Multi-source cooperative control method and device for new energy plant station

Abstract

The invention discloses a multi-source cooperative control method and device for a new energy plant station. The control method comprises the following steps: acquiring a state parameter initial value of the new energy plant station; constructing a comprehensive objective function of the new energy plant station; obtaining a plurality of groups of state parameter random values of the new energy plant station through a random disturbance mode; and based on the simulated annealing algorithm model, iteratively calculating an optimal solution of the comprehensive target function of the new energy plant station, and performing cooperative control on an energy storage assembly, a wind power assembly, a photovoltaic power generation assembly and reactive compensation equipment of the new energy plant station based on a state parameter random value corresponding to the optimal solution. By applying the simulated annealing algorithm to the aspect of multi-source cooperative control of a new energy plant station, local optimal limitation can be broken through in a complex solution space, global search of an optimal solution can be realized, operation strategies of all components can be rapidly adjusted, power supply reliability, electric energy quality and power generation cost can be accurately balanced, multi-source cooperation can be efficiently optimized, and the power generation efficiency can be improved. And the overall operation performance and stability of the new energy plant station are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power plant control, and particularly relates to a multi-source collaborative control method and device for a new energy power plant. Background Art

[0002] With the continuous improvement of the global attention to environmental protection and sustainable development, problems such as environmental pollution and energy depletion caused by the large-scale use of traditional fossil energy have become increasingly prominent. Against this background, countries around the world have accelerated the pace of energy structure transformation and vigorously promoted the development and utilization of new energy. As representatives of clean and renewable energy, wind energy and solar energy have been continuously increasing their proportion in the global energy structure. In the past decade, the global installed capacity of wind power and photovoltaic power generation has grown at an annual double-digit rate. A large number of wind power components and photovoltaic power generation components are connected to the power grid, becoming an important force to meet the growing power demand and reduce carbon emissions.

[0003] The energy sources of wind energy and solar energy are greatly affected by natural conditions and have significant intermittency and volatility. Wind power generation depends on wind speed, and the instability of wind speed causes the output power of wind turbines to fluctuate frequently and is difficult to predict. Photovoltaic power generation is closely related to light intensity and time, and factors such as day and night alternation and cloud cover will cause the output power of photovoltaic power plants to change significantly in a short period of time. This unstable power generation characteristic poses a huge challenge to the stable operation of the power system, resulting in difficult real-time balance between power supply and demand and seriously affecting power supply reliability. New energy power generation equipment is mostly connected to the power grid through power electronic devices, and these devices are prone to power quality problems such as harmonics, voltage fluctuations and flicker during operation. At the same time, due to the intermittency of wind power and photovoltaic power generation, it will cause an increase in grid voltage deviation and three-phase unbalance, affecting the normal operation of other equipment in the power grid and reducing the power quality of the entire power system. For example, when a large number of photovoltaic power plants experience rapid changes in light intensity, it may cause large fluctuations in the local grid voltage, affecting the service life and operation stability of electrical equipment of nearby residents and enterprises.

[0004] Energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment each play an important role in new energy power plants, but when they operate independently of each other, they cannot fully exert their overall efficiency. For example, if only the maximum power output of wind power and photovoltaic power generation is considered without considering the coordination of energy storage and reactive power compensation, problems such as excessive grid voltage fluctuations and reduced power supply reliability may occur. Therefore, realizing the multi-source collaborative control of energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment, and comprehensively optimizing power supply reliability, power quality and power generation cost, has become the key to improving the operation efficiency and stability of new energy power plants. The traditional decentralized control method can no longer meet the increasingly complex operation requirements of new energy power plants, and there is an urgent need for an advanced multi-source collaborative control method to solve the above problems. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a multi-source collaborative control method and device for a new energy plant station. By applying the simulated annealing algorithm to the multi-source collaborative control of a new energy plant station, it can break through the local optimal limit in a complex solution space, achieve global search for the optimal solution, quickly adjust the operation strategies of each component, accurately balance power supply reliability, power quality and power generation cost, efficiently optimize multi-source collaboration, and greatly improve the overall operation performance and stability of the new energy plant station.

[0006] To solve the above technical problems, the first aspect of the embodiments of the present invention provides a multi-source collaborative control method for a new energy plant station. The new energy plant station includes: an energy storage component, a wind power component, a photovoltaic power generation component, and a reactive power compensation device. The control method includes the following steps:

[0007] Obtain the initial values of the state parameters of the new energy plant station, including: the initial active power value of the energy storage component, the initial active power values and reactive power values of the wind power component and the photovoltaic power generation component, and the initial switching states of several reactive power compensation devices;

[0008] Construct a comprehensive objective function for the new energy plant station, where the comprehensive objective function includes a power supply reliability function, a power quality function, and a power generation cost function;

[0009] Obtain several sets of random values of the state parameters of the new energy plant station by means of random perturbation, including: the random active power values of the energy storage component, the random active power values and reactive power values of the wind power component and the photovoltaic power generation component, and the random switching states of several reactive power compensation devices;

[0010] Based on the simulated annealing algorithm model, iteratively calculate the optimal solution of the comprehensive objective function of the new energy plant station, and perform collaborative control on the energy storage component, the wind power component, the photovoltaic power generation component, and the reactive power compensation device of the new energy plant station based on the random values of the state parameters corresponding to the optimal solution.

[0011] Further, the iterative calculation of the optimal solution of the comprehensive objective function of the new energy plant station includes:

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

[0013] Based on the numerical value of the comprehensive objective function corresponding to the initial value of the state parameter and the numerical value of the comprehensive objective function corresponding to the random value of the state parameter, determine a better solution of the comprehensive objective function;

[0014] When the initial value of the state parameter is the better solution, calculate the probability value that the random value of the state parameter is the optimal solution according to the Metropolis criterion, randomly generate a random number within a preset range, and compare the random number with the probability value;

[0015] When the random number is less than the probability value, the random value of the state parameter is taken as the optimal solution of the new energy plant station; when 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 of the new energy plant station.

[0016] Iteratively calculate the random value of each state parameter to obtain the optimal solution of the comprehensive objective function.

[0017] Furthermore, the power supply reliability function is to calculate the influence values of the intermittency and volatility of the new energy power generation power on the system power outage time and power outage frequency. The calculation formula of the power supply reliability function R is:

[0018]

[0019] 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 power outage time of the i-th node, T max is the maximum operating power outage time of the i-th node, τ i is the reliability index of the i-th node;

[0020] The power quality function is to compare the actual voltage value and the rated voltage value of each node of the new energy plant station, and calculate the comprehensive index value of the voltage deviation of each node. The calculation formula of the power quality function Q is:

[0021]

[0022] where i is the node number of the new energy plant station, N is the number of nodes of the new energy plant station, ω i is the weight coefficient value of the voltage deviation value of the i-th node affecting the overall power quality, V i is the actual voltage value of the i-th node, V i,额定 is the rated voltage value of the i-th node, ρ i is the deviation index of the i-th node;

[0023] The power generation cost function is to calculate the sum of the output power cost of the energy storage components, the power generation costs of the wind turbines and the photovoltaic power generation equipment. The calculation formula of the power generation cost function F is:

[0024]

[0025] where C k is the unit active power cost coefficient of the k-th energy storage unit in the energy storage components, P k,有功 is the active power output of the k-th energy storage unit, σ is the carbon trading coefficient, W 弃风 is the curtailed wind power of the wind turbine, γ is the curtailed wind cost coefficient, P 弃光is the curtailed photovoltaic power of the PV power station, and θ is the curtailment cost coefficient;

[0026] The calculation formula of the comprehensive objective function is:

[0027] f(x j ) = θ 1 × R(x j ) + θ 2 × Q(x j ) + θ 3 × F(x j );

[0028] Among them, f(x j ) is the value of the comprehensive objective function corresponding to the random value of the j-th group of state parameters, θ 1 is the weight coefficient of the power supply reliability function in the comprehensive objective function, R(x j ) is the value of the power supply reliability function corresponding to the random value of the j-th group of state parameters, θ 2 is the weight coefficient of the power quality function in the comprehensive objective function, Q(x j ) is the value of the power quality function corresponding to the random value of the j-th group of state parameters, θ 3 is the weight coefficient of the power generation cost function in the comprehensive objective function, F(x j ) is the value of the power generation cost function corresponding to the random value of the j-th group of state parameters, 1 ≤ j ≤ M.

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

[0030] The calculation formula of the power quality function Q is:

[0031]

[0032] Among them, ω i1 - ω i5 are the weight coefficients of the voltage deviation value, voltage harmonics, three-phase unbalance, frequency deviation, and voltage flicker in the i-th node when calculating the power quality function, THDU i is the total voltage harmonic distortion rate in the i-th node, ε U2,i is the three-phase voltage unbalance degree in the i-th node, Δf i is the frequency deviation in the i-th node, P lt,i is the long-term flicker value in the i-th node;

[0033] The total voltage harmonic distortion rate THDU in the i-th node i The calculation formula is:

[0034]

[0035] Among them, U i,h is the effective value of the h-th harmonic voltage of the i-th node, and U i,1 is the effective value of the fundamental wave voltage of the i-th node;

[0036] The three-phase voltage unbalance degree in the i-th node is calculated by the formula:

[0037]

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

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

[0040]

[0041] Among them, f i is the corresponding frequency value of the i-th node, and f i,e is the nominal frequency value;

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

[0043]

[0044] Among them, ΔV i,k is the k-th voltage change amount of the i-th node, T is the measurement time length, and τ c is the long-term flicker degree time constant of the i-th node, and M is the number of voltage changes of the flicker degree of the i-th node.

[0045] Furthermore, after determining the optimal solution of the comprehensive objective function, it further includes:

[0046] Saving the random value of the state parameter and its corresponding comprehensive objective function value to the data storage unit.

[0047] Furthermore, before iteratively calculating the optimal solution of the comprehensive objective function of the new energy power station based on the simulated annealing algorithm model, it further includes:

[0048] Setting the initial values of the parameters of the simulated annealing algorithm model, and the initial values of the parameters include: the initial temperature value, the initial cooling rate value, and the maximum number of iteration values.

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

[0050] Update the initial temperature value in the simulated annealing algorithm model according to the initial cooling rate value until the number of iterations is equal to the maximum number of iteration values.

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

[0052] When the numerical value of the comprehensive objective function corresponding to the random value of the state parameter is continuously less than the preset threshold for several times, terminate the iterative calculation to obtain the optimal solution of the comprehensive objective function.

[0053] Correspondingly, a second aspect of the embodiments of the present invention provides a multi-source collaborative control device for a new energy plant station, which performs multi-source collaborative control on the new energy plant station based on the above multi-source collaborative control method for the new energy plant station. The new energy plant station includes: an energy storage component, a wind power component, a photovoltaic power generation component, and a reactive power compensation device, and includes:

[0054] An initial data acquisition module, which is used to acquire the initial values of the state parameters of the new energy plant station, including: the initial active power value of the energy storage component, the initial active power values and reactive power values of the wind power component and the photovoltaic power generation component, and the initial switching states of several reactive power compensation devices;

[0055] A target function construction module, which is used to construct the comprehensive target function of the new energy plant station, and the comprehensive target function includes a power supply reliability function, a power quality function, and a power generation cost function;

[0056] A random parameter generation module, which is used to obtain several groups of random values of the state parameters of the new energy plant station by means of random perturbation, including: the initial active power value of the energy storage component, the random active power values and reactive power values of the wind power component and the photovoltaic power generation component, and the random switching states of several reactive power compensation devices;

[0057] A multi-source collaborative control module, which is used to iteratively calculate the optimal solution of the comprehensive target function of the new energy plant station based on the simulated annealing algorithm model, and perform collaborative control on the energy storage component, the wind power component, the photovoltaic power generation component, and the reactive power compensation device of the new energy plant station based on the random value of the state parameter corresponding to the optimal solution.

[0058] Correspondingly, a third aspect of the embodiments 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above multi-source collaborative control method for the new energy plant station.

[0059] Correspondingly, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the multi-source collaborative control method of the new energy plant station described above is implemented.

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

[0061] 1. By constructing a comprehensive objective function including power supply reliability, power quality, and power generation cost functions, and using the simulated annealing algorithm to iteratively solve the optimal solution, the multi-faceted performance of the new energy plant station can be considered simultaneously, and the overall optimization of the new energy plant station can be achieved;

[0062] 2. Optimizing according to the random values of the state parameters obtained by random perturbation can effectively cope with the intermittency and volatility of new energy power generation, dynamically adjust the states of each component, and improve the adaptability of the plant station to environmental changes;

[0063] 3. By setting the parameters of the simulated annealing algorithm, updating the temperature, and setting the termination conditions, it can ensure that the algorithm efficiently searches for the optimal solution under reasonable computing resources, and improve the computing efficiency and practicability of collaborative control. Description of the Drawings

[0064] Figure 1 is a flowchart of the multi-source collaborative control method of the new energy plant station provided by the embodiments of the present invention;

[0065] Figure 2 is a block diagram of the multi-source collaborative control device of the new energy plant station provided by the embodiments of the present invention.

[0066] Reference Signs:

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

[0068] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

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

[0070] Step S100, obtain the initial values of the status parameters of the new energy power station, including: the initial active power value of the energy storage component, the initial active power values and the initial reactive power values of the wind power component and the photovoltaic power generation component, and the initial switching states of several reactive power compensation devices.

[0071] Specifically, the energy storage component plays a key role in energy regulation in the new energy power station. Its initial active power value reflects the initial level of energy storage or release by the energy storage system at the current moment. By real-time monitoring the charge and discharge status, remaining battery capacity (SOC), and its rated power of the energy storage device, etc., this initial value can be accurately obtained. For example, by using high-precision electricity sensors and power monitoring modules to measure the current and voltage of the energy storage battery pack in real time, and then calculating the active power. This initial value provides the basic data for subsequent judgment on how the energy storage system participates in energy regulation optimization.

[0072] The initial active power values of the wind power component and the photovoltaic power generation component are used to evaluate the actual power or set power of converting current wind energy and solar energy into electrical energy. The active power of a wind turbine is affected by factors such as wind speed, blade angle of the wind turbine, and wind turbine efficiency. By installing wind speed sensors, wind direction sensors on the wind turbine and feedback data from the power regulation device inside the wind turbine, the initial active power value can be obtained. For the photovoltaic power generation component, the light intensity, temperature, conversion efficiency of the photovoltaic panel, etc. determine its output power. By using light sensors, temperature sensors and measuring the electrical parameters of the photovoltaic array, the initial active power value can be obtained. At the same time, its initial reactive power value is also very important. Reactive power affects the voltage stability of the power system. The wind turbine and photovoltaic inverter can output or absorb reactive power by adjusting their control strategies. By measuring the phase difference between the output voltage and current of the inverter and combining its rated reactive power capacity, the initial reactive power value can be calculated.

[0073] Reactive power compensation devices (such as capacitor banks, static var compensators, etc.) are used to improve the power factor and voltage quality of the power system. Obtaining their initial switching states and determining which reactive power compensation devices are in the operating state and which are in the cut-off state currently can be achieved through feedback signals of the control circuit of the reactive power compensation device, data acquisition of the remote monitoring system, etc. For example, for a capacitor bank, by monitoring the auxiliary contact status of its control switch, it can be judged whether it is in the operating state.

[0074] Step S200, construct the comprehensive objective function of the new energy power station. The comprehensive objective function includes a power supply reliability function, a power quality function, and a power generation cost function.

[0075] Specifically, the power supply reliability function mainly measures the impact of the intermittency and volatility of new energy power generation on the system outage time and outage frequency. The instability of new energy (wind energy and solar energy) may lead to power supply interruptions or fluctuations. By calculating parameters such as the failure rate and outage time of each node, the power supply reliability of the entire substation is comprehensively considered. For example, for a new energy substation containing multiple wind turbines and photovoltaic arrays, each power generation unit has its own failure probability. Through statistical analysis of historical failure data and combined with the operating environment and maintenance records of the equipment, the failure rate of each node can be estimated; then, based on factors such as the repair time after failure and the switching time of the backup power supply, the outage time of each node can be determined; substituting these data into the power supply reliability function formula can quantitatively evaluate the power supply reliability level of the substation.

[0076] Specifically, in the power quality function, power quality mainly involves voltage deviation. Voltage deviation reflects the degree of deviation of the actual voltage from the rated voltage. By measuring the voltage values of each node and comparing them with the rated voltage, combined with the corresponding weight 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 the energy storage component, the power generation costs of wind turbines and photovoltaic power generation equipment. The cost of the energy storage component includes the charge and discharge losses and life losses of the battery. 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 component can be obtained; for wind turbines and photovoltaic power generation equipment, the power generation cost not only includes the investment and operation and maintenance costs of the equipment, but also considers the cost losses caused by wind curtailment and PV curtailment; through carbon trading coefficients, wind curtailment cost coefficients, PV curtailment cost coefficients, etc., combined with the actual wind curtailment power and PV curtailment power, the total power generation cost can be calculated.

[0078] Combining these three functions according to certain weight coefficients to construct a comprehensive objective function to balance the relationship among power supply reliability, power quality and power generation cost.

[0079] Step S300, obtain several groups of random values of the state parameters of the new energy substation through a random perturbation method, including: random values of the active power of the energy storage component, random values of the active power and reactive power of the wind power component and the photovoltaic power generation component, and the random switching states of several reactive power compensation devices.

[0080] To determine the impact of the energy storage component on the overall performance of the power station under different operating conditions, it is necessary to randomly perturb its active power. Within a certain power range, random active power values are generated according to the random number generation algorithm. For wind power components, considering the uncertainty of wind speed and the operating state of the wind turbines, within the adjustable power range, random active power values are generated through random perturbation. For example, according to the power curve of the wind turbine, within different wind speed intervals, the pitch angle or rotational speed control parameters of the wind turbine are randomly changed to obtain different active power outputs. At the same time, similar random perturbations are also performed on its reactive power. By changing the control strategy of the inverter, random reactive power values are generated within its reactive power regulation range. For photovoltaic power generation components, considering the changes in light intensity and temperature, within the maximum power point tracking (MPPT) control range of the photovoltaic array, the control parameters are randomly adjusted to generate random active power values. By changing the reactive power compensation function settings of the photovoltaic inverter, random reactive power values are generated. Random perturbation is performed on the switching state of the reactive power compensation equipment, that is, randomly determining which equipment is put into operation and which equipment is cut off.

[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 station, and perform coordinated control on the energy storage component, wind power component, photovoltaic power generation component, and reactive power compensation equipment of the new energy power station 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 the new energy power station are regarded as the atomic states of the solid, and the comprehensive objective function value is regarded as the energy of the solid. The algorithm starts from an initial state (i.e., the initial value of the state parameters obtained in step S100), and continuously generates new states through random perturbation (i.e., the random values of the state parameters obtained in step S300). In each iteration, the objective function value of the new state is compared with the objective function value of the current state. If the objective function value of the new state is better (such as the comprehensive objective function value is smaller, because it is usually desired to reduce the power generation cost while improving the power supply reliability and ensuring the 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, and this probability gradually decreases as the number of iterations increases.

[0083] First, substitute a random value of the state parameter into the comprehensive objective function to calculate the corresponding objective function value. Then, compare it with the comprehensive objective function value corresponding to the initial value of the state parameter to determine a better solution. When the initial value of the state parameter is the better solution, calculate the probability value that the random value of the state parameter is the optimal solution according to the Metropolis criterion. For example, let the current temperature be T, the objective function value corresponding to the initial value of the state parameter be E 1 and the objective function value corresponding to the random value of the state parameter be E 2, then the probability of accepting the new state (random value of the state parameter). Next, a random number between [0, 1] is randomly generated, and the random number is compared with the probability value. When the random number is less than the probability value, the random value of the state parameter is used as the current optimal solution of the new energy plant; when the random number is greater than or equal to the probability value, the initial value of the state parameter is used as the current optimal solution. After each iteration, the temperature of the simulated annealing algorithm is updated according to the preset cooling rate to reduce the probability of accepting inferior solutions. This process is continuously repeated until the preset termination condition is met (such as reaching the maximum number of iterations or the objective function value is less than the preset threshold for several consecutive times), and finally the optimal solution of the comprehensive objective function is obtained.

[0084] Based on the random value of the state parameter corresponding to this optimal solution, the energy storage components, wind power components, photovoltaic power generation components, and reactive power compensation devices of the new energy plant can be coordinated to achieve the optimal operation of the plant in terms of power supply reliability, power quality, and power generation cost.

[0085] Specifically, the iterative calculation of the optimal solution of the comprehensive objective function of the new energy plant in step S400 includes:

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

[0087] Select one from the numerous random values of the state parameter obtained by the random perturbation method and substitute it into the pre-constructed comprehensive objective function. This random value of the state parameter includes the random active power value of the energy storage component, the random active power values and reactive power values of the wind power component and the photovoltaic power generation component, and the random switching states of several reactive power compensation devices.

[0088] Step S420, determining a better solution of the comprehensive objective function based on the value of the comprehensive objective function corresponding to the initial value of the state parameter and the value of the comprehensive objective function corresponding to the random value of the state parameter.

[0089] In step S100, the initial value of the state parameter is obtained and substituted into the comprehensive objective function to calculate the initial value of the comprehensive objective function. In step S410, the value of the comprehensive objective function corresponding to a random value of the state parameter is calculated. The two are compared. If the value of the comprehensive objective function corresponding to the initial value is greater than the value of the comprehensive objective function 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 assuming to find the minimum value of the comprehensive objective function, 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 in different states, the more optimal state in the current comparison is selected, providing a basis for whether to accept a new randomly generated state parameter value as a possible optimal solution in the subsequent process, and driving the algorithm closer to the optimal solution.

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

[0092] When it is found that the initial value of the state parameter is a more optimal solution, calculate the probability value that the randomly generated state parameter value is the optimal solution according to the Metropolis criterion. Even if the comprehensive objective function value corresponding to the randomly generated state parameter value is worse than the initial value, according to the Metropolis criterion, there is still a certain probability of being accepted as the optimal solution, and the probability size depends on the temperature and the energy difference. Randomly generate a random number within a preset range (usually [0, 1]). Then compare this random number r with the calculated probability value p. The generation of this random number is to simulate the possibility that the system accepts a worse state with a certain probability, avoiding the algorithm falling into a local optimal solution.

[0093] Among them, the application of the Metropolis criterion is the core part of the simulated annealing algorithm, allowing the algorithm to accept a worse state with a certain probability, avoiding premature convergence to a local optimal solution, increasing the possibility of searching for the global optimal solution, and the generation and comparison of random numbers are the specific operations to implement this probability acceptance mechanism.

[0094] Step S440, when the random number is less than the probability value, use the randomly generated state parameter value as the optimal solution of the new energy plant station; when the random number is greater than or equal to the probability value, use the initial value of the state parameter as the optimal solution of the new energy plant station.

[0095] Continue the above comparison process. If r < p, then even if the comprehensive objective function value currently corresponding to the randomly generated state parameter value is worse, according to the probability of the Metropolis criterion, still use the randomly generated state parameter value as the optimal solution of the new energy plant station. Conversely, if r > p, a more conservative choice is preferred, and the initial value of the state parameter is used as the optimal solution of the new energy plant station.

[0096] Based on the comparison result of the probability and the random number, deciding whether to accept a state that seemingly is not the optimal provides an opportunity for the algorithm to jump out of the local optimum, which is a key operation for the simulated annealing algorithm to achieve global search ability.

[0097] Step S450, iteratively calculate each randomly generated state parameter value to obtain the optimal solution of the comprehensive objective function.

[0098] For each set of randomly generated state parameter values obtained through 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 certain rules (usually decreasing at a cooling rate), so that the algorithm gradually converges. For example, in the first iteration, a set of randomly generated state parameter values may be accepted as the optimal solution, and then continue to repeat the above steps with new randomly generated state parameter values, and the temperature continuously decreases. In subsequent iterations, the algorithm will gradually reduce the probability of accepting worse states 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 generated continuously for multiple times no longer have significant improvement (for example, less than a preset threshold for multiple consecutive times).

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

[0101] Furthermore, the power supply reliability function is used to calculate the numerical impact 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 total number of nodes in 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 max is the maximum operating outage time of the i-th node, τ i is the reliability index of the i-th node.

[0104] There are multiple nodes in the new energy plant station. These nodes can represent different power generation units or connection points. i is used to identify each specific node, and N represents the total number of nodes in the plant station. Reliability assessment is carried out for each node because the performance and conditions of different nodes may be different. For example, wind turbines, photovoltaic power generation units in different locations or connected to different electrical nodes may have different fault characteristics and impacts on system outages.

[0105] The failure rate λ iDenotes the probability of the i-th node failing, obtained through statistical analysis of historical operation 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, dust, etc.), and maintenance levels. The failure rate can be estimated based on the ratio of the number of times the node has failed to the total operating time over a past period.

[0106] Power outage time T i Refers to the actual duration of power outage when the i-th node fails, which varies depending on the type of failure and the timeliness of repair. In an actual system, when a node failure is detected, the time interval from the occurrence of the failure to the restoration of power supply is recorded, i.e., the power outage time.

[0107] Maximum operating power outage time T max Is the pre-set maximum allowable power outage duration for the i-th node, which can be determined according to the design requirements of the system and the needs of users. Different nodes have different importance for power supply, so the allowable power outage times are also different.

[0108] Reliability index τ i Reflects other reliability-related factors of the node, such as equipment redundancy, availability of backup power sources, etc. If a node has multiple backup power sources or redundant devices, its reliability index may be higher, indicating that it can restore power supply faster when a failure occurs, 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 the failure rate and power outage time of the node in real-time or regularly. Through the monitoring system and fault recording system of the substation, these data can be continuously updated to accurately evaluate the power supply reliability at different operating stages. For newly installed equipment or equipment that has undergone major maintenance upgrades, its failure rate may change, and relevant parameters need to be adjusted in a timely manner to ensure that the calculation of the reliability function conforms to the actual situation.

[0110] By analyzing the results of the power supply reliability function, corresponding measures can be taken to improve the power supply reliability. For example, if it is found that the failure rate of a certain node is high, the equipment of 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 the comparison of the actual voltage value and the rated voltage value of each node in the new energy substation, and calculates the comprehensive index value of the voltage deviation of each node. The calculation formula of the power quality function Q is:

[0112]

[0113] where \(i\) is the node number of the new - energy plant station, \(N\) is the number of nodes of the new - energy plant station, and \(\omega\) i is the weight coefficient value of the voltage deviation of the \(i\) - th node affecting the overall power quality, \(V\) i is the actual voltage value of the \(i\) - th node, \(V\) i,额定 is the rated voltage value of the \(i\) - th node, \(\rho\) i is the deviation index of the \(i\) - th node.

[0114] The weight coefficient value \(\omega\) of the voltage deviation affecting the overall power quality i , reflects the importance of the voltage deviation of the \(i\) - th node to the power quality of the entire plant station. Different nodes have different positions and functions in the plant station, and the influence weights of their voltage deviations on the system power quality will also be different. For example, for the nodes connecting key loads, their voltage deviations may have a more serious impact on the system, so they will be given a larger weight coefficient; while for some auxiliary nodes or nodes that are less sensitive to voltage fluctuations, their weight coefficients can be relatively small. And based on the difference between the actual voltage value \(V\) i and the rated voltage value \(V\) i,额定 , the voltage deviation is an important index for evaluating power quality, and the calculation of voltage deviation is usually based on the difference between the actual voltage and the rated voltage or the designed voltage. The deviation index \(\rho\) i is for some form of adjustment or non - linear processing of the voltage deviations of different nodes to better reflect their impact on power quality.

[0115] Furthermore, the power - generation cost function is the sum of the output power costs of energy - storage components, the power - generation costs of wind turbines and photovoltaic power - generation equipment. The calculation formula of the power - generation cost function \(F\) is:

[0116]

[0117] where \(C\) k is the unit active - power cost coefficient of the \(i\) - th energy - storage unit in the energy - storage component, \(P\) k,有功 is the active power output of the \(k\) - th energy - storage unit, \(\sigma\) is the carbon - trading coefficient, \(W\) 弃风 is the wind - curtailment electricity of the wind turbine, \(\gamma\) is the wind - curtailment cost coefficient, \(P\) 弃光 is the PV - curtailment electricity of the PV power station, and \(\theta\) is the PV - curtailment cost coefficient.

[0118] The unit active - power cost coefficient \(C\) kIt reflects the cost generated by the k-th energy storage unit in the energy storage component for outputting unit active electric energy, covering various costs, including the amortization of the initial investment cost of the energy storage device, the energy loss cost during operation (such as energy loss during battery charge and discharge), the equipment maintenance cost, and the replacement cost during the service life cycle, etc. For different types of energy storage technologies (such as lithium-ion batteries, lead-acid batteries, flow batteries, etc.), their unit active cost coefficients will have significant differences, which depend on their respective characteristics, such as energy conversion efficiency, service life, initial investment cost, etc. P k,有功 Refers to the active power actually output by the k-th energy storage unit during operation. Its magnitude will change dynamically according to the power supply and demand situation of the new energy power station.

[0119] The carbon trading coefficient σ represents the market trading price corresponding to unit carbon emissions. For wind power generation, although it hardly produces carbon emissions during operation, there will still be certain carbon emissions in the whole life cycle of wind turbine manufacturing, installation, maintenance, and decommissioning. The introduction of this coefficient makes the calculation of wind power generation cost more comprehensive and reflects its economic impact on carbon emission reduction.

[0120] The curtailed wind power of the wind turbine W 弃风 Due to the intermittency and volatility of wind energy, as well as the limitations of grid acceptance capacity, power dispatching and other factors, sometimes the electric energy generated by the wind turbine cannot be fully absorbed by the grid, resulting in the abandonment of some electricity, and the abandoned electricity is the curtailed wind power. By monitoring and analyzing data such as the power output of the wind turbine, the grid acceptance power, and the dispatching instructions, the curtailed wind power can be accurately counted.

[0121] The curtailed photovoltaic power of the photovoltaic power station P 弃光 Refers to the part of the electric energy generated by the photovoltaic power station that cannot be fully utilized and is abandoned due to reasons such as changes in light intensity, the matching problem between the photovoltaic power station and the grid, and the power consumption capacity. By analyzing data such as the output power of the photovoltaic power station, the power monitoring at the grid connection point, and the power dispatching records, the curtailed photovoltaic power can be determined.

[0122] Therefore, the calculation formula of the comprehensive objective function is:

[0123] f(x j )=θ 1 ×R(x j )+θ 2 ×Q(x j )+θ 3 ×F(x j )。

[0124] Among them, f(x j ) is the value of the comprehensive objective function corresponding to the random value of the j-th group of state parameters, θ 1is the weight coefficient of the power supply reliability function in the comprehensive objective function, and R(x j ) is the power supply reliability function value corresponding to the random value of the j-th group of state parameters. θ 2 is the weight coefficient of the power quality function in the comprehensive objective function, and Q(x j ) is the power quality function value corresponding to the random value of the j-th group of state parameters. θ 3 is the weight coefficient of the power generation cost function in the comprehensive objective function, and F(x j ) is the power generation cost function value corresponding to the random value of the j-th group of state parameters, where 1 ≤ j ≤ M.

[0125] For the determination of the above weight coefficients θ 1 , θ 2 , and θ 3 , first, it is necessary to evaluate the relative importance of power supply reliability, power quality, and power generation cost for new energy power plants and substations. If the location of the power plant and substation has extremely high requirements for the continuity of power supply, such as supplying power to important places like hospitals and data centers, then the weight coefficient of power supply reliability may be set relatively high. For example, in some industrial production scenarios that are extremely sensitive to power outages, it can be set to 0.5 to highlight the important position of power supply reliability. Secondly, when the operation goal of the power plant and substation focuses more on reducing costs to participate in market competition, the weight coefficient of the power generation cost function will increase accordingly. For example, for some large-scale new energy power generation enterprises, in order to gain a price advantage in the power market, it can be set to 0.4, while appropriately reducing the weights of other factors. Thirdly, for areas with high requirements for power quality, such as industrial parks with a large number of precision electronic devices, the weight coefficient of the power quality function should be increased. Its weight can be determined according to the specific requirements of the power grid for power quality and the possible impacts. 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 a specific implementation manner of the embodiment of the present 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 due to the use of non-linear loads such as power electronic devices. The content of each harmonic is measured by a harmonic analyzer, and then its impact on power quality is calculated according to the weight coefficient. The three-phase unbalance is calculated by measuring the effective values of the three-phase voltages to obtain the unbalance degree and its impact on power quality. The frequency deviation is obtained by comparing the actual frequency with the nominal frequency, and the voltage flicker is calculated by obtaining data such as voltage change amounts through specific measuring instruments. These factors are combined to construct the power quality function to comprehensively evaluate the power quality of the power plant and substation.

[0127] The calculation formula of the power quality function is:

[0128]

[0129] wherein, ω i1 -ω i5 are the weight coefficients of the voltage deviation value, voltage harmonics, three-phase unbalance degree, frequency deviation, and voltage flicker in the i-th node when calculating the power quality function, respectively. THDU i is the total harmonic distortion rate of the voltage in the i-th node, is the three-phase voltage unbalance degree in the i-th node, Δf i is the frequency deviation in the i-th node, P lt,i is the long-term flicker value in the i-th node.

[0130] Specifically, the calculation formula for the total harmonic distortion rate THDU of the voltage in the i-th node i is:

[0131]

[0132] wherein, U i,h is the effective value of the h-th harmonic voltage of the i-th node, U i,1 is the effective value of the fundamental wave voltage of the i-th node.

[0133] Specifically, the calculation formula for the three-phase voltage unbalance degree of the i-th node is:

[0134]

[0135] wherein, U i,a , U i,b , U i,c are the effective values of the three-phase voltages of the i-th node, respectively.

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

[0137]

[0138] wherein, f i is the corresponding frequency value of the i-th node, f i,e is the nominal frequency value.

[0139] Specifically, the calculation formula for the long-term flicker value P lt,i of the i-th node is:

[0140]

[0141] wherein, ΔV i,k is the k-th voltage change amount of the i-th node, T is the measurement time length, τ cis the long-term flicker degree time constant of the i-th node, and M is the number of voltage changes of the flicker degree of the i-th node.

[0142] In addition, the weight coefficient ω i1 -ω i5 can be determined based on three factors. First, load characteristics are dominant. The weights are determined according to the sensitivity of the loads supplied by the substation to different power quality factors. For example, in the area of precision equipment loads that are sensitive to voltage deviation, the voltage deviation weight coefficient is high; in the area with non-linear loads generating harmonic problems, the voltage harmonic weight coefficient is correspondingly increased. Second, grid requirements are referenced, referring to the grid access standards and specifications. If the grid has strict restrictions on a certain power quality index (such as frequency deviation), the corresponding weight coefficient should be set to better reflect the importance of this index. Third, equipment safety is considered, considering the damage degree of power quality problems to the substation and user-side equipment. For example, the three-phase unbalance degree has a great impact on three-phase equipment. In the power supply area with important three-phase equipment, its weight coefficient should be high enough to ensure equipment safety.

[0143] Further, after determining the optimal solution of the comprehensive objective function in step S420, it further includes:

[0144] Step S421, saving 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 and their corresponding comprehensive objective function values at each step helps to comprehensively understand the search trajectory of the algorithm and analyze the exploration of different state combinations by the algorithm at different stages. For example, by analyzing the saved data, it can be observed whether the algorithm has conducted intensive searches in certain areas and whether it has prematurely fallen into a local optimum. Optionally, the data storage unit can use a relational database (such as MySQL, PostgreSQL), or it can also use file storage, such as a CSV file.

[0146] Further, before iteratively calculating the optimal solution of the comprehensive objective function of the new energy substation based on the simulated annealing algorithm model in step S400, it further includes:

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

[0148] The initial temperature determines the probability of the simulated annealing algorithm accepting inferior 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 it can increase the globality of the search, it will cause the calculation time to be too long. 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 in a large solution space, which is inefficient. On the contrary, if the initial temperature is set too low, the algorithm may fall into a local optimal solution too early. Because at low temperatures, the probability of the algorithm accepting inferior solutions is very small, once it falls into a local optimal area, it is difficult to jump out. You can perform some simple experimental runs to observe the convergence of the algorithm, and then gradually adjust the initial temperature.

[0149] The cooling rate controls the rate at which 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 quickly, but it is likely to miss the global optimal solution. For example, if the cooling rate is set too large, the probability of accepting a poor solution will be greatly reduced at the beginning of the 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. Usually, the cooling rate can be set to a small fixed value (such as between 0.95-0.99), or an adaptive method can be used to dynamically adjust according to the operation of the algorithm.

[0150] In order to prevent the algorithm from running indefinitely, the maximum number of iterations sets the maximum number of attempts for the algorithm to search the solution space. If it is set too small, the algorithm may be forced to stop before finding a better solution. For example, in a complex multi-source coordinated control problem of a new energy plant, if the maximum number of iterations is set to 100, it may be far from enough for the algorithm to converge to a satisfactory solution.

[0151] Furthermore, after comparing the random number with the probability value in step S430, the following steps are further included:

[0152] Step S431, updating the initial temperature value in the simulated annealing algorithm model according to the initial cooling rate value until the number of iterations is equal to the maximum number of iterations value.

[0153] When the number of iterations reaches the maximum number of iterations, the algorithm stops iterating. This is a mandatory termination condition to ensure that the algorithm ends within a reasonable time and computing resources. For example, if the maximum number of iterations is set to 1000, when the algorithm completes the 1000th iteration, regardless of whether the optimal solution is found, the iteration process will stop and the current optimal solution (or approximate optimal solution) will be output.

[0154] Further, after the initial temperature value in the simulated annealing algorithm model is updated according to the initial cooling rate value in step S431, the method further includes:

[0155] Step S432: When the numerical 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, terminate the iterative calculation to obtain the optimal solution of the comprehensive objective function.

[0156] When the numerical value of the comprehensive objective function corresponding to the random value of the state parameter is less than the preset threshold for many consecutive times, this indicates that the algorithm has searched near a relatively optimal solution for many times, and the obtained solutions fluctuate within a relatively small range, indicating that the algorithm may have converged to a relatively optimal solution. For example, if the preset threshold is 0.01 and the numerical values of the comprehensive objective function obtained in 5 consecutive iterations are all less than 0.01, this means that the quality of the solutions obtained by the algorithm in the current area is relatively stable and has reached the precision requirement we set in advance.

[0157] In addition, the determination of the preset threshold needs to be decided according to the requirements of the specific problem and the nature of the objective function. If a higher precision requirement for the solution is needed, a smaller threshold can be set, such as 0.001; if the precision requirement for the solution is relatively low, or it is desired that the algorithm converges faster, a larger threshold can be set, such as 0.1. At the same time, a suitable threshold can also be determined by analyzing the running results of the algorithm under different thresholds.

[0158] Correspondingly, please refer to Figure 2 In the second aspect of the embodiments of the present invention, a multi-source collaborative control device for a new energy plant station is provided, which performs multi-source collaborative control on the new energy plant station based on the above-mentioned multi-source collaborative control method for the new energy plant station. The new energy plant station includes: an energy storage component, a wind power component, a photovoltaic power generation component, and a reactive power compensation device, and includes:

[0159] An initial data acquisition module 1, which is used to acquire the initial values of the state parameters of the new energy plant station, including: the initial active power value of the energy storage component, the initial active power values and reactive power values of the wind power component and the photovoltaic power generation component, and the initial switching states of several reactive power compensation devices;

[0160] A target function construction module 2, which is used to construct the comprehensive objective function of the new energy plant station. The comprehensive objective function includes a power supply reliability function, a power quality function, and a power generation cost function;

[0161] A random parameter generation module 3, which is used to obtain several groups of random values of the state parameters of the new energy plant station by means of random perturbation, including: the initial active power value of the energy storage component, the random active power values and reactive power values of the wind power component and the photovoltaic power generation component, and the random switching states 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 station based on the simulated annealing algorithm model, and perform collaborative control on the energy storage components, wind power components, photovoltaic power generation components, and reactive power compensation devices of the new energy power station based on the random values of the state parameters corresponding to the optimal solution.

[0163] Correspondingly, a third aspect of the embodiments 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute the multi-source collaborative control method of the above new energy power station.

[0164] Correspondingly, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the multi-source collaborative control method of the above new energy power station is implemented.

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

[0166] 1. By constructing a comprehensive objective function including power supply reliability, power quality, and power generation cost functions, and using the simulated annealing algorithm to iteratively solve the optimal solution, the multi-faceted performance of the new energy power station can be taken into account at the same time, and the overall optimization of the new energy power station can be realized;

[0167] 2. Optimizing according to the random values of the state parameters obtained by random perturbation can effectively cope with the intermittency and volatility of new energy power generation, dynamically adjust the states of each component, and improve the adaptability of the power station to environmental changes;

[0168] 3. By setting the parameters of the simulated annealing algorithm, updating the temperature, and setting the termination conditions, it can be ensured that the algorithm efficiently searches for the optimal solution under reasonable computing resources, and improves the computing efficiency and practicability of collaborative control.

[0169] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented 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 the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more flows and / or blocks in the flowchart. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0171] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one or more flows and / or blocks in the flowchart. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operating steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks in the flowchart. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

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

Claims

1. A multi-source collaborative control method for a new energy plant, characterized in that: The new energy plant includes: energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment. The control method includes the following steps: Obtain the initial values ​​of the state parameters of the new energy plant, including: the initial value of the active power of the energy storage component, the initial value of the active power and the initial value of the reactive power of the wind power component and the photovoltaic power generation component, and the initial switching state of several reactive compensation devices; Constructing a comprehensive objective function of a new energy plant, wherein the comprehensive objective function includes a power supply reliability function, a power quality function, and a power generation cost function; Through random disturbance, several groups of random values ​​of state parameters of new energy plants are obtained, 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 compensation devices; Based on the simulated annealing algorithm model, the optimal solution of the comprehensive objective function of the new energy plant is iteratively calculated, and the energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment of the new energy plant are collaboratively controlled based on the random values ​​of the state parameters corresponding to the optimal solution.

2. The multi-source coordinated control method of a new energy plant according to claim 1 is characterized in that: The iterative calculation of the optimal solution of the comprehensive objective function of the new energy plant includes: Substitute a random value of a state parameter into the comprehensive objective function; Determine a better solution of the comprehensive objective function based on the comprehensive objective function value corresponding to the initial value of the state parameter and the comprehensive objective function value corresponding to the random value of the state parameter; 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 the random number is compared with the probability value; When the random number is less than the probability value, the random value of the state parameter is used as the optimal solution of the new energy plant; when the random number is greater than or equal to the probability value, the initial value of the state parameter is used as the optimal solution of the new energy plant; The random value of each state parameter is iteratively calculated to obtain the optimal solution of the comprehensive objective function.

3. The multi-source coordinated control method of a new energy plant according to claim 2 is characterized in that: The power supply reliability function is a numerical value for calculating the impact of the intermittency and volatility of renewable energy power generation on the system power outage time and frequency. The calculation formula of the power supply reliability function R is: Among them, i is the node number of the new energy plant, N is the number of nodes of the new energy plant, λ i is the failure rate of the ith node, T i is the power outage time of the ith node, T max is the maximum power outage time of the ith node, τ i is the reliability index of the i-th node; The power quality function is to compare the actual voltage value of each node of the new energy plant with the rated voltage value, and calculate the comprehensive index value of the voltage deviation of each node. The calculation formula of the power quality function Q is: Among them, i is the node number of the new energy plant, N is the number of nodes of the new energy plant, ω i is the weight coefficient of the voltage deviation value of the ith node affecting the overall power quality, V i is the actual voltage value of the ith node, V i,额定 is the rated voltage value of the i-th node, ρ i is 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 component, the power generation cost of the wind turbine and the photovoltaic power generation equipment. The calculation formula of the power generation cost function F is: Among them, C k is the unit active cost coefficient of the kth energy storage unit in the energy storage component, P k,有功 is the active output of the kth energy storage unit, σ is the carbon trading coefficient, W 弃风 is the abandoned wind power of the wind turbine, γ is the abandoned wind cost coefficient, P 弃光 is the amount of abandoned photovoltaic power in the photovoltaic power station, and θ is the cost coefficient of abandoned photovoltaic power; The calculation formula of the comprehensive objective function is: f(x j )=θ1×R(x j )+θ2×Q(x j )+θ3×F(x j ); Among them, f(x j ) is the value of the comprehensive objective function corresponding to the random value of the jth group of state parameters, θ1 is the weight coefficient of the power supply reliability function in the comprehensive objective function, R(x j ) is the power supply reliability function value corresponding to the random value of the jth group of state parameters, θ2 is the weight coefficient of the power quality function in the comprehensive objective function, Q(x j ) is the power quality function value corresponding to the random value of the jth group of state parameters, θ3 is the weight coefficient of the power generation cost function in the comprehensive objective function, F(x j ) is the power generation cost function value corresponding to the random value of the j-th group of state parameters, 1≤j≤M.

4. The multi-source coordinated control method of a new energy plant according to claim 3 is characterized in that: The influencing factors of the power quality function also include: voltage harmonics, three-phase unbalance, frequency deviation and voltage flicker; The calculation formula of the power quality function Q is: Among them, ω i1 -ω i5 are the weight coefficients of voltage deviation value, voltage harmonics, three-phase unbalance, frequency deviation and voltage flicker in the ith node when calculating the power quality function, THDU i is the voltage total harmonic distortion rate in the i-th node, is the three-phase voltage unbalance degree in the i-th node, Δf i is the frequency deviation in the ith node, P lt,i is the long-term flicker value in the i-th node; The voltage total harmonic distortion rate THDU in the i-th node i The calculation formula is: Among them, U i,h is the effective value of the hth harmonic voltage at the ith node, U i,1 is the effective value of the fundamental voltage of the i-th node; The three-phase voltage unbalance degree in the i-th node The calculation formula is: Among them, U i,a , U i,b , U i,c are the effective values ​​of the three-phase voltages of the i-th node respectively; The frequency deviation Δf in the i-th node i The calculation formula is: Among them, f i is the frequency value corresponding to the i-th node, f i,e 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 is the kth voltage change of the ith node, T is the measurement time length, τ c is the long-term flicker time constant of the i-th node, and M is the number of voltage changes of the flicker at the i-th node.

5. The multi-source coordinated control method of a new energy plant according to claim 2 is characterized in that: After determining a better solution to the comprehensive objective function, the method further includes: The random values ​​of the state parameters and their corresponding comprehensive objective function values ​​are saved to the data storage unit.

6. The multi-source coordinated control method of a new energy plant according to claim 2, characterized in that: Before iteratively calculating the optimal solution of the comprehensive objective function of the new energy plant based on the simulated annealing algorithm model, the method further includes: The initial values ​​of the parameters of the simulated annealing algorithm model are set, and the initial values ​​of the parameters include: an initial temperature value, an initial cooling rate value, and a maximum number of iterations value.

7. The multi-source coordinated control method of a new energy plant according to claim 6, 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 is equal to the maximum number of iterations value.

8. The multi-source coordinated control method of a new energy plant according to claim 7, characterized in that: After the initial temperature value in the simulated annealing algorithm model is updated 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 the preset threshold for several consecutive times, the iterative calculation is terminated to obtain the optimal solution of the comprehensive objective function.

9. A multi-source collaborative control device for a new energy plant, characterized in that: The multi-source collaborative control method for a new energy plant station according to any one of claims 1 to 8 is used to perform multi-source collaborative control on a new energy plant station, wherein the new energy plant station includes: energy storage components, wind power components, photovoltaic power generation components and reactive power compensation equipment, including: An initial data acquisition module is used to obtain the initial values ​​of the state parameters of the new energy plant, including: the initial value of the active power of the energy storage component, the initial value of the active power and the initial value of the reactive power of the wind power component and the photovoltaic power generation component, and the initial switching state of several reactive compensation devices; An objective function construction module, which is used to construct a comprehensive objective function of a new energy plant, wherein the comprehensive objective function includes a power supply reliability function, a power quality function and a power generation cost function; A random parameter generation module is used to obtain several groups of random values ​​of state parameters of new energy plants and stations through random disturbance, 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 states of several reactive compensation devices; A multi-source collaborative control module is used to iteratively calculate the optimal solution of the comprehensive objective function of the new energy plant based on a simulated annealing algorithm model, and to collaboratively control the energy storage components, wind power components, photovoltaic power generation components and reactive compensation equipment of the new energy plant based on the random values ​​of the state parameters corresponding to the optimal solution.

10. An electronic device, characterized in that: include: at least one processor; And a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the multi-source collaborative control method of the new energy plant as described in any one of claims 1-8.

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