scholarly journals Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract)

2020 ◽  
Author(s):  
Alvaro Perez Diaz ◽  
Enrico H. Gerding ◽  
Frank McGroarty
Keyword(s):  
Electric Vehicle ◽  
Electricity Market ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Mathematical Framework ◽  
Alternating Direction ◽  
Strategic Manipulation ◽  
Real Market ◽  
Manipulation Detection ◽  
Very High

We consider a scenario where self-interested Electric Vehicle (EV) aggregators compete in the day-ahead electricity market in order to purchase the electricity needed to meet EV requirements. We propose a novel decentralised bidding coordination algorithm based on the Alternating Direction Method of Multipliers (ADMM). Our simulations using real market and driver data from Spain show that the algorithm is able to significantly reduce energy costs for all participants. Furthermore, we postulate that strategic manipulation by deviating agents is possible in decentralised algorithms like ADMM. Hence, we describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Our simulations show that our ADMM-based algorithm can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. At the same time, our proposed manipulation detection algorithm achieves very high accuracy.

scholarly journals Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

2020 ◽  
Vol 67 ◽  
pp. 437-470
Author(s):  
Alvaro Perez-Diaz ◽  
Enrico Harm Gerding ◽  
Frank McGroarty
Keyword(s):  
Electricity Markets ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Mathematical Framework ◽  
Detection Algorithms ◽  
Strategic Manipulation ◽  
Coordination Framework ◽  
Real Market ◽  
Manipulation Detection ◽  
The Impact

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 to 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.

scholarly journals AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

2018 ◽  
Vol IV-3 ◽  
pp. 83-90 ◽  
Author(s):  
Peter Fischer ◽  
Philipp Schuegraf ◽  
Nina Merkle ◽  
Tobias Storch
Keyword(s):  
Evolutionary Algorithm ◽  
Satellite Imagery ◽  
Image Matching ◽  
Optimal Solution ◽  
Search Problem ◽  
Computational Costs ◽  
Radar Sensors ◽  
Function Calls ◽  
Point Detection ◽  
Very High

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

scholarly journals Cloud screening in AVHRR digital data over Arctic regions

1993 ◽  
Vol 17 ◽  
pp. 386-390 ◽  
Author(s):  
Sonia C. Gallegos ◽  
Jeffrey D. Hawkins ◽  
Chiu Fu Cheng
Keyword(s):  
Single Channel ◽  
Screening Method ◽  
Detection Algorithm ◽  
Digital Data ◽  
Cloud Detection ◽  
Arctic Regions ◽  
Identification Technique ◽  
Multiple Band ◽  
Cloud Screening ◽  
Very High

A cloud screening method initially generated to mask cloud contaminated pixels over the ocean in visible/infrared imagery, has been revised and adapted to detect clouds over Arctic regions with encouraging results. Although the method is quite successful in eliminating very cold clouds, it underestimates low level clouds. However, this does not appear to interfere with monitoring of ice related features such as leads or the ice edge in Advanced Very High Resolution Radiometer (AVHRR) scenes. The method uses: a multiple-band approach to produce signatures not readily available in single channel data, an edge detection/dilation technique to locate features in the clouds and to join isolated edges, and a polygon identification technique to remove noise in the form of isolated pixels and separate clear regions from cloud contaminated areas. The method has been tested over a limited set of data with consistent results. Initial evaluation of the usefulness of this cloud-detection algorithm in data-fusion experiments indicate a potential in locating areas in AVHRR data which are cloud contaminated and which could yield a far superior representation of the ice features if replaced with data from a different sensor such as the Special Sensor Microwave/lmager (SSM/I).

Research on Second Distribution of Real Value Detectors Based on Heuristic Movement

2014 ◽  
Vol 1049-1050 ◽  
pp. 1506-1513
Author(s):  
Tian Bo Wang ◽  
Feng Bin Zhang ◽  
Chun He Xia
Keyword(s):  
Theoretical Analysis ◽  
Anomaly Detection ◽  
Heuristic Algorithm ◽  
Negative Selection ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Generalization Ability ◽  
Real Value ◽  
Omission Rate

Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. However, there still remain many problems such as the randomness of detector generation, incompleteness of self-set and the generalization ability of detectors, which would cause a lot of loopholes in non-self-space. This paper proposes a heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self-space in the first distribution. The algorithm proposed can distribute real value detectors through omission data based on the methods of partition and movement. A method is then proposed to solve the problem on how to get the optimal solution to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that the algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.

Optimal Quick Charging Station Placement for Electric Vehicles

2015 ◽  
Vol 785 ◽  
pp. 697-701 ◽  
Author(s):  
Md. Mainul Islam ◽  
Hussein Shareef ◽  
Azah Mohamed
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Optimal Solution ◽  
Optimization Method ◽  
Road Transport ◽  
Optimal Placement ◽  
Charging Station ◽  
Weighted Sum Method ◽  
Driving Range ◽  
Integrated Cost

Environmental concerns, dependency on imported petroleum and lower cost alternative to gasoline always motivated policymakers worldwide to introduce electric vehicles in road transport system as a solution of those problems. The key issue in this system is recharging the electric vehicle batteries before they are exhausted. Thus, the charging station should be carefully located to make sure the vehicle users can access the charging station within its driving range. This paper therefore proposes a multi-objective optimization method for optimal placement of quick charging station. It intends to minimize the integrated cost of grid energy loss and travelling of vehicle to quick charging station. Due to contrary objectives, weighted sum method is assigned to generate reference Pareto optimal front and optimized the overture by genetic algorithm. The results show that the proposed method can find the optimal solution of quick charging station placement that can benefit electric vehicle users and power grid.

A driving cycle for electrically-driven vehicles in Rome

2003 ◽  
Vol 217 (9) ◽  
pp. 781-789 ◽  
Author(s):  
A Alessandrini ◽  
F Orecchini
Keyword(s):  
Electric Vehicle ◽  
Driving Cycle ◽  
The Other ◽  
Maximum Speed ◽  
Electric Cars ◽  
High Maximum ◽  
Big City ◽  
Limited Power ◽  
The City ◽  
Very High

In the research results presented here, an average driving cycle is synthesized for an electrically driven car moving in the city of Rome. The technique of Lyons et al. [1] for synthesizing a statistically representative driving cycle was used on a 5 week acquisition set of data collected with a duly equipped electric Citroen Saxo that was driven for over 3100 km by six different drivers in the months of May and June 2001 in Rome. The driving cycle developed is compared with the other available cycles, especially the European ones. The comparison highlights the need for this new dedicated cycle to represent the driving conditions of electric cars in Rome, with a lower value of the acceleration-speed product on account of the limited power of the electric vehicle, frequent changes in the acceleration sign, typical of the trafic in a big city, and a very high maximum speed, typical of the driving behaviour of the inhabitants of Rome.

Development of a new over‐water Advanced Very High Resolution Radiometer dust detection algorithm

2006 ◽  
Vol 27 (18) ◽  
pp. 3903-3924 ◽  
Author(s):  
Amato T. Evan ◽  
Andrew K. Heidinger ◽  
Michael J. Pavolonis
Keyword(s):  
High Resolution ◽  
Detection Algorithm ◽  
Dust Detection ◽  
Very High
Sign in / Sign up

Export Citation Format

Share Document