A Data-Driven Two-Stage Distributionally Robust Planning Tool for Sustainable Microgrids

This paper presents a data-driven two-stage distributionally robust planning tool for sustainable microgrids under the uncertainty of load and power generation of renewable energy sources (RES) during the planning horizon. In the proposed two-stage planning tool, the first-stage investment variables are considered as here-and-now decisions and the second-stage operation variables are considered as wait-and-see decisions. In practice, it is hard to obtain the true probability distribution of the uncertain parameters. Therefore, a Wasserstein metric-based ambiguity set is presented in this paper to characterize the uncertainty of load and power generation of RES without any presumption on their true probability distributions. In the proposed data-driven ambiguity set, the empirical distributions of historical load and power generation of RES are considered as the center of the Wasserstein ball. Since the proposed distributionally robust planning tool is intractable and it cannot be solved directly, duality theory is used to come up with a tractable mixed-integer linear (MILP) counterpart. The proposed model is tested on a 33-bus distribution network and its effectiveness is showcased under different conditions

Comparison of AC Optimal Power Flow Methods in Low-Voltage Distribution Networks

Embedded with producers, consumers, and prosumers, active Low-Voltage Distribution Networks (LVDNs) with bi-directional power flows are rising to over-shadow the investment and operation planning in power systems. The Optimal Power Flow (OPF) has been extensively used in the recent years to solve different investment and operation planning problems in LVDNs. However, OPF is inherently a complex non-linear and non-convex optimization problem. Hence, different linearization and convexification models have been introduced in the literature to enhance the modeling accuracy and computational tractability of the OPF problem in LVDNs. In this paper, five multi-period OPF models (including the basic non-linear and non-convex one) are presented, with different linearizations/convexifications for the power flow equations. The proposed models are implemented on the IEEE 34-bus test system and their modeling accuracy and computational complexity are compared and discussed

Care seeking and treatment of febrile children with and without danger signs of severe disease in Northern Uganda: results from three household surveys (2018-2020)

Identification, stabilization, and prompt referral of children with signs of severe febrile disease (danger signs) in rural communities are crucial for preventing complications and death from severe malaria, pneumonia, and diarrhea. We set out to determine the treatment-seeking practices and treatment patterns for children < 5 years of age with an acute febrile illness, with or without danger signs of severe disease, in a highly malaria-endemic area of northern Uganda. Three household surveys were conducted from November through December each year in 2018, 2019, and 2020. Overall, 30% of the children in the study were reported to have had a WHO-classified danger sign including convulsions, unconsciousness/unusually sleepy, inability to feed or drink, and vomiting everything. Only half of the children in this study sought care from a health provider. However, significantly more children with danger signs of severe disease sought and received treatment and diagnostics from a health provider, compared with those without danger signs (adjusted odds ratio: 1.6, 95% confidence interval: 1.2-2.0; P < 0.01). In the total population studied, care seeking in the public sector was 26% and similar to care seeking in the private sector (24%). Community health workers were used as the first source of care by 12% of the children. Approximately 38% of the children who were reported to have danger signs of severe disease requiring prompt referral and treatment did not seek care from a health provider. Understanding and addressing barriers to accessing healthcare could contribute to better treatment seeking practices

Participatory monitoring and evaluation approaches that influence decision-making: lessons from a maternal and newborn study in Eastern Uganda

BACKGROUND: The use of participatory monitoring and evaluation (M&E) approaches is important for guiding local decision-making, promoting the implementation of effective interventions and addressing emerging issues in the course of implementation. In this article, we explore how participatory M&E approaches helped to identify key design and implementation issues and how they influenced stakeholders’ decision-making in eastern Uganda. METHOD: The data for this paper is drawn from a retrospective reflection of various M&E approaches used in a maternal and newborn health project that was implemented in three districts in eastern Uganda. The methods included qualitative and quantitative M&E techniques such as key informant interviews, formal surveys and supportive supervision, as well as participatory approaches, notably participatory impact pathway analysis. RESULTS: At the design stage, the M&E approaches were useful for identifying key local problems and feasible local solutions and informing the activities that were subsequently implemented. During the implementation phase, the M&E approaches provided evidence that informed decision-making and helped identify emerging issues, such as weak implementation by some village health teams, health facility constraints such as poor use of standard guidelines, lack of placenta disposal pits, inadequate fuel for the ambulance at some facilities, and poor care for low birth weight infants. Sharing this information with key stakeholders prompted them to take appropriate actions. For example, the sub-county leadership constructed placenta disposal pits, the district health officer provided fuel for ambulances, and health workers received refresher training and mentorship on how to care for newborns. CONCLUSION: Diverse sources of information and perspectives can help researchers and decision-makers understand and adapt evidence to contexts for more effective interventions. Supporting districts to have crosscutting, routine information generating and sharing platforms that bring together stakeholders from different sectors is therefore crucial for the successful implementation of complex development interventions

A Data-Driven Optimisation Model for Designing Islanded Microgrids

In practice, electrification of remote and islanded communities with no connection to the main grid is entangled with many techno-economic issues. These technical and more importantly economical challenges often justify the use of Micro-Grids (MGs) as self-sufficient electrical networks with a group of controllable/non-controllable consumers and producers in remote and islanded areas. However, the optimal design of sustainable MGs, even in small communities, is a complex optimisation problem due to the uncertain nature of load consumption and renewable production as well as the non-convex characteristics of network constraints. In this paper, we propose a model to design sustainable MGs using the notion of Distributionally Robust Optimisation (DRO) to handle the uncertainties arising from forecast data wherein the non-convex AC power flow equations are reformulated into convex constraints. Furthermore, a three-step approach is introduced to recast the tri-level DRO-based model into a tractable single-stage Mixed-Integer Linear Programming (MILP) problem. The proposed approach is tested on a modified Europrean CIGRE 18-bus test network and its performance is compared with the stochastic optimisation approach