Pricing and power strategy simulation system for distributed multi-microgrid distribution systems that takes into account flexibility resources

Abstract

The application aims to provide a distributed multi-microgrid power distribution system pricing and power strategy simulation system considering flexibility resources. Firstly, the operation models of the power distribution network and the microgrid are established respectively for the power distribution network and the microgrid belonging to different decision makers. According to the initial demand quantity reported by the user side flexibility resources, the consistency alternating direction multiplier method is used to solve the distribution network model and the microgrid model, and the microgrid injection power of the power distribution network access node is obtained. Secondly, the system optimization is carried out according to the node injection power, the dual variables of the active power node balance equation are solved, and the pricing strategy is established. The response model of the flexibility resources to the electricity price is established, and the new demand power is provided to the power distribution network according to the response model. Finally, the fixed point theorem is introduced to describe the dynamic game behavior between the flexibility resources and the power distribution network price, until the fixed point iteration convergence condition is met.

Description

Technical Field

[0001] This invention belongs to the field of optimized operation technology of multi-microgrid distribution systems, and specifically relates to a pricing and power strategy simulation system for distributed multi-microgrid distribution systems that takes into account flexibility resources. Background Technology

[0002] Currently, with the continuous development of new distribution networks, a large number of distributed power sources and microgrids are being connected to the generation side of the distribution system. While the development of microgrids can promote the local production and consumption of renewable energy, it will also lead to a bidirectional power flow distribution in the power grid, increasing the complexity of power grid operation. At the same time, considering that the distribution network and microgrid belong to different decision-makers and are highly sensitive to their own privacy information, how to coordinate the two to carry out reasonable resource allocation while preventing and avoiding risks such as voltage exceeding the limit of the power grid is an urgent problem to be solved by the distribution network.

[0003] On the other hand, user-side loads will be more proactive in the market environment. How to leverage the grid's ability to guide flexible resources to cope with changes in the power system's state and tap the potential of proactive responses from flexible resources is of great significance for improving system flexibility and promoting "source-load interaction". Summary of the Invention

[0004] To address real-world needs, this invention aims to provide a pricing and power strategy simulation system for distributed multi-microgrid distribution systems that takes into account flexible resources. This system simulates the dynamic game between the generation side and the user side. Through the construction and calculation of algorithm models, it obtains relatively optimal power injection and pricing schemes to achieve efficient resource allocation.

[0005] On the generation side, microgrids are considered as active participants coordinating the distribution network to set clearing prices for each node. To balance the interests of both sides, the ADMM algorithm is used to achieve distributed optimization of the distribution network and microgrids. On the distribution network user side, there are a large number of flexible resources. The fixed-point mapping theorem is introduced to describe the dynamic game process between the generation and user sides. Specifically, it is manifested as the closed-loop behavior of the node marginal price (the dual variable of the active power node balance equation of the distribution network) and the flexible resources, and this process has a unique game equilibrium solution.

[0006] The main feature of this system is that it adopts a two-layer fixed-point mapping algorithm to achieve the optimal allocation of flexibility resources between the power grid and the user side. By setting a reasonable sensitivity function for the flexibility resources, it obtains a unique stable fixed point using the compression mapping theorem.

[0007] The specific technical solution adopted by this invention to solve its technical problem is as follows:

[0008] A pricing and power strategy simulation system for distributed multi-microgrid distribution systems that takes into account flexibility resources, based on a computer system, characterized by comprising:

[0009] The objective function includes the distribution network and microgrid, as well as the operation model that considers the flexibility of distribution network user access resources, namely electric vehicles (EVs) and distributed energy storage (DES).

[0010] Injected power calculation module: It is used to solve distributed subproblems using the Consistent Alternating Directional Multiplier Method (ADMM) based on the given flexible resource demand power. When the ADMM convergence condition is met, the coupling variable value between the distribution network and the microgrid is obtained, that is, the injected power of the microgrid to the distribution network nodes.

[0011] Demand power calculation module: It is used to optimize the distribution network system based on the calculation results of the injected power calculation module, solve the dual variables of the active power node balance equation of the distribution network, and use them as the pricing scheme; and update the demand power of flexible resources based on the user-side flexible resource response model to the distribution network electricity price, provide the distribution network with new demand power, and update the injected power calculation module.

[0012] Iterative Judgment Module: This module is used to execute the iteration of the injected power calculation module and the demand power calculation module. It introduces the fixed point theorem to describe the dynamic non-cooperative game between flexible resources and distribution network electricity price. When the fixed point iteration termination condition is met, it outputs the nodal marginal electricity price and demand power of the game equilibrium solution.

[0013] Furthermore, each module was constructed using the gurobi solver on the Matlab platform.

[0014] Furthermore, the operational model includes:

[0015] Optimal power flow model of a second-order cone distribution network with multiple microgrids:

[0016] DN-OP:

[0017] Among them, the distribution network takes cost minimization as its objective function, and its expression is:

[0018]

[0019] The constraint set {CnsDN′} is as follows:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] In the formula, the variables include: Let be the active and reactive power injected into node j at time t. Let t be the power purchased by the distribution network from the upstream power grid. Let be the active and reactive power of line l at time t. Let be the square of the node voltage and line current amplitude at time t. Let be the charging and discharging power of the energy storage node at time t. Let be the EV charging power of the microgrid node at time t. Let t be the transmission power of the tie line between the distribution network and the microgrid at time t, with outflow from the distribution network being positive and inflow into the distribution network being negative;

[0028] The set includes: and These are the distribution network nodes and the line set, respectively. The node set consists of the slack node {0} and the remaining nodes. Composition, that is Π(j) represents the set of all line end nodes starting with node j. For the scheduling period, Δt is the time scale;

[0029] The parameters include: a j b j b0 represents the power generation cost coefficient, and b0 represents the electricity price sold by the upstream power grid. These are the resistance, reactance, and impedance of line l, respectively. For node reactive load, These are the upper and lower limits of the generator's active and reactive power, respectively. U represents the upper and lower limits of the node voltage amplitude, respectively. This is the upper limit of the line current amplitude. This refers to the upper limit of active and reactive power of the line. Active load of the node;

[0030] Optimal operation model of microgrid:

[0031] MG-OP:

[0032] The objective function expression for the microgrid is as follows:

[0033]

[0034] The constraint set {CnsMG'} is as follows:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] In the formula, the variables include: These represent the renewable energy output of the microgrid and the active power output of the gas turbine at time t, respectively. The controllable energy storage charging and discharging power of the microgrid at time t. The state of charge at time t represents the controllable energy storage. Let t be the transmission power of the tie line between the microgrid and the distribution network at time t, with positive values ​​for power flowing into the microgrid and negative values ​​for power flowing out of the microgrid;

[0043] The parameters include: Here, b0 represents the cost coefficient for gas turbine power generation, and b0 represents the electricity price sold to the upstream power grid. To control the cost coefficient of energy storage charging and discharging, Let be the predicted maximum daily output of the new energy source at time t. Let be the electrical load of the microgrid at time t. These are the upper and lower limits of the gas turbine's output power, respectively. η is the upper limit of the controllable energy storage charging and discharging power at time t; c η d These refer to the efficiency of controlled energy storage charging and discharging. These are the upper and lower limits of the controllable energy storage state of charge, respectively.

[0044] Given initial requirements for flexibility resources

[0045] Furthermore, the injected power calculation module specifically includes:

[0046] To achieve system partitioning, a set of variables is introduced to decouple and decompose the boundary regions, but the following consistency constraints must be satisfied:

[0047]

[0048]

[0049]

[0050] in, This is the upper limit of the maximum transmission power;

[0051] And perform the following steps:

[0052] Step S21: Given the initial iteration number r = 0 and the convergence tolerances ε1 and ε2, and set the Lagrange multipliers. and auxiliary variables

[0053] Step S22: Solve the distribution network subsystem optimization problem and the microgrid subsystem optimization problem respectively:

[0054] Distribution network issues: Micronetting problem:

[0055] And update the auxiliary variables:

[0056]

[0057] Step S23: Check whether the following convergence conditions for the original and dual residuals are met:

[0058]

[0059]

[0060] If the conditions are met, the iteration terminates and the optimization result is obtained; if the conditions are not met, proceed to step S24.

[0061] Step S24: Update the following Lagrange multipliers and return to step S22;

[0062]

[0063]

[0064] Furthermore, in the demand power calculation module: the nodal marginal price (LMP) is used as the price signal for adjusting user-side demand, which corresponds to the dual variable λ of the active power node balance equation. j,t ;

[0065] The mapping relationship between user-side EV / DES and electricity price is constructed as follows:

[0066] 1) Sensitivity function of electric vehicles (EVs) to electricity prices

[0067]

[0068] In the formula, For threshold electricity price, The maximum and minimum values ​​of the charging demand for EVs are given, and these values ​​are continuous nonlinear functions.

[0069] 2) Sensitivity function of distributed energy storage (DES) to electricity price

[0070]

[0071] In the formula, Indicates charging status. Indicates the discharge state. and This represents the maximum charging and discharging values ​​of distributed energy storage, from which we can know... There exists a zero point, and the corresponding price is called the critical price λ. 0,t The value is determined based on empirical data; above this electricity price, the node energy supply is relatively tight, and the DES is in a discharging state; below this electricity price, the node energy supply is relatively sufficient, and the DES is in a charging state.

[0072] EV and DES belong to the demand side of the distribution network. By constructing the above sensitivity function, we can reflect the demand adjustment decisions made by users in response to changes in electricity prices. The corresponding elasticity range is:

[0073] Furthermore, the iterative process of the iterative judgment module is as follows:

[0074] Step S41: Set the fixed-point iteration count k = 0 and the convergence tolerance ζ;

[0075] Step S42: Obtain the injected power from the microgrid to the distribution network node through the injected power calculation module. To optimize the distribution network system, calculate the dual variable λ of its active power node balance equation. j,t ;

[0076] Step S43: Calculate the demand based on the sensitivity function of flexible resources to electricity prices in the demand power calculation module. and Update;

[0077] Step S44: Determine if the following conditions are met:

[0078]

[0079] If satisfied, the iteration stops, and the equilibrium solution of the game, the electricity price and demand, are obtained. If not satisfied, let k = k + 1, and return to step S42.

[0080] Compared to existing technologies, this invention and its preferred solution promote bidirectional interaction between power sources and loads, alleviating power flow congestion in distribution networks. First, since distribution networks and microgrids belong to different decision-makers, separate operation models are established for the distribution network and microgrids. Based on the initial demand reported by user-side flexibility resources, a distributed solution is performed on the distribution network model and the microgrid model using the consistent alternating direction multiplier method to obtain the power injected by the microgrid to the distribution network access node. Second, the distribution network performs system optimization based on this injected power, solving for the dual variables of its active power node balance equations. This solution serves as a pricing strategy, establishing a response model of flexibility resources to electricity prices. Based on this response model, new demand power is provided to the distribution network. Finally, a fixed-point theorem is introduced to describe the dynamic game behavior between flexibility resources and distribution network electricity prices until the fixed-point iterative convergence condition is met. This scheme and the constructed system guide flexibility resources to adjust their electricity consumption patterns through price-based demand response, achieving a win-win situation for users and the power network through interactive iterative methods at both inner and outer layers. Attached Figure Description

[0081] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0082] Figure 1 This is a flowchart illustrating the solution process of the two-layer fixed-point mapping algorithm in an embodiment of the present invention.

[0083] Figure 2 This is a structural diagram of an improved 33-node power distribution system according to an embodiment of the present invention.

[0084] Figure 3 This is a graph showing the fixed-point iteration error in an embodiment of the present invention.

[0085] Figure 4 This is a diagram showing the convergence result of the ADMM iteration in an embodiment of the present invention.

[0086] Figure 5 The function graphs for different price-sensitive intervals are considered for embodiments of the present invention.

[0087] Figure 6 The diagram illustrates the convergence under different price-sensitive intervals in this embodiment of the invention.

[0088] Figure 7 This is a flowchart illustrating the system construction and algorithm solution of an embodiment of the present invention. Detailed Implementation

[0089] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0090] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0091] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0092] like Figure 7 As shown, the construction and operation process of the system solution of the present invention is first described, including the following steps:

[0093] Step S1: Since the distribution network and microgrid belong to different decision-makers, establish the objective function and operation model of the distribution network and microgrid respectively. Consider the flexible access resources (electric vehicles EV and distributed energy storage DES) on the user side of the distribution network, and complete the system optimization given the initial demand power.

[0094] Step S2: Based on the given power demand for flexible resources, the distributed subproblem is solved using the Alternating Directional Multiplier Method (ADMM). When the convergence condition of ADMM is met, the coupling variable value between the distribution network and the microgrid is obtained, that is, the power injected by the microgrid into the distribution network nodes.

[0095] Step S3: The distribution network performs system optimization based on the injected power of the node, solves the dual variables of the active power node balance equation of the distribution network, uses them as the pricing scheme, establishes the response model of user-side flexible resources to the distribution network electricity price, updates the power demand of flexible resources based on the response model, and provides the distribution network with new power demand.

[0096] Step S4: Introduce the fixed point theorem to describe the dynamic non-cooperative game between flexible resources and distribution network electricity price. When the fixed point iteration termination condition is met, the nodal marginal electricity price and demand power of the game equilibrium solution are obtained.

[0097] Step S5: Use the gurobi solver on the Matlab platform to solve the above steps, obtain the optimal demand for user-side flexibility resources, and at the same time alleviate the power flow congestion problem in the distribution network and reduce the operating cost of the distribution network.

[0098] In this embodiment, the specific content of establishing the objective function and operating model of the distribution network and microgrid in step S1 is as follows:

[0099] 1) The optimal power flow model of a distribution network with multiple microgrids, consisting of a second-order cone (SOC), can be expressed as follows: DN-OP:

[0100] Among them, the distribution network takes cost minimization as its objective function, and its expression is:

[0101]

[0102] The constraint set {CnsDN′} is as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] In the formula, ① includes the following variables: Let be the active and reactive power injected into node j at time t. Let t be the power purchased by the distribution network from the upstream power grid. Let be the square of the node voltage and line current amplitude at time t. Let be the charging and discharging power of the energy storage node at time t. Let be the EV charging power of the microgrid node at time t. Let t be the transmission power of the tie line between the distribution network and the microgrid, with outflow from the distribution network being positive and inflow into the distribution network being negative; ② The set includes: and These are the distribution network nodes and the line set, respectively. The node set consists of the slack node {0} and the remaining nodes. Composition, that is Π(j) represents the set of all line end nodes starting with node j. For the scheduling period, Δt is the time scale; in this paper, Δt = 1 hour is taken. ③ The parameters include: a j b j b0 is the power generation cost coefficient. These are the resistance, reactance, and impedance of line l, respectively. For node reactive load, These are the upper and lower limits of the generator's active and reactive power, respectively. U represents the upper and lower limits of the node voltage amplitude, respectively. This is the upper limit of the line current amplitude. This refers to the upper limit of active and reactive power of the line. This refers to the active load of the node.

[0111] 2) Optimized operation model of microgrids

[0112] MG-OP:

[0113] The objective function expression for the microgrid is as follows:

[0114]

[0115] The constraint set {CnsMG'} is as follows:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] In the formula, ① includes the following variables: These represent the renewable energy output of the microgrid and the active power output of the gas turbine at time t, respectively. The controllable energy storage charging and discharging power of the microgrid at time t. The state of charge at time t represents the controllable energy storage. ① The transmission power of the tie line between the microgrid and the distribution network at time t, with positive values ​​for power flowing into the microgrid and negative values ​​for power flowing out of the microgrid; ② Parameters include: This is the cost coefficient for gas turbine power generation. To control the cost coefficient of energy storage charging and discharging, Let be the predicted maximum daily output of the new energy source at time t. Let be the electrical load of the microgrid at time t. These are the upper and lower limits of the gas turbine's output power, respectively. η represents the upper limit of the controllable energy storage charging and discharging power at time t. c η dThese refer to the efficiency of controlled energy storage charging and discharging. These are the upper and lower limits of the state of charge of controllable energy storage, respectively.

[0124] Given initial requirements for flexibility resources

[0125] In this example, step S2 specifically includes the following steps:

[0126] First, to achieve system partitioning, a set of variables is introduced to decouple and decompose the boundary regions, but the following consistency constraints must be satisfied:

[0127]

[0128]

[0129]

[0130] in, This represents the maximum transmission power limit.

[0131] Step S21: Given the initial iteration number r = 0 and the convergence tolerances ε1 and ε2, and set the Lagrange multipliers. and auxiliary variables

[0132] Step S22: Solve the distribution network subsystem optimization problem and the microgrid subsystem optimization problem respectively:

[0133] Distribution network issues: Micronetting problem: And update the auxiliary variables:

[0134]

[0135] Step S23: Check whether the following convergence conditions for the original and dual residuals are met:

[0136]

[0137]

[0138] If the conditions are met, the iteration terminates and the optimized result is obtained; if not, proceed to step S24.

[0139] Step S24: Update the following Lagrange multipliers and return to step S22.

[0140]

[0141]

[0142] In this example, the specific content of establishing the response model of flexible resources to distribution network electricity prices in step S3 is as follows:

[0143] First, the locational marginal price (LMP) is used as the price signal for adjusting user-side demand, and its corresponding dual variable λ in the active power node balance equation is... j,t .

[0144] Drawing on consumer choice theory in economics, the demand for all goods and services decreases as prices rise. To simulate users' price sensitivity, the mapping relationship between user-side EV / DES and electricity prices is constructed as follows:

[0145] 1) Sensitivity function of electric vehicles (EVs) to electricity prices

[0146]

[0147] In the formula, For threshold electricity price, The maximum and minimum values ​​of the charging demand for EVs are given, and these values ​​are continuous nonlinear functions.

[0148] 2) Sensitivity function of distributed energy storage (DES) to electricity price

[0149]

[0150] In the formula, Indicates charging status. Indicates the discharge state. and This represents the maximum charging and discharging values ​​of distributed energy storage, from which we can know... There exists a zero point, and the corresponding price is called the critical price λ. 0,t This can be determined based on empirical data. Above this electricity price, node energy supply is relatively tight, and the DES is in a discharging state; below this price, node energy supply is relatively sufficient, and the DES is in a charging state.

[0151] In this example, step S4 specifically includes the following steps:

[0152] Step S41: Set the fixed-point iteration number k = 0 and the convergence allowable error ζ.

[0153] Step S42: Proceed to step 2 to obtain the microgrid's injected power to the distribution network nodes. To optimize the distribution network system, calculate the dual variable λ of its active power node balance equation. j,t .

[0154] Step S43: Based on step 3, update the demand according to the sensitivity function of flexible resources to electricity prices. and

[0155]

[0156] Step S44: Determine if the following conditions are met:

[0157]

[0158] If satisfied, the iteration stops, and the equilibrium solution of the game, the electricity price and demand, are obtained. If not satisfied, let k = k + 1, and return to step S42.

[0159] Preferably, in this example, the above steps are based on a two-layer fixed-point mapping algorithm, and the algorithm's solution flowchart is as follows: Figure 1 The inner layer employs ADMM distributed optimization, combining the distribution network and microgrids. The distribution network sets a clearing price (LMP) based on the power traded with the microgrids. This price serves as an intermediate variable for the outer layer's fixed-point algorithm. The fixed-point algorithm primarily represents a dynamic game between electricity price and flexible resources; users adjust their demand based on the price, and conversely, their demand also affects the price, forming a directed closed-loop adjustment. The compressibility mapping theorem is used to prove that a unique game equilibrium solution exists for this process.

[0160] The above embodiments employ a two-layer fixed-point mapping algorithm to simulate a scenario where efficient cooperative operation between the distribution network and the microgrid is achieved under the premise of limited information exchange. On the other hand, the marginal electricity price of the distribution network nodes can be formulated based on the power injected by the microgrid into the distribution network nodes, thereby guiding the user-side flexibility resource adjustment needs. The fixed-point theorem is introduced to describe this dynamic game process, and it is proven that a unique game equilibrium solution exists.

[0161] It should be noted that fixed-point iteration requires certain conditions to reach a convergent and unique game equilibrium solution. First, we define... The node marginal electricity price is obtained from the flexibility resource requirements in step S2, and this process is defined as mapping Φ(·):

[0162] The required power of flexible resources is obtained from the electricity price in step S3, and this process is defined as mapping Θ(·): The two mappings mentioned above actually constitute arrive Self-mapping, i.e. Ultimately, this can be described as a fixed-point problem: According to the compression mapping theorem, when the following conditions are met... This fixed-point algorithm is convergent and unique.

[0163] According to the definition of marginal nodal price, it is equivalent to the increase in the marginal cost of the power source caused by the increase in unit load at that node. When all the increase in node load is provided by the power source with the maximum marginal cost, the nodal marginal price reaches its maximum value, then:

[0164]

[0165] Among them, a max =max{a j} and b max =max{b j Let} be the maximum economic cost coefficient among all generators in the system, then:

[0166]

[0167] In summary, the prerequisite for fixed-point iteration to converge and be unique is:

[0168]

[0169] From this formula, we can see that the elastic modulus It will affect the convergence of the algorithm.

[0170] This example combines distributed optimization methods and the fixed point theorem. First, the ADMM distributed algorithm, as an inner algorithm, does not change the convex optimization properties and the feasible region set, so it does not affect the problem's shrinkability. By setting a reasonable sensitivity function, the fixed point converges and becomes unique.

[0171] This example uses marginal node pricing as a pricing strategy to accurately reflect the scarcity of electricity at each node in the system. At the same time, it introduces distributed energy storage as a flexible resource, which, compared with demand response, can participate in peak shaving and valley filling of the power grid, reduce the peak-valley difference in electricity prices, and reduce the operating costs of the distribution network.

[0172] Preferably, this embodiment employs a distributed algorithm to achieve cooperative operation between the distribution network and the microgrid while protecting the privacy information of each participating entity.

[0173] Preferably, this embodiment draws on consumer choice theory and constructs a sensitivity function to electricity prices to simulate users' game-theoretic behavior in real life.

[0174] Preferably, in this embodiment, the simulation system is built and test case simulation is performed in the MATLAB environment, the model is solved using the gurobi software package, and the convergence, uniqueness and convergence of the game equilibrium solution are verified.

[0175] The two-layer fixed-point mapping algorithm in this embodiment takes the minimum sum of operating costs of the distribution network and the microgrid as the objective function. The distribution network includes constraints on the balance of active and reactive power flow at nodes, constraints on the relationship between node voltage and branch power flow and current, constraints on the amplitude of line current and node voltage, constraints on line power limitation, and constraints on the active and reactive power output of generators.

[0176] Microgrids include active power balance constraints, maximum output limits for new energy sources, output power limits for controllable gas turbines, charge and discharge power limits for controllable energy storage, and state of charge constraints for controllable energy storage.

[0177] Implementation Simulation Case Study

[0178] The system design is applied to an improved IEEE-33 node system for verification, based on a specific example provided below, for an improved test system such as... Figure 2 As shown, nodes 10, 16, 19, 23, and 31 are connected to 5 distributed generation sources respectively; nodes 18, 22, 25, and 33 are connected to 4 microgrids, of which MG1 and MG3 are configured with photovoltaic units, and MG2 and MG4 are configured with wind turbine units; DES is connected to node 28. Node 1 is the common connection point (i.e., the slack node) between the distribution network and the upstream grid, with a voltage amplitude of 1.02 pu, and the allowable voltage deviation of the node is set to ±8%.

[0179] The maximum transmission power of the tie lines between the four microgrids and the distribution network is 1.5MW. The sensitivity function for the EV is set. It is 1.2MW. The value is 0.2MW, with a price elasticity range of [100, 200] yuan / MWh. Set the sensitivity function for DES. For a capacity of 1MW, the price elasticity range is [200, 300] yuan / MWh, corresponding to a critical electricity price of 250 yuan / MWh, and the convergence tolerances ε1, ε2, and ζ are 10. -3 All Lagrange multipliers and auxiliary variables are initialized to 0, and the penalty factor ρ = 50. Initial iteration requirement. The remaining parameters are shown in Table 1.

[0180] Table 1. Relevant parameters for the example.

[0181]

[0182] In this embodiment, Figure 3 and Figure 4 Iteration curves for the outer fixed-point algorithm and the inner distributed algorithm are given respectively, clearly showing that both have good convergence characteristics. Among them, Figure 3Fixed-point iteration can reduce the convergence error to a low order of magnitude with fewer iterations. By the 7th iteration, the iteration error has approached 0, indicating convergence. This curve verifies the existence, uniqueness, and convergence proven earlier, and demonstrates that it can quickly reach an equilibrium state.

[0183] Figure 4 As shown in the iterative curve of the distribution network operating cost in (a), it initially increases with each iteration, then begins to decrease after the 7th generation until it stabilizes. Furthermore, Figure 4 (b) Similarly, the objective function of each microgrid first increases and then dynamically adjusts until it no longer changes. This phenomenon reflects that when the proposed distributed algorithm is used for computation, the relaxed coupling constraints of each subproblem will be gradually satisfied with the algorithm iteration, thus causing the objective function value of each subproblem to have a dynamic change process of first increasing and then adjusting.

[0184] When EV users have high charging demands, their price tolerance should be stronger. To verify the impact of different price sensitivity ranges on the convergence of the fixed-point algorithm, three sensitivity functions for EV users were developed. Figure 5 To optimize convergence performance across different price-sensitive ranges, a fixed upper limit of 175 yuan / Wh was set for the elastic range, reflecting the price tolerance of users with varying needs. By setting different price-sensitive functions for all EV users accessing the microgrid nodes, the convergence results are as follows: Figure 6 As shown, it can be seen that the increase in the elastic range causes a change in the elastic coefficient. A decrease in the elastic modulus leads to faster convergence; conversely, a increase in the elastic modulus leads to a narrower elastic range and a decrease in the elastic modulus. As the elasticity increases, convergence slows down, and when the elasticity interval becomes small enough, it can even lead to non-convergence. Figure 6 The middle yellow line has the smallest elasticity range, and its demand is the largest when it finally converges, resulting in a higher marginal cost at the corresponding nodes. In real life, there are periods when electricity demand is high, and users will naturally adjust their sensitive price ranges to meet their needs as much as possible.

[0185] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0186] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0187] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0188] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0189] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0190] This patent is not limited to the above-described preferred embodiments. Anyone can derive other forms of pricing and power strategy simulation systems for distributed multi-microgrid power distribution systems that take into account flexible resources under the guidance of this patent. All equivalent changes and modifications made within the scope of the claims of this invention shall fall within the scope of this patent.

Claims

1. A pricing and power strategy simulation system for a distributed multi-microgrid distribution system that takes into account flexible resources, based on a computer system, characterized in that: include: The objective function includes the distribution network and microgrid, as well as the operation model that considers the flexibility of distribution network user access resources, namely electric vehicles (EVs) and distributed energy storage (DES). Injected power calculation module: It is used to solve distributed subproblems using the Consistent Alternating Directional Multiplier Method (ADMM) based on the given flexible resource demand power. When the ADMM convergence condition is met, the coupling variable value between the distribution network and the microgrid is obtained, that is, the injected power of the microgrid to the distribution network nodes. Demand power calculation module: It is used to optimize the distribution network system based on the calculation results of the injected power calculation module, solve the dual variables of the active power node balance equation of the distribution network, and use them as the pricing scheme; and update the demand power of flexible resources based on the user-side flexible resource response model to the distribution network electricity price, provide the distribution network with new demand power, and update the injected power calculation module. Iterative Judgment Module: Used to execute the iteration of the injected power calculation module and the demand power calculation module, and introduces the fixed point theorem to describe the dynamic non-cooperative game between flexible resources and distribution network electricity price. When the fixed point iteration termination condition is met, it outputs the nodal marginal electricity price and demand power of the game equilibrium solution. The operating model includes: Optimal power flow model of a second-order cone distribution network with multiple microgrids: DN-OP: Among them, the distribution network takes cost minimization as its objective function, and its expression is: Constraint Set Specifically as follows: In the formula, the variables include: , for The active and reactive power injected by the power source at time node j. for The power that the distribution network purchases from the upstream power grid at any given time. , for Timetable Active and reactive power, , for The square of the node voltage and the line current amplitude at any given time. for The charging and discharging power of the energy storage node at all times. for EV charging power of the Moment microgrid node for The transmission power of the tie line between the distribution network and the microgrid at any time is positive when flowing out of the distribution network and negative when flowing into the distribution network; The set includes: and These are the distribution network nodes and the line set, where the node set consists of the balancing node. and the remaining nodes Composition, that is ; This represents the set of all line terminators starting with node j. For the scheduling period, , Time scale; The parameters include: , , This is the power generation cost coefficient. The electricity price sold by the higher-level power grid. , , The lines are respectively Resistance, reactance and impedance, For node reactive load, , , , These are the upper and lower limits of the generator's active and reactive power, respectively. , These are the upper and lower limits of the node voltage amplitude, respectively. This is the upper limit of the line current amplitude. , This refers to the upper limit of active and reactive power of the line. Active load of the node; Optimal operation model of microgrid: MG-OP: The objective function expression for the microgrid is as follows: Constraint Set Specifically as follows: In the formula, the variables include: , microgrids in The active power output of renewable energy and gas turbines at any given time. , For microgrids in Controllable energy storage charging and discharging power at all times. For controllable energy storage State of charge at time t, for The transmission power of the tie line between the microgrid and the distribution network is positive when flowing into the microgrid and negative when flowing out of the microgrid. The parameters include: This is the cost coefficient for gas turbine power generation. , To control the cost coefficient of energy storage charging and discharging, For new energy in The predicted maximum output value for the day before the specified time. For microgrids in Electrical load at any given time , These are the upper and lower limits of the gas turbine's output power, respectively. , for The upper limit of the controllable charging and discharging power of energy storage at all times; , These refer to the efficiency of controlled energy storage charging and discharging. , These are the upper and lower limits of the controllable energy storage state of charge, respectively. Given initial requirements for flexibility resources , .

2. The pricing and power strategy simulation system for distributed multi-microgrid distribution systems considering flexibility resources according to claim 1, characterized in that: Each module was built using the gurobi solver on the Matlab platform.

3. The pricing and power strategy simulation system for distributed multi-microgrid distribution systems considering flexibility resources according to claim 1, characterized in that: The injection power calculation module specifically includes: To achieve system partitioning, a set of variables is introduced to decouple and decompose the boundary regions, but the following consistency constraints must be satisfied: in, This is the upper limit of the maximum transmission power; And perform the following steps: Step S21: Given the initial number of iterations and convergence tolerance , And set the Lagrange multipliers , and auxiliary variables ; Step S22: Solve the distribution network subsystem optimization problem and the microgrid subsystem optimization problem respectively: Distribution network issues: Micronetting problem: And update the auxiliary variables: Step S23: Check whether the following convergence conditions for the original and dual residuals are met: If the conditions are met, the iteration terminates and the optimization result is obtained; if the conditions are not met, proceed to step S24. Step S24: Update the following Lagrange multipliers and return to step S22; 。 4. The pricing and power strategy simulation system for a distributed multi-microgrid distribution system considering flexible resources according to claim 3, characterized in that: In the power demand calculation module, the nodal marginal price (LMP) is used as the price signal for adjusting user-side demand, and its corresponding active power nodal balance equation has dual variables. ; The mapping relationship between user-side EV / DES and electricity price is constructed as follows: 1) Sensitivity function of electric vehicles (EVs) to electricity prices In the formula, , For threshold electricity price, , The maximum and minimum values ​​of the charging demand for EVs are given, and these values ​​are continuous nonlinear functions. 2) Sensitivity function of distributed energy storage (DES) to electricity price In the formula, Indicates charging status. Indicates the discharge state. and This represents the maximum charging and discharging values ​​of distributed energy storage, from which we can know... There exists a zero point; the corresponding price is called the critical electricity price. The value is determined based on empirical data; above this electricity price, the node energy supply is relatively tight, and the DES is in a discharging state; below this electricity price, the node energy supply is relatively sufficient, and the DES is in a charging state. EV and DES belong to the demand side of the distribution network. By constructing the above sensitivity function, we can reflect the demand adjustment decisions made by users in response to changes in electricity prices. The corresponding elasticity range is: .

5. The pricing and power strategy simulation system for distributed multi-microgrid distribution systems considering flexibility resources according to claim 4, characterized in that: The iterative process of the iterative judgment module is as follows: Step S41: Set the number of fixed-point iterations and convergence tolerance ; Step S42: Obtain the injected power from the microgrid to the distribution network node through the injected power calculation module. To optimize the distribution network system, the dual variables of its active power node balance equations are calculated. ; Step S43: Calculate the demand based on the sensitivity function of flexible resources to electricity prices in the demand power calculation module. and Update; Step S44: Determine if the following conditions are met: If satisfied, the iteration stops, yielding the electricity price and demand for the game equilibrium solution. If not satisfied, let... Return to step S42.

Images