Engineering Applications
Thermoeconomic optimization of climate-adaptive solar and wind multi-generation systems using artificial intelligence and thermal energy recovery
This study presents a hybrid multi-generation energy system designed to overcome solar intermittency while meeting the global demand for integrated delivery of electricity, water, cooling, and sustainable fuels in the transition to decarbonization. The engineering application integrates solar thermal and wind energy with a modified Brayton cycle, a Steam Rankine Cycle (SRC), and a Thermoelectric Generator (TEG) to simultaneously produce electricity, fresh water via Reverse Osmosis (RO), hydrogen and oxygen via Proton Exchange Membrane Electrolyzer (PEME), and cooling (via absorption chiller) within a unified optimization framework. The system was modeled using Engineering Equation Solver (EES) and optimized via Response Surface Methodology (RSM) based on 11 decision variables. To address the complexity of optimization, a second phase applied Artificial Intelligence (AI) techniques: Adaptive Boosting (AdaBoost) for predictive modelling and Particle Swarm Optimization (PSO) for global optimization. Under optimal conditions, the Response Surface Methodology yielded an exergy efficiency of 45.8% with a cost rate of 576.76 United States Dollars per hour (USD/h), while AI reduced costs to 211.2 USD/h with a moderate efficiency trade-off. Simulation of the optimized configuration across eight diverse climates identified Quebec as most viable, generating 22,629.6 Megawatt-hours per year (MWh/year) of electricity and avoiding 4616.4 tons of Carbon Dioxide (CO2) emissions annually. Integration of wind energy stabilizes solar variability, enhancing performance. Al contributes to optimizing complex interactions, nonlinear constraints, and multiple conflicting objectives. The methodology offers a scalable, generalizable framework for designing intelligent, climate-resilient infrastructures. Future research includes AI-enabled real-time control, experimental validation, and broader deployment strategies.
Executive Impact Summary
Key performance indicators from the optimized multi-generation system highlight significant advancements in efficiency, cost reduction, and environmental sustainability.
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Exergy Efficiency (RSM)
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Cost Rate (RSM)
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CO2 Emissions Avoided
Deep Analysis & Enterprise Applications
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Optimization Approaches
RSM (Response Surface Methodology): RSM was initially used for its computational efficiency and statistical clarity in exploring the high-dimensional design space of the hybrid system. It provided a reliable baseline by striking a balanced trade-off between energy performance and cost, achieving an exergy efficiency of 45.8% and a cost rate of $576.76/h.
AI (AdaBoost + PSO): A second phase of optimization applied Artificial Intelligence techniques, specifically AdaBoost for predictive modeling and Particle Swarm Optimization (PSO) for global optimization. This AI-driven approach significantly reduced the cost rate to $211.2/h, albeit with a moderate efficiency trade-off (32.93% exergy efficiency).
Combined Approach: The dual-stage optimization strategy, combining RSM with AI, identifies the most reliable and cost-effective configuration under real-world conditions. It addresses RSM's limitations in capturing complex nonlinear relationships by refining initial results with AI, balancing thermodynamic efficiency and economic feasibility.
63.38%
Reduction in Cost Rate Achieved by AI Optimization
System Design and Optimization Workflow
System Design & Configuration
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Thermodynamic & Economic Modelling
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Validation
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Multi-Objective Optimization (RSM)
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Wind Power Allocation
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Best Performing System Configuration
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Simulate Optimal System in Eight Climates
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Environmental & Stability Analysis
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Multi-Objective Re-Optimization (AI)
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Comparison & Results
| Method | Exergy Efficiency | Cost Rate ($/h) | Advantages | Disadvantages |
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| RSM (Response Surface Methodology) | 45.8% | 576.76 |
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| AI (AdaBoost + PSO) | 32.93% | 211.2 |
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Climate Adaptability in Quebec City
Simulation across eight diverse climates identified Quebec as particularly viable for deployment. The region's high wind energy potential significantly stabilizes solar variability, especially in winter months when solar output is limited. This complementary relationship ensures consistent energy output, making the system resilient and adaptable to seasonal changes. Quebec's annual electricity generation reached 22,629.6 MWh/year, showcasing its strong potential for sustainable energy contribution.
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Annual Electricity Generation
Calculate Your Potential ROI with AI
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Estimated Annual Savings
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Your AI Implementation Roadmap
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Phase: Strategic AI Assessment
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Phase: Solution Design & Prototyping
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Phase: Full-Scale Integration & Deployment
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Phase: Performance Monitoring & Iteration
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Phase: Long-Term AI Strategy & Scaling
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Immediate Actionable Insights for Your Enterprise
Based on the advanced analysis of "Thermoeconomic optimization of climate-adaptive solar and wind multi-generation systems using artificial intelligence and thermal energy recovery," here are tailored recommendations:
- Prioritize AI-driven optimization for cost-sensitive projects, leveraging its ability to drastically reduce operational expenses while maintaining moderate efficiency.
- For projects where peak thermodynamic performance is paramount, RSM can provide a strong initial design, which can then be refined with AI for economic benefits.
- Integrate wind energy in solar-dominant regions to stabilize energy output and ensure reliability across seasons, as demonstrated by the Quebec case study.
- Invest in advanced component efficiencies (gas turbine, compressor) as they have the highest impact on overall system performance and cost reduction.
- Implement real-time AI-enabled control and experimental validation in future deployments to fully harness the system's adaptive capabilities and refine performance under dynamic conditions.
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