Micro-grid group electric energy-warrant mixed transaction optimization method

Abstract

The invention discloses a micro-grid group electric energy-warrant hybrid transaction optimization method, which comprises the following steps of: establishing a multi-micro-grid system architecture comprising a virtual operator decision-making layer and a micro-grid optimization layer, and constructing an electricity utilization right and power generation right transaction mechanism based on the architecture; constructing a double-layer game optimization model which comprises an upper-layer virtual operator price decision model and a lower-layer multi-microgrid resource scheduling model; solving the double-layer game optimization model by adopting a mixed solving strategy combining an improved particle swarm optimization algorithm and an fmincon function; and fairly distributing the income of the virtual operator among the multiple micro-grids based on an improved Shapley value method. According to the method, the virtual operator is introduced as a coordination center while the operation autonomy of each micro-grid is guaranteed, the mechanism can effectively aggregate the system flexibility and guide the multiple micro-grids to cooperatively participate in main grid interaction, organic unification of local scheduling autonomy and system global optimization is realized, and the overall operation efficiency and the renewable energy consumption capability are improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid warrant trading technology, and more specifically, to an optimized method for hybrid trading of electricity and warrants in microgrid clusters. Background Technology

[0002] With the acceleration of global energy transition and the deepening of power system reform, virtual power plants, as a new power operation organization model, are becoming a key tool for building a new power system and improving the efficiency of energy resource allocation.

[0003] Microgrids, as integrated units of distributed energy sources (photovoltaics, wind power, and energy storage) and flexible loads, are the core carriers for virtual power plants to achieve resource aggregation. The three major functions of virtual power plants—resource aggregation, market trading, and system regulation—essentially rely on the coordinated dispatch of microgrid clusters. Microgrid clusters, through interconnection, achieve coordinated dispatch of distributed power sources (such as photovoltaics and wind power) and energy storage devices, which can both absorb intermittent renewable energy and enhance grid reliability. However, the differing interests of microgrid stakeholders (such as the economics of electricity consumption in residential areas versus the reliability requirements of power supply in industrial areas) make it difficult for traditional centralized dispatch to balance individual rationality and collective optimality, necessitating the introduction of market mechanisms to coordinate resource allocation. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an optimization method for hybrid trading of electricity and warrants in microgrid clusters. Through a two-layer game optimization model, the effectiveness of the proposed mechanism in reducing operating costs, minimizing scheduling deviations, and ensuring fair revenue is verified, providing theoretical support and technical pathways for the market-oriented operation of microgrid clusters.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An optimized method for hybrid trading of electricity and warrants in microgrid clusters includes the following steps: A multi-microgrid system architecture comprising a virtual operator (MVNO) decision-making layer and a microgrid optimization layer is established, and a power consumption and generation rights trading mechanism is constructed based on this architecture. A two-layer game optimization model is constructed, including an upper-layer MVNO price decision-making model and a lower-layer multi-microgrid resource scheduling model. The two-layer game optimization model is solved using a hybrid solution strategy combining an improved particle swarm optimization algorithm and the fmincon function. The revenue of MVNOs is fairly distributed among the multi-microgrids based on an improved Shapley value method.

[0006] In the above scheme, the upper-layer virtual operator pricing decision model optimizes the electricity right price and the generation right price with the goal of maximizing overall revenue; the lower-layer multi-microgrid resource scheduling model optimizes the warrant trading volume and internal generation plan with the goal of minimizing operating costs.

[0007] In the above scheme, the microgrid can adjust its own electricity demand by purchasing or selling electricity rights to virtual operators, or adjust its own power generation plan by purchasing or selling power generation rights.

[0008] In the above scheme, the objective function of the upper-layer virtual operator pricing decision model is to maximize comprehensive revenue, and its decision variables are the electricity consumption right price and the power generation right price; The objective function of the lower-level multi-microgrid resource scheduling model is to minimize the operating cost of each microgrid, and its decision variables include the amount of electricity consumption rights traded and the amount of power generation rights traded.

[0009] In the above scheme, the hybrid solution strategy uses an improved particle swarm optimization algorithm to optimize the upper-level price decision variables, and calls the fmincon function to solve the multi-microgrid resource scheduling problem at the given price.

[0010] The electricity consumption rights and generation rights trading mechanism in the above scheme is specifically as follows: Step S1: The virtual operator sends signals of electricity consumption price and generation price to each microgrid; Step S2: Each microgrid, with the goal of minimizing operating costs, optimizes the amount of electricity consumption rights and generation rights transactions it submits to the virtual operator based on the price signal, and formulates an internal resource scheduling plan; Step S3: The virtual operator, aiming to maximize its overall revenue, updates the electricity consumption right price and the generation right price based on the transaction volume submitted by each microgrid. Step S4: Repeat steps S2 and S3 until both price and trading volume converge, obtaining the optimal price strategy and scheduling scheme.

[0011] In the above scheme, the electricity consumption right trading volume is the deviation between the actual electricity demand and the initial dispatch curve, and the power generation right trading volume is the deviation between the actual power generation output and the initial dispatch curve.

[0012] This invention constructs a two-layer architecture comprising a virtual operator (MVNO) decision-making layer and a microgrid optimization layer, and designs a power consumption and generation rights trading mechanism. While ensuring the operational autonomy of each microgrid, it introduces the MVNO as a coordination hub. This mechanism effectively aggregates system flexibility, guides multiple microgrids to collaboratively participate in main grid interaction, and achieves an organic unity of local scheduling autonomy and global system optimization, improving overall operational efficiency and renewable energy absorption capacity. The constructed upper-layer (price decision) and lower-layer (resource scheduling) game optimization model accurately simulates the strategic interaction behavior between the MVNO (aiming for profit maximization) and multiple microgrids (aiming for cost minimization). This model coordinates the interests of all parties through price signals, enabling the pricing strategies formulated by the MVNO in pursuing its own interests to automatically guide the microgrids to make decisions that conform to the overall system optimization goals, achieving incentive compatibility and ensuring the effectiveness and executability of the model solution. Addressing the complexity of solving the two-layer game model, this invention combines the advantages of the improved particle swarm optimization algorithm (strong global search capability) and the fmincon function (high accuracy in local optimization) to form an efficient hybrid solution strategy. This strategy can reliably and quickly obtain the optimal or satisfactory solution of the model, overcoming the shortcomings of traditional methods that are prone to getting trapped in local optima or have difficulty converging. It provides feasible technical support for real-time optimization scheduling of large-scale, multi-entity microgrid systems. Attached Figure Description

[0013] Figure 1 This is a flowchart of an optimized method for hybrid trading of electricity and warrants in microgrid groups according to the present invention; Figure 2 The diagram shows the scheduling curves for each microgrid in this invention. Figure 3 This is a diagram showing the optimized scheduling curve of microgrid 1 according to the present invention; Figure 4 This is a diagram showing the optimized scheduling curve of microgrid 2 according to the present invention; Figure 5 This is a diagram showing the optimized scheduling curve of microgrid 3 according to the present invention; Figure 6 This is a graph showing the change in deviation when the microgrid of the present invention does not cooperate; Figure 7 This is a schematic diagram illustrating the relationship between warrant prices and deviations during microgrid cooperation according to the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0015] The specific steps are as follows: Step 1: Establish a multi-microgrid system architecture, including a virtual operator decision-making layer and a microgrid optimization layer, and construct a trading mechanism for electricity consumption rights and generation rights; Step 2: Construct a two-layer game optimization model. The upper layer is a virtual operator pricing decision model, which optimizes the electricity right price and generation right price with the goal of maximizing comprehensive revenue. The lower layer is a multi-microgrid resource scheduling model, which optimizes the warrant trading volume and internal generation plan with the goal of minimizing operating costs. Step 3: Solve the two-layer game model using a hybrid solution strategy that combines the improved particle swarm optimization algorithm with the fmincon function; Step 4: Based on the improved Shapley value method, the revenue of virtual operators is fairly distributed to achieve surplus sharing in multi-microgrid cooperation.

[0016] For step 1, the core feature of the "distribution network-virtual operator-microgrid" three-layer interactive architecture lies in its hierarchical division of responsibilities and collaborative mechanism. As the upper layer of the system, the distribution network, based on regional power supply and demand, issues global dispatch curves and security constraints to the virtual operators, including the allowed upper limit of cluster deviation and deviation penalty coefficient. To meet the requirements of global supply and demand balance, the distribution network can issue dispatch curves to each microgrid, and also allows microgrids to aggregate together to meet the overall dispatch requirements of the distribution network for these microgrids. The distribution network settles electricity fees based on the electricity traded between each microgrid and the grid, forming an electricity trading system. Microgrids cannot directly trade electricity; they can only meet demands exceeding the dispatch curve through the transfer of electricity consumption rights or generation rights, forming a warrant trading system.

[0017] A Virtual Operator (MVNO) represents a group of microgrids that voluntarily unite together. It manages the trading rules of the microgrid group, sets prices for Electricity Rights Receivable (ECR) and Electricity Rights Receivable (EGR), and collects microgrid operational data, including electricity demand and generation supply. As an intermediary between the microgrid and the distribution network, the MVNO coordinates warrant trading among the microgrid groups. As a coordinator of interests among microgrids, the MVNO distributes all revenue generated from its coordination with the distribution network and individual microgrids to the participating microgrids, retaining no profit for itself.

[0018] Each microgrid, as an independent entity at the lower level, solves local optimization problems based on the warrant trading price signals released by the virtual operator, adjusts its own power generation and consumption strategies, and feeds back the power consumption and power generation rights to the virtual operator.

[0019] Electricity consumption rights (ECR) represent the right of a microgrid to obtain electricity from the main grid. The trading volume is the deviation between actual electricity demand and the initial dispatch curve. When the microgrid's electricity consumption exceeds the dispatch value, additional electricity consumption rights must be purchased; conversely, redundant electricity consumption rights can be sold. Generation rights (EGR) represent the right of a microgrid to inject electricity into the main grid. The trading volume is the deviation between actual generation output and the initial dispatch curve. When the microgrid's generation is lower than the dispatch value, generation rights must be purchased to fill the gap; conversely, excess generation rights can be sold.

[0020] In terms of the relationship between warrant trading and electricity trading, the two complement each other. Warrant trading mainly refers to the purchase and sale of electricity consumption and generation rights between microgrids, ensuring that each microgrid can obtain stable power resources within a specific future period; while electricity trading refers to the direct trading of actual electricity to meet immediate supply and demand. Through the guidance of virtual operators, microgrids can flexibly adjust their own trading strategies, locking in future electricity demand or selling generation rights in advance during warrant trading, while conducting real-time trading during electricity trading to optimize overall economic efficiency.

[0021] The electricity consumption rights (ECR) and generation rights (EGR) markets are centered on day-ahead dispatch, achieving the pre-allocation of electricity resources for the next day through two-way interaction between virtual operators (VOs) and microgrids. The process consists of three stages: price signal issuance, microgrid response feedback, and settlement and clearing. The VO selects the electricity consumption rights price and generation rights price at different times of the previous day as the initial price and issues them to the microgrid. Each microgrid optimizes its own electricity consumption rights and generation rights and feeds them back to the VO, forming a master-slave game. Settlement takes place after the game reaches equilibrium.

[0022] For step 2, the virtual operator, as the market leader, guides the market supply and demand balance by setting electricity consumption rights prices and generation rights prices. Its goal is to maximize overall revenue, which includes warrant trading revenue and cluster deviation penalty costs.

[0023] In the formula, T=24 indicates that the optimization period is one day, n is the number of microgrids, and the decision variable is the electricity right price. and electricity rights price . and Let represent the electricity consumption rights and generation rights trading volumes of microgrid i in time period t, respectively; This indicates the total deviation of the cluster's power consumption; As a penalty coefficient, considering the peak and off-peak electricity consumption of the distribution network, the penalty coefficient can be configured in three stages: peak, flat, and off-peak. Each microgrid, acting as a price taker, optimizes its warrant trading volume and operational strategies (electricity consumption or generation) based on the electricity prices published by the virtual operator to minimize costs.

[0024] In the formula, The total cost of microgrid i, This represents the cost of gas turbine power generation within microgrid i. This indicates the cost or benefit of buying and selling electricity rights in a microgrid. This indicates the cost or benefit of buying and selling power generation rights in a microgrid. This indicates the cost of electricity transactions in a microgrid. For electricity efficiency.

[0025] (1) Power generation cost of gas turbine The relationship between the power generation of a gas turbine and its power generation cost can usually be expressed as a quadratic function. Based on the operating characteristics of a micro gas turbine, its power generation cost can be expressed as:

[0026] In the formula: denoted by t, where t represents the power generation of the gas turbine in the microgrid; a, b, and c represent the cost coefficients of the gas turbine power generation.

[0027] (2) Costs or benefits of electricity use rights in microgrids

[0028] (3) Costs or benefits of microgrid generation rights

[0029] In the above formula, The price of electricity use rights, For the price of power generation rights, and These represent the electricity consumption rights and generation rights trading volumes of microgrid i during time period t, respectively. (4) Electricity trading costs Microgrid electricity trading cost represents the cost of purchasing electricity minus the revenue from selling electricity. Since a microgrid can only purchase or sell electricity to the distribution network within a given time period:

[0030] In the formula, Let t be the electricity purchase and sale price of microgrid i to the distribution network during time period t. Let i be the power purchased and sold by microgrid i to the distribution network during time period t. (5) Electricity consumption benefits In traditional microgrid optimization models, the electricity consumption benefit function typically considers only two dimensions: electricity procurement expenditure and electricity sales revenue. However, in real-world microgrid energy consumption scenarios, electricity, as a factor of production, has a significant value-added effect on microgrid economic activities. This value creation characteristic is particularly prominent in the industrial and commercial sectors, where electricity load and output scale show a significant positive correlation. To more accurately characterize the value transmission mechanism of microgrid energy consumption behavior, this study constructs an electricity consumption benefit model. Specifically, the electricity consumption benefit value function of microgrid i is defined as:

[0031] In the formula, Let be the electrical energy used by microgrid i during time period t; This is the initial power consumption curve for the microgrid; Let be the transaction volume of the electricity usage rights of microgrid i during time period t; The electricity consumption of the microgrid during time period t is equal to the sum of the initial electricity consumption and the amount of traded electricity warrants; For a microgrid i, the amount of electricity used in a day The benefits obtained; k is the electricity efficiency coefficient.

[0032] In step 3, during the solution process of the game model, the upper layer uses the particle swarm optimization (PSO) algorithm to handle the price optimization problem. This algorithm is suitable for continuous variables, nonlinear and nonconvex problems, and does not depend on gradient information.

[0033] The upper-layer virtual operator (MVNO) employs an improved particle swarm optimization algorithm. The upper-layer optimization objective is the MVNO's daily revenue. Each particle represents a combination of electricity and power generation prices over 24 hours, corresponding to a 48-dimensional search space. The particle's position vector corresponds to the chosen combination of electricity and power generation prices, while its velocity vector corresponds to the magnitude and direction of change in these prices during the optimization process. By iteratively optimizing the price combinations, the MVNO aims to maximize its overall revenue. In one ( In a 3D search space, there are ( ) particles form a group, the first The position of each particle is represented by a vector. Velocity is represented as a vector. .particle The best historical position is , representing the local optimal price combination of the particle swarm, and the global optimal position of the swarm. , representing the globally optimal price combination. In each iteration, the particle's position (the combination of electricity right price and power right price) and velocity (the amount and direction of change in electricity right price and power right price during optimization) are updated according to the following formula:

[0034] The inertia factor update formula is as follows: (This formula is used in conjunction with a linear differential decreasing inertia factor strategy.)

[0035] In the formula: t represents the current iteration number, This indicates the upper limit of the algorithm's iterations. This represents the upper limit of the inertia factor. This represents the lower limit of the inertia factor. The value of the inertia factor at the t-th iteration.

[0036] After the upper-level model optimizes and obtains the prices of electricity consumption rights and generation rights, the virtual operator distributes them to each microgrid. Based on these prices, the lower-level model uses the MATLAB optimization toolbox to solve the objective function, obtains the corresponding electricity consumption rights and generation rights, and feeds them back to the upper-level virtual operator, which then performs particle swarm optimization again.

[0037] The lower layer uses the nonlinear programming solver fmincon in MATLAB to solve the optimization model of each microgrid independently in order to obtain the optimal operating strategy under a given price.

[0038] Define convergence conditions: (1) Relative rate of change in prices:

[0039]

[0040]

[0041] In the formula, k For the number of iterations, Let $t$ be the price of electricity generation rights in the k-th iteration. The price of electricity generation rights in the (k-1)th iteration It is an infinitesimal quantity.

[0042] (2) Stability of trading volume (average value):

[0043]

[0044] In the formula, k is the number of iterations, n is the number of microgrids, and T is the optimization period. Let i be the volume of electricity consumption rights and generation rights warrants traded in the k-th iteration of microgrid i within time period t. Let i be the volume of electricity consumption rights and generation rights warrants traded in the k-th iteration of microgrid i within time period t. It is an infinitesimal quantity.

[0045] Combining the particle swarm optimization algorithm and the fmincon function, the following process is designed to solve the two-level game model of microgrid clusters: (1) Initialize parameters: Determine the parameters of the particle swarm algorithm, such as the number of particles, the maximum number of iterations, the inertia weight, the learning factor, etc.; initialize the position and velocity of the particles, the particle position is the initial price combination of the virtual operator's electricity use rights and power generation rights.

[0046] (2) Particle swarm iteration: For each particle, the price combination it represents is passed as input to the lower-level model.

[0047] (3) Model Solving: The fmincon function is used to solve the resource optimization model of the lower-level microgrid. Based on the electricity consumption rights and generation rights prices passed from the upper level, and with the goal of minimizing the total cost of the microgrid, the optimal electricity consumption rights trading volume, generation rights trading volume, and internal resource scheduling strategy (such as gas turbine power generation) of each microgrid are calculated under various constraints. These results are then fed back to the upper-level particle swarm optimization algorithm.

[0048] (4) Calculate the fitness of the upper layer: Based on the feedback from the lower layer, calculate the comprehensive revenue of the upper layer virtual operator and use it as the fitness value of the particle.

[0049] (5) Update particle state: Based on the update formula of particle swarm algorithm, update the velocity and position of the particles to obtain a new price combination.

[0050] (6) Determine the convergence condition: Check whether the convergence condition of the particle swarm optimization algorithm is met, such as reaching the maximum number of iterations or the fitness value changing very little. If it is met, stop the iteration and output the optimal price combination and the optimal resource scheduling strategy for each microgrid; otherwise, return to step 2 to continue the iteration.

[0051] Through the above process, the particle swarm optimization algorithm was used to optimize the pricing decision of virtual operators at the upper layer, and the fmincon function was used to solve the microgrid resource optimization allocation at the lower layer, effectively solving the problem of solving the two-layer game model of microgrid groups.

[0052] For step 4, in terms of revenue distribution, the Shapley value method is used to quantify the marginal contribution of each microgrid to the cooperative alliance, and the cooperative revenue is distributed fairly accordingly.

[0053] Regarding the revenue distribution mechanism, after a microgrid cluster forms a community of shared interests through coordinated scheduling, the fair and reasonable allocation of the cluster's total revenue is crucial to ensuring the stability of cooperation. Based on cooperative game theory, the Shapley value is used to evaluate contribution and thus distribute benefits.

[0054] For each microgrid, the deviations between microgrids are complementary, thus creating a need for cooperation and generating a cooperation surplus. The distribution network only charges a total penalty to the microgrid group, while each microgrid can cooperate with other microgrids according to its own needs. How to reasonably allocate the cooperation surplus is the goal of this paper's revenue distribution. Now, the revenue of the virtual operator is used as the cluster revenue for profit distribution.

[0055] Allocation targets must satisfy both individual and collective rationality. Individual rationality requires that the allocated revenue for each microgrid is no less than its independent operating revenue.

[0056] in Let i be the total cost when microgrid i is not cooperative (each microgrid operates independently). Costs incurred during cooperation (cooperative operation of microgrid groups under the virtual operator mechanism).

[0057] Collective rationality requires that the total benefit of the cluster be fully distributed:

[0058] In the formula For the benefits of microgrid i, This represents the total revenue of the cluster.

[0059] The formula for calculating the Shapley value assignment model is as follows:

[0060] in, This represents the revenue that microgrid i should receive; N is the set of all microgrids, and n is the number of microgrids; S is any subset that does not include microgrid i. This represents the alliance benefit of subset S; The overall contribution of microgrid i.

[0061] Based on the traditional Shapley value method for unit cost allocation, this paper optimizes and improves the traditional cost allocation coefficient by introducing positive coefficients based on the contribution of actual capacity output and the contribution of deviation scheduling improvement.

[0062] Define the overall contribution of microgrid i :

[0063] in: Reduce contribution, among which The total cost of microgrid i when it is not cooperative. Costs incurred during cooperation.

[0064] : Contribution to improving scheduling deviation, among which This represents the dispatch value issued by the distribution network. This indicates the trading volume of electricity consumption rights or power generation rights certificates. Ri: Actual power output contribution, calculated as the ratio of actual power generation to predicted power output. The weighting coefficients are determined by the entropy weighting method, and

[0065] The Shapley value calculates the marginal benefit of an individual joining the cluster, determines the contribution of each microgrid, and then fully distributes the cluster's benefits based on the contribution.

[0066] Case Analysis To verify the effectiveness of the proposed model and algorithm, a simulation system containing three microgrids was constructed: a residential microgrid, a commercial microgrid, and an industrial microgrid. The optimization runtime was one day. Each microgrid in the microgrid cluster was equipped with a small wind turbine, photovoltaic equipment, and a gas turbine. The parameters of each device, penalty coefficient, microgrid correlation coefficient, and time-of-use electricity price of the grid are set as shown in Tables 1-3.

[0067] Table 1 Penalty Coefficients

[0068] Table 2 Gas Turbine Parameters and Production Profit Coefficient

[0069] In Table 2, the production efficiency coefficients of 2000, 3000, and 4000 represent the benefits of using 1 additional kWh of electricity when the electricity consumption is 3000 kWh, respectively, which correspond to the microgrids in residential areas, commercial areas, and industrial areas.

[0070] Table 3 Time-of-use Electricity Prices

[0071] Dispatch curves issued by each microgrid are as follows: Figure 2 , 3 4, 5, the power consumption curve and power generation curve calculated through optimization are as follows: Figure 2 Under different dispatch requirements, the power consumption curve and power generation curve of each microgrid are different. For example, for microgrid 3, its dispatch curve is negative during the period from 10:00 to 15:00, which means that microgrid 3 is required to generate more power. In order to meet the dispatch requirements, microgrid 3 reduces its power consumption and increases the power generation of its gas turbine. Comparison of cooperative and non-cooperative operation of microgrid groups: Taking a microgrid cluster containing three microgrids as the research object, this study compares two scenarios: one without microgrid cooperation (each microgrid operates independently) and the other with microgrid cooperation (the microgrid cluster operates cooperatively under a virtual operator mechanism). In the non-cooperative scenario, the microgrid cluster deviation is 26309 kWh, with a total revenue of 78185 yuan; in the cooperative scenario, the microgrid cluster deviation decreases to 15201 kWh, and the total revenue increases to 82636 yuan (see Tables 4 and 5). When the microgrid cluster does not cooperate, differences in electricity demand among some microgrids lead to a significant increase in cluster deviation and penalties, resulting in reduced revenue; after cooperation, the collective deviation of the microgrid cluster decreases, and the revenue increases (see Tables 4 and 5). Figure 6 , Figure 7 The revenue of the virtual operator is distributed through Shapley values, which increases the revenue of each microgrid and reduces the cost of each microgrid. For example, the revenue of microgrid 1 increased from RMB 16,465 to RMB 17,596, an increase of about 6.8%; the revenue of microgrid 2 increased from RMB 25,063 to RMB 27,574, an increase of about 9.1%; and the revenue of microgrid 3 increased from RMB 34,565 to RMB 37,466, an increase of about 7.7%.

[0072] Table 4. Revenue and Deviation When Microgrids Do Not Cooperate

[0073] Table 5. Benefits and Deviations in Microgrid Cooperation

[0074] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0075] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0076] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0078] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A micro-grid group electricity-power warrant hybrid transaction optimization method, characterized in that, The method comprises the following steps: A multi-microgrid system architecture including a virtual operator decision layer and a microgrid optimization layer is established, and a power consumption right and power generation right transaction mechanism is constructed based on the architecture; A double-layer game optimization model is constructed, including an upper-layer virtual operator price decision model and a lower-layer multi-microgrid resource scheduling model; An improved particle swarm algorithm combined with a fmincon function is used as a hybrid solution strategy to solve the double-layer game optimization model; An improved Shapley value method is used to fairly distribute the virtual operator revenue among the multi-microgrids. 2.The microgrid swarm electric energy-put option hybrid trading optimization method according to claim 1, characterized in that, The upper-layer virtual operator price decision model optimizes the power consumption right price and the power generation right price with the goal of maximizing the comprehensive revenue; and the lower-layer multi-microgrid resource scheduling model optimizes the right certificate transaction volume with the goal of minimizing the operation cost. 3.The microgrid group electric energy-put option hybrid transaction optimization method according to claim 2, characterized in that, The microgrid adjusts its power consumption demand by purchasing or selling the power consumption right from the virtual operator, or adjusts its power generation plan by purchasing or selling the power generation right. 4.The microgrid group electric energy-put option hybrid transaction optimization method according to claim 2, characterized in that, The objective function of the upper-layer virtual operator price decision model is to maximize the comprehensive revenue, and the decision variables are the power consumption right price and the power generation right price. The objective function of the lower-layer multi-microgrid resource scheduling model is to minimize the operation cost of each microgrid, and the decision variables include the power consumption right transaction volume and the power generation right transaction volume.

5. The microgrid swarm electric energy-put option hybrid trading optimization method according to claim 4, characterized in that, The hybrid solution strategy uses the improved particle swarm algorithm to optimize the upper-layer price decision variables, and calls the fmincon function to solve the lower-layer multi-microgrid resource scheduling problem under a given price. 6.The microgrid swarm electric energy-put option hybrid trading optimization method according to claim 1, characterized in that, The power consumption right and power generation right transaction mechanism specifically comprises the following steps: Step S1: The virtual operator sends power consumption right price and power generation right price signals to each microgrid; Step S2: Each microgrid optimizes the power consumption right transaction volume and the power generation right transaction volume submitted to the virtual operator, and formulates an internal resource scheduling plan, with the goal of minimizing the operation cost according to the price signals; Step S3: The virtual operator updates the power consumption right price and the power generation right price according to the transaction volumes submitted by each microgrid, with the goal of maximizing the comprehensive revenue; Step S4: Steps S2 and S3 are repeatedly executed until the price and the transaction volume reach convergence, and the optimal price strategy and scheduling scheme are obtained.

7. The microgrid swarm electric energy-put option hybrid trading optimization method according to claim 6, characterized in that, The power consumption right transaction volume is the deviation between the actual power consumption demand and the initial scheduling curve, and when the actual power consumption demand is higher than the scheduling value, the power consumption right needs to be purchased, and vice versa; the power generation right transaction volume is the deviation between the actual power generation output and the initial scheduling curve, and when the actual power generation demand is higher than the scheduling value, the power generation right needs to be purchased, and vice versa. 8.The microgrid swarm electric energy-futures hybrid trading optimization method of claim 6, wherein, The objective function of the upper-layer virtual operator price decision model is: In the formula, T =24 indicates that the optimization period is one day. n For the number of microgrids, The price of electricity use rights, For the price of power generation rights, and They represent microgrids i During the period t The volume of electricity consumption rights and power generation rights transactions. This indicates the total deviation of the cluster's power. As a penalty coefficient, considering the peak and off-peak electricity consumption of the distribution network, the penalty coefficient can be configured in three stages: peak, flat, and valley. 9.The microgrid group electric energy-put option hybrid transaction optimization method of claim 8, wherein, The constraint conditions of the upper-layer virtual operator price decision model include a price fluctuation constraint and a market supply-demand balance constraint, The market supply-demand balance constraint in the same period is: In the formula, is t the electricity right price of the period, is t the power generation right price of the period, is the electricity right price of the period (i.e. the previous period), is the power generation right price of the period, is the maximum value of the change of the electricity right price of the adjacent period, is the maximum value of the change of the power generation right price of the adjacent period. The objective function of the lower-layer multi-microgrid optimization model is: wherein and respectively represent the microgrid i the electricity right trading volume of the microgrid t the electricity right trading volume of the microgrid represents the upper limit of the total electricity right trading volume of the microgrid represents the upper limit of the total electricity right trading volume of the microgrid 10.The microgrid swarm electric energy-futures hybrid trading optimization method of claim 1, wherein, a, b, c wherein, is the total cost of the micro-grid i, is the total cost of the micro-grid i is the cost of the internal combustion gas turbine power generation, is the total cost of the micro-grid in t is the power generation of the gas turbine at time t, The improved Shapley value method is represented by the following formula: is the cost coefficient of the gas turbine power generation; is the cost or benefit of the electricity right of the micro-grid i, is the cost or benefit of the power generation right of the micro-grid i, is the price of the electricity right, is the price of the power generation right, and respectively represent the electricity right and the power generation right transaction amount of the micro-grid i in the time period t ; is the electricity transaction cost of the micro-grid, is the electricity purchase and sale price of the micro-grid i to the power grid in t the time period, is the electricity purchase and sale power of the micro-grid i to the power grid in t the time period. is the electricity benefit; is the electricity energy used by the micro-grid i in t the time period; is the initial electricity curve of the micro-grid; is the electricity right transaction amount of the micro-grid i in t the time period; is the electricity amount of the micro-grid t time period, which is equal to the sum of the initial electricity amount and the electricity right transaction amount; is the benefit of the micro-grid i in a day by using the electricity energy ; k is the electricity benefit coefficient. 11.The microgrid swarm electric energy-futures hybrid trading optimization method of claim 1, wherein, ​ wherein, is the revenue that microgrid i should get; N is the set of all microgrids; S is any subset of microgrids i that does not contain microgrid i; is the coalition revenue of subset S; is the comprehensive contribution of microgrid i, n is the number of microgrids.

Images