Zone area photovoltaic voltage adjusting method and system based on Hemma optimization algorithm

Abstract

The invention discloses a transformer area photovoltaic voltage adjusting method and system based on a Hemma optimization algorithm. The method comprises the steps that 1, the deviation between the current voltage and the target voltage is detected; step 2, calculating an active regulation demand and a reactive regulation demand according to the deviation of the voltage; 3, the distribution amount is optimized and adjusted based on a Hemma optimization algorithm; and step 4, according to a result of optimizing and adjusting the distribution amount, dynamically allocating an adjusting task of each module, and realizing an effect of monitoring and adjusting. Adjusting distribution is optimized through the Hemma optimization algorithm, and the adjusting efficiency is improved; and on the basis of adjustment cost optimization distribution, the power grid adjustment cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system voltage regulation, and in particular to a photovoltaic voltage regulation method and system based on a Hippo optimization algorithm. Background Art

[0002] With the rapid development of distributed photovoltaic power generation, the problem of voltage fluctuation in power grids has become increasingly prominent. Traditional voltage regulation methods (such as inverter regulation or transformer voltage regulation) have problems such as limited regulation range, slow response speed, and lack of economic incentives. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of traditional voltage regulation means in the prior art, such as limited regulation range, slow response speed and lack of economic incentives, and to provide a photovoltaic voltage regulation method and system based on the Hippo optimization algorithm, which optimizes the regulation distribution through the dynamic combination of active and reactive power to achieve efficient and economical voltage regulation.

[0004] The purpose of the present invention is achieved through the following technical solutions: A photovoltaic voltage regulation method based on a Hippo optimization algorithm comprises the following steps: Step 1: Detect the deviation between the current voltage and the target voltage; Step 2: Calculate the active power regulation demand and reactive power regulation demand based on the voltage deviation; Step 3, optimizing and adjusting the allocation amount based on the Hippo optimization algorithm; Step 4: According to the result of optimizing the adjustment allocation, dynamically allocate the adjustment tasks of each module to achieve the effect of monitoring and adjustment.

[0005] Preferably, in step 1, the voltage deviation is: △V=V current -V target , Among them, V current Indicates the current voltage, V target Indicates the target voltage; In step 2, the formula for calculating the active power regulation demand and the reactive power regulation demand based on the voltage deviation is: Among them, △P represents the active power regulation demand, △Q represents the reactive power regulation demand, R represents the line resistance, and X represents the reactance.

[0006] Preferably, in step 3, the objective function of the distribution amount is: min(∑(c PV Q PV +c ESS ·P ESS +cSVC Q SVC +c load ·P load )), Among them, c PV 、c ESS 、c SVC 、c load are the unit regulation costs of photovoltaic inverter, energy storage system, reactive power compensation device and flexible load respectively, Q PV Indicates the reactive power regulation of the photovoltaic inverter, P ESS Indicates the active regulation of the energy storage system, Q SVC Indicates the reactive power regulation of the reactive power compensation device, P load Indicates the active power regulation of flexible load.

[0007] Preferably, the constraint conditions of the objective function are: P ESS +P load =△P, Q PV +Q SVC =△Q.

[0008] Preferably, the reactive power regulation amount of the photovoltaic inverter is specifically: Among them, is the capacity of the photovoltaic inverter and is the active power of the photovoltaic inverter.

[0009] Preferably, the Hippo optimization algorithm is specifically: Sub-step 1: randomly generate N hippopotamus individuals, each representing a power allocation scheme, and set the maximum number of iterations and the search space boundary, i.e., the reactive and active power regulation range; Sub-step 2: Calculate the fitness value of each individual and construct the objective function of the allocation amount; Sub-step 3, simulating the hippopotamus’s foraging behavior and updating the individual position; Sub-step 4: simulate the migration behavior of hippopotamus groups and update individual positions; Sub-step 5, simulate hippopotamus habitat behavior and randomly perturb individual positions; Sub-step 6, updating the global optimal solution and the group center position; Sub-step 7: Determine whether the maximum number of iterations has been reached or whether the objective function has converged. If so, output the optimal solution; otherwise, return to sub-step 2.

[0010] Preferably, the range of individual position adjustment is within the boundary of the contraction space.

[0011] A photovoltaic voltage regulation system based on the Hippo optimization algorithm, comprising: Photovoltaic inverter module for regulating reactive power; Energy storage system module, used to regulate active power; Reactive power compensation device module: used to adjust reactive power; Flexible load module: used to adjust active power; Hippo optimization algorithm module: calculates the optimal adjustment allocation based on real-time data and historical records; Coordination control module: Dynamically allocates the adjustment tasks of each module according to the calculation results of the Hippo optimization algorithm.

[0012] The beneficial effects of the present invention are as follows: the present invention optimizes regulation distribution through the Hippo optimization algorithm, thereby improving regulation efficiency; optimizes distribution based on regulation cost, thereby reducing grid regulation cost; combines the dynamic combination of active and reactive power, thereby making up for the limitations of single regulation means; the present invention is suitable for distribution network areas with distributed photovoltaic access, especially for areas with large fluctuations in photovoltaic power generation and poor voltage stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0014] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0015] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0016] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0017] Example: A photovoltaic voltage regulation method based on the Hippo optimization algorithm, such as Figure 1As shown, the following steps are included: Step 1: Detect the deviation between the current voltage and the target voltage; Step 2: Calculate the active power regulation demand and reactive power regulation demand based on the voltage deviation; Step 3, optimizing and adjusting the allocation amount based on the Hippo optimization algorithm; Step 4: According to the result of optimizing the adjustment allocation, dynamically allocate the adjustment tasks of each module to achieve the effect of monitoring and adjustment.

[0018] In step 1, the voltage deviation is: △V=V current -V target , Among them, V current Indicates the current voltage, V target Indicates the target voltage; In step 2, the formula for calculating the active power regulation demand and the reactive power regulation demand based on the voltage deviation is: Among them, △P represents the active power regulation demand, △Q represents the reactive power regulation demand, R represents the line resistance, and X represents the reactance.

[0019] In step 3, the objective function of the distribution amount is: min(∑(c PV Q PV +c ESS ·P ESS +c SVC Q SVC +c load ·P load )), Among them, c PV 、c ESS 、c SVC 、c load are the unit regulation costs of photovoltaic inverter, energy storage system, reactive power compensation device and flexible load respectively, Q PV Indicates the reactive power regulation of the photovoltaic inverter, P ESS Indicates the active regulation of the energy storage system, Q SVC Indicates the reactive power regulation of the reactive power compensation device, P load Indicates the active power regulation of flexible load.

[0020] The constraints of the objective function are: P ESS +P load =△P, Q PV +Q SVC =△Q.

[0021] The reactive power regulation of the photovoltaic inverter is specifically: Among them, is the capacity of the photovoltaic inverter and is the active power of the photovoltaic inverter.

[0022] The Hippo optimization algorithm is specifically: Sub-step 1: randomly generate N hippopotamus individuals, each representing a power allocation scheme, and set the maximum number of iterations and the search space boundary, i.e., the reactive and active power regulation range; Sub-step 2: Calculate the fitness value of each individual and construct the objective function of the allocation amount; Sub-step 3, simulating the hippopotamus’s foraging behavior and updating the individual position; Sub-step 4: simulate the migration behavior of hippopotamus groups and update individual positions; Sub-step 5, simulate hippopotamus habitat behavior and randomly perturb individual positions; Sub-step 6, updating the global optimal solution and the group center position; Sub-step 7: Determine whether the maximum number of iterations has been reached or whether the objective function has converged. If so, output the optimal solution; otherwise, return to sub-step 2.

[0023] In this embodiment, the Hippo optimization algorithm is specifically applied as follows: Sub-step 1: Initialization Parameter settings: The group size N is 20 (number of hippos); The maximum number of iterations T is 100; Search space boundaries (reactive and active power regulation range).

[0024] Initial population generation: Randomly generate N hippopotamus individuals, each of which represents a power allocation scheme: x i =[Q PV ,Q SVC ,P ESS ,P load ] Initialize the fitness value (objective function value) of each individual.

[0025] Sub-step 2: Fitness evaluation Calculate the fitness value of each individual, and the objective function is the total adjustment cost: f(x i )=(c PV Q PV +c SVC Q SVC +c ESS ·P ESS +c load ·P load ) Sub-step 3: Foraging behavior Simulate hippopotamus foraging behavior and update individual positions: Where: x best is the current optimal individual; α is the learning factor, which controls the foraging step length and is 0.5; r1 is a random vector in the range [0,1].

[0026] Sub-step 4: Migrate Behavior Simulate the migration behavior of hippopotamus groups and update individual positions: in: x center The center of the group; β is the migration factor, which controls the migration step length and is 0.3; r2 is a random vector in the range [0,1].

[0027] Sub-step 5: Perching behavior Simulate hippopotamus habitat behavior and randomly perturb individual positions: in: γ is the disturbance factor, which controls the disturbance amplitude and is 0.1; r3 is a random vector in the range [0,1].

[0028] Ensure that each individual's position is within the search space boundaries: PV inverter reactive power adjustment range: Reactive power compensation device reactive power adjustment range: Q SVC ∈[0,5.5]; Active power regulation range of energy storage system: P ESS ∈[0,10]; Flexible load active power adjustment range P load ∈[0,8].

[0029] Sub-step 6: Update the optimal solution Update the global optimal solution x best and the group center position x centert .

[0030] Sub-step 7: Termination Condition Determine whether the maximum number of iterations T is reached or the objective function converges: If the conditions are met, output the optimal solution x best ; Otherwise, return to sub-step 2.

[0031] A photovoltaic voltage regulation system based on the Hippo optimization algorithm, comprising: Photovoltaic inverter module for regulating reactive power; Energy storage system module, used to regulate active power; Reactive power compensation device module: used to adjust reactive power; Flexible load module: used to adjust active power; Hippo optimization algorithm module: calculates the optimal adjustment allocation based on real-time data and historical records; Coordination control module: Dynamically allocates the adjustment tasks of each module according to the calculation results of the Hippo optimization algorithm.

[0032] The specific code of the Hippo optimization algorithm in Python is as follows: #Hippo Optimization Algorithm (HOA) parameters N=20#Group size T=100#maximum number of iterations alpha = 0.5 # learning factor (foraging behavior) beta = 0.3 # Migration factor (migration behavior) gamma = 0.1# disturbance factor (habiting behavior) #Adjust range boundaries Q_PV_min, Q_PV_max = -10, 10 # PV inverter reactive power regulation range (kVAR) Q_SVC_min, Q_SVC_max = 0, 20 # reactive power compensation device reactive power regulation range (kVAR) P_ESS_min, P_ESS_max = -20, 20 # energy storage system active power regulation range (kW) P_load_min, P_load_max=-10,10#Flexible load active power adjustment range (kW) #Line parameters X=0.1# line reactance (Ω) R=0.08# line resistance (Ω) V_target = 230 # Target voltage (V) V_current=240#Current voltage (V) delta_V=V_current-V_target#voltage deviation (V) #Adjust cost (update to new cost) c_PV=0.16#PV inverter unit regulation cost c_SVC=0.16#Unit adjustment cost of reactive power compensation device c_ESS=0.18#Unit regulation cost of energy storage system c_load=0.2#Flexible load unit adjustment cost #Fitness function: total adjustment cost def fitness(Q_PV,Q_SVC,P_ESS,P_load): return c_PV*abs(Q_PV)+c_SVC*abs(Q_SVC)+c_ESS*abs(P_ESS)+c_load*abs(P_load)#voltage regulation effect calculation def voltage_effect(Q_PV,Q_SVC,P_ESS,P_load): return(X*(Q_PV+Q_SVC)+R*(P_ESS+P_load)) / V_target*1000 # Initialize the group def initialize_population(N): population=[] for_in range(N): Q_PV=np.random.uniform(Q_PV_min,Q_PV_max) Q_SVC=np.random.uniform(Q_SVC_min,Q_SVC_max) P_ESS=np.random.uniform(P_ESS_min,P_ESS_max) P_load=np.random.uniform(P_load_min,P_load_max) population.append([Q_PV,Q_SVC,P_ESS,P_load]) return np.array(population) #Boundary constraints def apply_boundaries(x): x[0]=np.clip(x[0],Q_PV_min,Q_PV_max) x[1]=np.clip(x[1],Q_SVC_min,Q_SVC_max) x[2]=np.clip(x[2],P_ESS_min,P_ESS_max) x[3]=np.clip(x[3],P_load_min,P_load_max) return x #Hippo optimization algorithm def HOA(): # Initialize the group population=initialize_population(N) best_solution=Nonebest_fitness=float('inf') #Iterative Optimization for t in range(T): for i in range(N): #Current individual x = population[i] #Calculate fitness current_fitness=fitness(x[0],x[1],x[2],x[3]) if current_fitness <best_fitness: best_fitness=current_fitness best_solution=x #Foraging behavior r1 = np.random.rand(4) x_new=x+alpha*r1*(best_solution-x) #Migration behavior r2 = np.random.rand(4) x_center=np.mean(population,axis=0) x_new=x_new+beta*r2*(x_center-x) #habiting behavior r3 = np.random.rand(4) x_new=x_new+gamma*r3 #Boundary constraints x_new=apply_boundaries(x_new) #Update individual population[i]=x_new # Output the current optimal solution print(f"Iteration{t+1}:Best Solution={best_solution},Best Fitness={best_fitness}")#Return the optimal solution return best_solution #Run the algorithm best_solution = HOA() print("\nFinal Optimal Solution:") print(f"Q_PV={best_solution[0]}kVAR") print(f"Q_SVC={best_solution[1]}kVAR") print(f"P_ESS={best_solution[2]}kW") print(f"P_load={best_solution[3]}kW") #Calculate voltage regulation effect voltage_effect_final=voltage_effect(best_solution[0],best_solution[1],best_solution[2],best_solution[3]) print(f"\nVoltage Effect:{voltage_effect_final}V").

[0033] The following is a specific plan for applying this plan in a certain area: Current voltage V current =240, target voltage V target =230; Line resistance R = 0.08Ω, reactance X = 0.1Ω; Unit adjustment cost (taking Zhejiang region as an example): Photovoltaic inverter: c PV =0.16 yuan / kVAR; Energy storage system: c ESS =0.18 yuan / kW; Reactive power compensation device: c SVC =0.16 yuan / kVAR; Flexible load: c load =0.2 yuan / kW.

[0034] Calculate voltage deviation: △V = 240-230 = 5V Calculation adjustment requirement: 0.08·△P+0.1·△Q=2300 Hippo optimization algorithm optimization: Photovoltaic inverter: Q PV =6kVAR; Energy storage system: P ESS =10kW; Reactive power compensation device: Q SVC =5.5kVAR; Flexible load: P load =4.375kW.

[0035] Total adjustment cost: Photovoltaic inverter: reactive power regulation 6kVAR, cost 0.96 yuan; Energy storage system: active power regulation 10kW, cost 1.8 yuan; Reactive power compensation device: Reactive power regulation 5.5kVAR, cost 0.88 yuan; Flexible load: active power regulation 4.375kW, cost 0.875 yuan; Total transaction cost: 4.515 yuan.

[0036] When only the PV inverter is regulated and the PV active output is at its maximum, the maximum reactive power regulation of the PV inverter is: Adjustment effect: The regulated voltage is 237.39V, which cannot meet the requirements. The regulation cost is 0.96 yuan.

[0037] When only reactive equipment participates in regulation, After adjustment, the voltage is 235V, which still cannot meet the requirements. The adjustment cost is 1.84 yuan.

[0038] When considering energy storage to participate in voltage regulation, but not considering flexible load participation, After adjustment, the voltage is 231.52V, which still cannot meet the requirements. The adjustment cost is 3.64 yuan.

[0039] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0040] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A photovoltaic voltage regulation method based on the Hippo optimization algorithm, characterized in that: The following steps are involved: Step 1: Detect the deviation between the current voltage and the target voltage; Step 2: Calculate the active power regulation demand and reactive power regulation demand based on the voltage deviation; Step 3, optimizing and adjusting the allocation amount based on the Hippo optimization algorithm; Step 4: According to the result of optimizing the adjustment allocation, dynamically allocate the adjustment tasks of each module to achieve the effect of monitoring and adjustment.

2. The photovoltaic voltage regulation method based on the Hippo optimization algorithm according to claim 1 is characterized in that: In step 1, the voltage deviation is: △V=V current -V target , Among them, V current Indicates the current voltage, V target Indicates the target voltage; In step 2, the formula for calculating the active power regulation demand and the reactive power regulation demand based on the voltage deviation is: Among them, △P represents the active power regulation demand, △Q represents the reactive power regulation demand, R represents the line resistance, and X represents the reactance.

3. The photovoltaic voltage regulation method based on the Hippo optimization algorithm according to claim 2 is characterized in that: In step 3, the objective function of the distribution amount is: min(∑(c PV ·Q PV +c ESS ·P ESS +c SVC ·Q SVC +c load ·P load )), Among them, c PV 、c ESS 、c SVC 、c load are the unit regulation costs of photovoltaic inverter, energy storage system, reactive power compensation device and flexible load respectively, Q PV Indicates the reactive power regulation of the photovoltaic inverter, P ESS Indicates the active regulation of the energy storage system, Q SVC Indicates the reactive power regulation of the reactive power compensation device, P load Indicates the active power regulation of flexible load.

4. The photovoltaic voltage regulation method based on the Hippo optimization algorithm according to claim 3 is characterized in that: The constraints of the objective function are: P ESS +P load =△P, Q PV +Q SVC =△Q。 5. The photovoltaic voltage regulation method based on the Hippo optimization algorithm according to claim 3 is characterized in that: The reactive power regulation of the photovoltaic inverter is specifically: Among them, is the capacity of the photovoltaic inverter and is the active power of the photovoltaic inverter.

6. The photovoltaic voltage regulation method based on the Hippo optimization algorithm according to claim 3 is characterized in that: The Hippo optimization algorithm is specifically: Sub-step 1: randomly generate N hippopotamus individuals, each representing a power allocation scheme, and set the maximum number of iterations and the search space boundary, i.e., the reactive and active power regulation range; Sub-step 2: Calculate the fitness value of each individual and construct the objective function of the allocation amount; Sub-step 3, simulating the hippopotamus’s foraging behavior and updating the individual position; Sub-step 4: simulate the migration behavior of hippopotamus groups and update individual positions; Sub-step 5, simulate hippopotamus habitat behavior and randomly perturb individual positions; Sub-step 6, updating the global optimal solution and the group center position; Sub-step 7: Determine whether the maximum number of iterations has been reached or whether the objective function has converged. If so, output the optimal solution; otherwise, return to sub-step 2.

7. The photovoltaic voltage regulation method based on the Hippo optimization algorithm according to claim 6 is characterized in that: The range of individual position adjustment is within the boundary of the contraction space.

8. A photovoltaic voltage regulation system based on the Hippo optimization algorithm, characterized by: include: Photovoltaic inverter module for regulating reactive power; Energy storage system module, used to regulate active power; Reactive power compensation device module: used to adjust reactive power; Flexible load module: used to adjust active power; Hippo optimization algorithm module: calculates the optimal adjustment allocation based on real-time data and historical records; Coordination control module: Dynamically allocates the adjustment tasks of each module according to the calculation results of the Hippo optimization algorithm.

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