Method and system for optimizing cooperation between transaction and operation of multi-micro grid in virtual power plant

Abstract

To provide a method and a system for optimizing the cooperation between a transaction and the operation of a multi-micro grid in a virtual power plant.SOLUTION: The method includes steps of: optimizing the cooperation between a transaction and the operation of a multi-micro grid in a virtual power plant by using a block chain, and each micro grid optimizing its operation on the previous day on the basis of a previous day operation optimization model to obtain the optimal previous day operation plan and a bidding decision including a price and the amount of transaction; executing, by each micro grid, a transaction matching mechanism in the block chain according to the bidding decision to complete matching of transaction participants with each other, and carrying out the transaction according to a result of the matching; and encrypting transaction information and uploading and storing the information in the block chain. The present invention can optimize the cooperation between an operation strategy inside the micro grid and a market transaction strategy, increase the utilization rate of energy, and effectively reduce the operation cost of the micro grid and the virtual power plant.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the field of power systems, and more particularly to a method and system for collaborative optimization of operation and trading of multiple microgrids within a virtual power plant. [Background technology]

[0002] The discussion in this section is intended only to provide background information related to the present invention and does not necessarily constitute prior art.

[0003] A microgrid (MG) is a localized power system primarily composed of generator sets, energy storage systems, loads, and power electronics. As distributed energy becomes more widely used, microgrids play an important role in facilitating on-site consumption of distributed resources. Microgrids also contribute to improving power supply reliability, facilitating energy interaction, and reducing operational costs. Multiple microgrids can be combined into a multi-microgrid system to provide power for larger loads. Compared with a single microgrid, a multi-microgrid system offers greater power supply reliability and energy efficiency, making it an important means of grid intelligence. Virtual power plants (VPPs), originally defined as the aggregation of distributed resources, have now become an effective means for integrating distributed energy into energy markets. While microgrids and VPPs are somewhat similar in system configuration, they have different focuses in various aspects, such as application scenarios and operation modes. With the development of distributed energy, the combination of microgrids and VPPs is expected to see further development.

[0004] Currently, the core research and focus of multi-microgrid systems is primarily on two points. One is the operation optimization of multi-microgrid systems, mainly related to the charging and discharging of energy storage systems, load demand response, and energy interactions. For grid-connected multi-microgrid systems, the main goal of operation optimization is to promote energy consumption and reduce operating costs. The other is the mechanism for power market trading. Peer-to-peer trading in the power market enables the sharing of idle resources within multi-microgrids, promoting efficient energy utilization and reducing operating costs. Currently, most studies focus on only one of these two points, and the lack of methods for co-optimizing operation and trading has resulted in significantly limited results. Summary of the Invention

[0005] In order to overcome the above-mentioned shortcomings of the prior art, the present invention provides a method and system for collaboratively optimizing the operation and trading of multiple microgrids within a virtual power plant, which can collaboratively optimize the operation strategy and market trading strategy within the microgrid, improve energy utilization rate, and effectively reduce the operation costs of the microgrid and the virtual power plant.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions.

[0007] A first aspect of the present invention provides a method for collaborative optimization of operation and trading of multiple microgrids within a virtual power plant.

[0008] The method for collaboratively optimizing the operation and transactions of multiple microgrids within a virtual power plant involves aggregating multiple microgrids to build a virtual power plant, and collaboratively optimizing the operation and transactions of the multiple microgrids within the virtual power plant using blockchain technology. Each microgrid performs day-ahead operation optimization based on the day-ahead operation optimization model to obtain an optimal day-ahead operation plan and a bidding decision including price and transaction volume; According to the bidding decision, each microgrid executes a transaction matching mechanism in the blockchain to complete the matching between transaction participants, and executes the transaction according to the matching result; and encrypting the transaction information and uploading and storing it on the blockchain; The day-ahead operation optimization model models the operating costs within the microgrid based on the planned bid price that reflects the relationship between prices and trading volume in the energy market, solves for minimizing the operating costs, and obtains a day-ahead operation plan and bidding decision for the microgrid.

[0009] Furthermore, the blockchain adopts a consortium chain with microgrids and virtual power plants as nodes, The virtual power plant is responsible for providing a transportation route for power trading between microgrids; The microgrid independently creates a day-ahead operation plan and submits bids and volumes to the power market within the virtual power plant.

[0010] Furthermore, the planned bid price is

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[0011] Furthermore, the objective function of the day-ahead operation optimization model is

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[0012] Furthermore, the day-ahead operation plan includes an output power of the micro gas turbine, a charging power and a discharging power of the energy storage system, and a load adjustment power; The bidding decision is the price and transaction volume submitted by the microgrids participating in the bidding.

[0013] Furthermore, the solution employs a method that combines the equilibrium optimizer algorithm and the CPLEX solver, and calculates the value of the day-ahead operation plan using the CPLEX solver during the iterative optimization process in which the equilibrium optimizer algorithm determines the bidding.

[0014] Furthermore, the transaction matching introduces a double auction mechanism based on priority indicators, and provides punitive measures against malicious bidding to limit malicious bidding behavior.

[0015] A second aspect of the present invention provides a system for collaborative optimization of operation and trading of multiple microgrids within a virtual power plant.

[0016] The system for collaborative optimization of multi-microgrid operation and transactions within a virtual power plant aggregates multiple microgrids to create a virtual power plant, and uses blockchain technology to collaboratively optimize the operation and transactions of the multi-microgrids within the virtual power plant. an optimization module configured for each microgrid to perform day-ahead operation optimization based on a day-ahead operation optimization model to obtain an optimal day-ahead operation plan and a bidding decision including a price and a trading volume; a trading module configured to execute a trading matching mechanism in the blockchain according to the bidding decision, complete the matching between trading participants, and conduct trading according to the matching result; a storage module configured to encrypt, upload, and store transaction information on the blockchain; The day-ahead operation optimization model models the operating costs within the microgrid based on the planned bid price that reflects the relationship between prices and trading volume in the energy market, solves for minimizing the operating costs, and obtains a day-ahead operation plan and bidding decision for the microgrid.

[0017] A third aspect of the present invention provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the steps of the method for coordinated optimization of operation and trading of multiple microgrids in a virtual power plant described in the first aspect of the present invention.

[0018] A fourth aspect of the present invention provides an electronic device comprising a memory, a processor, and a program stored in the memory and executable by the processor, the program being executed by the processor to implement steps in the method for collaborative optimization of operation and trading of multiple microgrids in a virtual power plant described in the first aspect of the present invention.

[0019] The above technical solutions have the following beneficial effects:

[0020] The present invention proposes a method for collaboratively optimizing the operation and trading of multiple microgrids within a virtual power plant, which aggregates multiple microgrids to build a virtual power plant model and proposes a distributed management strategy within the virtual power plant based on blockchain. Based on this, the operating costs within the microgrid are modeled based on the planned bidding price that reflects the relationship between prices and trading volume in the energy market, and a solution is found to minimize the operating costs. The method also proposes a method for collaboratively optimizing the operation and trading of multiple microgrids within a virtual power plant, which can be used to collaboratively optimize the operation strategy and market trading strategy within the microgrid, thereby increasing energy utilization and effectively reducing the operating costs of the microgrid and the virtual power plant.

[0021] Regarding the transaction mechanism, the present invention proposes a double auction mechanism that comprehensively considers the bid price and the transaction volume, which can reduce the influence of the bid price and restrict malicious bidding.

[0022] This invention proposes a solution method based on the combination of an equilibrium optimizer algorithm and a CPLEX solver to solve a non-convex optimization problem that combines operational optimization and market trading.

[0023] Additional aspect and advantages of the invention will be set forth in part in the description that follows, and in part will be obvious from the description, or may be learned by practice of the invention.

[0024] The accompanying drawings, which form a part of the present specification, are intended to provide a better understanding of the present invention. The illustrative examples of the present invention and the description thereof are for the purpose of illustrating the present invention and are not intended to unduly limit the present invention. [Brief explanation of the drawings]

[0025] [Figure 1] 2 is a flowchart of a method according to a first embodiment. [Figure 2] FIG. 2 is a configuration diagram of a VPP model constructed by aggregating multiple MGs in the first embodiment. [Figure 3] FIG. 1 is a schematic diagram of a VPP distributed management strategy according to a first embodiment. [Figure 4] 1 is a flowchart of a solution employing a combination of the equilibrium optimizer algorithm and the CPLEX solver in the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0026] It should be noted that the following detailed description is for illustrative purposes only and is intended to provide further explanation to the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art.

[0027] It should be noted that the terminology used herein is merely for the purpose of describing the mode for carrying out the invention and is not intended to limit the exemplary embodiments according to the present application. Unless the context clearly dictates otherwise, the singular forms used herein are also intended to include the plural forms. Also, when the terms "comprise" and / or "comprise" are used in this specification, they should be understood to specify the presence of features, steps, operations, devices, assemblies, and / or combinations thereof.

[0028] Example 1 In one embodiment of the present disclosure, a method for collaboratively optimizing the operation and trading of multiple microgrids in a virtual power plant is provided. As shown in FIG. 1 , the method includes aggregating multiple microgrids to form a virtual power plant, and collaboratively optimizing the operation and trading of the multiple microgrids in the virtual power plant using blockchain technology; Step S1: each microgrid performs day-ahead operation optimization based on the day-ahead operation optimization model to obtain an optimal day-ahead operation plan and a bidding decision including price and transaction volume; Step S2: each microgrid executes a transaction matching mechanism in the blockchain according to the bidding decision, completes the matching between transaction participants, and executes the transaction according to the matching result; Step S3 of encrypting the transaction information and uploading and storing it on the blockchain; The day-ahead operation optimization model models the operating costs within the microgrid based on the planned bid price that reflects the relationship between prices and trading volume in the energy market, solves for minimizing the operating costs, and obtains a day-ahead operation plan and bidding decision for the microgrid.

[0029] The process for implementing the method for coordinating and optimizing the operation and trading of multiple microgrids within a virtual power plant according to this embodiment will now be described in detail.

[0030] In this embodiment, a virtual power plant is constructed by aggregating multiple microgrids, and a distributed management strategy within the virtual power plant based on blockchain is proposed. Based on this, a method for collaboratively optimizing the operation and trading of multiple microgrids within the virtual power plant is proposed.

[0031] This method uses a planned bid price to establish a coupled relationship between operation optimization and market trading, allowing the microgrid to optimize the operation of its internal power grid and energy storage systems while taking market trading conditions into account. A double auction mechanism is proposed for the trading mechanism, which comprehensively considers bid prices and trading volumes. This mechanism can reduce the impact of bid prices and limit malicious bidding behavior. Finally, a solution method based on a combination of an equilibrium optimizer algorithm and the CPLEX solver is proposed to solve the non-convex optimization problem that combines operation optimization and market trading. This method can collaboratively optimize the microgrid's internal operation strategy and market trading strategy, increase energy utilization, and effectively reduce the operating costs of the microgrid and virtual power plant.

[0032] Below, we will explain in detail from three aspects: the system model, the mathematical model, and the solution flow.

[0033] 1. System model As shown in Figure 2, based on the differences between microgrid MGs and virtual power plant VPPs, we develop a virtual power plant VPP model that is constructed by aggregating multiple microgrid MGs. Considering the limited distributed resources of microgrid MGs, it is disadvantageous for microgrid MGs to participate in the electricity market alone. Therefore, by aggregating microgrid MGs within a certain area using a virtual power plant VPP, the communication and coordination functions of the virtual power plant VPP can promote the local electricity market.

[0034] A microgrid MG consists of a photovoltaic power generation system, an energy storage device, a micro gas turbine, and an adjustable load. Due to geographical limitations, a virtual power plant (VPP) is responsible for providing a transport route for power trading between MGs and also contributes to the aggregation of other microgrid MGs later. In addition, if power is not balanced within a region, the virtual power plant (VPP) is responsible for providing a transmission route to the main grid. Microgrid MGs participate in local power markets within the virtual power plant (VPP) to reduce operating costs. The virtual power plant (VPP) aims to incorporate energy resources distributed across regions into the energy market and increase energy utilization efficiency.

[0035] This example proposes a blockchain-based distributed VPP management strategy. As shown in Figure 3, a consortium chain is selected as the type of blockchain, with MG and VPP as nodes in the consortium chain, each with bookkeeping rights. Considering the limited number of nodes in the VPP model, a proof-of-stake mechanism is selected to improve security.

[0036] In decentralized management mode, MGs have greater autonomy and can independently create day-ahead operation plans and submit bid prices and transaction volumes to the power market within the VPP. The VPP is primarily responsible for data exchange and processing, creating market rules, and providing power transport services for the energy market. The VPP is also responsible for power billing and settlement services.

[0037] The blockchain-based VPP distributed management strategy process can be divided into three main steps:

[0038] First, after completing the operational optimization of the day-ahead scheduling stage, MG submits the bid price and trading volume to the market.

[0039] After that, the transaction matching mechanism written in the smart contract is automatically executed to complete the matching between trading participants, execute the transaction, and return the transaction results to MG and VPP.

[0040] Finally, the transaction results are encrypted and uploaded to the consortium chain for storage.

[0041] 2. Mathematical model In the day-ahead scheduling stage, each MG node prepares a day-ahead operation plan according to its capacity and load requirements.

[0042] (1) Planned bid price The estimated bid price is intended to reflect the relationship between price and volume in the energy market. According to economic principles, in an auction market, a higher bid is usually required to obtain a higher quantity, and the total volume in the market also affects the level of bidding. Therefore, the estimated bid price is expressed by formula (1).

[0043]

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[0044] Considering the willingness of MGs to participate in the power market within the VPP, the bid price from MGs needs to be more competitive than the price from the utility grid. Therefore, the bid price from MGs needs to satisfy the bidding constraint, which is expressed by formula (2).

[0045]

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[0046] (2) Day-ahead operation optimization model The day-ahead operation optimization of MG aims to minimize costs, and the strategic decision variables obtained through optimization include the transaction price, transaction volume, charge / discharge power of the energy storage device, output power of the micro gas turbine, load adjustment power, etc., that is, the bidding decision and day-ahead operation plan, and the objective function is expressed by formula (3).

[0047]

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[0048] The cost of participating in the electricity market is given by formula (4), which assumes that it is possible to meet all electricity supply needs within the local electricity market.

[0049]

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[0050] The operating cost of the energy storage device is given by formula (5). C ess,t =δ ess (P ess,ch,t +P ess,dis,t ) (5) where δ ess is the operating cost coefficient of the energy storage device. P ess,ch,t and P ess,dis,t represent the charging power and discharging power of the energy storage device at time t, respectively.

[0051] The operating cost of a micro gas turbine is given by formula (6).

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[0052] The compensation cost of load demand response adjustment is expressed by formula (7). Cdr,t=δ dr ΔP dr,t (7) where δ dr is the cost coefficient of load adjustment compensation, and ΔP dr,t is the load adjustment amount at time t.

[0053] The constraints are expressed by equations (1), (2), (8) to (18).

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[0054] However, equation (8) represents the power balance constraint, P load,t represents the initial load power at time t. P pv,t represents the photovoltaic power generation output power at time t, and equation (9) represents the load regulation constraint. dr,t,max represents the maximum value of the load adjustment at time t. Equations (10) to (14) respectively represent the charge constraint, discharge constraint, operating state constraint, energy constraint, and energy continuity constraint of the energy storage device. P ess,ch,max and P ess,dis,max represent the maximum charging power and maximum discharging power of the energy storage device, respectively. S ess,ch,t and S ess,dis,t is a 0-1 variable that represents the charge / discharge state of the energy storage device. ess,t represents the energy value of the energy storage device at time t. ess,min and E ess,max represent the minimum and maximum energy values ​​of the energy storage device, respectively. ess,ch and η ess,dis represent the charging and discharging efficiencies of the energy storage device, respectively. Equations (15) and (16) represent the output and ramp constraints of the micro gas turbine, respectively. P gt,maxrepresents the maximum output of the micro gas turbine. ΔP gt,max represents the maximum ramp power of the micro gas turbine. Equations (17) and (18) represent the power selling and purchasing state constraints and the transmission line power constraint, respectively. S sell,t and S buy,t is a 0-1 variable that represents the MG power selling and purchasing status, and P tr,max represents the maximum transport power value of the transmission line.

[0055] (3) Electricity market trading mechanism First, we define malicious bidding behavior by MGs. To encourage MGs to actively participate in local electricity markets and ensure discounted pricing of electric energy within the local electricity market (Equation (2)), MGs can reliably obtain profits by matching with local electricity market participants. Therefore, in order to obtain preferential matching rights, some MGs may adopt the most extreme pricing strategies. For example, an MG may set its selling price close to the main grid's electricity purchase price and its purchasing price close to the main grid's electricity sales price. Such prices are defined as marginal prices. The use of marginal prices by MGs may undermine the interests of competitors and affect the willingness of MGs that do not use marginal prices to participate in local electricity markets, thereby failing to establish a sound market order.

[0056] To address the above-mentioned malicious bidding behavior, we propose a double auction mechanism based on a priority index. First, the priority index takes price and transaction volume into consideration comprehensively, and thus price is not the only factor that determines whether matching can be successful. The calculation method for the priority index is shown in equations (19) and (20).

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[0057] The double auction mechanism is one of the common trading methods. In the double auction, first, the bid prices of the buyers and sellers are collected, and then the bid prices of the sellers are sorted in ascending order. Then, the seller sequence Q s The seller sequence Q s The lowest selling price is P s , the amount of electricity sold is E s The buyers are ordered in descending order of bid price, and the buyer sequence Q b The buyer sequence Q b The highest electricity purchase price is P b , the amount of electricity purchased is E b Let's say.

[0058] The highest power purchase price in the buyer sequence is equal to or greater than the lowest power selling price in the seller sequence, i.e., P s ≦P b If the price is equal to the average of both prices, the transaction is completed. s and E b Finally, the price of the transaction is put into the sequence P. After the transaction is completed, the completed part is removed from the matrix, and the incomplete part is updated in the matrix. The above process is repeated to continue the transaction.

[0059] The double auction mechanism must adhere to two principles: the time priority principle and the quantity priority principle. The time priority principle means that when two or more users submit the same bid, the earlier the bid is submitted, the higher the trading priority will be. The quantity priority principle means that when the time and price are the same, the larger the trading quantity, the higher the trading priority will be.

[0060] In addition to the double auction mechanism, a mechanism to punish malicious bidding is added to punish malicious use of marginal prices. As shown in Equation (21), if an MG is detected to repeatedly obtain priority matching rights by setting marginal prices, the MG is punished by being placed in the low-priority matching sequence for the optimization cycle.

[0061]

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[0062] In the process of market clearing, the average of the bid prices of both parties that successfully match will be the transaction price, and the smaller of the two transaction volumes will be the transaction volume.

[0063] 3. Solution flow Equilibrium Optimizer (EO) is a physics-based heuristic optimization algorithm for control-volume strongly mixed dynamic mass balancing, which is derived from control-volume mass balancing to estimate dynamic and equilibrium states. In EO, a search agent randomly updates the concentrations (positions) of some good particles, called equilibrium candidate particles, to ultimately reach an equilibrium state as the best result.

[0064] The mass balance equation of EO embodies the physical process of inflow, outflow, and generation of mass within the control volume, and is generally expressed as a first-order differential equation as shown in Eq. (22).

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[0065] where V is the test volume, C is the concentration in the test volume, Q is the volumetric flow rate into or out of the test volume, and C eq represents the concentration inside the control volume in the absence of mass generation (i.e., equilibrium), and G is the mass generation rate inside the control volume.

[0066] By solving the differential equation expressed by equation (22), C=C eq +(C0-C eq )F+G(1-F) / λV (23) F=exp(-λ(t-t0)) (24) where F is the coefficient of the exponential term, λ is the flow rate, and C 0 is the initial concentration in the test volume.

[0067] EO is updated based on equation (23). Specifically, C represents the new solution, C0 represents the old solution, and C eq represents the current optimal solution. Similar to the update equation of classical intelligent algorithms, here the cardinality represents the individual solution, and the solution update includes local search in the neighborhood of the current optimal solution and global random search within the optimization space. In order to meet the optimization needs of different problems, the specific operating process and parameters of the equilibrium optimizer (EO) are designed as follows:

[0068] 1) Initialization: As shown in Equation (25), the algorithm performs random initialization within the upper and lower bounds of each optimization variable.

[0069]

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[0070] 2) Equilibrium pool: To improve the global search ability of the algorithm and avoid falling into low-quality local optima, the equilibrium (i.e., optimal individual) in Eq. (23) is selected from the five currently optimal candidate solutions. The equilibrium pool consisting of these candidate solutions is: C eq,pool ={C eq,1 ,C eq,2 ,C eq,3 ,C eq,4 ,C eq,ave} (26) where C eq,1 , C eq,2 , C eq,4 and C eq,ave are the four best solutions found up to the current iteration, respectively, and C eq,ave represents the average state of these four solutions.

[0071] 3) The exponential coefficient F: To balance the local and global search of the algorithm, Eq. (24) is

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[0072] 4) Mass generation rate G: In order to enhance the local optimization ability of the algorithm, the generation rate is designed as follows:

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[0073] 5) Solution Update: For the optimization problem, based on Equation (23), the individual solutions can be updated as follows:

[0074] C=C eq +(CC eq )F+G(1-F) / λV(30)

[0075] The performance of the equilibrium optimizer (EO) is significantly superior to particle swarm optimization, genetic algorithms, gray wolf optimizers, gravitational search algorithms (GSA), and Salp swarm algorithms. However, because EO iteratively searches for the optimal solution, it still suffers from low solution efficiency. In this example, a method combining the equilibrium optimizer algorithm and the CPLEX solver is adopted. During the iterative optimization process in which the equilibrium optimizer algorithm makes bid decisions, the CPLEX solver calculates the value of the day-ahead operation plan. As shown in Figure 4, the solution flow can be summarized as follows:

[0076] (1) Select the bid price as the initial optimization variable and randomly generate one particle swarm. (2) Using the bid price, reverse-calculate the trading volume according to formula (1). (3) The known values ​​of the initial optimization variables and the objective function in formula (3) are input into the CPLEX solver to calculate the remaining optimization variables (such as the output power of the micro gas turbine, the charging and discharging power of the energy storage system, and the load adjustment power) and fitness values. (4) The optimal variables and optimal fitness are memorized. (5) Select the four optimal solutions and their average value to construct an equilibrium pool. (6) Update the parameters of the algorithm. (7) Update the bid price according to the equilibrium pool. (8) Return to step (2) and repeat the calculation until the maximum number of iterations is reached.

[0077] Based on the above-described solution flow, a specific solution algorithm is given.

[0078] Inputs: 1) MG solar power forecast; 2) MG load forecasting; 3) Power purchase and sale prices in the power grid; 4) The maximum charge / discharge power and capacity limits of the energy storage system (upper and lower limits of capacity), 5) Adjustable resistance, 6) Maximum transport power, 7) Maximum output power and maximum ramp power of the micro gas turbine; 8) Other parameters of MG, 9) Algorithm parameters.

[0079] Output: 1) MG's bidding decision (price and volume); 2) Day-ahead operation plan (charging and discharging power of energy storage system, power trading plan, output power of micro gas turbine, etc.).

[0080] start: 1: Select the bid price as the initial optimization variable and randomly generate one particle swarm.

[0081] #Algorithm iteration For Iterations = 1:Max Iterations: #Parallel calculation For Number of particles = 1:Maximum Number of particles:

[0082] 2: Calculate the trading volume using the bid price according to formula (1).

[0083] 3: The known values ​​of the initial optimization variables and the objective function are input into the CPLEX solver to calculate the remaining optimization variables (such as the output power of the micro gas turbine, the charging and discharging power of the energy storage system, and the load adjustment power) and fitness values.

[0084] 4: Remember the optimal variables and optimal fitness.

[0085] 5: Select the four optimal solutions and their average value to construct an equilibrium pool.

[0086] 6: Update the parameters of the algorithm.

[0087] 7: Update the bid price according to the equilibrium pool.

[0088] End for particle number

[0089] # End parallel calculation

[0090] End for iteration count

[0091] end

[0092] After solving the problem and obtaining the optimal day-ahead operation plan and bidding decision, the microgrid performs actual operation scheduling according to the day-ahead operation plan in terms of operation, and performs transaction matching and executes transactions according to the bidding decision in terms of trading. The transaction flow can be summarized as follows:

[0093] (1) Taking price and transaction volume into consideration comprehensively, the priority indexes of the microgrids that purchase electricity and the microgrids that sell electricity are calculated, and the priority index values ​​are arranged in descending order. The priority sequence of the microgrids that purchase electricity and the priority sequence of the microgrids that sell electricity are matched to obtain a priority matching sequence.

[0094] (2) Based on the mechanism of punishing malicious bidding, it is detected whether the microgrid repeatedly obtains the priority matching right by setting the limit price. If such a situation exists, the MG is put at the end of the priority matching sequence of the optimization cycle, and the same process is repeated as punishment to obtain an adjusted priority matching sequence.

[0095] (3) Based on the priority matching sequence, the matching is formed from the microgrid that buys electricity and the microgrid that sells electricity, with the highest priority index value, and the trading price is set to the average of the two prices. The trading amount of electricity is set to the smaller of the trading amount of electricity of the microgrid that buys electricity and the trading amount of electricity of the microgrid that sells electricity.

[0096] (4) After the transaction is completed, the completed part is removed from the matrix, and the incomplete part is updated in the matrix. The above process is repeated to continue the transaction.

[0097] A specific trade flow algorithm is given.

[0098] input: 1) MG's bidding decision (price and volume); 2) Day-ahead operation planning (charging and discharging power of energy storage systems, power trading plans, output power of micro gas turbines, etc.).

[0099] start: For time = 1: number of periods

[0100] 1. The priority index of the microgrids that purchase electricity is calculated, and they are sorted in descending order of priority index value.

[0101] 2. The priority index of the microgrids that sell electricity is calculated, and they are arranged in descending order of priority index value.

[0102] 3. Set the selling power number to 1 and the purchasing power number to 1.

[0103] While the number of microgrids that purchase electricity > 0 && the number of microgrids that sell electricity > 0

[0104] 4. Match the microgrid that sells electricity (power selling number) with the microgrid that buys electricity (power buying number).

[0105] 5. Liquidate in accordance with the liquidation rules.

[0106] 6. Update the information on the microgrid that purchases electricity and the microgrid that sells electricity.

[0107] 7. Update your power sales number and power sales number.

[0108] End for number of cycles

[0109] end

[0110] The above-mentioned flow is the basic logic of the transaction, and the consortium chain is the platform and storage support for the transaction logic. The specific implementation method is a smart contract in the blockchain, which is a program embedded in the blockchain. It is automatically executed when a condition is met. In this embodiment, this condition is receiving the bid price and transaction volume submitted by the microgrid that buys and sells electricity.

[0111] Example 2 In one embodiment of the present disclosure, a system for collaboratively optimizing the operation and trading of multiple microgrids within a virtual power plant is provided, which aggregates multiple microgrids to form a virtual power plant, and collaboratively optimizes the operation and trading of the multiple microgrids within the virtual power plant using a blockchain; an optimization module configured for each microgrid to perform day-ahead operation optimization based on a day-ahead operation optimization model to obtain an optimal day-ahead operation plan and a bidding decision including a price and a trading volume; a trading module configured to execute a trading matching mechanism in the blockchain according to the bidding decision, complete the matching between trading participants, and conduct trading according to the matching result; a storage module configured to encrypt, upload, and store transaction information on the blockchain; The day-ahead operation optimization model models the operating costs within the microgrid based on the planned bid price that reflects the relationship between prices and trading volume in the energy market, solves for minimizing the operating costs, and obtains a day-ahead operation plan and bidding decision for the microgrid.

[0112] Example 3 The present embodiment aims to provide a computer-readable storage medium.

[0113] The computer-readable storage medium stores a computer program, which, when executed by a processor, implements steps in a method for collaboratively optimizing operation and trading of multiple microgrids within a virtual power plant described in Example 1 of the present disclosure.

[0114] Example 4 The present embodiment aims to provide an electronic device.

[0115] The electronic device comprises a memory, a processor, and a program stored in the memory and executable by the processor, and when the processor executes the program, it realizes steps in a method for collaborative optimization of operation and trading of multiple microgrids within a virtual power plant described in Example 1 of the present disclosure.

[0116] The above merely describes the preferred embodiments of the present invention, but the present invention is not limited to these embodiments. Those skilled in the art can make various modifications and changes to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for collaboratively optimizing operation and transactions of a multi-microgrid within a virtual power plant, in which a virtual power plant is constructed by aggregating a plurality of microgrids, and the operation and transactions of the multi-microgrid within the virtual power plant are collaboratively optimized using a blockchain, Each microgrid performs day-ahead operation optimization based on the day-ahead operation optimization model to obtain an optimal day-ahead operation plan and a bidding decision including price and transaction volume; According to the bidding decision, each microgrid executes a transaction matching mechanism in the blockchain to complete the matching between transaction participants, and executes the transaction according to the matching result; and encrypting the transaction information and uploading and storing it on the blockchain; The day-ahead operation optimization model models the operation costs within the microgrid based on a planned bid price that reflects the relationship between prices and trading volumes in the energy market, and solves a problem to minimize the operation costs, thereby obtaining a day-ahead operation plan and bidding decision for the microgrid; The estimated bid price is intended to reflect the relationship between price and volume in the energy market. According to economic principles, in an auction market, a higher bid is usually required to obtain a higher quantity, and the total volume in the market also affects the level of bidding. The estimated bid price is [Equation 30] where: [Equation 31] denote the buying bid price and selling bid price of the i-th microgrid at time t, respectively; [Equation 32] represent the purchase price and sale price of the utility grid at time t, respectively, h is the bidding coefficient, [Equation 33] represent the purchase and sales amounts of the i-th microgrid at time t, respectively; [Equation 34] represents the total load in the VPP in period t; Considering the willingness of MGs to participate in the electricity market within the VPP, the bid price from MGs needs to be more competitive than the price from the utility grid, and the bid price from MGs needs to satisfy the bidding constraints. [Equation 35] where: [Equation 36] represent the buying bid price and selling bid price of the i-th MG at time t, respectively; [Equation 37] denote the purchase and sale prices of the utility grid at time t, respectively; The optimization of the day-ahead operation of MG aims to minimize costs. The strategic decision variables obtained through optimization include the transaction price, transaction volume, charge / discharge power of the energy storage device, output power of the micro gas turbine, load adjustment power, etc., i.e., bidding decision and day-ahead operation plan. The objective function of the day-ahead operation optimization model is: [Number 38] where C ｍｇ represents the total operating cost during the optimization period of the microgrid, and C ｔｒ，ｔ represents the cost of participating in the electricity market at time t, and C ｅｓｓ，ｔ represents the operating cost of the energy storage device at time t, and C ｇｔ，ｔ represents the operating cost of the micro gas turbine at time t, and C ｄｒ，ｔ represents the compensation cost of load demand response regulation at time t, T is the total scheduling period of the microgrid, The formula for the cost of participating in the electricity market is: [0.39] where: [Equation 40] represent the buying bid price and selling bid price of the i-th MG at time t, respectively; [Equation 41] and t respectively represent the predicted trading volume of the i-th MG at time t.

2. The blockchain uses a consortium chain with microgrids and virtual power plants as nodes, The virtual power plant is responsible for providing a transportation route for power trading between microgrids; The method for collaboratively optimizing operation and trading of multiple microgrids within a virtual power plant as described in claim 1, characterized in that the microgrids independently create day-ahead operation plans and submit bid prices and trading volumes to the electricity market within the virtual power plant.

3. the day-ahead operation plan includes an output power of the micro gas turbine, a charging power and a discharging power of the energy storage system, and a load adjustment power; The method for collaboratively optimizing operation and trading of multiple microgrids within a virtual power plant according to claim 1 , wherein the bidding decision is the price and trading volume submitted by the microgrids participating in the bidding.

4. The method for collaborative optimization of operation and trading of multi-microgrids in a virtual power plant according to claim 1, characterized in that the solution is obtained by adopting a method combining an equilibrium optimizer algorithm and a CPLEX solver, and calculating the value of the day-ahead operation plan by the CPLEX solver during the iterative optimization process in which bidding decisions are made by the equilibrium optimizer algorithm.

5. The method for collaboratively optimizing operation and trading of multiple microgrids within a virtual power plant as described in claim 1, characterized in that the trading matching mechanism introduces a double auction mechanism based on a priority index, and provides punitive measures for malicious bidding to restrict malicious bidding behavior.

6. A system for collaboratively optimizing operation and transactions of multiple microgrids in a virtual power plant based on the method according to any one of claims 1 to 5, comprising: aggregating multiple microgrids to construct a virtual power plant; and collaboratively optimizing operation and transactions of the multiple microgrids in the virtual power plant using a blockchain; an optimization module configured for each microgrid to perform day-ahead operation optimization based on a day-ahead operation optimization model to obtain an optimal day-ahead operation plan and a bidding decision including a price and a trading volume; a trading module configured to execute a trading matching mechanism in the blockchain according to the bidding decision, complete the matching between trading participants, and conduct trading according to the matching result; a storage module configured to encrypt, upload, and store transaction information on the blockchain; The system for collaboratively optimizing the operation and trading of multiple microgrids within a virtual power plant is characterized in that the day-ahead operation optimization model models the operating costs within the microgrid based on a planned bid price that reflects the relationship between prices and trading volume in the energy market, solves to minimize the operating costs, and obtains a day-ahead operation plan and bidding decision for the microgrid.

7. An electronic device, a memory for non-transitory storage of computer-readable instructions; a processor for executing the computer-readable instructions; 6. An electronic device, characterized in that the computer readable instructions, when executed by the processor, perform the method of any one of claims 1 to 5.

8. 6. A storage medium for non-transitory storage of computer-readable instructions, the computer-readable instructions, when executed by a computer, performing the method of any one of claims 1 to 5.

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