Reduced state space and cost function in reinforcement learning for demand response control of multiple EV charging stations

Electric vehicle (EV) charging stations represent a substantial load with significant flexibility. Balancing such load with model-free demand response (DR) based on reinforcement learning (RL) is an attractive approach. We build on previous RL research using a Markov decision process (MDP) to simultaneously coordinate multiple charging stations. The previously proposed approach is computationally expensive in terms of large training times, limiting its feasibility and practicality. We propose to a priori force the control policy to always fulfill any charging demand that does not offer any flexibility at a given point, and thus use an updated cost function. We compare the policy of the newly proposed approach with the original (costly) one, for the case of load flattening, in terms of (i) processing time to learn the RL-based charging policy, and (ii) overall performance of the policy decisions in terms of meeting the target load for unseen test data

Synthetic data generator for electric vehicle charging sessions : modeling and evaluation using real-world data

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets

Peer Support and Exclusive Breastfeeding Duration in Low and Middle-Income Countries: A Systematic Review and Meta-Analysis

Objective: To examine the effect of peer support on duration of exclusive breastfeeding (EBF) in low and middle-income countries (LMICs). Data Sources Medline, EMBASE, and Cochrane Central Register for Controlled Trials were searched from inception to April 2012. Methods: Two authors independently searched, reviewed, and assessed the quality of randomized controlled trials utilizing peer support in LMICs. Meta-analysis and metaregression techniques were used to produce pooled relative risks and investigate sources of heterogeneity in the estimates. Results: Eleven randomized controlled trials conducted at 13 study sites met the inclusion criteria for systematic review. We noted significant differences in study populations, peer counselor training methods, peer visit schedule, and outcome ascertainment methods. Peer support significantly decreased the risk of discontinuing EBF as compared to control (RR: 0.71; 95% CI: 0.61–0.82; I2 = 92%). The effect of peer support was significantly reduced in settings with >10% community prevalence of formula feeding as compared to settings with <10% prevalence (p = 0.048). There was no evidence of effect modification by inclusion of low birth weight infants (p = 0.367) and no difference in the effect of peer support on EBF at 4 versus 6 months postpartum (p = 0.398). Conclusions: Peer support increases the duration of EBF in LMICs; however, the effect appears to be reduced in formula feeding cultures. Future studies are needed to determine the optimal timing of peer visits, how to best integrate peer support into packaged intervention strategies, and the effectiveness of supplemental interventions to peer support in formula feeding cultures

Acute Malnutrition and Under-5 Mortality, Northeastern Part of India.

We assessed the prevalence of childhood acute malnutrition and under-five mortality rate (U5MR) in Darbhanga district, India, using a two-stage 49-cluster household survey. A total of 1379 households comprising 8473 people were interviewed. During a 90-day recall period, U5MR was 0.5 [95% confidence interval (CI), 0.2-1.4] per 10 000 per day. The prevalence of global acute malnutrition among 1405 children aged 6-59 months was 15.4% (NCHS) and 19.4% (2006 WHO references). This survey suggests that in Darbhanga district, the population is in a borderline food crisis with few food resources. Appropriate strategies should be developed to improve the overall nutritional and health status of children

Universal health coverage in India and health technology assessment: current status and the way forward

In India, there is a renewed emphasis on Universal Health Coverage (UHC). Alongside this, Health Technology Assessment (HTA) is an important tool for advancing UHC. The development and application of HTA in India, including capacity building and establishing institutional mechanisms. We emphasized using the HTA approach within two components of the Ayushman Bharat programme, and the section concludes with lessons learned and the next steps. The UHC has increased the importance of selecting and implementing effective technologies and interventions within national health systems, particularly in the context of limited resources. To maximize the use of limited resources and produce reliable scientific assessments, developing and enhancing national capacity must be based on established best practices, information exchange between different sectors, and collaborative approaches. A more potent mechanism and capacity for HTA in India would accelerate the country’s progress toward UHC

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid. In that respect, previous research mainly focused on energy management of individual buildings. In contrast, in this paper, we focus on aggregated control of a set of residential buildings, to provide grid supporting services, that eventually should include ancillary services. In particular, we present a real-life pilot study that studies the effectiveness of reinforcement-learning (RL) in coordinating the power consumption of 8 residential buildings to jointly track a target power signal. Our RL approach relies solely on observed data from individual households and does not require any explicit building models or simulators, making it practical to implement and easy to scale. We show the feasibility of our proposed RL-based coordination strategy in a real-world setting. In a 4-week case study, we demonstrate a hierarchical control system, relying on an RL-based ranking system to select which households to activate flex assets from, and a real-time PI control-based power dispatch mechanism to control the selected assets. Our results demonstrate satisfactory power tracking, and the effectiveness of the RL-based ranks which are learnt in a purely data-driven manner.Comment: 8 pages, 2 figures, workshop article accepted at RLEM'23 (BuildSys'23