Optimal Control for Distributed Energy Resources in Microgrids
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Solution Overview
Problem
The integration of high penetration levels of photovoltaic (PV) resources and other distributed energy resources (DERs) into electric power grids faces challenges due to intermittency and imbalance between supply and demand, limiting their effective utilization and causing strain on the grid.
Innovation Solution
A two-level control architecture is implemented, comprising a smart enterprise energy management system (SEEMS) and a distribution optimal control system (DOCS), using a constrained sampling based model predictive control method to optimize the allocation of DERs, such as solar PV and energy storage, in real-time to match demand and reduce grid strain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high penetration levels of photovoltaic and distributed energy resources are integrated into electric power grids, then renewable energy utilization is improved, but intermittency and imbalance between supply and demand worsen, causing strain on the grid
Solution Approach 1:
The system performs preliminary actions by forecasting future supply and demand conditions using machine learning models. It predicts renewable energy generation, load demand, and pricing signals in advance, allowing the optimal control system to pre-determine resource allocation strategies that prevent supply-demand imbalances before they occur, thereby maintaining grid stability while maximizing renewable utilization
Solution Approach 2:
The system implements continuous feedback loops where real-time data from distributed energy resources, loads, and grid conditions are monitored and fed back to the optimal control system. This feedback mechanism enables dynamic adjustment of resource allocation to respond to changing supply and demand conditions, resolving the contradiction between high renewable penetration and grid stability
2Productivity
If distributed energy resources are allocated to match demand in real-time, then operational efficiency is improved, but system complexity increases due to the need for advanced control systems
Solution Approach 1:
The control system is segmented into two hierarchical levels: a smart enterprise energy management system (SEEMS) that handles local optimization and a distribution optimal control system (DOCS) that manages broader resource allocation. This segmentation distributes computational complexity across multiple manageable components, enabling real-time operational efficiency without overwhelming system complexity
Solution Approach 2:
The system transforms the complex control problem into a more manageable form by changing parameters through the use of sampling-based model predictive control. By discretizing the control space and using probabilistic methods, the system achieves real-time optimization with reduced computational burden, balancing operational efficiency with acceptable system complexity
3Loss of energy
If constrained sampling based model predictive control method is used to optimize DER allocation, then energy cost reduction is improved, but computational requirements increase
Solution Approach 1:
The system applies partial action by using sampling-based approaches that explore a subset of possible control trajectories rather than exhaustively evaluating all options. This selective sampling achieves sufficient cost optimization without requiring excessive computational power, finding near-optimal solutions efficiently
Solution Approach 2:
The system replaces traditional deterministic optimization methods with probabilistic machine learning models and sampling-based control. This substitution transitions from computationally intensive mechanical optimization algorithms to more efficient statistical methods that achieve comparable or better cost reduction with lower computational requirements
Data Source
AI summary
Devices and methods of allocating distributed energy resources (DERs) to loads connected to a microgrid based on the cost of the DERs are provided. The devices and methods may determine one or more microgrid measurements. The devices and methods may determine one or more real-time electricity prices associated with utility generation sources. The devices and methods may determine one or more forecasts. The devices and methods may determine a cost associated with one or more renewable energy sources within the microgrid. The devices and methods may determine an allocation of the renewable sources to one or more loads in the microgrid.