Real-Time Energy Storage Control for Microgrid Cost Reduction
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Solution Overview
Problem
Conventional energy storage unit control systems in microgrids lack real-time adaptability to changes in power generation and demand, leading to inefficient battery operation and increased operational costs due to irregular charge and discharge patterns and lack of knowledge about real-time energy costs.
Innovation Solution
A method for controlling energy storage units that tracks each charging event, updates unit energy prices, compares these prices with other generation sources and grid tariffs, and selects the lowest cost combination to supply load, enabling real-time decision-making for optimal energy usage and cost reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If passive control methods (peak-shaving or schedule-based control) are used for energy storage units, then the control system is simple to implement, but the system cannot adapt to real-time changes in generation and demand levels, resulting in suboptimal operational cost
Solution Approach 1:
The control system transitions from static passive control to dynamic real-time control by continuously monitoring generation levels, demand levels, and operational costs, and adjusting battery charge/discharge decisions adaptively based on current system conditions
Solution Approach 2:
The system implements feedback mechanisms by calculating unit energy prices from all generation sources in real-time, comparing these prices, and using this information to make informed decisions about when to charge or discharge the battery, thereby optimizing operational cost
2Device complexity
If the battery operates without proper control to balance supply and demand, then the control system is simple, but the battery experiences irregular charge and discharge patterns, negatively impacting battery lifetime and increasing operational cost
Solution Approach 1:
The control system automatically manages battery charge and discharge operations based on real-time system conditions, eliminating the need for manual intervention while protecting battery health through intelligent decision-making about when to charge or discharge
Solution Approach 2:
The system proactively calculates unit energy prices and identifies optimal charge/discharge opportunities before they occur, allowing the battery to be charged when energy is cheapest and discharged when it provides maximum value, thereby optimizing both lifetime and operational cost
3Loss of energy
If real-time tracking of charging events and unit energy prices is implemented, then the operational cost is minimized through optimal battery control, but the system complexity increases
Solution Approach 1:
The system introduces an energy management controller as an intermediary that centralizes the calculation of unit energy prices from multiple generation sources and coordinates battery charge/discharge decisions, simplifying the overall system architecture while enabling complex optimization
Data Source
AI summary
A method for controlling an energy storage unit having a model which tracks each charging instances includes, for each charging event, capturing energy charged into the energy storage unit and an unit energy price of the charging source(s) at the time of charging; updating a unit price of total energy stored in the storage unit at each time-step; during a discharging event, updating the unit price of energy in the storage unit on a selected cost model; comparing the unit price of energy in the storage unit with other generation sources in the microgrid and a utility tariff; selecting a lowest cost combination from generation, storage, and grid and using the lowest cost combination to supply a load; and if a controller decides to use stored energy at any time, sending a discharge command to the energy storage unit.