Techno-Economic Analysis of Utility-Scale Solar Photovoltaic Plus Battery Power Plant

Decarbonizing the global power sector is a key requirement to fight climate change. Consequently, the deployment of renewable energy (RE) technologies, notably solar photovoltaic (PV), is proceeding rapidly in many regions. However, in many of these regions, the evening peak is predominantly being served by fossil-fired generators. Furthermore, as the evening peak is projected to increase in the coming years, there are plans to install more fossil-fired peaking generators. A cleaner alternative is to enable solar PV plants to provide clean power after sunset by pairing them with large-scale lithium-ion batteries to provide evening peak generation. In this work, we performed a techno-economic analysis of a solar PV plus battery (PVB) power plant using the island of Mauritius as a case study. We assessed the impacts of the battery size, inverter loading ratio (ILR), tracking type, and curtailment on the levelized cost of electricity (LCOE). The main results show that the LCOE of utility-scale PVB systems are comparable to that of fossil-fired peaking generators for this case study. Tracking was shown to exacerbate the clipping loss problem and its benefits on LCOE reduction decrease as the ILR increases. The availability of the PVB system to serve the evening peak was found to be high. The curtailment analysis also showed that planners must not rely solely on storage, but rather should also improve grid flexibility to keep PVB integration affordable. Overall, the practical insights generated will be useful to utility planners in charting their generation expansion strategy.

ASSESSMENT OF TRAVEL SPEED FOR URBAN STREETS USING GLOBAL POSITIONING SYSTEM

The travel speeds selected and trusted by the driver vary according to his behavior and the surrounding environment. Accordingly, this paper studies the change of the free-flow speed, influences on it, of the route linking Al-Bayaa and Bab Almudam sectors. A GPS device was used to collect data during 60 rounds for three periods (morning, day, and night), 50 of them during the off-peak time and 10 during peak time with drivers of both genders and different ages. The results showed a variation of the FFS values at off-evening peak time, also noting the posted speed limits corresponding to the statistically calculated 85, which was 69km\hr. along the route. The emergence of a significant impact of the security checkpoints in link 3, causing a general defect in the traffic accounts. Also, there was an increase in the speed with an increase in lane width by 13% and a decrease in the speed with an increase in the number of lanes by 45.18% and 18.6% between 2 and 4 lanes and 2 and 3 lanes respectively. It should be noted that male is bolder and more reckless in choosing speeds by 26.32% than female and that groups of 50 and below years old choose less speed by 13.52% of ages 35 and below years old who are more confident and familiar in dealing with roads and choosing the appropriate speed.

Demand Shifting as a Profitable Strategy for Solar Plant Operators

<p>As solar electricity generation increases, the daytime net load (total load less solar generation) decreases, reducing prices in the middle of the day. These low prices reduce motivation to invest in more solar electricity. In this study, the correlation between net load and price is quantified on a seasonal average basis, and used to predict resulting hourly price changes if demand can be shifted from evening peak hours to mid-day when solar generation is greatest. The results suggest such a strategy will be of economic benefit to solar generators by increasing the price at mid-day for all electricity delivered, while reducing the price and thus total expenditures for energy during evening peak hours, with a net overall savings for energy consumers. These financial benefits motivate solar plant owners and developers to promote load-shifting, both to increase the revenue from current solar plants and to create demand for more solar plants.</p>

Demand Shifting as a Profitable Strategy for Solar Plant Operators

<p>As solar electricity generation increases, the daytime net load (total load less solar generation) decreases, reducing prices in the middle of the day. These low prices reduce motivation to invest in more solar electricity. In this study, the correlation between net load and price is quantified on a seasonal average basis, and used to predict resulting hourly price changes if demand can be shifted from evening peak hours to mid-day when solar generation is greatest. The results suggest such a strategy will be of economic benefit to solar generators by increasing the price at mid-day for all electricity delivered, while reducing the price and thus total expenditures for energy during evening peak hours, with a net overall savings for energy consumers. These financial benefits motivate solar plant owners and developers to promote load-shifting, both to increase the revenue from current solar plants and to create demand for more solar plants.</p>

Optimization for a Multi-Constraint Truck Appointment System Considering Morning and Evening Peak Congestion

Gate and yard congestion is a typical type of container port congestion, which prevents trucks from traveling freely and has become the bottleneck that constrains the port productivity. In addition, urban traffic increases the uncertainty of the truck arrival time and additional congestion costs. More and more container terminals are adopting a truck appointment system (TAS), which tries to manage the truck arrivals evenly all day long. Extending the existing research, this work considers morning and evening peak congestion and proposes a novel approach for multi-constraint TAS intended to serve both truck companies and container terminals. A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. The aim of the proposed multi-constraint TAS model is to minimize the overall operation cost. The Lingo commercial software is used to solve the exact solutions for small and medium scale problems, and a hybrid genetic algorithm and simulated annealing (HGA-SA) is proposed to obtain the solutions for large-scale problems. Experimental results indicate that the proposed TAS can not only better serve truck companies and container terminals but also more effectively reduce their overall operation cost compared with the traditional TASs.

Identification Method for Optimal Urban Bus Corridor Location

Locating urban bus corridors based on corridor characteristics can increase the transportation capacity, improve transportation efficiency, and increase the attractiveness and commercial value of urban bus corridors. In this paper, we describe the comprehensive optimization of the urban bus corridor location and setting of bus lanes, while considering the aggregation effect of the corridor. First, we use a K-shortest path algorithm to generate a candidate set of bus corridors. Then, we analyze the influencing factors of the bus corridor. Following this, we take the minimum generalized cost and the maximum aggregation utility along the path as the objective function and design a bus corridor location identification optimization model, considering arc capacity, plot ratio, corridor development, and time constraints. Finally, we examine the real-world example of the Beijing city and identify the location of the bus corridors in the morning and evening peak hours. The one-way traffic of most of the roads identified as bus corridors was found to be greater than 6671 people/h. Thus, the location of the bus corridor and setting of bus lanes in the corridor are closely related to passenger flow, and the method can provide scientific guidance for transportation and urban planning departments and facilitate collaboration between these departments.

Extreme concentrations of fine particulate matter and black carbon during commute

&lt;p&gt;Commuters are vulnerable to traffic air pollutants, especially to fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) and black carbon (BC) because of their proximity to on-road vehicles. Both pollutants have been extensively associated to adverse health effects (i.e., stroke, diabetes, cardiovascular and respiratory diseases, and cancer). Therefore, this work aims to investigate the extreme concentrations of PM&lt;sub&gt;2.5&lt;/sub&gt; and BC occurrence in commuters in the megacity of S&amp;#227;o Paulo, Brazil. We carried out a field campaign measuring the commuter exposure to PM&lt;sub&gt;2.5&lt;/sub&gt; and BC concentrations inside buses, cars and undergrounds in S&amp;#227;o Paulo during morning and evening peak-hours. We fitted an Extreme Value Distribution to the collected data to investigate the behavior of the extreme values in the different transport modes and periods of the day. The results suggest that higher concentrations of PM&lt;sub&gt;2.5&lt;/sub&gt; and BC occur more frequently inside buses, followed by cars and undergrounds. Extreme concentrations for both pollutants are more likely to happen during morning peak-hours when compared to evening peak-hours. Our findings add further evidence that the transport mode and period of the day a&amp;#64256;ect substantially the PM&lt;sub&gt;2.5&lt;/sub&gt; and BC exposure in commuters. Furthermore, the results are quite useful for supporting urban policies that consider the improvement of the efficiency of air filtering systems inside public transport and private cars.&lt;/p&gt;