A Scenario Framework for Investment Appraisal and Shared Risk in Smart Microgrids in Industrial Zones

This study develops a scenario-based analytical framework for assessing smart microgrids in industrial zones as infrastructure investments and for analyzing multi-actor risk allocation and governance. No empirically validated return is reported for a specific Bulgarian industrial zone. Public institutional, market, financial, technology and environmental sources are instead used to benchmark the scenario assumptions. Three scenarios are evaluated for a reference zone with annual consumption of 12,000 MWh. The model incorporates photovoltaic (PV) degradation, battery round-trip efficiency of 85–90%, annual usable-capacity degradation, one modeled battery replacement within a 12–15-year service interval, component-based capital and operating expenditures, and an author-defined semi-quantitative likelihood–impact risk matrix. Net present value (NPV) is EUR −1,114,726 in the conservative scenario, EUR 697,836 in the baseline scenario and EUR 3,424,835 in the favorable scenario. Discounted payback is not achieved within 20 years in the conservative scenario and is approximately 14.1 and 7.0 years in the baseline and favorable scenarios, respectively. Deterministic one-at-a-time sensitivity analysis identifies electricity price, capital expenditures, and the direct PV self-consumption ratio as the dominant financial drivers.The indicative reduction in location-based emissions associated with grid electricity purchases is 802.9–1385.5 tonnes of carbon dioxide equivalent (tCO 2 e) per year under the selected grid-average electricity emission factor. The contribution lies in integrating public-data availability assessment, external parameter benchmarking, battery service life, degradation and replacement economics, investment appraisal, threshold analysis, semi-quantitative risk prioritization and contractual risk allocation within one reproducible framework. The outputs remain illustrative and require project-level validation using measured hourly loads, binding prices, financing terms and enforceable contracts.