Multi-type energy capacity configuration method and system for delivery through direct current island

Abstract

The invention discloses a multi-type energy capacity configuration method and system through direct current island delivery, and the method comprises the following steps: 1, constructing a classical scene set for modeling analysis according to the power generation characteristics of photovoltaic and wind power; 2, under the situation that multiple energy sources of water, wind, light and storage are sent out through an island, the influence of different energy source power generation outputs on system overvoltage is analyzed, and constraint conditions of various types of energy sources are quantified; 3, analyzing the influence of multi-energy configuration on the short-circuit ratio of the system under different outputs, analyzing the dynamic response characteristics of different energies in island delivery, and establishing a short-circuit ratio constraint; and 4, establishing mutual constraint conditions among multiple energy sources of water, wind, light and storage, determining the optimal output of water and electricity, establishing an optimization objective function, and solving through a particle swarm algorithm to obtain the optimal output ratio of the energy sources of water, wind, light and storage. According to the method, randomness and uncertainty of new energy power generation are considered, and more accurate energy configuration is ensured, so that the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible direct current transmission systems, and in particular to a method and system for configuring multi-type energy capacity through direct current island transmission. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, renewable energy such as hydropower, wind power, photovoltaics and energy storage have gradually become an important part of the modern power system. However, due to the intermittent, volatile and uncertain nature of these energy sources, how to efficiently and safely access the power system and achieve optimal dispatch has become a key issue in power system research.

[0003] In recent years, the application of high voltage direct current (HVDC) technology has provided a new solution for the large-scale access of renewable energy, especially in the scenarios of long-distance, large-scale transmission and multi-port connection. High voltage direct current transmission technology can effectively solve the problems of large losses and poor stability in the transmission process of AC power grid, and has higher transmission efficiency and better system scheduling capabilities. But at the same time, how to optimize scheduling in these complex systems to ensure the efficient use of multiple renewable energy sources and the stability of the power system has become a technical problem that needs to be solved urgently.

[0004] Short-circuit ratio (SCR) and overvoltage are important factors affecting the stability and safety of power systems. When the short-circuit ratio is low, the stability and anti-interference ability of the system are poor, and power fluctuations or system crashes are prone to occur. Overvoltage problems may cause equipment damage or power transmission interruption, especially in the case of unstable output such as wind power and photovoltaics, the risk of overvoltage is relatively high. Therefore, when optimizing the output ratio of renewable energy, factors such as short-circuit ratio and overvoltage must be comprehensively considered to ensure the voltage stability and operation safety of the system.

[0005] Existing optimization scheduling methods mostly focus on optimizing the output of a single energy source, and lack consideration of the overall optimization scheduling when multiple energy resources (such as water, wind, light, and storage) are connected and operated at the same time. In a multi-energy complementary system, considering the output ratio of a certain energy source alone often cannot take into account the global optimization and operation constraints of the system. In particular, how to take into account the short-circuit ratio and overvoltage constraints in the operation scenarios of multiple energy sources and achieve the optimal configuration of the system is an important topic in current research. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for configuring the capacity of multiple types of energy through DC island transmission, so as to improve the economy and efficiency of the power system while ensuring safety and stability through comprehensive scheduling of multiple renewable energy sources.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] On the one hand, the present invention provides a method for configuring the capacity of multiple types of energy through DC island transmission, comprising the following steps:

[0009] S1. Considering the strong randomness and uncertainty of photovoltaic and wind power generation, a classic scenario set is constructed for modeling and analysis based on the power generation characteristics of photovoltaic and wind power;

[0010] S2. Based on the typical photovoltaic and wind power operation scenarios obtained in S1, in the scenario where water, wind, light, and storage are transmitted through isolated islands, analyze the impact of different energy generation outputs on system overvoltage, and further quantify the constraints of each type of energy;

[0011] S3. Further analyze the impact of multi-energy configuration on the system short-circuit ratio under different outputs, analyze the dynamic response characteristics of different energy sources in island transmission, and establish short-circuit ratio constraints;

[0012] S4. Based on comprehensive consideration of the mutual constraints among water, wind, light, and energy storage, the optimal output of hydropower is determined, and an optimization objective function is established. The system with overvoltage and short-circuit ratio constraints is solved through the particle swarm algorithm to obtain the optimal output ratio of hydropower, wind power, photovoltaics, and energy storage.

[0013] In some embodiments, S1 comprises the following steps:

[0014] S11. Establish the probability density function of photovoltaic and wind power;

[0015] S12. Based on the probability density function in S11, the initial photovoltaic and wind power output scenario sets are generated by the Monte Carlo method, and the scenario sets are optimized and streamlined by the synchronous back-substitution reduction method.

[0016] In some embodiments, the probability density function of photovoltaic is:

[0017]

[0018] Where, μ is the mean of the prediction error; σ is the variance of the prediction error, which measures the degree of dispersion of the prediction error; P is the power output by the photovoltaic system;

[0019] The probability density function of wind power is:

[0020]

[0021] Where x and y are shape parameters and scale parameters, respectively, which are usually estimated through historical data; Γ(x) and Γ(y) are gamma functions.

[0022] In some embodiments, S12 includes the following steps:

[0023] S121. Randomly sample the photovoltaic output by using the Monte Carlo method to generate an initial photovoltaic or wind output scenario set C;

[0024] S122, using the synchronous back-reduction method (SBR) to reduce the scene set; initializing the reserved set A, making the reserved set A equal to the initial scene set C, and setting a stop condition;

[0025] S123, select the scene to delete, select each scene C in the reserved set A i The scene C with the smallest sum of distances to other scenes φ Delete it;

[0026] S124, add the selected scene to the deletion set B, and add the deleted scene C φ Put it in the deletion set B, and delete scene C from the retention set A φ , and update the number of scenes in the reserved set A;

[0027] S125. Find the scene C in the reserved set A. φ The closest scene C ψ , and set scene C φ The probability of occurrence p φ Add to scene C ψ Up, update probability;

[0028] S126, judging the stopping condition, if the number of scenes in the reserved set A has met the preset number of classic scenes G, then stopping the particle swarm; if the stopping condition is not met, returning to S123, continuing the selection and probability adjustment process until the number of scenes in the reserved set A meets the requirement.

[0029] In some embodiments, each of the scenes C i The calculation expression for the sum of the distances to other scenes is:

[0030]

[0031] Where i and j are different photovoltaic scenarios; d(C i , C j ) is the distance between scenes i and j; M is the number of scenes in the current reserved set A; n is the data length of each scene; p i For scene C i The probability of occurrence; pj For scene C j Probability of occurrence; D i For each scene C i The sum of the distances to other scenes; C i，k For scene C i The kth value of j，k For scene C j The kth value of .

[0032] In some embodiments, in S3, the calculation expression of the short-circuit ratio of clean energy access to multiple sites is:

[0033]

[0034] The short circuit ratio constraint is:

[0035]

[0036] Where P REi is the new energy active power injected into node i; P REj is the new energy active power injected into node j; U i is the voltage at node i; U j is the voltage at node j; Z EQii is the equivalent impedance of node i itself; Z EQij is the equivalent impedance between node i and node j; n is the total number of nodes; N MRSCR，min The minimum short-circuit ratio allowed for long-term system stability; N R is the total number of stations in the system.

[0037] In some embodiments, in S4, the mutual constraints among the water, wind, light, and storage energy sources are:

[0038] Power balance constraints of distribution network:

[0039]

[0040] Where P dc，t , P h，t are the DC transmission power and the active power of hydropower output in period t respectively; P wtk，t , P pvj，t , P esi，t are the active power of the kth wind farm, the active power of the jth photovoltaic output, and the active power of the ith energy storage output in time period t; n wt 、n pv 、n es They are the number of wind farms, the number of photovoltaic farms, and the number of energy storage stations;

[0041] Energy storage constraints:

[0042]

[0043] In the formula, are the upper and lower limits of the energy storage capacity connected to each node, and is the rated power of the node energy storage; α is the relationship between the power and capacity of the energy storage; is the maximum power of charging and discharging of the i-th energy storage; E es，i (t) is the charge state of the i-th energy storage at the t-th time; P dis，i , P ch，i They are respectively the charging power and discharging power of energy storage;

[0044] Photovoltaic constraints;

[0045]

[0046] V pv，min , V pv，max are the upper and lower limits of node voltage respectively; P pvj , P pv，mppj are the active power and maximum power generated by the jth photovoltaic cell respectively; Q pvj , S pvj are the reactive power and capacity output by the jth PV inverter at a certain moment respectively; is the lower limit of the photovoltaic inverter power factor; V n is; p is; v is;

[0047] Wind power constraints;

[0048]

[0049] V wt，min , V wt，max are the upper and lower limits of node voltage respectively; P wt，j , P wt，mppj are the active power and maximum power generated by the jth wind power respectively; Q wt，j , S wt，j are the reactive power and capacity output by the jth wind power inverter at a certain moment respectively; is the lower limit of wind power inverter power factor;

[0050] DC transmission constraints;

[0051]

[0052] P dc P is the DC output power; dc，max is the maximum capacity of DC transmission; P dc，t , P dc，t-1 are the DC transmission power at time t and time t-1 respectively; R dc，max is the maximum up and down climbing rate;

[0053] S46. Establish hydropower constraints;

[0054]

[0055] V h，min , V h，max are the upper and lower limits of node voltage respectively; P h , S h They are respectively the active power and maximum power generated by the hydropower unit; Q h , S h are the reactive power and capacity output by the hydropower unit at a certain moment; θ h is the generator power angle.

[0056] In some embodiments, in S4, the optimization objective function is:

[0057]

[0058] Where P esi，t , P pvj，t , P wtk，t , P h，t They are the i-th energy storage output, the j-th photovoltaic output, the k-th wind power output, and the hydropower output respectively.

[0059] In some embodiments, in S4, solving the system with overvoltage and short circuit ratio constraints by using a particle swarm algorithm includes the following steps:

[0060] S41, initializing a particle swarm, where each particle represents a set of rated power configurations of wind power, photovoltaic power, and energy storage;

[0061] S42, initializing the position and speed of particles: randomly initialize the rated power value (position) of each particle, and set the initial speed for each particle; the position of each particle represents a potential combination of wind power, photovoltaic, and energy storage rated power, and the speed controls the movement of the particle in the solution space;

[0062] S43, using the method of step S1 to calculate the photovoltaic and wind power output at each moment, and calculate the fitness according to the objective function. If the solution meets the constraints (such as overvoltage, short circuit ratio, output limit, etc.), the fitness value is the objective function value; if the constraints are not met, a penalty term is added; the fitness calculation expression is:

[0063]

[0064] S44, updating the individual best position and the global best position: for each particle, updating the individual best position according to its fitness value;

[0065] S45. Update the speed and position of particles:

[0066]

[0067] Where w is the inertia weight, which controls the inertia of the particle; c 1 、c 2 is the learning factor, which determines the particle's ability to learn to the individual optimal position and the global optimal position; rand 1 、rand 2 is a random function used to introduce randomness; is the k+1, kth update speed;

[0068] S46, check constraints: check whether the particles meet the voltage constraints and the output limit of each energy source. If a particle violates the constraints, it will be punished to reduce its fitness.

[0069] S47, setting convergence conditions;

[0070] S48. Output the optimal solution.

[0071] On the other hand, the present invention provides a multi-type energy capacity configuration system through DC island transmission, applying the above method, including:

[0072] Scenario set module: Based on the power generation characteristics of photovoltaic and wind power, it is used to build classic scenario sets for modeling and analysis;

[0073] Analysis module: In the scenario where water, wind, solar, and storage energy are transmitted through isolated islands, it is used to analyze the impact of different energy generation outputs on system overvoltage, and further quantify the constraints of each type of energy;

[0074] Short-circuit ratio constraint module: further analyzes the impact of multi-energy configuration on the system short-circuit ratio under different outputs, is used to analyze the dynamic response characteristics of different energy sources in island transmission, and establish short-circuit ratio constraints;

[0075] Optimization module: It is used to determine the optimal output of hydropower and establish the optimization objective function based on the comprehensive consideration of the mutual constraints among water, wind, light and energy storage. The particle swarm algorithm is used to solve the system with overvoltage and short-circuit ratio constraints to obtain the optimal output ratio of hydropower, wind power, photovoltaic and energy storage.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] 1) This invention proposes a new solution for comprehensively optimizing the output ratio of hydropower, wind power, photovoltaic power and energy storage through a multi-type energy capacity configuration method for DC island transmission. This invention models photovoltaic and wind power output through the Monte Carlo method, taking into account the randomness and uncertainty of renewable energy generation, ensuring more accurate energy configuration, thereby improving system stability and reliability.

[0078] 2) Aiming at the system overvoltage problem caused by multiple energy access, the present invention establishes a corresponding analysis module, which can quantify the constraints of different types of energy and effectively analyze their impact on the system voltage. In addition, through the short-circuit ratio constraint module, the system can ensure that the power system can maintain a stable short-circuit ratio in the island transmission mode, avoiding safety problems caused by improper configuration.

[0079] 3) The present invention can take into account the impact of each energy output on the stability of the power grid under the configuration of multiple types of energy, and ensure the stable operation of the system under short circuit or fault conditions. This is of great significance for improving the dynamic response characteristics and anti-disturbance ability of the power system.

[0080] 4) The present invention adopts a particle swarm algorithm, comprehensively considers constraints such as overvoltage and short-circuit ratio, and conducts a global search to determine the optimal energy output ratio, which can effectively improve the economy of the system and achieve accurate energy configuration in a variety of complex environments, avoiding the problem of local optimal solutions in traditional optimization methods.

[0081] 5) The present invention can effectively improve the stability, safety and economy of the power system in the island transmission mode. At the same time, by considering the uncertainty of renewable energy power generation and system constraints, it provides an efficient and feasible solution with significant application value and prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a schematic diagram of the process of Example 1 of the present invention;

[0083] Figure 2 This is a schematic diagram of the structure of Embodiment 1 of the present invention;

[0084] Figure 3 This is a simplified model schematic diagram of an AC system connected to multiple renewable energy stations in Example 1 of the present invention;

[0085] Figure 4 This is a schematic diagram of the structure of Example 2 of the present invention. DETAILED DESCRIPTION

[0086] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0087] Example 1

[0088] See also Figure 1-Figure 3 , a method for configuring the capacity of multiple types of energy through DC island transmission, comprising the following steps:

[0089] S1. Considering the strong randomness and uncertainty of photovoltaic and wind power generation, a classic scenario set is constructed for modeling and analysis based on the power generation characteristics of photovoltaic and wind power.

[0090] It is generally believed that the photovoltaic output prediction error follows a normal distribution, that is, ΔP~N(μ,σ 2 ), its probability density function is:

[0091]

[0092] Where μ is the mean of the prediction error (usually zero, indicating unbiased error); σ is the variance of the prediction error, which measures the degree of dispersion of the prediction error; and P is the power output of the photovoltaic system.

[0093] Since the active output prediction error of wind turbines usually exhibits large kurtosis and skewness, simply using normal distribution to describe its error characteristics may not fully reflect its true probability distribution, resulting in large estimation errors. In contrast, Beta distribution, due to its flexible shape parameters, can more effectively characterize the skewness and kurtosis characteristics of wind turbine output errors, thereby providing more accurate error modeling. The prediction error probability model expression is:

[0094]

[0095] Where x and y are shape parameters and scale parameters, respectively, which are usually estimated through historical data; Γ(x) and Γ(y) are gamma functions.

[0096] Based on the above probability density function, the initial photovoltaic and wind power output scenario set is generated by the Monte Carlo method. However, considering that the excessive number of scenarios may significantly increase the computational burden of stochastic optimization, the Simultaneous Backward Reduction (SBR) method is used to optimize and streamline the scenario set.

[0097] Step 1: Generate an initial scenario set using the Monte Carlo method. A large number of random samples are taken of the photovoltaic output through the Monte Carlo method to generate an initial photovoltaic or wind output scenario set C. Each scenario C i All of them contain a specific photovoltaic output error, and these scenarios satisfy the characteristics of normal distribution. In the initial scenario set C, the probability of occurrence of each scenario is equal, that is, p i =1 / N, where N is the total number of scenes in the scene set C.

[0098] Step 2: Use the synchronous back-reduction method (SBR) to reduce the scene set. Initialize the reserved set A and set the reserved set A equal to the initial scene set C i . Set the stopping condition: the number of scenes in the reserved set A reaches the preset number of classic scenes G.

[0099] Step 3: Select scenes to delete. For each scene C in the retained set A i , calculate the sum of the distances D between this scene and other scenes i , the calculation expression is:

[0100]

[0101] Where i and j are different photovoltaic scenarios; d(C i , C j ) is the distance between scenes i and j; M is the number of scenes in the current reserved set A; n is the data length of each scene; p i For scene C i The probability of occurrence; p j For scene C j Probability of occurrence; D i For each scene C i The sum of the distances to other scenes; C i，k For scene C i The kth value of j，k For scene C j Select the scene C with the smallest sum of distances. φ As a scene that should be eliminated.

[0102] Step 4: Add the selected scene to the deletion set B and remove the deleted scene C φ Place in delete set B. Delete scene C from keep set A φ , and update the number of scenes in the retained set: |A|→|A|-1.

[0103] Step 5: Scene clustering and probability adjustment, find the scene C in the reserved set A φ The closest scene C ψ , that is, find the scene with the smallest distance:

[0104]

[0105] Scene C φ The probability of occurrence p φ Add to scene C ψ Above, update probability:

[0106] p ψ =p ψ +p φ (5);

[0107] In the formula, p φ 、p ψ Scene C φ , C ψ The probability of occurrence is equivalent to C φ With C ψ group them into one category and group C φ The probability of assigning to C ψ .

[0108] Step 6: Determine the stopping condition. If the number of scenes in the reserved set A has met the preset number of classic scenes G, stop the particle swarm. If the stopping condition is not met, return to step 3 and continue the selection and probability adjustment process until the number of scenes in the reserved set meets the requirement.

[0109] S2. Based on the typical photovoltaic and wind power operation scenarios obtained in S1, in the scenario where water, wind, light and storage are transmitted through isolated islands, the impact of different energy generation outputs on system overvoltage is analyzed, and the constraints of each type of energy are further quantified.

[0110] 1) Voltage constraints under normal working conditions;

[0111] Under normal operating conditions, voltage constraints are crucial to ensure the stability and security of the power system. With the access of renewable energy generation systems such as photovoltaic and wind power, the voltage fluctuations of the power grid will be more complex, so it is necessary to effectively constrain the node voltages in the system.

[0112] After solving the system power flow according to the Gauss-Seidel particle swarm method, the voltage of each node is calculated and converted into a calibration value (pu value, unit is per unit value). In order to ensure that the system will not experience voltage instability or equipment damage under normal operating conditions, the voltage per unit value (V pu ) must meet the following constraints:

[0113] 0.95≤V pu，i ≤1.05 (6);

[0114] Where: V pu，iThe unit value of the operating voltage calculated for node i. In order to ensure that the voltage unit value is not higher than 1.05 and not lower than 0.95 under stable and normal operation of the system.

[0115] 2) New energy overvoltage under DC bipolar blocking;

[0116] like Figure 2 As shown in the figure, in the scenario where multiple energy sources are transmitted through isolated islands, due to the reactive power control of the DC system, the reactive power Q exchanged between the AC and DC systems ex It is very small, satisfying the "zero reactive exchange principle". In addition, the AC filter is the main reactive compensation equipment in the converter station. Therefore, the reactive power consumed by the DC system is approximately equal to the power compensated by the converter station filter. The filter removal strategy of the converter station during DC blocking will have an important impact on the reactive regulation effect of the DC converter station. For a DC bipolar blocking fault, such as a DC sending-end converter station in the northwest region equipped with a fast-cut strategy, the filter is completely cut off after a delay of 200ms during a blocking fault. However, for a DC unipolar blocking fault, in order to avoid exceeding the limit of bus voltage variation, the filter is based on U max Control and Q max The control strategy is to perform group removal, and the removal time of each group of filters is extended to 1s or even 10s. Therefore, after the DC single pole is locked, the reactive power imbalance state in the DC converter station lasts for a long time, forming a "vacuum zone" of reactive power regulation on a time scale.

[0117] When the DC system operates normally, the reactive power consumed by the DC system converter and the active power transmitted satisfy the following relationship:

[0118]

[0119] Where: Q dc , P dc , α and μ are the reactive power consumed by the DC converter, the active power transmitted by the DC converter, the converter power factor angle, the rectifier trigger angle, and the commutation angle, respectively.

[0120] When the DC blocking fault occurs, the rectifier trigger angle increases rapidly, and the DC transmission active power P dc The reactive power Q consumed by the DC system can be obtained from formula (7) dc Due to the delay in the removal of the AC filter equipment in the converter station, the converter reactive power compensation device generates reactive power Q c There is no time to drop, and the reactive power balance of the converter station is broken. Overvoltage occurs on the AC bus of the converter station, and the overvoltage amplitude and reactive power surplus satisfy the following relationship:

[0121]

[0122] Where ΔUs , Q dr , Q comp , S, S sc They are the AC bus overvoltage amplitude of the sending-end converter station, the reactive power surplus of the converter station, the reactive power compensation capacity of the rectifier station, the short-circuit capacity of the sending-end system, and the short-circuit capacity of the converter station. At this time, the reactive power surplus of the sending-end converter station is equal to the reactive power consumed by the DC system in steady state.

[0123] However, when a DC blocking fault occurs, a large amount of surplus reactive power from the converter station flows into the sending-end AC system at the initial blocking stage, causing a sudden increase in bus voltages of all levels. The overvoltage amplitude transmitted to the nearby renewable energy station is proportional to the equivalent impedance Z between the renewable energy station and the sending-end converter station. eq Inversely proportional to ΔU r (Z eq ) is approximately expressed. In particular, for wind farms compensated by fixed capacitors, the reactive power surplus of the capacitors will further cause overvoltage on the PCC busbar of the wind farm, which is expressed by the following formula (9):

[0124]

[0125] Where ΔU t , Q t1 , Q t2 , S t They are respectively the overvoltage amplitude of the new energy PCC bus, the capacity of the fixed capacitor, the surplus of reactive power in the new energy field after DC blocking, and the short-circuit capacity of the wind farm.

[0126] 3) Transient overvoltage of new energy sources under DC commutation failure;

[0127] After the DC system fails to commutate, the transient voltage increase of the new energy station is mainly caused by two factors: one is the reactive compensation capacity that is not fully consumed in the new energy station when the system voltage drops; the other is the reactive compensation amount provided to maintain the stable operation and voltage balance of the system. In the case of DC commutation failure, the transient voltage increase of the new energy station can be analyzed in the following ways.

[0128]

[0129] Where: ΔU w , ΔU r (Z eq ), Q n , Q e They are the overvoltage amplitude of the new energy PCC bus, the equivalent impedance between other substations and new energy stations, the reactive compensation provided under stable operation conditions, and the reactive power redundancy when the system voltage is reduced.

[0130] The overvoltage constraint conditions are:

[0131]

[0132] Where: ΔU t，max , ΔU w，max They are the maximum overvoltage allowed for new energy sources to avoid grid disconnection under DC blocking and DC commutation failure.

[0133] S3. Further analyze the impact of multi-energy configuration on the system short-circuit ratio under different outputs, analyze the dynamic response characteristics of different energy sources in island transmission, and establish short-circuit ratio constraints.

[0134] In the actual operation scenario where a large number of renewable energy power stations are connected to the power grid, these stations need to provide reactive power to compensate for the reactive power losses generated in their internal collection lines and external transmission paths. In particular, when multiple widely distributed renewable energy sites are connected to the grid, there may be significant impedance angle differences and initial phase angle differences between the bus nodes at the grid connection points of each station. However, the current method for evaluating the voltage stability of renewable energy grid-connected systems usually follows the calculation framework of the traditional DC short-circuit ratio, which ignores the influence of the resistance component in the system equivalent impedance and assumes that the voltage phase angles of all sites are exactly the same. This assumption fails to fully consider the reactive power characteristics of renewable energy power generation equipment and the actual changes in electrical quantities (such as amplitude and phase) between nodes within the station.

[0135] In order to more accurately characterize this complex characteristic, an improved calculation method for the multi-station short-circuit ratio (MRSCR) of renewable energy is proposed based on the basic theory of short-circuit ratio of power system. This method not only takes into account the amplitude and phase differences of electrical quantities between nodes, but also comprehensively considers the impact of reactive power output of renewable energy power generation equipment, thus providing a more comprehensive and sophisticated voltage stability assessment tool. At the same time, by introducing the Thevenin equivalent method, the AC system connected to renewable energy can be simplified into a model of an ideal voltage source and an equivalent impedance in series. This simplified model improves the efficiency and convenience of calculation while ensuring the accuracy of the analysis.

[0136] The multi-port Thevenin method can effectively simplify power networks, such as Figure 3 As shown in the figure, S REi and are the apparent power and grid-connected bus voltage of the new energy power generation equipment / station i, is the equivalent impedance between the grid connection points i and j, Z i The system-side equivalent impedance between the main grid equivalent power source i and the corresponding grid connection point, i, j = 1~n, is n new energy stations.

[0137] like Figure 3As shown in the figure, the new energy grid-connected busbar can represent the grid-side access point of the new energy power generation equipment or the grid-connected point of the new energy station (the high-voltage side busbar or node of the new energy station booster station). Assume that the current injected into the AC system by each new energy grid-connected busbar is I 1 , I 2 , ..., I n Then the voltage of each grid-connected bus node It can be expressed as:

[0138]

[0139] The short-circuit ratio is used to measure the relative size between the system nominal voltage and the voltage generated by the equipment after the equipment is connected to the system. Based on the above physical meaning, the MRSCR at the i-th renewable energy grid-connected bus in the system is:

[0140]

[0141] In the formula, is the AC grid equivalent impedance matrix Z at the new energy grid-connected busbar eq The i-th row and j-th column element of . Figure 3 of and For illustration only. There is no actual corresponding relationship between them. is the nominal voltage of the i-th grid-connected bus node; is the voltage generated by the power generation of the equipment at the i-th node, and the subscript RE represents the new energy power generation equipment / station; The short-circuit current provided for the i-th renewable energy power generation equipment / station. are the self-impedance and mutual impedance of the corresponding nodes in the equivalent impedance matrix on the grid side respectively; let the actual operating voltage of the i-th grid-connected bus node be Multiply the numerator and denominator of the formula by:

[0142]

[0143] Where: The actual apparent power of the renewable energy injected into the i-th renewable energy grid-connected bus node; It is the complex power conversion factor between the new energy grid-connected busbars i and j, reflecting the phase and amplitude differences between the electrical quantities of the grid-side access points of each new energy power generation equipment / new energy station grid-connected points.

[0144] In actual power grids, due to the reactance characteristics of transmission lines, Z i The reactance in is usually greater than the resistance. Therefore, the above formula is simplified to:

[0145]

[0146] In order to more intuitively reflect the impact of renewable energy grid connection on the power grid system. The short-circuit ratio constraint is:

[0147]

[0148] Where P REi is the new energy active power injected into node i; P REj is the new energy active power injected into node j; U i is the voltage at node i; U j is the voltage at node j; Z EQii is the equivalent impedance of node i itself; Z EQij is the equivalent impedance between node i and node j; n is the total number of nodes; N MRScR，min The minimum short-circuit ratio allowed for long-term system stability; N R is the total number of stations in the system.

[0149] S4. Based on comprehensive consideration of the mutual constraints among water, wind, light and energy storage, the optimal output of hydropower is determined, and an optimization objective function is established. The system with overvoltage and short-circuit ratio constraints is solved through the particle swarm algorithm to obtain the optimal output ratio of hydropower, wind power, photovoltaics and energy storage.

[0150] Power balance constraints of distribution network:

[0151]

[0152] Where P dc，t , P h，t are the DC transmission power and the active power of hydropower output in period t respectively; P wtk，t , P pvj，t , P esi，t are the active power of the kth wind farm, the active power of the jth photovoltaic output, and the active power of the ith energy storage output in time period t; n wt 、n pv 、n es They are the number of wind farms, the number of photovoltaic farms, and the number of energy storage stations respectively.

[0153] Energy storage constraints:

[0154]

[0155] In the formula, are the upper and lower limits of the energy storage capacity connected to each node, and is the rated power of the node energy storage; α is the relationship between the power and capacity of the energy storage; is the maximum power of charging and discharging of the i-th energy storage; E es，i (t) is the charge state of the i-th energy storage at the t-th time; Pdis，i , P ch，i They are the charging power and discharging power of energy storage respectively.

[0156] Photovoltaic constraints;

[0157]

[0158] V pv，min , V pv，max are the upper and lower limits of the photovoltaic node voltage respectively; P pvj , P pv，mppj are the active power and maximum power generated by the jth photovoltaic cell respectively; Q pvj , S pvj are the reactive power and capacity output by the jth PV inverter at a certain moment respectively; is the lower limit of the photovoltaic inverter power factor; V pvj is the actual photovoltaic voltage at the jth node.

[0159] Wind power constraints;

[0160]

[0161] V wt，min , V wt，max are the upper and lower limits of wind power node voltage respectively; P wt，j , P wt，mppj are the active power and maximum power generated by the jth wind power respectively; Q wt，j , S wt，j are the reactive power and capacity output by the jth wind power inverter at a certain moment respectively; is the lower limit of wind power inverter power factor; V wtj is the actual voltage of wind power at the jth node.

[0162] DC transmission constraints;

[0163]

[0164] P dc P is the DC output power; dc，max is the maximum capacity of DC transmission; P dc，t , P dc，t-1 are the DC transmission power at time t and time t-1 respectively; R dc，max The maximum up and down climbing rate.

[0165] S46, hydropower constraints;

[0166]

[0167] V h，min , V h，maxare the upper and lower limits of the hydropower node voltage respectively; P h , S h They are respectively the active power and maximum power generated by the hydropower unit; Q h , S h are the reactive power and capacity output by the hydropower unit at a certain moment; θ h is the generator power angle.

[0168] The optimization objective function takes the minimum total output of energy storage and hydropower as the objective function, which is expressed as:

[0169]

[0170] Where P esi，t , P pvj，t , P wtk，t , P h，t They are the i-th energy storage output, the j-th photovoltaic output, the k-th wind power output, and the hydropower output respectively.

[0171] First, determine the hydropower output range, and use the particle swarm optimization (PSO) algorithm to optimize the photovoltaic, wind power and energy storage output, while considering constraints such as overvoltage and short-circuit ratio. The specific steps are as follows:

[0172] S41, initializing particle swarm;

[0173] A particle swarm is composed of multiple particles, each of which represents a possible solution. Each particle has the following properties:

[0174] Position(x i ): represents the output configuration of the current solution (including the output of hydropower, wind power, photovoltaic power, and energy storage).

[0175] Speed ​​(v i ): The search speed of the particle, which controls the pace of the particle moving in the solution space.

[0176] The best position of an individual (p i ): The optimal solution experienced by the particle during the search process.

[0177] Global best position (g): the record of the best solution among all particles.

[0178] Each particle in the particle swarm represents a set of rated power configurations of wind power, photovoltaic power and energy storage. The dimension of each particle is x, which is composed of the rated power of wind power, photovoltaic power and energy storage. i =[P pv1，rate , ..., P pvn1，rate , P wt1，rate , ..., P wtn2，rate , P es1，rate , ..., P esn3，rate], n1, n2, n3 are the number of photovoltaic, energy storage and wind power respectively.

[0179] S42, initializing the position and velocity of particles;

[0180] The rated power value (position) of each particle is randomly generated and an initial velocity is assigned to it. The position of the particle represents a potential combination of wind power, photovoltaic, and energy storage rated power, while the velocity determines the direction and amplitude of the particle's movement in the solution space.

[0181] S43, calculating fitness;

[0182] The position of each particle represents a solution (i.e., a set of rated powers). Based on these rated powers, the method in step S1 is used to calculate the photovoltaic and wind power output at each moment, and the fitness is calculated based on the objective function:

[0183]

[0184] If the solution satisfies the constraints (such as overvoltage, short-circuit ratio, output limit, etc.), the fitness value is the objective function value; if the constraints are not met, a penalty term is added.

[0185] S44, updating the individual best position and the global best position;

[0186] For each particle, update the individual best position according to its fitness value. If the current particle's fitness value is better than its historical best fitness value, update the individual best position. Then, based on the individual best positions of all particles, select the particle with the best fitness as the global best position.

[0187] S45, updating the speed and position of the particle;

[0188] The particle's velocity and position are updated according to the following formula:

[0189]

[0190] Where w is the inertia weight, which controls the inertia of the particle; c 1 、c 2 is the learning factor, which determines the particle's ability to learn to the individual optimal position and the global optimal position; rand 1 、rand 2 is a random function used to introduce randomness; is the k+1, kth update speed.

[0191] S46, check constraints;

[0192] After updating the speed and position of the particle, it is necessary to check whether the voltage constraint (through voltage and short-circuit ratio limits) and the output limit of each energy source (for example, the output of wind power, photovoltaic and energy storage cannot exceed its rated power) are met. If a particle violates the constraint, it can be penalized to reduce its fitness.

[0193] S47, setting convergence conditions;

[0194] 1. Maximum number of particle swarms: Set a maximum number of particle swarms N. If the maximum number is reached, the optimization will stop.

[0195] 2. Convergence accuracy: If the optimal solution of the particle changes slightly (less than a predetermined threshold), the algorithm is considered to have converged and the optimization is stopped.

[0196] S48. Output the optimal solution.

[0197] The optimal solution of the final output is the global optimal position, that is, the optimal rated power configuration of wind power, photovoltaic and energy storage.

[0198] Example 2

[0199] like Figure 4 As shown, a multi-type energy capacity configuration system through DC island transmission includes:

[0200] Scenario set module: Based on the power generation characteristics of photovoltaic and wind power, it is used to build classic scenario sets for modeling and analysis;

[0201] Analysis module: In the scenario where water, wind, solar, and storage energy are transmitted through isolated islands, it is used to analyze the impact of different energy generation outputs on system overvoltage, and further quantify the constraints of each type of energy;

[0202] Short-circuit ratio constraint module: further analyzes the impact of multi-energy configuration on the system short-circuit ratio under different outputs, is used to analyze the dynamic response characteristics of different energy sources in island transmission, and establish short-circuit ratio constraints;

[0203] Optimization module: It is used to determine the optimal output of hydropower and establish the optimization objective function based on the comprehensive consideration of the mutual constraints among water, wind, light and energy storage. The particle swarm algorithm is used to solve the system with overvoltage and short-circuit ratio constraints to obtain the optimal output ratio of hydropower, wind power, photovoltaic and energy storage.

[0204] A multi-type energy capacity configuration system for DC islanding of the present invention can be installed in a computer device. The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a multi-type energy capacity configuration program for DC islanding. The memory includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The processor is the control core of the electronic device, and uses various interfaces and lines to connect the various components of the entire computer device, and executes various functions of the computer device and processes data by running or executing programs or modules stored in the memory, and calling data stored in the memory.

[0205] The module described in the present invention refers to a series of computer program segments that can be executed by a processor of a computer device and can complete fixed functions, and is stored in a memory of the computer device.

Claims

1. A method for configuring the capacity of multiple types of energy through DC island transmission, characterized in that: The following steps are involved: S1. Construct a classic scenario set for modeling and analysis based on the power generation characteristics of photovoltaic and wind power; S2. In the scenario where water, wind, solar, and storage energy sources are transmitted through isolated islands, analyze the impact of different energy generation outputs on system overvoltage, and further quantify the constraints of each type of energy; S3. Further analyze the impact of multi-energy configuration on the system short-circuit ratio under different outputs, analyze the dynamic response characteristics of different energy sources in island transmission, and establish short-circuit ratio constraints; S4. Based on comprehensive consideration of the mutual constraints among water, wind, light, and storage energy sources, the optimal output of hydropower is determined, and an optimization objective function is established. The system with overvoltage and short-circuit ratio constraints is solved through the particle swarm algorithm to obtain the output ratio of hydropower, wind power, photovoltaics, and energy storage, and complete the configuration of multi-type energy capacity.

2. A method for configuring the capacity of multiple types of energy through DC island transmission according to claim 1, characterized in that: S1 includes the following steps: S11. Establish the probability density function of photovoltaic and wind power; S12. Generate an initial photovoltaic and wind power output scenario set by using the Monte Carlo method, and optimize the scenario set using the synchronous back-substitution reduction method.

3. The method for configuring the capacity of multiple types of energy through DC island transmission according to claim 2 is characterized in that: The probability density function of photovoltaic is: Where, μ is the mean of the prediction error; σ is the variance of the prediction error, which measures the degree of dispersion of the prediction error; P is the power output by the photovoltaic system; The probability density function of wind power is: Where x and y are shape parameters and scale parameters, respectively, which are usually estimated through historical data; Γ(x) and Γ(y) are gamma functions.

4. The method for configuring the capacity of multiple types of energy through DC island transmission according to claim 2 is characterized in that: S12 includes the following steps: S121. Randomly sample the photovoltaic output by using the Monte Carlo method to generate an initial photovoltaic or wind output scenario set C; S122, initializing the reserved set A, setting the reserved set A to be equal to the initial scene set C, and setting a stop condition; S123, select each scene C in the reserved set A i The scene C with the smallest sum of distances to other scenes φ Delete it; S124, add the selected scene to the deletion set B, and add the deleted scene C φ Put it in the deletion set B, and delete scene C from the retention set A φ , and update the number of scenes in the reserved set A; S125. Find the scene C in the reserved set A. φ The closest scene C ψ , and set scene C φ The probability of occurrence p φ Add to scene C ψ Up, update probability; S126. If the number of scenes in the reserved set A has met the preset number of classic scenes G, the particle swarm is stopped; if the stopping condition is not met, the process returns to S123 and continues the selection and probability adjustment process until the number of scenes in the reserved set A meets the requirement.

5. The method for configuring the capacity of multiple types of energy through DC island transmission according to claim 4 is characterized in that: Each of the scenarios C i The calculation expression for the sum of the distances to other scenes is: Where i and j are different photovoltaic scenarios; d(C i ,C j ) is the distance between scenes i and j; M is the number of scenes in the current reserved set A; n is the data length of each scene; p i For scene C i The probability of occurrence; p j For scene C j Probability of occurrence; D i For each scene C i The sum of the distances to other scenes; C i,k For scene C i The kth value of j,k For scene C j The kth value of .

6. The method for configuring the capacity of multiple types of energy through DC island transmission according to claim 1 is characterized in that: In S3, the calculation expression of the short-circuit ratio of clean energy access in multiple stations is: The short circuit ratio constraint is: Where P REi is the new energy active power injected into node i; P REj is the new energy active power injected into node j; U i is the voltage at node i; U j is the voltage at node j; Z EQii is the equivalent impedance of node i itself; Z EQij is the equivalent impedance between node i and node j; n is the total number of nodes; N MRSCR,min The minimum short-circuit ratio allowed for long-term system stability; N R is the total number of stations in the system.

7. The method for configuring the capacity of multiple types of energy through DC island transmission according to claim 1 is characterized in that: In S4, the mutual constraints between the water, wind, light, and storage energy sources are: Power balance constraints of distribution network: Where P dc,t , P h,t are the DC transmission power and the active power of hydropower output in period t respectively; P wtk,t , P pvj,t , P esi,t are the active power of the kth wind farm, the active power of the jth photovoltaic output, and the active power of the ith energy storage output in time period t; n wt 、n pv 、n es They are the number of wind farms, the number of photovoltaic farms, and the number of energy storage stations; Energy storage constraints: In the formula, are the upper and lower limits of the energy storage capacity connected to each node, and is the rated power of the node energy storage; α is the relationship between the power and capacity of the energy storage; is the maximum power of charging and discharging of the i-th energy storage; E es,i (t) is the charge state of the i-th energy storage at the t-th time; P dis,i , P ch,i They are the charging power and discharging power of energy storage respectively; Photovoltaic constraints; V pp,min , V pv,max are the upper and lower limits of node voltage respectively; P pvj , P pv,mppj are the active power and maximum power generated by the jth photovoltaic cell respectively; Q pvj , S pvj are the reactive power and capacity output by the jth PV inverter at a certain moment respectively; is the lower limit of the photovoltaic inverter power factor; V n is; p is; v is; Wind power constraints; V wt,min ,V wt,max are the upper and lower limits of node voltage respectively; P wt,j , P wt,mppj are the active power and maximum power generated by the jth wind power respectively; Q wt,j , S wt,j are the reactive power and capacity output by the jth wind power inverter at a certain moment respectively; is the lower limit of the power factor of the wind power inverter; DC transmission constraints; P dc P is the DC output power; dc,max is the maximum capacity of DC transmission; P dc,t , P dc,t-1 are the DC transmission power at time t and time t-1 respectively; R dc,max is the maximum up and down climbing rate; S46. Establish hydropower constraints; V h,min ,V h,max are the upper and lower limits of node voltage respectively; P h , S h They are respectively the active power and maximum power generated by the hydropower unit; Q h , S h are the reactive power and capacity output by the hydropower unit at a certain moment; θ h is the generator power angle.

8. The method for configuring the capacity of multiple types of energy through DC island transmission according to claim 7 is characterized in that: In S4, the optimization objective function is: Where P esi,t , P pvj,t , P wtk,t , P h,t They are the i-th energy storage output, the j-th photovoltaic output, the k-th wind power output, and the hydropower output respectively.

9. The method for configuring the capacity of multiple types of energy through DC island transmission according to claim 7 is characterized in that: In S4, solving the system with overvoltage and short-circuit ratio constraints by particle swarm optimization includes the following steps: S41, initializing a particle swarm, where each particle represents a set of rated power configurations of wind power, photovoltaic power, and energy storage; S42, initializing the position and speed of particles: randomly initializing the rated power value of each particle and setting an initial speed for each particle; S43, calculating the photovoltaic and wind power output at each moment, and calculating the fitness according to the objective function. If the solution meets the constraint conditions, the fitness value is the objective function value; if the constraint conditions are not met, a penalty term is added; the fitness calculation expression is: S44, updating the individual best position and the global best position: for each particle, updating the individual best position according to its fitness value; S45. Update the speed and position of particles: Where w is the inertia weight, which controls the inertia of the particle; c1 and c2 are learning factors, which determine the particle's ability to learn to the individual optimal position and the global optimal position; rand1 and rand2 are random functions used to introduce randomness; are the k+1th and kth update speeds; S46, check constraints: check whether the particles meet the voltage constraints and the output limit of each energy source. If a particle violates the constraints, it will be punished to reduce its fitness. S47, setting convergence conditions; S48. Output the optimal solution.

10. A system for configuring the capacity of multiple types of energy through DC island transmission, applying the method for configuring the capacity of multiple types of energy through DC island transmission as claimed in any one of claims 1 to 9, comprising: Scenario set module: Based on the power generation characteristics of photovoltaic and wind power, it is used to build classic scenario sets for modeling and analysis; Analysis module: In the scenario where water, wind, solar, and storage energy are transmitted through isolated islands, it is used to analyze the impact of different energy generation outputs on system overvoltage, and further quantify the constraints of each type of energy; Short-circuit ratio constraint module: further analyzes the impact of multi-energy configuration on the system short-circuit ratio under different outputs, is used to analyze the dynamic response characteristics of different energy sources in island transmission, and establish short-circuit ratio constraints; Optimization module: It is used to determine the optimal output of hydropower and establish the optimization objective function based on the comprehensive consideration of the mutual constraints among water, wind, light and energy storage. The particle swarm algorithm is used to solve the system with overvoltage and short-circuit ratio constraints to obtain the optimal output ratio of hydropower, wind power, photovoltaic and energy storage.

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