Introducing user preferences for peer-to-peer electricity trading through stochastic multi-objective optimization

Peer-to-peer electricity markets are dedicated markets that enable the direct participation of small electricity end-users in energy trading activities. They are seen as a promising alternative that can empower end-users and accelerate the energy transition, by researchers, business developers, and legislators. Moreover, they can include environmental, social, or altruistic preferences that are relevant to end-users, in addition to the economic perspective. Such preferences are sometimes included in the modeling of P2P markets in the existing literature, but the assumptions behind them are rarely validated in practice. To investigate the desired attributes and preferences of end-users to participate in P2P markets, an online survey including a discrete choice experiment was conducted in The Netherlands The results of the survey are used to design a P2P electricity market with product differentiation. The participants in the market are residential end-users that are equipped with a home energy management system that can control some of the household appliances and automate the decision-making process for participation in the market. To facilitate this, a multi-objective stochastic optimization model is presented that incorporates results from the discrete choice experiment and real smart-meter measurements. The case study results demonstrate user preferences’ influence on market outcomes.</p

optimizing the operation of energy storage using a non linear lithium ion battery degradation model

Abstract Given their technological and market maturity, lithium-ion batteries are increasingly being considered and used in grid applications to provide a host of services such as frequency regulation, peak shaving, etc. Charging and discharging these batteries causes degradation in their performance. Lack of data on degradation processes combined with requirement of fast computation have led to over-simplified models of battery degradation. In this work, the recent experimental evidence that demonstrates that degradation in lithium-ion batteries is non-linearly dependent on the operating conditions is incorporated. Experimental aging data of a commercial battery have been used to develop a scheduling model applicable to the time constraints of a market model. A decomposition technique that enables the developed model to give near-optimal results for longer time horizons is also proposed

Regional cerebral blood flow and cellular environment in subarachnoid hemorrhage: A thermal doppler flowmetry and microdialysis study

Background Cerebral microdialysis enables assessment of regional metabolic physiology and provides biomarkers for clinical correlation in critical conditions, such as subarachnoid hemorrhage (SAH). The aim of our current study was to investigate the correlation between regional cerebral blood flow and microdialysis parameters (glucose, lactate, glycerol, pyruvate concentrations, and lactate/pyruvate metabolic ratio) in patients with SAH. Materials and methods Twenty-one patients with SAH were enrolled in our retrospective study. Cerebral blood flow (CBF) based on thermal diffusion methodology, the thermal coefficient K, and microdialysis biochemical markers were recorded. The duration of the brain monitoring was 10 days. Results Microdialysis glucose concentration was inversely related to the cerebral temperature and to the L/P ratio. Furthermore, it was positively correlated to all other microdialysis parameters but glycerol. The K coefficient was strongly and positively correlated with the temperature and marginally with the CBF. The L/P ratio was positively correlated with glycerol, while it was inversely correlated with the CBF. Patients who died had elevated L/P ratio and K coefficient compared to the survivors in our series. Conclusions Thermal conductivity coefficient may change over time as cerebral injury progresses and tissue properties alter. These alterations were found to be associated with the microdialysis metabolite concentrations and the CBF itself. The microdialysis biochemical indices of cell stress and death (glycerol, L/P ratio) were positively related to each other, while the measured L/P metabolic ratio was higher among patients who died

Flexibility Aggregation of Temporally Coupled Resources in Real Time Balancing Markets Using Machine Learning

In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio's actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator's offer. Once trained, the ML algorithms can make fast decisions about the portfolio's balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator's portfolio and minimizing the aggregator's imbalances.</p

Evaluation of load-following reserves for power systems with significant RES penetration considering risk management

In this study a novel two-stage stochastic programming based day-ahead joint energy and reserve scheduling model is developed. Demand-side as a reserve resource is explicitly modeled through responsive load aggregations, as well as large industrial consumers that directly participate in the scheduling procedure. Furthermore, a risk-hedging measure is introduced, namely the Conditional Value-at-Risk (CVaR), to analyze the behavior of energy and reserve scheduling by both the generation and the demand-side for a risk-averse ISO. The proposed methodology is tested on the practical non-interconnected insular power system of Crete, Greece, which is characterized by a significant penetration of Renewable Energy Sources (RES).Es la versión aceptada del documento. Se puede consultar la versión final en el DOI 10.1109/SEGE.2015.732457

RoboPol: AGN polarimetric monitoring data

We present uniformly reprocessed and re-calibrated data from the RoboPol programme of optopolarimetric monitoring of active galactic nuclei (AGN), covering observations between 2013, when the instrument was commissioned, and 2017. In total, the dataset presented in this paper includes 5068 observations of 222 AGN with Dec > -25 deg. We describe the current version of the RoboPol pipeline that was used to process and calibrate the entire dataset, and we make the data publicly available for use by the astronomical community. Average quantities summarising optopolarimetric behaviour (average degree of polarization, polarization variability index) are also provided for each source we have observed and for the time interval we have followed it.Comment: Accepted to MNRA