原文信息：
Integrated Optimization in Operations Control and Systems Design for Carbon Emission Reduction in Building Electrification with Distributed Energy Resources
原文链接:
https://www.sciencedirect.com/science/article/pii/S2666792423000239
Highlights
· An optimization framework for integrated system design and control optimization
· Deep learning is used to address computationally intractable optimization problem
· The framework optimizes distributed energy resources and electric building systems
· The framework reduces building carbon emissions by 80% and total costs by 2.7%
· The framework reduces building net electrical loads from 44.1 to 19.3 kWh/m2/year.
Abstract
Building electrification with distributed energy resources (DERs) is a promising strategy to decarbonize the building sector. Considering the inter-dependencies between operations control and systems design, integrating technology operations control optimization with DERs investment optimization can cost-effectively enhance such building decarbonization opportunities. This study proposes a multi-timescale integrated optimization framework to simultaneously optimize the design and control of DERs and electrification technologies for buildings. A novel building operational performance prediction model based on deep learning is developed to approximate and replace the computationally expensive control optimization. This helps resolve the challenging, computationally intractable multi-timescale integrated design and control optimization problem. Applying the proposed framework to a residential building, our results demonstrate its effectiveness in cost-efficient carbon emissions reduction. With integrated design and control optimization for DERs and electric building energy systems, the proposed framework reduces operational carbon emissions by 80% and total costs by 2.7% compared to a base case, which uses typical conventional building energy systems without DERs and control/design optimization. Separate optimization of operations control and system design cannot achieve such performance. Further scenario analyses indicate that as power grids become cleaner, the reliance on DERs can be alleviated but remain important in building carbon emission reduction under 2050 power grid scenario. Overall, as our results demonstrate, it is possible to reduce building operational carbon emissions simultaneously with net electrical load: compared to the base case, the proposed framework helps reduce the carbon emission by 80% while driving down the net electrical load from 44.1 to 19.3 kWh/m2/year.
Keywords
Carbon Emission
Building Electrification
Distributed Energy Resources
Deep Learning
Design Optimization
Model Predictive Control
Figure 1. Overview of the methodology for the development and performance assessment of the proposed deep-learning-based multi-timescale integrated optimization framework.
Figure 3. Schematic diagram of the MPC-based control optimization model.
Figure 6. Building performance results, including (a) cost, (b) carbon emission reduction, (c) net electrical load, (d) percent discomfort time, and (e) load shifting index, of the five simulation cases.
Figure 7. Building energy profiles in (a) base case, (b) no optimization case, (c) control optimization case, (d) design optimization case, and (e) integrated optimization case.
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