Smart Grid Demand Shaping via Two-Level Market Optimization
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Solution Overview
Problem
Current smart grid technologies face challenges in controlling energy flow effectively due to increasing complexity and variability from renewable sources, lacking mechanisms for optimal load shedding and shifting that balance user demand with energy procurement costs, and ensuring reliability and cost efficiency.
Innovation Solution
A method for controlling energy flow in smart power networks through optimal load shedding and shifting, utilizing a two-level market framework that includes day-ahead and real-time markets, and employing robust optimization techniques to manage stochastic demand and pricing, allowing for direct load control and price incentives to achieve equilibrium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If demand response methods are implemented to control energy flow, then system reliability and control capability are improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent segments demand response into two distinct markets: day-ahead market for load shifting and real-time market for load shedding. This segmentation allows each market to handle specific aspects of demand control, simplifying the overall system architecture while improving reliability through specialized control mechanisms for each market type.
Solution Approach 2:
The patent introduces a utility company as an intermediary that operates a demand response management system between renewable energy sources and consumers. This intermediary coordinates load shifting and shedding decisions, manages the two-level market structure, and balances supply-demand dynamics, thereby improving system reliability without requiring direct complex interactions between all system components.
2Loss of energy
If load shedding and shifting are implemented to shape demand, then energy procurement costs are minimized, but user convenience and ease of operation deteriorate
Solution Approach 1:
The patent implements dynamic demand control where load shifting and shedding profiles are adjusted in real-time based on market conditions, renewable energy availability, and user preferences. The system dynamically optimizes the balance between energy cost minimization and user convenience by allowing flexible participation levels and adjusting control strategies according to changing system states.
Solution Approach 2:
The patent changes key parameters including price signals, incentive structures, and control thresholds to optimize demand shaping. By adjusting pricing mechanisms in the day-ahead and real-time markets, the system can influence user behavior to shift or shed load during critical periods, thereby reducing energy procurement costs while maintaining acceptable user convenience through incentive-based approaches.
3Adaptability or versatility
If renewable power sources are introduced to increase sustainability, then environmental benefits are achieved, but output variance and system controllability worsen
Solution Approach 1:
The patent implements preliminary load shifting in the day-ahead market based on forecasted renewable energy generation. By proactively scheduling load shifts before actual generation variability is realized, the system prepares to absorb renewable output fluctuations, thereby reducing the impact of output variance while maintaining high renewable energy integration levels.
Solution Approach 2:
The patent employs feedback mechanisms where real-time market operations provide information about actual renewable generation and load response to day-ahead decisions. This feedback loop allows the system to learn from actual performance, adjust control strategies, and improve its ability to handle renewable output variance in subsequent operations while maintaining sustainability goals.
Data Source
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AI summary
A method (100) for demand shaping through load shedding and shifting in an electrical smart grid, comprising: a. obtaining an optimal load shedding profile for a user of the grid. b. obtaining an optimal billing structure and an optimal load shifting profile for the user. c. iterating the steps of obtaining an optimal load shedding profile and obtaining an optimal billing structure and an optimal load shifting profile until convergence is achieved. d. controlling the energy consumption/demand of the user based on the optimal load shedding profile, the optimal billing structure and the optimal load shifting profile. And determining how much energy to purchase on each of the day-ahead market and the real-time market to cover the load demand of the user after load shifting and load shedding.