AI-Optimized Energy Sharing Community with Smart Energy Trading and Automated Billing

Desired outcome

This project aims to develop an AI-powered energy management algorithm that optimizes renewable energy storage, consumption, and purchasing within an energy-sharing community. The community will consist of residential (private), commercial, and public (municipal) partners, who will co-own renewable energy sources and battery energy storage systems (BESS).
The AI algorithm will optimize:
1. Energy flow within the community – Ensuring efficient allocation of self-produced and stored energy.
2. Energy purchasing from external markets – Buying energy at optimal times from the grid or energy stock markets when community assets cannot fully meet demand.
3. Automated invoicing and billing – Creating transparent, fair, and automated billing based on each shareholder’s energy usage and financial participation.
Through this approach, the project will:
• Maximize self-consumption of renewable energy.
• Minimize overall energy costs through strategic buying and selling.
• Increase transparency in financial transactions within the community.
• Enhance sustainability by improving the efficiency of renewable energy use.
A software prototype will be developed to simulate and test different energy and financial optimization scenarios for the community.

Initial Problem Description

Energy communities enable collective generation, storage, and sharing of renewable energy. However, challenges arise when community-produced energy is insufficient, leading to:
• Higher costs from grid energy purchased during peak price periods.
• Lack of smart decision-making regarding when and how to buy energy externally.
• Complex billing and cost allocation among community members, leading to administrative burdens and potential disputes.
To address these challenges, this project will develop an AI-driven system that includes:
1. Smart Energy Flow Optimization
• Analyzing real-time energy generation, storage levels, and demand across all users.
• Predicting energy supply and consumption using machine learning to forecast fluctuations in 15-minute intervals.
• Dynamically allocating stored energy among community members based on usage patterns and cost efficiency.
2. Intelligent Energy Trading & Market Integration
• Monitoring external energy markets and electricity stock prices in real time.
• Predicting price trends and automatically purchasing electricity at the lowest rates when community assets cannot meet demand.
• Potentially selling excess energy to the grid or other market participants when prices are favorable.
• Supporting integration with peer-to-peer energy trading, allowing members to directly trade energy at negotiated rates.
3. Automated Invoicing and Cost Allocation
• Calculating each member’s energy usage, contributions, and costs in real time.
• Generating transparent invoices based on energy consumed, stored, and traded.
• Applying smart billing rules, including dynamic pricing based on usage patterns, storage contributions, and energy trading profits.
• Automating cost distribution based on ownership stakes and predefined agreements.
By integrating AI, battery storage, market trading, and automated billing, the system will create an intelligent energy-sharing model that maximizes economic and environmental benefits.

Context

This project aligns with the broader global trend of energy decentralization, digitalization, and decarbonization. It supports national and EU policies promoting renewable energy communities and intelligent energy trading.
Key challenges and opportunities include:
Regulatory & Market Framework
• Many countries are establishing legal frameworks for energy-sharing communities.
• Regulations support decentralized energy trading, but market mechanisms vary by region.
• Taxation and energy pricing policies need to be considered in automated billing models.
Technical Barriers
• Intermittency of renewables requires AI-driven forecasting and market purchasing strategies.
• Battery storage must be optimized to reduce grid dependency and arbitrage electricity prices.
• Scalability and interoperability with different grid infrastructures and trading platforms.
Financial Considerations
• The AI system will minimize costs by strategically purchasing energy from external markets.
• Automated billing ensures fair distribution of profits and expenses among members.
• The financial model will provide ROI calculations for community investments in renewable energy and storage assets.
Technological Innovations
The project will leverage:
• Artificial Intelligence & Machine Learning – For energy forecasting, price trend analysis, and dynamic allocation.
• Internet of Things (IoT) & Smart Meters – For real-time consumption tracking and billing accuracy.
• Blockchain & Smart Contracts (optional feature) – For secure, transparent, and automated energy transactions.

Connection to cross-cutting areas

1. Circular Economy & Decentralized Energy
• Promotes collective ownership and maximizes energy asset utilization.
• Encourages peer-to-peer energy trading, reducing dependence on centralized utilities.
2. Sustainability & Decarbonization
• Supports net-zero energy goals by maximizing renewable energy self-consumption.
• Reduces overall carbon footprint by minimizing grid energy purchases from fossil-fuel-based sources.
3. Industry 4.0 & Digitalization
• Uses AI and machine learning for smart decision-making in energy flow optimization and market trading.
• Automated invoicing and digital transactions reduce administrative costs and improve financial transparency.
4. Smart Cities & Urban Energy Transition
• Provides municipalities with cost-effective energy management solutions.
• Enhances energy resilience and security at the local level.

Input

Current Market Trends

Growing adoption of AI in energy management – Advanced analytics and automation are becoming key drivers in the energy sector.
Rising energy prices – Consumers and businesses are seeking solutions to lower energy costs through smart energy management.
Government incentives for energy communities – Many countries provide financial support for local renewable energy projects.

Expectations

Software prototype of the AI-driven optimization algorithm, capable of simulating different community setups. Energy-sharing model that can be replicated in different urban and rural settings. Demonstration of cost savings for community members through optimized energy distribution. In deployment phase the algorithm capable of predicting, measuring and analyzing the energy flows, trading (buying and selling) of energy via APIs on the market.

Desired Team Profile

Desired Team Profile
Technical Expertise
• AI & Machine Learning – Developing forecasting and optimization algorithms.
• Energy Systems & Market Trading – Understanding energy stock markets and real-time pricing.
• IoT & Data Science – Implementing real-time energy tracking and smart meter integration.
Sustainability & Energy Policy
• Knowledge of regulations on energy trading and local grid interactions.
• Experience with renewable energy integration and carbon reduction strategies.
Financial & Business Modeling
• Developing a cost-sharing and billing framework for community members.
• Assessing return on investment (ROI) for storage and market trading strategies.
Software Development & System Integration
• Building a scalable digital platform to manage the AI-driven optimization and invoicing process.

Additional Information

Additional Information
Competitive Landscape

The energy-sharing market is expanding, with growing interest from both public and private sectors.
Many existing energy management solutions focus on individual users, but community-wide AI optimization is still a relatively untapped market opportunity.

Regulatory & Policy Considerations

Compliance with local energy-sharing regulations is necessary.
Policies related to peer-to-peer energy trading, grid interaction, and incentives for BESS adoption will be taken into account.

Scalability & Replicability

The project is designed to be replicable in different communities, including residential districts, business parks, and municipal energy projects.
Potential for expansion by integrating EV charging, district heating, and demand response mechanisms.

Related Keywords

• Energy Technology
• Energy production, transmission and conversion
• Energy
• Energy Storage and Transport
• Renewable Sources of Energy