A Cooperative Optimization Operation Method and System for Multiple Microgrids and Shared Energy Storage

Abstract

The present disclosure provides a collaborative optimization operation method and system for a multi - microgrid and shared energy storage, relating to the technical field of collaborative optimization of multi - microgrids and shared energy storage, including: based on the structural data of a distribution network system containing multi - microgrids and shared energy storage, considering that the microgrid provides voltage support to the distribution network, a two - stage cooperative game optimization model for the multi - microgrid and shared energy storage is established by improving the Nash negotiation; the ADMM algorithm is used to perform distributed solution on the two - stage cooperative game optimization model to obtain the optimal interaction power and trading electricity price between the multi - microgrid and the shared energy storage; the present invention considers the economic benefits during the cooperative operation of the multi - microgrid and shared energy storage and the node voltage changes caused by the interaction power between the microgrid and the shared energy storage at different nodes of the distribution network, and incentives the microgrid to provide voltage support to the distribution network through voltage regulation rewards, while solving the problems of safety and economy.

Description

Technical Field

[0001] The present disclosure relates to the technical field of coordinated optimization of multi - microgrids and shared energy storage, and particularly relates to a coordinated optimization operation method and system for multi - microgrids and shared energy storage. Background Art

[0002] Distributed generation has been developed on a relatively large scale. As a small - scale power system containing distributed energy, energy storage, and loads, the microgrid MG will form an active distribution network system with multiple microgrids after being connected to the active distribution network. However, due to the intermittency and uncertainty of distributed energy generation, the access of microgrids may cause voltage fluctuations in the distribution network, which may further lead to voltage over - limit problems. As a flexible adjustable resource, the microgrid can operate either as a load or as a power generation device. By adjusting the interaction power with the distribution network, it can provide voltage support for the distribution network and improve the power quality. Energy storage is one of the main flexible resources in the microgrid. Configuring a certain capacity of energy storage can effectively relieve the grid - connection pressure brought by the net power generation, and at the same time, it can release electric energy during peak loads, adjust the power demand, and reduce the voltage over - limit situation. However, traditional energy storage faces dilemmas such as high construction, operation, and maintenance costs and low utilization rate, while the shared energy storage system SESS well overcomes the above problems.

[0003] The shared energy storage system developed under the background of the sharing economy can aggregate distributed energy storage or self - built energy storage devices to provide energy storage capacity leasing services or power interaction for multiple microgrids, becoming an effective means to help the power grid cope with the reverse peak - shaving characteristics of renewable energy output, improve energy - using economy, and enhance dispatching flexibility.

[0004] Regarding the research on the participation of microgrids in the voltage regulation of the distribution network, most current studies consider from the perspective of the distribution network and regulate the voltage by controlling the microgrid. However, in most cases, microgrids belong to different operators, and the distribution network cannot directly control and dispatch them. Requiring the microgrid to perform voltage regulation may cause problems such as increased operating costs, and it is necessary to further discuss how to incentivize the microgrid to provide voltage support through voltage regulation rewards.

[0005] Currently, many scholars have carried out a large number of studies on the trading mode and coordinated dispatching in the field of multi - microgrid - shared energy storage with the goal of economy. However, in most of the studies related to shared energy storage, the mutual influence on the operation of the power grid has not been considered, and only the various entities that interact with the shared energy storage in terms of energy have been focused on. In fact, when the shared energy storage interacts with the microgrids at different nodes of the distribution network in terms of power, it will affect the power flow of the distribution network and thus cause changes in the node voltage.

[0006] Therefore, the prior art does not comprehensively consider the energy interaction between the microgrid and the shared energy storage and its participation in solving the voltage problem of the distribution network, resulting in insufficient comprehensive consideration of safety and economy. Summary of the Invention

[0007] To solve the above problems, the present disclosure proposes a collaborative optimization operation method and system for multiple microgrids and shared energy storage, considering the economic benefits during the cooperative operation of multiple microgrids and shared energy storage, as well as the node voltage changes caused by the interaction power between the microgrids and shared energy storage at different nodes of the distribution network. By means of voltage regulation rewards, the microgrids are incentivized to provide voltage support to the distribution network, while solving the problems of security and economy.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions:

[0009] A collaborative optimization operation method for multiple microgrids and shared energy storage, comprising:

[0010] Based on the structural data of the distribution network system including multiple microgrids and shared energy storage, considering the voltage support provided by the microgrids to the distribution network, a two-stage cooperative game optimization model for multiple microgrids and shared energy storage is established through improved Nash bargaining.

[0011] The ADMM algorithm is used to perform distributed solution on the two-stage cooperative game optimization model to obtain the optimal interaction power and trading electricity price between the multiple microgrids and shared energy storage.

[0012] Among them, the two-stage cooperative game optimization model divides the optimization problem into two stages:

[0013] In the first stage, the distribution network is partitioned using the improved electrical distance, considering voltage regulation rewards, and with the goal of minimizing the cooperation cost between the multiple microgrids and shared energy storage, the optimal interaction power between the multiple microgrids and shared energy storage is solved.

[0014] In the second stage, based on the interaction power obtained from the solution of the first-stage problem, the contribution degree of each entity is quantified using a non-linear energy mapping function, and with the contribution degree of each entity as the bargaining power, asymmetric bargaining is performed based on the improved Nash bargaining to obtain the optimal trading electricity price.

[0015] According to some embodiments, the present disclosure adopts the following technical solutions:

[0016] A collaborative optimization operation system for multiple microgrids and shared energy storage, comprising a model construction module and a solution optimization module:

[0017] The model construction module is configured to: based on the structural data of the distribution network system including multiple microgrids and shared energy storage, considering the voltage support provided by the microgrids to the distribution network, establish a two-stage cooperative game optimization model for multiple microgrids and shared energy storage through improved Nash bargaining.

[0018] The solution optimization module is configured to: use the ADMM algorithm to perform distributed solution on the two-stage cooperative game optimization model to obtain the optimal interaction power and trading electricity price between the multiple microgrids and shared energy storage.

[0019] Among them, the two-stage cooperative game optimization model divides the optimization problem into two stages:

[0020] In the first stage, the improved electrical distance is used to partition the distribution network. Considering the voltage regulation reward, with the goal of minimizing the cooperation cost between the multi-microgrid and the shared energy storage, the optimal interaction power between the multi-microgrid and the shared energy storage is obtained by solving.

[0021] In the second stage, based on the interaction power obtained from the solution of the first-stage problem, the contribution degree of each entity is quantified by the non-linear energy mapping function. With the contribution degree of each entity as the bargaining power, asymmetric bargaining is carried out based on the improved Nash negotiation to obtain the optimal trading electricity price.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the collaborative optimization operation method of a multi-microgrid and a shared energy storage as described above.

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the collaborative optimization operation method of a multi-microgrid and a shared energy storage as described above is implemented.

[0026] According to some embodiments, the present disclosure adopts the following technical solutions:

[0027] An electronic device includes: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes and implements the collaborative optimization operation method of a multi-microgrid and a shared energy storage as described above.

[0028] Compared with the prior art, the beneficial effects of the present disclosure are:

[0029] The present disclosure proposes a collaborative optimization operation method for a multi-microgrid and a shared energy storage for voltage regulation of a distribution network. A model is established based on the improved Nash bargaining theory. Asymmetric bargaining is carried out considering the energy contribution of each microgrid in the cooperation and the change in the state of charge of the shared energy storage. The non-linear energy mapping function is used to measure the cooperation contributions of different entities, and the benefits are fairly and reasonably distributed.

[0030] In addition to considering economic benefits, this disclosure also takes into account the node voltage changes caused by the interactive power between the microgrids and shared energy storage at different nodes in the distribution network, designs voltage regulation rewards for the microgrids, considers the voltage support provided by the microgrids to the distribution network, and combines safety and economy. The proposed method has a significant voltage regulation effect. As an independent entity, the shared energy storage interacts with the microgrids in terms of energy, which promotes the local consumption of renewable energy, thereby optimizing the economic benefits. The operating benefits of each microgrid have been significantly improved, and the overall economic benefits of the multi-microgrid - shared energy storage system have also been greatly enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings forming a part of this disclosure are used to provide a further understanding of this disclosure. The schematic embodiments and descriptions thereof of this disclosure are used to explain this disclosure and do not constitute an improper limitation of this disclosure.

[0032] Figure 1 It is a framework diagram of a distribution network system with multiple microgrids and shared energy storage in Embodiment 1.

[0033] Figure 2 It is a schematic diagram of the topological structure after the distribution network is partitioned in Embodiment 1.

[0034] Figure 3 It is a flowchart of the ADMM algorithm for solving the two-stage problem in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The following further illustrates this disclosure in conjunction with the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further explanations of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this disclosure belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to this disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0038] Embodiment 1

[0039] In an embodiment of this disclosure, a collaborative optimization operation method for multiple microgrids and shared energy storage is provided, including:

[0040] Based on the structural data of a distribution network system with multiple microgrids and shared energy storage, considering the voltage support provided by the microgrids to the distribution network, an improved Nash negotiation is used to establish a two-stage cooperative game optimization model for multiple microgrids and shared energy storage;

[0041] The ADMM algorithm is used to perform distributed solution on the two-stage cooperative game optimization model to obtain the optimal interaction power and transaction electricity price between multiple microgrids and shared energy storage;

[0042] Among them, the two-stage cooperative game optimization model divides the optimization problem into two stages:

[0043] In the first stage, the distribution network is partitioned using the improved electrical distance. Considering the voltage regulation reward, with the goal of minimizing the cooperation cost between multiple microgrids and shared energy storage, the optimal interaction power between multiple microgrids and shared energy storage is obtained through solution;

[0044] In the second stage, based on the interaction power obtained from the solution of the first-stage problem, the contribution degree of each entity is quantified using a non-linear energy mapping function. With the contribution degree of each entity as the bargaining power, asymmetric bargaining is carried out based on the improved Nash negotiation to obtain the optimal transaction electricity price.

[0045] As an embodiment, a collaborative optimization operation method for multiple microgrids and shared energy storage of the present disclosure considers the economic benefits during the cooperative operation of multiple microgrids and shared energy storage and the node voltage changes caused by the interaction power between the microgrids and shared energy storage at different nodes of the distribution network. By means of voltage regulation rewards, the microgrids are incentivized to provide voltage support to the distribution network, while solving the problems of security and economy. The following details the specific implementation process.

[0046] The continuous increase in the penetration rate of distributed power sources will bring serious voltage over-limit problems to the distribution network. As a new energy storage technology, shared energy storage can meet the energy storage needs of microgrids, improve the adjustment ability of the interaction power between microgrids and the distribution network, and enable microgrids to provide effective voltage support to the distribution network; for this reason, this embodiment proposes a collaborative optimization operation method for multiple microgrids - shared energy storage for distribution network voltage regulation. By designing voltage regulation rewards, the microgrids are incentivized to participate in the voltage regulation of the distribution network. While ensuring the economy of the microgrids, the voltage can be effectively controlled within the specified range. The constructed asymmetric bargaining revenue distribution model can fairly distribute the revenue after the cooperation between multiple microgrids and shared energy storage, effectively improving the enthusiasm for participating in energy cooperation.

[0047] In this embodiment, the framework of the distribution network system with multiple microgrids and shared energy storage is as Figure 1 shown, and its topological structure and partition division results are as Figure 2As shown in the figure, there are 33 nodes in the distribution network, which are divided into 3 regions. Each microgrid MG is responsible for regulating the node voltage within its respective region. The microgrids connected to different nodes of the distribution network, the active power of power purchase and sale at the point of common coupling with the distribution network, and the change of photovoltaic reactive power in the microgrid will cause the change of node voltage in the corresponding region, thus providing voltage support for the distribution network. The application of shared energy storage in the microgrid can improve its voltage regulation ability for the distribution network, promote the utilization of renewable energy, reduce the operating cost, bring economic benefits and ensure the voltage security of the distribution network.

[0048] To optimize the above distribution network system with multiple microgrids and shared energy storage, first, based on the structural data of the distribution network system with multiple microgrids and shared energy storage, considering that the microgrid provides voltage support for the distribution network, a two-stage cooperative game optimization model of multiple microgrids and shared energy storage is established through improving the Nash negotiation. Then, the ADMM algorithm is used to solve the two-stage cooperative game optimization model distributively, and the optimal interaction power and transaction electricity price between multiple microgrids and shared energy storage are obtained. The two-stage cooperative game optimization model of multiple microgrids and shared energy storage will be described separately below.

[0049] In practice, the objects participating in the cooperation are all independent interest entities, and they all cooperate with the goal of maximizing their own cooperation benefits. As a kind of cooperative game, the Nash negotiation model can take into account both individual and collective interests, support cooperative members to make rational decisions, and after maximizing the interests of all participants, the cooperative revenue is distributed through mutual negotiation among multiple participants. Therefore, the two-stage cooperative game optimization model adopts the Nash negotiation standard model, as follows:

[0050]

[0051] Among them, is the cost before the cooperation of the participating subjects, that is, the Nash negotiation breakdown point, and C i is the operating cost of the negotiation subject after cooperation. The solution of formula (1) under the condition of satisfying is the Nash equilibrium solution.

[0052] Assume that each microgrid and shared energy storage are rational cooperative subjects. Nash bargaining is that the subjects obtain a satisfactory price through negotiation to solve the interest problems among the subjects. According to formula (1), the Nash negotiation model of this embodiment is as follows:

[0053]

[0054] Among them, C SESS,0 and C MG,k,0 are the operating costs of the shared energy storage and each microgrid before participating in the cooperation respectively; C SESS and C MG,kThey are the operating costs after their cooperation respectively.

[0055] Model (2) is a non-convex and non-linear model and cannot be directly solved using existing solvers. To reduce the difficulty of solving, it is decomposed into two stages to achieve iterative solution: the problem of minimizing the cooperation cost of the multi-microgrid - shared energy storage system and the problem of electricity payment negotiation after the cooperation between the microgrid and the shared energy storage. By solving the problems in the two stages, the optimal interaction power and transaction electricity price between the multi-microgrid and the shared energy storage can be obtained successively, and the optimal solution of formula (2) can be equivalently obtained, as Figure 3 shown, specifically as follows:

[0056] I. The first stage

[0057] Use the improved electrical distance to partition the distribution network, consider the voltage regulation reward, and take the minimum cooperation cost of the multi-microgrid and the shared energy storage as the goal to solve the optimal interaction power between the multi-microgrid and the shared energy storage. The specific steps are as follows:

[0058] (1) Use the improved electrical distance to partition the distribution network and consider the voltage regulation reward

[0059] Obtain the initial operation plan of the microgrid. Under the initial operation plan, the distribution network obtains the interaction power at the point of common coupling and and then calculates the power flow, constructs the voltage sensitivity matrix, and obtains the initial node voltage U 0,i,t ; Based on the voltage sensitivity matrix, use the improved electrical distance to partition the distribution network and set the voltage regulation reward.

[0060] The initial operation plan here is when the microgrid does not participate in the voltage regulation of the distribution network. Since there is no voltage support for the distribution network yet, there is no part of the voltage regulation reward in the objective function of the microgrid. Using existing technologies, an initial operation plan is optimized with the goal of its own economic optimality.

[0061] Based on the obtained interaction power and perform power flow calculation. From the power flow calculation results, the system Jacobian matrix Jac is as follows:

[0062]

[0063] where J Pθ 、J PU 、J Qθ 、J QU are sub-blocks in the Jacobian matrix.

[0064] Performing matrix transformation on formula (3) can obtain the voltage sensitivity matrix, and the relationship between the node voltage change and the power change can be expressed as follows:

[0065]

[0066] Among them, ΔP and ΔQ are the changes in active and reactive power of the node; Δθ and ΔU are the changes in the phase angle and amplitude of the node voltage; S PU and S QU are the degrees of change in the node voltage amplitude when the node injects a unit amount of active and reactive power and reactive power; S Pθ and S Qθ are the degrees of change in the node phase angle when the node injects a unit amount of active and reactive power and reactive power.

[0067] As can be seen from formula (4), in a distribution network with n nodes, the voltage change ΔU i of the i-th node is affected not only by its own factors but also by the power regulation ΔP j , ΔQ j of the remaining nodes j; therefore, when m nodes in the distribution network are connected to the microgrid, the influence on the voltage of node i can be expressed as:

[0068]

[0069] As can be seen from formula (5), when defining the electrical distance, it is not enough to only consider the position of the node in the distribution network, and the influence of the power change of other nodes connected to the microgrid on this node also needs to be considered; therefore, from the perspective of voltage control, using the above voltage sensitivity matrix, the electrical distance based on voltage sensitivity is defined as follows:

[0070]

[0071] In the power system, the electrical distance between nodes i and j can be represented by Z ij,eq , that is:

[0072] Z ij,eq =(Z ii -Z ij )-(Z ij -Z jj ) (7)

[0073] Among them, Z ij,eq is the impedance between nodes i and j; Z ij is the element in the i-th row and j-th column of the system node impedance matrix.

[0074] Taking into account the node position in the distribution network and the influence after the microgrid is connected, combining Z ij,eq with , the electrical distance between nodes i and j is redefined, that is, the improved electrical distance, which is expressed by the formula as:

[0075]

[0076] Among them, e(i,j) is the improved electrical distance between nodes i and j; and is the electrical distance based on voltage sensitivity; Re(Z ij,eq ) is the real part of the impedance between nodes i and j, and Im(Z ij,eq ) is the imaginary part of the impedance between nodes i and j.

[0077] Using the improved electrical distance, the K-means clustering method is used to partition the distribution network, and each microgrid is responsible for regulating the node voltages in its respective area. The basic steps are as follows:

[0078] Step 1: Take the 33 nodes of the distribution network as the data set, divide it into K clusters, and randomly select K data points as the initial centroids (i.e., cluster centers);

[0079] Step 2: According to the improved electrical distance e(i,j), calculate the distance between each data point in the data set and each centroid, and assign each data point to the cluster where the centroid closest to it is located;

[0080] Step 3: Calculate the average value of all data points in each cluster, and take the calculated average value as the new centroid;

[0081] Step 4: Repeat Steps 2 and 3 until the centroids no longer change significantly (i.e., the difference between the old and new centroids is very small) or the algorithm reaches the maximum number of iterations;

[0082] Step 5: Output the final K clusters and obtain the number of distribution network nodes included in each cluster.

[0083] For different clusters, they correspond to different areas divided by the distribution network. The microgrid is connected to the node of the distribution network. Whichever cluster the node belongs to, the microgrid is in that cluster.

[0084] (2) Due to the randomness of external natural conditions, the output power of photovoltaic power generation will be uncertain. Based on Latin hypercube sampling, the initial data is processed for correlation to generate a large number of scenarios, and then the improved K-means clustering method is used to obtain representative typical scenarios, that is, the output power data of the photovoltaic.

[0085] Specifically, first, scenario generation based on Latin hypercube sampling.

[0086] Determine the probability distribution function of photovoltaic power output based on historical data. Divide the vertical axis of the cumulative probability distribution function F(X) curve into N equal parts, with the distance between each interval being 1 / N and no overlap between intervals. For any i-th interval (i = 1, 2, …, N) among them, randomly generate a number r within the range of [0, 1], and then use this random number to obtain the cumulative probability function value q corresponding to interval i i :

[0087]

[0088] By substituting this cumulative probability function value q i into the inverse function F -1 (X) of the cumulative probability distribution function F(X), the i-th sampling value X i can be obtained, that is:

[0089] X i = F -1 (q i ) (10)

[0090] Then, the main steps of the improved K-means clustering are as follows:

[0091] Step 1: Determine the number of clusters k, and randomly select a data point as the initial cluster center, denoted as K1;

[0092] Step 2: Calculate the Euclidean distance between the remaining data points and K1, and select the data point with the farthest distance as the second cluster center, denoted as K2; subsequently, calculate the sum of the distances between the other data points and these two centers except for the two selected cluster centers, and select the data point with the largest sum of distances as the third cluster center, denoted as K3, and so on until k initial cluster centers are determined;

[0093] Step 3: Calculate the Euclidean distance between the remaining data points and each cluster center, and assign each data point to the nearest cluster center; subsequently, recalculate the average position of all data points in each cluster as the new cluster center;

[0094] Step 4: Define the clustering error H as the sum of the squares of the distances from all data points to their corresponding cluster centers. If the change in the clustering error H in two consecutive iterations is less than a preset threshold, it is considered that the algorithm has converged, and the final cluster centers are output. Otherwise, return to Step 3 to continue the iteration.

[0095] (3) Based on the power output data of the partition and photovoltaic, establish the power balance constraint for the operation of the microgrid, the upper and lower limits constraints of the curtailable / shiftable load, the energy interaction constraint with the outside world, and the photovoltaic reactive power constraint, and establish the state of charge constraint, charge and discharge constraint, and power balance constraint for the operation of the shared energy storage; with the goal of minimizing the purchase and sale electricity cost, demand response cost, electricity trading cost, curtailment penalty cost, and voltage regulation reward, construct the objective function for the operation of the microgrid, and with the goal of minimizing the charge and discharge loss cost and electricity trading cost, construct the objective function for the operation of the shared energy storage, so as to establish the microgrid model and the shared energy storage model.

[0096] 1) Shared energy storage model

[0097] The shared energy storage aggregates the power interaction demands of all microgrids, obtains the net charge and discharge demands, and determines the charge and discharge strategies to meet them. Its operating cost includes the charge and discharge loss cost C loss and the electricity interaction cost C trade with the microgrid, that is:

[0098] C SESS = min(C loss - C trade ) (11)

[0099] The specific representations of each part are as follows:

[0100]

[0101] Among them, τ is the charge and discharge cost coefficient of the energy storage; and are the charging power and discharging power of the shared energy storage respectively; is the electricity trading price between the shared energy storage and the microgrid; is the interaction power between the two, greater than 0 means selling electricity to the microgrid, and less than 0 means purchasing electricity from the microgrid.

[0102] The operation of the shared energy storage needs to meet the state of charge constraint, charge and discharge constraint, and power balance constraint, which are specifically represented as follows:

[0103]

[0104] Among them, SOC t is the state of charge of the shared energy storage; SOC max , SOC min are the upper and lower limits of the state of charge respectively; SOC0 and SOC T are the initial and final states of charge; η ch and η dis are the charge and discharge efficiencies of the shared energy storage respectively; E max is the maximum capacity of the shared energy storage; is the maximum charge and discharge power of the shared energy storage; μ ch and μ dis are 0-1 variables representing the charge and discharge states.

[0105] 2) Microgrid model

[0106] After considering the electricity trading between the microgrid and the shared energy storage and the microgrid's participation in regulating the distribution network voltage to obtain corresponding voltage regulation rewards, the operating cost of microgrid k includes the electricity purchase and sale cost C grid,k , the demand response cost C DR,k , the voltage regulation reward cost C reward,k , the electricity trading cost C trade,k with the SESS, and the curtailment penalty cost C cur,k , that is:

[0107] C MG,k = min(C grid,k + C DR,k - C reward,k - C trade,k + C cur,k ) (16)

[0108] Each part is specifically represented as follows:

[0109]

[0110] Among them, and are the electricity purchase and sale prices between the microgrid and the distribution network respectively; and are the electricity purchase and sale powers between the microgrid and the distribution network respectively; and are the compensation unit prices for load curtailment and load transfer respectively; and are the curtailable load and the transferable load respectively; α is the voltage regulation reward, n is the number of nodes in the area where the microgrid is located, and U 0,i,t is the initial node voltage; is the electricity trading price between the microgrid and the shared energy storage; is the interactive power between the two, greater than 0 means selling electricity to the shared energy storage, and less than 0 means purchasing electricity from the shared energy storage; is the curtailment penalty cost coefficient, is the curtailed electricity quantity.

[0111] The actual electrical load of the microgrid after demand response in period t consists of the fixed electrical load , the curtailable electrical load, and the transferable electrical load and is specifically represented as follows:

[0112]

[0113] The reducible and transferable loads need to satisfy certain upper and lower bound constraints:

[0114]

[0115] Among them, is the maximum reducible load allowed by the system, and k tran is the proportion of the electrical load that the system allows to adjust to the total electrical load.

[0116] During the operation of the microgrid, it is necessary to satisfy the constraints of energy interaction with the outside world, photovoltaic reactive power constraints, and power balance constraints:

[0117]

[0118] In the formula: and are the interaction powers at the point of common coupling, that is, the power of electricity purchase and sale, are the upper and lower limits of the interaction power between the microgrid and the shared energy storage respectively; is the upper limit of the power of electricity purchase and sale; is the capacity of the photovoltaic inverter, is the actual output value of the photovoltaic.

[0119] (4) Solve the microgrid model and the shared energy storage model to obtain the optimal interaction power between the multi-microgrids and the shared energy storage.

[0120] Through the equivalent transformation of formula (2), the optimization model of the first-stage problem is established as:

[0121]

[0122] Among them, C' SESS is the cost of electricity trading without the microgrid, and C' MG,k is the cost of electricity trading without the shared energy storage, that is:

[0123]

[0124] When is satisfied, it indicates that the amount of electricity that the shared energy storage expects to sell to the microgrid is the same as the amount of electricity that the microgrid expects to buy from the shared energy storage, and both parties reach a trading consensus.

[0125] Based on the ADMM algorithm, the interaction power and cooperation cost between the multi-microgrids and the shared energy storage are solved distributively. The Lagrange multipliers λ k,t and the penalty factor ρ t are introduced to construct the following distributed optimization model:

[0126]

[0127] According to the distributed iteration model, a distributed algorithm for the cooperative cost minimization problem is established, and the iteration formula is as follows:

[0128]

[0129] Iteration is carried out through Equation (29). When the convergence condition of Equation (30) is satisfied, the iteration stops, completing the solution of the system cooperative cost minimization problem and obtaining the optimal interaction power between the multi - micro - grid and the shared energy storage.

[0130] II. The second stage

[0131] Based on the interaction power obtained from the solution of the first - stage problem, the contribution degree of each entity is quantified by a non - linear energy mapping function. Taking the contribution degree of each entity as the bargaining power, asymmetric bargaining is carried out based on the improved Nash negotiation to obtain the optimal trading electricity price.

[0132] Specifically, the second - stage problem conducts cooperative bargaining based on the interaction power determined by the solution of the first - stage problem to complete the transaction payment. Due to the limitation of the charge - discharge efficiency of the energy storage device, when the power interaction between the micro - grid and the shared energy storage reaches balance, the state of charge of the shared energy storage will change. Therefore, this embodiment considers the energy contribution of each micro - grid in the cooperation and the change of the state of charge of the shared energy storage for asymmetric bargaining. A non - linear function based on the natural logarithm is used to quantify the contribution of different stakeholders in the cooperation. Taking the contribution degree of each entity as the bargaining power for cooperative bargaining, the trading electricity price between each micro - grid and the shared energy storage is determined, and the cooperative benefits are reasonably allocated. The specific formula is as follows:

[0133]

[0134]

[0135] Among them, are the electricity supplied and received by the MG respectively; are the reduction and increase amounts of the state of charge of the SESS respectively; are the maximum electric energies supplied and received respectively.

[0136] The above - mentioned non - linear function for constructing the bargaining power quantification based on the exponential function has the following functional characteristics: 1) Both receiving and supplying electric energy by the micro - grid will obtain contribution values, and supplying electric energy has a greater contribution than receiving electric energy; 2) If the state of charge of the shared energy storage decreases during the scheduling period, it will obtain a contribution value greater than 1, and the greater the reduction amount, the greater the contribution value; if the state of charge increases, it will obtain a contribution value less than 1, and the greater the increase amount, the smaller the contribution value.

[0137] Finally, according to the established bargaining power quantification models (31)-(35), substitute the optimal interaction power between the multi-microgrid and the shared energy storage obtained in the first stage into the second stage, construct an asymmetric bargaining revenue distribution model based on the Nash bargaining model (2), and equivalently transform it into a logarithmic form, which is specifically expressed as follows:

[0138]

[0139] Among them, the interaction cost is specifically expressed as:

[0140]

[0141] Among them, C' MG,k is the optimal operating cost of the microgrid without the interaction cost with the shared energy storage after participating in the cooperation, and C' SESS is the optimal operating cost of the shared energy storage without the interaction cost with the microgrid after participating in the cooperation.

[0142] Based on the ADMM algorithm principle, first introduce an auxiliary variable to decouple the trading electricity price: Let Introduce the Lagrange multiplier ψ k,t , the penalty factor χ t , and construct the following distributed optimization model:

[0143]

[0144] According to the distributed iteration model, establish a distributed algorithm for the revenue distribution problem, and the iteration formula is:

[0145]

[0146] Iterate through formula (40), and when the convergence condition of formula (41) is met, the iteration stops, complete the solution of the electricity negotiation payment problem after cooperation, and obtain the optimal trading electricity price between the multi-microgrid and the shared energy storage.

[0147] Embodiment 2

[0148] In an embodiment of the present disclosure, a collaborative optimization operation system for a multi-microgrid and a shared energy storage is provided, including a model construction module and a solution optimization module:

[0149] The model construction module is configured to: based on the structural data of the distribution network system including the multi-microgrid and the shared energy storage, considering the voltage support provided by the microgrid to the distribution network, establish a two-stage cooperative game optimization model for the multi-microgrid and the shared energy storage through improved Nash bargaining;

[0150] The solution optimization module is configured to: perform distributed solution on the two-stage cooperative game optimization model by using the ADMM algorithm to obtain the optimal interaction power and trading electricity price between the multi-microgrid and the shared energy storage;

[0151] Among them, the two-stage cooperative game optimization model divides the optimization problem into two stages:

[0152] In the first stage, the distribution network is partitioned by using the improved electrical distance, considering the voltage regulation reward, and with the goal of minimizing the cooperation cost between the multi-microgrid and the shared energy storage, the optimal interaction power between the multi-microgrid and the shared energy storage is obtained by solving;

[0153] In the second stage, based on the interaction power obtained by solving the problem in the first stage, the contribution degree of each entity is quantified by using the non-linear energy mapping function, and with the contribution degree of each entity as the bargaining power, asymmetric bargaining is carried out based on the improved Nash negotiation to obtain the optimal trading electricity price.

[0154] Embodiment 3

[0155] In an embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the cooperative optimization operation method of a multi-microgrid and a shared energy storage as described is implemented.

[0156] Embodiment 4

[0157] In an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, and the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the cooperative optimization operation method of a multi-microgrid and a shared energy storage as described is implemented.

[0158] Embodiment 5

[0159] In an embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes and implements the cooperative optimization operation method of a multi-microgrid and a shared energy storage as described.

[0160] This disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or steps for implementing the functions specified in one or more of the blocks.

[0162] Although the specific embodiments of the disclosure have been described above in conjunction with the accompanying drawings, they are not intended to limit the scope of protection of the disclosure. Those skilled in the art should understand that, based on the technical solutions of the disclosure, various modifications or variations that can be made by those skilled in the art without creative efforts are still within the scope of protection of the disclosure.

Claims

1. A method for collaborative optimization operation of multiple microgrids and shared energy storage, characterized in that: include: Based on the structural data of the distribution network system containing multiple microgrids and shared energy storage, considering that the microgrid provides voltage support to the distribution network, a two-stage cooperative game optimization model of multiple microgrids and shared energy storage is established through improved Nash negotiation. The ADMM algorithm is used to perform distributed solution on the two-stage cooperative game optimization model to obtain the optimal interactive power and transaction price between multiple microgrids and shared energy storage. The two-stage cooperative game optimization model divides the optimization problem into two stages: In the first stage, the distribution network is partitioned using the improved electrical distance, and the voltage regulation reward is considered. The goal is to minimize the cooperation cost of multi-microgrids and shared energy storage, and the optimal interaction power between multi-microgrids and shared energy storage is solved. In the second stage, based on the interactive power solved in the first stage, the contribution of each subject is quantified by a nonlinear energy mapping function. The contribution of each subject is used as the bargaining power, and asymmetric bargaining is carried out based on the improved Nash negotiation to obtain the optimal transaction electricity price.

2. A method for collaborative optimization operation of multiple microgrids and shared energy storage as claimed in claim 1, characterized in that: The improved electrical distance is used to partition the distribution network, specifically: Based on the initial operation plan of the microgrid, the distribution network obtains the interactive power at the common coupling point and performs power flow calculation; Perform matrix transformation on power flow calculation to obtain voltage sensitivity matrix; Based on the voltage sensitivity matrix and using the improved electrical distance, the K-means clustering method is used to partition the distribution network, and each microgrid is responsible for regulating the node voltage in its respective area.

3. The method for collaborative optimization operation of multiple microgrids and shared energy storage according to claim 1, characterized in that: The cooperation cost of the multi-microgrid and shared energy storage is composed of the operating cost of the multi-microgrid and the operating cost of the shared energy storage; The operating costs of the multi-microgrid include electricity purchase and sales costs, demand response costs, power transaction costs, abandoned solar penalty costs and voltage regulation rewards; The operating cost of the shared energy storage includes the energy storage charging and discharging loss cost and the energy interaction cost with the microgrid.

4. The method for collaborative optimization operation of multiple microgrids and shared energy storage according to claim 1, characterized in that: The first stage also includes constraints, specifically: Establish power balance constraints for microgrid operation, upper and lower limit constraints on loads that can be reduced and transferred, constraints on interaction with external energy, and constraints on photovoltaic reactive power; Establish state of charge constraints, charging and discharging constraints, and power balance constraints for shared energy storage operation.

5. The method for coordinated optimization operation of multiple microgrids and shared energy storage according to claim 1, characterized in that: The method of quantifying the contribution of each subject by a nonlinear energy mapping function is to use a nonlinear function based on natural logarithm to quantify the contribution of different stakeholders in energy cooperation.

6. The method for coordinated optimization operation of multiple microgrids and shared energy storage according to claim 1, characterized in that: The asymmetric bargaining based on improved Nash negotiation is based on Nash negotiation theory to build an asymmetric bargaining profit distribution model, based on ADMM algorithm to construct a distributed optimization model for the asymmetric bargaining profit distribution model, solve the profit distribution problem of the second stage, and obtain the optimal transaction electricity price between multiple microgrids and shared energy storage.

7. A collaborative optimization operation system of multiple microgrids and shared energy storage, characterized in that: Includes model building module and solution optimization module: The model building module is configured as follows: based on the structural data of the distribution network system containing multiple microgrids and shared energy storage, considering that the microgrid provides voltage support to the distribution network, and establishing a two-stage cooperative game optimization model of multiple microgrids and shared energy storage through improved Nash negotiation; The solution optimization module is configured to: use the ADMM algorithm to perform distributed solution on the two-stage cooperative game optimization model to obtain the optimal interactive power and transaction electricity price between multiple microgrids and shared energy storage; The two-stage cooperative game optimization model divides the optimization problem into two stages: In the first stage, the distribution network is partitioned using the improved electrical distance, and the voltage regulation reward is considered. The goal is to minimize the cooperation cost of multi-microgrids and shared energy storage, and the optimal interaction power between multi-microgrids and shared energy storage is solved. In the second stage, based on the interactive power solved in the first stage, the contribution of each subject is quantified by a nonlinear energy mapping function. The contribution of each subject is used as the bargaining power, and asymmetric bargaining is carried out based on the improved Nash negotiation to obtain the optimal transaction electricity price.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for collaborative optimization operation of multiple microgrids and shared energy storage as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, a collaborative optimization operation method of multiple microgrids and shared energy storage is implemented as described in any one of claims 1 to 6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes a method for collaborative optimization operation of multiple microgrids and shared energy storage as described in any one of claims 1 to 6.

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