On Optimal Battery Sizing for Households Participating in Demand-Side Management Schemes

TL;DR

In this research an in-depth analysis of the relation between optimal capacity, and demand and generation patterns is performed for households taking part in a community-wide demand-side management scheme based on a non-cooperative dynamic game approach.

Abstract

The smart grid with its two-way communication and bi-directional power layers is a cornerstone in the combat against global warming. It allows for the large scale adoption of distributed (individually-owned) renewable energy resources such as solar photovoltaic systems. Their intermittency poses a threat to the stability of the grid which can be addressed by the introduction of energy storage systems. Determining the optimal capacity of a battery has been an active area of research in recent years. In this research an in-depth analysis of the relation between optimal capacity, and demand and generation patterns is performed for households taking part in a community-wide demand-side management scheme. The scheme is based on a non-cooperative dynamic game approach in which participants compete for the lowest electricity bill by scheduling their energy storage systems. The results are evaluated based on self-consumption, the peak-to-average ratio of the aggregated load, and potential cost reductions. Furthermore, the difference between individually-owned batteries to a centralised community energy storage system serving the whole community is investigated.

Figures (9)

• Figure 1: Optimal sizing considerations. The self-consumption and the resulting effectiveness of an exemplary household are plotted over the battery size. The two vertical lines indicate the maximum of the effectiveness for the GTS and GTSWC approach and therefore the optimal size of the energy storage installation, respectively.
• Figure 2: Optimal battery sizes. The results are obtained through a process as described in Huang2018.Battery capacities between 1 and 27 kWh were analysed. The optimal battery sizes for the individual households from simulation runs over the period of an entire year are reported. Furthermore, statistical results for these simulations as well as independent seasonal simulations are shown.
• Figure 3: Self-consumption analysis. Statistical results for the self-consumption rates are shown for all seasons and an entire year. For each period, the reference case in which no storage is available is compared with a configuration that includes the optimally sized batteries for each individual household for both the GTS and the GTSWC approach.
• Figure 4: Peak-to-average ratio (PAR) of the aggregated load. A statistical analysis of the achieved daily PAR values over the respective seasons is shown. For each period, the reference case in which no storage is available is compared with a configuration that includes the optimally sized batteries for each individual household for both the GTS and the GTSWC approach.
• Figure 5: Cost reductions. Statistical analysis of the amount of savings from the electricity bill over various billing periods is presented for the GTS and the GTSWC approach. The calculation of the unit energy price depends on the aggregated load as introduced in \ref{['eqn:price']}.