Risk-Constrained VPP Optimization for Pool and Future Markets
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Solution Overview
Problem
Virtual Power Plants (VPPs) face challenges in optimizing energy production, storage, and demand management due to the volatility of renewable energy sources, lack of backup storage, and inadequate strategies for participating in both short-term and long-term energy markets, limiting their ability to maximize profits and manage financial risks.
Innovation Solution
The development of risk-constrained optimization models using information gap decision theory and stochastic dominance concepts to formulate mixed-integer linear programming problems, enabling VPPs to self-schedule energy production and consumption, select forward contracts, and optimize storage charging and discharging for both short-term and long-term markets, thereby maximizing revenue and minimizing energy costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If VPPs aggregate decentralized renewable energy units to achieve significant power capacity, then they can access lucrative power markets and reduce the need for individual unit participation, but the volatility of renewable sources causes total capacity to change constantly and become unreliable
Solution Approach 1:
The patent combines multiple decentralized renewable energy units into a Virtual Power Plant (VPP) that operates as a single aggregated entity. This merging allows the VPP to access power markets collectively, achieving economies of scale and market access that individual units cannot obtain alone, while the centralized control system manages the volatility of individual renewable sources.
Solution Approach 2:
The patent introduces a centralized control system as an intermediary between the decentralized renewable energy units and the power market. This intermediary manages the volatility and intermittency of renewable sources by coordinating production, storage, and trading activities, thereby ensuring reliable power supply to the market despite variations in individual renewable unit output.
2Stability of the object's composition
If VPPs combine multiple energy sources to prevent uneven power balance, then they improve power supply stability, but they still lack backup storage capacity to meet peak demand requirements
Solution Approach 1:
The patent creates a multi-functional energy ecosystem within the VPP that includes power producers, power storage units, power consumers, and power-to-X plants. This universal approach allows different energy sources and storage mechanisms to work together, where storage units provide backup capacity during peak demands while other units maintain power balance stability through their complementary operations.
3Ease of operation
If VPPs operate only as price-takers in conventional markets, then they simplify market participation, but they cannot diversify bidding strategies across pool and future markets to reduce financial risks and increase profits
Solution Approach 1:
The patent implements a dynamic bidding and offering strategy that adapts to different market conditions across pool and future markets. The centralized control system continuously adjusts bidding strategies based on real-time and forecasted market prices, renewable generation patterns, and storage availability, allowing the VPP to optimize revenues and manage financial risks through flexible, dynamic market participation rather than static price-taking.
4Device complexity
If VPPs lack battery storage infrastructure, then they reduce infrastructure costs and complexity, but they cannot meet power grid peak demand requirements at specific times
Solution Approach 1:
The patent changes the operational parameters of existing energy units and storage mechanisms to optimize peak demand fulfillment without requiring extensive new infrastructure. By adjusting charging/discharging rates, optimizing the operation of power-to-X plants, and dynamically coordinating multiple energy sources, the VPP can meet peak demands using existing infrastructure capabilities rather than building additional battery storage.
Data Source
AI summary
Embodiments for distributing energy for an energy system having an energy generation source, an energy storage system and a load. The method including identifying a risk level for the energy system, the risk level having objectives prioritized relative to one another. Calculating an objective function based on values including energy market values, electricity rates, and power producing, storing and consumption. Identifying an Optimized solution for charging or discharging the energy storage system based on the objective function. Controlling the distribution of energy to the energy storage system for charging according to the Optimized solution based on the objective function and discharging according to the Optimized solution. The objective function is Optimized using feasibility constraints, again Optimized using technical constraints and additional constraints. The Optimized solution maximizes an expected total pool market revenue and an expected total future market revenue, while minimizing an expected total energy cost for the energy system.