Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

Review of the cutting edge technologies for weed control in field crops

In 21st century,the rapid increase in population and industrialization not only limits the per capita arable land for crop production but also limits the productive potential of soil and agricultural crops due to the negative impacts of anthropogenic climate change.Besides the abiotic factors of the environment,among biotic factors limiting productivity,weeds contribute the maximum.Due to various limitations in conventional weed control methods,integrated weed management(IWM)practices have evolved for effective weed management in agriculture.In this era of information and technological evolution,artificial intelligence is moving at a faster pace in every sector to address the issues of various dimensions.The use of deep learning,machine learning,and artificial neural networks in AI-enabled robots and unmanned aerial vehicles,along with multi-and hyper-spectral image sensors,make the tools capable enough for quick and efficient weed management for harnessing the ultimate productive potential of different fields crops.No doubt,the IWM practices designed for various crops in different countries in different ecologies have advantages over the individual and traditional approaches to weed control,but the use of these AI-enabled software and tools can save time,resources,money,and labor when used along with the best IWM method.Sensor-based weed identification,mapping,and automation can be done for precise and effective management of weed flora using these modern approaches,which will be environmentally friendly and have a broader scope for achieving global food security.

Day-ahead industrial load forecasting for electric RTG cranes

Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports.The move towards electrified rubber-tyred gantry(RTG)cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecastsfor electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate.