Microgrid Predictive Planning via Discrete-Time Optimization
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Solution Overview
Problem
Current predictive operational planning in microgrids connected to primary grids lacks efficient integration of power exchange strategies, which can lead to instability and increased operational costs due to unoptimized power management across different states.
Innovation Solution
A discrete-time optimization method is implemented for predictive operational planning, classifying time intervals into states such as standby, power exchange requested, and active power exchange, allowing for adaptive target functions and constraints to manage power reserves and exchanges between microgrids and primary grids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power exchange between microgrid and primary grid is integrated into predictive operational planning, then stability and efficiency of power management is improved, but device complexity and computational requirements increase
Solution Approach 1:
The planning interval is segmented into multiple time intervals, each classified into different states (standby, power exchange requested, active power exchange). This segmentation allows the optimization to handle complex power exchange scenarios by breaking them down into manageable state-specific sub-problems, improving reliability without overwhelming system complexity.
Solution Approach 2:
The target function and constraints are dynamically determined based on the classified states of time intervals. The system adapts its optimization parameters in real-time according to the current operational state, enabling stable power management across varying conditions while maintaining computational tractability through state-based simplification.
2Loss of energy
If discrete-time optimization with multiple states is implemented, then operational costs are reduced through optimized power exchanges, but computational time and processing requirements increase
Solution Approach 1:
Time intervals are pre-classified into states before optimization execution. This preliminary classification organizes the computational workload in advance, allowing the optimization algorithm to efficiently process each state type with appropriate constraints and target functions, reducing operational costs while managing computational time through structured preprocessing.
Solution Approach 2:
The optimization approach changes parameters (target function and constraints) based on the classified states. By adapting parameters to match specific operational states rather than using a single fixed optimization model, the system achieves cost reduction through state-specific optimization while avoiding the computational burden of a monolithic complex optimization problem.
3Adaptability or versatility
If time intervals are classified into multiple states for optimization, then flexibility in responding to peak demands and generation fluctuations is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the operational timeline into distinct states (standby, power exchange requested, active power exchange) to capture different operational scenarios. This segmentation provides flexibility in responding to varying demand and generation conditions while maintaining manageable system complexity through clear state definitions and transitions.
Solution Approach 2:
The state-based classification framework serves multiple functions: it enables flexible adaptation to different operational conditions, structures the optimization problem, and provides a unified approach for handling various power exchange scenarios. This multi-functionality achieves versatility without proportionally increasing complexity, as the same state classification mechanism serves multiple purposes in the power management system.
Data Source
AI summary
Techniques for operational planning in a microgrid by means of optimization are provided. This involves a power exchange with a primary grid being taken into consideration. The operational planning in this case is performed by using an optimization, e.g. a (mixed) integer linear optimization. The various examples describe how a power exchange (balancing) can be provided for a primary grid to which the microgrid is connected. In particular, this is achieved by taking into consideration a distinction for the constraint of the optimization. In this manner, a lead time between a request for the power exchange and the actual activating of the power exchange can be taken into consideration.