Multi-Objective Optimization Model EPLANopt for Energy Transition Analysis and Comparison with Climate-Change Scenarios
2. Materials and Methods
3. Italian Case Study
3.2. PNIEC 2030 Scenario
3.3. Decision Variables and Assumptions
- Solar PV. For residential rooftop PV a couple of studies, Taylor et al.  and Vartiainen et al. , together with internal studies of Eurac research based on the Solar Tyrol project  identified a share of 2 kW per person as the maximum rooftop PV potential. Considering roughly 60 million inhabitants in Italy the final maximum potential for residential PV is assumed to be 120 GW. For what concerns utility scale PV, the maximum potential is taken from a study of the Energy Strategy Group  which studied the potential for the Italian territory evaluating the brownfield sites and unutilized rural areas. The overall estimated value is equal to 70 GW. An analysis of the land use for solar power by 2030 was realized by F. Mancini et al. . They demonstrated how the use of 10% of the soil already consumed could be sufficient to achieve the set objectives by 2030.
- On-shore wind power. Hoefnagels et al.  in the framework of the RE-shaping project estimated a maximum potential of 49 GW for Italy.
- Lithium-ion batteries. The maximum potential is evaluated through a series of simulations. A value above 600 GWh brings higher costs without any benefits in terms of renewable energy integration.
- Power to gas is managed through two variables: the produced hydrogen and the capacity of the electrolyzer. The produced hydrogen maximum potential is assumed to be 15% of the overall natural gas consumption. The maximum size of the electrolyzer is taken high enough to exploit the full potential of power-to-gas and low enough to contain the domain of the optimization problem.
- The installation of heat pumps is allowed only after a deep energy refurbishment of buildings. This decision variable is the percentage of the overall buildings that switched their heating system from boilers to heat pumps. For this reason, its maximum potential is 100%.
- The energy efficiency of buildings: the potential of energy efficiency by means of passive solutions is bound to the energy efficiency cost curve and is equal to 75%. The energy efficiency cost curve and the way it is implemented in the source code of EPLANopt is explained in a previous publication .
3.4. Optimization Problems Definition
- One case considering 10% electric mobility
- One case considering 20% electric mobility
- The PNIEC 2030 scenario produces a relevant reduction of CO2 emissions compared to the Baseline 2015. This reduction is in line with the CO2 emissions reduction target in 2030. The PNIEC 2030 scenario is found by Italian authorities through an optimization process but the assumptions on costs and efficiencies of the energy system components are not public. Therefore, it is important to validate the model and the PNIEC 2030 scenario. This result allows this validation which is added to the validation of the Baseline 2015 on CO2 emissions.
- The PNIEC 2030 scenario, characterized by 10% electric mobility penetration, is almost placed on the Pareto front characterized by 10% electric mobility. Thus, it is a solution close to the optimum. As already mentioned, the PNIEC 2030 scenario is found as a result of an optimization process by Italian authorities. In this study, the difference between the PNIEC 2030 scenario and the Pareto front with 10% electric mobility can be a consequence of different costs assumptions.
- With the same cost of the PNIEC 2030 scenario it is possible to reach higher CO2 emissions reduction by selecting a solution on the Pareto front with 20% electric mobility. The Advanced 2030 scenario showed that at the same costs of the PNIEC 2030 there are solutions which further reduce the CO2 emissions. In this case the Advanced 2030 scenario produces a further reduction of 10%.
- Another consideration that needs to be done is on the impact of electric mobility. The increase of electric mobility from 10 to 20% together with the optimal energy mix found by the optimization algorithm allows a further reduction of the CO2 emissions.
Conflicts of Interest
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|Electricity||Capacity of renewables||GSE|||
|Hourly profile for renewables||Terna, GSE||[53,54]|
|Capacity for other technologies||Terna|||
|Electricity demand||Terna, HRE||[50,53]|
|Heating||Generation and consumption data||HRE|||
|Electric vehicles demand and charge profile||RSE|||
|Equivalent Hours||PV||Wind Power||Hydro (River)||Geothermal|
|Equivalent hours 2011||1325||1563||4060||7324|
|Equivalent hours 2012||1312||1855||4379||7243|
|Equivalent hours 2013||1241||1793||4392||7321|
|Equivalent hours 2014||1211||1767||4454||7206|
|Equivalent hours 2015||1225||1683||4374||7534|
|Average equivalent hours||1263||1732||4332||7325|
|Residential PV (GW)||15||120|
|Utility scale PV (GW)||4||70|
|Wind power (GW)||9||49|
|Lithium-ion batteries (GWh)||0||600|
|Power to gas, H2 produced (%)||0||15|
|Power to gas, Electrolyzer max capacity (GW)||0||30|
|Advanced biomethane (TWh)||3||15|
|Energy efficiency of buildings (%)||0||75|
|Heat pumps (%)||0||100|
|Scenarios||PV||Wind Power||Stationary Batteries||Batteries of EV||Advanced Biomethane||Energy Efficiency of Buildings|
|Baseline 2015||19 GW||9 GW||0 GWh||0 GWh||3 TWh||0%|
|PNIEC 2030||59 GW||23 GW||40 GWh||200 GWh||15 TWh||15%|
|Advanced 2030||86 GW||48 GW||0 GWh||400 GWh||3 TWh||30%|
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Prina, M.G.; Manzolini, G.; Moser, D.; Vaccaro, R.; Sparber, W. Multi-Objective Optimization Model EPLANopt for Energy Transition Analysis and Comparison with Climate-Change Scenarios. Energies 2020, 13, 3255. https://doi.org/10.3390/en13123255
Prina MG, Manzolini G, Moser D, Vaccaro R, Sparber W. Multi-Objective Optimization Model EPLANopt for Energy Transition Analysis and Comparison with Climate-Change Scenarios. Energies. 2020; 13(12):3255. https://doi.org/10.3390/en13123255Chicago/Turabian Style
Prina, Matteo Giacomo, Giampaolo Manzolini, David Moser, Roberto Vaccaro, and Wolfram Sparber. 2020. "Multi-Objective Optimization Model EPLANopt for Energy Transition Analysis and Comparison with Climate-Change Scenarios" Energies 13, no. 12: 3255. https://doi.org/10.3390/en13123255