Smart Grid Demand Shaping via Iterative Load Shedding
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Solution Overview
Problem
Current smart grid technologies face challenges in efficiently controlling energy demand due to the integration of renewable sources with high output variance, leading to difficulties in predicting and managing energy flow, particularly in managing peak consumption and ensuring reliability and security.
Innovation Solution
A method that optimally sheds and shifts energy demand based on real-time market prices, using a two-step iterative process to minimize total energy procurement costs, involving direct load control and price incentives without explicit user preference information, and accounting for non-moveable demand, while utilizing a two-stage market structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If demand response methods are implemented to control energy demand, then energy procurement costs are reduced and peak-to-average ratio is improved, but system complexity increases due to the need for iterative optimization processes and market integration
Solution Approach 1:
The system performs preliminary actions by iteratively optimizing demand shedding and shifting decisions before final implementation. The two-step iterative process (shedding optimization followed by shifting optimization) prepares the demand profile in advance, allowing the system to anticipate and respond to price signals from day-ahead and real-time markets, thereby reducing procurement costs while systematically managing complexity through structured optimization steps.
Solution Approach 2:
The system applies dynamics by making the demand control mechanism adaptive and iterative rather than static. The optimization process dynamically adjusts shedding and shifting decisions based on evolving price signals and system conditions. This dynamic approach allows the system to respond flexibly to market variations while maintaining control over complexity through the structured two-step iterative framework.
2Loss of energy
If direct load control and price incentives are used to shed demand, then energy procurement costs decrease, but user convenience deteriorates due to load shedding without explicit user preference information
Solution Approach 1:
The system implements self-service by enabling automated demand shedding and shifting decisions to be made without requiring explicit user preferences or continuous user input. The iterative optimization process autonomously determines optimal shedding and shifting amounts based on price signals and system conditions, allowing the energy management system to serve itself by making intelligent decisions that reduce procurement costs while minimizing user burden.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring price signals from day-ahead and real-time markets and adjusting shedding and shifting decisions accordingly. The iterative optimization process incorporates feedback from previous iterations and market conditions to refine demand control strategies, ensuring that cost reductions are achieved while maintaining user convenience through automated adaptation to changing conditions.
3Loss of energy
If iterative optimization process is implemented to optimally shed and shift demand, then total energy procurement costs are minimized, but computational time and complexity increase
Solution Approach 1:
The system applies segmentation by dividing the complex demand optimization problem into two distinct iterative steps: first optimizing demand shedding decisions, then optimizing demand shifting decisions. This segmentation allows each sub-problem to be solved more efficiently and independently, reducing the overall computational burden while still achieving minimal total procurement costs through the coordinated interaction of the two optimization steps.
Solution Approach 2:
The system performs preliminary optimization of shedding decisions before proceeding to shifting optimization. This preliminary action allows the first step to establish a baseline optimized state that informs subsequent shifting decisions, reducing the computational search space for the second step and thereby minimizing total computational time while ensuring cost minimization through systematic progressive optimization.
Data Source
AI summary
A method (100) for electricity demand shaping through load shedding and shifting in an electrical smart grid.