Community Energy Balancing Groups for Renewable Supply Uncertainty
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Solution Overview
Problem
The increasing uncertainty in energy supply and demand due to unpredictable renewable energy sources, distributed generation, and limited storage technology poses challenges for power market operators in achieving accurate balance.
Innovation Solution
A method and system for balancing energy demand-supply at a community level through self-demand response, which involves dividing regions into communities, defining time windows, creating balancing groups with demand response and distributed energy resources, and utilizing reserve margins to manage supply-demand differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new and renewable energy generation is increased, then energy supply diversity is improved, but prediction accuracy of supply and demand deteriorates
Solution Approach 1:
The system segments the community into multiple balancing groups, each independently managing its own supply-demand balance. This segmentation allows localized optimization where each group can handle uncertainty autonomously, while collectively maintaining overall community balance despite prediction challenges.
Solution Approach 2:
The balancing groups operate autonomously to manage their own energy supply-demand balance without requiring external market operators. Each group uses its own resources (demand response and distributed energy) to self-regulate, reducing reliance on external prediction accuracy.
2Power
If distributed power generation is increased, then energy generation capacity is improved, but total power generation prediction becomes more difficult
Solution Approach 1:
Distributed power generation is organized into discrete balancing groups rather than treated as a single aggregate source. This segmentation allows each group's generation to be managed independently, making the total system more controllable despite the distributed nature of generation sources.
Solution Approach 2:
The system pre-defines balancing groups and their resource compositions in advance, establishing prediction and management frameworks before actual operation. This preliminary organization helps manage the complexity of predicting total distributed generation by breaking it into manageable group-level predictions.
3Device complexity
If energy storage technology is limited, then system simplicity is maintained, but supply-demand control capability deteriorates
Solution Approach 1:
The system uses existing demand response resources and distributed energy resources within each balancing group to self-regulate supply-demand balance without requiring complex external storage infrastructure. Each group leverages its own resources to maintain balance, achieving control capability through autonomous resource management rather than centralized storage.
Solution Approach 2:
The system changes operational parameters of existing resources (e.g., adjusting demand response levels, modifying distributed energy dispatch) to achieve supply-demand balance. This approach provides control capability by dynamically adjusting resource utilization rather than relying on physical storage capacity expansion.
4Extent of automation
If power market operators are not involved, then community autonomy is improved, but supply-demand balance accuracy may deteriorate
Solution Approach 1:
The community is divided into multiple balancing groups that independently manage their own balance. This segmentation distributes the balancing function across multiple autonomous units, each capable of maintaining accuracy within its scope without requiring centralized operator intervention.
Solution Approach 2:
The system implements feedback mechanisms where balancing groups continuously monitor their own supply-demand status and adjust their resource utilization accordingly. This internal feedback loop enables autonomous groups to maintain balance accuracy through self-correction without external market operators.
Data Source
AI summary
The present invention relates to a method of balancing energy demand-supply at a community level based on self-demand response, which obtains balance of energy demand-supply at a community level, and achieves and manages the balance of energy demand-supply within the community itself, by creating a balancing group for obtaining the balance of energy demand-supply in units of time zones and using the reserve margin of the balancing group, and the method may comprise the steps of: (a) dividing a predetermined region into a plurality of communities, by a community configuration module; (b) defining a window, which is a time period set to a predetermined time period, by a window definition module; (c) creating a balancing group including demand response resources and distributed energy resources included in one community for each of the windows, by a balancing module; and (d) reducing or eliminating a demand-supply difference of power in the community using the balancing group created in each window of the community, by the balancing module.