READ ME File For 'Dataset for "Coordination Mechanisms for Electric Vehicle Aggregators"'

Dataset DOI: 10.5258/SOTON/D1080

This dataset supports the thesis entitled 'Coordination Mechanisms for Electric Vehicle Aggregators'
AWARDED BY: Univeristy of Southampton
DATE OF AWARD: 2019

DESCRIPTION OF THE DATA
[This should include a detailed description of the data, how it was collected/created, any specialist software needed to view the data]

This dataset contains:

This dataset supports Chapters 7 and 9 in the PhD thesis titled "Coordination Mechanisms for Electric Vehicle Aggregators". The data supporting the other chapters has been previously published with the following independent DOIs:

- Dataset for "Coordination and payment mechanisms for electric vehicle aggregators" article DOI: 10.5258/SOTON/D0339
- Dataset for "Coordination of Electric Vehicle Aggregators: A Coalitional Approach" DOI: 10.5258/SOTON/D0413
- Dataset for "Fair Online Allocation of Perishable Goods and its Application to Electric Vehicle Charging" DOI: 10.5258/SOTON/D0926

In more detail, this dataset contains:
- Chapter 7. Strategic Manipulation of Decentralised Optimisation Algorithms

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark.

- Chapter 9. Forecasting Residual Supply Curves
We study the prediction of residual supply curves in day-ahead electricity markets, an essential ingredient for the application of bidding strategies. To this end, we apply neural network models, more specifically multilayer perceptrons, and forecast the residual supply curves for each of the twenty-four hours of the next day. In more detail, we consider both intra- and inter-hour models, and also incorporate exogenous explanatory variables such as wind generation and total demand forecasts. We present empirical results using  real data from the Spanish day-ahead market and show that our models outperform previous models in the literature, achieving up to 58.028% performance increase from the naive benchmark, compared to the previous 7.805% reported in the literature. Moreover, we find that inter-hour models achieve up to 6.028% performance increase when compared to intra-hour models.

Date of data collection: 1/1/2019 - 1/6/2019

Licence: CC BY
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Date that the file was created: 2019, September