Benchmarking Aggregation-Disaggregation Pipelines for Smart Charging of Electric Vehicles

TL;DR

This paper introduces a benchmark for evaluating various aggregation-disaggregation pipelines for smart EV charging, comparing their performance and error characteristics to guide practical energy system modeling.

Contribution

It provides a neutral, comprehensive benchmark for multiple aggregation methods, enabling direct comparison and evaluation of their performance in EV charging scenarios.

Findings

Representative profile approach incurs 2%-20% additional costs.

FO and DFO methods enable error-free disaggregation.

Different methods are optimal depending on the use case.

Abstract

As the global energy landscape shifts towards renewable energy and the electrification of the transport and heating sectors, national energy systems will include more controllable prosumers. Many future scenarios contain millions of such prosumers with individualistic behavior. This poses a problem for energy system modelers. Memory and runtime limitations often make it impossible to model each prosumer individually. In these cases, it is necessary to model the prosumers with representatives or in aggregated form. Existing literature offers various aggregation methods, each with strengths, drawbacks, and an inherent modeling error. It is difficult to evaluate which of these methods perform best. Each paper presenting a new aggregation method usually includes a performance evaluation. However, what is missing is a direct comparison on the same benchmark, preferably by a neutral third party that is not associated with any of the compared methods. This paper addresses this gap by introducing a benchmark to evaluate the end-to-end performance of multiple aggregation-disaggregation pipelines, specifically focusing on electric vehicles (EVs). Our study assesses the performance of the common representative profile (REP) approach, four different versions of the virtual battery (VB) approach, the Flex Object (FO) method, and the Dependency-based FlexOffer (DFO) method. The results show that each method has a clear use case. Depending on the price signal, additional median charging costs of 2%-50% are incurred using an aggregation method, compared to the optimal charging costs (i.e., the charging costs resulting from optimizing the EVs directly, without aggregation). The representative profile approach results in the lowest additional costs (2%-20%), while the FO and DFO methods allow for error-free disaggregation, which is advantageous in real-world use cases.