Load Control via Energy Block Sorting
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Solution Overview
Problem
Existing methods for controlling loads with variable power consumption are complex and inefficient, especially when multiple energy sources with different tariff structures and temporal availability are involved, leading to high numerical complexity and increased costs.
Innovation Solution
A method that determines energy blocks based on power and time intervals, allocates costs to these blocks depending on the energy source, and sorts them by cost to optimize energy consumption, allowing for cost-effective operation by prioritizing the lowest-priced energy sources and managing renewable energy fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If graph algorithms (e.g., Dijkstra's algorithm) are used to determine manipulated variables for actuators, then optimal energy operation can be achieved, but numerical complexity increases immensely
Solution Approach 1:
The patent segments the continuous energy optimization problem into discrete energy blocks with specific time intervals and power levels. Each energy block represents a quantized unit of energy consumption, allowing the system to evaluate multiple energy sources and tariff structures through simple comparison rather than complex graph algorithms. This segmentation transforms the optimization problem from a continuous mathematical challenge into a discrete selection process.
Solution Approach 2:
The patent changes the parameters of the optimization problem by introducing quantized energy blocks with discrete power levels and time intervals. Instead of solving for continuous manipulated variables using graph algorithms, the system evaluates pre-defined energy blocks characterized by specific power consumption levels, time durations, and associated costs. This parameter transformation simplifies the computational complexity while maintaining optimization capability.
2Reliability
If multiple auxiliary variables are introduced to map regulatable energy draw, then energy optimization can be achieved, but numerical complexity increases immensely
Solution Approach 1:
The patent extracts the complex optimization calculations from the real-time control process by pre-defining energy blocks with fixed characteristics. Instead of introducing multiple auxiliary variables to represent continuous energy draw, the system extracts energy consumption into discrete blocks that can be directly evaluated and selected based on current tariff structures and energy source availability. This extraction eliminates the need for complex auxiliary variable management.
Solution Approach 2:
The patent transforms the continuous energy draw parameters into discrete block parameters including fixed power levels, time intervals, and associated costs. This parameter change from continuous to discrete variables simplifies the optimization problem, allowing the system to compare and select energy blocks based on simple cost criteria rather than solving complex systems of equations with multiple auxiliary variables.
3Ease of operation
If energy blocks are sorted by cost and summed to reach total energy consumption, then cost-optimized operation is achieved, but computational steps increase
Solution Approach 1:
The patent applies preliminary action by pre-sorting energy blocks according to their cost characteristics before the actual energy consumption occurs. Energy blocks are pre-characterized with power levels, time intervals, and cost values, allowing the system to simply select from the pre-organized list rather than performing complex real-time optimization calculations. This preliminary organization of data significantly reduces computational burden during operation.
Data Source
AI summary
A method for controlling a load by providing a total energy consumption and a time window for the total energy consumption of the load from one or more energy sources is described. The method includes determining of energy blocks, wherein the energy blocks are based on a power interval during a time interval, the time intervals are within the time window and the energy blocks are allocated energy-source-dependent costs, sorting of the energy blocks in ascending order according to the level of the costs per energy block and subsequent summation of quantities of energy that the energy blocks contain in ascending order of sorting until the total energy consumption of the load is reached. From this, on and off times of the load are determined by means of the time intervals that belong to the summed energy blocks, wherein the power intervals belonging to the respective energy blocks determine a power consumption of the load at the respective instant, and the load is actuated in accordance with the on and off times. An apparatus for carrying out the method is likewise disclosed.