83 research outputs found
Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering
Among the various market structures under peer-to-peer energy sharing, one
model based on cooperative game theory provides clear incentives for prosumers
to collaboratively schedule their energy resources. The computational
complexity of this model, however, increases exponentially with the number of
participants. To address this issue, this paper proposes the application of
K-means clustering to the energy profiles following the grand coalition
optimization. The cooperative model is run with the "clustered players" to
compute their payoff allocations, which are then further distributed among the
prosumers within each cluster. Case studies show that the proposed method can
significantly improve the scalability of the cooperative scheme while
maintaining a high level of financial incentives for the prosumers.Comment: 6 pages, 4 figures, 2 tables. Accepted to the 13th IEEE PES PowerTech
Conference, 23-27 June 2019, Milano, Ital
Capturing diversity in electric vehicle charging behaviour for network capacity estimation
This paper proposes a stochastic data-driven model for uncontrolled charging that accurately captures diversity in individual consumer behaviour. This is important because understanding the diversity between consumers is necessary to accurately estimate the number of electric vehicles’ charging a distribution network could support without reinforcements. The model combines readily available travel survey data with high resolution data from an electric vehicle trial, using clustering and conditional probabilities. We demonstrate through a case study of UK residential charging that existing approaches may overestimate the increase in peak distribution network demand by 50%, which has implications for assessing the cost of network investments required. We also show that the peak charging demand varies regionally from 0.2–1.4 kW per household, demonstrating the importance of using locally representative vehicle usage data
Capturing diversity in electric vehicle charging behaviour for network capacity estimation
This paper proposes a stochastic data-driven model for uncontrolled charging that accurately captures diversity in individual consumer behaviour. This is important because understanding the diversity between consumers is necessary to accurately estimate the number of electric vehicles’ charging a distribution network could support without reinforcements. The model combines readily available travel survey data with high resolution data from an electric vehicle trial, using clustering and conditional probabilities. We demonstrate through a case study of UK residential charging that existing approaches may overestimate the increase in peak distribution network demand by 50%, which has implications for assessing the cost of network investments required. We also show that the peak charging demand varies regionally from 0.2–1.4 kW per household, demonstrating the importance of using locally representative vehicle usage data
Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty
In light of a reliable and resilient power system under extreme weather and
natural disasters, networked microgrids integrating local renewable resources
have been adopted extensively to supply demands when the main utility
experiences blackouts. However, the stochastic nature of renewables and
unpredictable contingencies are difficult to address with the deterministic
energy management framework. The paper proposes a comprehensive
distributionally robust joint chance-constrained (DR-JCC) framework that
incorporates microgrid island, power flow, distributed batteries and voltage
control constraints. All chance constraints are solved jointly and each one is
assigned to an optimized violation rate. To highlight, the JCC problem with the
optimized violation rates has been recognized to be NP-hard and challenging to
be solved. This paper proposes a novel evolutionary algorithm that successfully
tackles the problem and reduces the solution conservativeness (i.e. operation
cost) by around 50% comparing with the baseline Bonferroni Approximation.
Considering the imperfect solar power forecast, we construct three data-driven
ambiguity sets to model uncertain forecast error distributions. The solution is
thus robust for any distribution in sets with the shared moment and shape
assumptions. The proposed method is validated by robustness tests based on
those sets and firmly secures the solution robustness.Comment: Accepted by IEEE Transactions on Smart Gri
Coral records of reef-water pH across the central Great Barrier Reef, Australia: assessing the influence of river runoff on inshore reefs
The boron isotopic (δ11Bcarb) compositions of long-lived Porites coral are used to reconstruct reef-water pH across the central Great Barrier Reef (GBR) and assess the impact of river runoff on inshore reefs. For the period from 1940 to 2009, corals fro
Factors affecting B/Ca ratios in synthetic aragonite
Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here for personal use, not for redistribution. The definitive version was published in Chemical Geology 437 (2016): 67-76, doi:10.1016/j.chemgeo.2016.05.007.Measurements of B/Ca ratios in marine carbonates have been suggested to record
seawater carbonate chemistry, however experimental calibration of such proxies based on
inorganic partitioning remains limited. Here we conducted a series of synthetic aragonite
precipitation experiments to evaluate the factors influencing the partitioning of B/Ca
between aragonite and seawater. Our results indicate that the B/Ca ratio of synthetic
aragonites depends primarily on the relative concentrations of borate and carbonate ions
in the solution from which the aragonite precipitates; not on bicarbonate concentration as
has been previously suggested. The influence of temperature was not significant over the
range investigated (20 – 40°C), however, partitioning may be influenced by saturation
state (and/or growth rate). Based on our experimental results, we suggest that aragonite
B/Ca ratios can be utilized as a proxy of [CO32-]. Boron isotopic composition (δ11B) is an
established pH proxy, thus B/Ca and δ11B together allow the full carbonate chemistry of
the solution from which the aragonite precipitated to be calculated. To the extent that
aragonite precipitation by marine organisms is affected by seawater chemistry, B/Ca may
also prove useful in reconstructing seawater chemistry. A simplified boron purification
protocol based on amberlite resin and the organic buffer TRIS is also described.This work
was supported by the Australian Research Council (ARC) Centre of Excellence for Coral
Reef Studies. Research conducted at WHOI was supported by NSF grant OCE-1338320.
M.H. was supported by an ARC Super Science Fellowship and an NSF International
Postdoctoral Fellowship. T.D. was supported by a NSF Graduate Research Fellowship.
M.M. was supported by a Western Australian Premiers Fellowship and an ARC Laureate
Fellowship
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