Packetized DER Coordination for Grid Reserve and QoS Control
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Solution Overview
Problem
Current approaches to managing power systems with high renewable energy penetration require conventional generators to ramp quickly, leading to increased emissions and inefficiencies, while existing demand-side participation methods fail to fully utilize distributed energy resources' flexibility and ignore consumer constraints.
Innovation Solution
A distributed and anonymous packetized energy management (PEM) system that uses probabilistic automata to randomly request energy from or provide energy to the grid, ensuring fairness and quality of service by managing aggregate power consumption of thermostatically-controlled loads and bi-directional distributed energy storage systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional generators are used to provide operating reserves with fast-ramping capability, then reliability is improved, but emissions increase and fuel efficiency deteriorates
Solution Approach 1:
The patent enables distributed energy resources to self-regulate their power consumption and generation through autonomous feedback algorithms. Each DER independently adjusts its operation based on real-time grid conditions and local constraints, eliminating the need for conventional generators to idle and providing reserves without emissions.
Solution Approach 2:
The system dynamically changes operational parameters of distributed energy resources, including power setpoints, ramping rates, and response timing. This allows flexible adjustment of DER output to provide operating reserves without requiring conventional generators to maintain idle capacity, thereby reducing emissions while maintaining reliability.
2Productivity
If utility-centric valley-filling strategies are implemented, then system objectives are met, but consumer quality of service deteriorates
Solution Approach 1:
The patent implements dynamic, real-time coordination between utility objectives and consumer needs. The feedback algorithms continuously adjust DER operation based on current grid conditions and consumer constraints, allowing flexible load management that meets system objectives while maintaining consumer quality of service through adaptive rather than static control.
Solution Approach 2:
The system employs closed-loop feedback algorithms that monitor both grid conditions and consumer constraints in real-time. This feedback mechanism ensures that valley-filling strategies are implemented in a way that respects consumer quality of service requirements, adjusting operations dynamically to balance system efficiency with consumer needs.
3Ease of operation
If non-centralized optimal control algorithms are used for consumer-centric coordination, then consumer quality of service is improved, but convergence speed deteriorates
Solution Approach 1:
The patent segments the control problem into decentralized autonomous decisions at the consumer level, with each DER independently making optimization decisions based on local information. This segmentation eliminates the need for slow iterative convergence of centralized algorithms while maintaining consumer-centric quality of service through local autonomy and distributed intelligence.
Solution Approach 2:
Each distributed energy resource autonomously determines its optimal operation without requiring iterative coordination algorithms. The self-service approach allows immediate local decision-making that satisfies consumer quality of service requirements without the computational delays inherent in non-centralized optimal control methods.
4Manufacturing precision
If iterative optimal control methods are implemented, then optimality is improved, but computational complexity and time requirements increase
Solution Approach 1:
The patent replaces complex iterative optimization algorithms with simpler autonomous control logic that each DER executes independently. This self-service approach achieves near-optimal control outcomes without the computational burden of iterative methods, reducing both complexity and time requirements while maintaining effectiveness.
Solution Approach 2:
The system uses simple, computationally inexpensive control algorithms that can be executed rapidly and discarded each control interval, rather than expensive iterative optimization methods. This approach achieves sufficient control performance with minimal computational resources, effectively replacing complex algorithms with simpler alternatives that meet real-time requirements.
Data Source
AI summary
The present disclosure can provide a distributed and anonymous approach to demand response of an electricity system. The approach can conceptualize energy consumption and production of distributed-energy resources (DERs) via discrete energy packets that are coordinated by a cyber computing entity that grants or denies energy packet requests from the DERs. The approach leverages a condition of a DER, which is particularly useful for (1) thermostatically-controlled loads, (2) non-thermostatic conditionally-controlled loads, and (3) bi-directional distributed energy storage systems, among others. In a first aspect of the present approach, each DER independently requests the authority to switch on for a fixed amount of time (i.e., packet duration). The coordinator determines whether to grant or deny each request based electric grid and/or energy or power market conditions. In a second aspect, bi-directional DERs, such as distributed-energy storage systems (DESSs) are further able to request to supply energy to the grid.