Power distribution network source load storage resource scheduling method and device, electronic equipment and storage medium

Abstract

One or more embodiments of the invention provide a power distribution network source load storage resource scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring power data of an energy supply side, a power consumer side and an energy storage side in a power distribution network; constructing a source-load-storage multi-target resource scheduling model, and a target function and a constraint condition of the source-load-storage multi-target resource scheduling model, wherein the target function and the constraint condition cover sub-target functions and sub-constraint conditions of the energy supply side, the power consumer side and the energy storage side; the sub-objective function comprises the minimum network loss of the power distribution network, the minimum operation cost of the power distribution network, the maximum energy storage efficiency of the energy storage side and the minimum carbon emission of the energy supply side; the sub-constraint conditions comprise a node voltage constraint of the power distribution network, a distributed power supply output constraint of the energy supply side and an energy storage charging and discharging constraint of the energy storage side; and solving a global optimal solution of the source-load-storage multi-target resource scheduling model based on a quantum annealing algorithm to obtain a global resource scheduling scheme of the power distribution network.

Description

Technical Field

[0001] This disclosure relates to the field of power distribution network resource scheduling technology, and in particular to a method, apparatus, electronic device and storage medium for power distribution network source-load-storage resource scheduling. Background Technology

[0002] It should be noted that the above description of the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of the present invention and facilitating understanding by those skilled in the art. It should not be assumed that the above technical solutions are known to those skilled in the art simply because they have been described in the background section of this invention.

[0003] With the rapid popularization of new energy power generation technologies, the penetration rate of distributed power sources such as wind and solar energy in distribution networks continues to increase, driving the transformation of traditional unidirectional power flow distribution networks into new types of distribution networks with multi-source interaction and bidirectional power flow. As the core architecture of the new power system, the source-load-storage system's collaborative operation capability directly relates to the level of new energy absorption, grid operation stability, and energy utilization efficiency, becoming a key direction for the current transformation and development of the power industry. In scenarios with high new energy penetration, distribution networks face multiple challenges, including fluctuating power output, diverse load demands, and complex resource scheduling. Traditional operating models are no longer suitable for the dynamic balance requirements of the new power system, necessitating the construction of an efficient and collaborative scheduling system through technological innovation.

[0004] In related technologies, an integrated scheduling model is constructed to simulate the coordinated operation of energy storage in mitigating power output fluctuations and responding to flexible load demand, thereby achieving the goal of coordinated development of energy sources, loads, and storage. However, the modeling methods in these technologies suffer from insufficient accuracy, for example, focusing only on the power user side and failing to fully explore the potential of multi-party resource scheduling, resulting in low resource utilization efficiency. Summary of the Invention

[0005] In view of the above, the purpose of one or more embodiments of this disclosure is to provide a method, apparatus, electronic device and storage medium for scheduling power distribution network source-load-storage resources, so as to solve the problems mentioned in the background art.

[0006] Based on the above objectives, the first aspect of this disclosure provides a method for scheduling power generation, load, and storage resources in a distribution network, comprising: Acquire power data from the energy supply side, power user side, and energy storage side of the distribution network; A multi-objective resource scheduling model for energy supply, load, and storage is constructed. The objective function and constraints of the multi-objective resource scheduling model cover the sub-objective functions and sub-constraints of the energy supply side, the power user side, and the energy storage side. The sub-objective functions include minimizing the network loss of the distribution network, minimizing the operating cost of the distribution network, maximizing the energy storage efficiency of the energy storage side, and minimizing the carbon emissions of the energy supply side. The sub-constraints include the node voltage constraints of the distribution network, the distributed power output constraints of the energy supply side, and the energy storage charging and discharging constraints of the energy storage side. The global optimal solution of the source-load-storage multi-objective resource scheduling model is obtained by solving the quantum annealing algorithm, and the global resource scheduling scheme of the distribution network is obtained.

[0007] Optionally, the global optimal solution of the source-load-storage multi-objective resource scheduling model is obtained by solving the quantum annealing algorithm, resulting in a global resource scheduling scheme for the distribution network, including: A feasible solution to the objective function is randomly generated. The feasible solution includes the output of each distributed power source on the energy supply side, the voltage of each node on the energy supply side and the power user side, and the continuous variable values ​​of the charging and discharging power on the energy storage side. Based on the quantum annealing algorithm, the feasible solution is calculated iteratively until the preset iteration termination condition is met; The k-th iteration calculation process of the feasible solution includes: Based on the preset number of segments, the continuous variable values ​​of the output of each distributed power source on the energy supply side, the voltage of each node on the energy supply side and the power user side, and the charging and discharging power of the energy storage side are discretized to obtain the quantum bit code of the current feasible solution. The probability amplitude of the qubit is obtained based on the dynamic penalty factor, the objective function value corresponding to the qubit code, and the constraint violation degree of the objective function value. Based on the probability amplitude of the qubit, a quantum superposition state is obtained by performing a quantum rotation gate perturbation operation; Project the quantum superposition state as a feasible solution for this round.

[0008] Optionally, in response to the failure to meet the preset iteration switching condition, the k-th iteration process of the feasible solution further includes: The feasible solution is globally explored and optimized using the particle swarm optimization algorithm. The speedup constant of the particle swarm optimization algorithm decreases as the number of iterations increases, in order to adapt to the needs of local optimization.

[0009] Optionally, in response to satisfying a preset iteration switching condition, the k-th iteration process of the feasible solution further includes: The feasible solution is locally explored and optimized using the gradient descent algorithm.

[0010] Optionally, after randomly generating feasible solutions, the following steps are also included: Determine the priority of the node voltage constraints of the distribution network, the output constraints of the distributed power sources on the energy supply side, and the energy storage charging and discharging constraints on the energy storage side under the sub-constraint conditions. Determine the degree of constraint violation of the sub-constraint conditions by the feasible solution; Based on the priority of the sub-constraints and the degree of constraint violation, a greedy algorithm is used to progressively revise the feasible solution.

[0011] Optionally, the k-th iteration process of the feasible solution further includes: Based on the feasible solution, the network loss value of the distribution network is obtained; Based on the network loss value, adjust the weight coefficients of the sub-objective functions in the objective function in the next iteration.

[0012] Optionally, the k-th iteration process of the feasible solution further includes: The dynamic penalty factor is updated based on the cumulative constraint violation degree and iteration number of the feasible solution; The objective function in the next iteration is adjusted based on the dynamic penalty factor.

[0013] A second aspect of this disclosure provides a power distribution network source-load-storage resource dispatching device, comprising: The acquisition module is configured to acquire power data from the energy supply side, power user side, and energy storage side of the distribution network. The model building module is configured to construct a multi-objective resource scheduling model for energy supply, load, and storage. The objective function and constraints of this model cover sub-objective functions and sub-constraints for the energy supply side, the power user side, and the energy storage side. The sub-objective functions include minimizing network losses in the distribution network, minimizing the operating cost of the distribution network, maximizing the energy storage efficiency on the energy storage side, and minimizing carbon emissions on the energy supply side. The sub-constraints include node voltage constraints in the distribution network, distributed power output constraints on the energy supply side, energy storage charging and discharging constraints on the energy storage side, and adjustable load constraints on the power user side. The computing module is configured to solve the global optimal solution of the source-load-storage multi-objective resource scheduling model based on the quantum annealing algorithm, so as to obtain the global resource scheduling scheme of the distribution network.

[0014] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in the first aspect.

[0015] In a fourth aspect, this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in the first aspect.

[0016] As can be seen from the above, the present disclosure provides a distribution network source-load-storage resource scheduling method, device, electronic equipment, and storage medium. By combining the operational objectives and constraints of the energy supply side, power user side, and energy storage side, a multi-objective resource scheduling model for source, load, and storage is constructed, thereby achieving globally efficient allocation of resources among these three parties. The global solution of the multi-objective resource scheduling model is solved based on the quantum annealing algorithm. The quantum tunneling effect of the quantum annealing algorithm is used to overcome local optima limitations, thus obtaining the globally optimal solution of the model. Through the technical solution of this disclosure, globally efficient interaction and dynamic balance of multi-objective resources (source, load, and storage) can be achieved, resulting in a better distribution network source-load-storage resource scheduling scheme. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in one or more embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only one or more embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating one or more embodiments of the power distribution network source-load-storage resource scheduling method disclosed herein; Figure 2 This is a schematic diagram of the structure of a power distribution network source-load-storage resource scheduling device according to one or more embodiments of this disclosure; Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to one or more embodiments of this disclosure. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in one or more embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] In the embodiments of this disclosure, the energy supply side refers to the source end that provides various types of energy such as electricity, heat, and cold energy to the power user side in the distribution network; the power user side refers to the terminal in the distribution network that receives and consumes electricity and other energy; and the energy storage side refers to the node terminal in the distribution network that undertakes the functions of storing, releasing, and regulating electricity and other energy.

[0022] With the development of new energy technologies, the proportion of new energy power generation based on wind power, photovoltaics, biomass energy, etc., in the power system is getting higher and higher (the penetration rate is getting higher and higher). In this scenario of high penetration of new energy, the coordination capability of the energy supply side, the power user side and the energy storage side in the distribution network is crucial.

[0023] However, related technologies typically adjust the coordination strategies of the "source-load-storage" three sides in the distribution network based on a single load response mechanism. For example, the resource scheduling strategy of the "source-load-storage" three sides may be adjusted only with the goal of maximizing the energy consumption rate on the energy supply side or minimizing the economic losses on the power user side.

[0024] The relevant technologies have problems such as difficulty in fully tapping the potential of adjustable resources and neglecting the loss of energy storage equipment.

[0025] Therefore, some implementations of this disclosure provide a scheme for the scheduling of power generation, load, and energy storage resources in a distribution network. In this scheme, a multi-objective resource scheduling model covering the energy supply side, power user side, and energy storage side, with objective functions and sub-constraints, is constructed. The global optimal solution of this model is obtained based on the quantum annealing algorithm, leading to a global resource scheduling scheme for the distribution network. Through this scheme, the globally optimal scheduling of power generation, load, and energy storage resources in a distribution network can be achieved by relying on multi-dimensional power data and a multi-objective scheduling model, combined with the quantum annealing algorithm.

[0026] refer to Figure 1The present disclosure discloses one or more embodiments of a power distribution network source-load-storage resource scheduling method, including the following steps: Step S101: Obtain power data from the energy supply side, power user side, and energy storage side of the distribution network.

[0027] Step S102: Construct a multi-objective resource scheduling model for energy supply, load, and storage. The objective function and constraints of the multi-objective resource scheduling model cover the sub-objective functions and sub-constraints of the energy supply side, the power user side, and the energy storage side. The sub-objective functions include minimizing the network loss of the distribution network, minimizing the operating cost of the distribution network, maximizing the energy storage efficiency of the energy storage side, and minimizing the carbon emissions of the energy supply side. The sub-constraints include the node voltage constraints of the distribution network, the output constraints of distributed power sources on the energy supply side, the energy storage charging and discharging constraints on the energy storage side, and the adjustable load constraints on the power user side.

[0028] Step S103: Solve the global optimal solution of the source-load-storage multi-objective resource scheduling model based on the quantum annealing algorithm to obtain the global resource scheduling scheme of the distribution network.

[0029] In this embodiment of the disclosure, a multi-objective resource scheduling model for source, load and storage is designed with the goal of balancing the operating cost, reliability and environmental benefits of the distribution network system, taking into account current technical indicators. The objective function and constraints of this model are adapted to the energy supply side, the power user side and the energy storage side, and are designed with the concept of improving the economic and environmental benefits of the distribution network system while ensuring the stable operation of the system.

[0030] Specifically, in this embodiment of the disclosure, the objective function is designed based on four conditions: minimizing the network loss of the distribution network, minimizing the operating cost of the distribution network, maximizing the energy storage efficiency on the energy storage side, and minimizing the carbon emissions on the energy supply side. The constraints are designed based on three aspects: node voltage constraints of the distribution network, distributed power output constraints on the energy supply side, and energy storage charging and discharging constraints on the energy storage side.

[0031] In some embodiments, the network loss of a distribution network refers to the energy lost by the distribution network during the transmission and distribution process due to factors such as resistance and reactance.

[0032] Specifically, the formula for calculating distribution network losses can be: ; in, This represents the network loss of the distribution network. This represents the minimum operating rate of the distribution network at time t within a preset scheduling period. This represents the response load compensation value of the g-th power node in the distribution network. This represents the electrical energy obtained by the distribution network from the main grid at time t in the preset scheduling cycle. This represents the marginal electricity price of the g-th power node in the distribution network. This represents the cost coefficient for the h-th category of power loss, which indicates different time interval types (e.g., peak, flat, valley), power loss types, or network area types.

[0033] In some embodiments, the operating cost of the distribution network can be determined based on the electricity purchase cost and equipment operation and maintenance cost on the electricity user side, and the charging and discharging cost and equipment depreciation cost on the energy storage side.

[0034] Specifically, the formula for calculating the operating cost of a power distribution network can be: ; in, This indicates the operating cost of the power distribution network. This represents the time set at each moment in the preset scheduling period. and These represent the electricity purchase cost and equipment operation and maintenance cost on the electricity user side, respectively. and This represents the charging and discharging costs and equipment depreciation costs on the energy storage side. This represents the discharge power of the energy storage side at time t within the aforementioned scheduling period.

[0035] The energy storage efficiency reflects the ability of the energy storage side to effectively release stored electrical energy. Therefore, in some embodiments, the problem of maximizing the energy storage efficiency of the energy storage side can be transformed into the problem of minimizing the number of invalid charge-discharge cycles of the system, in order to simplify computational complexity and improve computational efficiency.

[0036] Specifically, the formula for calculating the energy storage efficiency on the energy storage side can be: ; in, This indicates the number of invalid charge / discharge cycles on the energy storage side. This indicates the total number of energy storage nodes on the energy storage side. Indicates the capacity of the energy storage side. This indicates the number of transferable loads on the energy storage side. This represents the adjustable power of the transferable load on the energy storage side. The depth of discharge on the energy storage side refers to the percentage of electricity released by an energy storage node during a single charge-discharge cycle relative to its rated total capacity. Load refers to the electrical equipment on the power user side (such as charging piles, air conditioners, and industrial motors), while transferable load refers to user equipment whose electricity usage time can be flexibly adjusted.

[0037] In some embodiments, the carbon emissions from the energy supply side can be calculated as follows: ; in, This indicates carbon emissions from the energy supply side. This indicates the number of distributed power sources on the energy supply side. The carbon emission factor representing distributed power generation. Indicates the first The power generation of a distributed power source under ideal conditions. The emission factor for nitrogen oxides from distributed power sources is represented by , where w indicates the pollutant type. Indicates the first Net load forecast for each distributed power source.

[0038] Therefore, in some embodiments, the objective function of the source-load-storage multi-objective resource scheduling model can be designed as follows: ; in, , , and These represent the weight coefficients of each of the aforementioned sub-objective functions. In some embodiments, these weight coefficients can be set manually. In other embodiments, these weight coefficients can be dynamically adjusted based on the function values ​​of the sub-objective functions at historical time points.

[0039] In some embodiments, the aforementioned weighting coefficients can be dynamically adjusted for each objective function solution or before each iteration of the objective function solution.

[0040] In some embodiments, the weighting coefficients can be adjusted based on the distribution network loss rate.

[0041] For example, the formula for calculating the distribution network loss rate at time t can be expressed as: ; in, This represents the network loss value of the distribution network at time t. This represents the steady-state network loss (or baseline network loss) when no energy storage equipment is added.

[0042] The strategy for adjusting the weighting coefficients can be expressed as: ; in, This represents the weight coefficient of the i-th sub-objective function at time t. This represents the base value of the weight coefficient for the i-th sub-objective function, which can be the weight coefficient value calculated in the previous round. This represents the penalty intensity coefficient, which can be set by the user (e.g., 1.2). This represents the threshold for the distribution network loss rate, which can be set by the user (e.g., to 0.05).

[0043] In some embodiments, higher weight values ​​can be assigned to more important sub-objective functions. For example, a higher weight can be assigned to the sub-objective function of minimizing distribution network losses. ; in, This indicates an enhanced stability constraint, which can be set by the user (e.g., to 1.5).

[0044] The sum of the updated weight coefficients should remain at 1. In some embodiments, when the sum of the weight coefficients does not meet the requirement of 1, the weight coefficients can be normalized: .

[0045] The algorithm dynamically adjusts the target weights, decoupling day-ahead scheduling from real-time scheduling, and achieves multi-scale resource interaction through a thermal inertial load response model and a dual-mode energy storage operation strategy.

[0046] In this embodiment of the disclosure, by setting corresponding constraints on the source-load-storage multi-objective resource scheduling model, the effectiveness and feasibility of the model within a specific range can be ensured, thereby guaranteeing that the power grid system maintains a safe and reliable operating state during the optimization scheduling process.

[0047] In some embodiments, the node voltage constraint of the distribution network can be set to require the voltage fluctuation of each node to be kept within an allowable safe range. The node voltage constraint of the distribution network can be expressed as: ; in, express Voltage fluctuation values ​​at nodes in the distribution network at all times. and They represent the qth element in The lower and upper limits of the voltage fluctuation at a node at a given time.

[0048] In some embodiments, the output constraint of distributed power sources on the energy supply side can be set to limit the actual output power of each distributed power source on the energy supply side. The output constraint of distributed power sources on the energy supply side can be expressed as: ; in, Indicates the first aspect of energy supply side A distributed power source in Output power at any moment and They represent the first A distributed power source in The lower limit and upper limit of output power at any given time.

[0049] In some embodiments, the energy storage charge and discharge constraints on the energy storage side can be set as limitations on the capacity and state of charge of the energy storage side. The energy storage charge and discharge constraints on the energy storage side can be expressed as: ; in, Indicates the energy storage side The capacity of each energy storage node at the start of a charging cycle. and They represent the first The lower and upper limits of the capacity of each energy storage node at the start of a charging cycle. This indicates the rated power of the interruptible load at the energy storage node. This represents the maximum value of the maximum dischargeable energy (or power) of the energy storage node.

[0050] In the process of realizing this disclosure, the inventors discovered that the algorithms used in related technologies are prone to getting trapped in local optima and cannot obtain the global optimal solution.

[0051] Quantum annealing is an optimization algorithm based on the principles of quantum mechanics. It can efficiently search for the global optimal solution to a problem by utilizing the quantum tunneling effect and quantum superposition state, and ultimately solve complex optimization problems by simulating the annealing process of a quantum system.

[0052] In some embodiments, before entering the iterative computation of the quantum annealing algorithm, the initial parameters of the quantum annealing algorithm are first set and the initial solution of the objective function is generated. Specifically, the initial parameters of the quantum annealing algorithm may include: initial temperature, cooling rate, number of iterations, number of qubits, etc.

[0053] In some embodiments, the initial temperature of the quantum annealing algorithm can be determined based on the number of optimization variables and the problem complexity to ensure that the initial temperature is high enough to cover the global solution space.

[0054] For example, the initial temperature It can be set as follows: ; in, Let C represent an empirical constant, ∈ [100, 500]. Indicates the number of independent variables. This represents the standard deviation of the variable used for dynamic scaling.

[0055] The cooling rate can be balanced between exploration and convergence using a piecewise exponential decay strategy.

[0056] For example, the cooling rate α(t) can be set as follows: ; in, Indicates the total number of iterations. , This represents the number of qubits in the computing resource. , Indicates the first The number of discretized segments of each sub-objective function. In other words, the total number of iterations can be calculated and allocated based on the variable dimensions of the source-load-storage multi-objective resource scheduling model.

[0057] In some embodiments, the initial feasible solution is a randomly generated feasible solution of the objective function that satisfies the above constraints.

[0058] In this embodiment of the disclosure, the initial feasible solution may include the output of distributed power sources on the energy supply side, the node voltage of the distribution network, and the energy storage charging and discharging power on the energy storage side.

[0059] In some embodiments, distributed power output It can be randomly generated under the constraint of distributed power source output; distributed power source output The initial feasible solution can be expressed as: ; in, Let ξ represent a uniformly distributed random number. U(0,1).

[0060] Node voltage The initial feasible solution can be randomly generated under the nodal voltage constraint, and the nodal voltage... The initial feasible solution can be expressed as: ; in, η represents a uniformly distributed random number. U(0,1).

[0061] Energy storage system charging and discharging power The initial feasible solution can be randomly generated based on the charge and discharge constraints, and the charge and discharge power of the energy storage system. The initial feasible solution can be expressed as: .

[0062] In some embodiments, when a randomly generated initial solution violates a constraint, the sub-constraints can be prioritized and the degree of constraint violation of each sub-constraint can be determined. Based on the priority ranking and the degree of constraint violation, a greedy algorithm can be used to correct the initial solution step by step.

[0063] In some embodiments, the node voltage constraints of the distribution network have a higher priority than the energy storage charging and discharging constraints of the energy storage side, and the energy storage charging and discharging constraints of the energy storage side have a higher priority than the distributed power output constraints of the energy supply side.

[0064] In some embodiments, when the initial solution violates the node voltage constraint, the adjustable load of the adjacent nodes of the over-limit node q can be adjusted. or distributed power output This allows the voltage to return to its normal range. Specifically, the voltage adjustment expression can be: ; in, This represents the algebraic sum of the total active power that needs to be adjusted across all adjacent nodes. This represents the actual measured or calculated voltage value of the problem node q at time t. This represents the midpoint of the safe operating range of the node q voltage. Let q represent the set of adjacent nodes of the node that exceeds the limit. Represents the sensitivity coefficient, α v This represents a correction step size coefficient between 0 and 1 (usually less than 1, such as 0.5), used to control the magnitude of a single adjustment.

[0065] In some embodiments, when the initial solution violates the energy storage charge and discharge constraints, the charge and discharge power can be made to meet the constraints by reducing the discharge power or charging power in the current period.

[0066] Specifically, when the discharge power exceeds the limit, the discharge power in the current period is reduced and the charging power in the subsequent period is increased; when the charging power exceeds the limit, the charging power in the current period is reduced and the discharge power in the subsequent period is increased.

[0067] The expression for adjusting charging and discharging power can be:

[0068] in, This represents the original planned charging and discharging power. A positive value indicates discharging (energy storage supplying power to the grid), a negative value indicates charging (the grid charging the energy storage), and a zero value indicates standby. This indicates the absolute value of the maximum allowable power of the energy storage device. The sign function is used to extract and preserve the direction of the original power, if >0, then =1; if <0, then =-1; if =0, then =0.

[0069] In some embodiments, when the initial solution does not meet the output constraints of the distributed power source, the over-limit distributed power source can be adjusted.

[0070] The distributed power regulation expression can be: ; in, Indicates the first The original planned output value of a distributed power source at time t. This represents the median output range of distributed power sources. , This indicates the distributed power sources participating in this coordinated adjustment. Indicates the first The original planned output value of a distributed power source at time t. This represents the median output range of distributed power sources. This represents the correction factor, typically 0 < <1, and Indicates the first The upper and lower limits of the adjustable output of a distributed power source.

[0071] In some embodiments, the initial solution may be iteratively adjusted until the violation degree value is equal to zero.

[0072] In some embodiments, the initial solution can be locally adjusted in each round of adjustment, and the local adjustment expression can be: ; in, This represents the feasible solution obtained in this iteration. This represents the feasible solution obtained in the previous iteration. Indicates step size, This represents the gradient of the running cost function.

[0073] In some embodiments, the violation degree value can be calculated as follows: ; In some embodiments, in each iteration, the continuous variable values ​​of the current solution (which is the initial solution in the first iteration) are first discretized to obtain the quantum bit code corresponding to the current solution (initial solution).

[0074] In some embodiments, the discretization of the charging and discharging power on the energy storage side in a feasible solution can be expressed as: ; in, o The index representing the discretized level is an integer. , Indicates the energy storage battery number. express , O This represents the number of discretized segments for the charging and discharging power.

[0075] In some embodiments, the discretization of the voltage at each node on the energy supply side and the power user side in a feasible solution can be expressed as: ; in, The index representing the discretized level is an integer. , express, , This represents the minimum allowable voltage at node q. This represents the number of discrete segments in the voltage.

[0076] By mapping the discretized variables to the probability amplitude of the qubit, we obtain the probability amplitude of the current solution.

[0077] Specifically, for a single variable, n qubits can be used to represent 2n discrete values. For example, four discrete values ​​can be mapped to two qubits.

[0078] For multivariate variables, multiple variables can be jointly encoded, and the total number of sub-digits can be represented as follows: ; in, Indicates the total number of digits. This represents the number of qubits of the i-th variable.

[0079] The probability amplitude of mapping the above qubits to quantum states: ; in, The probability amplitude of a qubit is represented by its quantum bit. Represents the normalization factor. Indicates the first The objective function value corresponding to each current solution. This represents parameters related to temperature.

[0080] To suppress infeasible solutions, a dynamic penalty factor can be introduced to correct the probability amplitude mentioned above: ; in, This represents the corrected probability amplitude. Indicates the penalty factor. This represents the constraint violation degree of the k-th current solution.

[0081] Based on the corrected probability amplitude, the quantum superposition state can be obtained: ; in, This represents the current solution space.

[0082] The above quantum superposition state is updated by generating a new solution through a quantum rotation gate perturbation operation.

[0083] In some embodiments, the rotation angle of the quantum rotating gate perturbation operation can be dynamically adjusted according to the Metropolis criterion: ; Where i and j are in θ The index represents the qubit. For example, θ 12 The rotation angle representing the operation performed on the first and second qubits determines the magnitude and direction of the disturbance to the quantum state. This represents the rotation step size, which can be set by the user (for example, it can be 0.1π). This represents the change in the objective function value. , This represents the comprehensive objective function value corresponding to the new solution. This represents the value of the integrated objective function corresponding to the current solution. This represents the historical average objective function value.

[0084] Performing a quantum rotation gate perturbation operation yields the updated quantum superposition state |ψ′k : ; in, Represents the identity matrix. Let X represent the Pauli matrix.

[0085] By introducing a non-uniform perturbation term (t) can inhibit premature convergence.

[0086] ; in, Indicates the initial disturbance magnitude. Indicates the current moment. Indicates the maximum number of iterations. This represents the decay index, which can be set manually (e.g., to 2).

[0087] Converting quantum states into binary sequences using projection measurements: , ; in, and The two probability amplitudes representing the state of the i-th qubit correspond to the ground state, respectively. and This probabilistic relationship is established by Born's law and satisfies... .

[0088] The statistical mean of M shots of repeated measurements is obtained. ; Among them, measure m ( ) represents the result of a single measurement of the i-th qubit during the m-th measurement.

[0089] The obtained binary sequence is mapped to a feasible solution for this round.

[0090] ; ; .

[0091] in, This represents a feasible solution for the energy storage charging and discharging power. This represents a feasible solution for the output of a distributed power source. This represents a feasible solution for the node voltage.

[0092] In some embodiments, a projection correction may be applied when a feasible solution goes out of bounds: ; .

[0093] In some embodiments, during each iteration, the feasible solution is further optimized globally using the particle swarm optimization algorithm or locally using the gradient descent algorithm.

[0094] In some embodiments, the feasible solutions of the first k iterations are globally explored and optimized, and after the preset iteration switching conditions are met, the feasible solutions are locally explored and optimized.

[0095] In some embodiments, the optimization parameters of the particle swarm optimization algorithm can be set as follows: Inertia weight ω: ; , ; acceleration constant , : ; Number of particles S: S = 2Q + 10, where Q represents the total number of qubits.

[0096] Among them, the acceleration constant can be gradually reduced with the number of iterations to enhance the local search capability, and the increase in the number of particles can ensure that the population diversity covers the solution space.

[0097] In each iteration, potential optimal regions can be searched through swarm intelligence to update particle positions and velocities.

[0098] The speed update formula is: ; in, and Let r1 and r2 represent random numbers uniformly distributed in the interval [0,1]. U(0,1), Let represent the optimal position of particle i in the j-th dimension in its historical history (up to the k-th iteration). This represents the optimal position in the j-th dimension throughout the entire history of the particle swarm (up to the k-th iteration). This represents the current position of particle i in the j-th dimension during the k-th iteration.

[0099] The position update formula is: ; In some embodiments, if the location violates the constraints, it can be corrected using a projection method: ; in, This represents the lower bound of the constraint on the variable. This indicates the upper limit of the constraint on the variable. The variable can be the active power output of a distributed generation source, node voltage, or the charging and discharging power of an energy storage system.

[0100] Specifically, when the variable represents the active power output of a distributed generation source, This indicates the minimum technical output of the distributed power source. This represents the maximum technical output of the distributed power source; when the variable represents node voltage, This indicates the lower limit of the safe operating voltage for this node. This indicates the upper limit of the safe operating voltage of the node; when the variable represents the charging and discharging power of the energy storage system, Indicates the maximum allowable charging power of the energy storage. This indicates the maximum permissible discharge power of the energy storage.

[0101] In some embodiments, the optimization parameters of the gradient descent algorithm can be set as follows: Learning rate η: , ; Momentum coefficient β: .

[0102] The learning rate decays with the number of iterations, and the momentum coefficient can be adjusted to balance the historical gradient with the current gradient.

[0103] In some embodiments, the gradient descent algorithm is specifically used to refine the energy storage charge and discharge curves in order to minimize the operating costs and network losses of the distribution network.

[0104] The gradient of the distribution network operating cost in the k-th iteration is: ; The gradient of the distribution network loss in the k-th iteration is: ; in, This represents the (reference) discharge power of the d-th energy storage node on the energy storage side. R represents the minimum operating speed of the system. g This represents the response load compensation value of the g-th node in the distribution network. This indicates that electricity was purchased from the main network at time t. This represents the marginal electricity price at the g-th node of the distribution network. This represents the energy loss cost coefficient.

[0105] ; To update the energy storage power value based on the two gradients calculated above, with a certain step size, in order to simultaneously reduce costs and network losses. Among these... This indicates the energy storage charging and discharging power before this round of iteration and update. Indicates the learning rate. This represents the network loss penalty coefficient.

[0106] The objective function value for this round can be determined based on the current solution obtained in this round.

[0107] In some embodiments, a penalty function term is introduced into the objective function to reduce the fitness of infeasible solutions through an exponential penalty mechanism: ; in, Represents the objective function value. Indicates the penalty factor. , This represents the initial penalty factor (which can be set manually, for example, γ0=10). Describe the sub-objective function. This represents a sub-constraint.

[0108] In some embodiments, after each iteration, the improved Metropolis criterion is used to determine the acceptance probability of the solution.

[0109] First, calculate the objective function value and constraint violation degree value corresponding to the current solution obtained in this iteration; Based on the objective function value and the constraint violation degree value, determine the acceptance probability value corresponding to the current solution: ,in, Indicates the current temperature. This represents the penalty coefficient for constraint violation, which can be set manually (e.g., any value between 10 and 100). Generate random number ξ0 For U(0, 1), if Paccept ≥ ξ0, then accept the new solution.

[0110] In some embodiments, the temperature update rule is as follows: ; Among them, α5=0.99, α6=0.95, and α7=0.90.

[0111] In some embodiments, a multi-objective Pareto front update is used to maintain the solution set for each iteration.

[0112] Specifically, an external archive is maintained to store non-dominated solutions. After each round of iteration, it is determined whether the conditions are met: If satisfied, then determine the new solution for this round. Dominate the archive .

[0113] The iteration terminates when any of the following conditions are met: Current temperature ( (Can be set manually); Reaching the maximum number of iterations ; continuous The objective function value did not improve in the next iteration; continuous Sub-iteration without Pareto front improvement; The objective function value converges to the preset precision. ,Right now .

[0114] The total number of iterations can be calculated using the following formula: ; Where N represents the total number of optimization variables (such as energy storage power, unit output, etc.).

[0115] Once the iteration termination condition is met, the final optimal solution set X is returned. That is, the corresponding objective function value: .in, This represents the feasible solution space. This represents the objective function value.

[0116] It is understandable that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities.

[0117] It should be noted that the methods of one or more embodiments of this disclosure can be executed by a single device, such as a computer or server. The methods of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to complete the process. In such a distributed scenario, one of these devices may execute only one or more steps of the methods of one or more embodiments of this disclosure, and the multiple devices will interact with each other to complete the method described.

[0118] It should be noted that the above description pertains to specific embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0119] Based on the same inventive concept, corresponding to any of the above-described embodiments, this disclosure also provides a power distribution network source-load-storage resource scheduling device. For example... Figure 2 As shown, the device includes: The acquisition module 11 is configured to acquire power data from the energy supply side, power user side, and energy storage side of the distribution network. Model building module 12 is configured to build a multi-objective resource scheduling model for energy supply, load, and storage. The objective function and constraints of the multi-objective resource scheduling model cover sub-objective functions and sub-constraints of the energy supply side, the power user side, and the energy storage side. The sub-objective functions include minimizing the network loss of the distribution network, minimizing the operating cost of the distribution network, maximizing the energy storage efficiency of the energy storage side, and minimizing the carbon emissions of the energy supply side. The sub-constraints include node voltage constraints of the distribution network, distributed power output constraints of the energy supply side, energy storage charging and discharging constraints of the energy storage side, and adjustable load constraints of the power user side. The calculation module 13 is configured to solve the global optimal solution of the source-load-storage multi-objective resource scheduling model based on the quantum annealing algorithm, so as to obtain the global resource scheduling scheme of the distribution network.

[0120] Optionally, the computing module 13 is specifically configured as follows: A feasible solution to the objective function is randomly generated. The feasible solution includes the output of each distributed power source on the energy supply side, the voltage of each node on the energy supply side and the power user side, and the continuous variable values ​​of the charging and discharging power on the energy storage side. Based on the quantum annealing algorithm, the feasible solution is calculated iteratively until the preset iteration termination condition is met; The k-th iteration calculation process of the feasible solution includes: Based on the preset number of segments, the continuous variable values ​​of the output of each distributed power source on the energy supply side, the voltage of each node on the energy supply side and the power user side, and the charging and discharging power of the energy storage side are discretized to obtain the quantum bit code of the current feasible solution. The probability amplitude of the qubit is obtained based on the dynamic penalty factor, the objective function value corresponding to the qubit code, and the constraint violation degree of the objective function value. Based on the probability amplitude of the qubit, a quantum superposition state is obtained by performing a quantum rotation gate perturbation operation; Project the quantum superposition state as a feasible solution for this round.

[0121] Optionally, the computing module 13 is specifically configured as follows: In response to the failure to meet the preset iteration switching conditions, the k-th iteration of the feasible solution uses the particle swarm optimization algorithm to perform global exploration and optimization on the feasible solution. The acceleration constant of the particle swarm optimization algorithm decreases as the number of iterations increases, in order to adapt to the local optimization requirements.

[0122] Optionally, the computing module 13 is specifically configured as follows: In response to the satisfaction of the preset iteration switching condition, the k-th iteration of the feasible solution uses the gradient descent algorithm to perform local exploration optimization on the feasible solution.

[0123] Optionally, the computing module 13 is specifically configured as follows: Determine the priority of the node voltage constraints of the distribution network, the output constraints of the distributed power sources on the energy supply side, and the energy storage charging and discharging constraints on the energy storage side under the sub-constraint conditions. Determine the degree of constraint violation of the sub-constraint conditions by the feasible solution; Based on the priority of the sub-constraints and the degree of constraint violation, a greedy algorithm is used to progressively revise the feasible solution.

[0124] Optionally, the computing module 13 is specifically configured as follows: Based on the feasible solution, the network loss value of the distribution network is obtained; Based on the network loss value, adjust the weight coefficients of the sub-objective functions in the objective function in the next iteration.

[0125] Optionally, the computing module 13 is specifically configured as follows: The dynamic penalty factor is updated based on the cumulative constraint violation degree and iteration number of the feasible solution; The objective function in the next iteration is adjusted based on the dynamic penalty factor.

[0126] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, when implementing one or more embodiments of this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0127] The apparatus described above is used to implement the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0128] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0129] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0130] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this disclosure are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0131] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0132] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0133] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0134] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this disclosure, and not necessarily all the components shown in the figures.

[0135] The electronic devices described above are used to implement the corresponding methods in the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0136] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0137] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0138] Additionally, to simplify the description and discussion, and to avoid obscuring one or more embodiments of this disclosure, the provided drawings may or may not show well-known power / ground connections to integrated circuit (IC) chips and other components. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring one or more embodiments of this disclosure, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which one or more embodiments of this disclosure will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuitry) are set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that one or more embodiments of this disclosure may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0139] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0140] This disclosure includes one or more embodiments intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for scheduling power generation, load, and storage resources in a distribution network, characterized in that, include: Acquire power data from the energy supply side, power user side, and energy storage side of the distribution network; A multi-objective resource scheduling model for energy supply, load, and storage is constructed. The objective function and constraints of the multi-objective resource scheduling model cover the sub-objective functions and sub-constraints of the energy supply side, the power user side, and the energy storage side. The sub-objective functions include minimizing the network loss of the distribution network, minimizing the operating cost of the distribution network, maximizing the energy storage efficiency of the energy storage side, and minimizing the carbon emissions of the energy supply side. The sub-constraints include the node voltage constraints of the distribution network, the distributed power output constraints of the energy supply side, and the energy storage charging and discharging constraints of the energy storage side. The global optimal solution of the source-load-storage multi-objective resource scheduling model is obtained by solving the quantum annealing algorithm, and the global resource scheduling scheme of the distribution network is obtained.

2. The method according to claim 1, characterized in that, The global optimal solution of the source-load-storage multi-objective resource scheduling model is obtained by solving the quantum annealing algorithm, resulting in a global resource scheduling scheme for the distribution network, including: A feasible solution to the objective function is randomly generated. The feasible solution includes the output of each distributed power source on the energy supply side, the voltage of each node on the energy supply side and the power user side, and the continuous variable values ​​of the charging and discharging power on the energy storage side. Based on the quantum annealing algorithm, the feasible solution is calculated iteratively until the preset iteration termination condition is met; The k-th iteration calculation process of the feasible solution includes: Based on the preset number of segments, the continuous variable values ​​of the output of each distributed power source on the energy supply side, the voltage of each node on the energy supply side and the power user side, and the charging and discharging power of the energy storage side are discretized to obtain the quantum bit code of the current feasible solution. The probability amplitude of the qubit is obtained based on the dynamic penalty factor, the objective function value corresponding to the qubit code, and the constraint violation degree of the objective function value. Based on the probability amplitude of the qubit, a quantum superposition state is obtained by performing a quantum rotation gate perturbation operation; Project the quantum superposition state as a feasible solution for this round.

3. The method according to claim 2, characterized in that, In response to the failure to meet the preset iteration switching condition, the k-th iteration process of the feasible solution further includes: The feasible solution is globally explored and optimized using the particle swarm optimization algorithm. The speedup constant of the particle swarm optimization algorithm decreases as the number of iterations increases, in order to adapt to the needs of local optimization.

4. The method according to claim 2, characterized in that, In response to the satisfaction of the preset iteration switching condition, the k-th iteration process of the feasible solution further includes: The feasible solution is locally explored and optimized using the gradient descent algorithm.

5. The method according to claim 2, characterized in that, After randomly generating feasible solutions, the following is also included: Determine the priority of the node voltage constraints of the distribution network, the output constraints of the distributed power sources on the energy supply side, and the energy storage charging and discharging constraints on the energy storage side under the sub-constraint conditions. Determine the degree of constraint violation of the sub-constraint conditions by the feasible solution; Based on the priority of the sub-constraints and the degree of constraint violation, a greedy algorithm is used to progressively revise the feasible solution.

6. The method according to claim 2, characterized in that, The k-th iteration process of the feasible solution also includes: Based on the feasible solution, the network loss value of the distribution network is obtained; Based on the network loss value, adjust the weight coefficients of the sub-objective functions in the objective function in the next iteration.

7. The method according to claim 2, characterized in that, The k-th iteration process of the feasible solution also includes: The dynamic penalty factor is updated based on the cumulative constraint violation degree and iteration number of the feasible solution; The objective function in the next iteration is adjusted based on the dynamic penalty factor.

8. A power distribution network source-load-storage resource dispatching device, characterized in that, include: The acquisition module is configured to acquire power data from the energy supply side, power user side, and energy storage side of the distribution network. The model building module is configured to construct a multi-objective resource scheduling model for energy supply, load, and storage. The objective function and constraints of this model cover sub-objective functions and sub-constraints for the energy supply side, the power user side, and the energy storage side. The sub-objective functions include minimizing network losses in the distribution network, minimizing the operating cost of the distribution network, maximizing the energy storage efficiency on the energy storage side, and minimizing carbon emissions on the energy supply side. The sub-constraints include node voltage constraints in the distribution network, distributed power output constraints on the energy supply side, energy storage charging and discharging constraints on the energy storage side, and adjustable load constraints on the power user side. The computing module is configured to solve the global optimal solution of the source-load-storage multi-objective resource scheduling model based on the quantum annealing algorithm, so as to obtain the global resource scheduling scheme of the distribution network.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executed by the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

Images