Flexible resource scheduling method and device based on master-slave game, terminal equipment and storage medium

Abstract

The invention discloses a flexibility resource scheduling method and device based on a master-slave game, terminal equipment and a storage medium, and belongs to the technical field of flexibility resource scheduling, and the method comprises the steps: converting a game relationship between a power distribution network and an aggregation transformer area into a quantifiable optimization model, and carrying out the optimization of the optimization model under multiple constraints; the upper-layer model and the lower-layer model are solved at the same time through a master-slave game mechanism, the decision of the upper-layer model serves as the premise of lower-layer optimization, and lower-layer feedback affects the achievement of an upper-layer target, so that the targets of the two parties can realize dynamic balance in the game process, and a finally solved flexible resource scheduling strategy is enabled to be more flexible. The benefit of the power distribution network is maximized, and the total power utilization cost of the flexible resource aggregation transformer area is minimized, so that the utilization rate of the flexible resources is improved. By implementing the invention, the problem that the utilization rate of flexible resources is low due to the fact that benefit conflicts between the power distribution network and the aggregation transformer area cannot be coordinated in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible resource scheduling, and in particular to a flexible resource scheduling method, device, terminal equipment and storage medium based on master-slave game. Background Art

[0002] Flexible resource scheduling generally refers to the rational arrangement of resources such as electric vehicle clusters, energy storage devices, and flexible loads within the distribution network to optimize system operation. This scheduling can improve resource utilization efficiency, reduce operating costs, and enhance grid stability and reliability.

[0003] Traditional flexibility resource scheduling technologies typically fail to consider the interaction between the distribution network and the flexible resource aggregation substations. Instead, they often employ single-objective optimization approaches, such as optimizing scheduling solely based on the distribution network's revenue or the aggregated substation's electricity costs. This fails to establish a master-slave game mechanism to coordinate the interests of both parties. Consequently, because existing technologies fail to consider the game relationship between the distribution network and the flexible resource aggregation substations, conflicting objectives between the distribution network and the aggregated substations remain unresolved, leading to low flexibility resource utilization. Summary of the Invention

[0004] The embodiments of the present invention provide a flexibility resource scheduling method, apparatus, terminal device and storage medium based on master-slave game, which converts the game relationship between the distribution network and the aggregation substation into a quantifiable optimization model, and realizes the coordinated optimization of the goals of both parties under multiple constraints, so that the flexibility resource scheduling strategy can not only ensure the economic operation of the distribution network, but also reduce the electricity cost of the aggregation substation, thereby improving the utilization rate of flexibility resources. It can effectively solve the problem that the existing technology does not consider the game relationship between the distribution network and the flexibility resource aggregation substation, resulting in conflicts of interest between the distribution network and the aggregation substation that cannot be coordinated and the low utilization rate of flexibility resources.

[0005] An embodiment of the present invention provides a flexible resource scheduling method based on a master-slave game, comprising:

[0006] Based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network power generation cost data, and flexibility resource operation data of the flexibility resource aggregation area, an upper-level model with the goal of maximizing the distribution network profit is constructed; wherein the flexibility resource aggregation area includes: electric vehicle clusters, energy storage devices, and flexible loads; the flexibility resource operation data includes: pricing strategy data of the flexibility resource aggregation area, output data of the flexibility resource aggregation area, and demand response incentive price data; the first constraints corresponding to the upper-level model are: pricing constraints, distribution network operation constraints, and node power balance constraints;

[0007] Based on the flexible resource operation data, a lower-level model is constructed with the goal of minimizing the total electricity cost of the flexible resource aggregation area; wherein the constraints of the lower-level model are: flexible resource output constraints;

[0008] Under the constraints of the first constraint condition and the flexible resource output constraint condition, a master-slave game is performed on the upper model and the lower model to solve a flexible resource scheduling strategy; wherein the flexible resource scheduling strategy includes: a first charging power of the electric vehicle cluster, a second charging power of the energy storage device, a target discharge power of the energy storage device, a target reducible load power of the flexible load, and a target transferable load power of the flexible load;

[0009] According to the flexible resource scheduling strategy, the output of the resource aggregation area is scheduled.

[0010] Preferably, the construction of an upper-level model with the goal of maximizing the benefits of the distribution network based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network power generation cost data, and flexibility resource operation data of the flexibility resource aggregation area includes:

[0011] Based on the distribution network topology data, distribution network operation data, distribution network electricity price data, and distribution network power generation cost data, an initial model is constructed with the goal of minimizing the distribution network operation cost and used to calculate the node marginal electricity price of the distribution network; wherein the second constraint conditions corresponding to the initial model are: distribution network operation constraints and node power balance constraints; the distribution network operation constraints include: grid equipment operation constraints, node voltage constraints, and line current constraints; the node power balance constraints include: node active power balance constraints and node reactive power balance constraints;

[0012] The initial model is solved under the constraint of the second constraint. When the operation cost of the distribution network is minimized, the node marginal electricity price of the distribution network is determined according to the Lagrange multiplier corresponding to the node active power balance constraint.

[0013] Based on the node marginal electricity prices of the distribution network and the operation data of flexibility resources, an upper-level model is constructed with the goal of maximizing the benefits of the distribution network.

[0014] Preferably, the pricing strategy data of the flexible resource aggregation area includes: charging price, discharging service fee, load price and demand response incentive price; the output data of the flexible resource aggregation area includes: charging power of electric vehicle cluster, charging power of energy storage, discharging power of energy storage, power consumption of flexible load after demand response and curtailable load power of flexible load;

[0015] The upper-level model is constructed based on the node marginal electricity price of the distribution network and the operation data of the flexibility resources, with the goal of maximizing the distribution network revenue, including:

[0016] Construct an electric vehicle pricing revenue function based on the node marginal electricity price of the distribution network, the charging price, and the charging power of the electric vehicle cluster;

[0017] Construct an energy storage pricing revenue function based on the node marginal electricity price of the distribution network, charging price, discharge service fee, energy storage charging power, and energy storage discharge power;

[0018] According to the node marginal electricity price of the distribution network, the load price and the power consumption of the flexible load after demand response, a flexible load pricing profit function is constructed;

[0019] Construct a target pricing revenue function based on the electric vehicle pricing revenue function, energy storage pricing revenue function, and flexible load pricing revenue function;

[0020] Constructing a demand response incentive cost function based on the demand response incentive price, the discharge power of the energy storage, and the curtailable load power of the flexible load;

[0021] An upper-level model is constructed according to the target pricing benefit function and the demand response incentive cost function.

[0022] Preferably, the construction of a lower-level model based on the flexible resource operation data with the goal of minimizing the total electricity cost of the resource aggregation area includes:

[0023] Construct an electric vehicle electricity cost function based on the charging price and the charging power of the electric vehicle cluster;

[0024] Construct an energy storage charging and discharging cost function based on the charging price, discharging service fee, demand response incentive price, energy storage charging power, and energy storage discharging power;

[0025] A load cost function is constructed based on the load price, the demand response incentive price, the power consumption of the flexible load after the demand response, and the curtailable load power of the flexible load;

[0026] A lower-level model is constructed based on the electric vehicle electricity cost function, the energy storage charging and discharging cost function, and the load cost function.

[0027] Preferably, the flexibility resource output constraint conditions include: electric vehicle cluster charging power constraint, electric vehicle cluster power constraint, energy storage charging and discharging power constraint, energy storage power constraint, curtailable load constraint, and transferable load constraint;

[0028] The performing of a master-slave game on the upper-layer model and the lower-layer model to solve a flexible resource scheduling strategy includes:

[0029] The functional form of the lower-level model is used as the objective function term, and the electric vehicle cluster charging power constraint, electric vehicle cluster power constraint, energy storage charging and discharging power constraint, energy storage power constraint, curtailable load constraint, and transferable load constraint are coupled with each Lagrangian multiplier to construct a Lagrangian function; wherein the Lagrangian multiplier is used to represent the marginal penalty of the constraint or the factor used to represent the establishment of the mandatory constraint;

[0030] Derivatives are taken for the Lagrangian function to extract a number of target constraints that satisfy preset constraints, wherein the preset constraints include: marginal balance between the target function and the constraints, mutually exclusive relationships between the inequality constraints and the Lagrangian multipliers corresponding to the inequality constraints, or non-negative Lagrangian multipliers corresponding to the inequality constraints;

[0031] Through dual transformation, the Lagrangian multiplier in the Lagrangian function is used as a new decision variable in the upper-level model, and the objective function term in the Lagrangian function is converted into a linear term of the upper-level model to construct a single-layer model; wherein the decision variables of the single-layer model include: charging price, discharge service fee, load price, demand response incentive price, charging power of electric vehicle cluster, charging power of energy storage device, discharge power of energy storage device, curtailable load power of flexible load, transferable load power of flexible load, and various Lagrangian multipliers;

[0032] Under the first constraint condition, the flexibility resource output constraint condition and the target constraint condition, the single-layer model is solved to output a flexibility resource scheduling strategy.

[0033] Preferably, the upper model includes:

[0034] max∑ t∈T {C n,P -C n,R};

[0035]

[0036] Among them, max∑ t∈T {C n,P -C n,R} represents the functional form of the upper model, C n,P represents the target pricing profit function, C n,R represents the demand response incentive cost function, T represents the preset time period, N is the total number of flexible resource aggregation areas, They represent the electric vehicle pricing revenue function, energy storage pricing revenue function, and flexible load pricing revenue function of the nth flexibility resource aggregation area respectively; represents the charging price, discharging service fee, and load price of the nth flexible resource aggregation area at time t; is the node marginal electricity price at time t of the node where the nth flexibility resource aggregation area is located;

[0037] is the charging power of the electric vehicle cluster in the nth flexible resource aggregation area at time t, They represent the charging power and discharging power of the energy storage in the nth flexible resource aggregation area at time t respectively; represents the power consumption of the flexible load in the nth flexible resource aggregation area after demand response at time t; represents the demand response incentive price for the nth flexibility resource aggregation area by the distribution network operator at time t; It represents the load power that can be reduced in the nth flexible resource aggregation area at time t.

[0038] Preferably, the lower layer model includes:

[0039]

[0040]

[0041]

[0042]

[0043] in, is the functional form of the lower model, is the electricity cost function of electric vehicles, is the energy storage charging and discharging cost function, is the load cost function.

[0044] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0045] An embodiment of the present invention provides a flexible resource scheduling device based on master-slave game, comprising: an upper layer model construction module, a lower layer model construction module, a model solving module, and a flexible resource scheduling module;

[0046] The upper-layer model construction module is used to construct an upper-layer model with the goal of maximizing the distribution network profit based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network power generation cost data, and flexibility resource operation data of the flexibility resource aggregation area; wherein the flexibility resource aggregation area includes: electric vehicle clusters, energy storage devices, and flexible loads; the flexibility resource operation data includes: pricing strategy data of the flexibility resource aggregation area, output data of the flexibility resource aggregation area, and demand response incentive price data; the first constraint conditions corresponding to the upper-layer model are: pricing constraints, distribution network operation constraints, and node power balance constraints;

[0047] The lower-layer model construction module is used to construct a lower-layer model with the goal of minimizing the total electricity cost of the flexible resource aggregation area based on the flexible resource operation data; wherein the constraint conditions of the lower-layer model are: flexible resource output constraint conditions;

[0048] The model solving module is used to perform a master-slave game on the upper model and the lower model under the constraints of the first constraint condition and the flexible resource output constraint condition to solve the flexible resource scheduling strategy; wherein the flexible resource scheduling strategy includes: the first charging power of the electric vehicle cluster, the second charging power of the energy storage device, the target discharge power of the energy storage device, the target curtailable load power of the flexible load, and the target transferable load power of the flexible load;

[0049] The flexibility resource scheduling module is used to schedule the output of the resource aggregation area according to the flexibility resource scheduling strategy.

[0050] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0051] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a flexible resource scheduling method based on master-slave game as described in the above-mentioned embodiment of the invention.

[0052] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0053] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a flexible resource scheduling method based on master-slave game as described in the above-mentioned embodiment of the invention.

[0054] The following beneficial effects are achieved by implementing the present invention:

[0055] The embodiment of the present invention provides a flexibility resource scheduling method, apparatus, terminal device and storage medium based on master-slave game. The upper model of the present invention aims to maximize the benefit of the distribution network and integrates data such as topology, electricity price and power generation cost; the lower model aims to minimize the electricity cost of the aggregated substation and can be modeled based on the flexibility resource operation data. Under the constraints of the first constraint and the flexibility resource output constraint, the upper model and the lower model can be solved simultaneously through the master-slave game mechanism. The decision of the upper model serves as the premise of the lower optimization, and the lower feedback affects the achievement of the upper goal, so that the goals of both parties can be dynamically balanced during the game process, so that the flexibility resource scheduling strategy finally solved satisfies both the maximization of the distribution network benefit and the minimization of the total electricity cost of the flexibility resource aggregated substation, thereby improving the utilization rate of flexibility resources. Compared with the existing technology, the present invention can transform the game relationship between the distribution network and the aggregated substation into a quantifiable optimization model, and realize the coordinated optimization of the goals of both parties under multiple constraints, so that the flexible resource scheduling strategy can not only ensure the economic operation of the distribution network, but also reduce the electricity cost of the aggregated substation. Moreover, the feasibility and efficiency of the scheduling strategy are guaranteed through multiple constraints, solving the problems of target conflict and low utilization in traditional technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flowchart of a flexible resource scheduling method based on master-slave game provided by one embodiment of the present invention.

[0057] Figure 2 It is a schematic diagram of the master-slave game solution process of a two-layer model provided by one embodiment of the present invention.

[0058] Figure 3 It is a structural diagram of a flexible resource scheduling device based on master-slave game provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] like Figure 1As shown, in order to solve the problem that the existing technology does not consider the game relationship between the distribution network and the flexible resource aggregation area, resulting in the conflict of interests between the distribution network and the aggregation area being unable to be coordinated and the low utilization rate of the flexibility resources, an embodiment of the present invention provides a flexibility resource scheduling method based on a master-slave game, including:

[0061] Step S1: Based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network generation cost data, and flexibility resource operation data of the flexibility resource aggregation area, an upper-level model is constructed with the goal of maximizing the distribution network benefit;

[0062] The flexible resource aggregation area includes: electric vehicle clusters, energy storage devices and flexible loads;

[0063] The flexibility resource operation data includes: pricing strategy data of the flexibility resource aggregation area, output data of the flexibility resource aggregation area, and demand response incentive price data;

[0064] The first constraints corresponding to the upper model are: pricing constraints, distribution network operation constraints, and node power balance constraints;

[0065] Schematically, the upper-level model, which aims to maximize the profit of the distribution network, actually maximizes the overall profit by optimizing the pricing strategy and operation mode of the distribution network.

[0066] By collecting distribution network topology data, we can understand the connection relationships between grid nodes (such as transformer and line layout). Distribution network operation data, generally including real-time voltage, current, load distribution, etc., can be collected and analyzed to ensure the safe operation of the power grid.

[0067] Furthermore, by using the distribution network electricity price data and the distribution network power generation cost data, we can combine the momentary fluctuations in electricity prices (such as peak and valley electricity prices) and power generation costs (such as coal-fired and renewable energy power generation costs) to calculate the difference between power supply revenue and costs.

[0068] The pricing strategy of the aggregated substations in the flexibility resource operation data, output data (such as energy storage charging and discharging status) and demand response incentive prices can be used as a reference for upper-level model decision-making.

[0069] Furthermore, pricing constraints can ensure that electricity prices are set within a reasonable range, preventing excessive price fluctuations from impacting user responses. Distribution network operation constraints, such as voltage deviation limits and line capacity limits, can prevent overvoltage and line overloads. Node power balance constraints ensure that the power input (e.g., power generation, energy storage discharge) of any node equals the output (e.g., load consumption, energy storage charging), ensuring grid power balance.

[0070] In the upper-level model, the distribution network can act as the main party and formulate electricity price strategies and demand response incentives based on the above data and constraints to guide the aggregated substations to adjust resource usage to maximize their own benefits. For example, it can reduce electricity purchase costs through peak-valley electricity price differences, or obtain additional benefits by incentivizing users to shave peak loads and fill valley loads.

[0071] Step S2: Based on the flexible resource operation data, construct a lower-level model with the goal of minimizing the total electricity cost of the flexible resource aggregation area; wherein the constraint conditions of the lower-level model are: flexible resource output constraint conditions;

[0072] Illustratively, the lower-level model of the present invention can optimize the operation mode of flexibility resources and reduce electricity costs under a given pricing strategy of the distribution network.

[0073] The output constraints of flexible resources can include the output constraints of electric vehicle clusters, the output constraints of energy storage devices, and the output constraints of flexible loads.

[0074] In principle, in the master-slave game, the aggregation substation acts as the slave, which adjusts the charging time of electric vehicles, the energy storage charging and discharging strategy, and the use of flexible loads according to the electricity price and incentive signal of the distribution network to minimize electricity costs. For example, it charges when electricity prices are low and discharges energy or reduces loads during peak hours.

[0075] Step S3: Under the constraints of the first constraint condition and the flexible resource output constraint condition, the upper model and the lower model are subjected to a master-slave game to solve a flexible resource scheduling strategy; wherein the flexible resource scheduling strategy includes: a first charging power of the electric vehicle cluster, a second charging power of the energy storage device, a target discharge power of the energy storage device, a target curtailable load power of the flexible load, and a target transferable load power of the flexible load;

[0076] Schematically, the master-slave game mechanism of the present invention means that the upper layer (distribution network) first formulates pricing strategies and constraints, and the lower layer (aggregation substation) optimizes its own strategy based on this; the lower layer's strategy is fed back to the upper layer, affecting the achievement of the upper layer's goals, and both parties iteratively optimize under constraints until a balance is reached.

[0077] In principle, the scheduling strategy can be used to determine the optimal charging power of the electric vehicle cluster at each time to avoid overloading the power grid due to centralized charging. It can also determine the charging power (storing energy at off-peak times) and discharging power (releasing energy at peak times) of the energy storage device, and the power at which the load can be reduced (such as shutting down some non-essential equipment) and the time when the load can be transferred.

[0078] For example, the distribution network increases electricity prices and provides discharge incentives during peak hours. The energy storage device responds to this signal and increases discharge power. At the same time, the flexible load shifts electricity consumption time, which not only reduces the electricity cost of the substation, but also alleviates the pressure on the power grid, achieving coordinated optimization of the goals of both parties.

[0079] Step S4: Scheduling the output of the resource aggregation substation according to the flexible resource scheduling strategy.

[0080] In schematic form, according to the flexibility resource scheduling strategy solved in step S3, the power of the electric vehicle charging pile, the charging and discharging status of the energy storage inverter, and the switching / regulation instructions of the flexible load can be controlled, thereby realizing the refined scheduling of flexibility resources to maximize the distribution network revenue and minimize the total electricity cost of the flexibility resource aggregation area.

[0081] Regarding step S1, in a preferred embodiment, constructing an upper-level model with the goal of maximizing distribution network benefits based on distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network generation cost data, and flexibility resource operation data of flexibility resource aggregation substations includes:

[0082] Based on the distribution network topology data, distribution network operation data, distribution network electricity price data, and distribution network power generation cost data, an initial model is constructed with the goal of minimizing the distribution network operation cost and used to calculate the node marginal electricity price of the distribution network; wherein the second constraint conditions corresponding to the initial model are: distribution network operation constraints and node power balance constraints; the distribution network operation constraints include: grid equipment operation constraints, node voltage constraints, and line current constraints; the node power balance constraints include: node active power balance constraints and node reactive power balance constraints;

[0083] The initial model is solved under the constraint of the second constraint. When the operation cost of the distribution network is minimized, the node marginal electricity price of the distribution network is determined according to the Lagrange multiplier corresponding to the node active power balance constraint.

[0084] Based on the node marginal electricity prices of the distribution network and the operation data of flexibility resources, an upper-level model is constructed with the goal of maximizing the benefits of the distribution network.

[0085] It can be understood that the present invention can first construct an initial model with the goal of minimizing the operating cost of the distribution network and used to calculate the node marginal electricity price of the distribution network, thereby obtaining the node marginal electricity price of the distribution network, using it as a benchmark, and then constructing the upper-level model.

[0086] Schematically, in the embodiment of the present invention, the node marginal electricity price of the distribution network refers to the marginal electricity prices corresponding to multiple nodes, such as the distribution network node electricity price of the node where the flexibility resource aggregation area is located. So that subsequent And the pricing benefits C of each flexible resource aggregation area n,P and demand response incentive cost C n,R , and construct an upper-level model with the goal of maximizing the benefits of the distribution network.

[0087] Furthermore, the pricing strategy data of the flexible resource aggregation area includes: charging price, discharge service fee, load price and demand response incentive price; the output data of the flexible resource aggregation area includes: charging power of electric vehicle clusters, charging power of energy storage, discharge power of energy storage, power consumption of flexible loads after demand response, and curtailable load power of flexible loads;

[0088] Then, based on the node marginal electricity price of the distribution network and the operation data of the flexibility resources, an upper-level model is constructed with the goal of maximizing the distribution network revenue, specifically including:

[0089] Construct an electric vehicle pricing revenue function based on the node marginal electricity price of the distribution network, the charging price, and the charging power of the electric vehicle cluster;

[0090] Construct an energy storage pricing revenue function based on the node marginal electricity price of the distribution network, charging price, discharge service fee, energy storage charging power, and energy storage discharge power;

[0091] According to the node marginal electricity price of the distribution network, the load price and the power consumption of the flexible load after demand response, a flexible load pricing profit function is constructed;

[0092] Construct a target pricing revenue function based on the electric vehicle pricing revenue function, energy storage pricing revenue function, and flexible load pricing revenue function;

[0093] Constructing a demand response incentive cost function based on the demand response incentive price, the discharge power of the energy storage, and the curtailable load power of the flexible load;

[0094] An upper-level model is constructed according to the target pricing benefit function and the demand response incentive cost function.

[0095] In a preferred embodiment, the initial model is a second-order cone optimal power flow model of the distribution network, which comprehensively considers the day-ahead spot market electricity transaction cost, photovoltaic power generation cost and micro diesel unit cost, and uses the Lagrange multiplier constrained by node active power balance. The marginal electricity price of the distribution network node is obtained, and its model can be expressed as follows:

[0096]

[0097] Where, represents the electricity transaction price in the spot market at time t; represents the power purchased by the distribution network at time t; represents the coal cost coefficient; represents the active power output of the g-th micro fuel generator set at time t; OM GEN / OM PV Indicates the operation and maintenance cost coefficient of micro fuel unit / photovoltaic; is the active power output of the pth photovoltaic unit at time t; G refers to the number of micro fuel units, and P refers to the number of photovoltaic units.

[0098] The distribution network operation constraint and node power balance constraint in the second constraint condition of the initial model are actually second-order cone optimal power flow constraints, which are expressed as follows:

[0099]

[0100]

[0101] Where, ψ N / ψ L represents the bus / line set of the distribution network; π(·) represents the line subnode; ‖·‖2 represents the two-norm; P ij,t / Q ij,t represents the active / reactive power of line ij at time t; r ij / x ij represents the resistance / reactance of line ij; represents the active / reactive power injected into node j at time t, mainly including the output of the micro fuel unit and photovoltaic at the node; represents the active / reactive load of node j at time t; π(j) is the set of line sub-nodes, representing the set of all downstream sub-nodes k connected to node j, then P jk,t is the active power of the downstream node k connected to node j at time t; Q jk,t is the reactive power of the downstream node k connected to node j at time t; is the node marginal electricity price of node j at time t;

[0102] ν i,t / l ij,t represents the square of the voltage of node i at time t and the square of the current of line ij; v j,t represents the square of the voltage at node j at time t;

[0103] Indicates the upper / lower limit of the node voltage; Indicates the upper limit of line current; Indicates the upper limit of active output and ramp rate of the g-th micro fuel generator set; It represents the reactive power / apparent power of the g-th micro fuel-fired unit at time t; It represents the reactive power / apparent power of the p-th photovoltaic unit at time t.

[0104] In a preferred embodiment, the present invention can use the polygonal inner approximation method to perform linearization processing on the quadratic nonlinear term involved in the micro fuel unit capacity constraint in the second-order cone optimal power flow. The constraint can be converted as follows:

[0105]

[0106] Where, Indicates The coordinates of a circle with radius φ obtained by cutting it inside the dodecagon.

[0107] Through the synergistic effect of the above parameters, the second-order cone optimal power flow model can solve the problem of reflecting the spatiotemporal value of electricity. Provide a scientific benchmark for the master-slave game pricing strategy of distribution network operators.

[0108] It is understandable that It reflects the increase in system operating cost when the unit active power demand at node j increases, for example:

[0109] The marginal cost of electricity production (e.g. fuel cost for oil-fired units);

[0110] The marginal cost of network losses (power loss due to line resistance);

[0111] The marginal cost of network congestion (transmission limitations due to node location).

[0112] The embodiment of the present invention can obtain the node of each flexibility resource aggregation area based on the marginal cost of each node. As a pricing benchmark in the electricity market, it reflects the spatiotemporal value of electricity at different nodes and times, and provides a basis for user-side demand response incentives.

[0113] Furthermore, after obtaining the node marginal electricity price of the distribution network, the upper-level model shown below can be constructed:

[0114]

[0115] Among them, max∑ t∈T {C n,P -C n,R} represents the functional form of the upper model, C n,P represents the target pricing profit function, C n,R represents the demand response incentive cost function, T represents the preset time period, N is the total number of flexible resource aggregation areas, They represent the electric vehicle pricing revenue function, energy storage pricing revenue function, and flexible load pricing revenue function of the nth flexibility resource aggregation area respectively; represents the charging price, discharging service fee, and load price of the nth flexible resource aggregation area at time t; is the node marginal electricity price at time t of the node where the nth flexibility resource aggregation area is located;

[0116] is the charging power of the electric vehicle cluster in the nth flexible resource aggregation area at time t, They represent the charging power and discharging power of the energy storage in the nth flexible resource aggregation area at time t respectively; represents the power consumption of the flexible load in the nth flexible resource aggregation area after demand response at time t; represents the demand response incentive price for the nth flexibility resource aggregation area by the distribution network operator at time t; It represents the load power that can be reduced in the nth flexible resource aggregation area at time t.

[0117] Furthermore, in the first constraint condition corresponding to the upper model, the pricing constraint is:

[0118]

[0119] Where: Indicates the pricing price / price adjustment coefficient for charging, discharging, and load in the nth flexible resource aggregation area, which is determined by the type and scale of electricity resources within each flexible resource aggregation area;

[0120] The electricity purchase price for the enterprise at time t; The pricing average value includes charging, discharging and basic load, so as to avoid excessive profit-seeking behavior in flexible resource aggregation areas and provide protection for users.

[0121] In a preferred embodiment, the marginal electricity price at the node where each flexibility resource aggregation area is located serves as the basis for the distribution network to formulate the user-side demand response incentive price, reflecting the importance of flexibility resources to the safe and economic operation of the power grid. The probability of load curtailment under the incentive price can be derived based on the principles of consumer psychology. Therefore, the correlation between the demand response incentive price and the node marginal electricity price of the distribution network can also be calculated according to the following formula:

[0122]

[0123]

[0124] Where α represents the excitation coefficient; ρ n,trepresents the probability of load reduction in the nth flexible resource aggregation area at time t; Indicates the upper / lower limit of the incentive electricity price; It represents the maximum response probability of load reduction in the nth flexible resource aggregation area.

[0125] It's understandable that demand response incentive prices are directly linked to the marginal electricity price at the node, allowing the intensity of the incentive to align with the real-time supply and demand tensions on the grid. For example, when the marginal electricity price at a node rises due to a load spike, the incentive price increases accordingly, encouraging users to reduce load and alleviate network congestion.

[0126] The node marginal electricity price of the node where the flexible resource aggregation area (such as electric vehicles, energy storage, and flexible loads) is located can be used as the basis for the distribution network to formulate user-side demand response incentive prices, thereby guiding users to adjust their electricity consumption behavior and achieve efficient resource scheduling.

[0127] Therefore, the initial model of the present invention takes the minimization of the distribution network operation cost as the goal, and can comprehensively consider the day-ahead spot market transaction costs, photovoltaic power generation costs, micro-diesel unit costs, etc., and ensure system safety through second-order cone constraints (such as grid equipment operation constraints and node voltage constraints), thereby providing a benchmark electricity price for economic operation for the upper-level model and avoiding the disconnection between pricing and actual costs.

[0128] The final upper-level model can maximize profits while taking into account user-side responses. The pricing benefit functions of electric vehicles, energy storage, and flexible loads are directly linked to node electricity prices. The higher the electricity price, the higher the profit the distribution network obtains from user-side charging / power consumption. Users are guided to reduce loads through incentive prices, which are dynamically adjusted based on node marginal electricity prices, ensuring the economy and effectiveness of the incentives.

[0129] Regarding step S2, in a preferred embodiment, the step of constructing a lower-level model based on the flexible resource operation data with the goal of minimizing the total electricity cost of the resource aggregation substation includes:

[0130] Construct an electric vehicle electricity cost function based on the charging price and the charging power of the electric vehicle cluster;

[0131] Construct an energy storage charging and discharging cost function based on the charging price, discharging service fee, demand response incentive price, energy storage charging power, and energy storage discharging power;

[0132] A load cost function is constructed based on the load price, the demand response incentive price, the power consumption of the flexible load after the demand response, and the curtailable load power of the flexible load;

[0133] A lower-level model is constructed based on the electric vehicle electricity cost function, the energy storage charging and discharging cost function, and the load cost function.

[0134] The lower layer model includes:

[0135]

[0136]

[0137]

[0138]

[0139] in, is the functional form of the lower model, is the electricity cost function of electric vehicles, is the energy storage charging and discharging cost function, is the load cost function.

[0140] Schematically, each flexible resource aggregation area can optimize its own electricity consumption behavior based on the pricing strategy, taking into account the internal electric vehicle electricity cost Energy storage charging and discharging costs and load costs Therefore, the electricity consumption behavior of adjustable resources within the resource aggregation area is optimized with the goal of minimizing the total electricity cost.

[0141] Among them, the present invention can construct an electric vehicle aggregation model, thereby modeling the electric vehicle cluster charging power constraints and electric vehicle cluster power constraints of the electric vehicle clusters in each resource aggregation area.

[0142] Specifically, considering the difference in time between a single electric vehicle entering and leaving the grid, the present invention can introduce an auxiliary variable Expanding the single-cell charging constraints to all times and building an aggregation model based on Minkowski addition to reduce the model dimension effectively solves the scalability problem faced by the single-layer master-slave game two-layer model. The following is the result:

[0143]

[0144]

[0145] Where, represents the time when the i-th electric vehicle goes off-grid or on-grid in the n-th flexibility resource aggregation area; represents the upper limit of charging power of the electric vehicle cluster in the nth flexible resource aggregation area at time t;

[0146] Indicates the upper limit of charging power for a single electric vehicle; Indicates the upper / lower limit of the power of the i-th electric vehicle in the n-th flexible resource aggregation area; It represents the upper / lower limit of the electric vehicle cluster power at time t in the nth flexible resource aggregation area; represents the amount of electricity consumed by the i-th electric vehicle on / off the grid in the n-th flexible resource aggregation area; Indicates the change in electricity consumption caused by electric vehicles connecting to and leaving the grid.

[0147] Schematically, the Minkowski addition algorithm aggregates the charging constraints of individual electric vehicles (such as grid connection / disconnection time and charging power) into cluster constraints, avoiding the need to model each electric vehicle individually and significantly reducing the model dimension. For example, auxiliary variables are used to expand the individual constraints to all time intervals, transforming the model variables from individual dimensions to cluster dimensions. This solves the dimensionality explosion problem of single-layer master-slave game models and improves the feasibility of solving large-scale systems.

[0148] The aggregated model parameters (such as the upper and lower limits of cluster charging power and power consumption) can be directly used for optimization calculations, reducing the number of iterations and solution time, and are suitable for collaborative optimization of multi-resource aggregation areas.

[0149] Furthermore, the energy state and charging and discharging constraints of the electric vehicle cluster are expressed as follows:

[0150]

[0151]

[0152]

[0153]

[0154] Where, It represents the upper limit of the charging power of the electric vehicle cluster and the power allocated by the distribution transformer in the nth flexible resource aggregation area at time t; It represents the electric vehicle cluster power consumption / off-grid expected power consumption at time t in the nth flexible resource aggregation station; represents the charging efficiency of electric vehicles; Δt represents the unit time length. This paper assumes that the electric vehicle charging load is supplied by a distribution transformer with a uniform fixed capacity. The upper limit of the total electric vehicle charging power at each flexible resource aggregation area depends on the proportion of charging piles at that node, and is therefore determined by the proportion of charging piles installed in the nth flexible resource aggregation area to the total number of charging piles.

[0155] In a preferred embodiment, the energy storage charging and discharging power constraint and the energy storage capacity constraint can be expressed as:

[0156]

[0157] Where, represents the charging / discharging power of the energy storage in the nth flexible resource aggregation area at time t; represents the amount of energy stored in the nth flexible resource aggregation area at time t; Indicates the upper limit of energy storage charging / discharging power in the nth flexible resource aggregation area; Indicates the upper / lower limit of energy storage capacity in the nth flexible resource aggregation area; Indicates the energy storage charge / discharge efficiency; Indicates the initial energy storage capacity of the nth flexible resource aggregation substation.

[0158] Furthermore, in the present invention, flexible loads are divided into curtailable loads and adjustable loads. The curtailable load constraints and the transferable load constraints can be expressed as:

[0159]

[0160] Where, Indicates that the upper limit of load can be reduced; represents the transferable load power of the flexible load resources in the nth flexible resource aggregation area at time t; Indicates the upper / lower limit of the transferable load.

[0161] In addition, the following equations are satisfied before and after flexible load adjustment:

[0162]

[0163] Where, It represents the initial power consumption of the load resources in the nth flexible resource aggregation area at time t.

[0164] In schematic form, through the constraints on reducible / transferable loads, the model supports the power grid to reduce unnecessary loads during peak load periods or transfer them to off-peak periods, and cooperates with electricity price signals to achieve peak shaving and valley filling, reducing the pressure on the power grid while reducing users' electricity costs.

[0165] For step S3, in a preferred embodiment, the flexibility resource output constraint conditions include: electric vehicle cluster charging power constraint, electric vehicle cluster power constraint, energy storage charging and discharging power constraint, energy storage power constraint, curtailable load constraint, and transferable load constraint;

[0166] The performing of a master-slave game on the upper-layer model and the lower-layer model to solve a flexible resource scheduling strategy includes:

[0167] The functional form of the lower-level model is used as the objective function term, and the electric vehicle cluster charging power constraint, electric vehicle cluster power constraint, energy storage charging and discharging power constraint, energy storage power constraint, curtailable load constraint, and transferable load constraint are coupled with each Lagrangian multiplier to construct a Lagrangian function; wherein the Lagrangian multiplier is used to represent the marginal penalty of the constraint or the factor used to represent the establishment of the mandatory constraint;

[0168] Derivatives are taken for the Lagrangian function to extract a number of target constraints that satisfy preset constraints, wherein the preset constraints include: marginal balance between the target function and the constraints, mutually exclusive relationships between the inequality constraints and the Lagrangian multipliers corresponding to the inequality constraints, or non-negative Lagrangian multipliers corresponding to the inequality constraints;

[0169] Through dual transformation, the Lagrangian multiplier in the Lagrangian function is used as a new decision variable in the upper-level model, and the objective function term in the Lagrangian function is converted into a linear term of the upper-level model to construct a single-layer model; wherein the decision variables of the single-layer model include: charging price, discharge service fee, load price, demand response incentive price, charging power of electric vehicle cluster, charging power of energy storage device, discharge power of energy storage device, curtailable load power of flexible load, transferable load power of flexible load, and various Lagrangian multipliers;

[0170] Under the first constraint condition, the flexibility resource output constraint condition and the target constraint condition, the single-layer model is solved to output a flexibility resource scheduling strategy.

[0171] Schematically, the preset constraints are KKT conditions, namely Karush–Kuhn–Tucker conditions. In the present invention, the KKT conditions are used to transform the two-layer model of the master-slave game into a single-layer mixed integer linear programming model. By extracting the optimality conditions of the lower-layer response model (such as zero gradient, complementary slackness, etc.), the model can be efficiently solved and global optimality can be guaranteed.

[0172] The present invention converts the optimality conditions (KKT conditions) of the lower-level model into upper-level constraints through Lagrangian functions, and directly constructs a single-level mixed integer linear programming (MILP) model, so that a commercial solver can be used directly for a one-time solution, thereby shortening the solution time.

[0173] Regarding the upper and lower models constructed by the present invention, the present invention transforms the lower response model into upper constraint conditions through the KKT (Karush-Kuhn-Tucker) condition and linearizes it accordingly using the optimal duality theory. The derivation and transformation process is as follows:

[0174] The first step is to construct the Lagrangian function of the lower-level response model, which can be divided into Lagrangian functions corresponding to electric vehicles, energy storage, and load resources respectively;

[0175] The Lagrangian function corresponding to the electric vehicle is:

[0176]

[0177] Where μ / σ represents the dual variable of the inequality / equality constraint, and the subscripts 1, 2, 3, 4, 5, etc. corresponding to μ / σ represent the marginal penalty or mandatory constraint establishment factor corresponding to the data item, such as μ 2,n,t The Lagrange multiplier corresponding to the upper limit constraint of charging power. The same applies to other order subscripts.

[0178] The Lagrangian function corresponding to energy storage is:

[0179]

[0180] Among them, μ / σ represents the dual variable of the inequality / equality constraint, and the order subscript corresponding to μ / σ represents the marginal penalty or mandatory constraint establishment factor corresponding to the item, such as μ 7,n,t Represents the marginal penalty of the upper limit of the energy storage charging power.

[0181] The Lagrangian function corresponding to the load resource is:

[0182]

[0183] Furthermore, the Lagrangian function is derived to extract the equality constraints and complementary relaxation conditions that satisfy the KKT conditions:

[0184] The KKT conditions corresponding to electric vehicles are:

[0185]

[0186] Among them, 0≤A⊥B≥0 in the complementary relaxation condition means that only one of A and B is strictly greater than 0, which can be linearized by the big M method and expressed as follows:

[0187] 0≤A≤Mb,0≤B≤M(1-b);#

[0188] Where M is a sufficiently large number and b is an introduced 0-1 variable. It should be noted that the value of M is usually slightly larger than the theoretical maximum value of the corresponding decision variable.

[0189] The KKT conditions corresponding to energy storage can be:

[0190]

[0191] Furthermore, the KKT condition corresponding to the load resource can be:

[0192]

[0193]

[0194] Furthermore, based on the strong duality theory, a single-layer mixed integer linear programming model is constructed, which is the final single-layer model, and its goal is also to maximize the benefits of the distribution network:

[0195]

[0196] It can be understood that the above μ and σ are different Lagrange multipliers (dual variables), corresponding to the marginal cost penalty of the constraint or the factor that characterizes the enforcement of the constraint.

[0197] After the above transformation, the master-slave game two-layer model constructed by the present invention can be transformed into a single-layer mixed integer linear programming model, which can be directly solved by commercial solvers while ensuring the global optimality of the pricing strategy.

[0198] It is understandable that in the upper model, the distribution network operator obtains the electricity price of the marginal node of the distribution network based on the day-ahead forecast information, and uses it as a reference for its pricing and incentive price formulation. The decision variables involved can include At the same time, under the guidance of the price signals of the upper multi-type pricing, the distribution network operator realizes the control of the internal flexibility resources of the smart substation, including flexible loads, energy storage and electric vehicles. Its decision variables include

[0199] Among them, electric vehicles, energy storage and transferable loads can shift the charging and electricity consumption periods to the pricing low period to reduce their electricity costs, while energy storage and curtailable loads optimize load distribution, alleviate network congestion and obtain profits by participating in demand response.

[0200] In the present invention, the two-layer model can solve the above-mentioned single-layer mixed integer linear programming model without the need for iteration to obtain the optimal day-ahead pricing result and the internal flexibility resource control and scheduling result of the smart substation, thereby achieving balanced optimization of the benefits and costs of the distribution network operators and the smart substation users, and achieving a mutually beneficial and win-win effect.

[0201] In a preferred embodiment, Figure 2 The schematic diagram of the master-slave game solution process of the two-layer model shown in the figure is as follows:

[0202] Step 1: Collect data on distribution network load, distributed generation (photovoltaic / micro-units), flexibility resources (electric vehicles / energy storage / flexible loads), and market electricity prices to provide basic input for the model.

[0203] Step 2: Using the second-order cone optimal power flow model, considering network constraints (voltage, current, unit output) and operating costs (power purchase and power generation costs), the node marginal price (DLMP) is solved to reflect the spatiotemporal value of electricity.

[0204] Step 3: For electric vehicles, energy storage, and flexible loads, use an aggregation model (such as Minkowski addition) to convert distributed resources into station-level constraints (power / power upper limit) to reduce model complexity.

[0205] Step 4: Construct a single-layer master-slave game model. The lower layer: Minimize resource costs. Use Lagrangian functions to couple constraints (power and energy limits). Extract the KKT conditions (zero gradient, complementary relaxation), linearize them, and embed them into the upper layer.

[0206] Upper layer: Lagrange multipliers are used as decision variables to integrate pricing strategies and resource responses to form a single-level mixed integer linear programming (MILP) model.

[0207] Step 5: Use a commercial solver to solve the single-layer MILP problem and output distribution network pricing (charging price, incentive price) and substation resource response strategy (charging and discharging power, load adjustment) to guide subsequent output scheduling.

[0208] According to the above steps, the present invention can realize the efficient solution of the master-slave game double-layer model to a single layer, and generate the optimal flexibility resource scheduling strategy.

[0209] For step S4, in a preferred embodiment, according to the flexibility resource scheduling strategy, the present invention can quickly respond to the scheduling strategy based on pricing guidance and instruction execution to balance the supply and demand of the power grid, thereby scheduling according to the output of the resource aggregation area.

[0210] The flexible resource scheduling strategy consists of two parts:

[0211] Upper layer (distribution network): sets prices for each substation (such as charging prices and incentive prices);

[0212] Lower layer (substation): How much electricity each resource should generate / use (such as electric vehicle charging power and energy storage discharge power).

[0213] Therefore, the embodiment of the present invention can decompose the flexibility resource scheduling strategy (such as "the charging power of electric vehicles in area A is 300kW, and the energy storage discharge is 100kW") into instructions that can be recognized by each device, and send instructions to the flexibility resources through the area intelligent control system (such as the charging pile management platform, energy storage EMS, and load controller). The resources automatically respond (such as reducing the power of the charging pile, switching the energy storage charging and discharging mode, and shutting down non-essential equipment on the load) to ensure that the actual output matches the strategy requirements.

[0214] Schematically, the present invention establishes an electric vehicle charging power aggregation model, which can reduce the dimension of the lower-level response model and the difficulty of KKT (Karrush-Kuhn-Tucker) conversion, and enhance the scalability of the master-slave game pricing technology solution in model solving.

[0215] Based on the second-order cone optimal power flow optimization model, the distribution network node electricity price is obtained as the pricing basis of the distribution network incentive mechanism, which can accurately incentivize flexible resources to participate in demand response, optimize load distribution, alleviate network congestion, and improve the safety and economy of distribution network operation.

[0216] The present invention can also transform the constructed double-layer model of master-slave game into a single-layer model, thereby achieving balanced optimization of benefits and costs between distribution network operators and smart substation users.

[0217] like Figure 3 As shown, based on the above-mentioned various embodiments of the flexible resource scheduling method based on the master-slave game, the present invention provides corresponding device embodiments;

[0218] An embodiment of the present invention provides a flexible resource scheduling device based on master-slave game, comprising: an upper layer model construction module, a lower layer model construction module, a model solving module, and a flexible resource scheduling module;

[0219] The upper-layer model construction module is used to construct an upper-layer model with the goal of maximizing the distribution network profit based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network power generation cost data, and flexibility resource operation data of the flexibility resource aggregation area; wherein the flexibility resource aggregation area includes: electric vehicle clusters, energy storage devices, and flexible loads; the flexibility resource operation data includes: pricing strategy data of the flexibility resource aggregation area, output data of the flexibility resource aggregation area, and demand response incentive price data; the first constraint conditions corresponding to the upper-layer model are: pricing constraints, distribution network operation constraints, and node power balance constraints;

[0220] The lower-layer model construction module is used to construct a lower-layer model with the goal of minimizing the total electricity cost of the flexible resource aggregation area based on the flexible resource operation data; wherein the constraint conditions of the lower-layer model are: flexible resource output constraint conditions;

[0221] The model solving module is used to perform a master-slave game on the upper model and the lower model under the constraints of the first constraint condition and the flexible resource output constraint condition to solve the flexible resource scheduling strategy; wherein the flexible resource scheduling strategy includes: the first charging power of the electric vehicle cluster, the second charging power of the energy storage device, the target discharge power of the energy storage device, the target curtailable load power of the flexible load, and the target transferable load power of the flexible load;

[0222] The flexibility resource scheduling module is used to schedule the output of the resource aggregation area according to the flexibility resource scheduling strategy.

[0223] It should be noted that the device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without paying any creative effort.

[0224] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0225] Based on the above-mentioned various embodiments of the flexible resource scheduling method based on master-slave game, the present invention provides corresponding embodiments of terminal equipment items.

[0226] An embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a flexible resource scheduling method based on master-slave game as described in any method embodiment of the present invention.

[0227] The terminal device may be a computing terminal device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0228] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0229] The memory can be used to store the computer program, and the processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0230] Based on the above-mentioned various embodiments of the flexible resource scheduling method based on master-slave game, the present invention provides corresponding storage medium item embodiments.

[0231] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a flexible resource scheduling method based on master-slave game as described in any method embodiment of the present invention.

[0232] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0233] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A flexible resource scheduling method based on master-slave game, characterized in that: include: Based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network power generation cost data, and flexibility resource operation data of the flexibility resource aggregation area, an upper-level model with the goal of maximizing the distribution network profit is constructed; wherein the flexibility resource aggregation area includes: electric vehicle clusters, energy storage devices, and flexible loads; the flexibility resource operation data includes: pricing strategy data of the flexibility resource aggregation area, output data of the flexibility resource aggregation area, and demand response incentive price data; the first constraints corresponding to the upper-level model are: pricing constraints, distribution network operation constraints, and node power balance constraints; Based on the flexible resource operation data, a lower-level model is constructed with the goal of minimizing the total electricity cost of the flexible resource aggregation area; wherein the constraints of the lower-level model are: flexible resource output constraints; Under the constraints of the first constraint condition and the flexible resource output constraint condition, a master-slave game is performed on the upper model and the lower model to solve a flexible resource scheduling strategy; wherein the flexible resource scheduling strategy includes: a first charging power of the electric vehicle cluster, a second charging power of the energy storage device, a target discharge power of the energy storage device, a target reducible load power of the flexible load, and a target transferable load power of the flexible load; According to the flexible resource scheduling strategy, the output of the resource aggregation area is scheduled.

2. A flexible resource scheduling method based on master-slave game as claimed in claim 1, characterized in that: The upper-level model aiming at maximizing the benefits of the distribution network is constructed based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network power generation cost data, and flexibility resource operation data of the flexibility resource aggregation area, including: Based on the distribution network topology data, distribution network operation data, distribution network electricity price data, and distribution network power generation cost data, an initial model is constructed with the goal of minimizing the distribution network operation cost and used to calculate the node marginal electricity price of the distribution network; wherein the second constraint conditions corresponding to the initial model are: distribution network operation constraints and node power balance constraints; the distribution network operation constraints include: grid equipment operation constraints, node voltage constraints, and line current constraints; the node power balance constraints include: node active power balance constraints and node reactive power balance constraints; The initial model is solved under the constraint of the second constraint. When the operation cost of the distribution network is minimized, the node marginal electricity price of the distribution network is determined according to the Lagrange multiplier corresponding to the node active power balance constraint. Based on the node marginal electricity prices of the distribution network and the operation data of flexibility resources, an upper-level model is constructed with the goal of maximizing the benefits of the distribution network.

3. The flexible resource scheduling method based on master-slave game according to claim 2, characterized in that: The pricing strategy data of the flexible resource aggregation area includes: charging price, discharge service fee, load price and demand response incentive price; the output data of the flexible resource aggregation area includes: charging power of electric vehicle clusters, charging power of energy storage, discharge power of energy storage, power consumption of flexible loads after demand response, and curtailable load power of flexible loads; The upper-level model is constructed based on the node marginal electricity price of the distribution network and the operation data of the flexibility resources, with the goal of maximizing the distribution network revenue, including: Construct an electric vehicle pricing revenue function based on the node marginal electricity price of the distribution network, the charging price, and the charging power of the electric vehicle cluster; Construct an energy storage pricing revenue function based on the node marginal electricity price of the distribution network, charging price, discharge service fee, energy storage charging power, and energy storage discharge power; According to the node marginal electricity price of the distribution network, the load price and the power consumption of the flexible load after demand response, a flexible load pricing profit function is constructed; Construct a target pricing revenue function based on the electric vehicle pricing revenue function, energy storage pricing revenue function, and flexible load pricing revenue function; Constructing a demand response incentive cost function based on the demand response incentive price, the discharge power of the energy storage, and the curtailable load power of the flexible load; An upper-level model is constructed according to the target pricing benefit function and the demand response incentive cost function.

4. A flexible resource scheduling method based on master-slave game as claimed in claim 3, characterized in that: The lower-level model based on the flexible resource operation data is constructed with the goal of minimizing the total electricity cost of the resource aggregation area, including: Construct an electric vehicle electricity cost function based on the charging price and the charging power of the electric vehicle cluster; Construct an energy storage charging and discharging cost function based on the charging price, discharging service fee, demand response incentive price, energy storage charging power, and energy storage discharging power; A load cost function is constructed based on the load price, the demand response incentive price, the power consumption of the flexible load after the demand response, and the curtailable load power of the flexible load; A lower-level model is constructed based on the electric vehicle electricity cost function, the energy storage charging and discharging cost function, and the load cost function.

5. The flexible resource scheduling method based on master-slave game according to claim 4, characterized in that: The flexibility resource output constraints include: electric vehicle cluster charging power constraints, electric vehicle cluster power constraints, energy storage charging and discharging power constraints, energy storage power constraints, curtailable load constraints, and transferable load constraints; The performing of a master-slave game on the upper-layer model and the lower-layer model to solve a flexible resource scheduling strategy includes: The functional form of the lower-level model is used as the objective function term, and the electric vehicle cluster charging power constraint, electric vehicle cluster power constraint, energy storage charging and discharging power constraint, energy storage power constraint, curtailable load constraint, and transferable load constraint are coupled with each Lagrangian multiplier to construct a Lagrangian function; wherein the Lagrangian multiplier is used to represent the marginal penalty of the constraint or the factor used to represent the establishment of the mandatory constraint; Derivatives are taken for the Lagrangian function to extract a number of target constraints that satisfy preset constraints, wherein the preset constraints include: marginal balance between the target function and the constraints, mutually exclusive relationships between the inequality constraints and the Lagrangian multipliers corresponding to the inequality constraints, or non-negative Lagrangian multipliers corresponding to the inequality constraints; Through dual transformation, the Lagrangian multiplier in the Lagrangian function is used as a new decision variable in the upper-level model, and the objective function term in the Lagrangian function is converted into a linear term of the upper-level model to construct a single-layer model; wherein the decision variables of the single-layer model include: charging price, discharge service fee, load price, demand response incentive price, charging power of electric vehicle cluster, charging power of energy storage device, discharge power of energy storage device, curtailable load power of flexible load, transferable load power of flexible load, and various Lagrangian multipliers; Under the first constraint condition, the flexibility resource output constraint condition and the target constraint condition, the single-layer model is solved to output a flexibility resource scheduling strategy.

6. A flexible resource scheduling method based on master-slave game as claimed in claim 5, characterized in that: The upper model includes: Among them, max∑ t∈T {C n,P -C n,R } represents the functional form of the upper model, C n,P represents the target pricing profit function, C n,R represents the demand response incentive cost function, T represents the preset time period, N is the total number of flexible resource aggregation areas, They represent the electric vehicle pricing revenue function, energy storage pricing revenue function, and flexible load pricing revenue function of the nth flexibility resource aggregation area respectively; represents the charging price, discharging service fee, and load price of the nth flexible resource aggregation area at time t; is the node marginal electricity price at time t of the node where the nth flexibility resource aggregation area is located; is the charging power of the electric vehicle cluster in the nth flexible resource aggregation area at time t, They represent the charging power and discharging power of the energy storage in the nth flexible resource aggregation area at time t respectively; represents the power consumption of the flexible load in the nth flexible resource aggregation area after demand response at time t; represents the demand response incentive price for the nth flexibility resource aggregation area by the distribution network operator at time t; It represents the load power that can be reduced in the nth flexible resource aggregation area at time t.

7. A flexible resource scheduling method based on master-slave game as claimed in claim 6, characterized in that: The lower layer model includes: in, is the functional form of the lower model, is the electricity cost function of electric vehicles, is the energy storage charging and discharging cost function, is the load cost function.

8. A flexible resource scheduling device based on master-slave game, characterized in that: include: Upper-level model building module, lower-level model building module, model solving module, and flexible resource scheduling module; The upper-layer model construction module is used to construct an upper-layer model with the goal of maximizing the distribution network profit based on the distribution network topology data, distribution network operation data, distribution network electricity price data, distribution network power generation cost data, and flexibility resource operation data of the flexibility resource aggregation area; wherein the flexibility resource aggregation area includes: electric vehicle clusters, energy storage devices, and flexible loads; the flexibility resource operation data includes: pricing strategy data of the flexibility resource aggregation area, output data of the flexibility resource aggregation area, and demand response incentive price data; the first constraint conditions corresponding to the upper-layer model are: pricing constraints, distribution network operation constraints, and node power balance constraints; The lower-layer model construction module is used to construct a lower-layer model with the goal of minimizing the total electricity cost of the flexible resource aggregation area based on the flexible resource operation data; wherein the constraint conditions of the lower-layer model are: flexible resource output constraint conditions; The model solving module is used to perform a master-slave game on the upper model and the lower model under the constraints of the first constraint condition and the flexible resource output constraint condition to solve the flexible resource scheduling strategy; wherein the flexible resource scheduling strategy includes: the first charging power of the electric vehicle cluster, the second charging power of the energy storage device, the target discharge power of the energy storage device, the target curtailable load power of the flexible load, and the target transferable load power of the flexible load; The flexibility resource scheduling module is used to schedule the output of the resource aggregation area according to the flexibility resource scheduling strategy.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method implements a flexible resource scheduling method based on a master-slave game as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a flexible resource scheduling method based on master-slave game as described in any one of claims 1 to 7.

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