# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 2 Related Work
### Renewable Heat Systems Optimization  
Optimization plays a critical role in enhancing the performance, efficiency, and sustainability of renewable heat systems. Incorporating optimization into renewable heat systems is particularly vital for ensuring adaptability to varying industrial requirements, such as load fluctuations and environmental constraints.  
The authors of [2] review optimization models for power system planning amid increasing integration of variable renewable energy (VRE). Analyzing 34 studies, the review identifies key shifts in planning and optimization models, emphasizing the growing importance of incorporating short-term operational constraints. These include flexible generation, interregional transmission, energy storage, and demand-side response. Unlike traditional models, VRE-focused planning introduces significant uncertainties, particularly around varying penetration levels. Almost all the reviewed studies perform uncertainty analysis using methods such as stochastic programming, Monte Carlo analysis, or scenario and sensitivity analysis. The work highlights the need for enhanced modeling approaches to accommodate the complexities of high VRE penetration in power systems.  
A recent review on optimization strategies for hybrid renewable energy system with hydrogen technologies [36] shows see a shift from classical methods to modern ones. The review authors identified several drawbacks of the classical methods--iterative, linear, and non-linear programming--such as inflexibility, slow response time or unresponsive to dynamic changes, rigorous processes. More modern methods have a fast convergence time finding local and global optimum with ease. Some of these methods are particle swarm optimization (PSO), genetic algorithm (GA), harmony search (HS), non-dominated sorting genetic algorithm (NSGA II).  
The integration of abundant renewable energy in smart cities necessitates novel optimization techniques to address the inherent variability and unpredictability of these sources such as solar and wind. These techniques must balance energy supply and demand dynamically, ensuring grid stability and efficiency. Reference [37] is a systematic review of optimization algorithms used in smart cities that make use of IoT and Cloud Computing technologies to better manage their energy systems. Most analyzed studies used GA, PSO, machine learning (ML), and ant colony optimization for energy optimization in multiple scenarios, from smart homes, building and factories to smart transportation.  
The renewable energy sector is also driven by advancements in artificial intelligence (AI) and deep learning (DL), which have enabled enhanced efficiency and sustainability across various domains.  
A review study on the artificial intelligence (AI) and deep learning (DL) potential in the renewable energy sector is done [4], which shows that it enables enhanced efficiency and sustainability. AI and DL methods, such as deep reinforcement learning and hybrid models combining clustering, convolutional neural networks (CNNs), and long short-term memory (LSTM), have proven effective in addressing challenges such as solar irradiance forecasting and optimal power flow prediction.