A DEMAND AND POLICY-DRIVEN FRAMEWORK FOR PHOTOVOLTAIC ASSESSMENT: THEORY, VALIDATION, AND APPLICATION IN QATAR

Abstract

Despite high solar irradiance, the adoption of distributed photovoltaic (PV) systems in Qatar remains limited, partly due to persistent uncertainty in estimating technical performance, economic viability, and the effects of policy mechanisms at the user level. Existing feasibility assessments exhibit a methodological gap: proven simulation tools, such as System Advisor Model (SAM), provide detailed and accurate outputs but require extensive technical inputs, while simplified approaches often reduce input requirements without systematically evaluating the resulting loss of accuracy. In addition, conventional PV sizing methods typically optimize levelized cost of electricity (LCOE) and do not account for the temporal mismatch between generation and demand. Policy mechanisms are also frequently analyzed independently of user-specific load profiles and system configurations. This study addresses these limitations through the development and validation of a demand-integrated PV feasibility framework. The proposed approach combines a simplified, physically-based PV performance model with high-resolution Advanced Metering Infrastructure (AMI) consumption data to enable user-level estimation of generation, self-consumption, and financial performance. The framework introduces a demand-driven system sizing methodology that determines PV capacity based on temporal load characteristics. It further incorporates a policy-sensitive evaluation module to assess the effects of alternative regulatory mechanisms, including capital subsidies, net metering, and time-of-use tariffs, across heterogeneous demand profiles. Model outputs are benchmarked against the System Advisor Model under consistent system and climatic assumptions. Results indicate that the simplified framework achieves close agreement with SAM, with a Mean Absolute Percentage Error of 2.32% and an R² of 0.98 for energy yield estimation. Case studies across various demand sectors including residential, commercial, hotels, and government, demonstrate that both optimal system sizing and financial performance are highly sensitive to load profiles and policy conditions, with significant variation in self-consumption ratios and payback periods Tariff scenarios including time-of-use (ToU), dynamic seasonal, and real-time pricing (RTP) show that RTP is the only structure that improves financial performance universally across all demand sectors. The proposed framework provides a transparent and relatively simple method for electricity users to estimate PV generation and associated financial outcomes under different system configurations. In addition, the results offer insights into how demand characteristics and policy mechanisms influence system sizing, self-consumption, and economic performance, which may be of relevance to utility providers and policymakers when evaluating distributed PV integration strategies.

Date of Award	2026
Original language	American English
Awarding Institution	HBKU College of Science and Engineering