Economic Optimization Controller for Electrical Systems
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Solution Overview
Problem
Existing automatic controllers of electrical systems are not economically optimal, as they rely on rule sets that become complex and difficult to maintain, and are not scalable to new rate tariffs or markets, failing to provide optimal control considering all costs and benefits.
Innovation Solution
A controller using an optimization algorithm to determine economically optimal control parameters for an electrical system, which includes an economic optimizer to define a control parameter set and a dynamic manager to implement these parameters, optimizing overall system economics by considering multiple value streams and costs simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rule sets and iteration are used to find operating commands, then control can be implemented, but the rule sets become complex quickly and are difficult to build and maintain
Solution Approach 1:
The patent replaces the mechanical rule-set-based control system with an optimization algorithm that uses mathematical optimization to determine operating commands. This substitution eliminates the need for complex, manually maintained rule sets while providing economically optimal control decisions based on real-time cost and benefit analysis.
Solution Approach 2:
The patent changes the fundamental parameter of control from fixed rule sets to dynamic optimization parameters that can adapt to different rate tariffs and market conditions. The optimization algorithm adjusts control parameters based on economic objectives, eliminating the need to rewrite rules for new scenarios.
2Adaptability or versatility
If rule sets are used for control, then basic control functionality is achieved, but the approach is not easily scalable to new rate tariffs or other markets
Solution Approach 1:
The optimization algorithm serves as a universal control mechanism that can handle multiple rate tariffs, markets, and economic objectives through a single framework. By formulating control as an optimization problem with configurable objective functions and constraints, the system adapts to new scenarios without requiring rule set rewrites, achieving true scalability.
3Reliability
If existing automatic controllers are used, then control commands can be generated, but they fail to provide economically optimal control considering all costs and benefits
Solution Approach 1:
The optimization algorithm incorporates feedback from real-time cost and benefit data to continuously adjust control decisions for economic optimality. By monitoring changing economic conditions, system state, and constraint violations, the algorithm dynamically optimizes operating commands to maximize economic value while satisfying all system requirements.
Data Source
AI summary
The present disclosure is directed to systems and methods for economically optimal control of an electrical system. A two-stage controller includes an optimizer and a high speed controller to effectuate a change to one or more components of the electrical system. The high speed controller receives a set of control parameters for an upcoming extended time period. The control parameters include a plurality of bounds for an adjusted net power of the electrical system. The high speed controller sets an energy storage system command control variable (ESS command) based on a state of adjusted net power of the electrical system and the set of control parameters.