HVAC Predictive Controller Battery Energy Optimization
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Solution Overview
Problem
Central energy facilities (CEFs) face challenges in minimizing power consumption and reducing energy costs due to the high power consumption of HVAC equipment like chillers, boilers, cooling towers, and pumps, which leads to increased operational expenses.
Innovation Solution
Implementing a predictive control system that includes a battery unit and a predictive controller to optimize energy usage by determining the optimal amount of energy to purchase from the grid and store or discharge from the battery unit, considering energy pricing data and demand charges, to minimize power consumption and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HVAC equipment operates continuously to meet building heating or cooling demands, then building thermal comfort is maintained, but power consumption and operational expenses increase
Solution Approach 1:
The battery unit stores electric energy in advance during periods of low energy cost or high renewable energy availability, enabling the HVAC equipment to operate at optimal levels while maintaining building thermal comfort through predictive energy management
Solution Approach 2:
The system dynamically adjusts operational parameters by optimizing the predictive cost function based on real-time energy pricing data, demand charge information, and weather forecasts to minimize power consumption while maintaining reliable HVAC performance
2Power
If electric energy is purchased from the energy grid during high demand periods, then powered CEF components can operate at full capacity, but energy costs increase due to demand charges
Solution Approach 1:
The battery unit pre-charges during off-peak hours or periods of high renewable energy generation, allowing the system to draw stored energy during high-demand periods and avoid peak demand charges while maintaining full power availability
Solution Approach 2:
The predictive controller continuously monitors energy pricing data, demand charge periods, and battery state of charge to dynamically adjust energy procurement and storage strategies, optimizing the balance between power availability and energy cost
3Adaptability or versatility
If renewable energy from photovoltaic panels is collected, then energy independence is improved, but determining optimal storage versus consumption creates control complexity
Solution Approach 1:
The system optimizes the predictive cost function with multiple parameters including renewable energy generation forecasts, energy pricing, demand charges, and battery state of charge to automatically determine optimal storage versus consumption decisions, managing control complexity through mathematical optimization
Solution Approach 2:
The predictive controller autonomously manages the complex decision-making process for renewable energy utilization, automatically adjusting battery charge/discharge operations based on real-time conditions without requiring manual intervention
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The predictive control system effectively reduces energy costs by optimizing energy usage based on real-time pricing and demand, allowing for efficient operation of HVAC equipment and minimizing peak power draw, thereby lowering operational expenses.
Implementation Method 1
The battery unit is configured to store electric energy from an energy grid and discharge the stored electric energy for use in powering the powered CEF components
Implementation Method 2
The CEF includes one or more photovoltaic panels configured to collect photovoltaic energy
Data Source
AI summary
A heating ventilating or air conditioning (HVAC) system includes a unit configured for use in a heating or a cooling operation for an environment. The unit includes an interface configured to receive electric energy from an alternative energy source and a controller configured to perform an optimization of an objective function for the unit to determine a first amount of electric energy to receive from an energy grid and a second amount of the electric energy from the alternative energy source for use in the heating or cooling operation at each time step of an optimization period in response to a cost characteristic of the electric energy received from the energy grid. The unit is configured to use the first amount and the second amount for the heating or cooling operation.