Microgrid Power Quality Optimization via Stochastic Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Microgrids using renewable energy sources like photovoltaics face challenges in providing continuous and reliable power due to the intermittency and randomness of energy production, leading to potential disruptions and losses in power quality, especially when operating in islanded mode.
Innovation Solution
The implementation of a Power Quality of Service (PQoS) model that utilizes stochastic optimization and adaptive scheduling to manage energy storage and load demand, ensuring a guaranteed availability of power by optimizing the interaction between photovoltaic systems, energy storage, and load management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If renewable energy sources like photovoltaics are used in microgrids, then sustainability and environmental friendliness are improved, but power reliability and continuity deteriorate due to intermittency and randomness
Solution Approach 1:
The system performs preliminary actions by predicting renewable energy generation and load demand in advance using stochastic models. Energy storage systems are charged during periods of high generation or low demand, and load scheduling is optimized beforehand to ensure power reliability during intermittent periods, thus resolving the contradiction between using renewable sources and maintaining continuous power supply.
2Reliability
If energy storage systems are added to microgrids, then power availability is improved, but system complexity and cost increase
Solution Approach 1:
The energy storage system is designed to perform multiple functions: storing excess energy during high generation periods, providing power during low generation periods, stabilizing voltage and frequency, and participating in load management. This multi-functionality justifies the added complexity by delivering comprehensive benefits that improve power availability while managing system complexity through integrated control.
3Reliability
If stochastic optimization and adaptive scheduling are implemented, then power quality is improved, but control complexity and computational requirements increase
Solution Approach 1:
The system employs dynamic stochastic optimization and adaptive scheduling that continuously adjust control parameters based on real-time conditions. The control system adapts to changing renewable generation patterns and load demands by updating probability distributions and optimization criteria dynamically, thereby improving power quality while managing control complexity through adaptive rather than static approaches.
4Adaptability or versatility
If microgrids operate in islanded mode, then energy independence is improved, but power stability and grid resilience worsen due to lack of main grid support
Solution Approach 1:
The microgrid implements comprehensive feedback mechanisms that continuously monitor generation, storage state, and load conditions. Stochastic models update probability distributions based on observed data, and adaptive scheduling adjusts operational strategies in real-time. This feedback loop enables the islanded microgrid to maintain stability and resilience by automatically responding to disturbances and optimizing resource allocation without main grid support.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the reliability and efficiency of microgrids by minimizing loss-of-load events and energy waste, ensuring a high probability of power availability, even in scenarios where renewable energy sources are unpredictable, thereby improving overall power quality and system resilience.
Implementation Method 1
microgrids utilizing photovoltaic or other renewable sources
Data Source
AI summary
Various examples are provided for power quality of service optimization for microgrids. In one example, among others, a microgrid includes a smart meter configured to control supply of electric power to loads based at least in part upon energy consumption scheduling of the loads, which is based at least in part upon an effective electric generation capacity associated with the microgrid. The energy consumption scheduling can be based at least in part upon estimated power output of a sustainable energy resource of the microgrid and a load demand characterization of the loads. In another example, a system comprises a plurality of microgrids and an advanced metering infrastructure (AMI) configured to monitor operations of the microgrids and to control supply of electric power from sustainable energy resources and energy storage systems to loads of the microgrids via smart meters based at least in part upon energy consumption scheduling of the loads.