Portable Energy Storage System Control via Decision Optimization
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Solution Overview
Problem
Current energy storage systems face challenges in efficiently managing and optimizing the operation of portable energy storage systems (PESS) to alleviate power network congestion, as they lack a comprehensive decision-making framework for energy charging, discharging, and travel strategies that maximize compensation while considering transportation and aging losses.
Innovation Solution
A decision optimization model is created to determine optimal energy charging and discharging decisions, travel routes, and energy storage unit loading for PESS, using mixed integer linear programming to maximize available compensation by balancing energy compensation, transportation loss, and aging loss, while adhering to energy, power, and travel constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a comprehensive decision-making framework is implemented to optimize energy charging, discharging, and travel strategies, then compensation maximization is achieved, but system complexity increases
Solution Approach 1:
The decision-making framework is segmented into three distinct but integrated modules: energy charging/discharging decision module, travel decision module, and energy storage unit loading decision module. Each module addresses a specific aspect of PESS operation, allowing complex optimization to be broken down into manageable components while achieving comprehensive compensation maximization through their coordinated interaction.
Solution Approach 2:
The control device is designed with multi-functional capabilities, integrating energy management, travel route optimization, and loading strategy determination into a single universal system. This universal framework simultaneously handles multiple decision types (charging, discharging, traveling, loading) that were previously managed separately, reducing overall system complexity while achieving comprehensive optimization.
2Productivity
If PESS operates across multiple nodes to alleviate power network congestion, then energy storage utilization increases, but transportation loss and aging loss increase
Solution Approach 1:
The system dynamically changes operational parameters based on real-time conditions, including state of charge (SOC) thresholds, power limits, and travel decisions. By adjusting these parameters adaptively, the system maximizes energy storage utilization at different nodes while minimizing transportation and aging losses through optimized charging/discharging timing and routing strategies.
Solution Approach 2:
The control device incorporates feedback mechanisms that continuously monitor PESS state (SOC, power levels) and external conditions (node congestion, compensation rates). This feedback enables the system to learn from past operations and optimize future decisions, balancing energy storage utilization against transportation and aging losses by adjusting charging/discharging schedules and travel routes based on accumulated operational data.
Data Source
AI summary
A method for controlling a portable energy storage system (PESS) includes: creating a decision optimization model for the PESS, which includes an objective function for maximizing available compensation of the PESS in the region to be applied; solving the decision optimization model to obtain a feasible solution that meets the objective function; and determining at least one of an energy charging and discharging decision, a travel decision, and an energy storage unit loading decision of the PESS in a region to be applied based on the feasible solution, and controlling operations of the PESS in the region to be applied based on at least one of the determined energy charging and discharging decision, the determined travel decision and the determined energy storage unit loading decision.