Chair: Jan Michalski, Technische Universität München, Germany
Analyzing Load Flexibility of Electric Vehicles for Renewable Energy Integration
Alexander Schuller (email@example.com)
The Smart Grid enables bidirectional communication between distributed actors and resources in the power system. In particular Electric Vehicles (EVs) are a new type of load that has a considerable flexibility in its demand. The approach evaluates the potential of a fleet of EVs with real-life driving profiles from two distinct socio-economic groups to distribute their demand in such a way that a given intermittent generation pattern (wind or solar-PV) can be balanced to the highest possible extent without affecting driving needs. For times in which renewable supply is not sufficient a conventional controllable generator is employed as a backup to guarantee EV supply. The model is formulated as a mixed-integer optimization problem that aims to minimize the amount of conventional generation employed, given the demand for driving energy of the EVs is met over the horizon of the analysis. Since the renewable generation amount is scaled to exactly meet the aggregated demand, the analysis focuses on the ability of EVs to shift their load to the given intermittent generation pattern. The results show that the EV fleet can substantially increase its direct utilization of renewable energy if charging is coordinated accordingly. On average, the utilization rate of wind-power can be doubled and the rate of PV can be tripled, as compared to the uncoordinated AFAP (as fast as possible) charging approach. A further reduction of the optimization horizon from one week-ahead to day-ahead reduces the direct utilization potential, but is also sensitive to assumptions about minimum battery energy levels, which if chosen to conservatively, highly affect the potential for renewable energy adoption.
Keywords: Load Flexibility, Renewable Energy, Electric Vehicle
Energy efficiency through automatic blind control
Rodrigo Leal (firstname.lastname@example.org), Francisco Regateiro (email@example.com), Maria Gomes (firstname.lastname@example.org)
Energy efficiency has been receiving considerable attention. One particular focus of attention concerns the big growth in electric energy demand, besides 1.3 billion people worldwide with no access to electric energy. The IEA (International Energy Agency) predicts a 50% growth in this demand until 2030 due to the industrial development and population growth. According to IEA, electric energy consumption in buildings represents 32% of the total energy consumption worldwide. The ECF (European Climate Foundation) states that 75% of buildings are residential buildings and of the other 25%, 23% are office buildings. Moreover, in spite of existing less office buildings, in Europe these consume around 306 kWh/m2 whereas residential buildings consume around 200 kWh/m2, i.e., office buildings consume about more 50% than residential buildings. Considering that energy efficiency may be improved in residential buildings, ADENE (the Portuguese Agency for Energy) recommends three most cost-effective fields of intervention: insulation (roof, walls, and floor), solar systems, DHW system (domestic hot water). It was concluded in observational studies and in surveys that manual blinds are typically not appropriately operated. In offices, natural light can improve work performance, reduce sleepiness, and improve the mood. Automated blind systems may contribute to energy savings that range between 22% and 94%. In spite of this variability in energy savings, automated blinds generally allow energy savings and better visual comfort conditions. Following the previous paragraphs, there is a need for further study and development of methods for automated control blind systems. These systems must consider many variables and the trade-off between energy saving and visual comfort. We start to identify these variables and relations among them. We propose an event based system, overridden by manual action if wanted, where automatic control rules use sensor, geographic, and indoor input data, together with modifiable thresholds. We use computational tools (EnergyPlus and Matlab/MLE+) to simulate building performance with our system. We conclude that our system may contribute to achieve better energy and visual comfort performances.
Keywords: Blind Automation, Energy Efficiency , Building Performance Simulation
The Economics of Electricity Storage for Renewable Electricity Generation
Jan Michalski (Jan.Michalski@tum.de)
In this paper we provide a detailed analysis of economics of different electricity storage technologies in combination with renewable generation from wind and PV in a given grid node. We have developed an LP optimisation model for investment and operation decisions from a single-firm perspective being sufficiently small to operate as a price taker without any market power. Important limitations for storage operations are represented by minimal demand to be met by the facility and maximal production constraint through limited grid capacities. We calculate the net present value and the corresponding costs associated with different combinations of storage and intermittent generation technologies and compare the results with two simple alternatives: generation by gas turbines when intermittent power is not sufficient to balance the load and spilling excess renewable electricity in case of oversupply. The model is applied for a case study for NaS batteries, redox flow batteries, pumped hydro storage, hydrogen storage and compressed air energy storage in combination with wind and PV in the context of the German electricity market and accounting for seasonality. We find that based on current technology costs and market conditions electricity storage is not profitable in comparison to simple alternatives. Our results also indicate that storage is more suitable for meeting minimal demand than coping with transmission congestion in case of wind. Due to seasonal effects storage value is 10-25 times higher in combination with wind rather than with PV in case of the minimal demand whereas the opposite is true for the maximal grid constraint. According to scenario analysis the results are rather sensitive to storage technology costs and fuel costs of the gas turbine allowing for positive business cases under some circumstances. The results also depend on the share of minimal demand and excess electricity of the overall amount of intermittent electricity.
Keywords: Electricity Storage, Renewable Energy, Single Firm Optimisation