Energy Storage System Scheduling via Historical Price Data
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Solution Overview
Problem
Existing energy storage systems lack an efficient method to optimize charging, discharging, and idling operations based on historical data, particularly price fluctuations, leading to suboptimal power distribution and increased operational costs in electrical networks.
Innovation Solution
An energy storage system comprising an energy storage device, a bidirectional inverter, a rectifier, and controllers that determine a schedule for charging, discharging, or idle states based on historical data, including price data, to maximize energy discharge during peak price periods and minimize genset runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy storage systems operate without optimization based on historical data, then operational simplicity is maintained, but operational costs increase and energy distribution efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of historical price data and demand patterns to pre-determine optimal charging and discharging schedules. The controller stores historical data and pre-calculates when to charge or discharge based on anticipated price fluctuations, enabling proactive rather than reactive energy management decisions.
Solution Approach 2:
The energy storage system autonomously monitors its own charge level, compares current conditions against historical data, and automatically adjusts its charging/discharging operations without external intervention. The controller independently identifies peak price periods and executes discharge commands to maximize revenue.
2Loss of energy
If energy storage systems discharge during all high demand periods, then revenue from peak pricing is maximized, but energy storage depletes too quickly and cannot serve future peak periods
Solution Approach 1:
The system selectively discharges during only the most critical peak price periods rather than all high demand periods. By analyzing historical price data, the controller identifies specific time windows with the highest price differentials and concentrates discharge capacity on those periods, leaving sufficient charge for future opportunities.
Solution Approach 2:
The controller dynamically adjusts discharge timing and duration based on varying price signals and charge level parameters. When charge levels are low, the system extends discharge into lower-price periods or skips less critical peaks. When charge levels are high, it can afford to discharge more aggressively during peak periods.
3Reliability
If genset runtime is extended to meet all demand, then power supply reliability is maintained, but fuel consumption and operational costs increase
Solution Approach 1:
The energy storage system acts as an intermediary between the genset and the electrical load. During peak demand periods, the storage system discharges to meet load requirements, allowing the genset to operate at lower, more efficient levels or remain offline, thereby reducing fuel consumption while maintaining power supply reliability.
Data Source
AI summary
Systems and methods for controlling power flow to and from an energy storage system are provided. One implementation relates to an energy storage system comprising an energy storage device, an inverter configured to control a flow of power out of the energy storage device, a rectifier configured to control the flow of power into the energy storage device and one or more controllers. The one or more controllers may be configured to determine a schedule of a plurality of time periods based on historical price data. Each of the plurality of time periods may be associated with one of a state of charging, discharging, or idle. The one or more controllers may be configured to control the inverter and the rectifier based on the determined schedule.