Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season

Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season

Transient Neonatal Hypocortisolism in Neonates with Hypoglycemia – Coexistence or Cause?

Introduction: Infants born preterm, with low birth weight (LBW), or with perinatal stress are at high risk for neonatal hypoglycemia. Low cortisol levels have also been demonstrated in this group of neonates, which is often transient. We report a series of neonates with transient hypocortisolism who had neonatal hypoglycemia. Methods: A descriptive study on clinic-biochemical parameters of a group of five neonates who had persistent neonatal hypoglycemia and had demonstrated low cortisol on critical sample testing. Results: All five neonates had birth weights below normal and four were born preterm. A history of perinatal asphyxia was seen in four cases and neonatal sepsis in two. During critical sample testing (when blood glucose [BG] was 2 mIU/ml) was seen in three infants whereas insulin was undetectable in two. The median cortisol during critical sample testing was 1.9 mcg/dl (0.88 – 3.7). Critical GH was normal in all, and ACTH ranged from 7.2 pg/ml to 41.3 pg/ml. None of the infants had overt clinical features of panhypopituitarism or primary adrenal insufficiency. USG brain revealed germinal matrix hemorrhage in two infants, which resolved on follow-up. USG adrenals and electrolytes were normal in all. Four of the five babies were started on oral hydrocortisone, to which they responded well with the resolution of hypoglycemia. No adverse events were noted. On follow-up, the median time to recover of serum cortisol to normal was 4 months. Conclusion: The contribution of transient hypocortisolism to hypoglycemia in infants at risk, including preterm, LBW, or those with perinatal stress, in the presence or absence of hyperinsulinism, is not well known. While the non-specific use of glucocorticoids is not advocated, the role of therapeutic glucocorticoids among at-risk neonates with documented hypocortisolism during hypoglycemia should be an area for research. Close follow-up of these neonates for spontaneous recovery of cortisol levels is warranted