Diesel Generators at the Customers Side as a Microgrid Connected to the Network

2021 ◽  
Author(s):  
Adel hamad Rafa ◽  
Hala Aqoub
Keyword(s):  
Diesel Generators

Total Petroleum Hydrocarbons Categories Concentration in Soils Within the Vicinity Housing Heavy-Duty Diesel Generators in Three Universities in Port Harcourt South-South Nigeria

2021 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Petroleum Hydrocarbons ◽  
Health Hazard ◽  
Soxhlet Extraction ◽  
Soil Samples ◽  
Total Petroleum Hydrocarbons ◽  
Heavy Duty ◽  
Flame Ionization ◽  
Port Harcourt ◽  
Diesel Generators ◽  
Heavy Duty Diesel

Total petroleum hydrocarbons pollution of soil samples randomly collected from three Nigeria Universities in Port Harcourt due to the use of heavy-duty diesel generators was studied to ascertains the level of concentration of the different hydrocarbons’ categories. The soil samples were collected at two different depths of 0.00-0.50m and 0.50-1.00m. The Universities were Ignatius Ajuru University of Education (IAUE), Rivers State University (RSU) and University of Port Harcourt (UNIPORT). The different total petroleum hydrocarbons categories were Gasoline Range Organics (GRO), Diesel Range Organics (DRO) and Lube Oil Range. Soxhlet extraction method was used in extracting the samples and due column clean-up was performed for chromatographic analysis. Gas Chromatography-Flame Ionization Detector was used to determine the level of concentrations of the different categories of total petroleum hydrocarbons. The results showed that at 0.00-0.50m depth, IAUE was 4.42145, 945.4784, and 525.66919 mg/Kg for GRO, DRO and lube oil range respectively, RSU was not detected, 494.44799 and 458.6715 mg/Kg for GRO, DRO and lube oil range respectively and UNIPORT was 4.40920, 501.2246 and 467.71426 mg/Kg for GRO, DRO and lube oil range respectively. At 0.50-1.00m depth IAUE was 2.75132, 596.35126, and 311.84451 mg/Kg for GRO, DRO and lube oil range respectively, RSU was not detected, 298.06899 and 270.61619 mg/Kg for GRO, DRO and lube oil range respectively and UNIPORT was 2.77780, 301.74701 and 276.88684 mg/Kg for GRO, DRO and lube oil range respectively. The level of soil contamination Showed that GRO > DRO > lube oil range. The observation showed that hydrocarbon pollution decreased with increase in depth. The level of DRO and lube oil range in the studied areas exceeded the limit acceptable and therefore adequate steps should be taken to remedy the situation so that it will not pose any health hazard to the workers operating the heavy-duty generators.

Renewable energy and energy storage to offset diesel generators at expeditionary contingency bases

2021 ◽  
pp. 154851292110513
Author(s):  
Scott M Katalenich ◽  
Mark Z Jacobson
Keyword(s):  
Air Pollution ◽  
Renewable Energy ◽  
Energy Storage ◽  
Carbon Emissions ◽  
Meteorological Data ◽  
Vertical Axis ◽  
Humanitarian Assistance ◽  
Diesel Generators ◽  
Combat Casualties ◽  
The Military

Expeditionary contingency bases (non-permanent, rapidly built, and often remote outposts) for military and non-military applications represent a unique opportunity for renewable energy. Conventional applications rely upon diesel generators to provide electricity. However, the potential exists for renewable energy, improved efficiency, and energy storage to largely offset the diesel consumed by generators. This paper introduces a new methodology for planners to incorporate meteorological data for any location worldwide into a planning tool in order to minimize air pollution and carbon emissions while simultaneously improving the energy security and energy resilience of contingency bases. Benefits of the model apply not just to the military, but also to any organization building an expeditionary base—whether for humanitarian assistance, disaster relief, scientific research, or remote community development. Modeling results demonstrate that contingency bases using energy efficient buildings with batteries, rooftop solar photovoltaics, and vertical axis wind turbines can decrease annual generator diesel consumption by upward of 75% in all major climate zones worldwide, while simultaneously reducing air pollution, carbon emissions, and the risk of combat casualties from resupply missions.

scholarly journals Solving optimal generation scheduling problem of microgrid using teaching learning based optimization algorithm

2020 ◽  
Vol 17 (3) ◽  
pp. 1632
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Demand Response ◽  
Operating Cost ◽  
Optimal Scheduling ◽  
Energy Resources ◽  
Successful Implementation ◽  
Scheduling Problem ◽  
Solar Pv ◽  
Teaching Learning Based Optimization ◽  
Diesel Generators ◽  
Teaching Learning

<p>This paper proposes a new optimal scheduling methodology for a Microgrid (MG) considering the energy resources such as diesel generators, solar photovoltaic (PV) plants, wind farms, battery energy storage systems (BESSs), electric vehicles (EVs) and demand response (DR). The penetration level of renewable and sustainable energy resources (i.e., wind, solar PV energy, geothermal and ocean energy) in power generation systems is increasing. In this work, the EVs and storage are used as flexible DR sources and they can be combined with DR to improve the flexibility of MG. Various uncertainties exist in the MGs due to the intermittent/uncertain nature of renewable energy resources (RERs) such as wind and solar PV power outputs. In this paper, these uncertainties are modeled by using the probability analysis. In this paper, the optimal scheduling problem of MG is solved by minimizing the total operating cost (TOC) of MG. The TOC minimization objective is formulated by considering the cost due to power exchange between main grid and MG, diesel generators, wind, solar PV units, EVs, BESSs, and DR. The successful implementation of optimal scheduling of MG requires the widespread use of demand response and EVs. In this paper, teaching-learning-based optimization (TLBO) algorithm is used to solve the proposed optimization problem. The simulation studies are performed on a test MG by considering all the components of MG.</p>

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