deviation credit includes actual output deviation sub-credit and communication fraud deviation sub-credit; at time t, the prediction error sub-credit, communication failure error sub-credit, actual output deviation sub-credit and communication fraud deviation sub-credit are updated according to the values ​​of the (t-1)th round of consensus and the (t-2)th round of consensus according to the following formula:

[0018]

[0019]

[0020]

[0021]

[0022] Among them, i∈I, is the prediction error sub-credit of the i-th distributed generation in the t-th round of consensus, is the communication failure error sub-credit of the i-th distributed power source in the t-th round of consensus, is the actual output deviation sub-credit of the i-th distributed generation in the t-th round of consensus, is the communication fraud deviation sub-credit of the i-th distributed power source in the t-th round of consensus, η obj is the error credit penalty coefficient, is the prediction error probability of the i-th distributed generation in the t-th round of consensus, is the communication failure probability of the i-th distributed power source in the t-th round of consensus, η h is the deviation credit reward coefficient, η m is the error credit reward coefficient, Ω h represents the set of honest nodes, Ω m Represents the set of untrustworthy nodes, is the prediction error sub-credit of the ith distributed generation in the (t-1)th round of consensus, is the prediction error sub-credit of the ith distributed generation in the (t-2)th round of consensus, is the communication failure error sub-credit of the i-th distributed power source in the (t-1)-th round of consensus, is the communication failure probability of the i-th distributed power source in the (t-2)th round of consensus, is the actual output deviation sub-credit of the ith distributed generation in the (t-1)th round of consensus, is the actual output deviation sub-credit of the ith distributed generation in the (t-2)th round of consensus, is the communication fraud deviation sub-credit of the i-th distributed power source in the (t-1)th round of consensus, is the communication fraud deviation sub-credit of the i-th distributed power source in the (t-2)-th round of consensus;

[0023] Step A2: There are V distributed power aggregators with self-organizing capabilities. The formula for the dual credit function for distributed power generation constructed by any aggregator is as follows:

[0024]

[0025] Where v∈V,C ivt is the dual credibility value of the i-th distributed power source in the t-th round of consensus, is the sub-weight for the prediction error sub-credit in the vth aggregator, is the weight of the sub-credit for the communication failure rate in the vth aggregator, is the weight of the actual output deviation sub-credit in the vth aggregator, is the weight of the sub-creditworthiness for communication fraud deviation in the vth aggregator, and the superscript T is transposed;

[0026] Step A3: The vth aggregator of distributed power with self-organizing ability builds a peer-to-peer network to connect various distributed power nodes. The i-th distributed power estimates the probability of becoming the winning node of the peer-to-peer network consensus in the vth aggregator based on the dual credibility. The probability estimation formula is:

[0027]

[0028] Among them, e is a natural constant, N vt is the total number of distributed power sources of the vth aggregator in the tth round of consensus, a is the communication round-trip delay coefficient of the peer-to-peer network, b is the attribute verification coefficient of the peer-to-peer network, ρ v The distribution function in the peer-to-peer network formed by the v-th aggregator.

[0029] As a further optimization scheme for the self-organized consensus, incentive and selection method of distributed power aggregators described in the present invention, the specific construction method of the triple incentive function in step B is as follows:

[0030] Step B1: The vth aggregator calculates the value of the dual credibility C ivt Accepting the power provided by the i-th distributed power source, when the i-th distributed power source chooses to participate in the v-th aggregator in the t-th round of consensus, the allocation function of its provided power is:

[0031]

[0032] Among them, P ivt The power provided by the i-th distributed power source when it chooses to participate in the v-th aggregator in the t-th round of consensus, is the maximum power that the i-th distributed power source can provide in the t-th round of consensus, is the total power that the vth aggregator needs to aggregate in the tth round of consensus;

[0033] Step B2: The aggregator provides each distributed power source with triple incentives, including power supply incentives, consensus winning incentives, and peer-to-peer data transmission incentives. The triple incentive function and its constraints are as follows:

[0034]

[0035] In the formula,

[0036]

[0037] Among them, R(v,P ivt ) is the i-th distributed power source that chooses to participate in the v-th aggregator in the t-th round of consensus and provides power P ivt The total expected return when vt is the power supply incentive price provided by the vth aggregator in the tth round of consensus, β vt , γ vt are the consensus winning incentive coefficient and peer-to-peer data dissemination incentive coefficient provided by the vth aggregator in the tth round of consensus, respectively, t is the external electricity market price in the tth round of consensus, z ivt is the total number of nodes that perform peer-to-peer data transmission in the vth aggregator by the i-th distributed power source in the t-th round of consensus.

[0038] As a further optimization scheme of the distributed power aggregator self-organization consensus, incentive and selection method described in the present invention, step B also includes that any aggregator distributes the incentives obtained by the distributed power source according to the triple incentive function.

[0039] As a further optimization scheme of the distributed power aggregator self-organization consensus, incentive and selection method described in the present invention, step C is as follows:

[0040] Step C1: At the beginning of the tth round of consensus, the following array is formed based on the number of distributed power sources, the number of aggregators, and the ratio of distributed power sources to initially select aggregators: Ω t is the proportion array of distributed power sources in each aggregator in the tth round of consensus, ω vt R(v,P ivt ;Ω t ) is when the ratio group is Ω t The total expected revenue of the i-th distributed generation is R(v,P ivt ), R(v,P ivt ) calculated according to step B2;

[0041] Step C2: The i-th distributed power source is based on the current quaternion array G t , evaluate the expected rate of change of return that can be obtained when choosing the vth aggregator. The evaluation formula is the following ordinary differential equation:

[0042]

[0043] in, is the expected rate of change of revenue of the vth aggregator in the tth round of consensus;

[0044] Step C3: The i-th distributed power source evaluates the expected rate of change of the currently selected v-th aggregator’s income. The changes of the distributed power sources in the v-th aggregator are divided into three scenarios: When , the i-th distributed generation keeps choosing the v-th aggregator or transfers to choosing the v-th aggregator, ω vt Increase; when When , the i-th distributed power source is transferred to other aggregators except the v-th aggregator, ω vt decrease; when When the i-th distributed generation does not adjust the selected v-th aggregator, ω vt constant;

[0045] Step C4: As the distributed power sources continue to adjust the selected aggregators, when the following evolutionary game equilibrium is reached, each distributed power source no longer adjusts its selected aggregator:

[0046]

[0047] Among them, Ω' t is the feasible solution for the proportion of DGs participating in the vth aggregator in the total number of DGs in the tth round of consensus, is the proportion of DGs that choose to participate in the vth aggregator in the total number of DGs when the evolutionary game equilibrium is reached in the tth round of consensus, R(v,P ivt ;Ω' t ) is when the ratio group is Ω' t The total expected revenue of the i-th distributed generation is R(v,P ivt ), When the ratio group is The total expected revenue of the i-th distributed generation is R(v,P ivt )

[0048] As a further optimization scheme of the self-organizing consensus, incentive and selection method of a distributed power aggregator described in the present invention, the distributed power source includes electric vehicles, energy storage devices, temperature control loads, distributed photovoltaics, distributed wind power distributed power supply equipment.

[0049] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0050] The present invention provides a self-organizing framework for the aggregation of distributed power sources that does not require centralized control or hierarchical and partitioned control. Distributed power individuals can freely and dynamically select participating aggregators based on the credit obtained by their own output uncertainty, communication reliability, etc., combined with the incentive function provided by different aggregators according to the required total aggregated power. While maximizing their own benefits, it can also improve the flexibility of two-way selection between distributed power individuals and aggregators, optimize the resource allocation between multiple aggregators, thereby increasing the enthusiasm of distributed resources to actively participate in power grid regulation and ensuring the reliability of the aggregation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a distributed power aggregator self-organizing consensus, incentive and selection method provided by the present invention.

[0052] Figure 2 An embodiment of the present invention provides an equilibrium point after the end of the t=9th round of evolutionary game and the ratio of the number of distributed power sources in each aggregator.

[0053] Figure 3 An embodiment of the present invention provides an equilibrium point after the end of the t=10th round of evolutionary game after adjusting the parameters and the ratio of the number of distributed power sources in each aggregator. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] Embodiment 1: The technical solution adopted by the present invention is a self-organizing consensus, incentive and selection method for distributed power aggregators. According to the number of distributed power sources and aggregators with self-organizing capabilities, combined with the dual credit of each distributed power source and the triple incentive of the aggregator, the balanced distribution of distributed power sources among the aggregators is achieved.

[0056] Combination Figure 1 In the embodiment of the present invention, 4000 distributed power sources and 2 distributed power source aggregators are taken as an example, and the specific steps include:

[0057] There are 4000 distributed power sources in total. At t = 0, the and The initial value is 1. The factors affecting the error credit, that is, the prediction error probability of the i-th distributed power source in the t-th round of consensus Probability of communication failure with the i-th distributed generation Obey and N(0.02,0.01 2 ) Normal distribution. The influencing factors of the deviation credit, that is, the i-th distributed power source shows communication fraud in the t-th round of consensus, which makes it a dishonest node Ω m The probability of , obeys the gamma distribution Γ(3,2). The rated power of each distributed power source The maximum power that the i-th distributed power source can provide in the t-th round of consensus is subject to the uniform distribution of U(5,25). obey of uniform distribution.

[0058] There are 2 distributed power aggregators with self-organizing capabilities. The communication round-trip delay coefficient a and attribute verification coefficient b of the constructed point-to-point network are 200 and 800 respectively. The empirical distribution of ρ1 and ρ2 conforms to the power law distribution, and the exponent values ​​are 1.2 and 2 respectively. The v=1th aggregator has the highest number of consensus rounds. η obj , η h , η m , β v , The above parameters of the v=2nd aggregator are 0.1, 0.5, 1.0, 5.0, 0.5, 0.05, 0.5, 0.03, and 0.2 respectively. The above parameters of the v=2nd aggregator are 0.1, 0.5, 1.0, 5.0, 0.5, 0.05, 0.5, 0.03, and 0.2 respectively in rounds t=1 to 9. In the t=10th round, some parameters were adjusted and the values ​​were 0.1, 0.5, 1.0, 5.0, 1.0, 0.05, 2.0, 0.05, and 0.3 respectively.

[0059] The selection equilibrium results of distributed power sources in the distributed power aggregator in the consensus observation of the t=9th round and the t=10th round are as follows Figure 2 and Figure 3 As shown. In the t=9th round, 82% of the distributed power sources chose to participate in the v=1st aggregator, and 18% of the distributed power sources chose to participate in the v=2nd aggregator. As the parameters changed, in the t=10th round, 43% of the distributed power sources chose to participate in the v=1st aggregator, and 57% of the distributed power sources chose to participate in the v=2nd aggregator. It can be seen from this embodiment that the distributed power aggregator can change the parameters of various incentives as needed in each round of consensus, and the distributed power sources can also choose to participate in the aggregator according to different expected returns, thereby achieving a balance in the number of distributed power sources in each aggregator. This organizational form fully considers the subjective and objective factors of the distributed power sources during aggregation, and improves the flexibility of the aggregator's organizational form.

[0060] In summary, the present invention has the following beneficial effects: it provides a self-organizing framework for the aggregation of distributed power sources, which does not require centralized control. Distributed power source individuals can freely and dynamically select participating aggregators based on a dual credibility proof consensus protocol and a triple incentive function constructed by the aggregator, and the equilibrium of the evolutionary game can also be achieved among the aggregators.

[0061] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A distributed power aggregator self-organizing consensus, incentive and selection method, characterized in that: The steps include: Step A: A distributed power aggregator with self-organizing capabilities constructs a dual credibility function and provides a dual credibility proof consensus protocol, wherein the dual credibility includes the error credit and deviation credit of each distributed power source, and the error credit and deviation credit of each distributed power source are combined with different weights, so as to provide a protocol for the distributed power sources in the aggregator to reach a consensus; Step B: Distributed power aggregators with self-organizing capabilities build a triple incentive function, where the triple incentives include power supply incentives, consensus winning incentives, and peer-to-peer data transmission incentives; Step C: After each round of consensus and selection begins, the distributed power source selects the aggregator that maximizes the incentive effect of the distributed power source based on the dual-credibility proof consensus protocol and triple incentive function provided by each aggregator self-organization and the evolutionary game equilibrium model under replication dynamics; Step A also includes calculating the probability of any distributed power source becoming a consensus winning node in a certain aggregator based on dual credibility; Step A is as follows: Step A1, the total number of distributed power sources is I, and the dual credibility of any distributed power source includes error credit and deviation credit, wherein the error credit includes prediction error sub-credit and communication failure error sub-credit, and the deviation credit includes actual output deviation sub-credit and communication fraud deviation sub-credit; at time t, the prediction error sub-credit, communication failure error sub-credit, actual output deviation sub-credit and communication fraud deviation sub-credit are updated according to the values ​​of the (t-1)th round of consensus and the (t-2)th round of consensus according to the following formula: Among them, i∈I, is the prediction error sub-credit of the i-th distributed generation in the t-th round of consensus, is the communication failure error sub-credit of the i-th distributed power source in the t-th round of consensus, is the actual output deviation sub-credit of the i-th distributed generation in the t-th round of consensus, is the communication fraud deviation sub-credit of the i-th distributed power source in the t-th round of consensus, η obj is the error credit penalty coefficient, is the prediction error probability of the i-th distributed generation in the t-th round of consensus, is the communication failure probability of the i-th distributed power source in the t-th round of consensus, η h is the deviation credit reward coefficient, η m is the error credit reward coefficient, Ω h represents the set of honest nodes, Ω m Represents the set of untrustworthy nodes, is the prediction error sub-credit of the ith distributed generation in the (t-1)th round of consensus, is the prediction error sub-credit of the ith distributed generation in the (t-2)th round of consensus, is the communication failure error sub-credit of the i-th distributed power source in the (t-1)-th round of consensus, is the communication failure probability of the i-th distributed power source in the (t-2)th round of consensus, is the actual output deviation sub-credit of the ith distributed generation in the (t-1)th round of consensus, is the actual output deviation sub-credit of the ith distributed generation in the (t-2)th round of consensus, is the communication fraud deviation sub-credit of the i-th distributed power source in the (t-1)th round of consensus, is the communication fraud deviation sub-credit of the i-th distributed power source in the (t-2)-th round of consensus; Step A2: There are V distributed power aggregators with self-organizing capabilities. The formula for the dual credit function for distributed power generation constructed by any aggregator is as follows: Where v∈V,C ivt is the dual credibility value of the i-th distributed power source in the t-th round of consensus, is the sub-weight for the prediction error sub-credit in the vth aggregator, is the weight of the sub-credit for the communication failure rate in the vth aggregator, is the weight of the actual output deviation sub-credit in the vth aggregator, is the weight of the sub-creditworthiness for communication fraud deviation in the vth aggregator, and the superscript T is transposed; Step A3: The vth aggregator of distributed power with self-organizing ability builds a peer-to-peer network to connect various distributed power nodes. The i-th distributed power estimates the probability of becoming the winning node of the peer-to-peer network consensus in the vth aggregator based on dual credibility. The probability estimation formula is: Among them, e is a natural constant, N vt is the total number of distributed power sources of the vth aggregator in the tth round of consensus, a is the communication round-trip delay coefficient of the peer-to-peer network, b is the attribute verification coefficient of the peer-to-peer network, ρ v The distribution function in the peer-to-peer network formed by the vth aggregator; The specific construction method of the triple excitation function in step B is as follows: Step B1: The vth aggregator calculates the value of the dual credibility C ivt Accepting the power provided by the i-th distributed power source, when the i-th distributed power source chooses to participate in the v-th aggregator in the t-th round of consensus, the allocation function of its provided power is: Among them, P ivt The power provided by the i-th distributed power source when it chooses to participate in the v-th aggregator in the t-th round of consensus, is the maximum power that the i-th distributed power source can provide in the t-th round of consensus, is the total power that the vth aggregator needs to aggregate in the tth round of consensus; Step B2: The aggregator provides each distributed power source with triple incentives, including power supply incentives, consensus winning incentives, and peer-to-peer data transmission incentives. The triple incentive function and its constraints are as follows: In the formula, Among them, R(v,P ivt ) is the i-th distributed power source that chooses to participate in the v-th aggregator in the t-th round of consensus and provides power P ivt The total expected return when vt is the power supply incentive price provided by the vth aggregator in the tth round of consensus, β vt , γ vt are the consensus winning incentive coefficient and peer-to-peer data dissemination incentive coefficient provided by the vth aggregator in the tth round of consensus, respectively, t is the external electricity market price in the tth round of consensus, z ivt is the total number of nodes that perform peer-to-peer data transmission in the vth aggregator by the i-th distributed power source in the t-th round of consensus; Step B also includes that any aggregator allocates the incentives obtained by the distributed power sources according to the triple incentive function; Step C is as follows: Step C1: At the beginning of the tth round of consensus, the following array is formed based on the number of distributed power sources, the number of aggregators, and the ratio of distributed power sources to initially select aggregators: Ω t is the proportion array of distributed power sources in each aggregator in the tth round of consensus, ω vt R(v,P ivt ;Ω t ) is when the ratio group is Ω t The total expected revenue of the i-th distributed generation is R(v,P ivt ), R(v,P ivt ) calculated according to step B2; Step C2: The i-th distributed power source is based on the current quaternion array G t , evaluate the expected rate of change of return that can be obtained when choosing the vth aggregator. The evaluation formula is the following ordinary differential equation: in, is the expected rate of change of revenue of the vth aggregator in the tth round of consensus; Step C3: The i-th distributed power source evaluates the expected rate of change of the currently selected v-th aggregator’s income. The changes of the distributed power sources in the v-th aggregator are divided into three scenarios: When , the i-th distributed generation keeps choosing the v-th aggregator or transfers to choosing the v-th aggregator, ω vt Increase; when When , the i-th distributed power source is transferred to other aggregators except the v-th aggregator, ω vt decrease; when When the i-th distributed generation does not adjust the selected v-th aggregator, ω vt constant; Step C4: As the distributed power sources continue to adjust the selected aggregators, when the following evolutionary game equilibrium is reached, each distributed power source no longer adjusts its selected aggregator: Among them, Ω' t is the feasible solution for the proportion of DGs participating in the vth aggregator in the total number of DGs in the tth round of consensus, is the proportion of DGs that choose to participate in the vth aggregator in the total number of DGs when the evolutionary game equilibrium is reached in the tth round of consensus, R(v,P ivt ;Ω' t ) is when the ratio group is Ω' t The total expected revenue of the i-th distributed generation is R(v,P ivt ), When the ratio group is The total expected revenue of the i-th distributed generation is R(v,P ivt ) 2. A distributed power aggregator self-organizing consensus, incentive and selection method according to claim 1, characterized in that: Distributed power sources include electric vehicles, energy storage devices, temperature control loads, distributed photovoltaics, distributed wind power and distributed power supply equipment.

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