Optimal Dispatch of Pumped Storage Hydro Cascade under Uncertainty

In this paper, we propose an optimal dispatch scheme for a pumped storage hydro cascade that maximizes the energy per cubic meter of water in the system taking into account uncertainty in the net load variations. To this end, we introduce a model to describe the behaviour of a pumped storage hydro cascade and formulate its optimal dispatch. We then incorporate forecast scenarios in the optimal dispatch, and define a robust variant of the developed system. The resulting optimization problem is intractable due to the infinite number of constraints. Using tools from robust optimization, we reformulate the resulting problem in a tractable form that is amenable to existing numerical tools and show that the computed dispatch is immunised against uncertainty. The efficacy of the proposed approach is demonstrated by means of a realistic case study based on the Seven Forks system located in Kenya

Enhanced power system operation with coordination and forecasting techniques

With the integration of renewable energy into power systems, traditional power systems face new challenges. Due to their inherent fluctuations and variability, the introduction of renewable energy in power systems poses new challenges in modelling uncertainty. Controlling and optimising the operation cost by adjusting the output generation of renewable energy resources makes the operation more reliable and secure. We first formulate the optimal power flow (OPF) problems for both the transmission and distribution systems and investigate the variables that greatly affect the outcome. Solving the power system optimal operation problem, we realise the importance of uncertainties involved with renewable energy due to the inherent variability of weather data. Accurate forecasting mechanisms that address their inherent intermittency and variability enable the smooth integration of such resources in power system operations. To solve this problem, in the next step, we propose a novel probabilistic framework to predict short-term PV output taking into account the variability of weather data over different days and seasons. We go beyond existing prediction methods, building a pipeline of processes, i.e., feature selection, clustering and Gaussian Process Regression (GPR). We make use of datasets that comprise power output and meteorological data such as irradiance, temperature, zenith, and azimuth. First, a correlation study is performed to select the weather features which affect solar output to a greater extent. Next, we categorise the data into four groups based on solar output and time using k-means clustering. Finally, we determine a function that relates the selected features with solar output using GPR and Matérn 5/2 as a kernel function. We validate our method with five solar generation plants in different locations and compare with the existing methodologies. More specifically, to test the proposed model, two different methods are used: (i) 5-fold cross-validation; and (ii) holding out 30 random days as test data. To confirm the model accuracy, we apply our framework 30 independent times on each of the four clusters. The average error follows a normal distribution, and with a 95% confidence level, it takes values between −1.6% to 1.4%. The proposed framework decreases the normalised root mean square error and mean absolute error by 54.6% and 55.5%, respectively, compared with other relevant works. Although we address the integration of a Microgrid into the distribution power network in the first research question, we yet need to address the transmission system constraints, as the incorporation of renewable energy into power systems poses serious challenges to the transmission and distribution power system operators (TSOs and DSOs). To fully leverage these resources, there is a need for a new market design with improved coordination between TSOs and DSOs. To answer the last research question, we propose two coordination schemes between TSOs and DSOs: one centralised and another decentralised that facilitate the integration of distributed based generation; minimise operational cost; relieve congestion; promote a sustainable system. To this end, we approximate the power equations with linearised equations so that the resulting OPFs in both the TSO and DSO become convex optimisation problems. In the resulting decentralised scheme, the TSO and DSO collaborate to allocate all resources in the system optimally. In particular, we propose an iterative bi-level optimisation technique where the upper level is the TSO that solves its own OPF and determines the locational marginal prices at substations. We demonstrate numerically that the algorithm converges to a near-optimal solution. We study the interaction of TSOs and DSOs and the existence of any conflicting objectives with the centralised scheme. More specifically, we approximate the Pareto front of the multi-objective optimal power flow problem where the entire system, i.e., transmission and distribution systems, is modelled. The proposed ideas are illustrated through a five-bus transmission system connected with distribution systems, represented by the IEEE 33- and 69-bus feeders

TSO-DSO Coordination Schemes to Facilitate Distributed Resources Integration

The incorporation of renewable energy into power systems poses serious challenges to the transmission and distribution power system operators (TSOs and DSOs). To fully leverage these resources there is a need for a new market design with improved coordination between TSOs and DSOs. In This paper we propose two coordination schemes between TSOs and DSOs: one centralised and another decentralised that facilitate the integration of distributed based generation; minimise operational cost; relieve congestion; and promote a sustainable system. In order to achieve this, we approximate the power equations with linearised equations so that the resulting optimal power flows (OPFs) in both the TSO and DSO become convex optimisation problems. In the resulting decentralised scheme, the TSO and DSO collaborate to optimally allocate all resources in the system. In particular, we propose an iterative bi-level optimisation technique where the upper level is the TSO that solves its own OPF and determines the locational marginal prices at substations. We demonstrate numerically that the algorithm converges to a near optimal solution. We study the interaction of TSOs and DSOs and the existence of any conflicting objectives with the centralised scheme. More specifically, we approximate the Pareto front of the multi-objective optimal power flow problem where the entire system, i.e., transmission and distribution systems, is modelled. The proposed ideas are illustrated through a five bus transmission system connected with distribution systems, represented by the IEEE 33 and 69 bus feeders

Clustering Sensitivity Analysis for Gaussian Process Regression Based Solar Output Forecast

Power system operations are becoming more challenging with the increasing penetration of renewable-based resources such as photovoltaic (PV) generation. In this regard, obtaining accurate solar power output forecasts allows a deepening penetration of renewable-based resources in a secure and reliable way. In this paper, we propose a probabilistic framework to predict short-term PV output taking into account the uncertainty of weather data as well as the variability of PV output over time. To this end, we use datasets comprising of meteorological weather data such as temperature, irradiance, zenith, and azimuth and solar power output. We cluster these data in categories and train a Matern 5/2 Gaussian Process Regression model for each cluster. ´More specifically, we cluster the data into one to eight different partitions by making use of the k-means algorithm. In order to identify the optimal number of clusters we use the Elbow and Gap methods. We compare the results obtained for the different number of clusters with the (i) 5-fold cross-validation; and (ii) holding out 30 representative days as test data. The results showed that the optimal number of clusters is four, since in comparison to higher number of clusters the increase in the forecast error was marginal

Spinach consumption and nonalcoholic fatty liver disease among adults: a case�control study

Background: Spinach has high antioxidants and polyphenols and showed protective effects against liver diseases in experimental studies. We aimed to assess the association between dietary intake of spinach and odds of nonalcoholic fatty liver disease (NAFLD) in a case�control study among Iranian adults. Methods: Totally 225 newly diagnosed NAFLD patients and 450 controls, aged 20�60 years, were recruited in this study. Participants� dietary intakes were collected using a valid and reliable 168-item semi-quantitative food frequency questionnaire (FFQ). The logistic regression test was used for assessing the association between total, raw, and boiled dietary spinach with the odds of NAFLD. Results: The mean (SD) age and BMI of participants (53 male) were 38.1 (8.8) years and 26.8 (4.3) kg/m2, respectively. In the final adjusted model for potential confounders, the odds (95 CI) of NAFLD in individuals in the highest tertile of daily total and raw spinach intake was 0.36 (0.19�0.71), P_trend = 0.001 and 0.47 (0.24�0.89), P_trend = 0.008, respectively compared with those in the lowest tertile. Furthermore, in the adjusted analyses, an inverse association was observed between the highest yearly intake versus no raw spinach consumption and odds of NAFLD (OR 0.41; 95% CI 0.18�0.96), P for trend = 0.013. However, there was no significant association between higher boiled spinach intake and odds of NAFLD. Conclusions: The present study found an inverse association between total and raw spinach intake with the odds of NAFLD. © 2021, The Author(s)

TFEB regulates murine liver cell fate during development and regeneration

It is well established that pluripotent stem cells in fetal and postnatal liver (LPCs) can differentiate into both hepatocytes and cholangiocytes. However, the signaling pathways implicated in the differentiation of LPCs are still incompletely understood. Transcription Factor EB (TFEB), a master regulator of lysosomal biogenesis and autophagy, is known to be involved in osteoblast and myeloid differentiation, but its role in lineage commitment in the liver has not been investigated. Here we show that during development and upon regeneration TFEB drives the differentiation status of murine LPCs into the progenitor/cholangiocyte lineage while inhibiting hepatocyte differentiation. Genetic interaction studies show that Sox9, a marker of precursor and biliary cells, is a direct transcriptional target of TFEB and a primary mediator of its effects on liver cell fate. In summary, our findings identify an unexplored pathway that controls liver cell lineage commitment and whose dysregulation may play a role in biliary cancer