Design of Wind-Solar Hybrid Microgrid System: Integrated Optimization of Forecasting, Energy Storage, and Dispatch

Design of Wind-Solar Hybrid Microgrid System: Integrated Optimization of Forecasting, Energy Storage, and Dispatch

As an effective vehicle for achieving regional energy self-sufficiency and clean energy transition, the core effectiveness of wind-solar hybrid microgrids depends on the accuracy of power forecasting, the rationality of energy storage configuration, and the synergy of intelligent dispatch. These three elements constitute an interdependent and dynamically coupled closed-loop system optimization system, aiming to solve the inherent intermittency and volatility problems of wind and solar power generation, and achieve safe, stable, and economical operation under a high proportion of renewable energy.

I. Power Forecasting: The Perception Foundation and Decision-Making Prerequisite for System Optimization

Accurate power forecasting is the foundation for the forward-looking regulation of wind-solar hybrid microgrids, and its error directly determines energy storage demand and dispatch costs. Modern forecasting systems adopt a fusion approach of "physical model + data-driven" method.

Input Data Layer: Integrates meteorological forecast data (wind speed, irradiance, cloud cover), historical power generation data, geographic information, and equipment status data.

Prediction Model Layer: Short-term forecasting (0-72 hours) primarily employs time-series analysis and machine learning models (such as LSTM and XGBoost) driven by numerical weather prediction (NWP) to capture the evolution patterns of weather systems. Ultra-short-term forecasting (0-4 hours) focuses on statistical extrapolation based on real-time data and cloud motion tracking to correct short-term forecast biases.

Application Value: High-precision forecasting allows the system to formulate economic dispatch plans hours or days in advance, reducing reserve capacity requirements and providing crucial support for participation in the electricity market. Forecast results and uncertainty quantification serve as core inputs for subsequent energy storage configuration and dispatch decisions.

II. Energy Storage Configuration: A Physical Buffer for Smoothing Fluctuations and Ensuring Power Supply The scientific configuration of energy storage systems is a key link in bridging fluctuating power generation and stable load demands, requiring coordinated optimization in both power (kW) and capacity (kWh) dimensions.

Functional Positioning: Energy storage must simultaneously fulfill multiple roles, including energy time-shifting (storing excess wind and solar power during periods of power shortage), power smoothing (smoothing power fluctuations from seconds to minutes), voltage and frequency support, and emergency backup.

Optimization Method: Based on long-term (typically over one year) wind and solar power generation and load data, time-series production simulation or mixed-integer programming methods are employed to determine the optimal energy storage type (e.g., lithium-ion, flow battery, flywheel combination) and power-to-capacity ratio, while meeting specific power supply reliability indicators (e.g., probability of insufficient power supply (LPSP)) and grid connection volatility requirements, with the goal of minimizing the total lifecycle cost.

Key Conclusion: Larger energy storage configurations are not always better; the optimal solution highly depends on wind and solar power output characteristics, load curves, electricity pricing policies, and technological costs. Typically, energy storage capacity needs to cover the load gap during typical "no wind or solar" periods (e.g., prolonged cloudy and windless weather) while maintaining a certain adjustment margin.

III. Intelligent Dispatch: The Decision-Making Hub for Multi-Objective Collaborative Optimization

The intelligent dispatch system is the "brain" of the microgrid. Based on predictive information and energy storage status, it coordinates various controllable resources in real time to achieve safe, economical, and efficient multi-objective dynamic optimization.

Resource Pool: The scheduling objects include wind/photovoltaic power generation units, energy storage systems (charging/discharging), controllable loads (interruptible/transferable), and backup diesel generators or power exchange with the main grid via tie lines.

Optimization Model and Algorithm: Model Predictive Control (MPC) is adopted as the core framework. In each scheduling cycle (e.g., 15 minutes), MPC, based on the latest ultra-short-term forecasts and system status, continuously optimizes scheduling instructions for the next few hours to minimize operating costs (including fuel costs, electricity purchase costs, equipment losses, etc.) or maximize self-sufficiency/revenue, while strictly meeting safety limits such as power balance, equipment operating constraints, and voltage/frequency.

Advanced Functions: The intelligent scheduling system must possess adaptive learning capabilities, able to iteratively optimize the prediction error model and scheduling strategy based on actual operating data; it must support multi-timescale coordination, achieving organic integration of day-ahead planning, intraday rolling adjustments, and real-time control; and in off-grid mode, it must have the ability to perform black start and islanded stable operation.

IV. System Integration and Collaborative Optimization: True system optimization is not a simple series connection of the three components, but rather a deep coupling achieved through a unified data platform and optimization engine. For example, the uncertainty of forecasts is explicitly incorporated into energy storage configuration models and robust scheduling strategies; the actual operating status of energy storage provides feedback for the next round of forecast correction. The future development trend is to introduce artificial intelligence and digital twin technology to build an intelligent "source-grid-load-storage" system capable of autonomous learning, simulation, and decision-making, ultimately transforming the wind-solar hybrid microgrid into a highly resilient, adaptive, and tradable community-level smart energy hub.

In summary, an efficient and reliable wind-solar hybrid microgrid is the product of the deep integration of a precise "weather forecaster," a moderate "electricity bank," and a smart "energy manager." Its design is a typical interdisciplinary systems engineering project. The key to success lies in taking a life-cycle perspective, comprehensively considering technical feasibility, economic optimization, and operational reliability, and maximizing the potential of wind and solar resources through refined modeling and intelligent decision-making, providing a solid unit foundation for building a zero-carbon energy system.