Big Lottery Fund (UK).

BackgroundAround a quarter of the world's neonatal and maternal deaths occur in India. Morbidity and mortality are highest in rural areas and among the poorest wealth quintiles. Few interventions to improve maternal and newborn health outcomes with government-mandated community health workers have been rigorously evaluated at scale in this setting.The study aims to assess the impact of a community mobilisation intervention with women's groups facilitated by ASHAs to improve maternal and newborn health outcomes among rural tribal communities of Jharkhand and Orissa.Methods/designThe study is a cluster-randomised controlled trial and will be implemented in five districts, three in Jharkhand and two in Orissa. The unit of randomisation is a rural cluster of approximately 5000 population. We identified villages within rural, tribal areas of five districts, approached them for participation in the study and enrolled them into 30 clusters, with approximately 10 ASHAs per cluster. Within each district, 6 clusters were randomly allocated to receive the community intervention or to the control group, resulting in 15 intervention and 15 control clusters. Randomisation was carried out in the presence of local stakeholders who selected the cluster numbers and allocated them to intervention or control using a pre-generated random number sequence. The intervention is a participatory learning and action cycle where ASHAs support community women's groups through a four-phase process in which they identify and prioritise local maternal and newborn health problems, implement strategies to address these and evaluate the result. The cycle is designed to fit with the ASHAs' mandate to mobilise communities for health and to complement their other tasks, including increasing institutional delivery rates and providing home visits to mothers and newborns. The trial's primary endpoint is neonatal mortality during 24 months of intervention. Additional endpoints include home care practices and health care-seeking in the antenatal, delivery and postnatal period. The impact of the intervention will be measured through a prospective surveillance system implemented by the project team, through which mothers will be interviewed around six weeks after delivery. Cost data and qualitative data are collected for cost-effectiveness and process evaluations.Study registrationISRCTN: ISRCTN31567106

Existing electricity supply systems face several challenges, including increasing energy prices with greenhouse gas (GHG) emissions and fossil fuel depletion.  These issues have a significant impact on all power system stakeholders, including customers/prosumers, utilities, and microgrid operators. Renewable energy incorporation and different energy managing strategies such as demand-side management (DSM), demand response (DR), and others may help to overcome these limitations. Campus microgrids are among the largest energy consumers in the United States, with high energy expenditures. This article presents a new energy management (EMS) system for a university campus microgrid with onsite solar PV and ESS that operates in a grid exchange scenario. The suggested EMS not only lowers power consumption costs by prolonging storage life; however, it also guarantees grid stability through limiting and shifting loads using price-based and incentive-based demand response methods. ESS is utilized as a stand-by energy reserve to maintain the microgrid system stability and to assist the utility network in the event of a power outage. In MATLAB, a quadratic approach is used to solve the proposed framework. According to the findings, the suggested EMS decreases the prosumer's operating cost and increasing self-consumption, minimizes peak load from the national grid, and encourages campus stakeholders and energy controllers to engage in large-scale ESS installations and distributed generation (DG).

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The automated gain control (AGC) units as well as other non AGC equipment may be utilized in real-time power transmitting (RTPD) to coordinate the operations (RTD). In order to guarantee high-probability system security and to save operating costs, it is essential to correctly define the probable Wind Energy Forecast (WPFE) mistakes in RTD. The Cauchy Distribution (CD) is the perfect match for the "leptokurtic" characteristic of WPFE small-scale distributions, following previous research and our onsite testing. In this study, the CD represents WPFE, which is suggested to provide a chance-controlled real-time dispatch (CCRTD) paradigm (Chance-Constrained Randomization). The suggested CCRTD Model may be analytically converted to the "Convex Optimisation Problem," which takes into consideration the dependency of the wind farm outputs because of the stability and attractive mathematical features of the CD. The inclusion of a refined control method that may also be used in combination with AGC systems is an additional aspect of the suggested model. This technique, when combined with the WPFE RTD Stage, allows the CCRTD to respond to the higher ramping power requirements as well as power variations on WPFE-generated transmittal lines. The proposed technique was shown to be trustworthy and efficient in numerical testing. It is nevertheless extremely effective as well as suitable for usage.