Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming. (1st June 2020)

• Record Type: Journal Article
• Authors: Martelli, Emanuele; Freschini, Marco; Zatti, Matteo
• Is Part Of: Applied energy. Volume 267(2020)
• Publication Date: 2020-06-01
• Subjects: Energy policy -- Incentives -- Carbon tax -- District energy systems -- MILP -- Black-box optimization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115089
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A bi-level approach reproducing the decision process to optimize energy policy. Renewable incentive and carbon tax is optimized by the government at the upper level. System design and operation is optimized by the owner/operators at the lower level. Application to four real case studies show improved cost-effectives of optimized policy. Abstract: The use of optimized Multi-Energy Systems, including renewables, combined heat and power units and energy storages, is proven to be effective in the reduction of fossil CO2 emissions. These systems can be efficiently operated to provide electricity, heating and cooling to energy districts and buildings. To increase the share of renewable sources and further decrease CO2 emissions, incentives and/or carbon taxes are set by governments. This work proposes a novel bi-level optimization approach which mimics the actual bilevel decision process to determine the optimal renewable subsidy and carbon tax for small-medium multi-energy systems. At the upper level the government decides the incentives/tax to meet the desired emission reduction target while minimizing its costs and, at the lower level, the owner/operator of the Multi-Energy System decides the optimal design and operation to minimize its Total Annual Cost (sum of investment and operating costs). We devise an efficient heuristic approach to solve the bilevel program and apply the approach to four different real-world applications, namely a university campus, a hospital, an urban district, and an office building.