Power Plant Network Simulation for Renewable Power Flow Optimization
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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 a power grid and behind-the-meter loads, and operators struggle to manage power distribution across multiple plants to maximize efficiency and income.
Innovation Solution
A data structure and simulation model that uses a graph database to model power flows from one or more power plants, including renewable energy sources and energy storage systems, to determine optimal energy distribution to various loads, grids, and other power plants, accounting for factors like weather, time, and contractual obligations, allowing for network-wide effects analysis and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If renewable energy sources are used to supply electricity, then environmental sustainability is improved, but power output variability and inefficiency increase
Solution Approach 1:
The simulation model performs preliminary analysis of power flow scenarios before actual operation, allowing operators to predict optimal power distribution strategies in advance. The system evaluates multiple hypothetical scenarios and selects the best course of action before implementing power distribution decisions.
Solution Approach 2:
The system continuously monitors actual power output from renewable sources and compares it with predicted values, using this feedback to dynamically adjust power flow optimization strategies. The simulation model incorporates real-time data to refine its predictions and improve subsequent power distribution decisions.
2Productivity
If power flow is optimized across multiple plants, then efficiency and income are improved, but system complexity increases
Solution Approach 1:
The simulation model divides the complex multi-plant power system into individual plant modules, each with its own power flow characteristics. By segmenting the system, the model can analyze and optimize each plant independently while still considering network-wide effects, making the overall optimization problem more manageable.
Solution Approach 2:
The simulation model serves multiple functions simultaneously: it predicts power output, optimizes power flow distribution, evaluates different operational scenarios, and provides decision support for operators. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single unified platform.
3Ease of operation
If power is distributed to meet varying demand, then customer satisfaction is improved, but energy waste increases
Solution Approach 1:
The simulation model dynamically adjusts power distribution strategies based on real-time demand conditions and predicted renewable energy output. Rather than using static distribution plans, the system continuously adapts its optimization approach to match varying customer demand patterns while minimizing energy waste through intelligent routing decisions.
Data Source
AI summary
A system for optimizing a network of power plants includes at least one memory storing a network model of a network of power plants and at least one processor. The network model includes a first power plant model of a first power plant including equipment models of power plant equipment of the first power plant, a second power plant model of a second power plant including equipment models of power plant equipment of the second power plant, and a plant relationship between the first power plant and the second power plant. The at least one processor is configured to receive a first input including an augmentation and replacement schedule for the second power plant, receive a second input modifying an attribute of a first equipment model of the first power plant model, and modify the schedule for the second power plant based on the second input and the plant relationship.