Cereal-legumes intercropping is among the most economical and effective agronomic strategies to boost forage biomass production, nutritional quality and monetary returns. This review synthesizes the research findings on how intercropping affects productivity, quality, competitiveness and economic viability of sorghum-legumes mixed, row and strip intercropping systems under varied pedo-climatic conditions. Though component crops show yield reductions in row (additive and row-replacement series), mixed (seed blended crops) and strip intercropping systems, in general overall productivity per unit land area increases to a great extent. The significantly higher resource capturing with better utilization efficacy by intercrops in temporal and spatial dimensions helps explain their greater productivity. In addition, forage intercrops result in improved nutritional quality as legumes contain protein in double quantity than cereals. Cereal-legumes intercropping systems yield higher quantities of lush green forage with improved quality traits, which ultimately increase monetary benefits. Furthermore, legumes inclusion as an intercrop with cereals has the potential to serve as a nitrogen-saving strategy due to the biological nitrogen fixation (BNF) process. Moreover, cereal-legume intercropping systems are effective in reducing weed infestations and soil erosion by providing extended soil cover, as well as in increasing water use efficiency and improving soil fertility. However, despite a significant increase in overall productivity, component crops suffer yield losses in intercropping systems owing to competition for the finite divisible pool of growth resources. Thus, there is a dire need to optimize spatial and temporal arrangements in sorghum-legumes intercropping systems to achieve maximum productivity and economic returns.

The achievement of sustainable energy goals warrants keen interest in promoting efficient buildings and renewable energy resources. Prominent among the energy-efficient building technologies is geothermal energy, which has a significant margin for improving energy utilization related to Heat, Ventilation, and Air Conditioning (HVAC). However, the efficient extraction of geothermal energy for HVAC applications requires stringent control of geometric parameters, boundary conditions, and environmental conditions. In this study a new approach has been devised to optimize the open loop Earth to Air Heat Exchanger (EAHE) system using a statistical optimization technique i.e., Response Surface Method (RSM). The study was conducted in the soil and weather conditions of Peshawar city in Pakistan. Parametric analysis was conducted for the three influencing variables, i.e., the pipe length, diameter, and air velocity using the EAHE model. The soil model predicts temperature in the range 20–26 °C for Peshawar at a depth above 3 m. Response Surface method was used to optimize the pipe length, diameter, and air velocity of the EAHE system. Analysis of Variance (ANOVA) indicates that all the three factors are significant. The EAHE system can effectively reduce the temperature by 15–18 °C and compensate the cooling load of single room for the parameters in the ranges of 50–70 m for the length, 0.18–0.25 m for the diameter, and 5–7 ms⁻¹ for the air velocity. A regression equation is developed to predict the cooling load for any input values of the three influencing variables according to the weather and soil conditions.

This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux (SMV) wind farm are used for this purpose. The upstream turbine (SMV6) is operated with a yaw offset ( $\alpha$ ) in a range of $-12$ ° to 8° for analysing the impact on the downstream turbine (SMV5). Simulations are performed with intelligent control strategies for estimating optimum $\alpha$ settings. Simulations show that optimal $\alpha$ can increase net production of the two turbines by more than 5%. The impact of $\alpha$ on SMV6 is quantified using the data obtained during the experiment. A comparison of the data obtained during the experiment is carried out with data obtained during normal operations in similar wind conditions. This comparison show that an optimum or near-optimum $\alpha$ increases net production by more than 5% in wake affected wind conditions, which is in confirmation with the simulated results.

This paper demonstrates, with live field experiment, the potential of increasing wind farm production through minimising wake effects by curtailing upstream wind turbines. Two 2MW turbines from the SMV (Le Sole de Moulin Vieux) wind farm are used for this. The farm is equipped with state of the art LiDARs (Light Detection And Ranging) for measuring wind characteristics, at a frequency of 1Hz. Simulations are performed using WindPRO for wake effects prediction. Optimised curtailment strategies are simulated for finding optimum curtailment settings of the upstream turbine. Results based on real time data are compared with simulated results. It is found that simulations are mostly in good agreement with field results, with a maximum difference of 1.5%. Analysis shows that a gain of up to 11.5% is possible in downstream turbine production, using a hard curtailment strategy by reducing power of the upstream turbine by about 17%. In this experiment, the combined production of the two turbines decreased with the hard curtailment strategy, indicating that the upstream turbine must be optimally curtailed for avoiding any production loss. To the best knowledge of the authors, this is the first practical implementation of LiDAR based coordinated control strategies in an operating wind farm.

Increasing large-scale integration of renewables in conventional power system has led to an increase in reserve power requirement owing to the forecasting error. Innovative operating strategies are required for maintaining balance between load and generation in real time, while keeping the reserve power requirement at its minimum. This research work proposes a control strategy for active power balance control without compromising power system security, emphasizing the integration of wind power and flexible load in automatic generation control. Simulations were performed in DIgSILENT for forecasting the modern Danish power system with bulk wind power integration. A high wind day of year 2020 was selected for analysis when wind power plants were contributing 76.7% of the total electricity production. Conventional power plants and power exchange with interconnected power systems utilize an hour-ahead power regulation schedule, while real-time series are used for wind power plants and load demand. Analysis showed that flexible load units along with wind power plants can actively help in reducing real-time power imbalances introduced due to large-scale integration of wind power, thus increasing power system reliability without enhancing the reserve power requirement from conventional power plants.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity–based Jensen wake model (TI–JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ( $C_P$ ) or deflecting wakes by applying yaw-offsets for maximising net farm production. Firstly, TI–JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimised strategies are evaluated using simulations based on TI–JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 s for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.