Researchers from the Polytechnic University of Milan have developed a new computational model to identify the best spatial distribution of renewable energy sources within an individual country or electricity system while avoiding problematic concentrations of technologies.
The new technique, which is intended to create what the scientists call spatially explicit, practically optimal results (SPORES) for renewable energy deployment, was applied to the Italian energy system. As a result, only PV and storage were found to be “vital components” for the country’s decarbonization plans by 2050. “Most alternative configurations are insensitive to cost and demand uncertainty, while dealing with adverse weather requires excess renewable generation and storage capacities,” the academics affirmed.
PV, on the other hand, is said to have a narrow distribution that peaks around 45% capacity utilization, which never falls below 15%, while all other technologies can be entirely substituted by functionally equivalent alternatives.
According to research team, the best cost configuration from the first attempt to apply the model to the Italian energy landscape has showed that PV may reach an installed power of 144.5 GW by the end of the first half of the century, followed by onshore and offshore wind with 59.6 GW and 17.6 GW, respectively.
Furthermore, hydrogen capacity may top 7.0 GW and methanation with CO2 capture 5.6 GW. “This allows 11.7 GW of existing combined-cycle gas turbines to be kept in operation,” the Italian group stressed.
This optimal configuration, however, would materialize only if solar and wind would see annual growth rates of 4.1 GW and 2.2 GW, respectively. In recent years both technologies grew only by a few hundred megawatts per year.
“Although capacities and deployment rates in the cost-optimal result are thus feasible, four features of the optimal solution stand out as highly relevant and potentially problematic for policy makers,” the scientists stated.
These are uneven distribution of wind and solar power across the peninsula, underutilized connections in expanded grid, a system design based on a specific weather year and public opposition caused by low social acceptance of renewable energy technologies. “Using our approach, we will investigate alternative but equally feasible solutions, generating a decision space to help navigate around the possibly problematic features highlighted above,” they further explained.
The proposed computational model developed by the Italian researchers is defined as a spatially explicit extension to the ‘‘modeling to generate alternatives’’ (MGA) method with the specific goal of being embedded in an energy system model with high spatial resolution.
The tool is said to be able to create a rich set of alternatives that can be used in the policy-making process. “To help with the urgent task of planning socially and politically acceptable energy system decarbonization strategies, our implementation of SPORES in the open-source energy systems modeling framework Calliope makes it accessible to a wide range of potential users,” the research team said. Calliope is a free and open-source software that makes it easy to build energy system models at scales ranging from urban districts to entire countries.
Furthermore, the SPORES tool is claimed to be able to identify key technologies for future energy systems, as well as those that are costly to replace. “A higher cost relaxation, which can be interpreted as a higher willingness to pay, is required for system configurations without these technologies,” the academics affirmed. “In the context of Italy, PV is found to be a must-have, while bioenergy, batteries and international transmission emerge as the costliest to replace.
The model is presented in the paper Policy Decision Support for Renewables Deployment through Spatially Explicit Practically Optimal Alternatives, published in Joule, and on the ScienceDirect website.
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