Regional integrated energy system optimization scheduling method based on voltage deviation treatment

Abstract

The invention discloses a regional integrated energy system optimization scheduling method based on voltage deviation treatment, which comprises the following steps of: firstly, focusing on load characteristics and electric energy quality problems of a regional integrated energy system, performing bad data processing and clustering analysis on annual load data of the system by adopting a data transverse processing method and a data longitudinal processing method; determining a typical daily load curve, and then combing a voltage deviation principle and a treatment scheme from the problem of long-time scale voltage deviation caused by distributed power supply output and load change; thirdly, establishing a multi-target optimization scheduling model of the regional integrated energy system, and solving a scheduling scheme with proper comprehensive performance by adopting a particle swarm algorithm; and finally, analyzing and comparing the voltage deviation value of each node of the system before and after optimization and the change of the operation cost of the system through an actual example, verifying an optimization scheduling strategy, guaranteeing the normal operation of the system, and managing the long-time scale voltage deviation so as to meet the power quality management requirement.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power distribution, and particularly to an optimized scheduling method for a regional integrated energy system based on voltage deviation control. Background Art

[0002] In recent years, technological innovations have driven the application of big data and artificial intelligence technologies in the power system, promoting the steady growth of the power quality management market. In 2022, the market size of the power quality management industry in China was 154.44 billion yuan, with a year-on-year growth of 8.44%. Such a growth rate of the market size on the one hand represents that most enterprises choose to install a certain amount of management equipment to solve the problem of power quality losses caused to their production or operation interests, and on the other hand, it also represents the universality of power quality problems in the current power system and the singularity of management methods.

[0003] There is a quite large-scale and high-growth power quality management market, which can clearly reflect the demand for high-quality power supply on the user side and the distribution network side. Compared with expensive management equipment, increasing the management cost and being unable to fundamentally solve the power quality problem, through the considerable power quality regulation potential within the regional integrated energy system, effective optimized scheduling strategies and the optimized configuration of related equipment can be studied and formulated to achieve the improvement effect of various power quality indicators, thereby significantly reducing the power quality management cost on the user side and the distribution network side, and having considerable development prospects. Summary of the Invention

[0004] In view of the importance and universality of current power quality problems in the power system, and the voltage deviation problem being a long-time-scale problem of power quality problems, considering the economic drawbacks of relying solely on professional management equipment to solve the voltage deviation problem, combined with the current situation of power quality problems in the regional integrated energy system. The present invention aims to provide an optimized scheduling method for a regional integrated energy system based on voltage deviation control. By establishing a multi-objective optimized scheduling model for the regional integrated energy system, the particle swarm algorithm is used to solve the scheduling scheme with suitable comprehensive performance. Through the analysis and comparison of the changes in the voltage deviation values of each node in the system and the system operation cost before and after optimization by actual case studies, it is verified that the optimized scheduling strategy can ensure the normal operation of the system while achieving the control of long-time-scale voltage deviation to meet the power quality management requirements, and also improve the economic efficiency of system operation.

[0005] To achieve the above object, the present invention is realized through the following technical solutions:

[0006] An optimized scheduling method for a regional integrated energy system based on voltage deviation control, comprising the following steps:

[0007] Conduct a comprehensive load characteristic analysis and power quality problem analysis on the regional integrated energy system; establish the system equipment model and system power flow model of the regional integrated energy system; take the minimum total system voltage deviation value as the optimization goal, and the minimum total system voltage deviation value and the lowest total operating cost as the objective function, and set constraints including equipment operation, power balance, and node safety; use the particle swarm algorithm to solve the problem, and update the particle velocity and position according to the individual and global optimal solutions; apply the obtained optimal scheduling strategy to the system and compare data such as the voltage amplitude of each node in the system before and after optimization, the total voltage deviation value, the number of nodes meeting the normal voltage range, and the system operating cost to verify the effectiveness and economy of voltage deviation control.

[0008] Furthermore, the specific load characteristic analysis of the regional integrated energy system includes:

[0009] Adopt the data horizontal processing method and data vertical processing method that rely more on data continuity. After eliminating the difference in the order of magnitude of load data through normalization, conduct clustering analysis of load data based on the shape of the load curve. Through clustering analysis, the commonality of various types of user loads can be refined and their energy consumption patterns can be deeply mastered, laying a theoretical basis for the subsequent optimal scheduling of the system.

[0010] Furthermore, the specific power quality problem analysis includes:

[0011] Voltage deviation refers to the instability of the system power supply voltage, with voltage rising or falling. The voltage deviation problem belongs to a long-time scale problem, and the optimization scheduling of the regional integrated energy system itself is used to control this problem.

[0012] Furthermore, the establishment of the system equipment model of the regional integrated energy system specifically includes:

[0013] The regional integrated energy system mainly consists of four parts: gas network, heat network, power grid, and energy conversion equipment. Among them, the gas network provides natural gas for loads and distributed energy stations, the heat network is a heat energy interaction network with water as the heat transfer medium, and the power grid exists in the form of a distribution network and is responsible for the power transmission and power distribution tasks between the superior power grid and each energy conversion equipment and load. Among them, the energy supply equipment involved in the power grid includes gas turbines, photovoltaic power generation, wind power generation, and energy storage equipment.

[0014] Furthermore, the system power flow model of the regional integrated energy system specifically includes:

[0015] For the lines with power quality problems, the influence of the power change of different nodes on the node voltage is mainly described by the node injection power model and the branch power flow model.

[0016] Furthermore, taking the minimum value of the total system voltage deviation as the optimization objective, and the minimum total system voltage deviation and the lowest total operating cost as the objective functions, specifically including:

[0017] (1) The total system voltage deviation function is F vol , and the specific expression is as follows:

[0018]

[0019] In the formula:

[0020] n—the number of system nodes (units);

[0021] U real —the actual voltage of the node (p.u.);

[0022] U rated —the rated voltage of the node (p.u.).

[0023] (2) The system operating cost: includes the cost of electric energy purchased from the external power grid and the operation and maintenance cost of the system. Let the total system operating cost be F sys , and the specific expression is as follows:

[0024] F sys =min(f grid +f om ) (2)

[0025] In the formula:

[0026] F sys ——the total system operating cost (yuan);

[0027] f grid ——the power purchase cost (yuan);

[0028] f om ——the operation and maintenance cost (yuan).

[0029] (4) Among them, the expression of the power purchase cost is as follows:

[0030]

[0031] In the formula:

[0032] ——the power purchase price from the external power grid at time t (yuan / kWh);

[0033] ——the power purchase power from the external power grid at time t (kW);

[0034] Δt—the dispatching duration (hours).;

[0035] (4) In multi-objective optimization problems, weight coefficients are often used to represent the importance of each optimization objective. Therefore, the total objective function F1 is constructed using a weight function as follows:

[0036] minF1 = (α1F sys + β1F vol ) (4)

[0037] Where:

[0038] F1 - The total objective function;

[0039] α1 - The weight corresponding to the total operating cost of the system, with a value of 0.5;

[0040] β1 - The weight corresponding to the node voltage deviation of the system, with a value of 0.5.

[0041] Furthermore, the above settings include constraint conditions in aspects such as equipment operation, power balance, and node safety, specifically including:

[0042] (1) Power balance constraint:

[0043] Optimal scheduling needs to consider the power balance constraints of various types of energy (electric energy, thermal energy) in each node at any time, that is, the output power is equal to the input power.

[0044]

[0045] Where:

[0046] P i - The active power of node i (W);

[0047] - The active power of the energy storage device operation at node i (W);

[0048] - The sum of the active powers of wind power and photovoltaic output at node i (W);

[0049] - The active power of the load at node i (W);

[0050] Q i - The reactive power of node i (Var);

[0051] - The reactive power of the energy storage device operation at node i (Var);

[0052] - The sum of the reactive powers of wind power and photovoltaic output at node i (Var);

[0053] - The reactive power of the load at node i (Var).

[0054] (2) Operating constraints of each device:

[0055] When the gas turbine, wind power generation unit, and photovoltaic power generation unit are generating power, they need to meet the requirements that the power generation power and the ramp rate are within a reasonable range. The other devices should also operate within the specified range, which is specifically expressed as follows:

[0056]

[0057]

[0058] In the formula:

[0059] —Lower limit of gas turbine operating power (kW);

[0060] —Upper limit of gas turbine operating power (kW);

[0061] —Lower limit of fan operating power (kW);

[0062] —Upper limit of fan operating power (kW);

[0063] —Lower limit of photovoltaic operating power (kW);

[0064] —Upper limit of photovoltaic operating power (kW);

[0065] —Lower limit of electric boiler operating thermal power (kW);

[0066] —Upper limit of electric boiler operating thermal power (kW);

[0067] —Lower limit of heat pump operating thermal power (kW);

[0068] —Upper limit of heat pump operating thermal power (kW);

[0069] —Lower limit of waste heat boiler operating thermal power (kW);

[0070] —Upper limit of waste heat boiler operating thermal power (kW).

[0071] (3) Node safety constraints:

[0072] When the system is operating normally, the voltage amplitudes of each node cannot exceed the limits, and the active power of the line cannot exceed the maximum value.

[0073]

[0074] P t ≤P max (14)

[0075] Wherein:

[0076] —Lower limit of node voltage amplitude (V);

[0077] —Upper limit of node voltage amplitude (V);

[0078] P t —Active power of the line at time t (W);

[0079] P max —Upper limit of active power of the line (W).

[0080] Furthermore, the particle swarm optimization algorithm is specifically as follows::

[0081] The particle swarm optimization algorithm is an intelligent evolutionary algorithm based on the enumeration method and with a certain optimization mechanism. It simulates the foraging behavior of a biological population and determines the search direction of a new solution through all the solutions found by the current known population. If a certain particle in the particle swarm finds the optimal solution, the remaining particles will all move in the direction of this particle. If a new optimal solution is found during the movement, they will move in a new direction, and so on, iterating until finally tending to an optimal solution direction, which is composed of two parts: the individual optimal solution and the global optimal solution.

[0082] Furthermore, the guidance of the particle velocity and position update based on the individual and global optimal solutions is specifically as follows:

[0083] The velocity and position update of particle i in each iteration are as follows:

[0084]

[0085] Wherein:

[0086] —Velocity of particle i in the (k + 1)-th iteration;

[0087] —Velocity of particle i in the k-th iteration;

[0088] C1—Learning factor;

[0089] r1—Random number between 0 and 1;

[0090] —Position of the individual optimal solution of particle i;

[0091] C2—Learning factor;

[0092] r2 — A random number between 0 and 1;

[0093] — The position of the global optimal point of the entire population;

[0094] — The position of particle i in the k-th iteration;

[0095] — The position of particle i in the (k + 1)-th iteration.

[0096] Furthermore, the iterative calculation of the optimal solution using the particle swarm algorithm specifically includes:

[0097] (1) Initialize the particle swarm and set parameters such as position, velocity, and number of iterations.

[0098] (2) Calculate the particle comfort level, that is, the system operation cost and voltage stability degree

[0099] (3) Update gbest and zbest according to the fitness, and update the position and velocity of the particles

[0100] (4) Determine whether the maximum number of iterations is reached or the global optimal position meets the requirements. If so, output the result; otherwise, execute step (2)

[0101] Through the load analysis of the regional integrated energy system and the analysis of the voltage deviation governance requirements, the present invention establishes the system equipment model and system power flow model of the regional integrated energy system; taking the minimum total system voltage deviation value as the optimization goal and the minimum total system voltage deviation value and the lowest total operation cost as the objective functions, setting constraint conditions including equipment operation, power balance, and node safety, etc., using the particle swarm algorithm to solve the problem, guiding the update of the particle velocity and position according to the individual and global optimal solutions; applying the obtained optimal scheduling strategy to the system to compare data such as the voltage amplitude of each node in the system before and after optimization, the total voltage deviation value, the number of nodes meeting the normal voltage range, and the system operation cost, etc., meeting the governance requirements of the long-time scale voltage deviation problem, while reducing the economic losses caused by voltage deviation to users, and having reference significance for solving the power quality governance problem. Description of the Drawings

[0102] Figure 1 Is the optimization flow chart of the voltage deviation problem considering the power quality governance requirements of the present invention

[0103] Figure 2 Is the flow chart of the K-means algorithm for load analysis of the present invention

[0104] Figure 3 Is the structure diagram of the regional integrated energy system of the present invention

[0105] Figure 4 Flow chart of the particle swarm optimization algorithm for solving the present invention

[0106] Figure 5 System diagram of the regional 33-node distribution network in the embodiment provided by the present invention

[0107] Figure 6 Load curve before and after optimization in the embodiment provided by the present invention Specific implementation manners

[0108] Next, the technical solutions in the embodiments of the present invention will be clearly and detailedly described in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0109] The technical solution of the present invention to solve the above technical problems is as follows:

[0110] The method of the present invention analyzes the load characteristics and power quality problems of the regional integrated energy system; based on the problem of analyzing the load characteristics of the regional integrated energy system, the data horizontal processing method and the data vertical processing method, which are more dependent on data continuity, are used. After eliminating the difference in the order of magnitude of the load data, the clustering analysis of the load data is carried out based on the shape of the load curve, and the K-means algorithm (k-means algorithm) is used for the clustering analysis of the load data. The solution process of the K-means algorithm is as Figure 2 shown.

[0111] The regional integrated energy system mainly consists of four parts: gas network, heat network, power grid, and energy conversion equipment. Among them, the gas network provides natural gas for the load and distributed energy stations. The heat network is a heat energy interaction network with water as the heat transfer medium. The power grid exists in the form of a distribution network and is responsible for the power transmission and power distribution tasks between the superior power grid and each energy conversion equipment and load. Among them, the energy supply equipment involved in the power grid includes gas turbines, photovoltaic power generation, wind power generation, and energy storage equipment; the energy supply equipment involved in the heat network includes gas turbines, waste heat boilers, and electric refrigerators. The structure of the regional integrated energy system is as Figure 3 shown.

[0112] To meet the requirements of power quality governance, with the minimum total voltage deviation value of the system as the optimization goal and the minimum total voltage deviation value and the lowest total operating cost of the system as the objective function, the constraint conditions including equipment operation, power balance, and node safety are set, and a multi-objective optimal scheduling model of the regional integrated energy system is established; the particle swarm optimization algorithm is used to solve the scheduling scheme with suitable comprehensive performance. The solution process of the particle swarm optimization algorithm is as Figure 4 shown.

[0113] This embodiment relates to an optimal scheduling method for a regional integrated energy system based on voltage deviation control. Taking an actual regional integrated energy system in a certain area in the south as the research object for case analysis, the initial voltage value of each node of the 33-node distribution network system is set to 1.0 p.u. according to the reference system data. Among them, photovoltaic power generation equipment with an access capacity of 1.6 MW is connected to nodes 7 and 20, wind power generation equipment with an access capacity of 1.5 MW is connected to nodes 16 and 29, a gas turbine is connected to node 3, and energy storage equipment with an access capacity of 1000 kWh is connected to nodes 12 and 32. The 33-node distribution network system of the region is as Figure 5 shown.

[0114] The all-day electricity purchase price of the system is shown in the following table:

[0115] Table 1 System electricity purchase price

[0116]

[0117] In this embodiment, after 100 iterations of the particle swarm, the fitness function F1 tends to the minimum value and remains stable. At this time, the generated optimal scheduling strategy reaches the optimum. After the output of the photovoltaic power generation equipment, wind power generation equipment, gas turbine, the operation state of the energy storage, and the system electricity purchase power in the system are determined, according to the analysis of the single-day load data of the system after optimization scheduling based on the typical daily load data obtained by cluster analysis, after the system implements the new operation plan, the operation stability of the system is enhanced, the peak value of the load curve is reduced from 11575 kW to 10236 kW, the load interval changes from (7746 kW - 11575 kW) to (8000 kW - 10236 kW), and the overall fluctuation degree of the load curve becomes gentler. The load curves before and after optimization are as Figure 6 shown.

[0118] In this embodiment, Figure 6 After optimization, the output of the gas turbine throughout the day remains relatively stable with a positive and negative value not exceeding 300 kW. The cooperation among photovoltaic, wind power, energy storage, and external network power purchase is obvious. The combined output of photovoltaic and wind power in the morning alleviates the load pressure. The load value is reduced to a certain extent compared with that before optimization for most of the time, and the energy storage equipment is in the discharge state, effectively suppressing the fluctuations brought by the output of photovoltaic and wind power to the system. In the afternoon, when the photovoltaic power generation is large and the output of the gas turbine reaches the peak, the increase in the electricity purchase price enables the system to significantly reduce the power purchase from the external network, improving the economy of the system. At night, as the load rises slightly, although the output of wind power still maintains a certain value, the zero output of photovoltaic power increases the burden on the power grid. The output of the gas turbine also increases appropriately after several hours of stability. The electricity purchase price reaches the lowest value at this time, and the system significantly increases the power purchase from the external network. The two energy storage devices also conduct a large amount of charging during this period to prepare for the load peak period of the next day..

[0119] Before optimization, due to the rapid increase in the output of distributed power sources during noon, the node voltage amplitude showed a rapid upward trend and exceeded the standard upper limit of 1.07 p.u. During the evening period, due to the sharp increase in user load to the peak, the line voltage dropped significantly, and the voltage amplitudes of some nodes were outside the standard lower limit of 0.93 p.u. for several hours.

[0120] In this embodiment, the analysis results of the total voltage deviation value, the over-limit partial voltage value, and the daily operating cost of the system before and after the implementation of the optimized dispatching scheme are shown in the following table:

[0121] Table 2 Analysis diagram of the objective function results

[0122]

[0123] After the regional integrated energy system implements a new operation strategy through optimized dispatching, the voltage deviation problem of the nodes has been significantly improved. For the overvoltage scenario caused by the increase in the output of distributed power sources, the number of nodes that meet the national standard requirements for voltage deviation has increased from 23 to 33, that is, the voltage deviations of all nodes in the system after optimization are within the normal range. The total voltage deviation value has also decreased from 1.471 p.u. to 0.856 p.u., achieving a 42% reduction. Since there will be certain losses during the transmission of electric energy, the voltage values of each node in actual operation have a certain difference from the nominal value, so the reduction percentage of the total voltage deviation value cannot well reflect the treatment effect. Therefore, the total voltage value outside the normal range is selected to represent the treatment effect of voltage deviation. Since all nodes are within the normal range, the over-limit partial voltage value is 0, indicating that the problem of high voltage (overvoltage) in the system has been solved.

[0124] The present invention analyzes the load of the regional integrated energy system and the requirements for power quality management; takes the minimum total voltage deviation value of the system as the optimization goal, and the minimum total voltage deviation value and the lowest total operating cost as the objective functions, sets the constraint conditions including equipment operation, power balance, and node safety, etc., uses the particle swarm optimization algorithm to solve the problem, and applies the obtained optimized dispatching strategy to the system to compare the voltage amplitudes, total voltage deviation values, the number of nodes meeting the normal voltage range, and the system operating cost data of each node before and after optimization, meeting the effectiveness and economy of voltage deviation management.

[0125] The attached drawings are only for illustrative purposes and should not be construed as a limitation to this patent; the above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as a limitation to the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A method for optimizing the scheduling of a regional integrated energy system based on voltage deviation control, characterized in that It includes the following steps: Conduct a comprehensive load characteristic analysis and power quality problem analysis on the regional integrated energy system; Establish a system equipment model and a system power flow model for the regional integrated energy system; Taking the minimum total system voltage deviation value as the optimization goal, and the minimum total system voltage deviation value and the lowest total operating cost as the objective functions, set constraints including equipment operation, power balance, and node safety, etc.; use the particle swarm algorithm to solve the problem, and update the particle velocity and position according to the individual and global optimal solutions; apply the obtained optimal scheduling strategy to the system and compare data such as the voltage amplitude of each node in the system, the total voltage deviation value, the number of nodes meeting the normal voltage range, and the system operating cost before and after optimization to verify the effectiveness and economy of voltage deviation control.

2. The optimization scheduling method for a regional integrated energy system based on voltage deviation governance according to claim 1, wherein, The specific load characteristic analysis of the regional integrated energy system includes: Adopt the data horizontal processing method and data vertical processing method that rely more on data continuity. The specific principles and steps are as follows; Horizontal processing method: The data horizontal processing method is based on the similarity of loads in a short period of time. By comparing the difference between the load value at a certain point in the system daily load curve and the loads at the previous point and the next point, and setting the maximum allowable range of the difference. If it exceeds this range, the data is corrected to the average value of the loads at the previous point and the next point. The method formula is as follows: If there exists: In the formula: S(t) - Load data value at time t (kW); S(t - 1) - Load data value at time t - 1 (kW); S(t + 1) - Load data value at time t + 1 (kW); A - Set the maximum allowable range of the difference (kW); B - Set the maximum allowable range of the difference (kW); Then the data at time t needs to be corrected to: The values of A and B selected in this paper are 3000 kW; Vertical processing method: The data vertical processing method is based on the operating characteristics of the system itself. It judges whether the data is bad data based on the regularity of the load at a certain moment under different weekly units, compares the data at a certain moment with the average load at the same moment in the previous week and the next week, and also sets a relatively reasonable threshold. The specific calculation formula is as follows: Set the threshold as: In the formula: C - Set the threshold (kW); - The average value (kW) of the load at time t for the bad data in the previous week and the next week; if it exists: In the formula: - The load value (kW) at time t last week; - Load value (kW) at time t in the next week; Then it is judged that the data at this moment is bad data and needs to be corrected. The formula is as follows: In the formula: S1(t) - Corrected value of the load data (kW); Adopt the minimum - maximum normalization method of linear transformation. The specific calculation formula is as follows: In the formula: S′ - Normalized load data (p.u); S max —— The maximum value of the load data (kW); S min —— The maximum value of the load data (kW); After eliminating the difference in the order of magnitude of the load data through normalization, clustering analysis of the load data will be carried out based on the shape of the load curve. Through clustering analysis, the commonality of various types of loads of users can be refined and their energy consumption patterns can be deeply mastered, laying a theoretical basis for the subsequent optimal scheduling of the system. The K-means algorithm is a classic direct clustering algorithm. This algorithm is simple and intuitive and is often used for data preprocessing and analysis. In this algorithm, it is first necessary to determine the number of categories to be divided (i.e., the K value) and the cluster center. After calculating the Euclidean distance from the sample to the cluster center, classification is performed according to the size of the comparative distance. The average value within each category is calculated and the average value is set as the new cluster center. The classification and coordinate mean calculation steps are repeated until the cluster center no longer moves significantly or the number of iterations has been reached, then the clustering process is terminated.

3. The optimal scheduling method for a regional integrated energy system based on voltage deviation governance according to claim 1, wherein The power quality problem analysis specifically includes: Voltage deviation refers to the instability of the system power supply voltage, where the voltage rises or falls. The voltage deviation problem is a long-term problem, and this problem is addressed by the optimized scheduling of the regional integrated energy system itself.

4. A method for optimizing the scheduling of a regional integrated energy system based on voltage deviation governance according to claim 1, characterized in that The system equipment model of the regional integrated energy system is established, specifically including: (1) Cogeneration unit model The energy conversion model is as follows: Where: —— The power generation of the cogeneration unit (W); ——The heating power of the cogeneration unit (W); V—the volume of natural gas consumed by the cogeneration unit (m 3 ); Q——calorific value of natural gas (J / m 3 ); —— Power generation efficiency of the cogeneration unit (%). —— Heating efficiency of the cogeneration unit (%). (2) Photovoltaic power generation equipment model Its power generation is as follows: Where: P WT —— Output power of the fan (kW); ρ——air density (kg / m 3 ); R——fan arm span radius (m): v——actual wind speed at output power (m / s); C p —— Wind energy utilization rate, with the maximum value taken as 0.593; (3) Wind power generation equipment model Its power generation is as follows: P PV = f PV P r G[1 + k(T - T stc )] / G SC In Equation (11): P PV —— Output power of the photovoltaic power generation system (kW); f PV —— Power reduction correction factor, taking 0.95; P r —— Rated output power (kW); G——Actual radiation intensity on the solar panel (KW / m 2 ); k——power temperature coefficient, T——actual temperature of the solar panel (℃); T stc —— Temperature under standard test conditions, taking 25°C; G SC —— Radiation intensity under standard conditions (kW / m 2 ) (4) Energy storage equipment The operation of energy storage devices needs to meet the relevant constraints of power and energy: Where: ——Charging power of the electrical energy storage at time t (W); ——Discharge power of the electrical energy storage at time t (W); —— A 0-1 variable representing the charging state of the electrical energy storage at time t; —— A 0-1 variable representing the discharging state of the electrical energy storage at time t; —— The installed capacity of the electrical energy storage (kWh); ——Energy state of electrical energy storage at time t (%) —— Installation power of electrical energy storage (W); ——Self-loss coefficient of electrical energy storage; η EES —— Charge-discharge efficiency coefficient; Δt——scheduling time interval (h); —— Energy state of the electrical energy storage at the 0th moment; —— Energy state of the electrical energy storage at the 24th moment.

5. The optimal scheduling method for a regional integrated energy system based on voltage deviation control according to claim 1, characterized in that The system flow model of the regional integrated energy system specifically includes: (1) Node injection power model Where: S a - Injection power of node a (W); P a - Active power of node a (W); Q a - Reactive power (Var) of node a; - Voltage vector at node a; - The conjugate complex number of the current vector at node a; - Node admittance Y ab = G ab + jB ab is the conjugate complex number; - The conjugate complex number of the voltage phasor at node b; The voltage at node a is: Where: U a - Effective voltage value (W) of node a; θ a - Initial phase angle of the voltage vector of node a (°); The current at node i is: Where: - Current vector of node i; - The conjugate complex number of the node admittance; - The conjugate complex number of the voltage phasor at node j; θ j - Initial phase angle of the current vector of node j (°); Combining the above three equations, we can get: P i +jQ i =U i ∑(G ij -jB ij )U j (cosθ ij +jsinθ ij ) (20) Where: P i - Active power of node i (W); Q i - Reactive power (Var) of node i; θ ij - Phase angle difference (°) between the voltages of nodes i and j; The power balance equation is as follows: Where: P i,in - Active power flowing into node i (W); P i，out - Active power flowing out of node i (W); Q i，in - Reactive power (Var) injected by node i; Q i,out - Reactive power (Var) flowing out of node i; (2) Branch flow model Voltage equation (Ohm's law): Where: V i - Voltage (V) of node i; V j - Voltage (V) of node j; I ij - The current (A) flowing from node i to node j; Z ij - Series impedance (Ω) between node i and node j; N-the set of system branches; The power at the head end of the branch is as follows: Where: S ij - Power at the beginning of the branch (W); - Impedance (Ω) between node i and node j; M-a collection of system nodes; The power balance equation of the branch node is as follows: Where: S jk ——Total power (W) flowing from node j to node k; S j —— Complex power (W) of node j; ——The total power (W) injected from node i to node j.

6. The optimization scheduling method for a regional integrated energy system based on voltage deviation governance according to claim 1, wherein The optimization objective is to minimize the total system voltage deviation value, and the objective function is to minimize the total system voltage deviation value and the total operating cost. Specifically, it includes: (1) The total system voltage deviation function is F vol , and the specific expression is as follows: Where: n-number of system nodes; U real - Actual voltage of node (p.u.); U rated - Rated voltage of node (p.u.); (2) System operation cost: including the cost of electric energy purchased from the external power grid and the operation and maintenance cost of the system. Let the total system operation cost be F sys , and the specific expression is as follows: F sys = min(f grid + f om ) (27) Where: F sys —— Total system operation cost (yuan); f grid —— Purchase electricity cost (yuan); f om —— Operation and maintenance cost (yuan); (3) The expression of electricity purchase cost is as follows: Where: ——Purchase price from the external power grid at time t (yuan / kWh); ——The purchased electric power from the external power grid at time t (kW); Δt——scheduling time (hours); (4) Multi-objective optimization problems often use weight coefficients to represent the importance of each optimization objective; therefore, the overall objective function F1 is constructed using the weight function as follows: minF1=(α1F sys +β1F vol ) (29) Where: F1-total objective function; α1-weight corresponding to the total operating cost of the system, with a value of 0.5; β1 - The weight corresponding to the system node voltage deviation, with a value of 0.

5.

7. An optimal scheduling method for a regional integrated energy system based on voltage deviation governance according to claim 6, characterized in that, The settings include constraints on equipment operation, power balance, and node safety, including: (1) Power balance constraints: Optimal scheduling needs to consider the power balance constraints of various types of energy (electrical energy, thermal energy) in each node at any time, that is, the output power is equal to the input power; Where: P i - Active power of node i (W); - Active power of the energy storage device of node i (W); - Active power sum (W) of wind power and PV output of node i; - Active power of node i load (W); Q i - Reactive power of node i (Var); - Reactive power (Var) of the energy storage device of node i; - Reactive power sum (Var) of wind power and PV output at node i; - Reactive power (Var) of Node i load; (2) Operation constraints of each device: The gas turbine, wind turbine and photovoltaic generator set must meet the power generation and ramp rate within a reasonable range when outputting power, and the other equipment should also operate within the specified range, as shown below: Where: - Lower limit of gas turbine operating power (kW); - Upper limit of gas turbine operating power (kW); - Lower limit of fan operating power (kW); - Upper limit of fan operating power (kW); - Lower limit of photovoltaic operating power (kW); - Upper limit of PV operating power (kW); - Lower limit of the operating thermal power of the electric boiler (kW); - Upper limit of operating thermal power of electric boiler (kW); - Lower limit of heat pump operating thermal power (kW); - Upper limit of heat power during heat pump operation (kW); - Lower limit of heat power during operation of waste heat boiler (kW); - Upper limit of operating thermal power of waste heat boiler (kW); (3) Node security constraints: When the system is operating normally, the voltage amplitude of each node shall not exceed the limit, and the active power of the line shall not exceed the maximum or minimum value; P t ≤P max In formula (39): - Lower limit of node voltage amplitude (V); - Upper limit of the node voltage amplitude (V); P t - Active power of the line at time t (W); P max - Upper limit of active power of the line (W).

8. An optimal scheduling method for a regional integrated energy system based on voltage deviation governance according to claim 1, characterized in that The specific particle swarm optimization algorithm is as follows: The particle swarm optimization algorithm is an intelligent evolutionary algorithm based on the enumeration method with a certain optimization mechanism; it simulates the foraging behavior of a biological group and determines the search direction of a new solution through all the solutions found by the currently known population; if a particle in the particle swarm finds the optimal solution, the remaining particles will move in the direction of this particle. If a new optimal solution is found during the movement, they will move in a new direction, and iterate in this way until finally approaching an optimal solution direction, which is composed of two parts: the individual optimal solution and the global optimal solution.

9. A method for optimizing the dispatching of a regional integrated energy system based on voltage deviation control, characterized in that, The guidance of the particle velocity and position update based on the individual and global optimal solutions is specifically as follows: The velocity and position update of particle i in each iteration are as follows: In the formula: - The velocity of particle i in the (k + 1)-th iteration; - The velocity of particle i at the k-th iteration; C1 - learning factor; r 1 - A random number between 0 and 1; - The position of the individual optimal solution of particle i; C2 - learning factor; r2 - a random number between 0 and 1; - The position of the global optimal point of the population; - The position of particle i in the k-th iteration; - The position of particle i in the (k + 1)-th iteration.

10. The optimization scheduling method of a regional integrated energy system based on voltage deviation governance according to claim 9, characterized in that, The iterative calculation of the optimal solution using the particle swarm algorithm specifically includes: (1) Initialize the particle swarm and set parameters such as position, velocity, and iteration times; (2) Calculate the particle comfort level, that is, the system operation cost and voltage stability level; (3) Update gbest and zbest according to the fitness, and update the position and velocity of the particles; (4) Determine whether the maximum iteration number is reached or the global optimal position meets the requirements. If so, output the result; otherwise, execute step (2).

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