Mountainous area urban power distribution network light storage and charging cooperative scheduling optimization method, system, device and medium

Abstract

The invention discloses a method, a system, equipment and a medium for optimizing light storage and charging cooperative scheduling of a power distribution network in a mountainous area city, and belongs to the technical field of light storage and charging scheduling of the power distribution network, and the method comprises the steps: predicting the photovoltaic power generation power and the load power; establishing an uncertainty set; establishing a photovoltaic power generation model, an energy storage system model and an electric vehicle charging model; constructing a multi-objective optimization model comprising an operation cost objective function, a network loss objective function and a power balance constraint condition; solving the multi-objective optimization model by adopting a robust optimization method to obtain a scheduling scheme; performing rolling optimization on the scheduling scheme by adopting a model prediction control strategy, and updating a control decision according to the real-time operation state of the power distribution network; and outputting the charging and discharging power control quantity of the energy storage system and the charging power control quantity of the electric vehicle charging pile. According to the method, the operation cost, the network loss and the power balance constraint are comprehensively considered, so that the overall performance optimization of the system is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution network light storage charging scheduling, in particular to a mountain city power distribution network light storage charging collaborative scheduling optimization method, system, device and medium. BACKGROUND

[0002] With the rapid development of distributed photovoltaic, energy storage system and electric vehicle charging piles in mountainous cities, the power distribution network is facing new operation challenges. The traditional scheduling method mainly optimizes single device or single target, and lacks consideration of the collaborative relationship between light storage and charging devices. Existing scheduling strategies are mostly based on deterministic models, which are difficult to cope with the uncertainty of photovoltaic power generation and load demand.

[0003] The terrain of mountainous city power distribution network is complex, the line is scattered, and the load density is uneven, so the traditional scheduling method has limited adaptability. The existing technology lacks consideration of environmental factors in mountainous areas, such as the impact of altitude on device performance and the impact of climate on photovoltaic power generation. There is a lack of unified modeling framework and optimization algorithm for collaborative operation of light storage and charging.

[0004] Existing energy management systems mostly use centralized architecture, which has problems such as large communication delay and low reliability when distributed energy is connected in large scale in mountainous areas. The traditional economic scheduling model does not fully consider the multiple values of energy storage and the mobility characteristics of electric vehicles. Uncertainty processing mostly uses probability prediction or scenario generation, which has high computational complexity and limited robustness.

[0005] Traditional control strategies use independent control methods, ignoring the coupling relationship between devices. The power change of one device will affect other devices through system coupling, and independent control is easy to cause system oscillation and uneven energy distribution. Existing methods lack adaptability to dynamic changes of photovoltaic and load, and it is difficult to achieve optimal power distribution and economic operation. SUMMARY

[0006] In view of the above problems, the present application provides a mountain city power distribution network light storage charging collaborative scheduling optimization method, system, device and medium.

[0007] Therefore, the technical problem solved by the present application is: for the problems of poor device collaboration, weak uncertainty processing ability, and difficulty in balancing economy and reliability in the collaborative scheduling of mountain city power distribution network light storage and charging, a light storage and charging collaborative operation model is established, a multi-objective optimization scheduling strategy is designed, the uncertainty of photovoltaic and load is processed, and dynamic adjustment of collaborative scheduling control is realized.

[0008] To solve the above technical problems, the present application provides the following technical solutions: a mountain city power distribution network light storage charging collaborative scheduling optimization method, which comprises, Collecting power generation data of photovoltaic power generation equipment, state of charge data of energy storage system, charging demand data of electric vehicle charging pile and load data of power distribution network in the power distribution network; Based on historical operation data and real-time environmental information, the photovoltaic power generation power and the load power are predicted; According to the deviation of the predicted value and the actual value of the photovoltaic power generation power and the deviation of the predicted value and the actual value of the load power, an uncertainty set is established; According to the rated power of the photovoltaic power generation equipment, the solar radiation intensity and the temperature, a photovoltaic power generation model is established, according to the state of charge of the energy storage system, the charging and discharging efficiency and the rated capacity, an energy storage system model is established, and according to the target state of charge of the electric vehicle, the current state of charge and the leaving time, an electric vehicle charging model is established; Based on the photovoltaic power generation model, the energy storage system model and the electric vehicle charging model, a multi-objective optimization model containing operation cost objective function, network loss objective function and power balance constraint condition is constructed; Based on the uncertainty set, the robust optimization method is used to solve the multi-objective optimization model, and the scheduling scheme of photovoltaic power generation equipment, energy storage system and electric vehicle charging pile is obtained; The rolling optimization of the scheduling scheme is carried out by using model predictive control strategy, and the control decision is updated according to the real-time operation state of the power distribution network; According to the updated control decision, the charging and discharging power control amount of the energy storage system and the charging power control amount of the electric vehicle charging pile are output.

[0009] As a preferred scheme of the mountainous city power distribution network photovoltaic storage charging collaborative scheduling optimization method, wherein: the photovoltaic power generation model includes establishing the relationship between photovoltaic power generation power and solar radiation intensity; The correction relationship between photovoltaic power generation power and temperature is established; Based on the relationship between photovoltaic power generation power and solar radiation intensity and the correction relationship between photovoltaic power generation power and temperature, the photovoltaic power generation model is established.

[0010] As a preferred scheme of the mountainous city power distribution network photovoltaic storage charging collaborative scheduling optimization method, wherein: the energy storage system model includes establishing the dynamic relationship between state of charge and charging and discharging power; The charging and discharging power constraint and the state of charge constraint of the energy storage system are set; Based on the dynamic relationship of the state of charge, the charging and discharging power constraint and the state of charge constraint, the energy storage system model is established.

[0011] As a preferred scheme of the mountainous city power distribution network light storage charging collaborative scheduling optimization method, the electric vehicle charging model is established by establishing the relationship between the charging demand and the target state of charge and the current state of charge. The relationship between the charging time window and the arrival time and the departure time is established. The electric vehicle charging model is established based on the charging demand relationship, the charging time window relationship and the maximum charging power constraint.

[0012] As a preferred scheme of the mountainous city power distribution network light storage charging collaborative scheduling optimization method, the multi-objective optimization model containing the operation cost objective function, the network loss objective function and the power balance constraint condition is constructed by establishing the operation cost objective function, the network loss objective function and the power balance constraint condition. The network loss objective function is established based on the line resistance and the line current of the power distribution network. The power balance constraint condition is established, which constrains the balance relationship between the photovoltaic power generation power, the energy storage system power, the electric vehicle charging power and the load power. The multi-objective optimization model is constructed based on the operation cost objective function, the network loss objective function and the power balance constraint condition.

[0013] The beneficial effects of the preferred technical scheme are that by introducing the prediction error of the photovoltaic power generation power and the load power as an uncertain parameter into the multi-objective optimization model, the robust optimization method is used to solve the optimal scheduling decision in the worst case within the range of all possible values of the uncertain parameter, and the adaptability of the scheduling scheme to the prediction error is improved. Compared with the deterministic optimization method which only considers the prediction value, the robust optimization method considers the range of the prediction error, so that the scheduling scheme can still maintain good performance when facing photovoltaic and load fluctuations in actual operation. The method avoids the high computational complexity of probability prediction and scenario generation, and improves the robustness of the system while ensuring the computational efficiency by constructing the uncertainty set and the worst case optimization.

[0014] As a preferred scheme of the mountainous city power distribution network light storage charging collaborative scheduling optimization method, the robust optimization method is used to solve the multi-objective optimization model by introducing the photovoltaic power generation power prediction error and the load power prediction error in the uncertainty set as uncertain parameters into the multi-objective optimization model. Within the range of all possible values of the uncertain parameter, the scheduling decision is solved to make the objective function optimal in the worst case. Obtain power generation scheduling quantity of photovoltaic power generation equipment, charge-discharge scheduling quantity of energy storage system and charging scheduling quantity of electric vehicle charging pile.

[0015] As a preferred scheme of the mountainous city power distribution network light storage charging collaborative scheduling optimization method, wherein: the control decision updating according to the real-time operation state of the power distribution network comprises: establishing a system state prediction equation, predicting the system state in the future period according to the current system state and the control input; The prediction time domain and the control time domain are set, and the optimization problem is solved in the prediction time domain; According to the deviation of the system state prediction value and the reference state and the control input, a rolling optimization objective function is established; In each control period, the rolling optimization objective function is re-solved based on the real-time operation state of the power distribution network, and the control decision is updated.

[0016] The beneficial effects of the preferred technical scheme are: by establishing a system state prediction equation to predict the system state in the future period, setting the prediction time domain and the control time domain to solve the optimization problem in the limited time domain, establishing a rolling optimization objective function according to the state deviation and the control input, and re-solving the optimization problem according to the real-time operation state in each control period to update the control decision, the dynamic adjustment collaborative scheduling control is realized. Compared with the static scheduling method of one-time optimization and execution, the model predictive control strategy has foresight and adaptability, and can dynamically adjust the control strategy according to the change of the system operation state. The rolling optimization mechanism makes the system re-plan based on the latest information in each control period, effectively coping with the dynamic change of photovoltaic output and load demand.

[0017] The application provides a mountainous city power distribution network light storage charging collaborative scheduling optimization system.

[0018] To solve the above technical problems, the application provides the following technical scheme: a mountainous city power distribution network light storage charging collaborative scheduling optimization system, comprising: a data acquisition module for acquiring power generation data of photovoltaic power generation equipment, state of charge data of energy storage system, charging demand data of electric vehicle charging pile and load data of power distribution network in the power distribution network; A prediction module is used for predicting photovoltaic power generation power and load power based on historical operation data and real-time environmental information. An uncertainty modeling module is used for establishing an uncertainty set according to the deviation of the predicted value and the actual value of the photovoltaic power generation power and the deviation of the predicted value and the actual value of the load power. The device modeling module is used for establishing a photovoltaic power generation model according to a rated power of a photovoltaic power generation device, a solar radiation intensity and a temperature, establishing an energy storage system model according to a state of charge of an energy storage system, a charging and discharging efficiency and a rated capacity, and establishing an electric vehicle charging model according to a target state of charge, a current state of charge and a leaving time of an electric vehicle. The multi-objective optimization module is used for constructing a multi-objective optimization model containing a running cost target function, a network loss target function and a power balance constraint condition based on the photovoltaic power generation model, the energy storage system model and the electric vehicle charging model. The robust optimization solving module is used for solving the multi-objective optimization model by using a robust optimization method based on the uncertainty set, and obtaining a scheduling scheme of the photovoltaic power generation device, the energy storage system and the electric vehicle charging pile. The model predictive control module is used for rolling optimization of the scheduling scheme by using a model predictive control strategy, and updating a control decision according to a real-time running state of the distribution network. The control output module is used for outputting a charging and discharging power control amount of the energy storage system and a charging power control amount of the electric vehicle charging pile according to the updated control decision.

[0019] The present application provides a kind of computer equipment, including memory and processor, the memory is stored with computer program, the processor executes the computer program when realizing the step of the mountainous city distribution network light storage and charge collaborative scheduling optimization method.

[0020] The present application provides a kind of computer readable storage medium, which is stored with computer program, the computer program is executed by processor when realizing the step of the mountainous city distribution network light storage and charge collaborative scheduling optimization method.

[0021] The beneficial effects of the present application are: by establishing photovoltaic, energy storage and electric vehicle models, the running characteristics of different devices are accurately described.Compared with single device scheduling, the system running efficiency is improved, and the new energy consumption rate is improved.

[0022] By establishing an uncertainty set of prediction errors, robust optimization is used to achieve optimal target in the worst case.Compared with deterministic optimization, good performance is maintained under different uncertainty levels, and the robustness of the scheduling scheme is improved.

[0023] By setting prediction time domain and control time domain, the optimization problem is re-solved according to real-time state in each control cycle.Rolling optimization mechanism enables the system to dynamically adjust the running strategy, and reduces the influence of prediction error.

[0024] By comprehensively considering the operation cost, network loss and power balance constraints, the overall performance optimization of the system is realized. The method provides technical support for the intelligent development of the distribution network in mountainous cities, and has practical value for promoting new energy consumption and improving operation economy. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and all other drawings obtained by those skilled in the art without creative labor should be within the protection scope of the present application.

[0026] Figure 1 The overall flowchart of a mountainous city distribution network light storage charging collaborative scheduling optimization method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the present application.

[0028] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a mountainous city distribution network light storage charging collaborative scheduling optimization method, comprising: Step 1: Collecting the power generation data of photovoltaic power generation equipment, the state of charge data of energy storage system, the charging demand data of electric vehicle charging pile and the load data of distribution network in the distribution network; Step 2: Predicting the photovoltaic power generation power and the load power based on historical operation data and real-time environmental information; Step 3: Establishing an uncertainty set according to the deviation of the predicted value and the actual value of the photovoltaic power generation power and the deviation of the predicted value and the actual value of the load power; Step 4: Establishing a photovoltaic power generation model according to the rated power of photovoltaic power generation equipment, solar radiation intensity and temperature, establishing an energy storage system model according to the state of charge of energy storage system, charging and discharging efficiency and rated capacity, and establishing an electric vehicle charging model according to the target state of charge of electric vehicle, current state of charge and leaving time; Step 5: Based on the photovoltaic power generation model, the energy storage system model and the electric vehicle charging model, a multi-objective optimization model containing operation cost objective function, network loss objective function and power balance constraint condition is constructed; Step 6: Based on the uncertainty set, a robust optimization method is used to solve the multi-objective optimization model to obtain the scheduling scheme of photovoltaic power generation equipment, energy storage system and electric vehicle charging pile; Step 7: The scheduling scheme is rolled and optimized using the model predictive control strategy, and the control decision is updated according to the real-time operation state of the distribution network; Step 8: According to the updated control decision, the charge and discharge power control quantity of the energy storage system and the charging power control quantity of the electric vehicle charging pile are output.

[0029] The core problem of coordinated scheduling of photovoltaic storage and charging in mountainous city distribution network is reflected in the coordinated control and uncertainty processing among multiple devices. The photovoltaic power generation data, energy storage state of charge data, charging demand data and load data collected in step 1 have time-varying and fluctuating characteristics, and direct use of these data cannot guarantee the scheduling accuracy. Step 2 is based on historical data and real-time environmental information for prediction, but photovoltaic output is greatly affected by weather, and load is affected by user behavior, so there is an error in prediction. Step 3 quantifies the prediction error as an uncertainty set, solving the mathematical description problem of uncertainty. Step 4 faces the problem of how to accurately describe the operating characteristics of different devices when establishing the model of three types of devices. Photovoltaic power generation is affected by radiation and temperature, energy storage state of charge changes dynamically, and electric vehicle charging is limited by time window. Step 5 needs to balance multiple objectives such as operating cost, network loss and power balance when constructing a multi-objective optimization model, and single objective optimization cannot meet the actual demand. Step 6 uses robust optimization to handle uncertainty, finding the optimal solution in the worst case within the prediction error range, solving the problem of insufficient robustness of deterministic optimization method. Step 7 model predictive control re-optimizes according to the real-time state in each period, solving the problem of slow response of static scheduling method to dynamic changes.

[0030] The embodiment realizes a complete mountainous city power distribution network light storage charging collaborative scheduling optimization method process. Step 1 obtains real-time information of system operation through data acquisition, providing a data basis for subsequent prediction and optimization. The prediction of step 2 provides input for the establishment of the uncertainty set of step 3 and the establishment of the device model of step 4, realizing effective utilization of data. The three types of device models established in step 4 provide a mathematical basis for the construction of the multi-objective optimization model of step 5, enabling the optimization model to accurately reflect the operation constraints and characteristics of each device. The multi-objective optimization model constructed in step 5 considers economy and reliability, providing optimization objectives and constraint conditions for the robust optimization of step 6. The robust optimization method of step 6 introduces an uncertainty set to solve the optimal scheduling scheme in the worst case, improving the adaptability of the scheduling scheme to photovoltaic and load fluctuations. The model predictive control of step 7 uses a rolling optimization strategy to update control decisions based on real-time operating conditions at each control period, realizing dynamic adjustment and forward control. Step 8 outputs the charge and discharge power control and the charging power control, completing the complete method process from data acquisition to control output, and realizing collaborative optimization scheduling of light storage charging equipment in mountainous city power distribution networks.

[0031] Embodiment 2, as an embodiment of the present application, based on the previous embodiment, provides a mountainous city power distribution network light storage charging collaborative scheduling optimization method, comprising: In step 4: a photovoltaic power generation model is established according to the rated power of the photovoltaic power generation device, the solar radiation intensity and the temperature, a storage system model is established according to the state of charge of the storage system, the charging and discharging efficiency and the rated capacity, and an electric vehicle charging model is established according to the target state of charge of the electric vehicle, the current state of charge and the departure time, comprising the following steps A1-A9: A1: a relationship between photovoltaic power generation and solar radiation intensity is established; A2: a correction relationship between photovoltaic power generation and temperature is established; A3: based on the relationship between photovoltaic power generation and solar radiation intensity and the correction relationship between photovoltaic power generation and temperature, a photovoltaic power generation model is established.

[0032] A4: a dynamic relationship between state of charge and charging and discharging power is established; A5: charging and discharging power constraints and state of charge constraints of the storage system are set; A6: based on the dynamic relationship of the state of charge, the charging and discharging power constraints and the state of charge constraints, a storage system model is established.

[0033] A7: a relationship between electric vehicle charging demand and target state of charge and current state of charge is established; A8: a relationship between a charging time window and arrival time and departure time is established; A9: establishing an electric vehicle charging model based on the charging demand relationship, the charging time window relationship, and the maximum charging power constraint.

[0034] In the embodiments of the present application, in step A3, the photovoltaic power generation model is established by: According to the rated power of the photovoltaic power generation device, the photovoltaic efficiency, the real-time solar radiation intensity, and the solar radiation intensity under standard test conditions, a proportional relationship between the photovoltaic power generation power and the solar radiation intensity is established. According to the temperature coefficient, the photovoltaic cell temperature, and the standard test temperature, a correction coefficient of the temperature deviation on the photovoltaic power generation power is established.

[0035] The photovoltaic power generation power is calculated as: wherein P is the photovoltaic power generation power, P0 is the rated power, η is the photovoltaic efficiency, I is the solar radiation intensity, I0 is the radiation intensity under standard test conditions, α is the temperature coefficient, T is the cell temperature, T0 is the standard test temperature.

[0036] In an optional embodiment, in step A3, the photovoltaic power generation model can be established by: Considering the influence of the altitude of mountainous areas on photovoltaic power generation, a photovoltaic power generation model containing an altitude correction is established. According to the altitude and the characteristic height, an altitude correction factor is calculated using an exponential function. The altitude correction factor is applied to the solar radiation intensity, and the corrected solar radiation intensity is the product of the measured radiation intensity and the altitude correction factor. The photovoltaic power generation power is calculated according to the corrected solar radiation intensity, the rated power, the photovoltaic efficiency, and the temperature correction relationship. This method takes into account the influence of the altitude of mountainous areas on atmospheric transparency and solar radiation intensity. The atmosphere is thinner at high altitudes, which reduces the attenuation of solar radiation, and the altitude correction factor improves the accuracy of the photovoltaic power generation power calculation. At the same time, considering the characteristic of lower temperature at high altitudes, the actual measured cell temperature is used in the temperature correction relationship.

[0037] In another optional embodiment, in step A3, the photovoltaic power generation model can also be established by: A photovoltaic power generation model considering shading effects is established. According to the terrain data and the position of the sun, the shading area ratio of photovoltaic components at different times is calculated as the shading coefficient. The shading coefficient ranges from zero to one, zero indicating complete shading and one indicating no shading. The shading coefficient is applied to the photovoltaic power generation power, and the photovoltaic power generation power under the shading effect is the product of the standard photovoltaic power generation power and the shading coefficient. According to the movement of the cloud layer and the change of the mountain shadow, the shading coefficient at different times is dynamically updated. Through the comparison of the actual power generation and the theoretical calculation value of the historical operation data, the calculation method of the shading coefficient is corrected. This method makes the photovoltaic power generation model reflect the shading effect caused by the complex terrain in mountainous areas, and improves the accuracy of photovoltaic power generation power prediction.

[0038] In the embodiment of the application, in step A6, the energy storage system model is established by: according to the current state of charge, charging power, discharging power, charging efficiency, discharging efficiency, rated capacity and time step of the energy storage system, a dynamic updating equation of the state of charge is established. The state of charge at the next time is equal to the current state of charge plus the influence of charging and discharging power on the state of charge. In the charging process, the state of charge increment is equal to the charging power multiplied by the charging efficiency divided by the rated capacity, and then multiplied by the time step. In the discharging process, the state of charge decrement is equal to the discharging power divided by the discharging efficiency divided by the rated capacity, and then multiplied by the time step.

[0039] The dynamic updating equation of the state of charge is: wherein is the photovoltaic power generation power, is the rated power, is the photovoltaic efficiency, is the solar radiation intensity, is the radiation intensity under standard test conditions, is the temperature coefficient, is the battery temperature, is the standard test temperature. The model comprehensively considers the effects of solar radiation intensity and temperature on photovoltaic power generation power, and is suitable for photovoltaic power generation power calculation under different altitudes and climate conditions in mountainous cities.

[0040] In an optional embodiment, in step A6, the establishment of the energy storage system model can be achieved by: establishing an energy storage system model considering the energy storage life attenuation. The cycle life of the energy storage system is related to the depth of discharge, and the greater the depth of discharge, the shorter the cycle life. The depth of discharge is defined as the ratio of the single discharge amount to the rated capacity. According to the depth of discharge and the life attenuation index, the cycle life of the energy storage system is calculated. On the basis of the state of charge dynamic updating equation, the life attenuation constraint condition is added. The cumulative cycle number is calculated according to the depth of discharge of each charge and discharge, and when the cumulative cycle number exceeds the calculated cycle life, the energy storage system needs to be replaced or maintained. In the optimization scheduling, the life of the energy storage system is prolonged by limiting the depth of discharge to avoid frequent deep discharge.

[0041] In another optional embodiment, in step A6, the establishment of the energy storage system model can also be achieved by: establishing an energy storage system model considering the dynamic change of charge and discharge efficiency. The charge and discharge efficiency of the energy storage system is not constant, but varies with the state of charge and the charge and discharge power. When the state of charge is low or high, the charge and discharge efficiency decreases. When the charge and discharge power is large, the charge and discharge efficiency also decreases due to the increase of internal resistance loss. According to the state of charge and the charge and discharge power of the energy storage system, a dynamic calculation relationship of the charge and discharge efficiency is established. The charge and discharge efficiency is determined by using a piecewise function or a lookup table method, and different efficiency values correspond to different state of charge intervals and power intervals. In the state of charge dynamic updating equation, the dynamically calculated charge and discharge efficiency is used instead of the fixed efficiency value.

[0042] In the embodiments of the present application, in step 4, the establishment of the electric vehicle charging model is achieved by: calculating the charging demand of the electric vehicle according to the target state of charge, the arrival state of charge and the battery capacity. The charging demand is equal to the difference between the target state of charge and the arrival state of charge multiplied by the battery capacity, which represents the amount of electricity that needs to be supplemented by the electric vehicle. The charging demand calculation formula is: wherein is the charging demand of the i-th electric vehicle, is the target state of charge, is the arrival state of charge, is the battery capacity. According to the arrival time and the departure time of the electric vehicle, the charging time window is determined. The charging time window is the available charging period between the arrival time and the departure time. According to the charging demand, the charging time window and the maximum charging power, the constraint condition of the electric vehicle charging power is established. The charging power does not exceed the maximum charging power, and the charging demand is completed within the charging time window. The calculation of the electric vehicle charging power needs to satisfy both the power limit and the time window constraint, and the charging power takes the smaller value between the maximum charging power and the demand power calculated according to the remaining time. Based on the charging demand, the charging time window and the maximum charging power constraint, the electric vehicle charging model is established.

[0043] In an optional embodiment, in step 4, the electric vehicle charging model can be established by: establishing an electric vehicle charging model considering ordered charging. In order to avoid the concentration of load caused by a large number of electric vehicles charging at the same time, a charging control factor is introduced to adjust the charging power. The charging control factor ranges from zero to one, representing the proportion of the charging power relative to the maximum charging power. When the charging control factor is one, the electric vehicle charges at the maximum power. When the charging control factor is less than one, the electric vehicle charges at a reduced power, realizing peak-shifting charging. The actual charging power of the i-th electric vehicle at time t is equal to the product of the charging control factor and the maximum charging power. By optimizing the adjustment of the charging control factor of different electric vehicles in different time periods, the time distribution optimization of the charging load is realized under the premise of meeting the charging demand of all electric vehicles.

[0044] In another optional embodiment, in step 4, the electric vehicle charging model can also be established by: establishing an electric vehicle charging model considering charging priority. Different electric vehicles have different degrees of urgency in charging demand. According to the length of the charging time window, the size of the charging demand and the user-set priority, each electric vehicle is assigned a charging priority. Electric vehicles with short charging time windows, large charging demands and high user priorities are assigned higher charging priorities. In the optimization scheduling, the charging demand of high-priority electric vehicles is prioritized. When the system power supply capacity is limited, the charging power of high-priority electric vehicles is prioritized, and the charging power of low-priority electric vehicles can be appropriately reduced or delayed. Through the priority mechanism, the limited charging resources are reasonably allocated under the premise of meeting the charging time window constraints.

[0045] In step 5: based on the photovoltaic power generation model, the energy storage system model and the electric vehicle charging model, a multi-objective optimization model containing an operation cost objective function, a network loss objective function and power balance constraints is constructed, including steps B1-B4: B1: establishing an operation cost objective function, the operation cost objective function including the cost of power exchange with the main grid, the cost of energy storage system use and the cost of electric vehicle charging service; B2: establishing a network loss objective function, the network loss objective function being calculated based on the line resistance and line current of the distribution network; B3: establishing power balance constraints, the power balance constraints constraining the balance relationship between photovoltaic power generation power, energy storage system power, electric vehicle charging power and load power; B4: based on the operation cost objective function, the network loss objective function and the power balance constraints, constructing a multi-objective optimization model.

[0046] In step 6: based on the uncertainty set, a robust optimization method is used to solve the multi-objective optimization model to obtain a scheduling scheme of the photovoltaic power generation device, the energy storage system and the electric vehicle charging pile, including steps C1-C3: C1: the photovoltaic power generation power prediction error and the load power prediction error in the uncertainty set are introduced into the multi-objective optimization model as uncertain parameters; C2: in all possible value ranges of the uncertain parameters, a scheduling decision is solved to make the objective function optimal in the worst case; C3: the power generation scheduling quantity of the photovoltaic power generation device, the charge-discharge scheduling quantity of the energy storage system and the charging scheduling quantity of the electric vehicle charging pile are obtained.

[0047] In the embodiment of the application, in step 6, the multi-objective optimization model is solved by introducing the photovoltaic power generation power prediction error and the load power prediction error as uncertain parameters into the multi-objective optimization model. The actual value of the photovoltaic power generation power is equal to the predicted value plus the prediction error, and the actual value of the load power is equal to the predicted value plus the prediction error. The prediction error varies within the range defined by the uncertainty set. A robust optimization model is established, and the optimization objective is to make the comprehensive objective function optimal in the worst case within all possible value ranges of the uncertain parameters. The mathematical expression of the robust optimization model is: wherein is a decision variable, including the photovoltaic power generation scheduling quantity, the energy storage charge-discharge scheduling quantity and the electric vehicle charging scheduling quantity, is an uncertain parameter, including the photovoltaic power generation power prediction error and the load power prediction error, is an uncertainty set, is a comprehensive objective function. The model first optimizes the decision variable, then finds the worst case of the objective function in all possible values of the uncertain parameters, and finally solves the decision scheme optimal in the worst case. By solving the robust optimization model, the power generation scheduling quantity of the photovoltaic power generation device, the charge-discharge scheduling quantity of the energy storage system and the charging scheduling quantity of the electric vehicle charging pile are obtained. The scheduling scheme can maintain good operation performance within the prediction error range of photovoltaic and load.

[0048] In an optional embodiment, in step 6, solving the multi-objective optimization model can be achieved by: adopting a box uncertainty set to describe the range of prediction errors. The box uncertainty set is defined as the prediction errors varying between respective independent upper and lower bounds, the photovoltaic power prediction error varying between a negative maximum deviation and a positive maximum deviation, and the load power prediction error varying between a negative maximum deviation and a positive maximum deviation. The robust optimization model is converted into a deterministic optimization problem for solving. The maximization problem is converted into a constraint condition by introducing auxiliary variables and constraint conditions. For each extreme value condition of the uncertain parameters, a corresponding constraint condition is established to ensure that the objective function meets the optimization requirements in all extreme conditions. The converted deterministic optimization problem is solved using mixed integer linear programming or quadratic programming.

[0049] In another optional embodiment, in step 6, solving the multi-objective optimization model can also be achieved by: adopting an ellipsoidal uncertainty set to describe the correlation of prediction errors. The ellipsoidal uncertainty set takes into account the correlation between different prediction errors, and is more consistent with the distribution characteristics of actual prediction errors than the box set. According to historical prediction error data, the covariance matrix of the photovoltaic power prediction error and the load power prediction error is calculated, and the ellipsoidal uncertainty set is determined by the mean and the covariance matrix. The robust optimization problem is converted into a deterministic optimization problem using duality theory. By Lagrange duality method, the dual form of the inner maximization problem is introduced into the constraint condition of the outer minimization problem. The dual variables represent the values of the uncertain parameters on the boundary of the ellipsoidal set, and the optimal solution of the original problem is obtained by solving the dual problem.

[0050] In step 7: adopting a model predictive control strategy to perform rolling optimization on the scheduling scheme, and updating the control decision according to the real-time operation state of the distribution network, including steps D1-D4: D1: establishing a system state prediction equation to predict the system state in the future period according to the current system state and control input; D2: setting a prediction time domain and a control time domain, and solving the optimization problem in the prediction time domain; D3: establishing a rolling optimization objective function according to the deviation of the system state prediction value from the reference state and the control input; D4: in each control period, re-solving the rolling optimization objective function based on the real-time operation state of the distribution network to update the control decision.

[0051] Embodiment 3, which is an embodiment of the present application, provides a mountainous city distribution network photovoltaic storage and charging collaborative scheduling optimization method based on the previous embodiment, comprising: In the system initialization and data collection phase, the basic database of the mountainous city distribution network is established, including network topology, device parameters, historical operation data, etc. The technical parameters and operation constraints of the light storage and charging equipment are configured, and the device model database is established. The parameters of the model predictive controller are initialized, including the prediction time domain, control time domain, weight matrix, etc.

[0052] A real-time data acquisition system is established to obtain real-time information such as photovoltaic power generation, energy storage status, electric vehicle charging demand, and load changes. Configure the communication network and data transmission protocol to ensure the real-time and reliability of the data. Establish a weather forecast interface to obtain environmental information such as light, temperature, and wind speed.

[0053] In the load and new energy prediction phase, based on historical data and real-time information, a deep learning model is used for short-term load prediction. Combined with weather forecast data, the photovoltaic power generation output in the next 24 hours is predicted. Analyze the charging behavior pattern of electric vehicles and predict the spatial and temporal distribution of charging demand. Establish a prediction error evaluation mechanism to calculate the prediction accuracy and confidence interval. According to the prediction error, an uncertainty set is constructed to provide input for robust optimization. An online update mechanism for the prediction model is established to continuously improve the prediction accuracy based on actual operation data.

[0054] In the multi-objective optimization solution phase, a multi-objective optimization model for light storage and charging collaborative scheduling is established, considering multiple objectives such as economy, reliability, and environmental protection. An improved multi-objective particle swarm optimization algorithm is designed to solve the Pareto optimal solution set. The elite preservation strategy and dynamic inertia weight are introduced to improve the convergence and diversity of the algorithm. Consider the operating constraints, including power balance, device capacity, voltage quality, etc. Handle uncertainty factors by using robust optimization methods to deal with prediction errors. Through weight coefficient adjustment, select the appropriate scheduling strategy under different operating scenarios.

[0055] In the model predictive control execution phase, based on the optimization results, the light storage and charging collaborative scheduling plan for the future period is developed. Implement a rolling optimization strategy, updating the optimization scheme once every control period. Adjust the control parameters and constraints according to the real-time system state. Implement energy storage charging and discharging control to optimize the role of energy storage in peak shaving, frequency regulation, etc. Implement orderly charging control for electric vehicles to avoid system impact caused by concentrated charging load. Coordinate photovoltaic power generation and load demand to maximize the use of local energy.

[0056] In the distributed energy trading phase, based on blockchain technology, an energy trading platform between transformers is established. According to the energy surplus or deficit of each transformer area, develop energy trading strategies. Automatically execute transactions through smart contracts to ensure the transparency and credibility of transactions. Calculate the transaction price and volume to achieve energy mutual aid between transformer areas. Record transaction history and establish a credit evaluation mechanism. Monitor the transaction execution process to ensure the safety and compliance of transactions.

[0057] System monitoring and evaluation phase, real-time monitoring system running state, including voltage, current, power and other key parameters. Evaluation of the implementation effect of scheduling strategy, analysis of economic and environmental benefits. Statistics system reliability indicators, including power supply reliability rate, voltage qualified rate, etc. Establish an abnormal detection mechanism, timely detection and handling of system failure. Generate operation report, provide decision support for operation personnel. Establish performance evaluation database, provide data basis for algorithm optimization and strategy improvement.

[0058] Specifically, in the light storage and charging collaborative operation model, the system state vector is defined as: (1) In the formula, is the state vector of the ith node, is the node voltage, is the node current, is the photovoltaic power, is the energy storage power, is the electric vehicle charging power.

[0059] Photovoltaic power generation model: (2) In the formula, is the photovoltaic power, is the rated power, is the photovoltaic efficiency, G(t) is the solar radiation intensity, is the radiation intensity under standard test conditions, is the battery temperature, is the standard test temperature. is the temperature coefficient, as shown in Table 1, which reflects the degree of photovoltaic cell output power decrease with temperature rise. Determined by photovoltaic module type.

[0060] Table 1, temperature coefficient correspondence table

[0061] Energy storage system model: (3) In the formula, is the state of charge, is the charging efficiency, is the charging power, is the discharging power, is the discharging efficiency, is the rated capacity, is the time step.

[0062] Electric vehicle charging model: (4) wherein, is the charging power of the ith electric vehicle, is the maximum charging power, is the target state of charge, is the current state of charge, is the departure time, is the battery capacity.

[0063] In the multi-objective optimization model, the objective function is: (5) wherein F is the comprehensive objective function, is the operation cost function, is the network loss function, is the carbon emission function, which is calculated by the product of unit power and carbon emission factor. is the weight coefficient, satisfying In the embodiment, is taken to embody the optimization strategy of giving priority to operation cost and giving priority to network loss and carbon emission.

[0064] The operation cost function is: (6) wherein, is the grid price, is the exchange power with the main grid, is the storage usage cost, is the charging service fee, T is the optimization time domain, is the number of electric vehicles.

[0065] The network loss function is: (7) wherein, is the resistance of the lth line, is the line current, is the number of lines.

[0066] The power balance constraint is: (8) wherein, is the load power, is the network loss power, is the number of photovoltaics, is the number of storages.

[0067] In the uncertainty modeling, the photovoltaic power generation prediction error model is: (9) wherein, is the predicted photovoltaic power, Ppv(t) is the actual photovoltaic power, Ppred(t) is the predicted error.

[0068] Load prediction uncertainty: (10) where, Ppred(t) is the predicted load power, Ppv(t) is the actual load power, Ppred(t) is the load prediction error.

[0069] Robust optimization model: (11) where, x(t) is the decision variable, p(t) is the uncertain parameter, P is the uncertainty set.

[0070] In the model predictive control strategy, the state prediction equation is: (12) where, x(t+k|t) is the state prediction at time t+k given the state at time t, A is the state transition matrix, B is the control input matrix, u(t) is the control input, w(t) is the process noise.

[0071] Rolling optimization objective: (13) where, J is the performance index, T is the prediction horizon, Q is the state weight matrix, R is the control weight matrix, xref is the reference state.

[0072] In the energy storage optimal scheduling, the energy storage power constraint is: (14) where, Pmin is the minimum power, Pmax is the maximum power.

[0073] State of charge constraint: (15) where, SOCmin is the minimum state of charge, SOCmax is the maximum state of charge.

[0074] Energy storage life model: (16) where, N is the cycle life, DOD is the depth of discharge, and is the life decay index.

[0075] In the electric vehicle scheduling strategy, the charging demand prediction: (17) where, is the charging demand of the i-th electric vehicle, is the state of charge upon arrival.

[0076] Charging time window: (18) where, is the arrival time, is the start charging time.

[0077] Orderly charging scheduling: (19) where, is the charging control factor, .

[0078] In the distributed energy transaction mechanism, inter-substation energy transaction: (20) where, is the transaction power of substation i to substation j, is the transaction coefficient, representing the response ability of the substation to the electricity price signal, whose value is determined by the internal exportable power and the bid deviation. is the marginal cost of substation i.

[0079] Blockchain consensus mechanism: (21) where, is the hash value of the new block, is the hash value of the previous block, Transactions is the transaction data, and Nonce is the random number.

[0080] Smart contract execution: (22) where, is the smart contract execution result, Conditions is the execution condition, States is the system state, and Actions is the execution action.

[0081] In the mountainous environment adaptability, the altitude correction factor: (23) where, is the altitude correction factor, h is the altitude, is the characteristic height.

[0082] Temperature influence model: (24) In the formula, is the corrected power, is the temperature correction coefficient, which is obtained by fitting the measured I-V curve of the component, and in this embodiment, a single crystal silicon component is used ; is the actual temperature, is the reference temperature.

[0083] In the system reliability evaluation, the power supply reliability rate: (25) In the formula, is the power supply reliability rate, is the normal power supply time, is the total time.

[0084] Voltage quality index: (26) In the formula, is the voltage quality index, is the rated voltage, and N is the number of measurement points.

[0085] In the economic benefit evaluation, the net present value calculation: (27) In the formula, is the net present value, is the cash flow in the tth year, r is the discount rate, is the initial investment.

[0086] Internal rate of return: (28) In the formula, is the internal rate of return.

[0087] Embodiment 4, an embodiment of the present application, provides a mountainous city power distribution network light storage charging collaborative scheduling optimization method, in order to verify the beneficial effects of the present application, scientific demonstration is carried out through experiment.

[0088] In this study, a typical station area in a mountainous city is taken as an example for simulation verification, which includes 5MW distributed photovoltaic, 2MWh energy storage system and 100 electric vehicle charging piles. MATLAB / Simulink and Python platform are used to build the simulation environment, considering the typical operation scenarios and extreme weather conditions in spring, summer, autumn and winter.

[0089] Table 1: Performance comparison of different scheduling methods

[0090] Table 2: Analysis of operation effect in different seasons

[0091] Table 3: Robustness analysis results

[0092] The simulation results show that the light storage and charging collaborative scheduling optimization method proposed in the patent has achieved significant improvement in multiple performance indicators. Compared with the traditional scheduling method, the daily operation cost is reduced by 31.6%, the network loss rate is reduced by 42.9%, the new energy consumption rate is increased to 95.6%, and the user satisfaction reaches 92.8%. In the adaptability analysis of different seasons, the summer benefit is the best, and the annual average economic benefit reaches 825,000 yuan. Robustness analysis shows that the method maintains good performance under various uncertainty levels, verifying the effectiveness and practicality of the method.

[0093] Embodiment 5 is an embodiment of the present application, which provides a light storage and charging collaborative scheduling optimization system for a mountainous city power distribution network, comprising: a data acquisition module for acquiring power generation data of photovoltaic power generation equipment, state of charge data of energy storage systems, charging demand data of electric vehicle charging piles, and load data of the power distribution network; a prediction module for predicting photovoltaic power generation power and load power based on historical operation data and real-time environmental information; an uncertainty modeling module for establishing an uncertainty set according to the deviation of the predicted value and the actual value of the photovoltaic power generation power and the deviation of the predicted value and the actual value of the load power; a device modeling module for establishing a photovoltaic power generation model according to the rated power, solar radiation intensity and temperature of the photovoltaic power generation equipment, establishing an energy storage system model according to the state of charge, charging and discharging efficiency and rated capacity of the energy storage system, and establishing an electric vehicle charging model according to the target state of charge, current state of charge and departure time of the electric vehicle; a multi-objective optimization module for constructing a multi-objective optimization model containing an operation cost objective function, a network loss objective function and a power balance constraint condition based on the photovoltaic power generation model, the energy storage system model and the electric vehicle charging model; a robust optimization solving module for solving the multi-objective optimization model based on the uncertainty set using a robust optimization method to obtain a scheduling scheme for photovoltaic power generation equipment, energy storage systems and electric vehicle charging piles; A model predictive control module is configured to perform rolling optimization on the scheduling scheme by using a model predictive control strategy, and update the control decision according to the real-time operation state of the power distribution network. A control output module is configured to output the charge-discharge power control quantity of the energy storage system and the charging power control quantity of the electric vehicle charging pile according to the updated control decision.

[0094] The embodiment also provides an electronic device suitable for the mountainous city power distribution network photovoltaic energy storage and charging collaborative scheduling optimization method, which comprises a memory and a processor.

[0095] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by the processor to implement the mountainous city power distribution network photovoltaic energy storage and charging collaborative scheduling optimization method.

[0096] The storage medium provided by the embodiment and the mountainous city power distribution network photovoltaic energy storage and charging collaborative scheduling optimization method provided by the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by software and necessary general hardware, and of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method of each embodiment of the present application.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for optimizing the coordinated scheduling of photovoltaic, energy storage, and charging power distribution networks in mountainous cities, characterized in that: include, Collect power generation data from photovoltaic power generation equipment, state of charge data from energy storage systems, charging demand data from electric vehicle charging piles, and load data from the distribution network. Predict photovoltaic power generation and load power based on historical operating data and real-time environmental information; An uncertainty set is established based on the deviations between the predicted and actual values ​​of photovoltaic power generation and the predicted and actual values ​​of load power. A photovoltaic power generation model is established based on the rated power, solar radiation intensity and temperature of the photovoltaic power generation equipment; an energy storage system model is established based on the state of charge, charging and discharging efficiency and rated capacity of the energy storage system; and an electric vehicle charging model is established based on the target state of charge, current state of charge and departure time of the electric vehicle. Based on the photovoltaic power generation model, the energy storage system model, and the electric vehicle charging model, a multi-objective optimization model is constructed, which includes an operating cost objective function, a network loss objective function, and power balance constraints. Based on the aforementioned uncertainty set, a robust optimization method is used to solve the multi-objective optimization model to obtain a scheduling scheme for photovoltaic power generation equipment, energy storage system, and electric vehicle charging piles. The scheduling scheme is continuously optimized using a model predictive control strategy, and the control decisions are updated based on the real-time operating status of the distribution network. Based on the updated control decisions, the outputs control quantities for the charging and discharging power of the energy storage system and the charging power of the electric vehicle charging pile are generated.

2. The method for coordinated scheduling optimization of photovoltaic, energy storage, and charging power distribution networks in mountainous cities as described in claim 1, characterized in that: The establishment of the photovoltaic power generation model includes establishing the relationship between photovoltaic power generation power and solar radiation intensity. Establish a corrected relationship between photovoltaic power generation and temperature variation; A photovoltaic power generation model is established based on the relationship between photovoltaic power generation and solar radiation intensity and the correction relationship between photovoltaic power generation and temperature.

3. The method for coordinated scheduling optimization of photovoltaic, energy storage, and charging power distribution networks in mountainous cities as described in claim 2, characterized in that: The establishment of the energy storage system model includes establishing the dynamic relationship between the state of charge and the charging and discharging power. Set charging and discharging power constraints and state of charge constraints for the energy storage system; Based on the dynamic relationship of the state of charge, the charging and discharging power constraints, and the state of charge constraints, an energy storage system model is established.

4. The method for coordinated scheduling optimization of photovoltaic, energy storage, and charging power distribution networks in mountainous cities as described in claim 3, characterized in that: The establishment of the electric vehicle charging model includes establishing the relationship between electric vehicle charging demand and target state of charge and current state of charge. Establish the relationship between the charging time window and the arrival and departure times; An electric vehicle charging model is established based on the charging demand relationship, the charging time window relationship, and the maximum charging power constraint.

5. The method for coordinated scheduling optimization of photovoltaic, energy storage, and charging power distribution networks in mountainous cities as described in claim 4, characterized in that: The construction of the multi-objective optimization model, which includes an operating cost objective function, a grid loss objective function, and power balance constraints, includes establishing an operating cost objective function, which includes the cost of exchanging power with the main grid, the cost of using the energy storage system, and the cost of electric vehicle charging services. A network loss objective function is established, which is calculated based on the distribution network line resistance and line current. Establish power balance constraints, which constrain the balance relationship between photovoltaic power generation, energy storage system power, electric vehicle charging power, and load power. A multi-objective optimization model is constructed based on the operating cost objective function, the network loss objective function, and the power balance constraints.

6. The method for coordinated scheduling optimization of photovoltaic, energy storage, and charging power distribution networks in mountainous cities as described in claim 5, characterized in that: The step of using a robust optimization method to solve the multi-objective optimization model includes introducing the photovoltaic power generation prediction error and load power prediction error in the uncertainty set as uncertainty parameters into the multi-objective optimization model. Within all possible values ​​of the uncertain parameter, find the scheduling decision that makes the objective function optimal in the worst case. It obtains the power generation scheduling volume of photovoltaic power generation equipment, the charging and discharging scheduling volume of energy storage system, and the charging scheduling volume of electric vehicle charging piles.

7. The method for coordinated scheduling optimization of photovoltaic, energy storage, and charging power distribution networks in mountainous cities as described in claim 6, characterized in that: The method of updating control decisions based on the real-time operating status of the distribution network includes establishing a system state prediction equation and predicting the system state for future periods based on the current system state and control input. Set up a prediction time domain and a control time domain, and solve the optimization problem in the prediction time domain; Based on the deviation between the predicted system state and the reference state, and the control input, a rolling optimization objective function is established; In each control cycle, the rolling optimization objective function is re-solved based on the real-time operating status of the distribution network, and the control decision is updated.

8. A photovoltaic-storage-charging coordinated scheduling optimization system for urban power distribution networks in mountainous areas, employing the photovoltaic-storage-charging coordinated scheduling optimization method for urban power distribution networks in mountainous areas as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to collect power generation data from photovoltaic power generation equipment, state-of-charge data from energy storage systems, charging demand data from electric vehicle charging piles, and load data from the distribution network. The prediction module is used to predict photovoltaic power generation and load power based on historical operating data and real-time environmental information; An uncertainty modeling module is used to establish an uncertainty set based on the deviations between the predicted and actual values ​​of the photovoltaic power generation and the predicted and actual values ​​of the load power. The equipment modeling module is used to establish a photovoltaic power generation model based on the rated power, solar radiation intensity and temperature of the photovoltaic power generation equipment; to establish an energy storage system model based on the state of charge, charging and discharging efficiency and rated capacity of the energy storage system; and to establish an electric vehicle charging model based on the target state of charge, current state of charge and departure time of the electric vehicle. The multi-objective optimization module is used to construct a multi-objective optimization model based on the photovoltaic power generation model, the energy storage system model, and the electric vehicle charging model, which includes an operating cost objective function, a network loss objective function, and power balance constraints. The robust optimization solution module is used to solve the multi-objective optimization model based on the set of uncertainties using a robust optimization method, so as to obtain the scheduling scheme of photovoltaic power generation equipment, energy storage system and electric vehicle charging pile; The model predictive control module is used to perform rolling optimization of the scheduling scheme using a model predictive control strategy and update control decisions based on the real-time operating status of the distribution network. The control output module is used to output the charging and discharging power control quantity of the energy storage system and the charging power control quantity of the electric vehicle charging pile based on the updated control decision.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for coordinated scheduling optimization of photovoltaic, energy storage and charging power distribution networks in mountainous cities as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for coordinated scheduling optimization of photovoltaic, energy storage and charging power distribution networks in mountainous cities as described in any one of claims 1 to 7.

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