Deep RL Solar Energy Scheduling for Generation-Demand Mismatch
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Solution Overview
Problem
The mismatch between solar energy generation during the day and peak power consumption in residential buildings, coupled with inefficient energy management strategies, leads to suboptimal energy consumption and trade.
Innovation Solution
A solar energy management system using deep reinforcement learning to schedule energy storage, discharge, consumption, or sale by processing energy information data through a deep neural network, determining optimal actions based on reward values and penalty adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If solar energy is generated during the daytime when sunlight intensity is strong, then solar energy production is maximized, but it does not coincide with peak power consumption time in the evening
Solution Approach 1:
The system performs preliminary action by storing solar energy in energy storage devices during the daytime when generation exceeds consumption. This allows the energy to be available for later use during peak evening consumption periods, resolving the time mismatch between generation and demand.
Solution Approach 2:
The deep reinforcement learning algorithm continuously monitors real-time data including solar generation amounts, power consumption patterns, energy storage levels, and market prices. This feedback mechanism enables dynamic adjustment of energy management decisions to optimize both immediate and future energy utilization.
2Adaptability or versatility
If solar energy is consumed in residential buildings, then self-sufficiency is improved, but there is no optimal energy consumption and energy trade due to lack of efficient management strategies
Solution Approach 1:
The system implements self-service by enabling residential buildings to autonomously manage their own energy consumption and trading decisions. The deep reinforcement learning algorithm automatically determines optimal actions for consuming, storing, or selling solar energy based on real-time conditions, eliminating the need for complex manual management while maximizing energy self-sufficiency.
Solution Approach 2:
The system dynamically adjusts multiple parameters including energy consumption rates, storage levels, and trading decisions based on changing conditions such as market prices, generation forecasts, and consumption patterns. This allows flexible adaptation to different scenarios while simplifying user operation through automated decision-making.
3Productivity
If deep reinforcement learning is used to optimize solar energy management, then energy efficiency is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex energy management problem into distinct decision components: consumption decisions, storage decisions, and trading decisions. The deep reinforcement learning algorithm processes these segmented decisions separately based on real-time data, improving computational efficiency while maintaining overall optimization performance.
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
Enhances efficient energy management by optimizing solar energy utilization in residential buildings, balancing generation with consumption patterns and market prices, thereby improving energy efficiency and economic outcomes.
Implementation Method 1
an amount of generated solar energy from a photovoltaic system
Data Source
AI summary
Disclosed is a solar energy management method using deep reinforcement learning including receiving, by at least one processor, at least one or more pieces of energy information data for deep reinforcement learning of a deep neural network, calculating, by the at least one processor, a reward value for controlling a solar energy management algorithm constituting the deep neural network using the at least one or more pieces of energy information data and performing the deep reinforcement learning, and determining, by the at least one processor, a schedule for storing, discharging, consuming or selling solar energy by inputting the at least one or more pieces of energy information data as input data of the solar energy management algorithm.