33 research outputs found
Performance of heuristic optimization in coordination of plug-in electric vehicles charging
A heuristic load management (H-LMA) algorithm is presented for coordination of Plug-in Electric Vehicles (PEVs) in distribution networks to minimize system losses and regulate bus voltages. The impacts of optimization period T (varied from 15 minutes to 24 hours) and optimization time interval (varied 15 minutes to one hour) on the performance, accuracy and speed of the H-LMA is investigated through detailed simulations considering enormous scenarios. PEV coordination is performed by considering substation transformer loading while taking PEV owner priorities into consideration. Starting with the highest priority consumers, HLMA will use time intervals to distribute PEV charging within three designated high, medium and low priority time zones to minimize total system losses over period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results generated in MATLAB are presented for a 449 node distribution network populated with PEVs in residential feeders
Distributed cooperative control for economic operation of multiple plug‐in electric vehicle parking decks
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138231/1/etep2348.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138231/2/etep2348_am.pd
Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators
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
Plug-and-Play Distributed Algorithms for Optimized Power Generation in a Microgrid
This paper introduces distributed algorithms that
share the power generation task in an optimized fashion among
the several Distributed Energy Resources (DERs) within a
microgrid. We borrow certain concepts from communication network
theory, namely Additive-Increase-Multiplicative-Decrease
(AIMD) algorithms, which are known to be convenient in terms
of communication requirements and network efficiency.We adapt
the synchronized version of AIMD to minimize a cost utility
function of interest in the framework of smart grids. We then
implement the AIMD utility optimisation strategies in a realistic
power network simulation in Matlab-OpenDSS environment, and
we show that the performance is very close to the full-communication
centralized case