Designing optimized energy scenarios for an Italian Alpine valley: the case of Giudicarie Esteriori. (1st December 2016)

• Record Type: Journal Article
• Authors: Mahbub, Md Shahriar; Viesi, Diego; Crema, Luigi
• Is Part Of: Energy. Volume 116:Part 1(2016)
• Page Start: 236
• Page End: 249
• Publication Date: 2016-12-01
• Subjects: Energy scenario design -- Multi-objective optimization -- Energy planning -- Energy modelling; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.09.090
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The design of future local energy scenarios, under the framework of covenant of mayors' initiative, is an important and challenging task for the energy and policy planners. Designing energy scenarios is a multi-objective optimization problem, hence, a framework that combines a multi-objective evolutionary algorithm and EnergyPLAN is employed to identify optimized scenarios. In this study, optimized scenarios for the policy makers of Giudicarie Esteriori are identified, so that they are able to face the challenges of minimizing energy costs and CO2 emissions, decreasing the dependency on foreign resources, and integrating large amount of renewable energy. The results show that economically attractive, environmental friendly and less dependent energy scenarios can be achieved by 1) increasing the capacity of photovoltaics, 2) maximizing local biomass usage through individual wood boilers, and 3) partially electrifying the thermal sector through ground source heat pumps. The modification of the transport sector by introducing electric cars is not economically viable under the current market conditions. Our kind of study can be performed for the policy makers of other regions as well, by 1) collecting energy data, 2) identifying local renewable resources, 3) modelling reference scenarios, 4) identifying optimized scenarios, 5) studying the scenarios according to the requirements. Highlights: Electric, thermal and transportation demands are collected and analyzed. A reference scenario is modelled and analyzed. Optimized energy scenarios are identified by considering multiple objectives. The reference scenario and interesting optimized scenarios are compared. Optimized scenarios show a significant improvement in most of the objectives.