A power distribution system low-carbon economic optimization method, system, device and medium based on considering user psychology and carbon potential deviation

Abstract

This invention discloses a low-carbon economic optimization method, system, equipment, and medium for power distribution systems based on considering user psychology and carbon potential deviation. The method includes calculating node carbon potential deviation values; generating charging and discharging subsidy strategies; constructing a differentiated travel demand model to generate electric vehicle load forecast data; constructing and solving a collaborative optimization model to obtain an optimized operating strategy, initial carbon emissions, and optimized carbon potential deviation; and performing iterative optimization to output the final operating strategy and final carbon emissions. This invention achieves perception and economic intervention of local carbon emissions in the power distribution network by calculating node carbon potential deviation values ​​and generating charging and discharging subsidy strategies. By constructing a differentiated travel demand model, it improves the accuracy of load forecasting and the targeting of regulation. The collaborative optimization model balances economic costs and low-carbon goals. Through intelligent algorithm solving and iterative feedback mechanisms, it outputs an economically feasible final operating strategy and final carbon emission output that reduces carbon emissions, thereby improving overall low-carbon economic efficiency.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a method, system, equipment and medium for low-carbon economic optimization of distribution systems based on consideration of user psychology and carbon potential deviation. Background Technology

[0002] Driven by the dual-carbon strategy, the power distribution system is rapidly developing towards a high proportion of renewable energy access and high electrification. As a key link connecting the main grid and diverse loads, the power distribution network not only needs to absorb the highly volatile wind and solar power output, but also faces enormous challenges brought about by the random access of large-scale electric vehicles (EVs). The charging and discharging behavior of EVs has spatiotemporal uncertainties, which, combined with the fluctuations in wind and solar power output, makes the spatiotemporal distribution of carbon emissions in the power distribution system extremely complex. This poses a severe test to the low-carbon and economical coordinated operation of the power distribution system. How to coordinate and optimize various resources such as sources, loads, and storage to achieve safe, low-carbon, and economical operation of the power distribution system has become a key technical challenge.

[0003] Current methods for modeling electric vehicle (EV) loads mostly treat EV cluster loads as a whole for prediction or scheduling, or use a single statistical model for characterization. They fail to deeply distinguish the essential differences between fast-charging and slow-charging users in terms of charging urgency, behavioral flexibility, and sensitivity to economic incentives. This makes it difficult to characterize real user behavior, resulting in demand response strategies that are not targeted and have limited regulatory effects. Furthermore, existing methods often use fixed time-of-use pricing or carbon signals based on the average carbon factor of the entire network to guide EV scheduling, ignoring the real-time differences in carbon emission intensity, i.e., carbon potential, between electrical nodes within the main grid and distribution network. The one-size-fits-all guidance signals cannot reflect the real low-carbon demand in local distribution networks, so the optimization results often only achieve rough emission reductions at the global level, and cannot make precise and effective interventions in local areas with high carbon potential, thus limiting the full realization of the overall low-carbon potential of the distribution system. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, device, and medium for optimizing the low-carbon economy of power distribution systems based on considering user psychology and carbon potential deviation. This addresses the problems of existing methods failing to distinguish between the behavior and response differences of fast-charging and slow-charging users in EV load modeling, resulting in insufficient targeting of control strategies; and the use of fixed electricity prices or average carbon signals in optimization guidance, ignoring the real-time differences in carbon potential at electrical nodes, making it difficult to achieve local emission reductions and limiting low-carbon potential.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a low-carbon economic optimization method for power distribution systems that considers user psychology and carbon potential deviation, comprising: Collect initial parameters of the distribution network, predicted wind and solar power output data, and main grid carbon potential, and calculate the node carbon potential deviation value based on the initial parameters of the distribution network, predicted wind and solar power output data, and main grid carbon potential. Based on the node carbon potential deviation value, a charge / discharge subsidy strategy is generated; A differentiated travel demand model is constructed by combining the charging and discharging subsidy strategy, and electric vehicle load forecast data is generated based on the differentiated travel demand model. A collaborative optimization model is constructed based on the electric vehicle load prediction data, the node carbon potential deviation value, and the charging and discharging subsidy strategy. The collaborative optimization model is solved to obtain the optimized operation strategy, the initial carbon emissions, and the corresponding optimized carbon potential deviation. The iteration stops under a preset condition. The charging and discharging subsidy strategy is iteratively optimized based on the optimized carbon potential deviation. When the iteration stops under the preset condition, the currently obtained optimized operating strategy and initial carbon emissions are output as the final operating strategy and final carbon emissions.

[0007] As a preferred embodiment of the low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in this invention, the step of calculating the node carbon potential deviation value includes: Based on the initial parameters of the distribution network and the predicted wind and solar power output data, power flow calculations are performed to determine the source of active power flowing into each electrical node; Calculate the carbon potential of each electrical node based on the active power source. The carbon potential of each electrical node is compared with the carbon potential of the main grid to obtain the node carbon potential deviation value.

[0008] The beneficial effects of this preferred technical solution are as follows: by comparing the carbon potential of electrical nodes with that of the main grid in real time, the spatial differences in carbon emissions within the distribution network can be quantified, providing a reliable spatiotemporal data basis for the subsequent formulation of charging and discharging subsidy strategies, supporting key interventions in areas with high carbon potential, achieving a leap from global extensive emission reduction to local fine control, and improving the overall low-carbon optimization potential and execution accuracy of the distribution network.

[0009] As a preferred embodiment of the low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation described in this invention, the step of generating a charging and discharging subsidy strategy includes: The node carbon potential deviation value is mapped to a differentiated charge and discharge subsidy value through a nonlinear function; Based on the aforementioned charge and discharge subsidy values, a charge and discharge subsidy strategy is generated to guide the charging and discharging behavior of fast-charging and slow-charging and discharging users.

[0010] The beneficial effects of this preferred technical solution are as follows: it transforms the node carbon potential deviation value into an economic incentive signal, reflects the regulation demand and cost-effectiveness through nonlinear mapping, generates stronger incentives in areas and periods with high carbon potential, guides users' charging and discharging behavior toward low-carbon goals, realizes the transformation of regulation strategy from fixed and rigid to dynamic and precise, and improves the pertinence and effectiveness of demand response.

[0011] As a preferred embodiment of the low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation described in this invention, the step of generating electric vehicle load forecast data includes: For fast-charging and discharging users, a fast-charging and discharging travel demand model is constructed based on the probability distribution process, in conjunction with the aforementioned charging and discharging subsidy strategy. For slow-charging and discharging users, a slow-charging and discharging travel demand model is constructed based on the time preference distribution, in conjunction with the aforementioned charging and discharging subsidy strategy. The fast-charging and discharging travel demand model and the slow-charging and discharging travel demand model are used as differentiated travel demand models, and electric vehicle load prediction data are generated based on the differentiated travel demand models.

[0012] The beneficial effects of this preferred technical solution are as follows: by constructing differentiated demand models for fast-charging and slow-charging users respectively, the differences in behavioral characteristics and incentive responses are characterized, thereby improving the spatiotemporal accuracy of load forecasting, providing a reliable basis for subsequent collaborative optimization, and supporting the formulation of more targeted and effective charging and discharging guidance strategies.

[0013] As a preferred embodiment of the low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in this invention, the step of constructing a collaborative optimization model including the electric vehicle load forecast data, the node carbon potential deviation value, and the charging and discharging subsidy strategy includes: An objective function is established with the goal of minimizing the total economic cost of the power distribution system. The electric vehicle load prediction data, the node carbon potential deviation value, and the charging and discharging subsidy strategy are used as inputs to construct a collaborative optimization model in conjunction with the objective function.

[0014] The beneficial effects of this preferred technical solution are as follows: It integrates electric vehicle load forecast data, node carbon potential deviation value and charging and discharging subsidy strategy into a collaborative optimization model with the goal of minimizing the total economic cost of the power distribution system. This achieves a trade-off and collaborative optimization of economic and low-carbon objectives within the same mathematical framework, ensuring that the final operation strategy can effectively reduce the operating cost and carbon emissions of the power distribution system, and providing a scientific and quantitative decision-making core for iterative optimization.

[0015] As a preferred embodiment of the low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation described in this invention, the steps of solving the collaborative optimization model to obtain the optimized operation strategy, initial carbon emissions, and corresponding optimized carbon potential deviation include: The collaborative optimization model is solved using an intelligent optimization algorithm to obtain the optimized operation strategy, initial carbon emissions, and optimized carbon potential deviation.

[0016] The beneficial effects of this preferred technical solution are as follows: by utilizing the strong search and optimization capabilities of intelligent optimization algorithms, it solves collaborative optimization models with multiple variables and constraints, avoids getting trapped in local optima, ensures the acquisition of high-quality and feasible optimization operation strategies and optimization carbon potential bias, provides a solid and reliable computational foundation for subsequent iterative feedback and final operation strategies, and guarantees the convergence of the overall optimization process and economic and low-carbon benefits.

[0017] As a preferred embodiment of the low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation described in this invention, the steps of outputting the final operating strategy and carbon emission results include: A preset deviation threshold is used as the iteration stopping condition to determine whether the optimized carbon potential deviation exceeds the deviation threshold. If the deviation threshold is exceeded, the charging and discharging subsidy strategy is adjusted according to the optimized carbon potential deviation, and the steps of constructing the fast charging and discharging travel demand model and the slow charging and discharging travel demand model are returned to be executed again. If the deviation threshold is not exceeded, the currently obtained optimized operating strategy and initial carbon emissions will be output as the final operating strategy and final carbon emissions.

[0018] The beneficial effects of this preferred technical solution are: by setting a deviation threshold and a closed-loop feedback mechanism, it ensures that the final operating strategy and the final carbon emissions are reliable solutions that have been iteratively optimized, avoiding the blindness of single optimization and improving the robustness and practicality of the final operating strategy.

[0019] Secondly, the present invention provides a low-carbon economic optimization system for power distribution systems that considers user psychology and carbon potential deviation, comprising: The data acquisition and processing module is used to collect initial parameters of the distribution network, wind and solar power output prediction data and main grid carbon potential, and calculate the node carbon potential deviation value. The subsidy strategy generation module is used to generate a charge / discharge subsidy strategy based on the node carbon potential deviation value. The load forecasting module is used to combine the charging and discharging subsidy strategy to distinguish between fast charging and discharging users and slow charging and discharging users to generate electric vehicle load forecasting data. The collaborative optimization solution module is used to construct and solve a collaborative optimization model containing the load forecast data, node carbon potential deviation value and subsidy strategy with the goal of minimizing the total economic cost of the power distribution system, so as to obtain the optimized operation strategy, initial carbon emissions and optimized carbon potential deviation. The iterative control and output module is used to preset the iteration stop condition, control the power distribution system to perform iterative operation according to the optimized carbon potential deviation, and output the final operation strategy and final carbon emission after the iteration stop condition is met.

[0020] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a low-carbon economic optimization method for power distribution systems that takes into account user psychology and carbon potential deviation.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method for optimizing the low-carbon economy of a power distribution system based on consideration of user psychology and carbon potential deviation.

[0022] Compared with existing technologies, the beneficial effects of this invention are as follows: by calculating the node carbon potential deviation value and generating a charging and discharging subsidy strategy, the invention enables perception and economic intervention of local carbon emissions in the distribution network; by distinguishing between fast-charging and slow-charging and discharging users and constructing a differentiated travel demand model, the invention improves the accuracy of load forecasting and the targeting of regulation; the constructed collaborative optimization model balances economic costs and low-carbon goals; and through intelligent algorithm solution and iterative feedback mechanism, the invention ultimately outputs an economically feasible and carbon-reduced final operation strategy and final carbon emission output, thereby improving the overall low-carbon economy. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the overall process of a low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation, as described in one embodiment of the present invention.

[0025] Figure 2 It is an improved IEEE 33-node system.

[0026] Figure 3This is a diagram of EV charging load power.

[0027] Figure 4 This is the EV discharge load power diagram.

[0028] Figure 5 This is a graph showing the carbon potential deviation of the EV connected to the electrical node.

[0029] Figure 6 This is a diagram showing the main grid power purchase capacity for the three schemes.

[0030] Figure 7 This is the carbon potential distribution diagram of the electrical nodes in Scheme 1.

[0031] Figure 8 This is the carbon potential distribution diagram of the electrical nodes in Scheme 2.

[0032] Figure 9 This is the carbon potential distribution diagram of the electrical nodes in Scheme 3.

[0033] Figure 10 These are the power response diagrams for the MT and energy storage systems of the three schemes.

[0034] Figure 11 This is a comparison chart of the total EV load before and after optimization. Detailed Implementation

[0035] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0036] Example 1, referring to Figures 1-11 As an embodiment of the present invention, a low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation is provided, comprising: S100: Collect initial parameters of the distribution network, wind and solar power output prediction data, and main grid carbon potential, and calculate the node carbon potential deviation value based on the initial parameters of the distribution network, wind and solar power output prediction data, and main grid carbon potential.

[0037] S200. Generate a charge / discharge subsidy strategy based on the node carbon potential deviation value.

[0038] S300. Construct a differentiated travel demand model based on the charging and discharging subsidy strategy, and generate electric vehicle load forecast data based on the differentiated travel demand model.

[0039] S400. Construct a collaborative optimization model based on the electric vehicle load prediction data, the node carbon potential deviation value, and the charging and discharging subsidy strategy. Solve the collaborative optimization model to obtain the optimized operation strategy, the initial carbon emissions, and the corresponding optimized carbon potential deviation.

[0040] S500: Preset iteration stop condition, iteratively optimize the charge and discharge subsidy strategy according to the optimized carbon potential deviation, and when the iteration stop condition is met, output the currently obtained optimized operating strategy and initial carbon emissions as the final operating strategy and final carbon emissions.

[0041] It should be noted that under the dual carbon targets, the distribution network faces the dual challenges of high-proportion renewable energy access and large-scale electric vehicle (EV) popularization. Wind and solar power output is highly volatile, and the charging and discharging behavior of EVs has spatiotemporal uncertainties, resulting in an extremely complex spatiotemporal distribution of carbon emissions in the distribution system. Most existing optimization methods rely on the average carbon factor of the entire network for extensive regulation, ignoring the real-time carbon potential differences between the main grid and the distribution network, and between various electrical nodes within the distribution network. This makes it difficult to achieve local emission reductions. The modeling of EV user behavior is too simplistic and fails to distinguish the essential differences between fast-charging and slow-charging users in terms of energy replenishment urgency, price sensitivity, and behavioral elasticity, resulting in insufficient targeting of demand response strategies and limited adjustment effects.

[0042] Therefore, addressing the issues of extensive carbon emission reduction regulation and homogeneous user behavior modeling, this paper employs steps S100-S500 to calculate the node carbon potential deviation value reflecting local carbon emission levels and generate differentiated charging and discharging subsidy strategies. Furthermore, it constructs a differentiated travel demand model to generate electric vehicle load forecast data. Finally, by constructing and iteratively solving a collaborative optimization model, it outputs the final operating strategy that minimizes the total economic cost of the distribution system and reduces carbon emissions. This achieves an optimization leap from global averaging to precise electrical node control, from homogeneous users to differentiated behaviors, and from static scheduling to dynamic iteration, providing a solution for the low-carbon, economical, and safe operation of the distribution network.

[0043] Example 2, refer to Figures 1-11 As an embodiment of the present invention, based on the above embodiment, a low-carbon economic optimization method for power distribution systems that considers user psychology and carbon potential deviation is provided.

[0044] In this embodiment, S100 involves collecting initial parameters of the distribution network, predicted wind and solar power output data, and the main grid carbon potential, and calculating the node carbon potential deviation value based on these parameters. Taking an improved IEEE 33 electrical node distribution network including distributed photovoltaic, wind power, energy storage, and multiple electric vehicle charging stations as an application scenario, the specific implementation of S100 (A1~A3) is as follows: A1. Based on the initial parameters of the power distribution network and the predicted wind and solar power output data, perform power flow calculations to determine the source of active power flowing into each electrical node.

[0045] Specifically, the initial parameters of the distribution network include distributed generation (DG) and energy storage system (ESS). Power flow calculations were performed using the forward-backward substitution method to determine the operating point of the distribution system under the predicted wind and solar power output data. Based on the power flow results, the carbon flow tracing method was used to determine the active power flowing into each electrical node i at time t, and its origin from the upstream branch. The active power components of the generator set g and the energy storage discharge.

[0046] A2. Calculate the carbon potential of each electrical node based on the active power source.

[0047] Specifically, the carbon potential of an electrical node is the carbon emission corresponding to a unit of active power flowing into that electrical node. Based on the principles of carbon flow tracking and superposition, the carbon potential of electrical node i at time t is... The calculation is as follows: in, For the carbon potential at the electrical node. The set of branches that inject active power into electrical nodes. Let i be the set of generator sets connected to electrical node i. , and The upstream branches at time t are respectively The active power injected into the electrical node by the generator set g and the energy storage discharge. upstream branch road carbon flux density, and The carbon emission intensity is calculated based on the carbon emission intensity of distributed power sources and energy storage systems, with wind and solar turbines and energy storage units having a carbon emission intensity of 0. The carbon emission intensity of micro gas turbines (MT) is calculated based on fuel consumption and emission coefficients.

[0048] A3. Compare the carbon potential of each electrical node with the carbon potential of the main grid to obtain the node carbon potential deviation value.

[0049] Specifically, obtain the main network carbon potential at time t. Calculate the nodal carbon potential deviation value for each electrical node i. The formula is: in, This represents the node carbon potential deviation value. Based on the main grid carbon potential, quantify the degree of deviation of the carbon emission level of electrical nodes in the distribution network relative to the main grid carbon potential; This indicates that the carbon potential of the electrical node is higher than that of the main grid, and it is necessary to guide the electric vehicles (EVs) to discharge or reduce charging to reduce local carbon emissions. This indicates that the carbon potential of the electrical node is lower than that of the main grid, and that it has a high proportion of clean energy, which can encourage electric vehicle (EV) charging.

[0050] In an optional implementation, a simplified carbon flow tracing algorithm can also be used in step S100. The steps are as follows: In step A1, for a radial distribution network, the carbon flow density and active power of the upstream branches can be allocated layer by layer from the root node to the terminal node according to the active power flow direction, and the equivalent carbon potential flowing into each electrical node can be calculated in combination to reduce the computational complexity.

[0051] In another optional implementation, step S100 may also consider multi-timescale carbon potential, wherein the steps are: not only calculating the real-time carbon potential deviation, but also, based on wind and solar power output forecast data and electric vehicle load forecast data, rolling calculation of the predicted carbon potential deviation for multiple future scheduling periods, such as the next 1 to 4 hours. This provides a forward-looking regulatory signal for subsequent subsidy strategies.

[0052] In this embodiment, step S200 involves generating a charge / discharge subsidy strategy based on the node carbon potential deviation value. Step S200 includes steps B1 to B2: B1. The node carbon potential deviation value is mapped to a differentiated charge / discharge subsidy value through a nonlinear function.

[0053] Specifically, a basic charging subsidy will be set. With basic discharge subsidy For the nodal carbon potential deviation value of electrical node i at time t Real-time charging subsidies are calculated using a saturation function. With real-time discharge subsidy The formula is: in, For real-time charging subsidies, For real-time discharge subsidies, , and , These are the adjustment coefficients for real-time charging subsidies and real-time discharging subsidies, used to control the magnitude and rate of subsidy changes with carbon potential deviation. As a time factor, it takes a value greater than 1 during peak load periods of the power distribution system. This indicates a time-varying conditioning effect that occurs around noon (12 noon), and can be adjusted during peak hours. Enhance guidance; This is the carbon potential deviation threshold. When the carbon potential deviation threshold is exceeded, the hyperbolic tangent function is activated. Nonlinear adjustment mechanism; function The sign function ensures that the carbon potential is high at the electrical node, i.e. The policy is to increase discharge subsidies and decrease charging subsidies when the carbon potential at the electrical node is low, and vice versa.

[0054] B2. Based on the aforementioned charge and discharge subsidy values, generate a charge and discharge subsidy strategy to guide the charging and discharging behavior of fast-charging and slow-charging and discharging users.

[0055] Specifically, the calculated and As the publicly disclosed subsidy price for electric vehicle (EV) users at time t at this electrical node, differentiating user guidance and generating charging and discharging subsidy strategies, such as using higher subsidies during high carbon potential node periods. Incentivizing electric vehicle (EV) users, especially those using slow charging, to discharge their batteries into the grid through lower... Suppress charging; during the low carbon potential node period, it uses higher... Encourage electric vehicle (EV) users to charge and utilize clean energy.

[0056] In an optional implementation, step S200 may also employ a piecewise linear function for mapping, wherein the step is: setting a carbon potential deviation threshold. ,when At that time, the charge / discharge subsidy is linearly related to the node carbon potential deviation; when At that time, the charging and discharging subsidies are maintained at a saturation level to control costs and cope with extreme situations.

[0057] In another optional implementation, step S200 may also consider pre-adjusting user response characteristics. The steps are as follows: after calculating the basic charging and discharging subsidy value in B1, the publicly disclosed subsidy price for the two user groups is fine-tuned based on the average price sensitivity coefficient of the fast charging and discharging user group and the slow charging and discharging user group in historical data. For example, for the fast charging and discharging user group, which is more sensitive to price, the subsidy change range is appropriately increased within the response range to obtain a better overall response effect.

[0058] In this embodiment of the application, step S300 involves constructing a differentiated travel demand model based on the charging and discharging subsidy strategy, and generating electric vehicle load forecast data based on the differentiated travel demand model. Step S300 includes C1~C3: C1. For fast-charging and discharging users, a fast-charging and discharging travel demand model is constructed based on the probability distribution process, in conjunction with the aforementioned charging and discharging subsidy strategy.

[0059] Specifically, the arrival process of fast-charging and discharging users is modeled using a Poisson distribution to construct the travel demand model. The number of fast-charging and discharging users served by centralized charging stations is assumed to be... The peak duration of the service area is Considering the impact of charging and discharging subsidy strategies on user motivation levels The influence of the arrival rate parameter of the Poisson distribution Set as: in, For arrival rate parameters, The influence coefficient represents the motivation level of fast charging users. The charging motive is determined by the publicly disclosed subsidy price in the charging and discharging subsidy policy. and electric motor The calculations are as follows: in, For charging motor, To discharge the electric motor, , These are the charging sensitivity coefficient and discharging sensitivity coefficient for fast charging users, respectively. To account for battery discharge loss costs, the charging process for fast-charging users is described using a stochastic service system model, such as the M / G / K model in queuing theory. The input is the charging and discharging subsidy strategy, which influences motivation levels. To adjust arrival rate The output is the probability distribution of the number of fast charging and discharging users arriving, queuing status, and charging and discharging power demand for any given time period, thus constructing a fast charging and discharging travel demand model.

[0060] C2. For slow-charging and discharging users, a slow-charging and discharging travel demand model is constructed based on the time preference distribution, in conjunction with the aforementioned charging and discharging subsidy strategy.

[0061] Specifically, the starting charging time for slow-charging users and the start of discharge time Preferences are respectively distributed normally. and In simulations, the charge / discharge subsidy strategy adjusts its time preference, specifically by adjusting the distribution parameters: in, and The baseline time expectation under no-incentive conditions. , These are the charging sensitivity coefficient and discharging sensitivity coefficient for slow charging and discharging users, respectively; The specific probability density functions at the start of charging and discharging are as follows: in, , Let represent the probability density functions at the start of charging and the start of discharging for slow-charging and discharging users, respectively. , The sensitivity coefficient for slow-charging and discharging users' willingness to charge and discharge is such that, when subsidies are high, the probability of charging and discharging behavior occurring at the corresponding time is linearly amplified. and The variance parameter indicates that the incentive makes slow charging and discharging user behavior more concentrated. Cost of discharge loss; A slow-charging / discharging travel demand model is constructed by simulating charging and discharging time preferences and calculating the probability density function at the start of charging and discharging.

[0062] C3. The fast-charging and discharging travel demand model and the slow-charging and discharging travel demand model are used as differentiated travel demand models, and electric vehicle load prediction data are generated based on the differentiated travel demand models.

[0063] Specifically, the fast-charging / discharging travel demand model and the slow-charging / discharging travel demand model are used as differentiated travel demand models. Based on these differentiated travel demand models, the behavior of each electric vehicle (EV) is simulated sequentially within the simulation period. The electric vehicle EV connected to electrical node i at any given time has the following initial state of charge: Target state of charge is The rated capacity of the battery is Its charging power at time t or discharge power From rated power Charge and discharge efficiency , and battery state of charge constraints Together, we determine the set of all electric vehicles (EVs) connected to charging at time t, aggregated at electrical node i. Electric vehicles (EVs) and electric vehicles that discharge electricity The power is used to obtain the total electric vehicle (EV) load forecast data for that electrical node. : in, This is the total electric vehicle (EV) load forecast data. Let k be the set of electric vehicles (EVs) connected to the charging station. index, Let be the set of electric vehicles (EVs) that are discharging, and s be the set of electric vehicles (EVs) that are connected to the discharge circuit. The index.

[0064] In an optional implementation, step S300 may further consider the queuing effect, wherein the following steps are taken: in the fast-charging / discharging user M / G / K queuing model of C1, a waiting time estimate based on the real-time queue length is introduced and fed back as a negative utility factor to the motivation level of the fast-charging / discharging user. The calculations more accurately reflect users' behavioral adjustments during peak hours when they might abandon charging due to waiting.

[0065] In another alternative implementation, response uncertainty may be introduced in step S300, wherein the sensitivity coefficient for each slow charge / discharge user is calculated in C2. , By introducing a random disturbance term that follows a specific distribution, such as a uniform distribution, we can simulate the individual differences and behavioral randomness within the same user group, making the load forecasting results more statistically robust.

[0066] In this embodiment of the application, step S400 involves constructing a collaborative optimization model based on the electric vehicle load prediction data, the node carbon potential deviation value, and the charging / discharging subsidy strategy; solving the collaborative optimization model to obtain the optimized operation strategy, the initial carbon emissions, and the corresponding optimized carbon potential deviation. Step S400 includes steps D1 to D3: D1. Establish the objective function with the goal of minimizing the total economic cost of the power distribution system.

[0067] Specifically, the total economic cost of the power distribution system Includes main grid electricity purchase costs System operation and maintenance costs Electric vehicle (EV) charging and discharging incentive subsidy costs and system carbon emission costs Therefore, the formula for minimizing the total economic cost of the power distribution system is: Among them, electricity purchase cost , For time-of-use electricity pricing during period t, , , These are the total load of electrical nodes in the power distribution system, network losses, and total output of distributed power sources, respectively. Operation and maintenance costs in, For the operation and maintenance costs of distributed generation (DG), For the operation and maintenance costs of the energy storage system ESS, The operation and maintenance costs of electric vehicle (EV) charging stations The operation and maintenance costs of the micro gas turbine (MT) are calculated. ; EV incentive subsidy costs ,in, and These represent the charging and discharging amounts of electric vehicles (EVs) at the corresponding electrical nodes during the corresponding time periods. carbon emission costs ,in, The unit carbon price, For the carbon potential at the electrical node. This represents the total load of the electrical nodes.

[0068] D2. Using the electric vehicle load prediction data, the node carbon potential deviation value, and the charging and discharging subsidy strategy as inputs, a collaborative optimization model is constructed in conjunction with the objective function.

[0069] Specifically, the decision variables of the collaborative optimization model include the output plans of each distributed power source. , The charging and discharging power of the energy storage system (ESS) The definition of charging as positive and discharging as negative is used to describe the output of a micro gas turbine. and total electric vehicle (EV) load forecast data The constraints of the collaborative optimization model include using linearized power flow models such as DistFlow to constrain the power flow of the distribution system; node voltage safety constraints: Power balance constraints: .

[0070] D3. The collaborative optimization model is solved using an intelligent optimization algorithm to obtain the optimized operation strategy, initial carbon emissions, and optimized carbon potential deviation.

[0071] Specifically, an improved particle swarm optimization (PSO) algorithm is used to solve the problem. The decision variables are encoded as particle positions, the population is randomly initialized within the feasible region, and the total economic cost of the solution corresponding to each particle is calculated. As a fitness value, and after verifying all constraints, particles that violate the constraints are penalized. An inertial weight adjustment strategy and a social learning factor are introduced to update the particle's velocity and position. Repeatedly calculate the fitness value and update the particle's velocity and position until the maximum number of iterations is reached or the optimal fitness value no longer improves after several consecutive generations. After the solution is completed, output the result. The combination of decision variables with the minimum value is used as the optimal operating strategy. Based on the system operating state under this optimal operating strategy, the carbon flow calculation in S100 is re-executed to obtain a new round of optimized carbon potential deviation. And the corresponding initial carbon emissions.

[0072] In an optional implementation, step S400 may also employ a distributed optimization framework, the steps of which are as follows: the entire distribution network is divided into several regions according to electrical coupling relationships or management authority, each region solves local optimization subproblems in parallel using distributed algorithms such as the alternating direction multiplier method, and exchanges boundary information, such as boundary node voltage and power exchange, through a coordination layer, and finally converges to the global optimal solution, thereby improving the efficiency of large-scale solution.

[0073] In another optional implementation, uncertainty optimization can also be considered in step S400. The steps are as follows: when building the model in D2, stochastic programming or robust optimization methods are used to model the wind and solar power output prediction data error and the electric vehicle (EV) load prediction data error as an uncertainty set or probability distribution. An expected cost term or a constraint to cope with the worst scenario is added to the objective function to obtain a more robust operating strategy under uncertain conditions.

[0074] In this embodiment, S500 involves setting a preset iteration stop condition, iteratively optimizing the charge / discharge subsidy strategy based on the optimized carbon potential deviation, and outputting the currently obtained optimized operating strategy and initial carbon emissions as the final operating strategy and final carbon emissions when the iteration stop condition is met. Step S500 includes E1~E3: E1. A preset deviation threshold is used as the iteration stopping condition to determine whether the optimized carbon potential deviation exceeds the deviation threshold.

[0075] Specifically, based on the precision requirements of low-carbon regulation, a global deviation threshold is preset. ,like The resulting new round of optimized carbon potential bias Calculate the maximum absolute value of the deviation of all electrical nodes over all time periods. The judgment condition is: if If the carbon potential distribution of the power distribution system is not yet balanced, iterative optimization is required; otherwise, it is considered that the iteration stopping condition has been met.

[0076] E2. When the deviation threshold is exceeded, the charging and discharging subsidy strategy is adjusted according to the optimized carbon potential deviation, and the steps of constructing the fast charging and discharging travel demand model and the slow charging and discharging travel demand model are re-executed.

[0077] Specifically, if step E1 determines that... Then the strategy adjustment mechanism will be activated. Larger electrical nodes and time periods require further strengthening of control measures, based on The sign and magnitude of the adjustment coefficient in the nonlinear mapping function described in step S200 are increased proportionally. or The value, such as for Furthermore, if the value is large, the discharge subsidy adjustment coefficient should be increased proportionally. and This will enable the generated discharge subsidy in the next iteration. Higher values ​​result in stronger incentives. After the adjustment is complete, a new charging and discharging subsidy strategy is generated using the updated coefficients, and the process returns to step S300. Based on the new charging and discharging subsidy strategy, steps C1-C3 are re-executed to construct a new differentiated travel demand model and generate new electric vehicle load forecast data, thus initiating a new round of optimization and solution in S400.

[0078] E3. If the deviation threshold is not exceeded, the currently obtained optimized operating strategy and initial carbon emissions will be output as the final operating strategy and final carbon emissions.

[0079] Specifically, if step E1 determines that... If the iteration process terminates, the optimized operating strategy obtained from the last S400 solution, including the optimal output plan for various units, the energy storage charging and discharging plan, and the EV pilot baseline, will be used as the final operating strategy that the power distribution system can execute. The carbon emissions calculated in this optimization will be used as the final operating strategy. The total carbon emissions of the power distribution system are used as the final carbon emission output to evaluate the low-carbon benefits of this optimization, together forming a power distribution system optimization and dispatch scheme that takes into account both economic efficiency and low carbon emissions.

[0080] In an optional implementation, step S500 may further set a maximum number of iterations as a dual stopping condition, the steps being: in E1, in addition to the deviation threshold judgment, a maximum number of iterations is also set. For example, 10 times, when the number of iterations reaches... When the deviation threshold condition is met, the iteration is forcibly terminated and E3 is executed to output the result, to prevent the algorithm from getting stuck in an infinite loop or oscillation in special cases.

[0081] In another optional implementation, step S500 may also employ threshold adjustment, the steps of which are: initially preset a large initial deviation threshold. During the iteration process, based on the historical iteration rounds... The rate of descent, tightening the initial deviation threshold to , In the initial optimization phase, the solution quickly approaches the optimal solution, and in the later phase, fine-tuning is performed to improve the overall convergence efficiency while ensuring accuracy.

[0082] In summary, this invention calculates the carbon potential deviation of nodes based on multi-source data from the distribution network and generates a charging and discharging subsidy strategy. By combining the charging and discharging subsidy strategy, it constructs a differentiated travel demand model for fast-charging and slow-charging and discharging users to predict the spatiotemporal distribution of electric vehicle load. Furthermore, with the goal of minimizing the total economic cost of the distribution system, it constructs a collaborative optimization model and iteratively solves it, ultimately outputting a final operating strategy that balances low carbon emissions and economic efficiency. This achieves a closed loop from accurate carbon potential perception and differentiated guidance of user behavior to source-load-storage collaborative optimization, providing an effective solution for refined scheduling of distribution networks with a high proportion of renewable energy and electric vehicles.

[0083] Example 3 illustrates a schematic scheme for a low-carbon economic optimization method for power distribution systems that considers user psychology and carbon potential deviation. It should be noted that the technical solution of this low-carbon economic optimization system for power distribution systems that considers user psychology and carbon potential deviation is based on the same concept as the aforementioned low-carbon economic optimization method for power distribution systems that considers user psychology and carbon potential deviation. Details not described in detail in this example can be found in the description of the aforementioned low-carbon economic optimization method for power distribution systems that considers user psychology and carbon potential deviation.

[0084] This embodiment also provides a low-carbon economic optimization system for power distribution systems that considers user psychology and carbon potential deviation, including: The data acquisition and processing module is used to collect initial parameters of the distribution network, wind and solar power output prediction data and main grid carbon potential, and calculate the node carbon potential deviation value. The subsidy strategy generation module is used to generate a charge / discharge subsidy strategy based on the node carbon potential deviation value. The load forecasting module is used to combine the charging and discharging subsidy strategy to distinguish between fast charging and discharging users and slow charging and discharging users to generate electric vehicle load forecasting data. The collaborative optimization solution module is used to construct and solve a collaborative optimization model containing the load forecast data, node carbon potential deviation value and subsidy strategy with the goal of minimizing the total economic cost of the power distribution system, so as to obtain the optimized operation strategy, initial carbon emissions and optimized carbon potential deviation. The iterative control and output module is used to preset the iteration stop condition, control the power distribution system to perform iterative operation according to the optimized carbon potential deviation, and output the final operation strategy and final carbon emission after the iteration stop condition is met.

[0085] This embodiment also provides an electronic device applicable to the low-carbon economic optimization of power distribution systems based on consideration of user psychology and carbon potential deviation, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the low-carbon economic optimization method of power distribution systems based on consideration of user psychology and carbon potential deviation proposed in the above embodiment.

[0086] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the low-carbon economic optimization method for power distribution systems based on consideration of user psychology and carbon potential deviation, as proposed in the above embodiments.

[0087] The storage medium proposed in this embodiment and the low-carbon economic optimization method for power distribution systems based on user psychology and carbon potential deviation proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0088] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0089] Example 4, building upon the previous three examples, provides a low-carbon economic optimization method for power distribution systems that considers user psychology and carbon potential deviation. To verify the beneficial effects of this invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments. Figure 2 The feasibility of the proposed low-carbon economic optimization method for power distribution systems is verified using the improved IEEE 33-node system as an example.

[0090] The integrated operation and maintenance costs for distributed photovoltaic (PV) and wind power are set at RMB 0.03 / kW and RMB 0.05 / kW, respectively. Wind turbines with an installed capacity of 300kWh are installed at nodes 14 and 30, and PV array generators with an installed capacity of 500kWh are installed at nodes 4 and 23. The ESS (Electric Power Supply) has rated parameters of 600kW / 800kW·h and a carbon price of RMB 50 / t. MT (Metallic Transmission Unit) generators are connected at node 16, with a maximum output of 0.25MW. EV centralized fast charging and discharging stations are connected at nodes 11 and 29, with each node equipped with 5 V2G public charging piles and a rated charging power of 80kW. EV slow charging and discharging stations are connected at nodes 16, 21, 24, and 31. There are 200 EV private vehicles in the region using slow charging and discharging, with each slow charging and discharging station equipped with 13 home V2G charging piles and a rated charging power of 30kW. The system's carbon potential error preset threshold is set to 0.05, the per-unit value of the reference voltage of each node in the system is 1, the system reference voltage is 12.66kV, the optimization time granularity Δt is 1h, the optimization period is 24h, and the improved particle swarm optimization algorithm is used to optimize the system.

[0091] Figure 3 The data shows differences in the type and timing of EV user charging behavior. Fast-charging users exhibit stable loads distributed throughout the day, reflecting their rapid energy replenishment needs. Slow-charging users show a significant increase in load starting at 3 PM, peaking at 9 PM, reflecting a concentrated charging trend among slow-charging users. This load structure is basically consistent with... Figure 5 The node carbon potential deviation curves are identical. A larger carbon potential deviation decreases the total EV charging load, while a smaller carbon potential deviation increases the total EV charging load. This indicates that the carbon potential deviation effectively guides user charging through an incentive mechanism, improving the efficiency of clean energy utilization. Combined with... Figure 4 and Figure 5 It is evident that the discharge behavior of EV users generally aligns with the changing trends of carbon potential deviation at each node, demonstrating a certain degree of load feedback characteristics. During the early morning period when carbon potential deviation is high, both fast-charging and slow-charging users exhibit active discharge behavior, indicating that the subsidy mechanism can effectively guide users to participate in system regulation. As carbon potential deviation decreases, the willingness of slow-charging users to respond declines rapidly, especially after 8 PM when they almost completely cease discharging, while fast-charging users maintain a moderate to high level, reflecting that fast-charging users are more sensitive to the incentive threshold.

[0092] This embodiment analyzes the low-carbon emission reduction capability, economic operation capability, and load peak shaving capability of the power distribution system under different optimization methods using three optimization schemes. Scheme 1 optimizes the power distribution system based solely on nodal carbon potential, without considering the dynamic response characteristics of EV loads. Scheme 2 introduces a traditional EV response mechanism, employing a fixed time-of-use electricity price subsidy strategy to guide EV users' charging and discharging behavior within a specific time period, but without considering the temporal changes in carbon emissions. Scheme 3 adopts the EV user response strategy based on the nodal carbon potential deviation feedback mechanism proposed in this invention, dynamically guiding the charging and discharging behavior of EV users according to changes in carbon potential deviation.

[0093] Figure 6 The graph shows the main grid power purchase for the three schemes. As can be seen, Scheme 1's 24-hour main grid power purchase is significantly higher, exceeding Scheme 2 by 35.02% and Scheme 3 by 39.54%. Scheme 2 is only 3.35% higher than Scheme 3. This indicates that Scheme 1, lacking EV (Electric Vehicle) regulation capability, significantly increases the system's dependence on the main grid. Schemes 2 and 3, both incorporating EV response mechanisms, possess a certain load regulation capability, thus their main grid power purchase is not significantly different.

[0094] Figure 7 , Figure 8 and Figure 9 The projected 3D surface plots illustrate the nodal carbon potential distribution of the three schemes at different time points. The results show that the nodal carbon potential of Scheme 1 at 1 AM is significantly higher than that of Schemes 2 and 3. This is mainly because the charging and discharging regulation mechanism for EV users is not introduced during this period, resulting in a lack of effective carbon potential reduction. For example, at node 11, the carbon potential of Scheme 1 reaches 0.49 kg / kWh, while Scheme 3, guided by the carbon potential deviation, allows EV users to reverse discharge, significantly reducing the carbon potential level to 0.08 kg / kWh, a reduction of 85.4%.

[0095] Overall, although Scheme 1 resulted in wind power output almost covering load demand at certain times, causing carbon potential at some nodes to approach zero, Schemes 2 and 3, by introducing EV demand response mechanisms, further reduced the overall carbon potential level of the system, making the distribution of carbon emissions more balanced throughout the day. In particular, Scheme 3 not only maintained a low carbon potential at several key nodes, but also effectively suppressed the duration of carbon potential peaks, demonstrating a stronger ability to dynamically regulate carbon emissions.

[0096] To further compare the impact of the three optimization schemes on the system's local energy regulation capability, Figure 10The diagram illustrates the response power of the MT (Metal Transport Unit) and energy storage system in three schemes. As shown in the figure, in Scheme 1, the MT operates at maximum output for extended periods, only reaching minimum output during a few low-load periods, exhibiting rigid operation characteristics. The energy storage system discharges most of the time, indicating a significant dependence of Scheme 1 on the main grid and distributed generation units. Scheme 2 shows increased MT output volatility compared to Scheme 1, with energy storage participating in charging during some low-carbon periods, but it does not consider the temporal changes in carbon emissions, resulting in limited regulatory effects. In Scheme 3, the MT unit output exhibits a high-output peak and low-output off-peak pattern. Output is increased during periods of high load, high electricity prices, or insufficient renewable energy to reduce electricity purchase costs, while output is reduced during periods of low load or abundant wind and solar power, prioritizing the use of clean energy to reduce carbon emissions. Energy storage regulation reflects a synergistic mechanism between carbon potential deviation and load timing. During peak periods, if the carbon potential deviation is large, discharge is prioritized to reduce emissions; when the carbon potential deviation is small and there is power redundancy, energy storage is charged, achieving coordinated optimization of source-storage-load.

[0097] Figure 11 In the comparison, the total EV load before optimization corresponds to Scheme 2, and the total EV load after optimization corresponds to Scheme 3. As can be seen from the comparison, the total EV load is significantly reduced overall after optimization, and the peak load during peak hours, such as from 7 pm to 11 pm, is also reduced. This indicates that the control strategy effectively suppressed the expansion of EV load, especially reducing the peak load during peak electricity consumption periods and alleviating the pressure on the power grid.

[0098] To analyze the impact of the proposed low-carbon economic optimization method for power distribution systems, which considers user psychology and carbon potential deviation, on the system's economic efficiency, Table 1 compares the cost results of the three optimization schemes.

[0099] Table 1 Costs of Three Optimization Schemes

[0100] Option 1 lacks an EV demand response mechanism and user discharge guidance, resulting in a high reliance on grid power during high-load or high-carbon periods. Grid power purchase costs are 33.74% and 38.05% higher than Options 2 and 33.05%, respectively. Furthermore, due to the significantly higher MT output compared to the other two options, Option 1 has the highest system operation and maintenance costs among the three options. Although Option 1 lacks EV incentive expenditures, its total cost is still slightly higher than Option 3 by 1.92%. Moreover, because it fails to guide EVs to participate in regulation during high-carbon periods, its total carbon emissions are the highest among the three options, exceeding Options 2 and 3 by 76.52% and 90.89%, respectively, placing it at a clear disadvantage in terms of low-carbon performance.

[0101] Option 2 introduces a traditional EV response mechanism, employing a fixed charge / discharge subsidy strategy. While this strategy guides EV user behavior to some extent, it fails to adjust to dynamic changes in the system's carbon potential, exhibiting response lag and inaccurate adjustment, thus failing to effectively reduce some peak carbon emissions. Ultimately, its carbon emissions are still 8.15% higher than Option 3. Furthermore, the fixed subsidy method lacks flexibility, resulting in an EV incentive subsidy that is 17,185.75 yuan higher than Option 3. This high incentive cost drives up its overall system cost, ultimately exceeding Option 3 by 4.68%.

[0102] Overall, Option 3 performs best in terms of both system carbon emission control and economic efficiency. This option not only achieves the lowest carbon emissions but also delivers optimal operating costs due to its more rational load regulation strategy.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential bias, characterized in that, include: Collect initial parameters of the distribution network, predicted wind and solar power output data, and main grid carbon potential, and calculate the node carbon potential deviation value based on the initial parameters of the distribution network, predicted wind and solar power output data, and main grid carbon potential. Based on the node carbon potential deviation value, a charge / discharge subsidy strategy is generated; A differentiated travel demand model is constructed by combining the charging and discharging subsidy strategy, and electric vehicle load forecast data is generated based on the differentiated travel demand model. A collaborative optimization model is constructed based on the electric vehicle load prediction data, the node carbon potential deviation value, and the charging and discharging subsidy strategy. The collaborative optimization model is solved to obtain the optimized operation strategy, the initial carbon emissions, and the corresponding optimized carbon potential deviation. The iteration stops under a preset condition. The charging and discharging subsidy strategy is iteratively optimized based on the optimized carbon potential deviation. When the iteration stops under the preset condition, the currently obtained optimized operating strategy and initial carbon emissions are output as the final operating strategy and final carbon emissions.

2. The low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in claim 1, characterized in that, The steps for calculating the nodal carbon potential deviation include: Based on the initial parameters of the distribution network and the predicted wind and solar power output data, power flow calculations are performed to determine the source of active power flowing into each electrical node; Calculate the carbon potential of each electrical node based on the active power source. The carbon potential of each electrical node is compared with the carbon potential of the main grid to obtain the node carbon potential deviation value.

3. The low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in claim 2, characterized in that, The steps for generating a charge / discharge subsidy strategy include: The node carbon potential deviation value is mapped to a differentiated charge and discharge subsidy value through a nonlinear function; Based on the aforementioned charge and discharge subsidy values, a charge and discharge subsidy strategy is generated to guide the charging and discharging behavior of fast-charging and slow-charging and discharging users.

4. The low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in claim 3, characterized in that, The steps to generate electric vehicle load forecast data include: For fast-charging and discharging users, a fast-charging and discharging travel demand model is constructed based on the probability distribution process, in conjunction with the aforementioned charging and discharging subsidy strategy. For slow-charging and discharging users, a slow-charging and discharging travel demand model is constructed based on the time preference distribution, in conjunction with the aforementioned charging and discharging subsidy strategy. The fast-charging and discharging travel demand model and the slow-charging and discharging travel demand model are used as differentiated travel demand models, and electric vehicle load prediction data are generated based on the differentiated travel demand models.

5. The low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in claim 4, characterized in that, The steps for constructing a collaborative optimization model that includes the electric vehicle load forecast data, the nodal carbon potential deviation value, and the charging and discharging subsidy strategy include: An objective function is established with the goal of minimizing the total economic cost of the power distribution system. The electric vehicle load prediction data, the node carbon potential deviation value, and the charging and discharging subsidy strategy are used as inputs to construct a collaborative optimization model in conjunction with the objective function.

6. The low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in claim 5, characterized in that, The steps for solving the collaborative optimization model to obtain the optimized operating strategy, initial carbon emissions, and corresponding optimized carbon potential deviation include: The collaborative optimization model is solved using an intelligent optimization algorithm to obtain the optimized operation strategy, initial carbon emissions, and optimized carbon potential deviation.

7. The low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in claim 6, characterized in that, The steps for outputting the final operating strategy and carbon emission results include: A preset deviation threshold is used as the iteration stopping condition to determine whether the optimized carbon potential deviation exceeds the deviation threshold. If the deviation threshold is exceeded, the charging and discharging subsidy strategy is adjusted according to the optimized carbon potential deviation, and the steps of constructing the fast charging and discharging travel demand model and the slow charging and discharging travel demand model are returned to be executed again. If the deviation threshold is not exceeded, the currently obtained optimized operating strategy and initial carbon emissions will be output as the final operating strategy and final carbon emissions.

8. A low-carbon economic optimization system for power distribution systems based on considering user psychology and carbon potential deviation, employing the method described in any one of claims 1-7, characterized in that, include: The data acquisition and processing module is used to collect initial parameters of the distribution network, wind and solar power output prediction data and main grid carbon potential, and calculate the node carbon potential deviation value. The subsidy strategy generation module is used to generate a charge / discharge subsidy strategy based on the node carbon potential deviation value. The load forecasting module is used to combine the charging and discharging subsidy strategy to distinguish between fast charging and discharging users and slow charging and discharging users to generate electric vehicle load forecasting data. The collaborative optimization solution module is used to construct and solve a collaborative optimization model containing the load forecast data, node carbon potential deviation value and subsidy strategy with the goal of minimizing the total economic cost of the power distribution system, so as to obtain the optimized operation strategy, initial carbon emissions and optimized carbon potential deviation. The iterative control and output module is used to preset the iteration stop condition, control the power distribution system to perform iterative operation according to the optimized carbon potential deviation, and output the final operation strategy and final carbon emission after the iteration stop condition is met.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the low-carbon economic optimization method for power distribution systems based on considering user psychology and carbon potential deviation as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the low-carbon economic optimization method for a power distribution system based on considering user psychology and carbon potential deviation as described in any one of claims 1 to 7.

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