A distributed control method for microgrids considering dynamic trading of shared energy storage space

Abstract

The present invention relates to the technical field of shared energy storage scheduling, and discloses a distributed control method for a microgrid group considering dynamic trading of shared energy storage space, comprising the following steps: step one: obtaining relevant data and processing the relevant data; the relevant data processing includes synthetic sampling, outlier processing and sliding average; step two: constructing a microgrid user optimization scheduling model considering dynamic allocation of shared energy storage space and electric energy trading; step three: using a consensus algorithm based on combined adaptation to solve the shared energy storage space trading problem between users to achieve the optimal effect; the present invention adopts a consensus algorithm based on combined adaptation, which reduces the dependence on centralized communication equipment and centralized computing equipment on the basis of effectively solving the user's free trading energy storage space, and protects the user's privacy to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of shared energy storage scheduling, and in particular to a distributed control method for a microgrid group considering dynamic trading of shared energy storage space. Background Art

[0002] With the rapid development of technology, energy storage technology has also developed accordingly. At present, traditional energy storage equipment faces problems such as high investment and construction costs, low equipment utilization, and difficulty in capacity customization. The sharing economy model that has emerged in recent years can provide new ideas for solving the above problems. At present, many regions have proposed innovative business models for shared energy storage. However, general shared energy storage equipment can solve the problem of high individual investment and construction costs to a certain extent, but the problem of low utilization of energy storage equipment still exists.

[0003] There are two ways to deal with the problem of idle energy storage space. One is to hand over all energy storage space information to the energy storage center for unified dispatch. However, some areas are not equipped with a central control system, and local market operators are required to collect information and provide price signals, which requires intensive computing power and communication infrastructure, which means huge computing and communication pressure, high facility costs, and exposure of user data privacy; the other is to allow users to trade freely according to their own needs, but how to spontaneously complete efficient energy storage space transactions by multiple users within a limited time remains to be studied.

[0004] Therefore, the research on how to optimize the scheduling of shared energy storage and balance communication, computing pressure, user privacy, and the efficiency of transactions between users to share energy storage space has practical and economic significance. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed control method for a microgrid group taking into account dynamic transactions in a shared energy storage space, so as to improve the utilization efficiency of the energy storage space, reduce the operating costs of users, and utilize renewable energy more efficiently.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A distributed control method for a microgrid group considering dynamic trading of shared energy storage space includes the following steps:

[0008] Step 1: Obtain relevant data and process the relevant data;

[0009] The relevant data are processed including synthetic sampling, outlier handling and sliding average;

[0010] Step 2: Construct a microgrid user optimization scheduling model that considers dynamic allocation of shared energy storage space and electricity trading;

[0011] Step 3: Use a consensus algorithm based on combined adaptation to solve the shared energy storage space transaction problem between users to achieve the optimal effect.

[0012] As a further solution of the present invention: in step one, the relevant data includes the rigid load and flexible load of each user within the microgrid, the power generation of self-generating users within the microgrid, weather conditions, electricity prices in different time periods, and the amount of energy storage space purchased by users.

[0013] As a further solution of the present invention: in step 2, the optimization scheduling model uses the economic index of each user in the microgrid as the objective function;

[0014] The objective function combines power balance constraints, demand response constraints, energy storage system constraints, and state of charge constraints.

[0015] As a further solution of the present invention: the objective function is:

[0016]

[0017] In the formula, C user,n represents the total operating cost of user n on the day, which is composed of the daily operating cost C including the dynamic transaction of energy storage space SE,n (t) and the electricity transaction cost C EEX,n (t) The sum of the parts in the overall cycle, T represents the total operating time of one day;

[0018]

[0019] In the formula is the energy storage space purchase and sale price of user n at the kth iteration at time t, Δe n,k (t) is the amount of energy storage space purchased and sold by user n at the kth iteration at time t, V grid (t) is the unit power price of electricity purchased from the power grid at time t, V is the power purchased by user n from the power grid at the kth iteration at time t, comp (t) is the unit power subsidy price given by the power grid to users for demand response at time t, The load power shifted by user n in response to demand at time t;

[0020]

[0021] Where; is the unit power price of electricity purchased and sold by user n to other users at the kth iteration at time t, is the power purchased and sold by user n from other users in the kth iteration at time t; θ buy It is the transmission fee parameter charged by the power grid when users conduct transactions. The unit power price of electricity sold by user n to the upper grid at time t is: The power sold by user n to the upper grid at the kth iteration at time t.

[0022] As a further solution of the present invention: the power balance constraint is composed of the power purchased by the user from the upper grid, the power generated by the user, the power traded with the shared energy storage, and the power traded between users, that is,

[0023]

[0024] In the formula is the load power of user n after demand response at time t; is the load power of user n at time t without demand response; The load power transferred by user n for demand response at time t; The electric power generated by user n at time t after the energy storage device is charged and discharged and the sub-generation device generates electricity to meet the load power at that time; is the power generated by the self-generating equipment of user n at time t; The discharge power of the energy storage device of user n at time t; The charging power of the energy storage device of user n at time t.

[0025] As a further solution of the present invention: the demand response constraint is a translatable load;

[0026] Right now

[0027] In the formula is the power transferred by user n at time t in response to demand, The power transferred by user n in response to demand at time t; The maximum value of the load transferred to respond to user demand; It is the load power transferred by user n at time t, which is the final load power transferred by user n participating in demand response.

[0028] As a further solution of the present invention: the energy storage system is constrained to allow a shared energy storage device to accept simultaneous charging and discharging by multiple users, but the same user can only choose one state, charging or discharging, at a time;

[0029]

[0030] Where α1(t) and α2(t) are binary variables used to restrict users to only charge or discharge at the same time. and is the power used by user n to discharge and charge the energy storage system at time t, and The maximum power at which users can discharge and charge the energy storage system.

[0031] As a further solution of the present invention: the state of charge constraint is the state of charge constraint of the shared energy storage space owned by each user at the current moment, and the upper and lower limits of the shared energy storage state space of each user are taken as the minimum value of the upper limit and the maximum value of the lower limit of all shared energy storage devices, so as to maximize the life of the shared energy storage device, that is,

[0032]

[0033] Where SOC n (t) is the state of charge of the energy storage space of user n at time t, δ is the self-consumption coefficient of the energy storage device itself, which is 0.03, η ch , η dch is the charging and discharging efficiency of the energy storage device, E n,k (t) is the energy storage capacity of user n after k iterations at time t, It is the minimum and maximum value of the state of charge of the energy storage space.

[0034] As a further solution of the present invention: in step 3, the demand of user n for energy storage space at time t is determined, each user solves the model in step 2 through a commercial solver, and the demand of the user for energy storage space is divided into two categories: expansion and contraction according to the solution result;

[0035] The state of expansion and contraction is determined by the state of charge SOC of the energy storage space owned by user n at the current time t n (t) Is the value less than or equal to If equal and If it is greater than 0, it is considered to be in the expansion state N + , if less than and If it is greater than 0, it is considered to be in a shrinking state N - , and the required capacity is calculated as follows:

[0036]

[0037] In the formula is the initial buying and selling capacity of user n at time t. It should be noted that buying is a positive value and selling is a negative value;

[0038] All microgrid users freely establish information connections, so that the entire communication network forms a strong connectivity graph. According to the strong connectivity graph with surrounding users and the shrinking and expanding states of surrounding users, the capacity flow of each user into the collection is determined. And out of the set gather and The number of elements in is recorded as and Calculate the user's iteration weight x in ,y in , and set the iteration duration to the transaction interval T tra ;

[0039]

[0040] Initialize the iteration conditions of the shared energy storage space, and each user initializes his own transaction price λ n (0), whose size is freely set, and the demand is set to the initial unbalanced space e n (0);

[0041] Start the iteration of shared energy storage space trading;

[0042] The variables involved in the transaction include the energy storage space transaction price λ, the intermediate variable λ′, the unbalanced space amount e, and the purchased energy storage space amount E in 、Selling energy storage space E out , for each user

[0043]

[0044] Where n is the iterative main user, i is the user directly connected to user n, and λ′ n (k) is the intermediate variable of the kth iteration of user n, e n (k) is the imbalanced space of user n in the kth iteration, λ n (k) is the transaction price of user n in the kth iteration, Δe n (k) is the difference between the amount of space purchased and the amount of space sold by user n in the kth iteration, and ε is a small positive disturbance;

[0045] Integrate the post-transaction imbalance space into its own energy storage space, and calculate the imbalance space and transaction price for the next iteration. The integration formula is as follows:

[0046] E n,k+1 (t) = E n,k (t)+e n (k+1)

[0047] Then record the shared energy storage transaction time t tra,k ;

[0048] Repeat the above steps until the loop iteration stop condition is met, and stop the shared energy storage space trading loop. The shared energy storage space trading loop iteration stop condition is:

[0049]

[0050] Where T tra Iterate the time limit for the shared energy storage space transaction, t tra,k is the time required for the kth iteration, K is the total number of iterations;

[0051] After the shared energy storage space transaction iteration is completed, each user will use the commercial solver to solve the microgrid user optimization scheduling model obtained in the kth iteration again, and divide the user's demand for electricity trading into two categories: buying and selling based on the solution results;

[0052] The buying and selling status is determined by user n at the current time t. If it is greater than 0, it is considered to be in the selling state R + ,like If it is greater than 0, it is considered to be in the buy state N - , and the required capacity is calculated as follows:

[0053]

[0054] In the formula is the initial selling and buying power of user n at time t, where buying is a positive value and selling is a negative value;

[0055] All microgrid users freely establish information connections, so that the entire communication network forms a strong connectivity graph. According to the strong connectivity graph with surrounding users and the buying and selling status of surrounding users, the energy flow into the collection of each user is determined. And out of the set gather and The number of elements in is recorded as and Calculate the user's iteration weight a in , b in , and set the iteration duration to the transaction interval

[0056]

[0057] Initialize the iteration conditions of power trading, and each user initializes its own trading power price μ n (0), its size can be freely set within a certain range, and the demand obtained is set as the initial unbalanced electric energy w n (0), the range of user transaction electricity prices is limited as follows:

[0058]

[0059] Started the iteration of electric energy trading;

[0060] The variables involved in the transaction include the energy storage space transaction price μ, the intermediate variable μ′, the unbalanced space volume w, and the purchased energy storage space volume. Energy storage space sold For each user

[0061]

[0062] Where n is the iterative main user, i is the user directly connected to user n, μ′ n (k) is the intermediate variable of the kth iteration of user n, is the unbalanced power of user n in the kth iteration, μ n (k) is the transaction price of user n in the kth iteration, is the difference between the purchased and sold electricity of user n in the kth iteration, and ε is a small positive disturbance;

[0063] Integrate the unbalanced power after the transaction into its own power balance, and calculate the unbalanced power and transaction price for the next iteration. The integration formula is as follows:

[0064]

[0065] Then record the electricity transaction time

[0066] Repeat the above steps until the power trading cycle iteration stop condition is met, and then stop the power trading cycle.

[0067] As a further solution of the present invention: the electric energy trading cycle iteration stop condition is:

[0068]

[0069] or

[0070] In the formula Iterate the time limit for shared energy storage space transactions. is the time required for the kth iteration, and K is the total number of iterations.

[0071] Beneficial effects of the present invention: Starting from the perspective of user economy, the present invention takes into account the characteristics of shared energy storage and electric energy trading that can absorb renewable energy, and establishes a microgrid user optimization scheduling model that dynamically allocates shared energy storage space and electric energy trading. It can make full use of the changes in the shared energy storage space of users when using energy storage to improve user benefits, and effectively absorb renewable energy by combining shared energy storage and electric energy trading. In addition, the present invention adopts a consensus algorithm based on combined adaptation, which reduces the dependence on centralized communication equipment and centralized computing equipment on the basis of effectively solving the user's free trading energy storage space, and protects the user's privacy to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The present invention will be further described below in conjunction with the accompanying drawings.

[0073] Figure 1 It is a flow chart of a distributed control method of a microgrid group considering dynamic trading of shared energy storage space according to the present invention;

[0074] Figure 2 It is a schematic diagram of a microgrid structure that considers electric energy trading and dynamic trading of shared energy storage space in a distributed control method of a microgrid group that considers dynamic trading of shared energy storage space of the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] See also Figure 1-Figure 2 As shown, the present invention is a distributed control method for a microgrid group considering dynamic trading of shared energy storage space, comprising the following steps:

[0077] Step 1: Obtain relevant data and process the data through methods such as synthetic sampling, outlier processing, and sliding average to obtain practical and usable data;

[0078] Step 2: Construct a microgrid user optimization scheduling model that considers dynamic allocation of shared energy storage space and electricity trading;

[0079] Step 3: Use a consensus algorithm based on combined adaptation to solve the shared energy storage space transaction problem between users to achieve the optimal effect.

[0080] Specifically: In Step 1, relevant data is obtained, such as the rigid load and flexible load of each microgrid user, the power generation of self-generated users within the microgrid, weather conditions, electricity prices in each period, and the amount of energy storage space purchased by users. Then, outlier processing and sliding average methods are used to clean and smooth the data, reduce the impact of data errors and noise, and obtain practical and usable data.

[0081] Step 2: Construct a microgrid user optimization scheduling model that considers dynamic allocation of shared energy storage space and electricity trading.

[0082] The optimization scheduling model in Step 2 takes the economic indicators of each user in the microgrid as the objective function, considers power balance constraints, demand response constraints, energy storage system constraints, and charge state constraints, and constructs a microgrid user optimization scheduling model that considers dynamic allocation of shared energy storage space and electricity trading.

[0083] (1) Objective function

[0084] Each user in the microgrid uses the economic index as the objective function. The specific expression of the objective function is as follows:

[0085]

[0086] Where C user,n represents the total operating cost of user n on the day, which is composed of the daily operating cost C including the dynamic transaction of energy storage space SE,n (t) and the electricity transaction cost C EEX,n (t) is the sum of the parts in the overall cycle, T represents the total operating time of one day, and T is taken as 24 here.

[0087]

[0088] In the formula is the energy storage space purchase and sale price of user n at the kth iteration at time t, Δe n,k (t) is the amount of energy storage space purchased and sold by user n at the kth iteration at time t, V grid (t) is the unit power price of electricity purchased from the grid at time t. Here, the price of electricity purchased from the grid is selected as the time-of-use electricity price. When the time is 00:00-08:00, the time-of-use electricity price is 0.75 yuan; when the time is 09:00-16:00 or 19:00-23:00, the time-of-use electricity price is 1.00 yuan; when the time is 17:00-18:00, the time-of-use electricity price is 0.55 yuan. V is the power purchased by user n from the power grid at the kth iteration at time t, comp (t) is the unit power subsidy price given by the power grid to users for demand response at time t, which is 0.35 yuan. The load power shifted by user n in response to demand at time t.

[0089]

[0090] Where; is the unit power price of electricity purchased and sold by user n to other users at the kth iteration at time t, is the power purchased and sold by user n from other users in the kth iteration at time t; θ buyIt is the parameter of the access fee charged by the power grid when users conduct transactions. Based on the principle of fairness, both the buyer and the seller need to bear the access fee charged by the power grid. The value is 0.01. The unit power price of electricity sold by user n to the upper grid at time t is taken as 0.15. The power sold by user n to the upper grid at the kth iteration at time t.

[0091] (2) Constraints

[0092] Construct a microgrid user optimization scheduling model constraint that considers dynamic allocation of shared energy storage space and electricity trading, including power balance constraints, demand response constraints, energy storage equipment constraints and state of charge constraints.

[0093] Power balance constraints

[0094] The power balance constraint in this patent is composed of the power purchased by the user from the upper power grid, the power generated by the user, the power traded with the shared energy storage, and the power traded between users. It is worth noting that the power generated by the user can be a positive value, indicating that the user has a surplus of electricity at the current moment, or a negative value, indicating that the user needs to purchase electricity from other entities at the current moment. In addition, the power traded between users is not 0 only during the power trading phase, and is considered 0 at other times.

[0095]

[0096] In the formula is the load power of user n after demand response at time t; is the load power of user n at time t without demand response; The load power transferred by user n for demand response at time t; The electric power generated by user n at time t after the energy storage device is charged and discharged and the sub-generation device generates electricity to meet the load power at that time; is the power generated by the self-generating equipment of user n at time t; The discharge power of the energy storage device of user n at time t; The charging power of the energy storage device of user n at time t.

[0097] Demand response constraints

[0098] The flexible load participating in the demand response in this patent is a movable load, and is restricted to be transferred only to a later time.

[0099]

[0100] In the formula is the power transferred by user n at time t in response to demand, The power transferred by user n in response to demand at time t; The maximum value of the load transferred to respond to user demand; It is the load power transferred by user n at time t, which is the final load power transferred by user n participating in demand response.

[0101] Energy storage system constraints

[0102] Shared energy storage devices can accept simultaneous charging and discharging by multiple users, but the same user can only choose to charge or discharge at a time.

[0103]

[0104] Where α1(t) and α2(t) are binary variables used to restrict users to only charge or discharge at the same time. and is the power used by user n to discharge and charge the energy storage system at time t, and The maximum power at which users can discharge and charge the energy storage system.

[0105] 4) State of charge constraints

[0106] The state of charge constraint in this patent is the state of charge constraint of the shared energy storage space owned by each user at the current moment. The upper and lower limits of each user's shared energy storage state space are taken as the minimum value of the upper limit and the maximum value of the lower limit of all shared energy storage devices, so as to maximize the life of the shared energy storage equipment.

[0107]

[0108] Where SOC n (t) is the state of charge of the energy storage space of user n at time t, δ is the self-consumption coefficient of the energy storage device itself, which is 0.03, η ch , η dch is the charging and discharging efficiency of the energy storage device, E n,k (t) is the energy storage capacity of user n after k iterations at time t, are the minimum and maximum values ​​of the state of charge of the energy storage space, with the maximum value being 0.8 and the minimum value being 0.2.

[0109] Step 3: Adopt a consensus algorithm based on combined adaptation to solve the shared energy storage space transaction and electricity transaction problems between users through information exchange between adjacent nodes, so as to help various market players realize decentralized transactions and achieve the best results.

[0110] The specific steps of the solution algorithm are as follows.

[0111] Step 3.1: Determine user n’s demand for energy storage space at time t.

[0112] Each user solves the model in Step 2 using a commercial solver, and divides the user's demand for energy storage space into two categories: expansion and contraction. The expansion and contraction states are determined by the state of charge SOC of the energy storage space owned by user n at the current time t. n (t) Is the value less than or equal to If equal and If it is greater than 0, it is considered to be in the expansion state N + , if less than and If it is greater than 0, it is considered to be in a shrinking state N - At the same time, the required capacity is calculated as follows.

[0113]

[0114] In the formula is the initial buying and selling capacity required by user n at time t. It should be pointed out here that buying is a positive value and selling is a negative value.

[0115] Step 3.2: All microgrid users freely establish information connections, so that the entire communication network forms a strong connectivity graph. According to the strong connectivity graph with surrounding users and the shrinking and expanding states of surrounding users, the capacity flow of each user into the collection is determined. And out of the set gather and The number of elements in is recorded as and Then, the user's iteration weight x is calculated by equation (9) and equation (10): in ,y in , and set the iteration duration to the transaction interval T tra , take the transaction interval T tra It is 15min, or 0.25h.

[0116]

[0117] Step 3.3: Initialize the iteration conditions of the shared energy storage space, and each user initializes his own transaction price λ n (0), its size can be freely set, and the demand obtained in Step 3.1 is set as the initial unbalanced space e n (0).

[0118] Step 3.4: Start the iteration of shared energy storage space transaction. Each user conducts transactions through the information of the entities adjacent to itself, such as the transaction price of each user and the amount of unbalanced space.

[0119] The variables involved in the transaction include the energy storage space transaction price λ, the intermediate variable λ′, the unbalanced space amount e, and the purchased energy storage space amount E in 、Selling energy storage space E out For each user

[0120]

[0121] Where n is the iterative subject user, and i is the user directly connected to user n. n (k) is the intermediate variable of the kth iteration of user n, e n (k) is the imbalanced space of user n in the kth iteration, λ n (k) is the transaction price of user n in the kth iteration, Δe n (k) is the difference between the amount of space purchased and the amount of space sold by user n in the kth iteration, and ε is a small positive disturbance.

[0122] Then, the post-transaction imbalance space is integrated into its own energy storage space, and the imbalance space and transaction price of the next iteration are calculated. The integration formula is as follows:

[0123] E n,k+1 (t) = E n,k (t)+e n (k+1) (12)

[0124] Then record the shared energy storage transaction time t tra,k .

[0125] Step 3.5: Repeat step 3.4 until the loop iteration stop condition is met, and stop the shared energy storage space trading loop. The shared energy storage space trading loop iteration stop condition is:

[0126]

[0127] Where T tra Iterate the time limit for the shared energy storage space transaction, t tra,k is the time required for the kth iteration, and K is the total number of iterations.

[0128] Step 3.6: After the shared energy storage space transaction iteration is completed, each user uses the commercial solver to solve the microgrid user optimization scheduling model obtained in the kth iteration in Step 3.4 again, and divides the user's demand for power trading into two categories: buying and selling through the solution results. The buying and selling status is determined by user n at the current time t. If it is greater than 0, it is considered to be in the selling state R + ,like If it is greater than 0, it is considered to be in the buy state N -At the same time, the required capacity is calculated as follows.

[0129]

[0130] In the formula It is the initial selling and buying power of user n at time t, where buying is a positive value and selling is a negative value.

[0131] Step 3.7: All microgrid users freely establish information connections, so that the entire communication network forms a strong connectivity graph. According to the strong connectivity graph with surrounding users and the buying and selling status of surrounding users, the energy flow into the collection of each user is determined. And out of the set gather and The number of elements in is recorded as and Then, the user's iteration weight a is calculated by equation (15) and equation (16): in , b in , and set the iteration duration to the transaction interval Take transaction interval It is 15min, or 0.25h.

[0132]

[0133] Step 3.8: Initialize the iteration conditions of power trading, and each user initializes its own trading power price μ n (0), its size can be freely set within a certain range, and the demand obtained in Step 3.6 is set as the initial unbalanced electric energy w n (0). The range of user transaction electricity prices is limited as follows:

[0134]

[0135] Step 3.9: Start the iteration of power trading. Each user trades through the information of the entities adjacent to itself, such as the power prices and unbalanced power of each user.

[0136] The variables involved in the transaction include the energy storage space transaction price μ, the intermediate variable μ′, the unbalanced space volume w, and the purchased energy storage space volume. Energy storage space sold For each user

[0137]

[0138] Where n is the iterative main user, and i is the user directly connected to user n. n (k) is the intermediate variable of the kth iteration of user n, is the unbalanced power of user n in the kth iteration, μ n (k) is the transaction price of user n in the kth iteration, is the difference between the purchased and sold electricity of user n in the kth iteration, and ε is a small positive disturbance.

[0139] Then, the unbalanced power after the transaction is integrated into its own power balance, and the unbalanced power and transaction price of the next iteration are calculated. The integration formula is as follows:

[0140]

[0141] Then record the electricity transaction time

[0142] Step 3.10: Repeat step 3.9 until the power trading cycle iteration stop condition is met, and then stop the power trading cycle. The power trading cycle iteration stop condition is:

[0143]

[0144] In the formula Iterate the time limit for shared energy storage space transactions. is the time required for the kth iteration, and K is the total number of iterations.

[0145] It only needs to satisfy one of the two iteration loop stop conditions.

[0146] After the iteration is completed, in the proposed distributed trading model, market entities exchange their own transaction prices and unbalanced capacity information with adjacent entities, and use local information for iterative optimization, thereby achieving global convergence of multi-agent transactions, and obtaining a user optimization scheduling model after shared energy storage space transactions and electricity transactions.

[0147] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A distributed control method for microgrids considering dynamic trading of shared energy storage space, characterized in that: The following steps are involved: Step 1: Obtain relevant data and process the relevant data; The relevant data are processed including synthetic sampling, outlier processing and sliding average; Step 2: Construct a microgrid user optimization scheduling model that considers dynamic allocation of shared energy storage space and electricity trading; Step 3: Use a consensus algorithm based on combined adaptation to solve the shared energy storage space transaction problem between users to achieve the optimal effect.

2. According to claim 1, a distributed control method for microgrid groups considering dynamic trading of shared energy storage space is characterized in that: In step one, the relevant data include the rigid load and flexible load of users within each microgrid, the power generation of self-generating users within the microgrid, weather conditions, electricity prices in different time periods, and the amount of energy storage space purchased by users.

3. According to claim 1, a distributed control method for microgrid groups considering dynamic trading of shared energy storage space is characterized in that: In step 2, the optimization dispatch model uses the economic indicators of each user in the microgrid as the objective function; The objective function combines power balance constraints, demand response constraints, energy storage system constraints, and state of charge constraints.

4. A distributed control method for microgrids considering dynamic trading of shared energy storage space according to claim 3, characterized in that: The objective function is: In the formula, C user,n represents the total operating cost of user n on the day, which is composed of the daily operating cost C including the dynamic transaction of energy storage space SE,n (t) and the electricity transaction cost C EEX,n (t) The sum of the parts in the overall cycle, T represents the total operating time of one day; In the formula is the energy storage space purchase and sale price of user n at the kth iteration at time t, Δe n,k (t) is the amount of energy storage space purchased and sold by user n at the kth iteration at time t, V grid (t) is the unit power price of electricity purchased from the power grid at time t, V is the power purchased by user n from the power grid at the kth iteration at time t, comp (t) is the unit power subsidy price given by the power grid to users for demand response at time t, The load power shifted by user n in response to demand at time t; Where: is the unit power price of electricity purchased and sold by user n to other users at the kth iteration at time t, is the power purchased and sold by user n from other users in the kth iteration at time t; θ buy It is the transmission fee parameter charged by the power grid when users conduct transactions. The unit power price of electricity sold by user n to the upper grid at time t is: The power sold by user n to the upper grid at the kth iteration at time t.

5. A distributed control method for microgrids considering dynamic trading of shared energy storage space according to claim 4, characterized in that: The power balance constraint is composed of the power purchased by the user from the upper grid, the power generated by the user, the power traded with the shared energy storage, and the power traded between users, that is, In the formula is the load power of user n after demand response at time t; is the load power of user n at time t without demand response; The load power transferred by user n for demand response at time t; The electric power generated by user n at time t after the energy storage device is charged and discharged and the sub-generation device generates electricity to meet the load power at that time; is the power generated by the self-generating equipment of user n at time t; The discharge power of the energy storage device of user n at time t; The charging power of the energy storage device of user n at time t.

6. A distributed control method for microgrids considering dynamic trading of shared energy storage space according to claim 4, characterized in that: Demand response constraints are shiftable loads; In the formula is the power transferred by user n at time t in response to demand, The power transferred by user n in response to demand at time t; The maximum value of the load transferred to respond to user demand; It is the load power transferred by user n at time t, which is the final load power transferred by user n participating in demand response.

7. A distributed control method for microgrids considering dynamic trading of shared energy storage space according to claim 4, characterized in that: The energy storage system is constrained to allow shared energy storage devices to accept simultaneous charging and discharging by multiple users, but the same user can only choose to charge or discharge at a time. Where α1(t) and α2(t) are binary variables used to restrict users to only charge or discharge at the same time. and is the power used by user n to discharge and charge the energy storage system at time t, and The maximum power at which users can discharge and charge the energy storage system.

8. A distributed control method for microgrids considering dynamic trading of shared energy storage space according to claim 4, characterized in that: The state of charge constraint is the state of charge constraint of the shared energy storage space owned by each user at the current moment. The upper and lower limits of each user's shared energy storage state space are taken as the minimum upper limit and the maximum lower limit of all shared energy storage devices, which maximizes the life of the shared energy storage device, that is, Where SOC n (t) is the state of charge of the energy storage space of user n at time t, δ is the self-consumption coefficient of the energy storage device itself, which is 0.03, η ch , η dch is the charging and discharging efficiency of the energy storage device, E n,k (t) is the energy storage capacity of user n after k iterations at time t, It is the minimum and maximum value of the state of charge of the energy storage space.

9. A distributed control method for microgrids considering dynamic trading of shared energy storage space according to claim 1, characterized in that: In step 3, the demand for energy storage space of user n at time t is determined. Each user solves the model in step 2 using a commercial solver, and the demand for energy storage space is divided into two categories: expansion and contraction based on the solution results. The state of expansion and contraction is determined by the state of charge SOC of the energy storage space owned by user n at the current time t n (t) Is the value less than or equal to If equal and If it is greater than 0, it is considered to be in the expansion state N + , if less than and If it is greater than 0, it is considered to be in a shrinking state N - , and the required capacity is calculated as follows: In the formula is the initial buying and selling capacity of user n at time t. It should be noted that buying is a positive value and selling is a negative value; All microgrid users freely establish information connections, so that the entire communication network forms a strong connectivity graph. According to the strong connectivity graph with surrounding users and the shrinking and expanding states of surrounding users, the capacity flow of each user into the collection is determined. And out of the set gather and The number of elements in is recorded as and Calculate the user's iteration weight x in ,y in , and set the iteration duration to the transaction interval T tra ; Initialize the iteration conditions of the shared energy storage space, and each user initializes his own transaction price λ n (0), whose size is freely set, and the demand is set to the initial unbalanced space e n (0); Start the iteration of shared energy storage space trading; The variables involved in the transaction include the energy storage space transaction price λ, the intermediate variable λ′, the unbalanced space amount e, and the purchased energy storage space amount E in 、Selling energy storage space E out , for each user Where n is the iterative main user, i is the user directly connected to user n, and λ n ′(k) is the intermediate variable of user n’s kth iteration, e n (k) is the imbalanced space of user n in the kth iteration, λ n (k) is the transaction price of user n in the kth iteration, Δe n (k) is the difference between the amount of space purchased and the amount of space sold by user n in the kth iteration, and ε is a small positive disturbance; Integrate the post-transaction imbalance space into its own energy storage space, and calculate the imbalance space and transaction price for the next iteration. The integration formula is as follows: E n,k+1 (t)=E n,k (t)+e n (k+1) Then record the shared energy storage transaction time t tra,k ; Repeat the above steps until the loop iteration stop condition is met, and stop the shared energy storage space trading loop. The shared energy storage space trading loop iteration stop condition is: Where T tra Iterate the time limit for the shared energy storage space transaction, t tra,k is the time required for the kth iteration, K is the total number of iterations; After the shared energy storage space transaction iteration is completed, each user will use the commercial solver to solve the microgrid user optimization scheduling model obtained in the kth iteration again, and divide the user's demand for electricity trading into two categories: buying and selling based on the solution results; The buying and selling status is determined by user n at the current time t. If it is greater than 0, it is considered to be in the selling state R + ,like If it is greater than 0, it is considered to be in the buy state N - , and the required capacity is calculated as follows: In the formula is the initial selling and buying power of user n at time t, where buying is a positive value and selling is a negative value; All microgrid users freely establish information connections, so that the entire communication network forms a strong connectivity graph. According to the strong connectivity graph with surrounding users and the buying and selling status of surrounding users, the energy flow into the collection of each user is determined. And out of the set gather and The number of elements in is recorded as and Calculate the user's iteration weight a in 、b in , and set the iteration duration to the transaction interval Initialize the iteration conditions of power trading, and each user initializes its own trading power price μ n (0), its size can be freely set within a certain range, and the demand obtained is set as the initial unbalanced electric energy w n (0), the range of user transaction electricity prices is limited as follows: Started the iteration of electric energy trading; The variables involved in the transaction include the energy storage space transaction price μ, the intermediate variable μ′, the unbalanced space volume w, and the purchased energy storage space volume. Energy storage space sold For each user Where n is the iterative main user, i is the user directly connected to user n, μ n ′(k) is the intermediate variable of the kth iteration of user n, is the unbalanced power of user n in the kth iteration, μ n (k) is the transaction price of user n in the kth iteration, is the difference between the purchased and sold electricity of user n in the kth iteration, and ε is a small positive disturbance; Integrate the unbalanced power after the transaction into its own power balance, and calculate the unbalanced power and transaction price of the next iteration. The integration formula is as follows: Then record the electricity transaction time Repeat the above steps until the power trading cycle iteration stop condition is met, and then stop the power trading cycle.

10. A distributed control method for microgrids considering dynamic trading of shared energy storage space according to claim 9, characterized in that: The conditions for stopping the electric energy trading cycle iteration are: or In the formula Iterate the time limit for shared energy storage space transactions. is the time required for the kth iteration, and K is the total number of iterations.

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