A multi-source cooperative control method and device for a new energy plant station

Abstract

The application discloses a kind of new energy plant station's multi-source collaborative control method and device, control method includes: obtaining the initial value of the state parameter of new energy plant station;The comprehensive objective function of new energy plant station is constructed;By random disturbance mode obtains several groups of state parameter random values of new energy plant station;Based on simulated annealing algorithm model, iteratively calculate the optimal solution of new energy plant station comprehensive objective function, based on the state parameter random value corresponding to optimal solution the energy storage component of new energy plant station, wind power component, photovoltaic power generation component and reactive power compensation equipment are collaboratively controlled.By using simulated annealing algorithm for new energy plant station multi-source collaborative control aspect, can in complex solution space, break through local optimal limit, realize global search optimal solution, quickly adjust the operation strategy of each component, accurately weigh power supply reliability, power quality and power generation cost, efficiently optimize multi-source collaboration, greatly improve new energy plant station overall operation performance and stability.

Description

Technical Field

[0001] This invention relates to the field of new energy power plant control technology, and in particular to a multi-source collaborative control method and device for new energy power plants. Background Technology

[0002] With increasing global emphasis on environmental protection and sustainable development, the environmental pollution and energy depletion resulting from the massive use of traditional fossil fuels are becoming increasingly prominent. Against this backdrop, countries worldwide are accelerating their energy structure transformation and vigorously promoting the development and utilization of new energy sources. Wind and solar energy, as representatives of clean and renewable energy, are seeing their share in the global energy mix continue to rise. Over the past decade, global installed capacity for wind and solar power has grown at an average annual rate of double digits. A large number of wind and solar power modules have been connected to the grid, becoming a crucial force in meeting the growing electricity demand and reducing carbon emissions.

[0003] The energy sources of wind and solar power are greatly affected by natural conditions, exhibiting significant intermittency and volatility. Wind power generation depends on wind speed, and the instability of wind speed leads to frequent and unpredictable fluctuations in the output power of wind turbines. Photovoltaic power generation is closely related to sunlight intensity and time; factors such as day-night cycles and cloud cover can cause significant changes in the output power of photovoltaic power plants within a short period. This unstable power generation characteristic poses a significant challenge to the stable operation of the power system, making it difficult to balance power supply and demand in real time and seriously affecting power supply reliability. Most new energy power generation equipment is connected to the grid through power electronic devices, which are prone to generating power quality problems such as harmonics, voltage fluctuations, and flicker during operation. Simultaneously, the intermittency of wind and solar power generation can lead to increased grid voltage deviation and three-phase imbalance, affecting the normal operation of other equipment in the grid and reducing the overall power quality of the power system. For example, when a large number of photovoltaic power plants experience rapid changes in sunlight intensity, it can cause significant fluctuations in local grid voltage, affecting the lifespan and operational stability of electrical equipment used by nearby residents and businesses.

[0004] Energy storage modules, wind power modules, photovoltaic (PV) power generation modules, and reactive power compensation equipment each play a crucial role in renewable energy power plants. However, when they operate independently, their overall efficiency cannot be fully realized. For example, considering only the maximum power output of wind and PV power generation without considering the synergy of energy storage and reactive power compensation can lead to excessive grid voltage fluctuations and reduced power supply reliability. Therefore, achieving multi-source coordinated control of energy storage modules, wind power modules, PV power generation modules, and reactive power compensation equipment to comprehensively optimize power supply reliability, power quality, and generation costs is key to improving the operational efficiency and stability of renewable energy power plants. Traditional decentralized control methods can no longer meet the increasingly complex operational needs of renewable energy power plants, necessitating an advanced multi-source coordinated control method to address these issues. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-source collaborative control method and device for new energy power plants. By applying the simulated annealing algorithm to the multi-source collaborative control of new energy power plants, it can break through the limitations of local optima in complex solution space, achieve global search for optimal solutions, quickly adjust the operating strategies of each component, accurately balance power supply reliability, power quality and power generation costs, efficiently optimize multi-source collaboration, and significantly improve the overall operating performance and stability of new energy power plants.

[0006] To address the aforementioned technical problems, a first aspect of this invention provides a multi-source coordinated control method for a new energy power plant. The new energy power plant includes: energy storage components, wind power components, photovoltaic power generation components, and reactive power compensation equipment. The control method includes the following steps:

[0007] Acquire the initial values ​​of the status parameters of the new energy power plant, including: the initial value of the active power of the energy storage module, the initial value of the active power and reactive power of the wind power module and the photovoltaic power generation module, and the initial switching status of several reactive power compensation devices;

[0008] Construct a comprehensive objective function for new energy power plants, which includes a power supply reliability function, a power quality function, and a power generation cost function;

[0009] Several sets of random values ​​of state parameters of new energy power plants are obtained through random perturbation, including: random values ​​of active power of energy storage components, random values ​​of active power and reactive power of wind power components and photovoltaic power generation components, and random switching states of several reactive power compensation devices.

[0010] Based on the simulated annealing algorithm model, the optimal solution of the comprehensive objective function of the new energy power plant is calculated iteratively. Based on the random values ​​of the state parameters corresponding to the optimal solution, the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of the new energy power plant are coordinated and controlled.

[0011] Furthermore, the iterative calculation of the optimal solution to the comprehensive objective function of the new energy power plant includes:

[0012] Substitute a random value of a state parameter into the comprehensive objective function;

[0013] Based on the numerical values ​​of the integrated objective function corresponding to the initial values ​​of the state parameters and the numerical values ​​of the integrated objective function corresponding to the random values ​​of the state parameters, a better solution to the integrated objective function is determined;

[0014] When the initial value of the state parameter is a better solution, the probability value of the random value of the state parameter being the optimal solution is calculated according to the Metropolis criterion, and a random number within a preset range is randomly generated and compared with the probability value.

[0015] If the random number is less than the probability value, the random value of the state parameter is taken as the optimal solution for the new energy power plant; if the random number is greater than or equal to the probability value, the initial value of the state parameter is taken as the optimal solution for the new energy power plant.

[0016] The optimal solution of the comprehensive objective function is obtained by iteratively calculating the random value of each state parameter.

[0017] Furthermore, the power supply reliability function is a numerical value used to calculate the impact of the intermittency and volatility of new energy power generation on the system's outage time and frequency. The formula for calculating the power supply reliability function R is:

[0018]

[0019] Where i is the node number of the new energy power plant, N is the number of nodes in the new energy power plant, and λ i Let T be the failure rate of the i-th node. i Let T be the power outage time of the i-th node. max Let τ be the maximum operating power outage time of the i-th node. i Let be the reliability index of the i-th node;

[0020] The power quality function is a comprehensive index value calculated by comparing the actual voltage value with the rated voltage value at each node of the new energy power plant, and determining the voltage deviation at each node. The formula for calculating the power quality function Q is as follows:

[0021]

[0022] Where i is the node number of the new energy power plant, N is the number of nodes in the new energy power plant, and ω i V represents the weighting factor of the voltage deviation at node i affecting the overall power quality. i V is the actual voltage value of the i-th node. i,额定 Let ρ be the rated voltage value of the i-th node. i Let be the deviation index of the i-th node;

[0023] The power generation cost function is the sum of the power output cost of the energy storage module, the power generation cost of the wind turbine, and the power generation cost of the photovoltaic power generation equipment. The formula for calculating the power generation cost function F is:

[0024]

[0025] Among them, C k Let P be the unit active power cost coefficient of the k-th energy storage unit in the energy storage system. k,有功 Let W be the active power output of the k-th energy storage unit, σ be the carbon trading coefficient, and W be the active power output of the k-th energy storage unit. 弃风 γ represents the amount of wind power curtailed by the wind turbine generators, γ is the cost factor for wind curtailment, and P 弃光θ represents the amount of solar power curtailed by the photovoltaic power plant, and θ is the curtailment cost coefficient.

[0026] The formula for calculating the comprehensive objective function is as follows:

[0027] f(x j )=θ1×R(x j )+θ2×Q(x j )+θ3×F(x j );

[0028] Where, f(x) j ) represents the numerical value of the comprehensive objective function corresponding to the random values ​​of the j-th group of state parameters, θ1 represents the weighting coefficient of the power supply reliability function in the comprehensive objective function, and R(x) represents the value of the comprehensive objective function. j ) represents the power supply reliability function value corresponding to the random value of the j-th group of state parameters, θ2 is the weighting coefficient of the power quality function in the comprehensive objective function, and Q(x) is the power supply reliability function value. j ) represents the power quality function value corresponding to the random value of the j-th group of state parameters, θ3 is the weighting coefficient of the power generation cost function in the comprehensive objective function, and F(x) j ) represents the power generation cost function value corresponding to the random value of the j-th group of state parameters, 1≤j≤M.

[0029] Furthermore, the influencing factors of the power quality function also include: voltage harmonics, three-phase imbalance, frequency deviation, and voltage flicker;

[0030] The formula for calculating the power quality function Q is:

[0031]

[0032] Where, ω i1 -ω i5 These are the weighting coefficients for voltage deviation, voltage harmonics, three-phase unbalance, frequency deviation, and voltage flicker at the i-th node when calculating the power quality function, THDU. i Let ε be the total harmonic distortion of the voltage at the i-th node. U2,i Let Δf be the three-phase voltage imbalance at the i-th node. i P represents the frequency deviation in the i-th node. lt,i This represents the long-duration flicker value in the i-th node;

[0033] Total harmonic distortion (THDU) of voltage at the i-th node i The calculation formula is:

[0034]

[0035] Among them, U i,h U is the effective value of the h-th harmonic voltage at the i-th node. i,1This represents the effective value of the fundamental voltage at the i-th node.

[0036] Three-phase voltage imbalance at the i-th node The calculation formula is:

[0037]

[0038] Among them, U i,a U i,b U i,c These are the effective values ​​of the three-phase voltages at the i-th node;

[0039] The frequency deviation Δf in the i-th node i The calculation formula is:

[0040]

[0041] Among them, f i f is the frequency value corresponding to the i-th node. i,e This is the nominal frequency value;

[0042] The long-term flicker value P in the i-th node lt,i The calculation formula is:

[0043]

[0044] Where, ΔV i,k Let τ be the voltage change at the i-th node for the k-th time, T be the measurement time length, and τ be the voltage change at the i-th node for the k-th time. c Let M be the long-term flicker time constant of the i-th node, and M be the number of voltage changes in the flicker of the i-th node.

[0045] Furthermore, after determining a better solution to the integrated objective function, the process further includes:

[0046] The random values ​​of the state parameters and their corresponding comprehensive objective function values ​​are saved to the data storage unit.

[0047] Furthermore, before iteratively calculating the optimal solution of the comprehensive objective function of the new energy power plant based on the simulated annealing algorithm model, the following steps are also included:

[0048] Set the initial values ​​of the parameters of the simulated annealing algorithm model. The initial values ​​of the parameters include: initial temperature value, initial cooling rate value, and maximum number of iterations value.

[0049] Furthermore, after comparing the random number with the probability value, the process further includes:

[0050] The initial temperature value in the simulated annealing algorithm model is updated according to the initial cooling rate value until the number of iterations equals the maximum number of iterations.

[0051] Furthermore, after updating the initial temperature value in the simulated annealing algorithm model according to the initial cooling rate value, the method further includes:

[0052] When the value of the comprehensive objective function corresponding to the random value of the state parameter is less than a preset threshold for several consecutive times, the iterative calculation is terminated, and the optimal solution of the comprehensive objective function is obtained.

[0053] Accordingly, a second aspect of the present invention provides a multi-source coordinated control device for a new energy power plant, which performs multi-source coordinated control of the new energy power plant based on the above-mentioned multi-source coordinated control method for the new energy power plant. The new energy power plant includes: energy storage components, wind power components, photovoltaic power generation components, and reactive power compensation equipment, including:

[0054] The initial data acquisition module is used to acquire the initial values ​​of the status parameters of the new energy power plant, including: the initial value of the active power of the energy storage module, the initial value of the active power and the initial value of the reactive power of the wind power module and the photovoltaic power generation module, as well as the initial switching status of several reactive power compensation devices.

[0055] The objective function construction module is used to construct the comprehensive objective function of the new energy power plant. The comprehensive objective function includes the power supply reliability function, the power quality function, and the power generation cost function.

[0056] The random parameter generation module is used to obtain several sets of random values ​​of state parameters of new energy power plants through random perturbation, including: the initial value of active power of energy storage components, random values ​​of active power and reactive power of wind power components and photovoltaic power generation components, and random switching status of several reactive power compensation devices.

[0057] The multi-source collaborative control module is used to iteratively calculate the optimal solution of the comprehensive objective function of the new energy power plant based on the simulated annealing algorithm model, and to perform collaborative control of the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of the new energy power plant based on the random values ​​of the corresponding state parameters of the optimal solution.

[0058] Accordingly, a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to execute the above-described multi-source coordinated control method for new energy power plants.

[0059] Accordingly, a fourth aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-described multi-source coordinated control method for new energy power plants.

[0060] The above-described technical solutions of the embodiments of the present invention have the following beneficial technical effects:

[0061] 1. By constructing a comprehensive objective function that includes power supply reliability, power quality, and power generation cost functions, and using the simulated annealing algorithm to iteratively solve for the optimal solution, the overall performance of new energy power plants can be taken into account at the same time, thus achieving overall optimization of new energy power plants;

[0062] 2. Optimization based on random values ​​of state parameters obtained from random disturbances can effectively address the intermittency and volatility of new energy power generation, dynamically adjust the state of each component, and improve the plant's adaptability to environmental changes;

[0063] 3. By setting the simulated annealing algorithm parameters, updating the temperature, and setting the termination conditions, the algorithm can be guaranteed to efficiently search for the optimal solution under reasonable computing resources, thereby improving the computational efficiency and practicality of collaborative control. Attached Figure Description

[0064] Figure 1 This is a flowchart of the multi-source collaborative control method for new energy power plants provided in this embodiment of the invention;

[0065] Figure 2 This is a block diagram of a multi-source collaborative control device module for new energy power plants provided in an embodiment of the present invention.

[0066] Figure label:

[0067] 1. Initial data acquisition module; 2. Objective function construction module; 3. Random parameter generation module; 4. Multi-source collaborative control module. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0069] Please refer to Figure 1 The first aspect of this invention provides a multi-source coordinated control method for a new energy power plant. The new energy power plant includes: energy storage components, wind power components, photovoltaic power generation components, and reactive power compensation equipment. The control method includes the following steps:

[0070] Step S100: Obtain the initial values ​​of the status parameters of the new energy power plant, including: the initial value of the active power of the energy storage module, the initial value of the active power and the initial value of the reactive power of the wind power module and the photovoltaic power generation module, and the initial switching status of several reactive power compensation devices.

[0071] Specifically, energy storage components play a crucial role in energy regulation in new energy power plants. Their initial active power reflects the initial level of energy stored or released by the energy storage system at the current moment. This initial value can be accurately obtained by real-time monitoring of the energy storage device's charge / discharge status, remaining charge (SOC), and rated power. For example, high-precision power sensors and power monitoring modules can be used to measure the current and voltage of the energy storage battery pack in real time, thereby calculating the active power. This initial value provides the foundational data for subsequent decisions on how the energy storage system should participate in energy regulation optimization.

[0072] The initial active power value of wind turbine and photovoltaic (PV) power generation modules is used to assess the actual or set power output of current wind and solar energy resources converted into electricity. The active power of a wind turbine is affected by factors such as wind speed, turbine blade angle, and turbine efficiency. Its initial active power value can be obtained through feedback data from wind speed and direction sensors installed on the turbine, as well as the power regulation device inside the turbine. For PV power generation modules, sunlight intensity, temperature, and the conversion efficiency of the photovoltaic panels determine their output power. Their initial active power value can be obtained through measurements of sunlight sensors, temperature sensors, and the electrical parameters of the photovoltaic array. Simultaneously, their initial reactive power value is also important, as reactive power affects the voltage stability of the power system. Wind turbines and PV inverters can output or absorb reactive power by adjusting their control strategies. The initial reactive power value can be calculated by measuring the phase difference between the inverter's output voltage and current, combined with its rated reactive power capacity.

[0073] Reactive power compensation equipment (such as capacitor banks and static var compensators) is used to improve the power factor and voltage quality of power systems. Obtaining their initial switching status and determining which reactive power compensation equipment is currently in operation and which is disconnected can be achieved through feedback signals from the control circuits of the reactive power compensation equipment and data acquisition from remote monitoring systems. For example, for capacitor banks, monitoring the status of the auxiliary contacts of their control switches can determine whether they are in operation.

[0074] Step S200: Construct the comprehensive objective function of the new energy power plant. The comprehensive objective function includes the power supply reliability function, the power quality function, and the power generation cost function.

[0075] Specifically, the power supply reliability function primarily measures the impact of the intermittency and volatility of renewable energy generation on system outage time and frequency. The instability of renewable energy sources (wind and solar) can lead to power supply interruptions or fluctuations. By calculating parameters such as the failure rate and outage time of each node, the overall power supply reliability of the entire power plant is comprehensively considered. For example, in a renewable energy power plant containing multiple wind turbines and photovoltaic arrays, each power generation unit has its own failure probability. Through statistical analysis of historical failure data, combined with the equipment's operating environment and maintenance records, the failure rate of each node can be estimated. Then, based on factors such as post-failure repair time and backup power switching time, the outage time of each node can be determined. Substituting these data into the power supply reliability function formula allows for a quantitative assessment of the power plant's power supply reliability level.

[0076] Specifically, in the power quality function, power quality mainly involves voltage deviation. Voltage deviation reflects the degree of deviation between the actual voltage and the rated voltage. By measuring the voltage values ​​at each node and comparing them with the rated voltage, and combining this with the corresponding weighting coefficients, the impact of voltage deviation on power quality can be calculated.

[0077] Specifically, the power generation cost function comprehensively considers the output power cost of energy storage components, the power generation cost of wind turbines and photovoltaic power generation equipment. The cost of energy storage components includes battery charging and discharging losses, lifespan losses, etc. By calculating the unit active power cost coefficient and its active power output of each energy storage unit, the power generation cost of the energy storage components can be obtained. For wind turbines and photovoltaic power generation equipment, the power generation cost includes not only the investment and operation and maintenance costs of the equipment, but also the cost losses caused by wind and solar curtailment. The total power generation cost is calculated by using carbon trading coefficients, wind curtailment cost coefficients, solar curtailment cost coefficients, etc., combined with the actual amount of wind and solar curtailment.

[0078] By combining these three functions with certain weighting coefficients, a comprehensive objective function is constructed to balance the relationship between power supply reliability, power quality, and power generation cost.

[0079] Step S300: Obtain several sets of random values ​​of state parameters of the new energy power plant through random perturbation, including: random values ​​of active power of energy storage components, random values ​​of active power and reactive power of wind power components and photovoltaic power generation components, and random switching states of several reactive power compensation devices.

[0080] To determine the impact of energy storage components on the overall performance of the power plant under different operating conditions, it is necessary to randomly perturb their active power. Within a certain power range, random active power values ​​are generated using a random number generation algorithm. For wind power components, considering the uncertainties of wind speed and turbine operating conditions, random active power values ​​are generated through random perturbation within their adjustable power range. For example, based on the turbine's power curve, the turbine's pitch angle or speed control parameters are randomly changed within different wind speed ranges to obtain different active power outputs. Simultaneously, similar random perturbation is applied to their reactive power, generating random reactive power values ​​within their reactive power adjustment range by altering the inverter's control strategy. For photovoltaic (PV) power generation components, considering variations in irradiance and temperature, control parameters are randomly adjusted within the PV array's maximum power point tracking (MPPT) control range to generate random active power values. Random reactive power values ​​are generated by changing the reactive power compensation function settings of the PV inverter. Randomly perturbing the switching status of reactive power compensation equipment means randomly determining which equipment is put into operation and which is disconnected.

[0081] Step S400: Based on the simulated annealing algorithm model, iteratively calculate the optimal solution of the comprehensive objective function of the new energy power plant, and coordinately control the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of the new energy power plant based on the random values ​​of the corresponding state parameters of the optimal solution.

[0082] In the simulated annealing algorithm, the random values ​​of the state parameters of a renewable energy power plant are treated as the atomic states of a solid, and the overall objective function value is considered as the energy of the solid. The algorithm starts from an initial state (i.e., the initial values ​​of the state parameters obtained in step S100) and generates new states (i.e., the random values ​​of the state parameters obtained in step S300) through continuous random perturbation. In each iteration, the objective function value of the new state is compared with that of the current state. If the objective function value of the new state is better (e.g., a smaller overall objective function value, as it is generally desirable to reduce generation costs while improving power supply reliability and ensuring power quality), then the new state is accepted as the current state. If the objective function value of the new state is worse, then the new state is accepted with a certain probability, which gradually decreases as the number of iterations increases.

[0083] First, a random value of the state parameter is substituted into the comprehensive objective function to calculate the corresponding objective function value. Then, this value is compared with the comprehensive objective function value corresponding to the initial state parameter value to determine the better solution. When the initial state parameter value is a better solution, the probability value of the random state parameter value being the optimal solution is calculated according to the Metropolis criterion. For example, if the current temperature is T, the objective function value corresponding to the initial state parameter value is E1, and the objective function value corresponding to the random state parameter value is E2, then the probability of accepting the new state (random state parameter value) is calculated. Next, a random number between [0,1] is generated and compared with the probability value. When the random number is less than the probability value, the random state parameter value is taken as the current optimal solution for the new energy plant; when the random number is greater than or equal to the probability value, the initial state parameter value is taken as the current optimal solution. After each iteration, the temperature of the simulated annealing algorithm is updated according to a preset cooling rate to reduce the probability of accepting a poor solution. This process is repeated until a preset termination condition is met (such as reaching the maximum number of iterations or the objective function value being less than a preset threshold for several consecutive times), finally obtaining the optimal solution of the comprehensive objective function.

[0084] Based on the random values ​​of the state parameters corresponding to this optimal solution, the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of new energy power plants can be coordinated and controlled to achieve optimal operation of the power plants in terms of power supply reliability, power quality and power generation cost.

[0085] Specifically, step S400 involves iteratively calculating the optimal solution to the comprehensive objective function of the new energy power plant, including:

[0086] Step S410: Substitute a random value of a state parameter into the comprehensive objective function.

[0087] One random value of the state parameters obtained through random perturbation is selected and substituted into the pre-constructed comprehensive objective function. This random value of the state parameters includes the random values ​​of the active power of the energy storage module, the random values ​​of the active power and reactive power of the wind power module and the photovoltaic power generation module, as well as the random switching states of several reactive power compensation devices.

[0088] Step S420: Based on the numerical values ​​of the integrated objective function corresponding to the initial values ​​of the state parameters and the numerical values ​​of the integrated objective function corresponding to the random values ​​of the state parameters, determine a better solution for the integrated objective function.

[0089] In step S100, the initial value of the state parameter is obtained and substituted into the comprehensive objective function to calculate the initial comprehensive objective function value. In step S410, the comprehensive objective function value corresponding to a random value of the state parameter is calculated and compared with the initial value. If the comprehensive objective function value corresponding to the initial value is greater than the comprehensive objective function value of the random value, it means that the state corresponding to the random value of the state parameter can make the comprehensive objective function obtain a better result (usually it is assumed that the minimum value of the comprehensive objective function is sought, so the smaller the function value, the better). Then the random value of the state parameter is temporarily considered to be a better solution; otherwise, the initial value of the state parameter is temporarily considered to be a better solution.

[0090] By comparing the comprehensive objective function values ​​under different states, the state that is better in the current comparison is selected, which provides a basis for whether to accept new random values ​​of state parameters as possible optimal solutions in the future, and pushes the algorithm closer to the optimal solution.

[0091] Step S430: When the initial value of the state parameter is a better solution, calculate the probability value of the random value of the state parameter as the optimal solution according to the Metropolis criterion, and randomly generate a random number within a preset range, and compare the random number with the probability value.

[0092] When the initial state parameter values ​​are found to be a better solution, the probability that a random state parameter value is the optimal solution is calculated according to the Metropolis criterion. Even if the comprehensive objective function value corresponding to the random state parameter value is worse than the initial value, it still has a certain probability of being accepted as the optimal solution according to the Metropolis criterion; the probability depends on the magnitude of the temperature and energy difference. A random number is randomly generated within a preset range (usually [0,1]). This random number r is then compared with the calculated probability value p. The generation of this random number is to simulate the possibility that the system will accept a worse state with a certain probability, thus avoiding the algorithm from getting trapped in local optima.

[0093] The application of the Metropolis criterion is the core of the simulated annealing algorithm. It allows the algorithm to accept a poor state with a certain probability, avoids getting trapped in a local optimum too early, and increases the possibility of searching for the global optimum. The generation and comparison of random numbers are the specific operations to implement this probability acceptance mechanism.

[0094] In step S440, if the random number is less than the probability value, the random value of the state parameter is taken as the optimal solution for the new energy power plant; if the random number is greater than or equal to the probability value, the initial value of the state parameter is taken as the optimal solution for the new energy power plant.

[0095] Continue the above comparison process. If r < p, even if the comprehensive objective function value corresponding to the current random value of the state parameter is poor, according to the probability of the Metropolis criterion, the random value of the state parameter is still taken as the optimal solution of the new energy plant station. On the contrary, if r > p, a more conservative choice is preferred, and the initial value of the state parameter is taken as the optimal solution of the new energy plant station.

[0096] The comparison result of probability and random number determines whether to accept a seemingly non-optimal state, providing an opportunity for the algorithm to jump out of the local optimum, which is the key operation for the simulated annealing algorithm to achieve global search ability.

[0097] Step S450: Iteratively calculate each random value of the state parameter to obtain the optimal solution of the comprehensive objective function.

[0098] For each set of random values of the state parameter obtained by random perturbation, the operations in steps S410 to S440 need to be repeated. Continuously update the currently considered optimal solution, and after each iteration, update the temperature T of the simulated annealing algorithm according to a certain rule (usually decreasing according to the cooling rate) to make the algorithm gradually converge. For example, in the first iteration, a set of random values of the state parameter may be accepted as the optimal solution, and then continue to repeat the above steps with the new random values of the state parameter, and the temperature continuously decreases. The algorithm will gradually reduce the probability of accepting a worse state in subsequent iterations and finally gradually converge to the optimal solution.

[0099] This process will continue until the termination condition of the algorithm is met, such as reaching the maximum number of iterations, or the comprehensive objective function values corresponding to the randomly generated state parameter values for several consecutive times no longer have significant improvement (for example, being less than the preset threshold for several consecutive times).

[0100] The above steps reflect the iterative nature of the simulated annealing algorithm. By continuously evaluating and comparing different random values of the state parameter, combined with the Metropolis criterion and the temperature update mechanism, the optimal combination of state parameters that makes the comprehensive objective function reach the optimum is finally found through multiple iterations, so as to achieve the optimal control of the new energy plant station.

[0101] Furthermore, the power supply reliability function is to calculate the influence values of the intermittency and volatility of new energy power generation on the system outage time and outage frequency. The calculation formula of the power supply reliability function R is:

[0102]

[0103] where i is the node number of the new energy plant station, N is the number of nodes of the new energy plant station, λ i is the failure rate of the i-th node, T i is the outage time of the i-th node, T maxLet τ be the maximum operating power outage time of the i-th node. i Let be the reliability index of the i-th node.

[0104] New energy power plants have multiple nodes, which can represent different power generation units or connection points. 'i' identifies each specific node, while 'N' represents the total number of nodes in the plant. Reliability assessments are performed on each node because the performance and conditions of different nodes may vary. For example, wind turbines and photovoltaic power generation units located in different locations or connected to different electrical nodes will have different fault characteristics and impacts on system power outages.

[0105] Failure rate λ i This represents the probability of the i-th node failing, obtained through statistical analysis of historical operating data. For example, for a wind turbine node, its failure rate may be affected by equipment aging, environmental conditions (such as strong winds, lightning strikes, and dust storms), and maintenance levels. The failure rate can be estimated based on the ratio of the number of failures of the node over a past period to its total operating time.

[0106] Power outage time T i This refers to the actual power outage duration when the i-th node experiences a fault, which varies depending on the type of fault and the timeliness of repair. In a real system, when a node fault is detected, the time interval from the occurrence of the fault to the restoration of power is recorded, i.e., the power outage time.

[0107] Maximum operating power outage time T max This is a pre-defined maximum allowable outage duration for the i-th node, which can be determined based on system design requirements and user needs. Different nodes have different levels of importance to the power supply, and therefore their allowable outage times also differ.

[0108] Reliability index τ i This reflects other reliability-related factors of the node, such as equipment redundancy and the availability of backup power. If a node has multiple backup power supplies or redundant equipment, its reliability index may be higher, indicating that power can be restored more quickly in the event of a failure, thus improving the overall reliability of the node.

[0109] To accurately calculate the power supply reliability function, it is necessary to collect and update data such as node failure rates and outage times in real time or periodically. These data can be continuously updated through the plant's monitoring and fault recording systems to accurately assess power supply reliability at different operational stages. For newly installed equipment or equipment that has undergone major maintenance and upgrades, its failure rate may change, requiring timely adjustments to relevant parameters to ensure that the reliability function calculation reflects actual conditions.

[0110] By analyzing the results of the power supply reliability function, corresponding measures can be taken to improve power supply reliability. For example, if a node is found to have a high failure rate, the equipment at that node can be inspected and maintained, or redundant equipment can be added to reduce λ. i Or increase τ i .

[0111] Furthermore, the power quality function is a comprehensive index value calculated by comparing the actual voltage value with the rated voltage value at each node of the new energy power plant, and determining the voltage deviation at each node. The formula for calculating the power quality function Q is:

[0112]

[0113] Where i is the node number of the new energy power plant, N is the number of nodes in the new energy power plant, and ω i V represents the weighting factor of the voltage deviation at node i affecting the overall power quality. i V is the actual voltage value of the i-th node. i,额定 Let ρ be the rated voltage value of the i-th node. i Let be the deviation index of the i-th node.

[0114] The weighting factor ω of the voltage deviation value on the overall power quality i This reflects the importance of the voltage deviation at node i to the overall power quality of the power plant. Different nodes have different positions and roles within the power plant, and therefore their voltage deviations will have different weights on the system's power quality. For example, nodes connecting critical loads may have a more severe impact on the system due to their voltage deviations, and thus will be assigned a larger weighting factor; while auxiliary nodes or nodes less sensitive to voltage fluctuations can have relatively smaller weighting factors. Based on the actual voltage value V... i and rated voltage value V i,额定 Voltage deviation is an important indicator for evaluating power quality, and its calculation is typically based on the difference between the actual voltage and the rated or design voltage. The deviation index ρ i This is to adjust or nonlinearly process the voltage deviation at different nodes in some way to better reflect its impact on power quality.

[0115] Furthermore, the power generation cost function is the sum of the power output cost of the energy storage module, the power generation cost of the wind turbine, and the power generation cost of the photovoltaic power generation equipment. The formula for calculating the power generation cost function F is:

[0116]

[0117] Among them, C k Let P be the unit active power cost coefficient of the i-th energy storage unit in the energy storage system. k,有功Let W be the active power output of the k-th energy storage unit, σ be the carbon trading coefficient, and W be the active power output of the k-th energy storage unit. 弃风 γ represents the amount of wind power curtailed by the wind turbine generators, γ is the cost factor for wind curtailment, and P 弃光 Let θ represent the amount of solar power curtailed from the photovoltaic power plant, and θ be the curtailment cost coefficient.

[0118] Unit active power cost coefficient C k This reflects the cost generated by the k-th energy storage unit outputting a unit of active electrical energy within an energy storage system. It encompasses multiple costs, including the amortization of the initial investment cost of the energy storage equipment, energy loss costs during operation (such as energy loss during battery charging and discharging), equipment maintenance costs, and replacement costs over the lifespan of the equipment. Different types of energy storage technologies (such as lithium-ion batteries, lead-acid batteries, and flow batteries) will have significantly different unit active cost coefficients, depending on their respective characteristics, such as energy conversion efficiency, lifespan, and initial investment cost. k,有功 This refers to the actual active power output of the k-th energy storage unit during operation. Its magnitude will dynamically change according to the power supply and demand situation of the renewable energy power plant.

[0119] The carbon trading factor σ represents the market trading price corresponding to a unit of carbon emissions. While wind power generation produces almost no carbon emissions during operation, it still generates some carbon emissions throughout its entire lifecycle, including the manufacturing, installation, maintenance, and decommissioning of wind turbines. The introduction of this factor makes the calculation of wind power generation costs more comprehensive, reflecting its economic impact on carbon reduction.

[0120] Wind power curtailment W 弃风 Due to the intermittent and fluctuating nature of wind energy, as well as limitations imposed by grid capacity and power dispatching, sometimes not all the electricity generated by wind turbines can be absorbed by the grid, resulting in some electricity being wasted. This wasted electricity is known as wind curtailment. By monitoring and analyzing data such as the power output of wind turbines, the grid's capacity, and dispatching instructions, the amount of wind curtailment can be accurately calculated.

[0121] Curtailed solar power P 弃光 This refers to the portion of electricity generated by a photovoltaic (PV) power plant that cannot be fully utilized and is thus discarded due to factors such as changes in sunlight intensity, matching issues between the PV power plant and the power grid, and power absorption capacity. The amount of discarded solar power can be determined by analyzing data such as the output power of the PV power plant, power monitoring at the grid connection point, and power dispatch records.

[0122] Therefore, the formula for calculating the comprehensive objective function is:

[0123] f(x j )=θ1×R(x j )+θ2×Q(x j)+θ3×F(x j ).

[0124] Where, f(x) j ) represents the numerical value of the comprehensive objective function corresponding to the random values ​​of the j-th group of state parameters, θ1 represents the weighting coefficient of the power supply reliability function in the comprehensive objective function, and R(x) represents the value of the comprehensive objective function. j ) represents the power supply reliability function value corresponding to the random value of the j-th group of state parameters, θ2 is the weighting coefficient of the power quality function in the comprehensive objective function, and Q(x) is the power supply reliability function value. j ) represents the power quality function value corresponding to the random value of the j-th group of state parameters, θ3 is the weighting coefficient of the power generation cost function in the comprehensive objective function, and F(x) j ) represents the power generation cost function value corresponding to the random value of the j-th group of state parameters, 1≤j≤M.

[0125] The determination of the aforementioned weighting coefficients θ1, θ2, and θ3 first requires assessing the relative importance of power supply reliability, power quality, and generation cost to renewable energy power plants. If the location of the power plant has extremely high requirements for power supply continuity, such as supplying power to important locations like hospitals and data centers, then the weighting coefficient for power supply reliability may be set higher. For example, in some industrial production scenarios that are extremely sensitive to power outages, it can be set to 0.5 to highlight the importance of power supply reliability. Secondly, when the operational goal of the power plant focuses more on reducing costs to participate in market competition, the weighting coefficient for the generation cost function will be increased accordingly. For example, for some large-scale renewable energy power generation companies, in order to gain a price advantage in the electricity market, it can be set to 0.4, while appropriately reducing the weights of other factors. Thirdly, for areas with high power quality requirements, such as industrial parks with a large number of precision electronic devices, the weighting coefficient for the power quality function should be increased. Its weight can be determined based on the specific requirements of the power grid for power quality and the potential impact. For example, in areas sensitive to harmonics, it can be set between 0.4 and 0.6 to focus on and improve power quality.

[0126] In one specific embodiment of this invention, the influencing factors of the power quality function further include: voltage harmonics, three-phase unbalance, frequency deviation, and voltage flicker. Voltage harmonics are generated by the use of nonlinear loads such as power electronic equipment. The content of each harmonic is measured using a harmonic analyzer, and its impact on power quality is calculated based on weighting coefficients. Three-phase unbalance is calculated by measuring the effective values ​​of the three-phase voltages to determine the degree of imbalance and its impact on power quality. Frequency deviation is obtained by comparing the actual frequency with the nominal frequency, and voltage flicker is calculated using data such as voltage changes obtained through specific measuring instruments. These factors are combined to construct the power quality function for a comprehensive assessment of the power quality of power plants.

[0127] The formula for calculating the power quality function is:

[0128]

[0129] Where, ω i1 -ω i5 These are the weighting coefficients for voltage deviation, voltage harmonics, three-phase unbalance, frequency deviation, and voltage flicker at the i-th node when calculating the power quality function, THDU. i Let be the total harmonic distortion of the voltage at the i-th node. Let Δf be the three-phase voltage imbalance at the i-th node. i P represents the frequency deviation in the i-th node. lt,i Let be the long-term flicker value in the i-th node.

[0130] Specifically, the total harmonic distortion (THDU) of the voltage at the i-th node i The calculation formula is:

[0131]

[0132] Among them, U i,h U is the effective value of the h-th harmonic voltage at the i-th node. i,1 Let be the effective value of the fundamental voltage at the i-th node.

[0133] Specifically, the three-phase voltage imbalance in the i-th node The calculation formula is:

[0134]

[0135] Among them, U i,a U i,b U i,c These are the effective values ​​of the three-phase voltages at the i-th node.

[0136] Specifically, the frequency deviation Δf in the i-th node i The calculation formula is:

[0137]

[0138] Among them, f i f is the frequency value corresponding to the i-th node. i,e This is the nominal frequency value.

[0139] Specifically, the long-time flicker value P in the i-th node lt,i The calculation formula is:

[0140]

[0141] Where, ΔV i,k Let τ be the voltage change at the i-th node for the k-th time, T be the measurement time length, and τ be the voltage change at the i-th node for the k-th time.c Let M be the long-term flicker time constant of the i-th node, and M be the number of voltage changes in the flicker of the i-th node.

[0142] In addition, the weighting coefficient ω i1 -ω i5 The weighting can be determined based on three factors: First, load characteristics play a dominant role, with the weights determined according to the sensitivity of the power plant's load to different power quality factors. For example, in areas with precision equipment loads sensitive to voltage deviation, the voltage deviation weighting coefficient is higher; in areas with nonlinear loads generating harmonic problems, the voltage harmonic weighting coefficient is correspondingly increased. Second, grid requirements are taken into account, referring to grid access standards and specifications. If the grid imposes strict restrictions on a certain power quality indicator (such as frequency deviation), its corresponding weighting coefficient should be set to better reflect the importance of that indicator. Third, equipment safety is considered, taking into account the degree of damage that power quality problems can cause to power plant and user-end equipment. For example, three-phase imbalance has a significant impact on three-phase equipment, and in power supply areas with important three-phase equipment, its weighting coefficient should be high enough to ensure equipment safety.

[0143] Furthermore, after determining a better solution to the integrated objective function in step S420, the process also includes:

[0144] Step S421: Save the random values ​​of the state parameters and their corresponding comprehensive objective function values ​​to the data storage unit.

[0145] Recording the random values ​​of the state parameters at each step and their corresponding comprehensive objective function values ​​helps to fully understand the algorithm's search trajectory and analyze its exploration of different state combinations at different stages. For example, by analyzing the saved data, it is possible to observe whether the algorithm performs intensive searches in certain areas or gets trapped in local optima prematurely. Optionally, the data storage unit can use a relational database (such as MySQL or PostgreSQL) or file storage, such as a CSV file.

[0146] Furthermore, before iteratively calculating the optimal solution of the comprehensive objective function of the new energy power plant based on the simulated annealing algorithm model in step S400, the following steps are also included:

[0147] Step S401: Set the initial values ​​of the parameters of the simulated annealing algorithm model. The initial values ​​of the parameters include: initial temperature value, initial cooling rate value, and maximum number of iterations value.

[0148] The initial temperature determines the probability that the simulated annealing algorithm will accept poor solutions in the initial stage. If the initial temperature is set too high, the algorithm will accept a large number of inferior solutions in the initial stage. Although this increases the globality of the search, it will lead to excessively long computation time. For example, if the initial temperature is set to a maximum value, almost all new solutions will be accepted, and the algorithm will blindly search within a large solution space, resulting in low efficiency. Conversely, if the initial temperature is set too low, the algorithm may get trapped in local optima too early. This is because at low temperatures, the probability of the algorithm accepting poor solutions is very small, and once trapped in a local optimum, it is difficult to escape. Some simple experimental runs can be performed to observe the convergence of the algorithm, and then the initial temperature can be gradually adjusted.

[0149] The cooling rate controls how quickly the probability of accepting a poor solution decreases as the number of iterations increases. If the cooling rate is too fast, the algorithm will converge rapidly, but may miss the global optimum. For example, if the cooling rate is set too high, the probability of accepting a poor solution will be significantly reduced in the early stages of iteration, and the algorithm may stop searching before fully exploring the solution space. If the cooling rate is too slow, the algorithm will linger between different solutions for a long time, resulting in low computational efficiency. Typically, the cooling rate can be set to a small fixed value (e.g., between 0.95 and 0.99), or it can be adaptively adjusted dynamically based on the algorithm's performance.

[0150] To prevent the algorithm from running indefinitely, the maximum number of iterations is set as the maximum number of attempts the algorithm can make to search the solution space. If this number is set too low, the algorithm may be forced to stop before finding a satisfactory solution. For example, in a complex multi-source coordinated control problem for a new energy power plant, a maximum number of iterations of 100 might be far from sufficient for the algorithm to converge to a satisfactory solution.

[0151] Furthermore, after comparing the random number with the probability value in step S430, the process also includes:

[0152] Step S431: Update the initial temperature value in the simulated annealing algorithm model according to the initial cooling rate value until the number of iterations equals the maximum number of iterations.

[0153] The algorithm stops iterating when the maximum number of iterations is reached. This is a mandatory termination condition that ensures the algorithm terminates within a reasonable time and computational resource limit. For example, if the maximum number of iterations is set to 1000, the algorithm will stop iterating after the 1000th iteration, regardless of whether it has found the optimal solution, and will output the currently obtained optimal solution (or near-optimal solution).

[0154] Furthermore, after updating the initial temperature value in the simulated annealing algorithm model according to the initial cooling rate value in step S431, the process also includes:

[0155] Step S432: When the value of the comprehensive objective function corresponding to the random value of the state parameter is less than the preset threshold for several consecutive times, the iterative calculation is terminated, and the optimal solution of the comprehensive objective function is obtained.

[0156] When the value of the comprehensive objective function corresponding to the random value of the state parameter is less than the preset threshold multiple times consecutively, it indicates that the algorithm has performed multiple searches around a relatively optimal solution, and the solutions obtained fluctuate within a small range, suggesting that the algorithm may have converged to a better solution. For example, if the preset threshold is 0.01, and the value of the comprehensive objective function obtained in 5 consecutive iterations is less than 0.01, this means that the solution quality obtained by the algorithm in the current region is relatively stable and has reached our preset accuracy requirement.

[0157] Furthermore, the determination of the preset threshold needs to be based on the requirements of the specific problem and the properties of the objective function. If a high level of accuracy is required for the solution, a smaller threshold, such as 0.001, can be set; if the accuracy requirement is relatively low, or if faster convergence of the algorithm is desired, a larger threshold, such as 0.1, can be set. Additionally, a suitable threshold can be determined by analyzing the algorithm's results under different thresholds.

[0158] Accordingly, please refer to Figure 2 The second aspect of this invention provides a multi-source coordinated control device for a new energy power plant, which performs multi-source coordinated control of the new energy power plant based on the above-mentioned multi-source coordinated control method for the new energy power plant. The new energy power plant includes: energy storage components, wind power components, photovoltaic power generation components, and reactive power compensation equipment.

[0159] Initial data acquisition module 1 is used to acquire the initial values ​​of the status parameters of the new energy power plant, including: the initial value of the active power of the energy storage module, the initial value of the active power and the initial value of the reactive power of the wind power module and the photovoltaic power generation module, as well as the initial switching status of several reactive power compensation devices.

[0160] Objective function construction module 2 is used to construct the comprehensive objective function of new energy power plants. The comprehensive objective function includes the power supply reliability function, the power quality function, and the power generation cost function.

[0161] The random parameter generation module 3 is used to obtain several sets of random values ​​of state parameters of the new energy power plant through random perturbation, including: the initial value of active power of the energy storage component, the random values ​​of active power and reactive power of the wind power component and the photovoltaic power generation component, and the random switching state of several reactive power compensation devices.

[0162] The multi-source collaborative control module 4 is used to iteratively calculate the optimal solution of the comprehensive objective function of the new energy power plant based on the simulated annealing algorithm model, and to perform collaborative control of the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of the new energy power plant based on the random values ​​of the corresponding state parameters of the optimal solution.

[0163] Accordingly, a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to execute the above-described multi-source coordinated control method for new energy power plants.

[0164] Accordingly, a fourth aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-described multi-source coordinated control method for new energy power plants.

[0165] The embodiments of the present invention aim to protect a multi-source coordinated control method and device for new energy power plants, which has the following effects:

[0166] 1. By constructing a comprehensive objective function that includes power supply reliability, power quality, and power generation cost functions, and using the simulated annealing algorithm to iteratively solve for the optimal solution, the overall performance of new energy power plants can be taken into account at the same time, thus achieving overall optimization of new energy power plants;

[0167] 2. Optimization based on random values ​​of state parameters obtained from random disturbances can effectively address the intermittency and volatility of new energy power generation, dynamically adjust the state of each component, and improve the plant's adaptability to environmental changes;

[0168] 3. By setting the simulated annealing algorithm parameters, updating the temperature, and setting the termination conditions, the algorithm can be guaranteed to efficiently search for the optimal solution under reasonable computing resources, thereby improving the computational efficiency and practicality of collaborative control.

[0169] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0171] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-source coordinated control method for new energy power plants, characterized in that, The new energy power plant includes: energy storage modules, wind power modules, photovoltaic power generation modules, and reactive power compensation equipment. The control method includes the following steps: Acquire the initial values ​​of the status parameters of the new energy power plant, including: the initial value of the active power of the energy storage module, the initial value of the active power and reactive power of the wind power module and the photovoltaic power generation module, and the initial switching status of several reactive power compensation devices; Construct a comprehensive objective function for new energy power plants, which includes a power supply reliability function, a power quality function, and a power generation cost function; Several sets of random values ​​of state parameters of new energy power plants are obtained through random perturbation, including: random values ​​of active power of energy storage components, random values ​​of active power and reactive power of wind power components and photovoltaic power generation components, and random switching states of several reactive power compensation devices. Based on the simulated annealing algorithm model, the optimal solution of the comprehensive objective function of the new energy power plant is calculated iteratively. Based on the random values ​​of the state parameters corresponding to the optimal solution, the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of the new energy power plant are coordinated and controlled. The iterative calculation of the optimal solution to the comprehensive objective function of new energy power plants includes: Substitute a random value of a state parameter into the comprehensive objective function; Based on the numerical values ​​of the integrated objective function corresponding to the initial values ​​of the state parameters and the numerical values ​​of the integrated objective function corresponding to the random values ​​of the state parameters, a better solution to the integrated objective function is determined; When the initial value of the state parameter is a better solution, the probability value of the random value of the state parameter being the optimal solution is calculated according to the Metropolis criterion, and a random number within a preset range is randomly generated and compared with the probability value. If the random number is less than the probability value, the random value of the state parameter is taken as the optimal solution for the new energy power plant; if the random number is greater than or equal to the probability value, the initial value of the state parameter is taken as the optimal solution for the new energy power plant. The optimal solution of the comprehensive objective function is obtained by iteratively calculating the random value of each state parameter. The power supply reliability function is used to calculate the impact of the intermittency and volatility of new energy power generation on system outage time and frequency. The formula for calculating the power supply reliability function R is as follows: Where i is the node number of the new energy power plant, N is the number of nodes in the new energy power plant, and λ i Let T be the failure rate of the i-th node. i Let T be the power outage time of the i-th node. max Let τ be the maximum operating power outage time of the i-th node. i Let be the reliability index of the i-th node; The power quality function is a comprehensive index value calculated by comparing the actual voltage value with the rated voltage value at each node of the new energy power plant, and determining the voltage deviation at each node. The formula for calculating the power quality function Q is as follows: Where i is the node number of the new energy power plant, N is the number of nodes in the new energy power plant, and ω i V represents the weighting factor of the voltage deviation at node i affecting the overall power quality. i V is the actual voltage value of the i-th node. i,额定 Let ρ be the rated voltage value of the i-th node. i Let be the deviation index of the i-th node; The power generation cost function is the sum of the power output cost of the energy storage module, the power generation cost of the wind turbine, and the power generation cost of the photovoltaic power generation equipment. The formula for calculating the power generation cost function F is: Among them, C k Let P be the unit active power cost coefficient of the k-th energy storage unit in the energy storage system. k,有功 Let W be the active power output of the k-th energy storage unit, σ be the carbon trading coefficient, and W be the active power output of the k-th energy storage unit. 弃风 γ represents the amount of wind power curtailed by the wind turbine generators, γ is the cost factor for wind curtailment, and P 弃光 θ represents the amount of solar power curtailed by the photovoltaic power plant, and θ is the curtailment cost coefficient. The formula for calculating the comprehensive objective function is as follows: f(x j )=θ1×R(x j )+θ2×Q(x j )+θ3×F(x j ); Where, f(x) j ) represents the numerical value of the comprehensive objective function corresponding to the random values ​​of the j-th group of state parameters, θ1 represents the weighting coefficient of the power supply reliability function in the comprehensive objective function, and R(x) represents the value of the comprehensive objective function. j ) represents the power supply reliability function value corresponding to the random value of the j-th group of state parameters, θ2 is the weighting coefficient of the power quality function in the comprehensive objective function, and Q(x) is the power supply reliability function value. j ) represents the power quality function value corresponding to the random value of the j-th group of state parameters, θ3 is the weighting coefficient of the power generation cost function in the comprehensive objective function, and F(x) j ) represents the power generation cost function value corresponding to the random value of the j-th group of state parameters, 1≤j≤M.

2. The multi-source coordinated control method for new energy power plants according to claim 1, characterized in that, The factors affecting the power quality function also include: voltage harmonics, three-phase imbalance, frequency deviation, and voltage flicker. The formula for calculating the power quality function Q is: Where, ω i1 -ω i5 These are the weighting coefficients for voltage deviation, voltage harmonics, three-phase unbalance, frequency deviation, and voltage flicker at the i-th node when calculating the power quality function, THDU. i Let be the total harmonic distortion of the voltage at the i-th node. Let Δf be the three-phase voltage imbalance at the i-th node. i The frequency deviation in the i-th node, This represents the long-duration flicker value in the i-th node; Total harmonic distortion (THDU) of voltage at the i-th node i The calculation formula is: Among them, U i,h U is the effective value of the h-th harmonic voltage at the i-th node. i,1 This represents the effective value of the fundamental voltage at the i-th node. Three-phase voltage imbalance at the i-th node The calculation formula is: Among them, U i,a U i,b U i,c These are the effective values ​​of the three-phase voltages at the i-th node; The frequency deviation Δf in the i-th node i The calculation formula is: Among them, f i f is the frequency value corresponding to the i-th node. i,e This is the nominal frequency value; The long-term flicker value P in the i-th node lt,i The calculation formula is: Where, ΔV i,k Let τ be the voltage change at the i-th node for the k-th time, T be the measurement time length, and τ be the voltage change at the i-th node for the k-th time. c Let M be the long-term flicker time constant of the i-th node, and M be the number of voltage changes in the flicker of the i-th node.

3. The multi-source coordinated control method for new energy power plants according to claim 1, characterized in that, After determining a better solution to the integrated objective function, the process further includes: The random values ​​of the state parameters and their corresponding comprehensive objective function values ​​are saved to the data storage unit.

4. The multi-source coordinated control method for new energy power plants according to claim 1, characterized in that, Before iteratively calculating the optimal solution of the comprehensive objective function of new energy power plants based on the simulated annealing algorithm model, the following steps are also included: Set the initial values ​​of the parameters of the simulated annealing algorithm model. The initial values ​​of the parameters include: initial temperature value, initial cooling rate value, and maximum number of iterations value.

5. The multi-source coordinated control method for new energy power plants according to claim 4, characterized in that, After comparing the random number with the probability value, the method further includes: The initial temperature value in the simulated annealing algorithm model is updated according to the initial cooling rate value until the number of iterations equals the maximum number of iterations.

6. The multi-source coordinated control method for new energy power plants according to claim 5, characterized in that, After updating the initial temperature value in the simulated annealing algorithm model according to the initial cooling rate value, the method further includes: When the value of the comprehensive objective function corresponding to the random value of the state parameter is less than a preset threshold for several consecutive times, the iterative calculation is terminated, and the optimal solution of the comprehensive objective function is obtained.

7. A multi-source coordinated control device for a new energy power plant, characterized in that, The multi-source coordinated control method for new energy power plants according to any one of claims 1-6 is used to perform multi-source coordinated control on the new energy power plants. The new energy power plants include: energy storage modules, wind power modules, photovoltaic power generation modules, and reactive power compensation equipment. The initial data acquisition module is used to acquire the initial values ​​of the status parameters of the new energy power plant, including: the initial value of the active power of the energy storage module, the initial value of the active power and the initial value of the reactive power of the wind power module and the photovoltaic power generation module, as well as the initial switching status of several reactive power compensation devices. The objective function construction module is used to construct the comprehensive objective function of the new energy power plant. The comprehensive objective function includes the power supply reliability function, the power quality function, and the power generation cost function. The random parameter generation module is used to obtain several sets of random values ​​of state parameters of new energy power plants through random perturbation, including: the initial value of active power of energy storage components, random values ​​of active power and reactive power of wind power components and photovoltaic power generation components, and random switching status of several reactive power compensation devices. The multi-source collaborative control module is used to iteratively calculate the optimal solution of the comprehensive objective function of the new energy power plant based on the simulated annealing algorithm model, and to perform collaborative control of the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of the new energy power plant based on the random values ​​of the corresponding state parameters of the optimal solution.

8. An electronic device, characterized in that, include: At least one processor; The system includes a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to cause the at least one processor to perform the multi-source coordinated control method for new energy power plants as described in any one of claims 1-6.

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