Evaluating Optimal Capacity and Investment Strategies for Renewable Energy Projects: A Combined Technical and Financial Approach
再生可能エネルギープロジェクトの最適容量と投資戦略の評価：技術的・財務的アプローチの結合 (AI 翻訳)
Helen Josephine, Indhumathi Shanmugasundaram, Sharad Gupta, Manjari Sharma
🤖 gxceed AI 要約
日本語
風力・太陽光発電の変動性と需要パターンを考慮し、線形計画法を用いて最適設備容量を求めるフレームワークを提案。LCOE等の財務指標も統合的に評価し、インドを事例に実証。
English
This study presents a simulation-driven framework that combines hourly wind and solar generation modeling with linear programming to determine optimal renewable capacity. It integrates financial analysis including LCOE, working capital, and debt repayment, using Indian data to demonstrate practical viability.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でもFIT/FIP制度下での再エネ導入拡大が進む中、系統安定性と投資効率の両立は重要課題。本フレームワークは日本の地域特性に応じた電源構成最適化にも応用可能。
In the global GX context
As renewable penetration increases globally, balancing reliability and cost-effectiveness is critical. This framework offers a practical tool for developers and policymakers to design robust portfolios, relevant for markets like India and other emerging economies.
👥 読者別の含意
🔬研究者:Provides a validated methodology for integrating technical and financial optimization in renewable energy planning.
🏢実務担当者:Offers a practical framework for evaluating capacity investments and LCOE under realistic generation patterns.
🏛政策担当者:Demonstrates how to assess system reliability and cost impacts of renewable deployment, useful for capacity planning.
📄 Abstract（原文）
The global shift toward clean energy is accelerating, and by 2050 renewable sources are expected to supply more than 85% of the world’s electricity. This transition, however, introduces new layers of complexity. Wind and solar energy behave as interconnected subsystems whose output fluctuates with weather, season, and geography. Their interaction with fixed hourly demand, capital-intensive investments, and financing structures creates a multi-dimensional system in which small changes can trigger significant operational and economic consequences. This study presents a simulation-driven framework designed to understand and optimize this complex behaviour. The framework models hourly wind and solar generation alongside projected demand to examine how intermittency influences system reliability. A linear programming model forms the analytical core, identifying the minimum required installed capacity while still meeting demand across varying conditions. To reflect the real-world behaviour of energy infrastructure, the framework evaluates financial components such as Levelized Cost of Energy (LCOE), working capital requirements, depreciation, and long-term debt repayment based on the optimized system configuration. Using realistic capacity utilization factors, 37.89% for wind and 30.42% for solar, the model estimates LCOEs of ₹2.3672/kWh and ₹2.3089/kWh, respectively. Despite the higher capital cost of wind installations, its stronger utilization compensates for the investment, leading to comparable long-term energy costs. This outcome highlights how site-specific resource patterns shape overall system performance. By combining technical modelling with financial analytics, the study conceptualizes renewable energy planning as a complex socio-technical system. The proposed framework equips developers, investors, and policymakers with practical insights to design renewable portfolios that remain reliable, cost-effective, and adaptive in an increasingly dynamic energy landscape.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.63562/2577-8439.1157first seen 2026-06-13 04:34:15
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