Mobile Energy Storage Bidding for Risk-Aware Virtual Power Plants
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Solution Overview
Problem
Conventional bidding strategies for virtual power plants with fixed energy storages fail to maximize total expected profits due to insufficient utilization of mobile energy storages, which can enhance reserve response capability and smooth load and renewable fluctuations.
Innovation Solution
A stochastic optimization-based energy and reserve bidding strategy for virtual power plants with mobile energy storages, utilizing conditional value at risk (CVaR) to manage risks and optimize delivery schedules across multiple buses, considering uncertain market prices and demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy storages are located at fixed locations in conventional bidding strategies, then the bidding problem is simpler to solve, but the virtual power plant cannot maximize total expected profit due to inability to utilize mobile energy storages at critical locations and times
Solution Approach 1:
The patent transforms the static energy storage location problem into a dynamic optimization problem by allowing energy storages to be mobile across multiple buses. The stochastic optimization model determines optimal delivery schedules that dynamically allocate mobile energy storages to different locations based on uncertain market prices, renewable productions, and demand conditions, thereby maximizing the virtual power plant's total expected profit while accounting for the complexity of mobile deployment
Solution Approach 2:
The patent changes the key parameter of energy storage location from fixed to variable. By introducing delivery schedule decisions as new optimization variables and incorporating mobility constraints, the model enables energy storages to be transported between buses, fundamentally changing the system configuration from static to dynamic and enabling profit maximization through spatial flexibility
2Productivity
If mobile energy storages are used to relieve network congestion and smooth load fluctuations, then the economic benefits increase, but the optimization model becomes more complex with additional constraints and variables
Solution Approach 1:
The patent segments the optimization problem into distinct components: energy market bidding, reserve market bidding, and mobile energy storage delivery scheduling. Each component has its own set of variables and constraints, but they are integrated through the stochastic optimization framework that simultaneously optimizes all segments to maximize total expected profit while managing the overall complexity
Solution Approach 2:
The patent creates a universal optimization model that handles multiple functions simultaneously: arbitrage between day-ahead and real-time energy markets, participation in operating and regulation reserve markets, and mobile energy storage delivery scheduling. This multi-functional model consolidates various bidding and scheduling decisions into a single comprehensive framework, improving economic benefits while managing complexity through integration
3Productivity
If energy storages are used for real-time arbitrage and reserve market participation, then the profit increases, but the system requires fast response capability that mobile storages may not provide
Solution Approach 1:
The patent applies preliminary action by making delivery schedule decisions in the day-ahead market based on stochastic optimization, positioning mobile energy storages at optimal locations before real-time operations. This advance planning allows the system to capture arbitrage opportunities and reserve market profits in real-time without the delay of last-minute relocation, effectively bridging the response time gap between mobile and fixed storages
Data Source
AI summary
Systems and methods for allocating energy including distributing and receiving energy using a mobile energy storage (MEES) system at locations of a power supplier in an energy market system by a user. Determine allocating amounts of energy for the MEES system and for each time interval for all time intervals for an upcoming operating time period based on a set of uncertain parameters using an optimization model. Base on calculating an objective function using uncertain parameters. Update the objective function using constraints. While optimizing the objective function for a value hierarchy associated with energy and reserve bidding scenarios for the user and delivery scheduling for the MEES system based upon the stored user risk preferences. Control scheduling of the MEES system between the locations of the power supplier, according to allocating of the amount of the electrical energy for the MEES system at the locations of the power supplier.