Energy Storage In Flexible Microgrids
Edmund Schaefer is a PhD student in the Department of Mathematics of Operations Research. (Co)Promotors are prof.dr. J.L. Hurink and dr.ir. G. Hoogsteen from the Faculty of Electrical Engineering, Mathematics and Computer Science and dr.ir. R.P. van Leeuwen from Saxion.
The 21st century is defined by reconciling humanity's desire for both growth and comfort with our planet's need for a stable equilibrium in nature. Due to human activities over the past 200 years, the climate of the planet is changing at an alarming rate. In order to curb this climate change, and eventually reverse it, humanity is currently in the process of switching from fossil fuel based energy systems to systems that derive the majority of their energy from sustainable sources. In such an energy system, most energy is provided by intermittent renewable energy sources such as wind and solar in the form of electricity. These changes in how energy is produced also necessitate a shift in our energy demand. Many traditional non-electric applications, such as heating and mobility, are becoming electric, as appliances such as heat pumps and electric vehicles are rapidly introduced for mass usage.
As renewables and electrified appliances are introduced to electricity grids worldwide, the need for grid flexibility (i.e., the grid's ability to balance energy demand and production at every moment under uncertainty) increases dramatically. Flexibility can be provided by connected electricity grids, when available. In many countries such as the Netherlands, the electricity grid is congested and is currently being upgraded, a costly process both in financial and time related terms. If upgrades are not timely, this leads to delays in the integration of renewable systems. Furthermore, the electricity grid is moving away from the traditional architecture where energy is produced in one central location before being transported and used elsewhere, towards a system where energy is produced closer to the end user. This requires a re-evaluation of how flexibility should be provided in grids. Dividing electricity grids into smaller subsets, so-called microgrids, allows for more flexibility and matching to be provided locally, for example by energy storage or intelligently controlled flexible devices. Therefore, by providing flexibility locally, the need for connected grid reinforcement can be reduced.
The goal of this thesis is to investigate how local flexibility affects and potentially reduces the dependence of microgrids on connected grids. Specifically, it investigates the interaction between energy storage devices and the other flexibility available in microgrids, such as flexibility from flexible loads and the connected grid. It presents options to alleviate grid congestion problems in the short term, and reduce required grid reinforcements in the long term. This research investigates a number of relevant use cases using a simulation based approach (with measured data where possible), and provides methods and corresponding tools to distil the dynamics of microgrids in which energy storage and other systems are controlled.
The first contributions of this thesis is two methods (and the resulting tools), the Load Profile Analysis Tool (LPAT) and the Energy Storage System toolKit (ESSKit). LPAT is a holistic method for decomposing a given load profile into several timescale based sub-profiles using frequency based filtering. Each profile is then analysed and storage requirements are derived. This method is useful in pre-design phases, when storage technology independent sizing is required at low computational cost. Furthermore, LPAT can size multiple storages simultaneously. In contrast, ESSKit selects and sizes a single device, considering specific storage technologies. Furthermore, when sizing ESSKit incorporates both the operational scheduling of the storage devices and other microgrid flexibility, such as a connected grid. It is applicable during a pre-design phase when a more precise storage size is required. Both methods are evaluated based on a single household application case where grid connection reduction is desired. Here we find that the grid connection can be reduced by 80% when 8 kWh of storage is introduced (approximately 1 kWh per 10% reduction). Furthermore, we find that input data resolution should be one minute or less to ensure accurate storage sizing.
The second contribution of this thesis is an empirical study of the effect of controlling four different classes of flexible loads on energy storage sizing, which includes existing technologies such as EV chargers, heat pumps and white-goods. A Profile Steering (i.e., load flattening) control strategy is used to minimize the power imbalance over the considered period. Furthermore, parameter sweeps are used to investigate the influences of load availability and load demand on the energy storage sizing, whereby energy storage reductions are visualised with heat maps. The results show that significant energy storage sizing reductions are possible if flexible loads are considered, where the reductions depend on the flexibility type and characteristics (e.g., availability, buffering capacity). We conclude that it is important to consider local flexible devices when sizing storage in order to avoid (significant) over-dimensioning.
The third contribution of this thesis consists of three simulation studies investigating distinct microgrid use cases. The first case is an investigation into the environmental and financial effects of adding solar PV and storage to off-grid microgrids to reduce or remove diesel usage. To investigate the effects, we select a military base use case, and conduct a Life Cycle Analysis (LCA) and a financial analysis using Levelized Cost of Electricity (LCOE). The results show that significant environmental and financial cost reductions are possible by (partially) switching to renewables and energy storage.
The second case is an investigation into the storage sizing requirements of an EV charging hub for a package distribution centre with solar PV. The results show that storage capacity requirements can be reduced by partially charging during daytime breaks. Furthermore, we found that controlling EV charging sessions can reduce storage sizing requirements by 17-63%, while curtailing PV reduces storage sizing by 63-84%.
The third case is a broader investigation into a neighbourhood microgrid, the Aardehuizen energy community. In this case, we analysed storage requirements in respect to connected grid capacities, while also examining the effects of different control objectives, control of flexible loads and PV curtailment on grid and storage requirements and utilization. We found that a load flattening control strategy is the most suitable for the community, and that controlling flexible loads locally can reduce storage requirements from 32 to 25 storage units when considering a 100 kW grid connection. Furthermore, we found that curtailing PV can reduce the requirements of the grid connection by 52.9\% to 72.7\%. For this case, preliminary microgrid measurements are also analysed, which shows that rudimentary control of the storage already leads to significant grid peak reduction, while reducing overall dependence on the connected grid for flexibility.
