(4lf) Optimizing Renewable Energies through Consumer Engagement: Media Influence and System Design

- Net-negative utility-free multicarrier systems;

- Data-driven techno-econo-socio-environmental sustainable strategies;

- Water-Exergy-Carbon nexus;

- Optimal multi-scale decision-making;

- Innovative demand management;

Teaching interests:

Graduate school lectures:

Thermal systems design

Renewable energies and multigeneration systems

Optimal scheduling and operation of networks

Big data analysis

Big data visualization

Machine learning algorithms

Machine learning applications

Undergraduate lectures:

Mathematical programming

Environmental statistics

Thermodynamics and environment

Abstract:

Optimal design and operation of Renewable Energy Systems (RES) critically depend on energy production technologies, available resources, and consumer demand patterns^1,2. Although green energy technologies share similarities, significant variations in weather, demographics, and policies across regions introduce complexities in dynamic energy demand management³. Recent global events, including pandemics and international conflicts, have underscored the importance of effective demand control in RES⁴. Traditional demand response programs predominantly rely on economic incentives or prices, overlooking the potential of direct consumer engagement, particularly during peak demand periods^5,6. Direct consumer involvement can substantially reduce RES size and costs by minimizing the mismatch between energy consumption and supply. This study explores the efficacy of media platforms as tools for demand control, assessing their impact on electricity consumption and RES sizing across three continents. A comprehensive intercontinental field survey was conducted in Hawaii (USA), Incheon (Korea), and Melbourne (Australia) using a questionnaire designed around the Stimulus-Organism-Response theory⁷. The survey aimed to evaluate the influence of both traditional and social media content on consumers' energy use behaviors during peak times, covering 20 categories of energy-intensive appliances. Using Cochran’s sampling formulae⁸, a sample of 640 participants was involved, allowing for a nuanced understanding of energy reduction potentials under various media influence scenarios. The sample consisted of random energy consumers from diverse ethnics, age groups, educational digress, and income levels to represent the whole population with an acceptable confidence. The survey results facilitated the generation of controlled demand time series, enabling the optimal design of RES in each location. Simulations compared the effectiveness of media-driven demand control against traditional methods, analyzing configurations of solar PV panels, wind turbines, and batteries in both standalone and hybrid systems⁹. The findings reveal significant benefits of media-based demand control, including reduced net present costs, smaller system sizes, and lower losses across all case studies. The choice between AI-driven and human decision-making in demand control was also examined, highlighting the potential for a comprehensive global energy reduction strategy. This presentation demonstrates the transformative potential of media platforms in enhancing the efficiency and efficacy of renewable energy systems through direct consumer engagement. By extending this research to additional case studies, a global plan for energy reduction that harnesses the power of media influence can be developed, offering new pathways for sustainable energy management.

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