Local Grid Energy Storage Scheduling for Real-Time Cost Control
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Solution Overview
Problem
Current intelligent energy management systems (EMSs) rely on weather forecasts to predict future energy use, which is not efficient for real-time optimization of energy usage and cost in local electrical grids.
Innovation Solution
The system employs a load distribution controller, coupled with a PID controller and a Viterbi algorithm, to dynamically manage energy usage by optimizing the charging and discharging of energy storage devices based on real-time energy cost and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Force
If weather forecast is used to predict future energy use, then energy management can be performed in advance, but real-time optimization efficiency is insufficient
Solution Approach 1:
The system transitions from static weather forecast-based predictions to dynamic real-time optimization using a PID controller that continuously adjusts energy management decisions based on current grid conditions, cost signals, and actual consumption patterns, enabling both advance planning and real-time responsiveness
Solution Approach 2:
The invention implements feedback mechanisms where the PID controller receives real-time information about energy costs, grid conditions, and consumption patterns, then adjusts charging/discharging schedules dynamically to optimize energy usage efficiency in real-time rather than relying solely on predetermined forecasts
2Reliability
If energy storage device charges at all times, then energy availability is maximized, but energy cost increases
Solution Approach 1:
The system performs preliminary charging actions during off-peak hours when energy costs are lower, using the PID controller to anticipate future energy needs and charge the storage device in advance during economically favorable periods, thereby reducing overall energy costs while maintaining availability
Solution Approach 2:
The invention dynamically changes the charging parameters of the energy storage device based on real-time cost signals and grid conditions, adjusting charge rates and timing to optimize the balance between energy availability and cost efficiency through continuous parameter adaptation
3Productivity
If PID controller with feedback loop is used for real-time optimization, then energy cost optimization improves, but system complexity increases
Solution Approach 1:
The PID controller serves as an intermediary component that simplifies the control architecture by providing a well-established, modular feedback control mechanism that can process multiple inputs (cost signals, grid conditions, consumption patterns) and generate optimized charging/discharging commands without requiring complex custom control logic
Data Source
AI summary
Systems and methods for dynamic management of energy usage in local grids are provided. An example method is performed by a load distribution controller coupled to a power source and an energy storage device configured to accumulate power. The method includes acquiring reference levels of using energy of the power source at time slots within a time period and cost values corresponding to the reference levels, dynamically determining, based on the reference levels and the cost values, a schedule indicating charge levels of the energy storage device at the time slots within the time period, and configuring the energy storage device to charge or discharge the power the power according to the schedule. The reference levels are obtained using the Viterbi algorithm based on costs of the energy of power source at past times slots of a past time period and rates of charging and discharging the energy storage device.