Reinforcement Learning Improves Smart Grid Management

Smart technology systems have improved legacy grids by optimizing operations and using higher degrees of regulation and advanced system monitoring. The various aspects—central system operator, transmission and distribution networks, electricity production plants, and advanced metering infrastructure—also integrate renewable energy sources. 

Managing rooftop solar panels and user-side batteries is not an easy task. Advanced metering infrastructure, which uses advanced control techniques, provides two-way communication between the smart meter and system operator to periodically measure electricity consumption so home batteries can be better managed. 

Reinforcement learning is a key control technique. Researchers at SUNY Polytechnic Institute have studied how reinforcement learning algorithms can better manage energy among distributed renewables and battery systems that give and receive power from the grid.

Smart grid depiction. Image used courtesy of  Adobe Stock

Smart Grid Energy Management

Smart grid energy management involves controlling energy consumption, generation, and distribution while maintaining grid reliability, energy costs, and supply. Introducing distributed renewables makes management more complex because they add variability to the grid.

Energy management strategies must optimize energy usage with supply and demand, grid stability, and energy costs. Optimization, control, and machine learning algorithms are often employed in smart grids to manage these various parameters. In-home battery chargers add challenges, such as avoiding battery depletion and fairly distributing energy among multiple users.

Using Reinforcement Learning Models

Reinforcement learning models are an effective energy management approach because they learn without needing a pre-defined model and can navigate the complex factors of energy management,

Reinforcement models use an agent and an environment with continuous interaction over time. At defined periods, the algorithm’s agent takes an action—such as checking the state of charge of the user’s batteries—and sends it to the environment. The agent then receives a reward or a penalty from the environment based on how good or bad the decision was for a given state or scenario. Reinforcement models aim to maximize rewards by self-learning a policy where the best decisions for different states are mapped and implemented.

The agent (actor) learns by receiving feedback based on its actions in an environment. Image used courtesy of Elshazly et al.

SUNY researchers used reinforcement learning to improve home charging efficiency. Renewable energy users’ batteries were optimized to balance energy movement between the batteries and the grid and to ensure all users had access to stored power and a fair distribution of power. In the study, reinforcement learning could interact with multiple battery systems simultaneously to learn and implement optimal charging operations to keep the grid stable during these operations. 

Rewards and Penalties of Reinforcement Models

The algorithms’ responses received rewards and penalties based on their actions to reach optimal smart grid function. Fixed penalties occur when the battery’s critical state of charge (SoC) is below a defined threshold. This aims to avoid situations where batteries become critically low. When the SoC exceeds another threshold, fixed rewards can ensure the batteries are not overcharged.

Other rewards enable the many parameters to be balanced. For example, grid rewards increase the maximum power allocated for charging to prevent exceeding the maximum power.

Reinforcement Learning and Efficient Charging Coordination

Reinforcement learning models can efficiently navigate and coordinate the parameters to ensure enhanced grid stability, provide evenly distributed energy, and balance power saving.

Smart grid system. Image used courtesy of Elshazly et al.

In grid stability, reinforcement learning prevents grid overload by optimizing charging schedules so excessive power demands from user batteries in the network are avoided, creating a more robust and reliable grid. This is achieved by adopting an adaptive power allocation approach to monitor the individual batteries’ charge levels to allocate power based on real-time grid constraints.

For evenly distributing energy, reinforcement learning algorithms can make decisions to ensure that all users receive a fair energy distribution to avoid significant disparities among the SoC of all the home batteries in the local grid network. This also ensures that users have enough energy in their batteries and that these levels are maintained above a specific threshold, avoiding battery depletion.

How Reinforced Learning Differs From Other Optimization Techniques

Many optimization techniques rely on fixed parameters and objective functions to solve energy problems. Reinforcement learning is a dynamic approach that continuously learns through a continued interaction with its environment. This enables reinforcement learning algorithms to adjust their policies based on the received rewards and penalties and enables the control algorithm to continuously adapt to ever-changing energy landscapes.