Towards Understanding Home Battery Adoption in the Netherlands

Abstract

Introduction

The Dutch electricity system increasingly needs flexibility, because electricity generation from variable renewable sources such as solar and wind does not always match electricity demand. Battery Energy Storage Systems (BESS) can provide this flexibility by storing electricity and releasing it later. They respond quickly, scale across system sizes, and are already commercially proven. These properties have led market actors to increasingly deploy utility-scale BESS. At household level, residential BESS, or home batteries, are also growing rapidly and make up ~45% of installed capacity in the Netherlands.

Despite technical similarity, installed capacity of the two types of BESS grows in fundamentally different ways. Utility-scale capacity growth results from a limited number of professional investment decisions and is therefore relatively predictable. Residential BESS capacity growth is more difficult to predict, because it emerges from many separate household decisions shaped by financial and non-financial considerations. Various actors can only influence the conditions under which households evaluate one. In this thesis, these conditions are conceptualized as home battery attributes, such as investment costs, payback time, certainty of payback time, self-consumption and emergency power.

Literature Review

Existing literature shows that residential BESS adoption follows from many household decisions to invest in a home battery. This decision should not be understood as purely techno-economic. Financial drivers such as costs, payback time and lower electricity bills matter, but non-financial drivers such as energy autonomy, emergency power and environmental motives also shape the household decision. A structured literature search identified a broad set of adoption drivers, which were organized using a framework for categorizing drivers of household investment decisions. Although literature is rich in possible drivers, it does not clearly show which home battery attributes matter most to households, how important these attributes are relative to one another, nor how changes in these attributes affect choices. Existing prioritizations rely mainly on stated importance rather than observed choice trade-offs, and evidence is concentrated in Australian studies, limiting transferability to the Dutch context.

The main research question of this thesis is therefore: How do changes in home battery attributes affect the household investment decision to buy a home battery? It is answered through three sub-questions. First, the thesis identifies the most important attributes affecting the household investment decision. Second, it estimates their relative importance. Third, it explores how changes in these attributes affect the probability that a household chooses a home battery.

Methodology

The research combines a literature analysis with Discrete Choice Modeling (DCM). The literature analysis screened the broad set of drivers down to a set of 12 attributes, from which we derived a subset of five key home battery attributes: investment costs, payback time, certainty of payback time, energy autonomy, and backup power.

A discrete choice experiment (DCE) was designed where respondents repeatedly chose between Battery A, Battery B and no battery. Each battery was described by a set of these attributes and their levels. The final experiment included eight choice tasks and was completed by 529 usable respondents. From the total pool of respondents, based on household characteristics, a representative sample for the average Dutch household was made. A panel mixed logit model estimated relative importance of the key attributes and the choice probabilities for choosing a baseline home battery with consistent attributes.

Results

A panel mixed logit model estimated on the choice data shows that all five attributes have a statistically significant effect on household choices. The model provides significant explanatory power (ρ² = 0.2477). The relative importance of the attributes for the average Dutch household is shown in Figure . A choice probability analysis shows the probability for choosing a baseline home battery is 0.60 in a data sample with solar PV owners, and 0.53 in the representative sample. These values should not be interpreted as real market shares, but as controlled model outputs.

Conclusion

Changes in home battery attributes affect the household investment decision by changing the probability that a household selects the battery alternative. The five selected attributes are therefore relevant "knobs" through which residential BESS adoption can be understood or influenced. However, the model only tests the included attributes: it shows that these five matter, not that they are the five most important in every real-world decision.

From the key attributes and their relative importances we found that the household investment decision is strongly financial, but not purely economically rational. Costs, payback time and certainty of payback time together account for most of the relative importance, yet households do not appear to evaluate a battery as a simple profitability calculation. Payback time, a key metric for professional investors, is the least important attribute in the representative sample, and households did not accept more risk in exchange for shorter payback.

Investment costs and certainty of payback time are the strongest attributes. This has direct implications for policy and market actors. Measures that lower upfront costs, such as subsidies or tax rebates, target the most important part of the household decision. Measures that stabilize or guarantee returns target the second most important attribute. This is relevant because current Dutch policies mainly affect payback time and self-consumption, certainty of payback time is affected only indirectly. Barely any policies affect investment costs and certainty of payback time directly. Grid operators could use this insight by offering contracts that give households more certain returns in exchange for control over battery operation to stabilize grids.

The attributes do not tell the full picture. Comparing the representative and solar samples shows that targeting the right households can shift choice probabilities by a similar magnitude as large attribute changes. Actors seeking to promote adoption should therefore not only improve the battery proposition, but also focus on groups already more willing to adopt.

The results should be read within the limits of the method. The experiment simplifies a complex real-world decision, measures stated rather than actual choices, and only tests the included attributes. Future work should expand and formalize the attribute selection, test transferability across countries, quantify how policies change home battery attributes, and connect these preference estimates to diffusion models that translate attribute changes into capacity growth.