Powering the Future: Choosing the Right AI for Virtual Power Plants

A. Introduction

The power industry is experiencing a major shift, with the rise of distributed energy sources (DERs). A key future player in this transformation is the virtual power plant (VPP). This network combines DERs, including solar, batteries, electric vehicles, and heat pumps, to act like a single large power plant. It will also integrate residential, commercial & industrial loads over time. VPPs are becoming essential for managing modern energy systems and deep decarbonization.

While VPP adoption has grown steadily over the past decade, it remains below its potential. This can be attributed to the novelty of the concept, regulation, size, and the complexity of managing a vast portfolio of different technologies and individual assets. As VPPs become more established, exploring the use of artificial intelligence (AI) for optimization could be a game-changer.

Why consider advanced optimization techniques for VPPs?

Managing the diverse assets and loads of a VPP in real-time poses significant challenges and there are several factors supporting the usage of AI:

1. Resource Coordination and Aggregation: DER resources need to be efficiently coordinated and aggregated to maximize their collective impact. Optimization algorithms?ensure that DERs work together seamlessly, responding to grid conditions and energy demand in real time.
2. Energy Management: VPPs must balance energy supply and demand across diverse resources. This involves optimizing the dispatch of energy from various DERs. Advanced techniques consider factors like resource availability, load profiles, and grid constraints to minimize costs and enhance reliability.
3. Market Participation: VPPs participate in electricity markets, selling surplus energy or providing ancillary services. Effective participation requires strategic decision-making. Optimization models help VPPs determine optimal bidding strategies, considering market prices, demand fluctuations, and regulatory requirements.
4. Resource Uncertainty: DERs are inherently uncertain due to factors like weather conditions, EV charging patterns, and battery degradation. Advanced optimization accounts for this uncertainty, adapting resource schedules dynamically.
5. Control and Communication Challenges: Managing a diverse set of DERs involves control and communication complexities. Optimization techniques address issues related to data exchange, latency, and synchronization.

In summary, advanced optimization techniques empower VPPs to operate efficiently, adapt to changing conditions, and contribute to a more sustainable energy future.

B. AI approaches for Virtual Power Plants

Which AI approach is relevant for VPPs? A short overview of the key techniques at hand:

1. Supervised learning?involves training a model using labeled data, where each input is associated with a correct output. In supervised learning, the model learns to map inputs to predefined outputs based on the provided labels. One example for using supervised learning in a VPP is power flow optimization.[1]
2. Unsupervised learning?deals with unlabeled data, aiming to discover patterns or structures within the data. Clustering and dimensionality reduction are common tasks in unsupervised learning. Unlike supervised learning, there are no explicit output labels to guide the learning process. One example is again power flow optimization.[2]
3. Deep learning uses neural networks with multiple layers (deep architectures). It excels at tasks like image recognition, natural language processing, and speech synthesis. Deep learning models learn hierarchical representations from raw data and typically are trained using large amounts of labeled or unlabeled data.
4. Reinforcement Learning is an agent-based approach and is distinct for VPPs in several ways:Sequential Decision-Making: Reinforcement learning involves making a series of decisions over time, where each action affects subsequent states.Trial and Error: Instead of relying on labeled data, so called agents learn through trial and error. They explore the environment, receive rewards or penalties, and adjust their behavior accordingly. Data is not part of the input but collected through trial and error. Through trial and error, the agent interacts with the VPP environment by making decisions (e.g. dispatching power, charging batteries). It observes the resulting rewards and learns to choose actions that maximize the long-term reward.Maximizing Cumulative Reward: The goal is to learn optimal behavior to maximize cumulative rewards over time.Interactivity: Unlike supervised learning, reinforcement learning interacts with the environment, gathering data as it goes.Reinforcement Learning is an iterative cycle of exploration, feedback, and improvement. In summary, while supervised and unsupervised learning operate on existing datasets, Reinforcement Learning actively explores and learns from its environment, making it well-suited for dynamic decision-making and complex control tasks such as VPPs and their optimization.[3] [4] [5]

C. Reinforcement Learning for Virtual Power Plants

Let’s have a brief look how a Reinforcement Learning system is structured. This helps to understand better the way it can work in a VPP context.

There are two main characters in Reinforcement Learning: the?agent?and the environment. The?agent?is the decision-maker or learner. It interacts with the environment. The?environment?encompasses everything outside the agent that the agent interacts with.

The VPP environment consists of the DERs connected to it, such as solar panels, wind turbines, and battery storage units. These provide data on their current power generation, storage levels, and operational constraints. The agent resides within the VPP control system. It receives data from the environment about the current state (e.g. total power generation, demand forecast, electricity prices) and decides on actions to optimize the performance.

There are additional components within the system and some of them are as follows:

Reward: The environment provides immediate feedback to the agent in the form of rewards or penalties based on its actions. The reward function is designed to incentivize the agent to make decisions that align with the VPP goals. These goals can include: (1) Maximizing profit: Selling electricity to the grid at peak prices and buying when prices are low. (2) Maintaining grid stability: Responding to fluctuations in demand by adjusting power output from DERs. (3) Minimizing operational costs: Optimizing energy use within the VPP to reduce reliance on expensive sources.

Based on the chosen reward function, the agent receives positive rewards for actions that achieve these goals and negative rewards for actions that don't.

State: A state represents the complete description of the world at a given moment. It includes all relevant information.

Action: Actions are the choices the agent can make. They influence the environment.

Return: The cumulative reward over time, which the agent aims to maximize. The agent’s objective is to learn behaviors that maximize its cumulative reward. Reinforcement Learning formalizes the idea that rewarding or punishing an agent shapes its future behavior.

Policy: Defines how the agent behaves at a given time.

Reinforcement Learning in VPPs uses several mathematical principles:

1. Optimization Theory: VPPs aim to optimize the use of various energy resources (e.g., solar, wind, battery storage) to meet electricity demand efficiently. Optimization techniques such as linear programming, nonlinear optimization, and mixed-integer programming are crucial for formulating and solving the optimization problems encountered in VPPs.
2. Stochastic Processes: VPPs often operate in uncertain environments due to the variability of renewable energy sources (e.g., solar irradiance, wind speed). Understanding stochastic processes, including time series analysis, probabilistic forecasting, and Monte Carlo simulation, is essential for modeling and predicting the behavior of these energy resources.
3. Power Systems Analysis: Knowledge of power systems engineering principles is essential for understanding the interactions between different components in VPPs, such as generators, transformers, and transmission lines. Concepts such as power flow analysis, voltage stability, and frequency control are relevant for designing and operating VPPs efficiently.
4. Markov Decision Processes (MDPs): MDPs provide a mathematical framework for modeling the sequential decision-making process in VPPs. States represent the current system state (e.g. energy demand, energy prices), actions represent the control decisions (e.g. dispatching resources), and rewards represent the system objectives (e.g. profit, reliability). Algorithms can be applied to learn optimal control policies for VPP operation based on MDP formulations.[6]

D. Improvements for VPPs from Reinforcement Learning & Examples

Applying these techniques to a VPP can unlock significant improvements across different functions:

1. Dynamic Optimization and Real-Time Decision Making:

VPPs operate in a constantly changing environment with fluctuating energy prices, weather patterns, and consumer demand. Traditional rule-based systems struggle to adapt effectively. Reinforcement Learning excels in such scenarios. The VPP agent can continuously learn from real-time data and interactions on energy generation, grid conditions, and market prices.

Based on rewards (e.g. maximizing profit, minimizing grid imbalance) and penalties, the agent can dynamically adjust VPP operations:

• Optimizing energy generation: The agent can learn to strategically charge and discharge batteries to store excess renewable energy for peak demand periods, maximizing profit by selling electricity when prices are high.
• Participating in demand response programs: The agent can learn to adjust energy consumption of participating homes and businesses based on real-time grid pricing signals, reducing peak demand charges, and contributing to grid stability.
• Assists in strategic bidding and market participation.
• Learn optimal bidding strategies, maximizing revenue while adhering to regulations.

2. Improved Forecasting and Proactive Management:

Forecasting energy generation from renewable sources is crucial for VPPs. Traditional methods rely on historical data and weather patterns, which can be inaccurate. Reinforcement Learning can integrate historical data with real-time weather updates and continuously learn from past forecasting errors. This allows for more accurate predictions, enabling the VPP to proactively manage resources and make better decisions.

3. Advanced Asset Management and Maintenance Scheduling:

VPPs rely on a mix of DERs and maintaining these assets is vital for optimal performance. Reinforcement Learning can analyze sensor data from DERs to predict potential equipment failures. Based on this prediction, the agent can schedule preventive maintenance, minimizing downtime and maximizing VPP efficiency.

4. Self-Healing and Grid Resilience:

• Disruption: Power grids are susceptible to disruptions from natural disasters or equipment failures. VPPs with Reinforcement Learning can play a crucial role in enhancing grid resilience. The agent can learn to identify and respond to grid disturbances in real-time. It can automatically adjust VPP operations to isolate faults, maintain grid stability, and facilitate faster recovery.
• Uncertainty Handling: DERs are uncertain (e.g., solar output varies with clouds). Reinforcement Learning models adapt to this uncertainty. They learn to adjust schedules dynamically, improving reliability.
• Safe Exploration: Reinforcement Learning balances exploration (trying new actions) and exploitation (choosing known good actions). In VPPs, safe exploration ensures reliable operation without risking critical failures.

A good example for Reinforcement Learning in a VPP is the Deep Deterministic Policy Gradient (DDPG) algorithm. The DDPG is a popular algorithm for learning continuous action policies. It has been used for strategic bidding of VPPs in day-ahead electricity markets. The algorithm enables VPPs to learn competitive bidding strategies without requiring an accurate market model.?Additionally, enhancements like a projection-based safety shield and a penalty for shield activation are introduced to account for the complex internal physical constraints of VPPs.[7]

Centralized coordination and control of DERs is problematic (e.g. failure and system disruption, security, and privacy). Research also proposes novel based distributed optimization methods for VPP coordination. These methods expedite solution search, reduce convergence times, and outperform traditional approaches.[8]

E. Challenges for AI in a VPP

There are also significant challenges for AI in VPPs and a few highlights are:

1. Complexity and Training:?Implementing and fine-tuning AI algorithms for VPPs requires expertise and substantial resources. The complex and dynamic nature of the power grid can make it challenging for the AI agent to learn effectively.
2. Data Security and Privacy:?VPPs handle sensitive energy data. Ensuring data security and user privacy while collecting data for AI training is crucial. Reinforcement Learning is “data-hungry”: it requires even more data than supervised learning, as well as many interactions, to learn effectively. Getting enough training data is hard. Most of the data for AI stems from IoT devices. This raises additional security concerns.
3. Explainability of Decisions:?Understanding the rationale behind the AI agent's decisions can be very difficult. This is a concern for ensuring VPP operations comply with safety and grid regulations. System-level and physical constrains are not easy to integrate and this can create additional security risks. [9]

F. Conclusion

Despite these challenges, Reinforcement Learning holds immense potential for optimizing VPP operations and performance. As VPP innovation and technology matures, Reinforcement Learning can revolutionize VPPs, leading to a more sustainable, efficient, profitable, and resilient energy system.?

[1] 'Learning-Accelerated ADMM for Distributed DC Optimal Power Flow', Biagioni et al., IEEE, 2020

[2] 'Learning-aided Asynchronous ADMM for Optimal Power Flow', Mohammadi et al., IEEE, 2021

[3] 'Distributed Optimization for Distribution Grids with Stochastic DER Using Multi-Agent Deep Reinforcement Learning', Al-Saffar et al., IEEE, 2021

[4] ‘Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy’, Lin et al., IEEE, 2020

[5] Virtual power plant containing electric vehicles scheduling strategies based on deep reinforcement learning, Wang et al., EPSR, 2022

[6] ‘A Markov Decision Model for Cooperative Virtual Power Plants Market Participation', Paniah et al., Journal of Clean Energy Technologies, 2015

[7] 'Safe Reinforcement Learning for Strategic Bidding of Virtual Power Plants in Day-Ahead Markets', Stanojev et al., ETH Zurich, 2023

[8] 'Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets', Li and Mohammadi, IEEE, 2023

[9] 'Data-driven energy management of virtual power plants: A review' Ryan et al., Advances in Applied Energy, 2024