Renewable Power Plant Simulation for Multi-Load Power Flow Forecasting
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Solution Overview
Problem
Renewable energy power plants face challenges in optimizing power flow due to variations in renewable energy source output and demand, leading to inefficiencies and waste, especially when connected to multiple power plants and energy storage systems.
Innovation Solution
A data structure and simulation model that uses a graph database to model power flows between renewable energy sources, energy storage systems, and power grids, allowing for the determination of optimal energy distribution across various loads and power plants to maximize efficiency and income.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If renewable energy sources are connected to multiple power plants and energy storage systems, then power supply capacity is improved, but system complexity increases making optimization difficult
Solution Approach 1:
The system is segmented into modular components including renewable energy sources, energy storage systems, and controllable loads, each represented as discrete objects in the simulation model. This modular segmentation allows complex power flow optimization to be broken down into manageable segments that can be independently analyzed and optimized while maintaining overall system coordination.
Solution Approach 2:
A simulation model acts as an intermediary between the complex renewable energy system and operators. This intermediary processes the complex multi-plant interactions and presents simplified optimization scenarios, enabling operators to make informed decisions without being overwhelmed by system complexity.
2Productivity
If real-time control of power flow is implemented across multiple power plants, then efficiency is improved, but computational requirements and system complexity increase
Solution Approach 1:
The simulation model performs preliminary analysis of power flow scenarios before actual operation. By pre-calculating optimal power distribution strategies across multiple plants and storing these as reference scenarios, the system enables rapid real-time control decisions without requiring complex real-time computations for every scenario.
Solution Approach 2:
The simulation creates virtual copies of the power plant system to test different operational scenarios. These digital replicas allow operators to evaluate efficiency improvements from various control strategies without implementing complex real-time control systems, by simply selecting from pre-simulated optimal scenarios.
3Measurement precision
If comprehensive simulation of equipment modifications is performed, then decision accuracy is improved, but simulation time and computational resources increase
Solution Approach 1:
The simulation model implements selective modeling where only critical equipment modifications and their direct impacts are fully simulated, while secondary effects are estimated or omitted. This partial action approach maintains sufficient decision accuracy for operational purposes while significantly reducing simulation time and computational requirements compared to exhaustive modeling.
Data Source
AI summary
A system for determining power flows in a power plant comprising an energy storage system (ESS) and a power generation system includes at least one memory storing a power plant model of the power plant and at least one processor. The power plant model includes an ESS model including a battery model, a power generation system model, a power price schedule for first and second loads configured to receive power from the power plant, and system relationships between the models. The at least one processor is configured to receive a power demand schedule representing expected power demands from the first load and the second load and determine, based on the power demand schedule, the power price schedule, and the system relationships, a power flow rate forecast for at least one point in the power plant.