Shared Battery Control for Collective Self-Consumption in Energy Communities
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Solution Overview
Problem
Existing energy community management systems fail to efficiently manage shared energy resources, particularly electrical batteries, due to non-optimal control of charging and discharging cycles, lack of consideration for collective self-consumption and ancillary services, and inefficient distribution of storage capacity, leading to suboptimal energy usage and remuneration.
Innovation Solution
A method and system for managing distributed energy resources that decouples real-time control of shared batteries from user remuneration, optimizing energy management through centralized control of electrical batteries, virtualization of user contributions, and fair distribution of energy and power flexibility among users, ensuring efficient collective self-consumption and ancillary service provision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtualisation systems are used to manage energy communities based on individual user advantages, then individual user flexibility is improved, but collective control and maximisation of common income are deteriorated
Solution Approach 1:
The system segments the energy community into individual user entities with separate virtual accounts, each capable of independent decision-making and flexibility. This segmentation allows individual users to optimize their own energy consumption patterns while the aggregate of these individual optimizations achieves collective efficiency without requiring centralized control.
Solution Approach 2:
Each user operates autonomously to manage their own energy consumption and flexibility contributions. The system enables self-service by allowing users to independently adjust their energy usage patterns, participate in flexibility markets, and optimize their own cost structures without requiring centralized coordination, thereby achieving both individual flexibility and collective efficiency simultaneously.
2Ease of operation
If real-time control is implemented to follow individual user energy consumption variations, then user-specific energy needs are satisfied, but control delays and non-optimal system behavior increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing flexibility profiles, energy consumption patterns, and optimization strategies in advance. Instead of reacting in real-time to user consumption variations, the system prepares optimization routines beforehand that can be executed efficiently, eliminating control delays while still satisfying user-specific energy needs when conditions change.
3Productivity
If shared battery storage is used to maximize collective self-consumption, then energy cost savings are improved, but state of charge management and ancillary service provision become more complex
Solution Approach 1:
The system implements dynamic state of charge management that automatically adjusts battery charging and discharging operations based on real-time conditions including user consumption patterns, grid prices, and ancillary service opportunities. This dynamic approach simplifies management complexity by using adaptive algorithms that respond to changing conditions, maximizing collective self-consumption while maintaining optimal battery state of charge for both energy savings and ancillary service provision.
4Adaptability or versatility
If individual virtual behaviour control is used for continuous battery charging and discharging, then user energy profiles are optimized, but battery state of health deteriorates due to excessive cycles
Solution Approach 1:
The system maintains continuous useful action by optimizing battery operations to provide steady-state charge/discharge cycles that meet user energy profile requirements without creating excessive cycling. The continuous optimization algorithm smooths out individual user demand variations through aggregation, transforming intermittent individual requests into continuous, predictable battery operations that extend battery lifespan while still achieving user profile optimization.
Data Source
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AI summary
A method and system for managing shared energy resources in an energy community, wherein said energy community comprises a plurality of users (20) having one or more items of electrical equipment to be powered. Said energy community defining a DER system and comprising shared batteries (2) for storing electrical energy/power and an apparatus (30) for the production of electrical energy/power, preferably renewable, connected with said batteries (2) so that the electrical energy/power produced can be stored in said batteries (2). The method comprises: assigning to each of the users a virtual share of electrical energy production and a virtual share of storage of electrical energy of said shared batteries; calculating the total energy/power that the community exchanges with an electrical grid and a control step carried out in the following manner: discharging into the grid (100) the energy/power stored in the batteries (2) so as to decrease the value of energy/power stored in the batteries (2) (in order to increase the total energy/power exchanged; charging the batteries (2) by means of said apparatus (30) for the production of electrical energy/power so as to increase the value of energy/power stored in the batteries (2) in order to lower the total energy/power exchanged.