Case Study: Battery Sizing
The global energy market is undergoing continuous changes and the price of energy has become an increasingly important consideration in the design and construction of buildings. As energy costs continue to rise and concerns about climate change mount, architects and engineers are under pressure to create buildings that are more energy-efficient and environmentally sustainable while remaining economically viable. The design choices and the sizing of energy systems can have significant impact on the energy consumption, the initial investment, and the overall cost of operation of a building, irrespective of the building is a single household, a group of households or an industrial plant. Specific choices have to be made about the size and design of the photovoltaic system, the size of the battery, the design and size of the heating system including thermal storage, and even the design and size of a long-term energy storage system in the form of hydrogen or other energy carriers.
The design and size of an energy system not only depends on the components itself, but also on the topology, insulation, and usage of the building(s), the location and the weather conditions, the anticipated evolution of the energy price, and on the operation and control strategy. Hence, a complex set of questions with many trade-offs arise when designing an energy system.
To answer these questions, we have developed a toolbox which allows the calculation of the cost optimal design of an energy system for a given scenario in terms of energy price, weather and environmental conditions, marginal cost of the components and energy demand. Furthermore, on top of the optimal design, the best operational strategy is calculated. This approach of simultaneous optimization of the design and the operation strategy is a key prerequisite to make different energy system configurations comparable. Additionally, the obtained optimal operation strategy can be used as a basis for the finally implemented energy system control system.
Designing the energy system of a modern building
Designing the energy system of a modern building involves several steps, which can be summarized as follows:
- Define the building’s electrical energy requirements and warm water demand.
- Analyze the site and boundary conditions: weather conditions throughout the year, building orientation and shading, local energy sources such as solar or wind power.
- Define the building envelope, including insulation, windows, and doors to derive the heat demand.
- Select energy efficient technologies and define the architecture of the energy system.
- Define the sizing of each subsystem to provide the desired energy requirements in the most efficient and cost-effective
- Implement the energy management system to monitor and control the energy use in the building.
Going through the design process, many questions will arise: How do the energy requirements change when the building design changes? How much is it worth to pay for each technology to assure a reasonable return on investment? How should the system be operated and how should it adapt to changing external conditions and changing energy demands?
Case study: Choice of a battery for electrical energy storage
Let us assume a single home with an electricity consumption of 6300 kWh/year and a heat consumption of 18000 kWh/year. The building is located in Eastern Switzerland at an elevation of 750 m above sea level. Average ambient temperatures range from -1°C in January to 16°C in July. The building has a flat roof and shall be equipped with a photovoltaic system, mounted with an angle of ten degrees facing east and west direction. The heating system of the building incorporates an air heat pump, a water tank for the storage of warm water, and one for the heating system. The building has a strong exposure to the south with significant window areas, resulting in a rapid heat-up when the sun is shining. It is a well-insulated brick and concrete building. Hence, the structure incorporates a considerable heat storage capacity.
We want to find out, how the optimal size of a battery changes dependent on its marginal price (i.e. CHF/kWh). To solve this problem, we make use of the AIMotroniX modelling toolbox atbMod and the optimization toolbox atbOpt in the following steps:
- Compose a simulation model of the house and the energy system
- Assemble the ambient conditions (temperature and solar irradiation) profiles over one year (resolution: 1 h).
- Assemble electrical consumption profile (without heat pump) over one year (resolution: 1 h).
- Assemble grid tariffs (both for buying and selling electricity) over one year (resolution: 1 h): In this example, we assume constant tariffs for buying and selling electricity throughout the year.
- Define marginal and operational costs of optimized components
- Run the optimizer: Calculate the optimal sizes of the components which result in the lowest overall cost. Notice that both the sizes of the components and the operation of the system is optimized at the same time.
- Rerun the optimizer with a set of marginal cost levels of the battery.
The optimization problem incorporates approximately 180’000 optimization variables and is solved within a few minutes on a regular laptop. The results are shown in the figure below. A battery including mounting work costs
approximately 725 CHF/kWh today. It is expected that this price is significantly decreasing over the next years with the advent of new technologies (such as sodium-ion batteries). The results indicate that with today’s prices, the optimizer proposes a rather small battery (6 kWh). It is economically more viable to sell energy to the grid than to store it for later use. However, with decreasing battery prices, it becomes economically viable to install a bigger battery, as the optimization indicates. There is a somewhat flatter characteristic in the intermediate price area (500-850 CHF/kWh). The capacity here approximately corresponds to the energy demand during a summer night. With lower battery prices, it is obviously worth to exploit the longer nights during spring and autumn, although the battery will not be completely drained during the summer nights anymore.
Of course, the optimal configuration is also impacted by many other factors such as the grid tariffs and the marginal cost of other equipment. The focus here is on the methodology, so we shall not further elaborate on all other impact factors.
Conclusion
Using the right methodologies and tools, complex design and control problems such as for the energy system of buildings can be tackled efficiently. Very often, knowledge about the sensitivities is more viable than the specific design choices themselves. In other words, it is not only important to see, how e.g. a battery should be designed today, but how the solution varies, if boundary conditions such as energy- and component prices, the topology of the building, or interest rates change. This allows the anticipation of various scenarios, which greatly supports the design decisions for the energy system of a building. Additionally, the optimization approach provides guidance for the development of an optimal control system.
This case study was conducted using the AIMotroniX toolboxes for modelling atbMod and optimization atbOpt. Get in touch with us if you want to learn more about these tools and our approach to tackle optimization problems for energy systems.
This text has been written by real people from AIMotroniX.