Micro-grid group distributed energy management method and device and storage medium

Abstract

The application discloses a kind of micro-grid group distributed energy management method, device and storage medium, wherein method includes: constructing the mathematical model of unit operation of micro-grid, and the mathematical model of unit operation includes fan and photovoltaic unit output model, water turbine output model and micro gas turbine output model;Based on the mathematical model of unit operation, the day-ahead distributed energy management model is constructed with the lowest operation cost of each micro-grid;The day-ahead distributed energy management model is solved using alternating direction multiplier method, and the optimal day-ahead energy management strategy is obtained.In the embodiment of the application, the energy supply and use characteristics of the regional micro-grid are considered to construct the micro-grid model, and the day-ahead distributed energy management model based on alternating direction multiplier method is constructed, which can effectively improve the operation efficiency of the micro-grid, thereby effectively improving the consumption of renewable energy.

Description

Technical Field

[0001] This invention relates to the field of microgrid management technology, and in particular to a method, device and storage medium for distributed energy management of microgrid clusters. Background Technology

[0002] Microgrids achieve a high degree of autonomy by connecting distributed fossil fuel and renewable energy generating units and rationally scheduling power generation to meet load demand. Furthermore, connecting multiple microgrids within the same distribution network area to form a microgrid cluster allows for cooperative operation, promoting the local consumption of clean energy and improving utilization efficiency. However, the uncertainty of renewable energy output and load consumption within microgrids presents challenges to multi-microgrid management. Therefore, researching microgrid cluster management that considers source-load uncertainty is crucial for optimizing grid operation.

[0003] Existing energy management methods for microgrid clusters are often unable to provide reasonable energy control and management for microgrids, resulting in excessive energy waste. Summary of the Invention

[0004] This invention provides a method, device, and storage medium for distributed energy management of microgrid clusters, in order to solve the technical problem that existing energy management methods for microgrid clusters are generally unable to perform reasonable energy control and management of microgrids, resulting in excessive energy waste in microgrids.

[0005] One embodiment of the present invention provides a distributed energy management method for microgrid groups, comprising: A mathematical model for the operation of microgrid units is constructed, which includes a power output model for wind turbines and photovoltaic units, a power output model for water turbines, and a power output model for micro gas turbines. Based on the aforementioned mathematical model of unit operation, a day-ahead distributed energy management model is constructed with the lowest operating cost for each microgrid. The day-ahead distributed energy management model is solved using the alternating direction multiplier method to obtain the optimal day-ahead energy management strategy.

[0006] Furthermore, the power output model for the wind turbine and photovoltaic module is as follows:

[0007]

[0008] in, and These represent the actual output values ​​of the photovoltaic units and the wind turbine units of microgrid i at time t, respectively. and These are the maximum predicted output values ​​for photovoltaic units and wind turbine units, respectively. The turbine output model is as follows:

[0009]

[0010] in, Let be the output of the turbine unit in the i-th microgrid at time t; The water flow rate of the watershed where the i-th microgrid is located at time t; and These are the turbine output coefficient and the turbine operating head, respectively. and These are the upper and lower limits of the turbine unit's output, respectively. and These represent the upper and lower limits of the turbine's reference flow rate at time t, respectively, for the watershed where the unit is located. The natural inflow rate of the watershed where the unit is located at time t; The output model of the micro gas turbine is as follows:

[0011]

[0012] in, Let be the output value of the micro gas turbine of the i-th microgrid at time t; and These are the upper and lower limits of the output of the micro gas turbine unit; and These represent the vertical and horizontal climbing limits of the micro gas turbine.

[0013] Furthermore, a day-ahead distributed energy management model is constructed to minimize the operating costs of each microgrid, including: Considering the day-ahead distributed energy management cost of microgrid clusters, a day-ahead distributed energy management model is constructed with the lowest operating cost of each microgrid. The day-ahead distributed energy management cost includes the generation cost of controllable units, the energy interaction cost between microgrids, the energy interaction cost between microgrids and distribution networks, and the cost of curtailing renewable energy. The day-ahead distributed energy management model is as follows:

[0014] in, , and These represent the operating costs of the MT unit, the operating costs of the turbine, and the energy interaction costs with the distribution network for the i-th microgrid during time period t, respectively. and Let $t$ be the cost of wind and solar power curtailment penalties for the $i$-th microgrid during time period $t$. and This is the power generation cost coefficient for MT units; and This is the power generation cost coefficient for the hydro turbine unit; and These are the penalty coefficients for wind curtailment and solar curtailment, respectively. and These are the predicted output values ​​of the wind and solar turbines for the i-th microgrid during time period t; and These represent the electricity purchased and sold between the i-th microgrid and its upstream distribution network at time t; The time-of-use electricity price for period t; The on-grid electricity price is for period t.

[0015] Furthermore, the constraints of the day-ahead distributed energy management model include power balance constraints, power purchase and sale constraints, and unit operation constraints; The power balance constraint is:

[0016]

[0017] in, and These represent the electricity purchased and sold between the i-th microgrid and the p-th microgrid during time period t; The power purchase and sale constraints are as follows:

[0018]

[0019]

[0020]

[0021] in, and These represent the upper limits of the electricity purchased and sold between the i-th microgrid and the distribution network during time period t; and These represent the power purchase and sale limits of the i-th microgrid with other microgrids during time period t; The mathematical model of unit operation is used as the constraint for unit operation.

[0022] Furthermore, the method of using alternating direction multipliers to solve the day-ahead distributed energy management model to obtain the optimal day-ahead energy management strategy includes: The coupling constraints in the power balance constraints are embedded into the objective function of the day-ahead distributed energy management model by means of the augmented Langevin relaxation method, resulting in a convex function; Based on the convex function, the optimization problem is decoupled and decomposed into multiple sub-optimization problems according to the principle of Lagrange dual decomposition. A distributed algorithm is used to solve multiple sub-optimization problems to obtain the optimal day-ahead energy management strategy.

[0023] Furthermore, after obtaining the optimal day-ahead energy management strategy, it also includes: The predicted power output is obtained based on the optimal day-ahead energy management strategy. The predicted power output is then compared with the actual power output to obtain the energy deviation of each microgrid. Real-time energy management strategies for each microgrid are constructed based on a two-way auction mechanism.

[0024] Furthermore, based on a two-way auction mechanism, real-time energy management strategies for each microgrid are constructed, including: Initial electricity trading information is formulated based on the energy deviation, and the initial electricity trading information includes the bidding volume and bidding price. At the deadline for submitting transaction information, the transaction information of each microgrid is reviewed. After the review is completed, each transaction entity conducts the transaction according to the two-way auction mechanism between the buyer and seller. When there is a mismatch between the prices of electricity purchase and sale, the quotations of both parties will be adjusted until the prices of the purchase and sale are matched. When the transaction matching round ends or all users have completed their transactions, the smart meter will be used to transfer and settle the electricity transaction.

[0025] One embodiment of the present invention provides a distributed energy management device for microgrid clusters, comprising: The unit operation mathematical model construction module is used to construct the unit operation mathematical model of the microgrid. The unit operation mathematical model includes the power output model of wind turbine and photovoltaic unit, the power output model of water turbine and the power output model of micro gas turbine. The day-ahead distributed energy management model construction module is used to construct a day-ahead distributed energy management model based on the unit operation mathematical model, with the lowest operating cost of each microgrid. The optimal day-ahead energy management strategy solution module is used to solve the day-ahead distributed energy management model using the alternating direction multiplier method to obtain the optimal day-ahead energy management strategy.

[0026] One embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the microgrid distributed energy management method as described above.

[0027] In this embodiment of the invention, a microgrid model is constructed by comprehensively considering the energy supply and consumption characteristics of the regional microgrid, and a day-ahead distributed energy management model based on the alternating direction multiplier method is constructed, which can effectively improve the operating efficiency of the microgrid and thus effectively improve the absorption of renewable energy.

[0028] Furthermore, in this embodiment of the invention, the predicted power output is obtained based on the optimal day-ahead energy management strategy. The predicted power output is compared with the actual power output to obtain the energy deviation of each microgrid. Based on the two-way auction mechanism, a real-time energy management strategy for each microgrid is constructed. This can effectively improve the computational efficiency and matching success rate of real-time distributed transactions, thereby effectively solving the problem of power deviation caused by source-load uncertainty, and thus improving the reliability of microgrid operation. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the distributed energy management method for microgrid clusters provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the microgrid cluster architecture provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the matching process of the two-way auction mechanism provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the predicted renewable energy output provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the predicted load values ​​of each microgrid provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of a one-day energy management plan for a microgrid provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of a microgrid energy management plan two days in advance, provided in an embodiment of the present invention. Figure 8 This is a schematic diagram of a microgrid energy management plan three days in advance, provided in an embodiment of the present invention. Figure 9 This is a schematic diagram of a microgrid energy management plan four days in advance provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of a microgrid's 5-day energy management plan provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the transaction volume provided in an embodiment of the present invention; Figure 12 This is a schematic diagram of the transaction price provided in an embodiment of the present invention; Figure 13 This is a schematic diagram of the structure of the microgrid distributed energy management device provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0032] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0033] Please see Figure 1 An embodiment of the present invention provides a distributed energy management method for microgrid groups, comprising: S1. Construct a mathematical model for the operation of the microgrid units. The mathematical model for the operation of the units includes the output models of wind turbines and photovoltaic units, the output model of water turbines, and the output model of micro gas turbines. Please see Figure 2The management architecture of a microgrid cluster includes the microgrids themselves and the distribution network operator. Each microgrid primarily contains elements such as micro gas turbines, wind turbines, photovoltaic units, hydro turbines, energy storage systems, and residential loads. When a microgrid has surplus energy after achieving its own energy supply and demand balance, it can act as a producer, selling the surplus energy to other microgrids with demand. When a microgrid's own renewable energy output is insufficient, it can act as a consumer, purchasing energy from other microgrids to achieve its own energy balance. The distribution network operator mainly provides ancillary services to each microgrid. When the energy supply of all microgrids is insufficient or excessive, the distribution network operator will supplement the energy supply to the microgrids or purchase energy. Furthermore, the distribution network operator will provide an information exchange platform for distributed energy management among the microgrids, guiding each microgrid in energy management, achieving energy balance within the entire microgrid cluster, and thus promoting the local consumption of renewable energy.

[0034] In the process of distributed energy optimization management of microgrid groups, energy supply and demand balance can be achieved by adjusting the adjustable capacity of micro gas turbines and energy storage systems, or by conducting energy trading within the microgrid group or with the upper-level distribution network to meet the energy needs of the microgrid itself.

[0035] In this embodiment of the invention, the microgrid has abundant wind and solar energy, and renewable energy generation can be achieved by building wind turbines and installing rooftop photovoltaics in the region.

[0036] S2. Based on the mathematical model of unit operation, construct a day-ahead distributed energy management model with the lowest operating cost for each microgrid. In this embodiment of the invention, the operating cost of a microgrid includes the power generation cost of the controlled generators, the power exchange cost between microgrids, the power exchange cost between the microgrid and the distribution network, and the cost of abandoning renewable energy sources such as wind and solar power.

[0037] S3. Solve the day-ahead distributed energy management model using the alternating direction multiplier method to obtain the optimal day-ahead energy management strategy.

[0038] In this embodiment of the invention, a microgrid model is constructed by comprehensively considering the energy supply and consumption characteristics of the regional microgrid, and a day-ahead distributed energy management model based on the alternating direction multiplier method is constructed, which can effectively improve the operating efficiency of the microgrid and thus effectively improve the absorption of renewable energy.

[0039] In one embodiment, the power output model for wind turbines and photovoltaic modules is as follows: (1) (2) in, and These represent the actual output values ​​of the photovoltaic units and the wind turbine units of microgrid i at time t, respectively. and These are the maximum predicted output values ​​for photovoltaic units and wind turbine units, respectively. The turbine output model is as follows: (3) (4) in, Let be the output of the turbine unit in the i-th microgrid at time t; The water flow rate of the watershed where the i-th microgrid is located at time t; and These are the turbine output coefficient and the turbine operating head, respectively. and These are the upper and lower limits of the turbine unit's output, respectively. and These represent the upper and lower limits of the turbine's reference flow rate at time t, respectively, for the watershed where the unit is located. The natural inflow rate of the watershed where the unit is located at time t; In this embodiment of the invention, hydropower can be generated in rivers within the region to replace traditional thermal power units, thereby reducing environmental pollution. The output of the hydropower units used for hydropower generation is affected by the upstream and downstream water levels and flow rates of the river basin.

[0040] The output model of the micro gas turbine is as follows: (5) (6) in, Let be the output value of the micro gas turbine of the i-th microgrid at time t; and These are the upper and lower limits of the output of the micro gas turbine unit; and These represent the vertical and horizontal climbing limits of the micro gas turbine.

[0041] In this embodiment of the invention, biomass energy can be generated through micro gas turbines to improve its energy utilization efficiency. In some regions, natural gas pipelines are being constructed to replace traditional fossil fuels. This embodiment of the invention allows the deployment of micro gas turbines in microgrids within these regions for power supply and flexible control, constructing a micro gas turbine output model that comprehensively considers both the output and control characteristics of the micro gas turbine.

[0042] In this embodiment of the invention, step S2, constructing a day-ahead distributed energy management model with the lowest operating cost for each microgrid, may further include the following sub-steps: Considering the day-ahead distributed energy management cost of microgrid clusters, a day-ahead distributed energy management model is constructed with the lowest operating cost of each microgrid. The day-ahead distributed energy management cost includes the generation cost of controllable units, the energy interaction cost between microgrids, the energy interaction cost between microgrids and distribution networks, and the cost of curtailing renewable energy. The day-ahead distributed energy management model is as follows: (7) (8) in, , and These represent the operating costs of the MT unit, the operating costs of the turbine, and the energy interaction costs with the distribution network for the i-th microgrid during time period t, respectively. and Let $t$ be the cost of wind and solar power curtailment penalties for the $i$-th microgrid during time period $t$. and This is the power generation cost coefficient for MT units; and This is the power generation cost coefficient for the hydro turbine unit; and These are the penalty coefficients for wind curtailment and solar curtailment, respectively. and These are the predicted output values ​​of the wind and solar turbines for the i-th microgrid during time period t; and These represent the electricity purchased and sold between the i-th microgrid and its upstream distribution network at time t; The time-of-use electricity price for period t; The on-grid electricity price is for period t.

[0043] In this embodiment of the invention, equation (7) is the objective function of the day-ahead distributed energy management model.

[0044] In one embodiment, the constraints of the day-ahead distributed energy management model include power balance constraints, power purchase and sale constraints, and unit operation constraints; The power balance constraint is: (9) (10) in, and These represent the electricity purchased and sold between the i-th microgrid and the p-th microgrid during time period t; The power consumption constraints for purchasing and selling electricity are: (11) (12) (13) (14) in, and These represent the upper limits of the electricity purchased and sold between the i-th microgrid and the distribution network during time period t; and These represent the power purchase and sale limits of the i-th microgrid with other microgrids during time period t; The mathematical model of unit operation is used as the constraint for unit operation.

[0045] In this embodiment of the invention, the mathematical model for unit operation is formula (1)-(6).

[0046] In one embodiment, step S3, solving the day-ahead distributed energy management model using the alternating direction multiplier method to obtain the optimal day-ahead energy management strategy, may further include the following sub-steps: S31. By using the augmented Langevin relaxation method, the coupling constraints in the power balance constraint are embedded into the objective function of the day-ahead distributed energy management model to obtain a convex function; In this embodiment of the invention, the alternating direction multiplier method combines the characteristics of dual decomposition and enhanced Lagrangian constraint optimization. It uses the alternating direction multiplier method to coordinate the solution of local optimization problems in order to achieve the solution of the global optimization problem. The standard form of the alternating direction multiplier method is: (15) In the formula, and They are convex functions; and These are variables; , and These are the coefficients of the equation. When and In variables and When the function is convex within the range of values, the algorithm can converge to the optimal solution.

[0047] In this embodiment of the invention, problem (15) can be obtained using the augmented Langron relaxation method: (16) In the formula, These are dual variables, i.e., Lagrange multipliers; The penalty coefficient is used in the alternating direction multiplier method for convex optimization problems. It can converge to the optimal solution regardless of the different values ​​it takes.

[0048] According to the alternating direction multiplier method, an iterative form as shown in equation (17) can be formed. When the values ​​of the original residual and the dual residual are less than a small number, the algorithm is considered to have converged.

[0049] (17) It should be noted that the day-ahead distributed energy management model in this embodiment of the invention is a convex optimization problem to find the minimum value, which can satisfy the condition for reliable convergence of the alternating direction multiplier method.

[0050] The transaction variables among different entities within a microgrid cluster satisfy the corresponding coupling consistency characteristics, namely the coupling constraint in the power balance constraint of equation (10). During the same operating period, the electrical energy purchased by microgrid i from microgrid p should be equal to the electrical energy sold by microgrid p to microgrid i, i.e. and These are two coupling variables between microgrid i and microgrid p.

[0051] In this embodiment of the invention, the coupling constraints of equation (10) are embedded into the objective function of energy optimization management using the augmented Lagrange relaxation method, resulting in the following form: (18) Among them, equation (18) is a convex function.

[0052] S32. Based on convex functions, the optimization problem is decoupled and decomposed into multiple sub-optimization problems according to the principle of Lagrange dual decomposition. In this embodiment of the invention, based on the convex function of formula (18), the optimization problem can be decoupled and decomposed into multiple sub-optimization problems according to the principle of Lagrange dual decomposition.

[0053] S33. A distributed algorithm is used to solve multiple sub-optimization problems to obtain the optimal day-ahead energy management strategy.

[0054] In this embodiment of the invention, the sub-optimization objective function is solved independently by each microgrid entity participating in distributed energy management, and its sub-optimization objective function is: (19) in, The physical meaning of i is the transaction price of the microgrid p.

[0055] In this embodiment of the invention, a distributed algorithm is used to iteratively solve the sub-optimization objective function, that is, the Lagrange multipliers are updated using equation (20).

[0056] (20) in, The gradient coefficient is fixed and is taken as a positive number to ensure that the iteration process can converge; k is the number of iterations.

[0057] In this embodiment of the invention, for interconnected microgrid systems, coupling variables can be exchanged through a local communication network, thereby updating transaction data, and global variables can be updated according to equations (21) and (22): (twenty one) (twenty two) When the original residual and dual residual calculated by each microgrid satisfy (23), it is considered that the iterative process has reached the set accuracy requirement, the iterative process terminates, and the optimal day-ahead energy management strategy is output.

[0058] (twenty three) In the formula, and The coefficient for determining the convergence of the iteration will affect the error in the supply and demand balance between microgrids in the optimization results. Its value needs to be confirmed through negotiation between the trading platform and various market participants.

[0059] In one embodiment, after obtaining the optimal day-ahead energy management strategy in step S3, the method further includes: S4. Based on the optimal day-ahead energy management strategy, the predicted power output is obtained, and the predicted power output is compared with the actual power output to obtain the energy deviation of each microgrid. In this embodiment of the invention, due to the uncertainty in source-load processing, there may be a certain deviation between the day-ahead energy management strategy of the microgrid and the actual situation. This embodiment of the invention compares the predicted processing results with the actual output results to obtain the energy deviation of each microgrid.

[0060] S5. Construct real-time energy management strategies for each microgrid based on a two-way auction mechanism.

[0061] In this embodiment of the invention, a real-time energy management strategy for the power grid cluster is constructed based on a two-way auction mechanism to achieve energy complementarity among the various microgrids, thereby effectively improving the operational reliability of each microgrid.

[0062] It should be noted that the continuous two-way auction mechanism refers to a trading model in a multi-participant market where participants can submit bids at any time within a specified trading period, and a transaction can be quickly completed once the bids meet the matching conditions. In this mechanism, participants form a matching queue based on the principle of "price priority, time priority," with buyer prices sorted in descending order and seller prices sorted in ascending order. If there are identical bids, the order is determined by the time the bids were submitted.

[0063] Please see Figure 3This is a price matching diagram of a continuous two-way auction mechanism. When the buyer's bid is higher than the seller's bid, the two parties can reach a match, and the transaction price is the average of the two bids, until the buyer's highest bid is lower than the seller's lowest bid.

[0064] In real-time, when the microgrid has a power surplus, it acts as a power seller; when there is a power deficit, it acts as a power buyer. In a distributed market structure with multiple trading entities, the continuous two-way auction mechanism can complete the signing of power trading contracts between the power buyers and sellers through simple bidding and matching. This helps improve the operational stability of each entity and increase economic efficiency, thereby reducing unnecessary energy interaction between the microgrid and the distribution network and improving energy utilization.

[0065] In one embodiment, the continuous two-way auction transaction process is as follows: Step 1: Each trading entity submits its bid price and volume based on its own supply and demand relationship. After the trading platform receives the valid bids from each microgrid, it will generate the power seller queue and the power buyer queue according to the order of bid increase and bid decrease, as shown in Equation (24) and Equation (25).

[0066] (twenty four) (25) In the formula, and These are the queues for electricity purchasers and sellers during time period t, where the queue for electricity purchasers contains the following: In the queue of electricity purchasers, those who meet the requirements .

[0067] Step 2: Matching begins from the top of the electricity purchaser queue and the electricity seller queue respectively. When the price quoted by electricity purchaser m is greater than the price quoted by electricity seller n, the transaction between electricity purchaser m and electricity seller n is successfully matched. The transaction price is the average of the bid prices of both parties, as shown in Equation (26), and an electricity trading contract is signed. The trading entities that have successfully matched and whose bid electricity quantities have been matched will be removed from the queue. When the price quoted by the electricity purchaser is lower than the price quoted by the electricity seller, the current round of trading ends, and the remaining trading entities that have not been successfully matched will enter the next round of trading matching.

[0068] (26) In one embodiment, to prevent malicious bidding by trading entities, each microgrid entity's smart meter is pre-installed with automatic bidding program code. When the distributed energy trading market opens, the smart meter will automatically execute the program code to conduct bidding.

[0069] For the electricity purchaser, the initial bidding strategy is as follows: (27) In the formula, Let m be the initial price quoted by the m-th microgrid during time period t. and These represent the renewable energy output and load forecast deviation values ​​for the m-th microgrid during time period t, respectively. For the m-th microgrid's willingness to participate, .

[0070] For electricity retailers, their initial bidding strategy is as follows: (28) In the formula, This represents the initial price quoted by the nth microgrid during time period t.

[0071] When the prices of electricity purchase and sale parties do not match, price adjustments are required. In this case, all trading entities that did not reach a transaction in the previous round will subtract or add a corresponding step size to their initial price to quickly reach a transaction, as shown in equations (29) and (30).

[0072] (29) (30) In the formula, and These are the previous round of bids from the electricity purchaser and the electricity seller, respectively. and These are the price adjustment steps for electricity buyers and sellers, respectively. and These represent the maximum and minimum values ​​of the price adjustment step size, respectively. It is uniformly distributed.

[0073] In one embodiment, step S5, constructing a real-time energy management strategy for each microgrid based on a two-way auction mechanism, may further include the following sub-steps: S51. Formulate initial electricity trading information based on energy deviation. The initial electricity trading information includes the bidding volume and bidding price. S52. At the deadline for submitting transaction information, the transaction information of each microgrid shall be reviewed. After the review is completed, each transaction entity shall conduct the transaction in accordance with the two-way auction mechanism between the buyer and seller. In this embodiment of the invention, if false information or other misconduct is discovered during the review process, the trading party will be prohibited from participating in this round of electricity trading.

[0074] S53. When there is a mismatch between the prices of electricity purchase and sale, adjust the quotations of both parties until the prices of the two parties match. In this embodiment of the invention, adjusting the quotations of the buyer and seller can be as follows: when the buyer's price is lower than the seller's price, the two parties will enter the quotation adjustment stage, whereby the buyer appropriately raises its quotation and the seller appropriately lowers its quotation until the prices of the two parties can be matched.

[0075] S54. When the transaction matching round ends or all users have completed their transactions, the smart meter will be used to transfer and settle the electricity based on the transaction matching information.

[0076] In this embodiment of the invention, the number of transaction rounds can be limited to prevent the transaction matching process from failing to terminate, and the transaction discussion can be set according to the number of subjects.

[0077] In one embodiment, to verify the effectiveness of the microgrid distributed optimization management method considering source-load uncertainty proposed in this invention, five microgrids on the distribution network side were selected to form a microgrid group for simulation testing. Simulation testing was conducted on the day-ahead real-time two-stage energy management plan of the microgrid group in a certain region, using a 1-hour timescale.

[0078] Please see Figure 4-5 These represent the renewable energy output and load forecasts within each microgrid. The unit parameters and time-of-use tariffs for interaction with the distribution network in the day-ahead energy management plan are shown in Tables 1 and 2, respectively. Furthermore, the maximum real-time deviation for wind power, solar power, and load is set as... .

[0079] Table 1 System Operating Parameters

[0080] Table 2. Time-of-use pricing for distribution networks

[0081] In this embodiment of the invention, the results of day-ahead energy optimization management are analyzed: Please see Figure 6-10 The diagram illustrates the day-ahead energy management plans for each microgrid. In the overall day-ahead distributed energy management results, microgrids 1, 3, and 4 primarily act as electricity sellers, while microgrids 2 and 5 primarily act as electricity buyers. This is because microgrids 1 and 4 have relatively low load levels and relatively high generator output, allowing them to sell surplus electricity to other microgrids when they have a surplus. For microgrid 3, although its load level is high, it is powered not only by wind turbines and solar PV units but also by small hydropower, thus it can also sell surplus electricity to other microgrids to generate economic benefits when its generator output is sufficient. For microgrids 2 and 5, both have high load levels and low generator output, therefore they need to purchase electricity from other microgrids to achieve their own supply and demand balance.

[0082] During the 8:00-21:00 period, the output of MT (Mechanical Transmission Unit) and hydro-turbine units in each microgrid is relatively high. For microgrids 1, 3, and 4, the electricity purchase price from the distribution network and the electricity sales price to other microgrids during this period are higher than the generation costs of the MT and hydro-turbine units. Therefore, after meeting their own supply and demand balance, microgrids 1, 3, and 4 will also generate electricity through these units and sell it to other microgrids to obtain more sales revenue. For microgrids 2 and 5, since the electricity purchase price from other microgrids is slightly higher, they will prioritize supplying electricity through their own internal units to reduce their own electricity purchase costs and thus improve their economic efficiency. Furthermore, during certain periods, some grids, such as microgrids 2, 3, and 4, simultaneously purchase and sell electricity. This is because during the iterative solution process, there will be certain differences in the electricity purchase prices between different microgrids. Therefore, some microgrids can earn a certain price difference by "buying low and selling high," thereby obtaining certain economic benefits. Specifically, the load level of microgrid 5 is much higher than its unit output, resulting in a high demand for electricity purchase. Meanwhile, the unit output of microgrid 1 is higher, resulting in a higher demand for electricity sales. Moreover, the transaction price with other grids is lower. Therefore, microgrids 2, 3, and 4 can earn a certain intermediate profit by purchasing electricity from microgrid 1 and then selling it to microgrid 5 at a slightly higher price, thereby improving their own economic efficiency.

[0083] In this embodiment of the invention, a comparison of the economics of day-ahead energy management plans is provided: To verify the economic efficiency of the distributed energy optimization management constructed in this invention, a simulation comparison analysis was conducted between the distributed trading situation among microgrid groups and the individual operation situation of each microgrid. The operating costs of each microgrid and the wind and solar curtailment costs are shown in Table 3.

[0084] Table 3. Economic Comparison of Different Methods

[0085] As can be seen from the table, considering distributed energy trading within a microgrid cluster can effectively reduce the operating costs of microgrids 2-5 and increase the economic benefits of microgrid 1. This is because the transaction price for distributed energy trading between microgrids is lower than the time-of-use price for purchasing electricity from the distribution network, but higher than the grid-connected price for selling electricity to the distribution network. Therefore, each microgrid can purchase surplus electricity at a lower price or sell surplus electricity at a higher price when participating in distributed energy optimization management. Furthermore, inter-microgrids can fully absorb renewable energy through energy trading to reduce the curtailment costs of wind and solar power, thereby reducing the amount of energy interaction with the distribution network and ensuring the safety of the distribution network operation. In summary, the microgrid cluster distributed energy optimization management model proposed in this invention has better economic efficiency compared to the independent operation of each microgrid.

[0086] In this embodiment of the invention, the real-time energy optimization management results are analyzed as follows: Please see Figure 11-12 This displays the real-time energy optimization management results of the regional microgrid cluster and the transaction prices between microgrids. Please refer to [link / reference]. Figure 11 The electricity demand among microgrids can be met. However, during the periods of 23:00-7:00 and 10:00-19:00, the microgrid network experiences a "supply surplus" because the deviation in the predicted renewable energy output is greater than the load deviation. Therefore, microgrids with surplus electricity must exchange power with the distribution network to achieve their own supply-demand balance. Conversely, during the periods of 8:00 and 20:00-23:00, the microgrid network experiences a "supply shortage," requiring microgrids with a power deficit to purchase electricity from the distribution network to meet their needs. Please refer to [link to relevant documentation]. Figure 12 The transaction price of electricity in inter-microgrid electricity trading is between the time-of-use price and the grid connection price for that period. Therefore, compared with direct electricity exchange with the distribution network, the electricity seller can increase its electricity sales revenue, and the electricity buyer can save its electricity purchase cost, thereby improving the economic benefits of the entire microgrid group.

[0087] In this embodiment of the invention, the real-time transaction matching process is analyzed as follows: To illustrate the transaction matching process, we take the electricity transaction at 20:00 as an example to show the matching process within a complete transaction cycle. The bidding information and transaction results of each microgrid are shown in Tables 4 and 5, respectively.

[0088] Table 4. Price information for each microgrid

[0089] Table 5 Transaction Matching Results

[0090] As shown in the table, in the first round of bidding, microgrid 5 submitted the lowest bid among the power sellers, while microgrid 1 submitted the highest bid among the power buyers. Therefore, microgrid 1 and microgrid 5 were prioritized for a transaction, with a transaction price of 0.68 yuan / kW and a transaction volume of 31kW. At this point, microgrid 5 had sold all its surplus power, while microgrid 2's bid was 1.11 yuan / kW, both higher than the bids of all other microgrids among the power buyers, thus requiring a price adjustment phase. After several rounds of rebidding, microgrid 2 reached power trading contracts with microgrids 1, 3, and 4 for 16kW, 63kW, and 16kW respectively, with transaction prices of 0.91 yuan / kW, 0.88 yuan / kW, and 0.85 yuan / kW respectively. Finally, because this round of trading was in a state of "supply shortage," microgrid 4, due to its lower bid among the power buyers, still had some demand for power and would reach a power trading agreement with the distribution network at a higher price. Once all transactions are completed, the electricity purchaser and seller will finalize the electricity delivery and transfer based on the transaction matching.

[0091] In this embodiment of the invention, the effectiveness analysis of the pricing strategy is as follows: To verify the effectiveness of the pricing strategy proposed in this embodiment of the invention, a comparison was made between pricing adjustments using a zero-information strategy and those using a zero-information strategy during the pricing adjustment phase. A 24-hour simulation of distributed trading within a regional microgrid cluster was conducted, yielding results shown in Table 6. It can be seen that using a zero-information strategy for pricing adjustments significantly improves the success rate of transaction matching. This is because when prices do not match, the electricity purchase and sale entities can automatically and quickly adjust their bids without needing to obtain any information, thereby increasing the distributed trading success rate within the microgrid cluster and ultimately improving the economic efficiency of the transactions.

[0092] Table 6 Comparison Results of Whether or Not Price Adjustment Strategies Were Considered

[0093] Implementing the embodiments of the present invention has the following beneficial effects; In this embodiment of the invention, a microgrid model is constructed by comprehensively considering the energy supply and consumption characteristics of the regional microgrid, and a day-ahead distributed energy management model based on the alternating direction multiplier method is constructed, which can effectively improve the operating efficiency of the microgrid and thus effectively improve the absorption of renewable energy.

[0094] Furthermore, in this embodiment of the invention, the predicted power output is obtained based on the optimal day-ahead energy management strategy. The predicted power output is compared with the actual power output to obtain the energy deviation of each microgrid. Based on the two-way auction mechanism, a real-time energy management strategy for each microgrid is constructed. This can effectively improve the computational efficiency and matching success rate of real-time distributed transactions, thereby effectively solving the problem of power deviation caused by source-load uncertainty and improving the reliability of microgrid operation.

[0095] Please see Figure 13 Based on the same inventive concept as the above embodiments, one embodiment of the present invention provides a distributed energy management device for microgrid groups, comprising: The unit operation mathematical model construction module 10 is used to construct the unit operation mathematical model of the microgrid. The unit operation mathematical model includes the output models of wind turbines and photovoltaic units, the output model of water turbines, and the output model of micro gas turbines. The day-ahead distributed energy management model construction module 20 is used to construct a day-ahead distributed energy management model based on the unit operation mathematical model, with the lowest operating cost of each microgrid. The optimal day-ahead energy management strategy solution module 30 is used to solve the day-ahead distributed energy management model using the alternating direction multiplier method to obtain the optimal day-ahead energy management strategy.

[0096] One embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the microgrid distributed energy management method as described above.

[0097] In one embodiment, the power output model for wind turbines and photovoltaic modules is as follows:

[0098]

[0099] in, and These represent the actual output values ​​of the photovoltaic units and the wind turbine units of microgrid i at time t, respectively. and These are the maximum predicted output values ​​for photovoltaic units and wind turbine units, respectively. The turbine output model is as follows:

[0100]

[0101] in, Let be the output of the turbine unit in the i-th microgrid at time t; The water flow rate of the watershed where the i-th microgrid is located at time t; and These are the turbine output coefficient and the turbine operating head, respectively. and These are the upper and lower limits of the turbine unit's output, respectively. and These represent the upper and lower limits of the turbine's reference flow rate at time t, respectively, for the watershed where the unit is located. The natural inflow rate of the watershed where the unit is located at time t; The output model of the micro gas turbine is as follows:

[0102]

[0103] in, Let be the output value of the micro gas turbine of the i-th microgrid at time t; and These are the upper and lower limits of the output of the micro gas turbine unit; and These represent the vertical and horizontal climbing limits of the micro gas turbine.

[0104] In one embodiment, the day-to-day distributed energy management model building module 20 is also used for: Considering the day-ahead distributed energy management cost of microgrid clusters, a day-ahead distributed energy management model is constructed with the lowest operating cost of each microgrid. The day-ahead distributed energy management cost includes the generation cost of controllable units, the energy interaction cost between microgrids, the energy interaction cost between microgrids and distribution networks, and the cost of curtailing renewable energy. The day-ahead distributed energy management model is as follows:

[0105] in, , and These represent the operating costs of the MT unit, the operating costs of the turbine, and the energy interaction costs with the distribution network for the i-th microgrid during time period t, respectively. and Let $t$ be the cost of wind and solar power curtailment penalties for the $i$-th microgrid during time period $t$. and This is the power generation cost coefficient for MT units; and This is the power generation cost coefficient for the hydro turbine unit; and These are the penalty coefficients for wind curtailment and solar curtailment, respectively. and These are the predicted output values ​​of the wind and solar turbines for the i-th microgrid during time period t; and These represent the electricity purchased and sold between the i-th microgrid and its upstream distribution network at time t; The time-of-use electricity price for period t; The on-grid electricity price is for period t.

[0106] In one embodiment, the constraints of the day-ahead distributed energy management model include power balance constraints, power purchase and sale constraints, and unit operation constraints; The power balance constraint is:

[0107]

[0108] in, and These represent the electricity purchased and sold between the i-th microgrid and the p-th microgrid during time period t; The power consumption constraints for purchasing and selling electricity are:

[0109]

[0110]

[0111]

[0112] in, and These represent the upper limits of the electricity purchased and sold between the i-th microgrid and the distribution network during time period t; and These represent the power purchase and sale limits of the i-th microgrid with other microgrids during time period t; The mathematical model of unit operation is used as the constraint for unit operation.

[0113] In one embodiment, the optimal day-ahead energy management strategy solving module 30 is further configured to: By embedding the coupling constraints in the power balance constraint into the objective function of the day-ahead distributed energy management model through the augmented Langevin relaxation method, a convex function is obtained. Based on convex functions, the optimization problem is decoupled and decomposed into multiple sub-optimization problems according to the principle of Lagrange dual decomposition. A distributed algorithm is used to solve multiple sub-optimization problems to obtain the optimal day-ahead energy management strategy.

[0114] In one embodiment, the device further includes a real-time energy management strategy construction module, used for: The predicted power output is obtained based on the optimal day-ahead energy management strategy. The predicted power output is then compared with the actual power output to obtain the energy deviation of each microgrid. Real-time energy management strategies for each microgrid are constructed based on a two-way auction mechanism.

[0115] In one embodiment, a real-time energy management strategy for each microgrid is constructed based on a two-way auction mechanism, including: Initial electricity trading information is formulated based on energy deviation, and the initial electricity trading information includes the bidding volume and bidding price. At the deadline for submitting transaction information, the transaction information of each microgrid is reviewed. After the review is completed, each transaction entity conducts the transaction according to the two-way auction mechanism between the buyer and seller. When there is a mismatch between the prices of electricity purchase and sale, the quotations of both parties will be adjusted until the prices of the purchase and sale are matched. When the transaction matching round ends or all users have completed their transactions, the smart meter will be used to transfer and settle the electricity transaction.

[0116] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distributed energy management method for microgrid groups, characterized in that, include: A mathematical model for the operation of microgrid units is constructed, which includes a power output model for wind turbines and photovoltaic units, a power output model for water turbines, and a power output model for micro gas turbines. Based on the aforementioned mathematical model of unit operation, a day-ahead distributed energy management model is constructed with the lowest operating cost for each microgrid. The optimal day-ahead energy management strategy is obtained by solving the day-ahead distributed energy management model using the alternating direction multiplier method. After obtaining the optimal day-ahead energy management strategy, the following is also included: The predicted power output is obtained based on the optimal day-ahead energy management strategy. The predicted power output is then compared with the actual power output to obtain the energy deviation of each microgrid. Real-time energy management strategies for each microgrid are constructed based on a two-way auction mechanism; A real-time energy management strategy for each microgrid is constructed based on a two-way auction mechanism, including: Initial electricity trading information is formulated based on the energy deviation, and the initial electricity trading information includes the bidding volume and bidding price. For the electricity purchaser, the initial bidding strategy is as follows: In the formula, Let m be the initial price quoted by the m-th microgrid during time period t. and These represent the renewable energy output and load forecast deviation values ​​for the m-th microgrid during time period t, respectively. For the m-th microgrid's willingness to participate, ; For electricity retailers, their initial bidding strategy is as follows: In the formula, The initial price quoted by the nth microgrid during time period t; At the deadline for submitting transaction information, the transaction information of each microgrid is reviewed. After the review is completed, each transaction entity conducts the transaction according to the two-way auction mechanism between the buyer and seller. When there is a mismatch between the prices of electricity purchase and sale, the quotations of both parties will be adjusted until the prices of the purchase and sale are matched. When the transaction matching round ends or all users have completed their transactions, the smart meter will be used to transfer and settle the electricity transaction based on the transaction matching status. The power output model for the wind turbine and photovoltaic module is as follows: in, and These represent the actual output values ​​of the photovoltaic units and the wind turbine units of microgrid i at time t, respectively. and These are the maximum predicted output values ​​for photovoltaic units and wind turbine units, respectively. The turbine output model is as follows: in, Let be the output of the turbine unit in the i-th microgrid at time t; The water flow rate of the watershed where the i-th microgrid is located at time t; and These are the turbine output coefficient and the turbine operating head, respectively. and These are the upper and lower limits of the turbine unit's output, respectively. and These represent the upper and lower limits of the turbine's reference flow rate at time t, respectively, for the watershed where the unit is located. The natural inflow rate of the watershed where the unit is located at time t; The output model of the micro gas turbine is as follows: in, Let be the output value of the micro gas turbine of the i-th microgrid at time t; and These are the upper and lower limits of the output of the micro gas turbine unit; and These represent the vertical and horizontal climbing limits of the micro gas turbine.

2. The microgrid distributed energy management method as described in claim 1, characterized in that, A day-ahead distributed energy management model is constructed to minimize the operating costs of each microgrid, including: Considering the day-ahead distributed energy management cost of microgrid clusters, a day-ahead distributed energy management model is constructed with the lowest operating cost of each microgrid. The day-ahead distributed energy management cost includes the generation cost of controllable units, the energy interaction cost between microgrids, the energy interaction cost between microgrids and distribution networks, and the cost of curtailing renewable energy. The day-ahead distributed energy management model is as follows: in, , and These represent the operating costs of the MT unit, the operating costs of the turbine, and the energy interaction costs with the distribution network for the i-th microgrid during time period t, respectively. and Let $t$ be the cost of wind and solar power curtailment penalties for the $i$-th microgrid during time period $t$. and This is the power generation cost coefficient for MT units; and This is the power generation cost coefficient for the hydro turbine unit; and These are the penalty coefficients for wind curtailment and solar curtailment, respectively. and These are the predicted output values ​​of the wind and solar turbines for the i-th microgrid during time period t; and These represent the electricity purchased and sold between the i-th microgrid and its upstream distribution network at time t; The time-of-use electricity price for period t; The on-grid electricity price is for period t.

3. The microgrid distributed energy management method as described in claim 1, characterized in that, The constraints of the day-ahead distributed energy management model include power balance constraints, power purchase and sale constraints, and unit operation constraints. The power balance constraint is: in, and These represent the electricity purchased and sold between the i-th microgrid and the p-th microgrid during time period t; The power purchase and sale constraints are as follows: in, and These represent the upper limits of the electricity purchased and sold between the i-th microgrid and the distribution network during time period t; and These represent the power purchase and sale limits of the i-th microgrid with other microgrids during time period t; The mathematical model of unit operation is used as the constraint for unit operation.

4. The microgrid distributed energy management method as described in claim 3, characterized in that, The method of using alternating direction multipliers to solve the day-ahead distributed energy management model to obtain the optimal day-ahead energy management strategy includes: The coupling constraints in the power balance constraints are embedded into the objective function of the day-ahead distributed energy management model by means of the augmented Langevin relaxation method, resulting in a convex function; Based on the convex function, the optimization problem is decoupled and decomposed into multiple sub-optimization problems according to the principle of Lagrange dual decomposition. A distributed algorithm is used to solve multiple sub-optimization problems to obtain the optimal day-ahead energy management strategy.

5. A distributed energy management device for microgrid clusters, characterized in that, include: The unit operation mathematical model construction module is used to construct the unit operation mathematical model of the microgrid. The unit operation mathematical model includes the power output model of wind turbine and photovoltaic unit, the power output model of water turbine and the power output model of micro gas turbine. The day-ahead distributed energy management model construction module is used to construct a day-ahead distributed energy management model based on the unit operation mathematical model, with the lowest operating cost of each microgrid. The optimal day-ahead energy management strategy solution module is used to solve the day-ahead distributed energy management model using the alternating direction multiplier method to obtain the optimal day-ahead energy management strategy. After obtaining the optimal day-ahead energy management strategy, the following is also included: The predicted power output is obtained based on the optimal day-ahead energy management strategy. The predicted power output is then compared with the actual power output to obtain the energy deviation of each microgrid. Real-time energy management strategies for each microgrid are constructed based on a two-way auction mechanism; A real-time energy management strategy for each microgrid is constructed based on a two-way auction mechanism, including: Initial electricity trading information is formulated based on the energy deviation, and the initial electricity trading information includes the bidding volume and bidding price. For the electricity purchaser, the initial bidding strategy is as follows: In the formula, Let m be the initial price quoted by the m-th microgrid during time period t. and These represent the renewable energy output and load forecast deviation values ​​for the m-th microgrid during time period t, respectively. For the m-th microgrid's willingness to participate, ; For electricity retailers, their initial bidding strategy is as follows: In the formula, The initial price quoted by the nth microgrid during time period t; At the deadline for submitting transaction information, the transaction information of each microgrid is reviewed. After the review is completed, each transaction entity conducts the transaction according to the two-way auction mechanism between the buyer and seller. When there is a mismatch between the prices of electricity purchase and sale, the quotations of both parties will be adjusted until the prices of the purchase and sale are matched. When the transaction matching round ends or all users have completed their transactions, the smart meter will be used to transfer and settle the electricity transaction based on the transaction matching status. The power output model for the wind turbine and photovoltaic module is as follows: in, and These represent the actual output values ​​of the photovoltaic units and the wind turbine units of microgrid i at time t, respectively. and These are the maximum predicted output values ​​for photovoltaic units and wind turbine units, respectively. The turbine output model is as follows: in, Let be the output of the turbine unit in the i-th microgrid at time t; The water flow rate of the watershed where the i-th microgrid is located at time t; and These are the turbine output coefficient and the turbine operating head, respectively. and These are the upper and lower limits of the turbine unit's output, respectively. and These represent the upper and lower limits of the turbine's reference flow rate at time t, respectively, for the watershed where the unit is located. The natural inflow rate of the watershed where the unit is located at time t; The output model of the micro gas turbine is as follows: in, Let be the output value of the micro gas turbine of the i-th microgrid at time t; and These are the upper and lower limits of the output of the micro gas turbine unit; and These represent the vertical and horizontal climbing limits of the micro gas turbine.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the microgrid distributed energy management device as described in any one of claims 1 to 4.

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