A Smart Solar Grid: Using AI for Energy Storage and Trading

A renewable energy provider who owns and operates multiple solar farms in the US wanted to leverage battery storage systems to optimize the use of renewable energy resources, contribute to a more stable and efficient AI energy grid, and gain a competitive edge in the industry. 

They sought a sophisticated, custom-built solution to forecast energy market prices and solar production, identify energy deficits and surpluses, and manage battery charging and discharging accordingly.

The Apriorit team delivered an advanced smart grid system featuring two interconnected AI-powered modules and a dispatch agent, along with seamless integration into the client’s infrastructure.

The client

Our client is a midsized organization operating within the renewable energy sector in the US market. They own and operate solar farms that use advanced energy storage systems. 

The challenge

Our client sought to optimize their energy operations and maximize profitability by implementing an intelligent, automated system. Their key goals were to:

• Automate station management to reduce manual operations, improve the efficiency of energy production, and utilize AI in energy storage to the max
• Optimize energy trading to be able to sell energy at the most favorable market conditions while ensuring regulatory compliance and avoiding penalties

To get software that perfectly fits their needs and infrastructure, the client decided to build a custom solution that could provide accurate forecasts, real-time decision-making, and seamless automation of their energy trading operations.

After extensive research and comparison of different AI development service providers, the client chose Apriorit for our expertise in AI-driven solutions. Our experience in developing customized, precise software systems is perfectly aligned with the client’s objectives.

The result

The Apriorit team has built a comprehensive AI-powered grid system designed to automate operations, optimize energy trading, and enhance profitability. 

This system includes: 

• Two AI-powered forecasting modules that provide critical insights into energy production and market demand
• An automated energy dispatch agent that uses these insights to sell and store energy at optimal times, minimizing manual intervention and maximizing profit
• An analytical dashboard that is user-friendly and provides a clear and comprehensive overview of the system’s performance, allowing the client to visualize key data points

By implementing this custom-built solution, the client received full control over their energy operations without relying on third-party forecasting services, thus reducing external dependencies and getting tailored and precise predictions.

How we did it

After the initial contact and project discussion, we decided to split this project into three phases:

Phase 1. Requirements elicitation

Our collaboration started with a series of requirements elicitation sessions to understand the client’s vision and challenges and finalize requirements. Given the need for accurate predictions and real-time automation, we assembled a team and selected a tech stack that would ensure high performance and scalability.

As we recognized the complexity of this project, we brought in a business analyst to work closely with the client. Our BA researched the market, agreed on the needs that were validated with stakeholders, and prepared project requirements for developers.

To ensure the client achieves their goals, we suggested building a complex system with three modules:

• Solar energy production forecast
• Demand and supply analysis
• Energy dispatch agent

We also offered to include an analytical dashboard to help the client manage the system and energy trading. After approving the plan with the client, we moved to development.

Phase 2. Development and testing

We developed a sophisticated system consisting of two AI-powered modules and a dispatch agent, each designed to address a specific aspect of energy network management.

Solar energy production forecast module

The client’s main goal was to be able to precisely plan their energy sales and storage strategies. This module provides highly accurate predictions of solar energy production for the next 24 hours. 

Previously, the client relied on data from multiple weather forecast suppliers. 

We developed a personalized AI forecasting module that combines open-source weather forecast data from relevant sources and real-time inputs from the client’s solar farms. This tailored approach generates site-specific forecasts, providing a more reliable foundation for further trading.

Demand and supply analysis module

This module predicts market fluctuations, identifies periods of energy deficit and surplus, and determines the most profitable times for energy sales and battery utilization. It manages battery usage strategies based on analysis of energy production and market demand. Our experts embedded AI-driven algorithms into the client’s ERP system, creating an intelligent system that automates key trading decisions. The module considers factors such as:

• Predicted solar energy production
• Historical and real-time market data to forecast demand and supply imbalances
• The client’s declared energy generation and the associated penalty risks for under-delivery

Based on this analysis, the system identifies optimal times to charge batteries during surplus periods and discharge them during periods of high demand and higher prices. This module also lays the groundwork for the energy dispatch agent to effectively execute its functions.

Energy dispatch agent

This module is a JavaScript-based bot that executes energy trading operations. It automates the process of placing orders on the energy exchange using the following strategies:

• Scheduled sale — selling energy according to a predefined schedule
• Surplus sale — selling surplus energy in the amount determined by the model
• Battery energy sales — selling stored battery energy during deficit periods for maximum profit

The agent places these requests on the exchange platform via the exchange’s API, streamlining and automating the entire process. To provide the client with real-time insights and visualizations, we also created an analytical dashboard that helps the client optimize smart grid solar energy processes and enhance the system’s overall usability.

Phase 3. Integration with the client’s system

This phase was crucial for deploying the AI-powered energy optimization system within the client’s existing infrastructure. We deployed the entire infrastructure and all integrations using AWS, focusing on high availability and performance. 

To protect sensitive operational data, we configured a private network with secure VPN access. The infrastructure is designed to support real-time data processing and enable efficient automation of energy trading operations. We also implemented:

• Real-time data updates. Every five minutes, the AI models receive fresh data from multiple sources, including weather stations, satellite images, solar power plants, and energy consumption tracking systems.
• Real-time energy trading. The system generates market requests based on AI-driven price forecasts and uploads them to the exchange either hourly or once every 24 hours.

With these integrations, our data-driven smart grid and solar energy system work seamlessly with the client’s other systems and external exchange platform.

The impact

As a result of our collaboration, the client now has a fully customized energy grid management system. By leveraging AI-driven decision-making, they reduced energy waste, lowered operational costs, and enhanced sustainability, gaining a competitive advantage in the renewable energy sector. 

Since this is a fully custom-built solution, the client no longer needs to pay fees and licensing costs associated with third-party services. And by increasing the share of energy sold at higher prices to 30% of total production, they significantly improved their profit. In the future, we plan to help the client retrain the AI model to get more tailored results and meet their evolving business needs.