CN117132017A - Shared energy storage operation optimization method based on cooperative game theory and CPS layered architecture - Google Patents
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Abstract
The invention relates to a shared energy storage operation optimization method based on a cooperative game theory and a CPS layered architecture, which comprises the following steps: establishing a CPS architecture suitable for a distributed shared energy storage mechanism; the sensor measures the physical state quantity on site, converts the physical state quantity into digital information and transmits the digital information to a control center managed by an energy storage operator through the terminal equipment; the energy storage operator establishes a charging and discharging strategy of the energy storage equipment according to the received digital information and combining the user requirement, the new energy output and the grid time-sharing electricity price, and transmits the responsive control information to the driver; secondly, a distributed shared energy storage operation mechanism based on a cooperative game theory is designed: (1) establishing a user electricity consumption operation cost model; (2) calculating the energy storage life by a rain flow counting method; (3) on the basis of a user electricity consumption operation cost model, a distributed energy storage operation model is established; and establishing an alternate direction multiplier ADMM algorithm suitable for the distributed shared energy storage mechanism. The invention can realize energy storage control and benefit distribution.
Description
Technical Field
The invention relates to the technical field of energy storage, in particular to a shared energy storage operation optimization method based on a cooperative game theory and a CPS layered architecture.
Background
Under the background of energy crisis and environmental pollution, new energy power generation rapidly develops, and renewable clean energy represented by photovoltaic, wind energy and the like is an important means for realizing a double-carbon target. The traditional power system is gradually changed into a novel high-proportion new energy power system, and the fluctuation, randomness and intermittence of new energy power generation make the safe operation and economic dispatch of a power grid face significant challenges.
The energy storage has flexible regulating capability, so that the fluctuation of new energy power generation can be stabilized, the impact of the new energy power generation on a power grid is reduced, and the energy storage is widely applied, but the development of the current energy storage has a plurality of problems. On the one hand, the distributed energy storage installation positions are scattered, the utilization rate is generally low, and unified scheduling and settlement are difficult to achieve. On the other hand, the energy storage cost investment is high, so that the investment enthusiasm of the user is insufficient.
The cyber-physical system (CPS) is a novel engineering system, is a unified whole capable of carrying out information interaction on a network level and a physical level, and has been applied to the aspects of automobiles, medical treatment, transportation, energy sources and the like by students, but the CPS has less research content in the energy storage field and lacks an overall framework and research method.
In addition, for high-efficiency application of energy storage resources, shared energy storage becomes a research hotspot, a learner discusses a new form of shared energy storage, namely 'cloud energy storage', introduces a business mode and a control strategy of the shared energy storage from the perspective of an energy storage operator, and does not study a user side. The two-layer energy storage configuration method for the user side energy storage proposal and the investment benefits of the user and the energy storage suppliers is considered by the learner, but only the centralized energy storage investment is focused, and the distributed energy storage investment is not integrated. Still other scholars consider shared energy storage operation analysis including user distributed energy storage, but do not make objective benefit distribution of the system.
Disclosure of Invention
The invention aims to provide a shared energy storage operation optimization method based on a cooperative game theory and CPS layered architecture, which realizes energy storage control and benefit distribution.
In order to solve the problems, the shared energy storage operation optimization method based on the cooperative game theory and CPS layered architecture comprises the following steps:
(1) Establishing a CPS architecture suitable for a distributed shared energy storage mechanism;
the sensor measures the physical state quantity on site, converts the physical state quantity into digital information and transmits the digital information to a control center managed by an energy storage operator through the terminal equipment; the energy storage operator establishes a charging and discharging strategy of the energy storage equipment according to the received digital information and combining the user requirement, the new energy output and the grid time-sharing electricity price, and transmits the responsive control information to the driver;
(2) Designing a distributed shared energy storage operation mechanism based on a cooperative game theory:
(1) establishing a user electricity consumption operation cost model;
(2) calculating the energy storage life by a rain flow counting method;
(3) on the basis of a user electricity consumption operation cost model, a distributed energy storage operation model is established;
the first stage: the users share the energy preferentially, namely, the power of different users is mutually used;
and a second stage: the energy storage operator provides energy storage service;
the overall power balance equation for distributed energy storage sharing is considered as follows:
wherein:load power of the user i in the t period; />Charging power of the distributed energy storage i in a t period;purchasing electric power for a power grid of a user i in a t period; />Is new toThe energy power generation i outputs power in the t period; />Discharging power of the distributed energy storage i in a t period;
the individual power balance equation for distributed energy storage sharing is considered as follows:
wherein:the sum of the link power between the users; />Defining the positive direction of power as flowing from user i to user j;
the method comprises the following steps of:
x i ＝V(S)-V(S-{i})
wherein: x is x i A marginal contribution to the individual; s is individual x i A participating federation; v (S) is x i The benefits generated by the whole alliance when participating in the alliance S; v (S- { i) is x i The benefits generated by the whole alliance when the alliance S is exited;
wherein: w is individual x i Weights separated within federation S; s is the number of participants within federation S; n is the overall number of participants;
wherein:benefits that are separated from the overall benefits for the user; w (w) s For individual x i A set of weights within the federation S;
establishing an alternate direction multiplier ADMM algorithm suitable for a distributed shared energy storage mechanism:
the power is equal using tie gon as follows:
the following augmented lagrangian function was constructed:
wherein: lambda is the Lagrangian multiplier; ρ is a penalty coefficient; f is an independent optimization objective function of a system where each distributed energy storage is located;
the iterative solution process is as follows:
the convergence conditions were as follows:
in the step (1), a user electricity consumption operation cost model is established and carried out according to an objective function, wherein the objective function consists of two parts, including the electricity purchasing cost and the energy storage operation cost of a power grid;
the objective function is:
wherein:the purchase cost of the user; />Charge and discharge service fees paid to the energy storage operator for the user;
electric power purchasing cost of each user power grid
Wherein:the electricity selling price of the power grid in the period t is represented; />Representing the power purchased from the grid by user i during period t; NT represents the entire scheduling period, taking 1h as one scheduling period, nt=24h;
charge and discharge service charge of energy storage operators:
wherein: c op The operation and maintenance cost of the energy storage unit power is;charging power of the energy storage device configured for i users in a t period; />Discharging power of the energy storage device configured for i users in a t period;
the daily operation cost of energy storage is:
wherein: c inv Investment cost for energy storage unit capacity; e (E) ESS Is the rated capacity of energy storage; t is the energy storage service life.
The method for calculating the energy storage life by using the rain flow counting method in the step (2) is as follows;
1) Reconstructing and rearranging the charge state change curve along with time so that the maximum peak or the maximum valley is used as a starting point to calculate the period;
2) Rotating the SOC-t curve by 90 degrees clockwise, and beginning the raindrops to flow downwards along the maximum peak position; if the lower roof is shielded, the raindrops fall to the shielding roof to continue falling, if the lower roof is not shielded, the raindrops change directions and fall to the end points;
3) Recording maximum peak value SOC of rain drop flowing path 1 Maximum valley SOC 2 As a complete cycle, this cycleDepth of discharge dod=soc 1 -SOC 2 ；
4) Deleting the path through which the raindrops flow, and repeating the rain flow counting process for the rest paths until all calculation is finished;
obtaining the relation between the energy storage cycle life and the discharge depth through fitting; the equivalent cycle times N' of the energy storage device in the operation period and the maximum operation days T of energy storage are as follows:
wherein: n (N) t (1) The maximum cycle number of the stored energy corresponding to the depth of charge and discharge of 1; n (N) t (d k ) Is charged and discharged with depth d k And the corresponding maximum cycle times of energy storage.
The constraint conditions of the distributed energy storage operation model in the step (3) are individual power balance, overall power balance and energy storage constraint; the energy storage constraint consists of an energy storage charge state continuity constraint and an energy storage charge and discharge constraint; wherein:
energy storage state of charge continuity constraints:
wherein: e (t) is the capacity of the stored energy at the moment t, eta ch And eta dis Charging efficiency and discharging efficiency SOC of energy storage respectively min And SOC (System on chip) max The energy storage minimum charge state and the maximum charge state;
energy storage charge-discharge constraint:
wherein:and->The energy storage charge state bit and the discharge state bit; />And->Respectively storing maximum charging power and discharging power.
Compared with the prior art, the invention has the following advantages:
1. the CPS framework suitable for the distributed shared energy storage mechanism is provided, the interaction of digital information and physical information is realized by combining an advanced detection technology and a communication technology, and an energy storage charging and discharging plan is reasonably arranged according to the collected related information.
2. The invention designs a distributed shared energy storage operation mechanism based on a cooperative game theory, and users with different source load characteristics can perform capacity shared power mutual aid and perform benefit distribution through a shape value method.
3. The invention considers the privacy of users and proposes an ADMM algorithm suitable for a distributed shared energy storage mechanism according to the power coupling relation of the connecting lines among different users.
Drawings
The following describes the embodiments of the present invention in further detail with reference to the drawings.
Fig. 1 is a schematic diagram of an information physical architecture of the shared energy storage of the present invention.
Fig. 2 is a schematic diagram of the rain flow counting scheme of the present invention.
Fig. 3 is a graph of the power load and photovoltaic curves of the users 1,2,3 of the present invention.
Fig. 4 is a graph of the user 1 electrical load balance of the present invention.
Fig. 5 is a graph of the electrical load balancing for user 2 of the present invention.
Fig. 6 is a graph of the electrical load balance of user 3 of the present invention.
FIG. 7 is a graph of the energy storage investment recovery analysis of the present invention.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The shared energy storage operation optimization method based on the cooperative game theory and CPS layered architecture comprises the following steps:
(1) In order to realize the control of distributed energy storage throughout, a CPS architecture suitable for a distributed shared energy storage mechanism is established.
Based on advanced detection technology and communication technology, interaction between digital information and physical information is realized, and energy storage charging and discharging plans are reasonably arranged according to collected related information.
Fig. 1 is a physical architecture of information sharing energy storage, where a sensor measures physical state quantities (including SOC of the energy storage and information for load prediction and new energy output prediction, etc.), converts the physical quantities into digital information, and transmits the digital information to a control center managed by an energy storage operator through a terminal device; and the energy storage operator establishes a charging and discharging strategy of the energy storage equipment according to the received digital information by combining the user requirement, the new energy output and the time-of-use electricity price of the power grid, and transmits the responsive control information to the driver.
The driver converts the control information into a physical driving signal, acts on the energy storage system and realizes the control of the energy storage system.
(2) Designing a distributed shared energy storage operation mechanism based on a cooperative game theory:
(1) establishing a user electricity consumption operation cost model:
to ensure economical operation, the operation cost is minimized as an optimization target. Therefore, the user electricity consumption operation cost model is established according to an objective function, and the objective function consists of two parts, namely the electricity purchasing cost and the energy storage operation cost of the power grid.
The objective function is:
wherein:the purchase cost of the user; />Charge and discharge service fees paid to the energy storage operator for the user;
electric power purchasing cost of each user power grid
Wherein:the electricity selling price of the power grid in the period t is represented; />Representing the power purchased from the grid by user i during period t; NT represents the entire scheduling period, taking 1h as one scheduling period, nt=24h;
charge and discharge service charge of energy storage operators:
wherein: c op The operation and maintenance cost of the energy storage unit power is;charging power of the energy storage device configured for i users in a t period; />Discharging power of the energy storage device configured for i users in a t period;
the daily operation cost of energy storage is:
wherein: c inv Investment cost for energy storage unit capacity; e (E) ESS Is the rated capacity of energy storage; t is the energy storage service life. The service life of the stored energy is obtained by a rain flow counting method.
(2) The stored energy life is calculated by a rain flow counting method, also known as "overhead method", as shown in fig. 2. The method comprises the following steps:
1) Reconstructing and rearranging the charge state change curve along with time so that the maximum peak or the maximum valley is used as a starting point to calculate the period;
2) Rotating the SOC-t curve by 90 degrees clockwise, and beginning the raindrops to flow downwards along the maximum peak position; if the lower roof is covered, the roof falls to the covered roof to continue falling: if the lower roof is not shielded, the raindrops change direction and fall to the end points;
3) Recording maximum peak value SOC of rain drop flowing path 1 Maximum valley SOC 2 As a complete cycle, depth of discharge dod=soc for this cycle 1 -SOC 2 ；
4) Deleting the path through which the raindrops flow, and repeating the rain flow counting process for the rest paths until all calculation is finished.
The cycles were ABC ' DEJ ' K, BCC ', EFG ' HIJ ' J ", FGG ', IJJ ', respectively.
The corresponding cycle life of a certain type of lithium iron phosphate battery under different charge and discharge depths is shown in table 1.
TABLE 1 depth of charge and discharge and cycle times
| Depth of charge and discharge | Cycle life/time |
| 0.2 | 8110 |
| 0.4 | 6340 |
| 0.6 | 5510 |
| 0.8 | 4430 |
| 1 | 3680 |
The relationship between the energy storage cycle life and the depth of discharge obtained by fitting is shown in the following formula:
N t (d)＝-6354×d 3 +14630×d 2 -15220×d+10590
wherein: d is the depth of energy storage charge and discharge;
the equivalent cycle times N' of the energy storage device in the operation period and the maximum operation days T of energy storage are as follows:
wherein: n (N) t (1) The maximum cycle number of the stored energy corresponding to the depth of charge and discharge of 1; n (N) t (d k ) Is charged and discharged with depth d k And the corresponding maximum cycle times of energy storage.
(3) On the basis of a user electricity consumption operation cost model, a distributed energy storage operation model is established;
the first stage: the users share energy preferentially, namely, the power of different users is mutually matched.
The power output between different new energy sources among different users has complementarity, and the load characteristics among different users are also different, so that the power can be mutually used among different users. The users directly perform power mutual compensation, so that the energy storage charging and discharging transaction link can be omitted, and the service life loss caused by the energy storage charging and discharging operation is reduced.
And a second stage: the energy storage operator provides energy storage services.
When the mutual power between users cannot fully meet the power requirements between the users, the unbalanced amount of the power is needed to be born through the charge and discharge service of the energy storage, so that the supply and demand balance of the power is realized.
The overall power balance equation for distributed energy storage sharing is considered as follows:
wherein:load power of the user i in the t period; />Charging power of the distributed energy storage i in a t period;purchasing electric power for a power grid of a user i in a t period; />Generating output power of the new energy source in the period t; />Discharging power of the distributed energy storage i in a t period;
the individual power balance equation for distributed energy storage sharing is considered as follows:
wherein:the sum of the link power between the users; />Defining the positive direction of power as flowing from user i to user j;
the method comprises the following steps of:
x i ＝V(S)-V(S-{i})
wherein: x is x i A marginal contribution to the individual; s is individual x i A participating federation; v (S) is x i The benefits generated by the whole alliance when participating in the alliance S; v (S- { i) is x i The benefits generated by the whole alliance when the alliance S is exited;
wherein: w is individual x i Weights separated within federation S; s is the number of participants within federation S; n is the overall number of participants;
wherein:benefits that are separated from the overall benefits for the user; w (w) s For individual x i A set of weights within the federation S.
Constraint conditions of the distributed energy storage operation model are individual power balance, overall power balance and energy storage constraint; the energy storage constraint consists of an energy storage charge state continuity constraint and an energy storage charge and discharge constraint; wherein:
energy storage state of charge continuity constraints:
wherein: e (t) is the capacity of the stored energy at the moment t, eta ch And eta dis Charging efficiency and discharging efficiency SOC of energy storage respectively min And SOC (System on chip) max The energy storage minimum charge state and the maximum charge state;
energy storage charge-discharge constraint:
wherein:and->The energy storage charge state bit and the discharge state bit; />And->Respectively storing maximum charging power and discharging power.
Establishing an alternate direction multiplier ADMM algorithm suitable for a distributed shared energy storage mechanism:
the power is equal using tie gon as follows:
the following augmented lagrangian function was constructed:
wherein: lambda is the Lagrangian multiplier; ρ is a penalty coefficient; f is an independent optimization objective function of a system where each distributed energy storage is located;
the iterative solution process is as follows:
the convergence conditions were as follows:
example 1
The embodiment is based on 24 hours before the day, and considers the industrial users considering the power requirements of 3 different types, namely, flat peak type, double peak type and peak avoidance type, and photovoltaic power generation and wind power generation are respectively arranged. The load and new energy output curves are shown in fig. 3, and the electricity purchase price of the power grid is shown in table 2. The shared energy storage service cost is 0.3 yuan (kW.h), the maximum charge and discharge power of each energy storage device is 100kW, and the maximum transmission power of a connecting line between users is 40kW.
Table 2 grid electricity purchase price
To compare and verify the superiority of the shared energy storage, two different modes of operation are set:
operation mode 1: a single user independently configures the distributed energy storage.
Operation mode 2: multiple users share distributed energy storage.
In the embodiment, under the mode that the users independently configure the energy storage, the energy storage is only served for a single user, and the maximum benefit of individuals is targeted; in the multi-user shared distributed energy storage mode, all the distributed energy storage modes operate in a coordinated mode, and can perform power mutual aid, serve the user group together and aim at the maximum benefit of the group. The investment costs for the different modes of operation are shown in the table below.
TABLE 3 comparison of results in independent and shared modes
| Type(s) | Electricity charge/yuan | Waste amount/KW.H | Energy storage cost/element |
| User 1 | 860.274 | 144.2845 | 168.5349 |
| User 2 | 935.426 | 426.3145 | 249.2125 |
| User 3 | 100.1331 | 184.7362 | 84.4832 |
| Sharing mode | 902.2303 | 0 | 503.0866 |
As can be seen from analysis table 3, when the user configures the energy storage independently, the energy storage cannot completely absorb the new energy output, the wind and the light are abandoned relatively much, and a large amount of electric energy needs to be purchased from the power grid. When the multi-user shares the distributed energy storage, the users form a alliance, and users with different electricity requirements can perform power mutual aid and share the energy storage capacity, so that the wind and light abandoning is reduced, the power generation utilization rate of new energy is improved, and the electricity purchasing cost of a power grid is reduced.
The distributed shared energy storage scheduling results are shown in fig. 4, 5 and 6. As can be seen from fig. 4, the user 1 is a flat peak load, photovoltaic power generation is installed, 1:00-6:00 photovoltaic power generation is not performed, the user 2 and the user 3 transmit power to the user 1, the load power consumption is met, at the moment, the load is in a power price valley period, power is purchased from a power grid and stored, and energy storage and charging are performed. 7:00-19:00 photovoltaic power generation, and redundant electric energy is transmitted to the users 2 and 3 while the self power utilization is met, and the rest electric energy is stored in the energy storage. 20:00-24:00 photovoltaic does not generate electricity, balancing load power by discharging previously stored electrical energy and accepting electrical energy from users 2 and 3.
As can be seen from FIG. 5, the user 2 is a double-peak load, a wind power generation device is arranged, 1:00-8:00 wind power generation is more, the load is less in power consumption, and redundant electric energy is transmitted to the user 1 and the user 3 and stored in the energy storage equipment. 9:00-13:00 and 17:00-20:00 are two load electricity consumption peaks, wind power generation is less, and load electricity consumption is balanced through energy storage discharge and receiving other user electric energy. The load of 14:00-16:00 uses less electricity, and a large amount of purchased and stored electric energy is used in the second electricity consumption peak period. Wind power generation is gradually increased from 21:00 to 24:00, load power consumption is gradually reduced, and redundant electric energy is conveyed outwards.
As can be seen from FIG. 6, the user 3 is a peak avoidance load, wind power generation is installed, more users are loaded at the loads of 1:00-6:00 and 22:00-24:00, wind power generation is more at the moment, wind power generation is basically consistent with load electricity utilization curves, electricity is purchased from a power grid to store electric energy in valley period on the premise of meeting self electricity utilization, and the electric energy is transmitted to other users so as to reduce the total electricity utilization cost of the system.
And (3) carrying out cost allocation on the users by using a shape value method, wherein a characteristic function takes the difference between the energy storage operation cost of the users independently used and the energy storage operation cost shared by the users, and the solving process and the distribution result are shown in tables 4 and 5.
TABLE 4 characterization function and marginal contribution
Table 5 distribution results vs. original costs
| User numbering | Shape value assignment | Paying for costs | Original cost |
| User 1 | 309.31 | 719.50 | 1028.8089 |
| User 2 | 351.90 | 832.73 | 1184.6385 |
| User 3 | 331.53 | -146.91 | 184.6163 |
From tables 4 and 5, it can be seen that the user alliance gain is greater than the sum of individual gains, i.e. V (C.u.D). Gtoreq.V (C) +V (D), the alliance satisfies the individual rationality while the alliance also satisfies the overall rationality, and the condition that the alliance is stable is satisfied. The user 1 saves the cost by 30.06%, the user 2 saves the cost by 30.47%, the user 3 saves the cost by 179.57%, and net income is obtained after joining the alliance, because the load curve and the wind power output curve of the user 3 have high similarity, the requirement on shared energy storage is small, convenience is provided for other users after joining the shared energy storage, and the cost is saved, so that economic benefit is obtained.
And (3) analyzing the energy storage investment recovery, wherein the relationship among the static energy storage investment recovery period, the daily energy storage running cost, the service income and the service pricing is shown in figure 7 for researching the economic problem of the energy storage operator investment energy storage power station. The graph can be used for observing that the service income of the energy storage power station is positively correlated with the service pricing, and the recovery period is negatively correlated with the service pricing. The daily operation cost of energy storage is calculated according to the operation condition of the energy storage by a rain flow counting method, and the daily operation cost of the energy storage is not greatly changed in the service pricing interval.
Claims (4)
1. The shared energy storage operation optimization method based on the cooperative game theory and CPS layered architecture comprises the following steps:
(1) Establishing a CPS architecture suitable for a distributed shared energy storage mechanism;
the sensor measures the physical state quantity on site, converts the physical state quantity into digital information and transmits the digital information to a control center managed by an energy storage operator through the terminal equipment; the energy storage operator establishes a charging and discharging strategy of the energy storage equipment according to the received digital information and combining the user requirement, the new energy output and the grid time-sharing electricity price, and transmits the responsive control information to the driver;
secondly, a distributed shared energy storage operation mechanism based on a cooperative game theory is designed:
(1) establishing a user electricity consumption operation cost model;
(2) calculating the energy storage life by a rain flow counting method;
(3) on the basis of a user electricity consumption operation cost model, a distributed energy storage operation model is established;
the first stage: the users share the energy preferentially, namely, the power of different users is mutually used;
and a second stage: the energy storage operator provides energy storage service;
the overall power balance equation for distributed energy storage sharing is considered as follows:
wherein:load power of the user i in the t period; />Charging power of the distributed energy storage i in a t period; />Purchasing electric power for a power grid of a user i in a t period; />Generating output power of the new energy source in the period t; />Discharging power of the distributed energy storage i in a t period;
the individual power balance equation for distributed energy storage sharing is considered as follows:
wherein:the sum of the link power between the users; />Defining the positive direction of power as flowing from user i to user j;
the method comprises the following steps of:
x i ＝V(S)-V(S-{i})
wherein: x is x i A marginal contribution to the individual; s is individual x i A participating federation; v (S) is x i The benefits generated by the whole alliance when participating in the alliance S; v (S- { i) is x i The benefits generated by the whole alliance when the alliance S is exited;
wherein: w is individual x i Weights separated within federation S; s is the number of participants within federation S; n is the overall number of participants;
wherein:benefits that are separated from the overall benefits for the user; w (w) s For individual x i A set of weights within the federation S;
establishing an alternate direction multiplier ADMM algorithm suitable for a distributed shared energy storage mechanism:
the power is equal using tie gon as follows:
the following augmented lagrangian function was constructed:
wherein: lambda is the Lagrangian multiplier; ρ is a penalty coefficient; f is an independent optimization objective function of a system where each distributed energy storage is located;
the iterative solution process is as follows:
the convergence conditions were as follows:
2. the shared energy storage operation optimization method based on the cooperative game theory and CPS layered architecture as claimed in claim 1, wherein the method comprises the following steps: in the step (1), a user electricity consumption operation cost model is established and carried out according to an objective function, wherein the objective function consists of two parts, including the electricity purchasing cost and the energy storage operation cost of a power grid;
the objective function is:
wherein:the purchase cost of the user; />Charge and discharge service fees paid to the energy storage operator for the user;
electric power purchasing cost of each user power grid
Wherein:the electricity selling price of the power grid in the period t is represented; />Representing the power purchased from the grid by user i during period t; NT represents the entire scheduling period, taking 1h as one scheduling period, nt=24h;
charge and discharge service charge of energy storage operators:
wherein: c op The operation and maintenance cost of the energy storage unit power is;charging power of the energy storage device configured for i users in a t period; />Discharging power of the energy storage device configured for i users in a t period;
the daily operation cost of energy storage is:
wherein: c inv Investment cost for energy storage unit capacity; e (E) ESS Is the rated capacity of energy storage; t is the energy storage service life.
3. The shared energy storage operation optimization method based on the cooperative game theory and CPS layered architecture as claimed in claim 1, wherein the method comprises the following steps: the method for calculating the energy storage life by using the rain flow counting method in the step (2) is as follows;
1) Reconstructing and rearranging the charge state change curve along with time so that the maximum peak or the maximum valley is used as a starting point to calculate the period;
2) Rotating the SOC-t curve by 90 degrees clockwise, and beginning the raindrops to flow downwards along the maximum peak position; if the lower roof is shielded, the raindrops fall to the shielding roof to continue falling, if the lower roof is not shielded, the raindrops change directions and fall to the end points;
3) Recording maximum peak value SOC of rain drop flowing path 1 Maximum valley SOC 2 As a complete cycle, depth of discharge dod=soc for this cycle 1 -SOC 2 ；
4) Deleting the path through which the raindrops flow, and repeating the rain flow counting process for the rest paths until all calculation is finished;
obtaining the relation between the energy storage cycle life and the discharge depth through fitting; the equivalent cycle times N' of the energy storage device in the operation period and the maximum operation days T of energy storage are as follows:
wherein: n (N) t (1) The maximum cycle number of the stored energy corresponding to the depth of charge and discharge of 1; n (N) t (d k ) Is charged and discharged with depth d k And the corresponding maximum cycle times of energy storage.
4. The shared energy storage operation optimization method based on the cooperative game theory and CPS layered architecture as claimed in claim 1, wherein the method comprises the following steps: the constraint conditions of the distributed energy storage operation model in the step (3) are individual power balance, overall power balance and energy storage constraint; the energy storage constraint consists of an energy storage charge state continuity constraint and an energy storage charge and discharge constraint; wherein:
energy storage state of charge continuity constraints:
wherein: e (t) is the capacity of the stored energy at the moment t, eta ch And eta dis Charging efficiency and discharging efficiency SOC of energy storage respectively min And SOC (System on chip) max The energy storage minimum charge state and the maximum charge state;
energy storage charge-discharge constraint:
wherein:and->The energy storage charge state bit and the discharge state bit; />And->Respectively storing maximum charging power and discharging power.
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