Power Management System Optimizing Grid Imports
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Solution Overview
Problem
Managing utility costs is complex due to variable electricity pricing based on demand, with challenges in minimizing grid power usage under multi-component time-of-use tariff schedules and integrating off-grid renewable resources effectively.
Innovation Solution
A computer-based system predicts load and off-grid power supply over a prospective time period, using a cost function to minimize grid power imports by considering different tariff rates, battery state, and historical data, and stores energy in rechargeable batteries for optimal consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If off-grid renewable resources are used to offset grid energy costs, then energy costs can be reduced, but the complexity of managing variable load demand and multi-component tariff schedules increases
Solution Approach 1:
The system continuously monitors grid pricing signals, load demand, and off-grid resource availability, using this feedback to dynamically adjust power import/export decisions. The cost function incorporates real-time pricing data and historical patterns to optimize energy management strategies under varying tariff conditions.
Solution Approach 2:
The system changes operational parameters based on pricing signals and resource availability. It adjusts the rate of power import from the grid, modifies charging/discharging schedules of energy storage systems, and varies the utilization of off-grid resources according to time-of-use tariff rates and real-time pricing signals.
2Loss of energy
If constant rate power consumption is maintained to minimize load charges, then energy costs are reduced, but the ability to meet variable load demand becomes difficult
Solution Approach 1:
The system performs preliminary actions by charging energy storage systems during low-cost periods before peak demand occurs. It proactively stores energy when pricing signals indicate low rates or when off-grid resources are abundant, ensuring power availability during high-demand periods without incurring high load charges.
Solution Approach 2:
The system dynamically adjusts power consumption rates based on real-time pricing signals and load conditions. Rather than maintaining a fixed constant rate, it modulates the discharge rate of energy storage systems and the import rate from the grid to balance load charge minimization with meeting actual demand requirements.
3Loss of energy
If power is imported during low-cost periods to meet peak demand, then energy costs are reduced, but the complexity of predicting load and off-grid supply increases
Solution Approach 1:
The system performs preliminary actions by predicting future load demand and off-grid resource supply to determine optimal power import schedules. It uses historical data and forecasting models to anticipate when power should be imported at low costs, storing energy before peak demand periods occur.
Solution Approach 2:
The system uses historical load patterns and off-grid supply data as copies of future behavior to make predictions. By analyzing past performance under similar conditions, it infers future load requirements and resource availability, reducing the complexity of direct real-time prediction.
4Measurement precision
If real-time pricing models are implemented with instantaneous load and energy charges, then pricing accuracy is improved, but the complexity of minimizing costs increases
Solution Approach 1:
The system uses real-time pricing signals as feedback to continuously optimize power management decisions. It incorporates instantaneous load charges and energy charges into its cost function, adjusting import/export schedules based on current pricing conditions while considering the impact on both load and energy charge components.
Solution Approach 2:
The system segments the cost minimization problem into separate load charge and energy charge components. By treating these as distinct optimization targets with different weighting factors, it simplifies the overall complexity of responding to real-time pricing models that include both instantaneous load and energy charges.
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
This approach effectively reduces utility costs by optimizing energy storage and release based on real-time pricing, minimizing grid power imports during high-demand periods and utilizing off-grid resources efficiently.
Implementation Method 1
storing electrical power received from a power grid and storing the power in a large-scale rechargeable battery
Implementation Method 2
off-grid sources of electrical power, such as wind turbines, solar panels, and hydroelectric generators
Implementation Method 3
off-grid sources of electrical power, such as wind turbines, solar panels, and hydroelectric generators
Implementation Method 4
off-grid sources of electrical power, such as wind turbines, solar panels, and hydroelectric generators
Data Source
AI summary
A method includes: calculating a load prediction arising from an power consumption entity over a prospective time period; calculating a predicted off-grid power supply from an off-grid power supply; minimizing, for a respective time interval, a calculated amount of electrical-power to import from a power grid, in accordance with output from a cost function; and, for at least one interval, importing, from the power grid, the corresponding calculated amount of electrical-power corresponding to the at least one interval. The cost function uses: (i) a multi-component time of use tariff schedule associated with the power grid that includes a first power rate during a prospective time period and second power rate during a second time period, (ii) the load prediction over the prospective time period, (iii) the predicted off-grid electrical-power supply over the prospective time period, (iv) a state of a rechargeable battery.