Power Grid Energy Capacity Allocation Under Market Uncertainty
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Solution Overview
Problem
The increasing volatility of renewable energy sources (RES) in power grids due to fluctuations in production leads to instability and higher operational costs, necessitating additional balancing resources like gas power plants, which increases electricity prices. Investing in battery energy storage systems (BESS) for stability is costly, and participating in energy trading is challenging due to price uncertainties, leading to potential financial losses.
Innovation Solution
A method that simplifies the handling of stochastic scenarios in energy trading by 'flattening' the scenario tree, allowing for efficient distribution of energy across markets while optimizing BESS usage, ensuring grid stability and profitability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large fleet of gas power plants is engaged to support RES production, then grid stability is ensured, but total operational cost increases and electricity prices rise
Solution Approach 1:
The patent introduces a stochastic optimization system as an intermediary between RES production and grid stability requirements. This system uses scenario trees to model uncertainty and determines optimal BESS dispatch strategies that reduce reliance on expensive gas power plants while maintaining grid stability through probabilistic constraints.
Solution Approach 2:
The patent changes the operational parameters of BESS from deterministic to stochastic optimization. By incorporating probability distributions and scenario trees, the system dynamically adjusts BESS charging/discharging decisions based on predicted RES production and electricity prices, thereby reducing operational costs while ensuring reliability.
2Reliability
If BESS is used to balance RES fluctuations, then power output stability improves, but investment and operational costs increase
Solution Approach 1:
The patent applies dynamic optimization to BESS operations, transitioning from static investment decisions to dynamic dispatch strategies. The stochastic optimization model adjusts BESS behavior in real-time based on scenario outcomes, allowing the system to maximize the utilization of existing BESS capacity and reduce the need for additional investments.
Solution Approach 2:
The patent implements a feedback mechanism through scenario trees that capture the stochastic nature of RES production and electricity prices. The optimization model uses this feedback to adjust BESS dispatch decisions across multiple time stages, ensuring power output stability while minimizing the need for additional BESS investment.
3Productivity
If perfect energy price forecast is available, then BESS capacity usage optimization becomes trivial and additional profits are achieved, but in reality price uncertainties exist
Solution Approach 1:
The patent performs preliminary actions by constructing scenario trees that predict multiple possible future price paths with associated probabilities. The stochastic optimization model uses these pre-computed scenarios to make robust BESS dispatch decisions before actual price realizations occur, thereby capturing profits under uncertainty rather than relying on perfect forecasts.
Solution Approach 2:
The patent accepts that perfect price forecast is unattainable and instead takes partial action by optimizing BESS operations based on probabilistic scenarios. The model makes decisions that are optimal across multiple scenarios rather than chasing unattainable perfect accuracy, thereby achieving robust profitability despite price uncertainties.
4Reliability
If rigorous multi-stage stochastic optimization is applied, then solution robustness improves, but computational complexity explodes with too many variables
Solution Approach 1:
The patent segments the complex multi-stage stochastic optimization problem into manageable components using scenario trees. Each scenario represents a possible realization of uncertainty, and the optimization model processes these segmented scenarios independently, thereby reducing computational complexity while maintaining solution robustness through probabilistic constraints.
Solution Approach 2:
The patent uses scenario trees as simplified copies of the uncertain future. Instead of modeling every possible variable interaction in the full stochastic space, the system creates representative scenario copies that capture the essential uncertainty structure, thereby reducing computational complexity while preserving robustness.
Data Source
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AI summary
The present disclosure relates to a method for determining a distribution of existing capacity of energy in a power grid across various energy markets and products, comprising: obtaining a plurality of scenarios for the various energy markets and products in the power grid with associated probabilities based on predictions of energy demand, energy production and available energy storage, estimating the distribution for a given product in one of the energy markets at a given time by selecting a scenario based on the probability of the selected scenario individually for the given product, and constraining the total volume of the distributed energy to the physical limits of the power grid, thereby optimizing the usage of the energy storage enabling larger volumes for supporting grid stability.