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High-proportion renewable energy integration causes serious power fluctuations and voltage deviations. This paper proposes a collaborative optimization method for energy storage capacity and inverter control parameters to reduce fluctuations and deviations by at least 30%. A 100 kW photovoltaic-energy storage system model is established and simulated in MATLAB/Simulink. Based on L27 orthogonal test and minimum search algorithm, six PID parameters are optimized under four energy storage capacities. Results show that power fluctuation is reduced by 61.9%–81.0%, voltage deviation exceeds 90%, bus voltage is stable at 1000 V, and THD < 3%. The method effectively improves grid stability and renewable energy consumption.

• OPTIMIZATION OF ENERGY STORAGE CAPACITY AND ITS INVERTER PARAMETERS FOR SMOOTHING FLUCTUATIONS IN RENEWABLE ENERGY GENERATION
• Peter the Great St.Petersburg Polytechnic
• University Institute of
• Energy
• For the implementation of master's thesis
• 1. INTRODUCTION
  • 1.1. Renewable Energy Systems and Energy Storage Systems
    • 1.1.1. Overview of Renewable Energy Systems
    • 1.1.2. Overview of Energy Storage Systems
    • 1.1.3. Synergistic Operation of Renewable Energy and Energy Storage
  • 1.2. Inverters
    • 1.2.1. Basic Information of Inverters
    • 1.2.2. Core Characteristics of Inverters
    • 1.2.3. Inverter Circuit Structure
  • 1.3. Drivers
    • 1.3.1. Driver Types
    • 1.3.2. Role of the Driver in the System
    • 1.3.3. Driver Connection Methods
  • 1.4. Impact of Inverters and Energy Storage on the Power Grid
    • 1.4.1. Positive Impacts
    • 1.4.2. Negative Impacts
• 2. MATHEMATICAL MODEL OF DISTRIBUTION NETWORK NODES
  • 2.1. Introduction
  • 2.2. Model Prerequisites and Conventions
  • 2.3. Distribution Network Node Model Elements
    • 2.3.1. Mathematical Model of PV Inverter Unit
    • 2.3.2. Mathematical Model of Energy Storage System Unit
    • 2.3.3. Mathematical Model of Power Load Unit
    • 2.3.4. Mathematical Model of Node Interface Unit
  • 2.4. Photovoltaic-Storage Joint Operation Mode
    • 2.4.1. Photovoltaic Output Fluctuation Smoothing Mode
    • 2.4.2. Constant Power Grid Connection Mode
    • 2.4.3. Peak-Valley Arbitrage + Power Support Mode
    • 2.4.4. Low Voltage Ride-Through (LVRT) Collaborative Support Mode
  • 2.5. Overall Mathematical Model of Distribution Network Nodes Including Sources, Loads, and Storage
    • 2.5.1. Node Power Balance Equation
    • 2.5.2. Multi-Dimensional Constraint System of the Model
  • 2.6. Typical Engineering Example
    • 2.6.1. Example Parameter Settings
    • 2.6.2. Example Model Equations
  • 2.7. Chapter Summary
• 3. MODELING AND SIMULATION ANALYSIS OF A 100KW PHOTOVOLTAIC‑ENERGY STORAGE GRID‑CONNECTED SYSTEM
  • 3.1. System Architecture and Modeling Methods
    • 3.1.1. Modeling of the 100kW Photovoltaic Array
    • 3.1.2. Modeling of the 100kW Energy Storage System
    • 3.1.3. Grid-Connected Inverter and LCL Filter Design
    • 3.1.4. Three-Phase 380V Power Grid Model
  • 3.2. Core control strategy
    • 3.2.1. Photovoltaic MPPT Control (Variable Step Size Perturbation-Observation Method)
    • 3.2.2. Energy Storage Dual Closed-Loop Control (Voltage Outer Loop + Current Inner Loop)
    • 3.2.3. Grid-connected control
  • 3.3. Simulation Results and Analysis
    • 3.3.1. Photovoltaic Output and MPPT Performance (100kW Steady-State)
    • 3.3.2. DC Bus Voltage Characteristics
    • 3.3.3. Grid-connected Voltage and Current Waveforms (220V, THD<3%)
    • 3.3.4. Grid-Connected Power Smoothing Effect
    • 3.3.5. Energy Storage Operation Characteristics (SOC Management, Charge/Discharge Switching)
  • 3.4. Model Constraint Verification
  • 3.5. Chapter Summary
• 4. CO-OPTIMIZATION OF ENERGY STORAGE CAPACITY AND INVERTER CONTROL PARAMETERS
  • 4.1. Introduction
  • 4.2. Determination of Optimization Criteria
    • 4.2.1. Physical Significance of Optimization Criteria
    • 4.2.2. Mathematical Definition of Optimization Criterion
  • 4.3. Selection of Optimization Variables
    • 4.3.1. Variable Selection Principles
    • 4.3.2. Definition and Physical Meaning of Optimization Variables
    • 4.3.3. Range of Optimization Variables and Orthogonal Design
  • 4.4. Power smoothing strategy and baseline determination
    • 4.4.1. Low-Pass Filter Power Smoothing Strategy
    • 4.4.2. Benchmark Value Determination Method
  • 4.5. Minimum Search Implementation
    • 4.5.1. Basic Algorithm Principles
    • 4.5.2. Algorithm Implementation Steps
  • 4.6. Optimization Results and Analysis
    • 4.6.1. Optimization Results for 50 kW Energy Storage Capacity
    • 4.6.2. Optimization Results for 25 kW Energy Storage Capacity
    • 4.6.3. Optimization Results for 10 kW Energy Storage Capacity
    • 4.6.4. Optimization Results of 5 kW Energy Storage Capacity
  • 4.7. Comparative Analysis of Optimization Effects under Different Energy Storage Capacities
    • 4.7.1. Comprehensive Comparison
    • 4.7.2. Impact of Energy Storage Capacity on Optimization Results
    • 4.7.3. Discussion and Explanation of Optimization Results
  • 4.8. Chapter Summary
• Conclusions and Discussion
• REFERENCES