Power Controller Mixed Integer Programming for Building Energy Scheduling
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Solution Overview
Problem
Determining an optimum charging-discharging schedule for power storage devices in buildings that consider various dynamic factors such as power consumption, generation by solar power generators, and unit electricity prices is challenging, making it difficult to minimize electric bills and carbon dioxide emissions.
Innovation Solution
A power supply system that includes a solar power generator, a power storage device, and a power controller, which calculates predicted consumption and generation data using historical usage and weather forecasts to formulate a mixed integer programming problem for determining an optimal charging-discharging schedule that minimizes the evaluation index, such as the power price, to control the charging and discharging of the power storage device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If an optimum charging-discharging schedule is determined by taking into account various dynamic factors (power consumption, solar generation, electricity prices), then electric bill and carbon dioxide emissions are minimized, but the complexity of the control system increases significantly
Solution Approach 1:
The power controller calculates predicted consumption data based on usage history and predicted generation data based on weather forecasts before determining the charging-discharging schedule. This preliminary calculation of future states enables the system to plan ahead and minimize emissions without requiring complex real-time adjustments during operation
Solution Approach 2:
The patent introduces a mixed integer programming formulation as an intermediary mathematical model that bridges the gap between multiple dynamic factors (power consumption, solar generation, electricity prices) and the charging-discharging control decisions. This mathematical framework systematically integrates various constraints and objectives, transforming a complex multi-factor optimization problem into a solvable structured problem that minimizes emissions while managing system complexity
2Loss of energy
If various dynamic factors (power consumption, solar generation, electricity prices, battery state) are considered in the charging-discharging schedule, then the electric bill is minimized, but the difficulty of determining the schedule increases
Solution Approach 1:
The system performs preliminary calculations of predicted consumption data using usage history and predicted generation data using weather forecasts before schedule determination. This advance preparation of data reduces the computational burden during schedule optimization and makes the determination process more manageable
Solution Approach 2:
The mixed integer programming formulation serves as a mathematical intermediary that systematically integrates multiple dynamic factors (power consumption, solar generation, electricity prices, battery state constraints) into a unified optimization framework. This structured approach transforms the difficult multi-factor scheduling problem into a standardized mathematical problem that can be solved efficiently
3Loss of energy
If the charging-discharging schedule is optimized using mixed integer programming with predicted consumption and generation data, then the evaluation index (power price) is minimized, but the computational complexity increases
Solution Approach 1:
The power controller calculates predicted consumption data based on usage history and predicted generation data based on weather forecasts before formulating the optimization problem. This preliminary data preparation reduces the complexity of the actual optimization calculation by providing pre-processed input data, allowing the mixed integer programming to focus on finding the optimal schedule rather than processing raw data
Solution Approach 2:
The mixed integer programming formulation acts as a mathematical intermediary that structures the optimization problem in a systematic way, separating the computational tasks into data preparation (prediction) and optimization (schedule determination). This structured approach makes the computational process more manageable and efficient
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
The system effectively optimizes the charging and discharging of power storage devices, reducing electric bills and carbon dioxide emissions by aligning energy usage with cheaper power rates and maximizing the use of solar-generated power, thereby minimizing the net power purchase from the grid.
Implementation Method 1
a solar power generator (16) which generates solar power from sunlight
Data Source
AI summary
A power supply system for supplying a grid power to a building includes a power generator, a power storing device, and a power controller. The power generator generates off-grid power from a predetermined energy. The power storing device stores the grid power and the off-grid power and supplies the stored power to the building. The power controller controls consumptions of the grid power and the off-grid power. The power controller calculates predicted consumption data related to power consumed in the building and predicted generation data related to power generated by the power generator. The power controller calculates a charging-discharging schedule for the power storing device based on the predicted consumption data and the predicted generation data by formulating the charging-discharging schedule as a mixed integer programming problem.