Multiscale energy models for designing energy systems with electric vehicles André Pina 16/06/2010

Overview  Introduction  Proposed Methodology  Medium-term model  Short-term model  Integrated Model Framework  Flores case study  Case study application  Results  Conclusions

Introduction To increase sustainability and security of supply, several options must be considered: Renewable resources Energy storage Consumer behavior Energy efficiency Alternative transportation fuels (biofuels, electricity, others) To effectively design future energy systems, the interactions between the possible options must be accounted for: Intermittency of renewable resources Evolution of energy consumption Impact of energy efficiency policies Charging of electric vehicles Energy demand Energy storage Renewable energies

Introduction The introduction of electric vehicles has been identified as one of the key issues for the next years due to the potential gains that can be achieved: Tackling the consumption of fossil fuels in the transportation, which has only a few alternatives Reduction of fossil fuel imports Maximization of renewable energies use Possibility of having V2G for grid stabilization in the future However, they can present some challenges: Expansion of current electricity system needs to be addressed If not properly managed, the vehicles could be charged using electricity produced from fossil fuels What are the possible gains for different hours, days, months or seasons? Watt Drive, ponto EDP para veículos eléctricos

Introduction To address these issues, a large number of tools have been developed with very different scopes, resolution and algorithms. HourYear Region /Country Small communities/ Household level Second Spatial resolution Temporal resolution Projections Optimization Methodology Lack the ability to look into several years Lack the ability to account for hourly dynamics

Proposed methodology The work being developed proposes a methodology that combines a Medium and a Short-term models to plan the evolution of energy systems. Medium-term model Multi-year optimization of investments in renewable energies Some hourly dynamics Detailed description of energy consumption: Across different sectors Different types of energy carriers Capable of understanding the evolution of the system: Economic growth Fuel prices Energy demand Short-term model Hourly optimization of electricity production for one year, taking into account: Start-up costs and efficiencies for thermal engines Variability of renewable resources Solar Wind Hydro Fuel prices Demand profiles Dynamic demand options (EVs, others) Optimization of use of energy storage systems Test feasibility of the results, one year at a time Introduction of restrictions for optimization of investments

The medium-term model aims at optimizing the installation of new generation capacity by looking at an extended time horizon. In this work, a 25 years time period was considered. To include some dynamics, the medium-term model divides the year in: 4 seasons 3 days per season 24h per day The following assumptions are considered in the model: The model is deterministic and has perfect foresight; Electricity demand grows at the same rate for each time period for each sector. A mixed integer programing approach was used in this study due to the small scale of the region being analyzed. TIMES (The Integrated MARKAL-EFOM System) was used. Medium-term model

Short-term model The short-term model optimizes the operation of the electric system by minimizing the operation costs. This model considers the following assumptions: The installed capacities are provided by the medium-term simulation and are constant throughout the short-term simulation. The demand is provided by the medium-term model, but a stochastic term is introduced for each day. There is no continuity on the electricity production from renewables from the end of one day (23 hours and 30 minutes) to the beginning of the following day (0 hours). The model has a high time resolution: 365 days of the year. Hourly steps (24 periods) for each day. MATLAB was used to build this model.

Integrated modeling framework Both models are used in an iterative cycle: The medium-term model is used to optimize the investment in new generation capacity. The short-term model is used to calculate the energy balances over one year with higher time resolution (hourly or less) by optimizing the operational costs. Interactions between both models: The medium-term model provides to the short-term model the installed capacities of each energy source that it has to consider for the simulation of each year. The short-term model updates the medium-term model constraints parameters regarding the operation of the electricity generation facilities, such as the impossibility to start the operation on a certain year or the limitation on the amount of electricity that can be absorbed by the grid (effectively lowering the availability factor in that year). Generally, the framework works as shown in the figure.

Flores case study Flores is one of the smallest and most isolated island of the Azores archipelago (located in the middle of the North Atlantic Ocean) Population of ~4200 habitants Area of ~141 km LDV in 2008 High renewable electricity penetration In 2009, 54% of the electricity came from renewables Electricity production in Flores,

Flores case study – The electricity production system The electricity system is a combination of Wind-Hydro-Diesel. To maintain frequency and voltage stability at the grid level, a flywheel energy storage system is in place The system is able to achieve 100% renewable electricity In 2009, this happened for several hours of the day in at least 12 days of the year. Future investments: Hydro, 1600 kW, Hydro, 1040 kW, 2012.

Case study application The model had to make two decisions: If and when should the 1600 kW hydro turbine come online; If and when should the 1040 kW hydro turbine come online. Operation conditions: The operation condition consisted on the difference between the amount of hydro power produced in the MATLAB model and the TIMES model being smaller than 5%. A scenario approach was used based on the type of charging and the penetration of electric vehicles. EVs salesType of charging No EVsNone Scenario 1Low (10%)Fixed Scenario 2LowFlexible Scenario 3High (20%)Fixed Scenario 4HighFlexible 160 vehicles 64 vehicles

The overall share of renewable energies in the electricity mix is fairly equal for all scenarios due to the low penetration of electric vehicles For Scenarios 1 and 2, less than 2% of regular electricity consumption For Scenarios 3 and 4, less than 8% of regular electricity consumption Results – Medium-term evolution of the system The scenarios with flexibility in the charging hours achieve higher shares of renewables Scenarios 2 and 4 have roughly the double of renewables than scenarios 3 and 4, respectively

Results – Short-term analysis for the year 2025 Very different situations for different months Winter months have more renewables The introduction of solar energy could help increase the penetration in Summer months Flexibility in the charging hours maximizes introduction of renewables The scenarios with flexibility in the charging hours level out the consumption throughout the day: Scenarios 1 and 3 charge mainly during peak hours, when people arrive at home Scenarios 2 and 4 have higher consumption during the nights and lower during the peak hours

Conclusions This work proposes an integrated modeling framework for energy systems planning, that combines: Medium-term planning for the investment in new generation capacity. Short-term systems operation. The methodology was applied to study the impact of introducing electric vehicles in Flores. The results show that by combining the medium- and short-term approaches it is possible to identify the strengths and weaknesses of introducing electric vehicles. Future work is being developed in order to understand how the introduction of storage systems, other sources of renewable energies and application of energy policies can help increase the sustainability of the electric vehicles. The methodology can also be applied to study other energy projects, with or without electric vehicles

End Ackowledgements: This work has been supported by the FCT scholarship SFRH/BD/35334/2007 within the MIT-Portugal. Any questions? Comments? Ideas? André Pina