Evaluation of hydraulics characteristics and management strategies of subsurface drainage system in Indira Gandhi Canal Command

The present study revealed the performance of subsurface drainage systems for long-term sustainability of irrigated agriculture.  The performance of subsurface drainage systems was evaluated on the basis of drain spacing equations for disposal of effluent and hydraulic characteristics of envelop materials, like entrance resistance created by envelop and hydraulic conductivity.  Three important synthetic envelopes, HG 22, SAPP 240 and CAN 2 were tested in laboratory using sand tank model and permeability apparatus to compare their performances in terms of entrance resistance and hydraulic conductivity of soil envelope system.  The hydraulic conductivity for SAPP 240 filter was found the highest and entrance resistance the lowest. Performance of four unsteady state drain spacing equations viz.  Glover-Dumm, Van Schilfgaarde, Integrated Hooghoudt and Modified Glover equations were also tested to evaluate disposal efficiency of excess water.  The percentage deviation between predicted drain spacing and actual spacing was -33.31% to -31.55%, 9.40% to 17.07%, 11.84% to 20.83% and 6.10% to 14.62% for Glover-Dumm, Van Schilfgaarde, Integrated Hooghoudt and Modified Glover equations, respectively.  Modified Glover equation showed minimum deviation from actual drain spacing due to its versatile applicability.  Therefore, the Modified Glover equation with SAPP 240 filter was recommended for subsurface drainage system in sandy soil texture areas.Keywords: subsurface drainage, unsteady drain spacing equations, evaluation hydraulic characteristics, management strategie

Influence of bidding mechanism and spot market characteristics on market power of a large genco using hybrid DE/BBO

Generation company (Genco) bidding in an electricity market (EM) aims to maximize its profit under uncertain market characteristics and a regulated bidding mechanism. This paper addresses the strategic bidding for a large price maker Genco and empirically investigates the effect of a step-wise multiple segment bidding mechanism and EM characteristics, such as demand and rivals' behavior, on its market power (MP) potential and efficiency. The methodology of using novel hybrid differential evolution with biogeography-based optimization (DE/BBO), employing the sinusoidal migration model, is proposed for strategic bidding. DE exploration with BBO exploitation enhances global optimization. Uncertain rival behavior is modeled as normal distribution and simulated by the Monte Carlo technique. The proposed approach is validated for large Genco bidding in spot EM, under changing market characteristics and bidding segments. The implicit MP potential and efficiency of Genco for corresponding strategies is assessed using the criteria of expected profit, risk of profit variance, and failure rate of Genco. This assessment discovers an underlying correlation between the market characteristics and bidding segments, which would aid Genco in optimizing its bidding strategy and market performance.</p

Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system

Hybrid differential evolution with BBO for Genco's multi-hourly strategic bidding

In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.</p

Long-term expansion planning of the transmission network in India under multi-dimensional uncertainty

Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system

Neuronal development is promoted by weakened intrinsic antioxidant defences due to epigenetic repression of Nrf2

Forebrain neurons have weak intrinsic antioxidant defences compared with astrocytes, but the molecular basis and purpose of this is poorly understood. We show that early in mouse cortical neuronal development in vitro and in vivo, expression of the master-regulator of antioxidant genes, transcription factor NF-E2-related-factor-2 (Nrf2), is repressed by epigenetic inactivation of its promoter. Consequently, in contrast to astrocytes or young neurons, maturing neurons possess negligible Nrf2-dependent antioxidant defences, and exhibit no transcriptional responses to Nrf2 activators, or to ablation of Nrf2’s inhibitor Keap1. Neuronal Nrf2 inactivation seems to be required for proper development: in maturing neurons, ectopic Nrf2 expression inhibits neurite outgrowth and aborization, and electrophysiological maturation, including synaptogenesis. These defects arise because Nrf2 activity buffers neuronal redox status, inhibiting maturation processes dependent on redox-sensitive JNK and Wnt pathways. Thus, developmental epigenetic Nrf2 repression weakens neuronal antioxidant defences but is necessary to create an environment that supports neuronal development

Hybrid differential evolution with BBO for Genco's multi-hourly strategic bidding

In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.</p