Hybrid Power Dispatch Optimization With Maintenance and Degradation Costs
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Solution Overview
Problem
Hybrid power systems face complexity in managing multiple types of power assets, leading to sub-optimal operation and high computational costs due to the difficulty in accounting for type-specific aspects like maintenance, degradation, and varying energy costs.
Innovation Solution
A controller-based system that performs both prospective and on-line optimizations, accounting for asset degradation, maintenance costs, and energy costs, using cost functions and constraints to determine active power commands for each power asset group, including gensets, energy storage, and photovoltaic systems, to optimize total operating costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional optimization techniques are applied to hybrid power systems, then operational costs may be reduced, but computational complexity increases significantly
Solution Approach 1:
The optimization problem is segmented into two distinct parts: (1) a prospective optimization that determines scheduled active power commands considering long-term asset degradation and maintenance costs, and (2) an on-line optimization that determines real-time active power commands based on current system state. This segmentation allows each optimization to focus on specific time horizons and computational requirements, reducing overall complexity while maintaining comprehensive optimization.
Solution Approach 2:
The prospective optimization is performed in advance to determine scheduled active power commands that account for asset degradation, maintenance costs, and energy costs over a future horizon. These pre-computed commands are then used as inputs for the on-line optimization, eliminating the need to recalculate all optimization parameters in real-time and significantly reducing computational burden during operation.
2Ease of operation
If rule-based algorithms are used for power distribution decisions, then implementation is simple, but optimization performance deteriorates due to inability to handle edge cases
Solution Approach 1:
The system transitions from fixed rule-based parameters to dynamic optimization parameters that adapt to changing system conditions. The cost function includes variable parameters such as asset degradation rates, maintenance cost coefficients, and energy prices that are updated based on current system state, allowing the optimization to handle edge cases and varying operating conditions effectively.
3Power
If the number of power assets increases to improve system capacity, then power supply capability improves, but management complexity increases rapidly
Solution Approach 1:
The optimization framework uses a universal cost function and constraint structure that can accommodate any number and type of power assets (gensets, energy storage systems, photovoltaic systems, power grid connections). Each asset type is modeled with its own parameters within the same mathematical framework, allowing the system to scale without requiring separate management approaches for different asset quantities.
Data Source
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AI summary
Systems and methods for operating a hybrid power system (100) are disclosed. A controller (135) may perform operations, including: obtaining load data for the hybrid power system (100); obtaining power availability data and energy cost data for each power asset in each power asset group of a plurality of power asset groups (115); and determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost, wherein: the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and operating each power asset based on the determined active power commands.