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