Log in

Crete

The figures and content herein were published in: Applied Energy, Terlouw, T., Savvakis, N., Bauer, C., McKenna, R., & Arampatzis, G. (2024), Designing multi-energy systems in Mediterranean regions towards energy autonomy, Volume 377, Part B, 124458.

Figure 1 shows the layout of a decentralized MES in Crete, illustrating various technologies that can be installed for island decarbonization. This system can integrate several technologies for energy generation, conversion, and storage.

Electricity can be locally generated by installing onshore wind, offshore wind, and solar PV systems. Additionally, this locally produced electricity can be fed into the local power grid, which could also supply the MES if connected to the larger power grid network. However, entirely off-grid MESs would not have this option, which means no power grid connection. Heat can be produced locally as well, using solar thermal systems.

Figure 1. MES is installed in Crete, including all possible energy technologies that can be installed. This figure has been adapted and is reproduced from Ref. (Terlouw et al., 2023). The figure is published in Terlouw et al. (2024).

Figure 1. MES is installed in Crete,  including all possible energy technologies that can be installed. This figure has been adapted and is reproduced from Ref. (Terlouw et al., 2023). The figure is published in Terlouw et al. (2024).

This MES in Crete features a wide variety of conversion technologies. Alternatives for converting electricity into heat include electric boilers, residential electric heaters, and air-source heat pumps. It is essential to differentiate between low-temperature heat (below 100°C), typically used in residential applications, and high-temperature heat (above 100°C), which is typically required for industrial processes. Diesel boilers, electric boilers, or advanced CHP units can generate high-temperature heat. These advanced CHP can utilize hydrogen, syngas (a mix of CO and H2 from biomass gasification), and biogas (CH4 and CO2 from anaerobic digestion), with the gases mixed by using a gas mixing unit.

Hydrogen might play a key role in decarbonizing decentralized MESs. As such, PEM electrolyzers and PEMFCs can be installed to generate hydrogen and convert it back into heat and electricity, respectively. The MES includes various energy storage technologies to enhance operational flexibility, including long-term hydrogen storage and batteries. For residential mobility, options include gasoline vehicles or BEVs.

Scenarios

When comparing different MES design options, different scenarios are considered, including those focused on the bakery industry (BI), which requires high-temperature heat. Further, larger-scale MES scenarios are included considering the industry, residential households, and residential (personal) mobility sectors. Due to different system boundaries, direct comparisons are challenging. Table 1 summarizes these scenarios and their energy demands. The BAU is used as a baseline for comparison with the design scenarios given below:

  • BAU (BI): The current energy system for the bakery industry in 2022, relying on fossil fuels (diesel boiler) and GHG-intensive power from the local Cretan grid.
  • Cost-Min (BI): Minimum-cost optimization for the bakery industry, excluding environmental factors and location-specific regulations.
  • Cost-Min-Constr (BI): Minimum-cost optimization for the bakery industry, excluding environmental factors but considering current local regulations (e., max. 0.5 MW solar PV, 0.06 MW onshore wind, with a micro wind turbine).
  • BAU: The current energy system for the entire MES in 2022, including the bakery industry, residential power, cooling, heating, and mobility, relying on fossil fuels and GHG-intensive power from the Cretan grid.
  • Cost-Min: Minimum-cost optimization for the entire MES, excluding environmental factors and location-specific regulations.
  • Cost-Min-Constr: Minimum-cost optimization for the entire MES, excluding environmental factors but considering local regulations regarding maximum renewable capacity for onshore wind and solar PV.
  • GHG-Min: Optimization focusing on minimizing life cycle GHG emissions, excluding cost considerations and location-specific regulations for solar PV and onshore wind capacity.
  • Off-grid: Minimum-cost optimization for the entire MES without a connection to power and gas grids, operating entirely off-grid and excluding power and hydrogen export.
  • Balanced Autonomy: Minimum-cost optimization for the entire MES with connections to power and gas grids, ensuring that local renewable power production (from solar PV, wind, and biomass) meets or exceeds annual power consumption, achieving balanced autonomy. This scenario does not allow hydrogen export.

 

Scenario

Industrial

Residential

Mobility

Location-specific regulations

Power grid

Cost opt.

GHG opt.

High-temperature heat demand [GWh]

Low-temperature heat demand [GWh]

Power demand [GWh]

BAU (BI)

x

      

x

    

3.04

0

1.06

Cost-Min (BI)

x

      

x

x

  

3.04

0

1.06

Cost-Min-Constr (BI)

x

    

x

x

x

  

3.04

0

1.06

BAU

x

x

x

  

x

    

3.04

0.86

3.43

Cost-Min

x

x

x

  

x

x

  

3.04

0.86

3.43

Cost-Min-Constr

x

x

x

x

x

x

  

3.04

0.86

3.43

GHG-Min

x

x

x

  

x

  

x

3.04

0.86

3.43

Off-Grid

x

x

x

    

x

  

3.04

0.86

3.43

Balanced Autonomy

x

x

x

  

x

x

  

3.04

0.86

3.43

Results

 

Figure 2 compares the cost and life cycle GHG emissions between the eight scenarios, with the left subplot focusing on the industrial sector and the right illustrating the entire MES. Each scenario includes two stacked bars—annual cost on the left and annual GHG emissions on the right. The colored stack segments represent contributions from different technologies and energy carriers. Dashed lines (with percentages) indicate changes in cost due to location-specific regulations versus ‘unconstrained’ conditions.

 

Figure 2. Overall results: annual cost and life cycle GHG emissions of optimal MES designs in Crete. The diamond markers represent the net annual costs and GHG emissions. The figure is published in Terlouw et al. (2024).

 

The results reveal that implementing MESs can significantly reduce costs and GHG emissions in Crete. For the bakery industry, costs can be reduced by up to 81%, while the entire MES could reach a 30% cost reduction compared to the BAU scenarios. Even under location-specific regulations and constraints, cost and GHG reductions are substantial, although cost savings for the entire MES are halved to approximately € 0.8 million.

Current regulations, such as limits on wind and solar PV capacity, reduce the decarbonization potential, primarily due to restrictions on exporting power to the grid. An unconstrained design, however, could reduce costs by over 67% through the potential export of locally generated renewable energy.

 

Figure 3 illustrates six spider graphs showing the environmental trade-offs of the optimal MES designs across the six scenarios. The impacts are normalized against the scenario with the highest impact in each category. The BAU scenario shows the worst overall environmental performance, with the largest dark blue area, driven by substantial fossil fuel utilization and GHG-intensive grid electricity. In contrast, scenarios with energy export, especially those allowing a power grid connection and hydrogen export, show avoided environmental burdens (negative impacts, thus shown as ‘zero’ impact). Optimization scenarios generally reduce environmental burdens due to the increased implementation of cost-effective solar PV and onshore wind resulting in lower environmental burdens for most environmental impact categories. However, the ‘Cost-Min-Constr’ scenario faces trade-offs in land use, mainly from biomass needed for the advanced CHP to generate industrial high-temperature heat. Off-grid MESs exhibit trade-offs in material use, water consumption, and human toxicity due to the oversizing of renewables (and curtailment) and energy storage installations.

 

Figure 3. Spider graph with the different scenarios considered for the entire MES in Crete, and associated life cycle environmental burdens on selected normalized environmental impact categories. LT = land transformation. AC = acidification. CC = climate change. ETF = ecotoxicity: freshwater. ETFI = ecotoxicity: freshwater, inorganics. ETFO = ecotoxicity: freshwater, organics. ER = energy resources: non-renewable. EFF = eutrophication: freshwater. EFM = eutrophication: marine. EFT = eutrophication: terrestrial. HTC = human toxicity: carcinogenic. HTCI = human toxicity: carcinogenic, inorganics. HTCO = human toxicity: carcinogenic, organics. HTNC = human toxicity: non-carcinogenic. HTNCO = human toxicity: non-carcinogenic, organics. HTNCI = human toxicity: non-carcinogenic, inorganics. IR = ionizing radiation: human health. MM = material resources: metals/minerals. OD = ozone depletion. PM = particulate matter formation. PF = photochemical oxidant formation: human health. WU = water use. The figure is published in Terlouw et al. (2024).

 

Thus, integrating a comprehensive life cycle approach is key to evaluate overall environmental sustainability, trade-offs, and co-benefits of MESs.

All data and code of this work have been provided in a GitHub repository: https://github.com/tomterlouw/optimes.