Energy Storage Dispatch for Online Peak-Demand Reduction
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Solution Overview
Problem
Large-load consumers face significant challenges in reducing peak-demand charges, which account for a large portion of their electricity bills, due to the volatility of small-scale demands and self-owned renewable generations.
Innovation Solution
An optimal online algorithm is developed to maximize peak-demand reduction using energy storage, achieving the best possible competitive ratio among all online algorithms by solving a linear number of linear-fractional problems and extending to an adaptive algorithm for improved average-case performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If time-of-use pricing strategies are used to promote load shifting to off-peak hours, then off-peak energy consumption increases, but rebound peaks occur that increase peak-demand charges
Solution Approach 1:
The system performs preliminary actions by charging energy storage devices during off-peak hours before the peak-demand period arrives. This pre-positioning of energy allows the system to discharge during peak periods, preventing rebound peaks while maintaining off-peak consumption benefits.
Solution Approach 2:
Energy storage devices serve as an intermediary between the grid and the consumer's load. They buffer the timing mismatch between off-peak charging and peak-period discharge, enabling load shifting without creating rebound peaks on the grid.
2Power
If energy storage is used to reduce peak-demand charge, then peak-demand reduction is achieved, but future demand volatility and uncertainty make optimal decisions difficult
Solution Approach 1:
The system performs preliminary charging actions during off-peak hours based on current information, preparing energy storage devices for potential peak-period discharge. This allows the system to act proactively without knowing future demand patterns.
Solution Approach 2:
The energy storage system serves itself by automatically charging during off-peak periods and discharging during peak periods based on price signals, without requiring perfect future information or complex external control.
3Use of energy by moving object
If self-owned renewable generations are used to reduce energy purchased from grid, then green energy consumption increases, but net demand curve becomes more fluctuating and peak-demand charge is not reduced
Solution Approach 1:
Energy storage devices act as an intermediary that smooths the fluctuating output from renewable generators. By charging during high renewable generation periods and discharging during low generation or high demand periods, they stabilize the net demand curve while maintaining renewable energy consumption benefits.
Solution Approach 2:
The system provides beforehand cushioning by storing excess renewable energy when generation is high, creating a buffer that compensates for periods when renewable generation is low or demand is high, thus stabilizing the net demand curve.
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 algorithm effectively reduces peak-demand charges by strategically utilizing energy storage, achieving peak reductions comparable to optimal offline solutions with perfect information, while outperforming other alternatives.
Implementation Method 1
an electricity storage device to reduce the use of a mains electricity grid during peak cost charging periods
Data Source
AI summary
A method of managing real-time electrical energy storage management from an electricity storage device to maximise the reduction of the use of a mains electricity grid during peak cost charging periods on the mains electricity grid, including: calculating an ideal offline use of the electricity storage device using ideal predicted parameters; calculate a plurality of electrical storage device storage and electrical discharge models using a plurality of algorithms based on recorded data; calculate the competitive ratio for each of the algorithms and the ideal offline use; and use power from the electricity storage device based on an optimal competitive ratio.