Electricity consumption is a critical indicator of economic development and societal well-being, but climate change necessitates innovative energy solutions. This work addresses the scarcity of models for energy management projects that balance electricity demand, economic viability, and environmental concerns. The proposed model integrates Real Options, Measurement and Verification (M&V) methods, and time series analysis to assess the technical and financial aspects of energy management projects involving distributed generation and energy efficiency.
The Measurement and Verification (M&V) methods play a central role in Energy Conservation Services Companies (ESCOs), focusing on energy efficiency, supply-side management, and distributed generation. The International Performance Measurement and Verification Protocol (IPMVP) and ASHRAE Guideline 14 provide guidelines, incorporating statistical metrics for reliable results and addressing uncertainties associated with baseline models. The investment analysis utilizes Real Options methodology, emphasizing flexibility in decision-making and considering economic, technical, and strategic uncertainties through decision trees, sensitivity analysis, and Monte Carlo simulation.
The proposed conceptual model comprises two phases. The first evaluates candidate projects with distributed generation and energy conservation measures, aligning with ISO 50001:2015 guidelines. The second phase employs Real Options models for financial assessments, considering uncertainties in energy prices using Monte Carlo Simulation. The model's applicability is demonstrated in a pharmaceutical industry case study, showcasing a 4% average reduction in energy consumption post-implementation of energy conservation measures and revealing the feasibility of a distributed solar generation project.
Results from the pharmaceutical industry case study indicate the effectiveness of the baseline model with statistical metrics confirming the energy savings. The financial analysis using the Real Options approach considers uncertainties in energy prices, revealing a significant increase in project value when exercising the expansion option. The proposed model allows for a comprehensive technical-financial evaluation, integrating technical uncertainties into the financial analysis.
In conclusion, the proposed conceptual model proves effective in evaluating energy management projects, providing insights into project scope planning, M&V processes, and financial assessments. The dynamic regression model for M&V demonstrates a robust fit to accurate data, and the Real Options approach effectively addresses economic, technical, and strategic uncertainties. While the model has limitations, it presents a valuable framework for future works in exploring uncertainties and optimizing energy management projects. Overall, the model offers a nuanced approach to decision-making in the evolving landscape of energy management and sustainability.
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