SPLODER (Smart Planning and Operation of Distributed Energy Resources (DER)) was developed at IIT (Institute for Research in Technology).

The SPLODER model, which is referred to in the literature in its System version, is a planning tool for both centralized and distributed generation and storage resources of the electric power system. This version has been developed for regulatory analysis and to support the strategic decisions of electric utilities. It includes a version formerly known as Building, which allows detailed consumption modeling.

By means of input data and an optimization problem, the model is able to provide the optimal mix of technologies in economic terms to meet the demand, both in terms of energy consumed and in terms of guarantee or firmness of supply. Therefore, the objective function of the model minimizes the cost of investments in new centralized and distributed resources, as well as the operating cost of both these new investments and existing resources.

To be computationally efficient, a generation and storage expansion model such as SPLODER needs to simplify the representation of system operation. That is why the model simplifies the system operation by considering 4 representative weeks of the year with hourly granularity. Each representative week has a different weight according to the number of weeks it represents:

• Week 1: December, January and February.
• Week 2: March, April, October and November.
• Week 3: May, June, July and September.
• Week 4: August.

In order to adequately reflect the loading and unloading cycles of the storage facilities and the management of hydraulic production, the technologies that allow some type of storage comply with the fact that the load level is the same in the first and last hour of the representative week. Except for the case of pumping, which is allowed to store water from one week to another because otherwise its potential would not be well collected.

To mitigate the effect of working with four representative weeks of the year and to better evaluate the contribution to the firmness of supply of the different technologies, the SPLODER model incorporates an explicit restriction that establishes as a requirement to cover the firmness needs of the system, using predetermined coefficients of contribution to the firmness of each of the competing technologies. The competitiveness of the technologies may depend significantly (together with their other characteristics of CAPEX, OPEX, performance, storage capacity, etc.) on the firmness contribution coefficient attributed to them.

The input parameters for each of the case studies include:

1. Solar and wind generation profiles. Up to 5 profiles for each technology, which are intended to capture the different geographical areas available in which to install.
2. Maximum generation profile for cogeneration and run-of-river hydro. In addition to the water available for conventional hydropower.
3. The installed capacity and the maximum and minimum capacity that can be installed by technology, together with their firmness coefficients.
4. For each generation technology the following are available: annualized installation costs, annual operation and maintenance (O&M) costs, variable costs (fuels and variable O&M), start-up and shut-down costs, typical plant capacity, technical minimum, ramps up and down, availability percentage and their CO2 emissions whenever applicable.
5. For storage and distributed resources, the loading and unloading cycle and efficiency are also introduced.
6. Grid losses, technology taxes and CO2 emissions cost.
7. Demand profiles broken down by sector and geographical area, and if available by type of consumption. In case the breakdown by consumption typology is not available, it would be necessary to remove the consumption associated to air conditioning from the demand profile entered and give the model an outdoor temperature profile for each geographical area considered, since SPLODER uses a thermal model that uses the outdoor temperatures to know how much to heat/cool the house based on comfort requirements, considering the thermal inertia of the buildings.  
8. Total number of electric vehicles (EVs) in the study year and their associated consumption.
9. Percentage of flexible demand (DR) available for each type of consumption: air conditioning, DHW and EV.
10. Distributed resources installed by geographical area.

Once the model has been executed, the following variables are obtained as results:

1. Investment decisions in the most profitable centralized and distributed resources, taking into account the available generation park and the energy needs, both electrical and thermal, of the consumers, guaranteeing the security of supply.
2. Investment and operation costs of the resources selected by the model.
3. Cost recovery analysis by technology according to existing price signals.
4. Optimal dispatch costs.
5. Operation in the representative weeks at hourly level of the different technologies.
6. Price of the resulting energy.
7. Output demand profile modified by the consumption of EVs and DR.
8. Total CO2 emissions.

The model also allows imposing other restrictions that are very useful when designing scenarios:

• Renewable production requirement: the percentage of energy that must be produced by renewable sources is introduced.
• Emissions limit: The maximum value of emissions from electricity generation is introduced.

The model has three price signals. First the market price that it estimates with the selected mix, then it considers a retribution to the technologies for their contribution to the system with firm capacity in case it requires them only for this reason and not for cost competitiveness, and finally an additional payment to the renewable energy sources in case it is necessary to reach a certain established percentage of renewables. The tariff design of the model consists of:

• A percentage on the final price for all technologies.
• Access tolls €/MWh for all technologies.
• Specific taxes €/MWh for gas and coal.

Funded Projects

• FLEXENER: New 100% renewable, flexible and robust energy system for the integration of new technologies in generation, networks and demand - Scenarios. Developed for Iberdrola Generación España, S.A.U. Oct 2020-Dec 2023.
• MODESC – Platform of innovative models for speeding the energy transition towards a decarbonized economy. Developed for Ministerio de Ciencia e Innovación (MCIN), Agencia Estatal de Investigación (AEI). Sep/2020-Dec/2023.
• Analysis of the generation mix in 2030 under different demand and generation management scenarios. Developed for Repsol S.A.. Sep 2019-Oct 2019.
• Analysis of the expansion and operation of the Spanish electricity system for a 2030-2050 time horizon. Developed for Iberdrola España S.A.U. Jan 2019-Dec 2023.
• Analysis of the Spanish nuclear power plants´ lifespan extension, from the plant owners perspective. Developed for Iberdrola S.A. May 2018-Dec 2018.
• The future of the Spanish electricity sector. Developed for Iberdrola S.A.. Apr 2018-Sep 2018.
• Analysis of different technology based scenarios in power systems for the 2040 horizon. Developed for Iberdrola S.A. Feb 2017-Dec 2017.
• Energy scenario assessment in a context of cost reductions of distributed energy resources. Developed for Gas Natural SDG, S.A. Dec 2016-Dec 2017.

References

[1] Francisco Martín Martínez. Modeling tools for planning and operation of DERs and their impact in microgrids and centralized sources. (2017).

[2] F. Martín-Martínez, A. Sánchez-Miralles, M. Rivier, C.F. Calvillo, Centralized vs distributed generation. A model to assess the relevance of some thermal and electric factors. Application to the Spanish case study, Energy. 134 (2017) 850–863.

[3] T. Gerres, J.P.C. Ávila, F.M. Martínez, M.R. Abbad, R.C. Arín, Á.S. Miralles, T.G. San Román, “Rethinking the electricity market design: Remuneration mechanisms to reach high RES shares. Results from a Spanish case study” (Energy Policy (2019) 129 (1320–1330), (S0301421519301995), (10.1016/j.enpol.2019.03.034)), Energy Policy. 131 (2019) 434. https://doi.org/10.1016/j.enpol.2019.04.014.

[4] T. Freire-Barceló, F. Martín-Martínez, Á. Sánchez-Miralles, M. Rivier, T.G.S. Román, S. Huclin, J.P.C. Ávila, A. Ramos, Storage and demand response contribution to firm capacity: analysis of the Spanish electricity system, Energy Reports. 8 (2022) 10546–10560. https://doi.org/10.1016/j.egyr.2022.08.014.

[5] M. Rivier, F. Martín, T. Freire-Barceló, Solar and wind production profiles and 2030 disaggregated electricity demand profiles to feed the SPLODER generation expansion planning model. Project: FLEXENER. Funded by Siemens Gamesa Renewable Energy Innovation & Technology S.L. Dec/2022.

[6]T. Freire-Barceló, F. Martín, A. Sánchez. System planning with demand assets in balancing markets. International Journal of Electrical Power & Energy Systems. 156 (2024).