[Paper Review] Day-ahead Trading of Aggregated Energy Flexibility - Full Version

Emmanouil Valsomatzis, Torben Bach Pedersen+1 more|arXiv (Cornell University)|May 7, 2018

Electric Vehicles and Infrastructure22 references3 citations

TL;DR

This paper proposes three market-based aggregation techniques for electric vehicle (EV) energy flexibility, transforming individual EV charging loads into flexible orders (FOs) that can be traded in the Nordic day-ahead market. By leveraging real market constraints and optimizing for price arbitrage, the methods achieve an average 24.4% cost reduction in energy procurement—up to 27.4% compared to optimal benchmarks—demonstrating significant financial gains for Balance Responsible Parties (BRPs).

ABSTRACT

Flexibility of small loads, in particular from Electric Vehicles (EVs), has recently attracted a lot of interest due to their possibility of participating in the energy market and the new commercial potentials. Different from existing work, the aggregation techniques proposed in this paper produce flexible aggregated loads from EVs taking into account technical market requirements. They can be further transformed into the so-called flexible orders and be traded in the day-ahead market by a Balance Responsible Party (BRP). As a result, the BRP can achieve at least 20% cost reduction on average in energy purchase compared to traditional charging based on 2017 real electricity prices from the Danish electricity market.

Motivation & Objective

• Address the gap in existing EV flexibility trading models that fail to account for real technical market constraints, such as minimum bid sizes and flexible order requirements.
• Develop aggregation techniques that produce flexible aggregated loads (flex-offers) compliant with actual market rules, particularly the Nordic flexible order (FO) framework.
• Enable Balance Responsible Parties (BRPs) to reduce energy purchase costs by strategically bidding aggregated EV flexibility in the day-ahead market.
• Evaluate the financial and technical performance of the proposed techniques using real 2017 Danish electricity prices and EV plug-in data.
• Demonstrate that market-based aggregation outperforms traditional fixed-scheduling and baseline bidding approaches in cost efficiency and market compliance.

Proposed method

• Introduce the flex-offer (FO) model to represent EV charging flexibility, defining each FO by a time window, energy amount, and power constraints based on EV battery state-of-charge (SOC) and charging efficiency.
• Formulate the market-based FO aggregation problem as a constrained optimization task, where aggregated FOs must satisfy market-specific requirements such as minimum bid size and time flexibility.
• Propose three heuristic aggregation algorithms: Dynamic Programming (DP), Dynamic Time-First (DTF), and Load Prioritization (LP), each designed to maximize cost savings while respecting market and technical constraints.
• Use real-world data from 5,000 to 40,000 EVs with known plug-in times and charging profiles to simulate aggregation and bidding in the day-ahead market.
• Transform aggregated FOs into flexible orders (FOs) that can be submitted to the market, allowing the market to schedule activation times within the defined flexibility windows.
• Evaluate performance using real Elspot electricity prices from 2017, comparing total energy costs across techniques and baselines, including imbalance costs under forecast uncertainty.

Experimental results

Research questions

• RQ1 How can EV charging flexibility be effectively aggregated into market-compliant flexible orders that meet technical constraints such as minimum bid size and time flexibility?
• RQ2 What is the financial impact of using market-aware aggregation techniques compared to traditional fixed-scheduling or baseline bidding strategies in the day-ahead electricity market?
• RQ3 How do different aggregation heuristics (DP, DTF, LP) perform under varying price dynamics and EV plug-in patterns, particularly in periods of low or negative electricity prices?
• RQ4 To what extent do forecast uncertainties in EV plug-in behavior affect the financial outcomes of aggregated flexibility trading, and how do they impact BRP profitability?
• RQ5 How does the market-based aggregation approach compare to the theoretical optimal solution in terms of cost reduction, and what fraction of the optimal savings can be achieved in practice?

Key findings

• The Dynamic Programming (DP) heuristic achieves an average cost reduction of 24.4% in energy purchase costs across 365 trading periods, outperforming DTF (20.2%) and LP (19.1%).
• DP achieves 98.3% of the optimal cost reduction for the FOs that participate in aggregation, indicating high efficiency in leveraging market flexibility.
• The optimal solution yields a 27.4% average cost reduction, and DP achieves 88.9% of this potential, demonstrating strong near-optimality.
• In periods with negative electricity prices—such as -50 €/MWh on December 24, 2017—cost reductions exceeded 800%, highlighting the method’s effectiveness in volatile pricing environments.
• Under forecast uncertainty, when more than 23% of EVs fail to participate as expected, the cost of energy via flexible orders exceeds the plug-in charging cost, indicating a financial risk threshold.
• DP achieves the highest cost reduction in 66% of trading periods, while DTF leads in the remaining 34%, depending on the shape and timing of price curves.