BESS DRL Controller for 5G Base Station Energy Management
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Solution Overview
Problem
The high energy consumption of 5G base stations poses significant resource and cost challenges for mobile operators, with existing solutions failing to effectively manage energy expenditure and reduce reliance on fossil fuels.
Innovation Solution
A battery energy storage system (BESS) integrated with renewable energy sources, utilizing deep reinforcement learning (DRL) for strategic battery charging and discharging to maximize renewable energy utilization and minimize grid power usage, thereby reducing energy costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 5G base stations are deployed to provide higher bandwidth and lower latency, then network performance is improved, but energy consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by charging the battery during periods of low power demand and high renewable energy availability. The DRL agent learns optimal charging strategies in advance to prepare energy reserves before peak demand periods, thereby reducing the need for grid power during high-performance operations.
Solution Approach 2:
The system changes operational parameters by dynamically adjusting battery charge/discharge rates based on real-time conditions. The DRL agent modifies charging power, discharging power, and grid power acquisition parameters to optimize the balance between network performance requirements and energy consumption.
2Loss of energy
If renewable energy generators are installed to reduce energy costs, then fossil fuel utilization is reduced, but energy supply stability deteriorates due to intermittency
Solution Approach 1:
The battery energy storage system serves as an intermediary between renewable energy generators and the 5G base station. It buffers the intermittent renewable energy supply, storing excess energy when available and releasing it when renewable generation is insufficient, thereby maintaining stable power supply and reducing reliance on fossil fuels.
Solution Approach 2:
The system performs preliminary energy storage by charging the battery during periods of high renewable energy generation. This advance preparation ensures that energy reserves are available to compensate for future intermittency, maintaining supply stability without increasing fossil fuel usage.
3Loss of energy
If battery energy storage system is used to store renewable energy, then renewable energy utilization is maximized, but system complexity increases
Solution Approach 1:
The DRL-based control system enables the BESS to self-manage its charging and discharging operations autonomously. The agent learns optimal strategies through interaction with the environment and makes real-time decisions without requiring complex external control systems, thereby maximizing renewable energy utilization while keeping control architecture relatively simple.
Solution Approach 2:
The system simplifies complexity management by dynamically adjusting key operational parameters such as charging power, discharging power, and grid power acquisition. The DRL agent optimizes these parameters based on learned patterns, replacing complex rule-based control systems with a more adaptable and potentially simpler learning-based approach.
4Loss of energy
If deep reinforcement learning is applied to control battery operations, then energy cost is reduced, but computational complexity increases
Solution Approach 1:
The DRL system implements continuous feedback loops where the agent observes system states (battery charge level, renewable energy availability, power demand, grid prices), takes actions (charging, discharging, grid power acquisition), and receives rewards (energy cost savings). This feedback mechanism enables the system to learn optimal control strategies that reduce energy costs while managing computational complexity through experience-based learning rather than exhaustive optimization.
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 proposed solution achieves a monthly saving of up to $50.7 per base station (with a 74.8% saving ratio) by efficiently managing energy demand and supply, leveraging intermittent renewable energy sources and optimizing battery operations.
Implementation Method 1
a battery storage configured to store surplus renewable energy
Implementation Method 2
renewable energy generators... photovoltaic (PV) modules
Data Source
AI summary
A battery energy storage system (BESS) including: a battery storage configured to store surplus renewable energy; a controller coupled to the battery storage and configured to control charging operations and discharging operations of the battery storage; a generation meter coupled to the controller and configured to measure renewable energy; a renewable energy generator coupled to the generation meter and configured to generate renewable energy; and a standard meter coupled to the controller and configured to measure energy provided by a power grid; wherein the controller is configured to manage energy expenditure of the BESS according to the following steps: initializing a replay buffer configured to store state transition samples; initializing a main net configured to generate a current Q-value; initializing a target net configured to generate a target Q-value; obtaining an environment state of the BESS; and selecting an action based on an ϵ-greedy policy.