Добірка наукової літератури з теми "Electric power consumption Measurement"

The electric railways is a specific consumer of the electric power system. For the purpose of using electric energy rationally and making adequate savings, efforts are made to optimize electric energy consumption of electric trains and other electric railway facilities. The work shows the train movement simulation algorithm which serves to determine primarily the mechanical and then also the electric power required for traction. The sections of the electrified tracks are supplied from the electric traction substation (TS) and, for the requirements of the electric traction calculation, an electric network is formed. Based on the maximum time table for a certain time period, calculation is done of the electric circumstances; electricity, voltage, electric power, as well as the total consumed electric energy. For the determination of the electric energy supply of the traction unit, movement resistances of the certain train on each section need to be calculated. Input data necessary for such a calculation are the tracks profile parameters, planned movement speeds on certain sections, and the properties of the train and the locomotive. Besides the train movement simulation model, the article also shows the analysis of impact factors on the electric energy consumption for the electromotor train which travels the Croatian suburban rails. The results are obtained by the train movement simulation algorithm, by virtue of which the locations of trains are calculated, as well as their mechanical and electric powers necessary for traction. The particular example of the supply of the existing SS serves for comparing the results obtained by electric traction calculation and measurement. Some of the results are given of the electric traction simulation for the Zaprešić SS at the supply of the suburban Podsused factory − Samobor − Bregana which is planned for construction.

This research aims to the development of reader equipment as well as control the load limitation of electric power using Atmega 8535 microcontroller. In the development of equipment of reading and controlling electrical energy consumptions, the modified KWH (Kilo Watt Hour) meter was used by placing the optocoupler sensor as the enumerator indicator the electric power consumption on the disc. Atmega 8535 microcontroller was used to control and limitation of the electric power consumption. In this research, the measuring and control system was developed to record the amount of electrical power load used, and it can be used as an alternative to the current divider for the achievement of the efficiency of practical electrical energy consumption. The results of the measurement comparison between the measured load and the output load tended to be stable with an average percentage error of 6.3%, and it was still below the optimum threshold value of the error factor, which around 10%. Therefore, results of testing developed equipment KWH digital meter using Atmega 8535 microcontroller that was produced a good performance.

Since the 1991 Gulf War, Iraq is still suffering from the crisis and irregular in the processing of electric power to customers.Damage to the network, the old measurement devices for measuring electrical energy consumption, and irregularities and corruption in theprocess of measuring and calculating fees of the consumption, all these reasons led to the deterioration of the performance of thedistribution network in Iraq. In this paper, Wireless Electric Power Meter Based on XBee Module has been designed and implementedwhich ZigBee wireless sensor network (WSN) will be used for wireless electric power meter communication supported by PICmicrocontroller which used for power unit measurements. Wireless metering system will be controlled by central computer which place inbase station; also database will be design to record and manage all data and information of consumers.

Datasets with measurements of both solar electricity production and domestic electricity consumption separated into the major loads are interesting for research focussing on (i) local optimization of solar energy consumption and (ii) non-intrusive load monitoring. To this end, we publish the iHomeLab RAPT dataset consisting of electrical power traces from five houses in the greater Lucerne region in Switzerland spanning a period from 1.5 up to 3.5 years with a sampling frequency of five minutes. For each house, the electrical energy consumption of the aggregated household and specific appliances such as dishwasher, washing machine, tumble dryer, hot water boiler, or heating pump were metered. Additionally, the data includes electric production data from PV panels for all five houses, and battery power flow measurement data from two houses. Thermal metadata is also provided for the three houses with a heating pump.

The objective of smart power consumption system is to make the large industrial user communicate actively with electric power operator by the means of smart measurement and control. The necessity of interactions between electric power corporation and large industrial users was analyzed. Then the interactive service architecture for large industrial user in smart power consumption and the relations among parts of the architecture were studied. The functions and key technologies of the interactive service architecture were also discussed. Further, by collecting the information such as internal power consumption information, device running information, laws of production, and development tend information et al., the electric power corporation can offer personalized, diversified, facilitated and interactive services for large industrial users. And the corporation can provide necessary information support for electric grid dispatching, power distribution planning, electric energy quality, line loss and power supply reliability.

Electric power has become a vital element for life today. Despite this importance, electric power consumers in Iraq suffer from the problem of noncontinuity and daily electric power supply interruption. This problem led to the use of various sources of electric power as an alternative to compensate for the shortage of electric power provided by the Iraqi national grid. In this work, a smart wireless changeover device is designed using wireless sensor networks technology aiming to solve problem caused by the multiplicity of power sources received at home and governmental buildings in Iraq by controlling operation of some electrical devices (which consume high current) in the home or workplace automatically when changing source of electricity from one to another. This solution will help to ensure the continuity of electric current feeding from power sources of variable capacities, also, to rationalize power consumption by assigning an operation priority to electric devices. Furthermore, a statistical measurement as a case study was performed in a building with a total power consumption of 160.8 KW/h. The result showed that the device functions effectively and it is capable of achieving an average saving in power of about 50% to 86% depending on the applied priorities and case study scenario.

Problem. The most effective energy management strategies for hybrid vehicles and electric vehicles are optimization-based strategies. These strategies require prior knowledge of the driving cycle, which is not easy to predict. Goal. The goal is to combine the Worldwide harmonized light vehicles test cycle (WLTC) with short trips on small sections with real traffic levels to predict the energy and fuel consumption of hybrid vehicles and electric vehicles. Methodology. Research methods are experimental and mathematical. First of all, eight characteristic parameters are extracted from real speed profiles used on urban road sections in the city of Kharkiv under various road conditions, as well as on short WLTC trips. The minimum distance algorithm is used to compare parameters and determine three traffic levels (heavy, medium, and low traffic) for short WLTC trips. Thus, for each route determined using Google Maps, the energy and fuel consumption of hybrid vehicles and electric vehicles are estimated using short trips by the WLTC, adjusted for distances and traffic levels. In addition, a numerical model of the vehicle was implemented. It was used to test the accuracy of predicting fuel and energy consumption in accordance with the proposed methodology. Originality. For the methodology using only GM information is required as input data; no other device or software is required. This aspect makes the methodology extremely economical. Then, the algorithm regulating traffic levels shown by GM is unique and valid in all urban centers. This aspect makes the methodology universal. WLTC takes into account the driving styles of drivers around the world, so the methodology can be applied to any car driver. Prediction accuracy can be increased by taking into account other input information, such as the distribution of traffic light signals or the driver's typical gear shifting style. Results. The results are promising, as the average absolute percentage error between experimental driving cycles and projected ones is 3.89 % for fuel consumption, increasing to 6.80 % for energy consumption. Practical value. The possibility of energy forecasting and fuel consumption for a hybrid vehicle and an electric vehicle makes it possible to develop energy consumption management systems for HEVs that can manage the energy reserve to ensure full travel by electric traction in limited traffic zone (LTZ) or minimize local air pollution; increase the service life of energy reserves (usually batteries) by maintenance costs and disposal problems reducing; optimize the transmission-use efficiency due to fuel consumption and pollutants emissions reduction.

Abstract The efficient use of energy and its implementation in domestic automation systems is an essential requirement in Smart Cities. However, this requires appropriate measurement devices and an M2M control-communication alternative that offers real-time visualization of the information. This article proposes a prototype of the home electric power consumption management platform, as an advance in generating a model for future Smart Grid. Therefore, the research implements smart socket devices and a coordinator of the communication of a home area network between these measuring devices to have an intelligent control of electric power. As a result, real-time data of the defined electrical parameters have been obtained. This information has been stored in the Internet cloud, also allowing remotely programming and controlling these measurement devices. The present research contributes to generating a profile of total load consumption for residential users and allowing them to know and compare their real consumption with what was reported by the distributors.

This research aims to design an electric panel monitoring system using the Internet of Things technology in company buildings so that consumers can monitor real-time electricity consumption. The energy consumption monitoring method that we propose uses PM2100 by implementing a real-time monitoring function of the power consumption of a 3-phase electric panel. The monitoring system implementation results show that the value is very close to measuring the digital multimeter measuring instrument. The monitoring system produces a current measurement accuracy of 97.38% with an error of 2.62%, while the 3-phase voltage measurement error is 0.616%. This system design helps companies obtain information faster to be considered data to improve efficiency in the Company.

The article considers the using intelligent controls possibility in low-voltage rural electric networks to minimize the unbalance modes consequences. The proposed technology includes the digital data transmission compilation on the electrical energy parameters with a new balancing technical means the electrical network operating mode. Digital feedback is provided for changes the balancing device (BD) parameters by the unbalancing power consumption changing level. Based on the developed methods compilation, software for calculating unbalancing modes has been created, which makes it possible to assess the currents and voltages unbalancing effect on the power quality and its additional losses change. The “green” technology proposed version, which increases the economic and the electric energy environmental safety use in the rural electric power industry, contains a new constructive solution for the balancing device implementation. The proposed technology was tested on the measurement data basis in existing electrical networks. Based on the MALAB technologies use, changes studied indicators visualization in the before and after BD integration in the electrical network was carried out and its analysis was makes. Used on the “neural networks” MALAB technology, a preventive assessment of the unbalancing power consumption events development in the investigated operating electrical network is presented, as well as the proposed technology effectiveness assessment was carried out.

The climate change has been an ongoing debate throughout the years. There are already some devices that monitor the changing of the climate, CanSat is a good example. The main goal of this project is to create a CanSat prototype and analyse the power used by it. The focus is on several factors such as the use of power with and without an upload program, the use of power when the sensors are switched on/off and the duration of the battery using the prototype. Some parts of the analysis have been done theoretically and practical. The project has been conducted with the aid of Arduino, an ammeter, and a voltmeter. The results show that the prototype would not spare much power if the sensors are switched off and on, this because not all the sensors have implemented the “sleep mode”. The difference between sleep mode and the normal functionality is equal to 0.026𝑊. Moreover, the difference in power when there is an upload program and when there is not an upload program is equal to 0.057𝑊. The duration of battery in the prototype is equal to 1 hour and 45 minutes according to the theoretical part, while the practical part showed a duration of 1 hour and 11 minutes. Moreover, the results show also that the prototype send the wrong values for some of the sensors when the battery have a low value. It was estimated a value of 7V of battery left to guarantee credible measurements. From the result it is possible to deduce that the decrease of power used from the CanSat prototype can be improved by finding sensors that have implemented the sleep mode, by having a small code and by having an electric platform that consume less power than Arduino. Keywords: CanSat, Power consumption, Battery life, Arduino.

Our thesis is about development of equipment, which combine two technology. First technology is wattmeter which is used to energy measurement and second is Power line communication. Wattmeter needs two variables for measurement, voltage and current. Voltage measurement is less difficult than measurement of current. Voltage can be reduce and measured by A/D converter. Current has to be transform to voltage and than can be measured same way. To do that we use current transformer. Than we design program for the microcontroller, involvement and PCB. Wattmeter is connect to PLC modem which has been chosen. Fusing this two technology we create wattmeter network. Next step was program which is used to network control and data collecting. This data are used to create charts in web application.

An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.

In this study, in order to investigate the power consumption of feed drive system, a mathematical model to predict for the electric power consumption of feed drive systems is proposed by using the single-axis experimental apparatus. This can be driven by either of ball screw or linear motor and it is possible to change the mechanical properties of the machine such as grease viscosity of the table. The power consumption is simulated by proposed simulation method based on the mathematical model of feed drive system and the simulated results are compared with the measured results of the experimental apparatus to confirm the validity of the simulated results. In addition, it is clarified that the energy usages of the feed drive system. The energy losses of the feed drive system are divided into the loss of viscous friction, coulomb’s friction, servo amplifier, and motor. These energy losses are calculated by the proposed model. Then, it is investigated that the influence of the velocity and the friction to the power consumption of feed drive system experimentally. As the results, it is confirmed that proposed simulation method can accurately predict the power consumption of the ball-screw feed drive system. It is also clarified that the friction energy loss of ball-screw drive is larger than one of linear motor drive, and the friction characteristics of linear guides influences the power consumption of linear motor drive system.

This work demonstrates the effects of the active use of renewable energy sources (RES) in the unified energy system (UES) of Ukraine, in particular, maintaining the balance in the network for its reliable and high-quality operation. In the energy strategy for the development of renewable energy sources in Ukraine until 2035, a rapid increase of the installed RES capacity is planned, i.e. the task of maintaining balance sheet reliability in the conditions of an increase in the share of RES in the UES of Ukraine is quite urgent. Forecasting of the electricity balance is based on the generation forecast, electricity consumption in a given period of time, as well as fuel reserves and the volume of generating capacity reserves. Renewable energy sources have a non-uniform nature of generation, and therefore endanger aspects of balance reliability. This problem is key for Ukraine in light of the integration of Ukraine's energy system into the European ENTSO-E network. As a result of the conducted research, it can be stated that the improvement of the situation is possible only with an integrated approach and the implementation of appropriate technologies, namely: technical, economic and institutional technologies. The introduction of technologies will make it possible to improve monitoring and management of energy systems; economic implementations will contribute to the reform of retail pricing and taxation of electricity supplies, taking into account the payment they supply for electricity and covering part of the cost of the general infrastructure; institutional innovations will change the functions and responsibilities of management subjects. Improving coordination between transmission and distribution network operators will become a priority.

The ever-tightening government-enforced regulations for more energy efficient and less polluting machines and the simultaneous fast development of electric drives have caused hydraulic systems to lose ground to electric drives. One promising solution to improve the status of hydraulics in this competition are the Direct Driven Hydraulic (DDH) systems, aka electro-hydraulic actuators (EHAs), which are characterized by a closed circuit type and a servo motor driven speed-controlled pump controlling the actuator. Due to this topology, they offer a possibility of reaching higher energy efficiencies compared to traditional open circuit type valve-controlled systems and simultaneously they offer the high accuracy and dynamics of these. Typical applications where DDHs have been used are, in the area of mobile equipment, modern commercial and military aircrafts and some lift trucks, and in the area of stationary applications, mostly presses. In all of these, the actuators produce relatively slow motions. In this experimental study, a DDH system is applied to a stationary industrial vertical position control application where a very rapid movement of a heavy load is required. This brings out some unwanted fluctuation phenomena not encountered with slower motion velocities. Here we are striving for avoiding these phenomena by adding damping to the system. In addition, it is studied whether the good energy efficiency of DDH systems could be enhanced with load-compensation. The presented measurement results include the system behavior regarding the smoothness of positioning, the fluctuations of pressures, forces, and power, and finally the energy consumption with three different system configurations: basic DDH, load-compensated DDH, and load-compensated and damped DDH. The measured energy consumptions are compared against results gained in simulating a conventional valve-controlled system driving the same application. The measurement results manifest that energy consumption wise significant benefits are achievable with DDH, especially in combination with hydraulic load compensation. However, without added damping the motion involved marked vibrations in the end of the upward and downward strokes. Added damping eliminated these vibrations, but at the cost of reduced energy efficiency. Due to this, the solution for the fluctuation and vibration problem should be sought by developing a control strategy that produces a smoother but as fast motion.

The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.

1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lower than estimates from January’s Monetary Policy Report (-7.2%). High-frequency indicators suggest that economic performance was significantly more dynamic than expected in January, despite mobility restrictions and quarantine measures. This has also come amid declines in total and core inflation, the latter of which was below January projections if controlling for certain relative price changes. This suggests that the unexpected strength of recent growth contains elements of demand, and that excess capacity, while significant, could be lower than previously estimated. Nevertheless, uncertainty over the measurement of excess capacity continues to be unusually high and marked both by variations in the way different economic sectors and spending components have been affected by the pandemic, and by uneven price behavior. The size of excess capacity, and in particular the evolution of the pandemic in forthcoming quarters, constitute substantial risks to the macroeconomic forecast presented in this report. Despite the unexpected strength of the recovery, the technical staff continues to project ample excess capacity that is expected to remain on the forecast horizon, alongside core inflation that will likely remain below the target. Domestic demand remains below 2019 levels amid unusually significant uncertainty over the size of excess capacity in the economy. High national unemployment (14.6% for February 2021) reflects a loose labor market, while observed total and core inflation continue to be below 2%. Inflationary pressures from the exchange rate are expected to continue to be low, with relatively little pass-through on inflation. This would be compatible with a negative output gap. Excess productive capacity and the expectation of core inflation below the 3% target on the forecast horizon provide a basis for an expansive monetary policy posture. The technical staff’s assessment of certain shocks and their expected effects on the economy, as well as the presence of several sources of uncertainty and related assumptions about their potential macroeconomic impacts, remain a feature of this report. The coronavirus pandemic, in particular, continues to affect the public health environment, and the reopening of Colombia’s economy remains incomplete. The technical staff’s assessment is that the COVID-19 shock has affected both aggregate demand and supply, but that the impact on demand has been deeper and more persistent. Given this persistence, the central forecast accounts for a gradual tightening of the output gap in the absence of new waves of contagion, and as vaccination campaigns progress. The central forecast continues to include an expected increase of total and core inflation rates in the second quarter of 2021, alongside the lapse of the temporary price relief measures put in place in 2020. Additional COVID-19 outbreaks (of uncertain duration and intensity) represent a significant risk factor that could affect these projections. Additionally, the forecast continues to include an upward trend in sovereign risk premiums, reflected by higher levels of public debt that in the wake of the pandemic are likely to persist on the forecast horizon, even in the context of a fiscal adjustment. At the same time, the projection accounts for the shortterm effects on private domestic demand from a fiscal adjustment along the lines of the one currently being proposed by the national government. This would be compatible with a gradual recovery of private domestic demand in 2022. The size and characteristics of the fiscal adjustment that is ultimately implemented, as well as the corresponding market response, represent another source of forecast uncertainty. Newly available information offers evidence of the potential for significant changes to the macroeconomic scenario, though without altering the general diagnosis described above. The most recent data on inflation, growth, fiscal policy, and international financial conditions suggests a more dynamic economy than previously expected. However, a third wave of the pandemic has delayed the re-opening of Colombia’s economy and brought with it a deceleration in economic activity. Detailed descriptions of these considerations and subsequent changes to the macroeconomic forecast are presented below. The expected annual decline in GDP (-0.3%) in the first quarter of 2021 appears to have been less pronounced than projected in January (-4.8%). Partial closures in January to address a second wave of COVID-19 appear to have had a less significant negative impact on the economy than previously estimated. This is reflected in figures related to mobility, energy demand, industry and retail sales, foreign trade, commercial transactions from selected banks, and the national statistics agency’s (DANE) economic tracking indicator (ISE). Output is now expected to have declined annually in the first quarter by 0.3%. Private consumption likely continued to recover, registering levels somewhat above those from the previous year, while public consumption likely increased significantly. While a recovery in investment in both housing and in other buildings and structures is expected, overall investment levels in this case likely continued to be low, and gross fixed capital formation is expected to continue to show significant annual declines. Imports likely recovered to again outpace exports, though both are expected to register significant annual declines. Economic activity that outpaced projections, an increase in oil prices and other export products, and an expected increase in public spending this year account for the upward revision to the 2021 growth forecast (from 4.6% with a range between 2% and 6% in January, to 6.0% with a range between 3% and 7% in April). As a result, the output gap is expected to be smaller and to tighten more rapidly than projected in the previous report, though it is still expected to remain in negative territory on the forecast horizon. Wide forecast intervals reflect the fact that the future evolution of the COVID-19 pandemic remains a significant source of uncertainty on these projections. The delay in the recovery of economic activity as a result of the resurgence of COVID-19 in the first quarter appears to have been less significant than projected in the January report. The central forecast scenario expects this improved performance to continue in 2021 alongside increased consumer and business confidence. Low real interest rates and an active credit supply would also support this dynamic, and the overall conditions would be expected to spur a recovery in consumption and investment. Increased growth in public spending and public works based on the national government’s spending plan (Plan Financiero del Gobierno) are other factors to consider. Additionally, an expected recovery in global demand and higher projected prices for oil and coffee would further contribute to improved external revenues and would favor investment, in particular in the oil sector. Given the above, the technical staff’s 2021 growth forecast has been revised upward from 4.6% in January (range from 2% to 6%) to 6.0% in April (range from 3% to 7%). These projections account for the potential for the third wave of COVID-19 to have a larger and more persistent effect on the economy than the previous wave, while also supposing that there will not be any additional significant waves of the pandemic and that mobility restrictions will be relaxed as a result. Economic growth in 2022 is expected to be 3%, with a range between 1% and 5%. This figure would be lower than projected in the January report (3.6% with a range between 2% and 6%), due to a higher base of comparison given the upward revision to expected GDP in 2021. This forecast also takes into account the likely effects on private demand of a fiscal adjustment of the size currently being proposed by the national government, and which would come into effect in 2022. Excess in productive capacity is now expected to be lower than estimated in January but continues to be significant and affected by high levels of uncertainty, as reflected in the wide forecast intervals. The possibility of new waves of the virus (of uncertain intensity and duration) represents a significant downward risk to projected GDP growth, and is signaled by the lower limits of the ranges provided in this report. Inflation (1.51%) and inflation excluding food and regulated items (0.94%) declined in March compared to December, continuing below the 3% target. The decline in inflation in this period was below projections, explained in large part by unanticipated increases in the costs of certain foods (3.92%) and regulated items (1.52%). An increase in international food and shipping prices, increased foreign demand for beef, and specific upward pressures on perishable food supplies appear to explain a lower-than-expected deceleration in the consumer price index (CPI) for foods. An unexpected increase in regulated items prices came amid unanticipated increases in international fuel prices, on some utilities rates, and for regulated education prices. The decline in annual inflation excluding food and regulated items between December and March was in line with projections from January, though this included downward pressure from a significant reduction in telecommunications rates due to the imminent entry of a new operator. When controlling for the effects of this relative price change, inflation excluding food and regulated items exceeds levels forecast in the previous report. Within this indicator of core inflation, the CPI for goods (1.05%) accelerated due to a reversion of the effects of the VAT-free day in November, which was largely accounted for in February, and possibly by the transmission of a recent depreciation of the peso on domestic prices for certain items (electric and household appliances). For their part, services prices decelerated and showed the lowest rate of annual growth (0.89%) among the large consumer baskets in the CPI. Within the services basket, the annual change in rental prices continued to decline, while those services that continue to experience the most significant restrictions on returning to normal operations (tourism, cinemas, nightlife, etc.) continued to register significant price declines. As previously mentioned, telephone rates also fell significantly due to increased competition in the market. Total inflation is expected to continue to be affected by ample excesses in productive capacity for the remainder of 2021 and 2022, though less so than projected in January. As a result, convergence to the inflation target is now expected to be somewhat faster than estimated in the previous report, assuming the absence of significant additional outbreaks of COVID-19. The technical staff’s year-end inflation projections for 2021 and 2022 have increased, suggesting figures around 3% due largely to variation in food and regulated items prices. The projection for inflation excluding food and regulated items also increased, but remains below 3%. Price relief measures on indirect taxes implemented in 2020 are expected to lapse in the second quarter of 2021, generating a one-off effect on prices and temporarily affecting inflation excluding food and regulated items. However, indexation to low levels of past inflation, weak demand, and ample excess productive capacity are expected to keep core inflation below the target, near 2.3% at the end of 2021 (previously 2.1%). The reversion in 2021 of the effects of some price relief measures on utility rates from 2020 should lead to an increase in the CPI for regulated items in the second half of this year. Annual price changes are now expected to be higher than estimated in the January report due to an increased expected path for fuel prices and unanticipated increases in regulated education prices. The projection for the CPI for foods has increased compared to the previous report, taking into account certain factors that were not anticipated in January (a less favorable agricultural cycle, increased pressure from international prices, and transport costs). Given the above, year-end annual inflation for 2021 and 2022 is now expected to be 3% and 2.8%, respectively, which would be above projections from January (2.3% and 2,7%). For its part, expected inflation based on analyst surveys suggests year-end inflation in 2021 and 2022 of 2.8% and 3.1%, respectively. There remains significant uncertainty surrounding the inflation forecasts included in this report due to several factors: 1) the evolution of the pandemic; 2) the difficulty in evaluating the size and persistence of excess productive capacity; 3) the timing and manner in which price relief measures will lapse; and 4) the future behavior of food prices. Projected 2021 growth in foreign demand (4.4% to 5.2%) and the supposed average oil price (USD 53 to USD 61 per Brent benchmark barrel) were both revised upward. An increase in long-term international interest rates has been reflected in a depreciation of the peso and could result in relatively tighter external financial conditions for emerging market economies, including Colombia. Average growth among Colombia’s trade partners was greater than expected in the fourth quarter of 2020. This, together with a sizable fiscal stimulus approved in the United States and the onset of a massive global vaccination campaign, largely explains the projected increase in foreign demand growth in 2021. The resilience of the goods market in the face of global crisis and an expected normalization in international trade are additional factors. These considerations and the expected continuation of a gradual reduction of mobility restrictions abroad suggest that Colombia’s trade partners could grow on average by 5.2% in 2021 and around 3.4% in 2022. The improved prospects for global economic growth have led to an increase in current and expected oil prices. Production interruptions due to a heavy winter, reduced inventories, and increased supply restrictions instituted by producing countries have also contributed to the increase. Meanwhile, market forecasts and recent Federal Reserve pronouncements suggest that the benchmark interest rate in the U.S. will remain stable for the next two years. Nevertheless, a significant increase in public spending in the country has fostered expectations for greater growth and inflation, as well as increased uncertainty over the moment in which a normalization of monetary policy might begin. This has been reflected in an increase in long-term interest rates. In this context, emerging market economies in the region, including Colombia, have registered increases in sovereign risk premiums and long-term domestic interest rates, and a depreciation of local currencies against the dollar. Recent outbreaks of COVID-19 in several of these economies; limits on vaccine supply and the slow pace of immunization campaigns in some countries; a significant increase in public debt; and tensions between the United States and China, among other factors, all add to a high level of uncertainty surrounding interest rate spreads, external financing conditions, and the future performance of risk premiums. The impact that this environment could have on the exchange rate and on domestic financing conditions represent risks to the macroeconomic and monetary policy forecasts. Domestic financial conditions continue to favor recovery in economic activity. The transmission of reductions to the policy interest rate on credit rates has been significant. The banking portfolio continues to recover amid circumstances that have affected both the supply and demand for loans, and in which some credit risks have materialized. Preferential and ordinary commercial interest rates have fallen to a similar degree as the benchmark interest rate. As is generally the case, this transmission has come at a slower pace for consumer credit rates, and has been further delayed in the case of mortgage rates. Commercial credit levels stabilized above pre-pandemic levels in March, following an increase resulting from significant liquidity requirements for businesses in the second quarter of 2020. The consumer credit portfolio continued to recover and has now surpassed February 2020 levels, though overall growth in the portfolio remains low. At the same time, portfolio projections and default indicators have increased, and credit establishment earnings have come down. Despite this, credit disbursements continue to recover and solvency indicators remain well above regulatory minimums. 1.2 Monetary policy decision In its meetings in March and April the BDBR left the benchmark interest rate unchanged at 1.75%.

Research objectives : Identify genetic potential and community structure of soil and rhizosphere microbial community structure as affected by treated wastewater (TWW) irrigation. This objective was achieved through the examination soil and rhizosphere microbial communities of plants irrigated with fresh water (FW) and TWW. Genomic DNA extracted from soil and rhizosphere samples (Minz laboratory) was processed for DNA-based shotgun metagenome sequencing (Green laboratory). High-throughput bioinformatics was performed to compare both taxonomic and functional gene (and pathway) differences between sample types (treatment and location). Identify metabolic pathways induced or repressed by TWW irrigation. To accomplish this objective, shotgun metatranscriptome (RNA-based) sequencing was performed. Expressed genes and pathways were compared to identify significantly differentially expressed features between rhizosphere communities of plants irrigated with FW and TWW. Identify microbial gene functions and pathways affected by TWW irrigation*. To accomplish this objective, we will perform a metaproteome comparison between rhizosphere communities of plants irrigated with FW and TWW and selected soil microbial activities. Integration and evaluation of microbial community function in relation to its structure and genetic potential, and to infer the in situ physiology and function of microbial communities in soil and rhizospere under FW and TWW irrigation regimes. This objective is ongoing due to the need for extensive bioinformatics analysis. As a result of the capabilities of the new PI, we have also been characterizing the transcriptome of the plant roots as affected by the TWW irrigation and comparing the function of the plants to that of the microbiome. *This original objective was not achieved in the course of this study due to technical issues, especially the need to replace the American PIs during the project. However, the fact we were able to analyze more than one plant system as a result of the abilities of the new American PI strengthened the power of the conclusions derived from studies for the 1ˢᵗ and 2ⁿᵈ objectives. Background: As the world population grows, more urban waste is discharged to the environment, and fresh water sources are being polluted. Developing and industrial countries are increasing the use of wastewater and treated wastewater (TWW) for agriculture practice, thus turning the waste product into a valuable resource. Wastewater supplies a year- round reliable source of nutrient-rich water. Despite continuing enhancements in TWW quality, TWW irrigation can still result in unexplained and undesirable effects on crops. In part, these undesirable effects may be attributed to, among other factors, to the effects of TWW on the plant microbiome. Previous studies, including our own, have presented the TWW effect on soil microbial activity and community composition. To the best of our knowledge, however, no comprehensive study yet has been conducted on the microbial population associated BARD Report - Project 4662 Page 2 of 16 BARD Report - Project 4662 Page 3 of 16 with plant roots irrigated with TWW – a critical information gap. In this work, we characterize the effect of TWW irrigation on root-associated microbial community structure and function by using the most innovative tools available in analyzing bacterial community- a combination of microbial marker gene amplicon sequencing, microbial shotunmetagenomics (DNA-based total community and gene content characterization), microbial metatranscriptomics (RNA-based total community and gene content characterization), and plant host transcriptome response. At the core of this research, a mesocosm experiment was conducted to study and characterize the effect of TWW irrigation on tomato and lettuce plants. A focus of this study was on the plant roots, their associated microbial communities, and on the functional activities of plant root-associated microbial communities. We have found that TWW irrigation changes both the soil and root microbial community composition, and that the shift in the plant root microbiome associated with different irrigation was as significant as the changes caused by the plant host or soil type. The change in microbial community structure was accompanied by changes in the microbial community-wide functional potential (i.e., gene content of the entire microbial community, as determined through shotgun metagenome sequencing). The relative abundance of many genes was significantly different in TWW irrigated root microbiome relative to FW-irrigated root microbial communities. For example, the relative abundance of genes encoding for transporters increased in TWW-irrigated roots increased relative to FW-irrigated roots. Similarly, the relative abundance of genes linked to potassium efflux, respiratory systems and nitrogen metabolism were elevated in TWW irrigated roots when compared to FW-irrigated roots. The increased relative abundance of denitrifying genes in TWW systems relative FW systems, suggests that TWW-irrigated roots are more anaerobic compare to FW irrigated root. These gene functional data are consistent with geochemical measurements made from these systems. Specifically, the TWW irrigated soils had higher pH, total organic compound (TOC), sodium, potassium and electric conductivity values in comparison to FW soils. Thus, the root microbiome genetic functional potential can be correlated with pH, TOC and EC values and these factors must take part in the shaping the root microbiome. The expressed functions, as found by the metatranscriptome analysis, revealed many genes that increase in TWW-irrigated plant root microbial population relative to those in the FW-irrigated plants. The most substantial (and significant) were sodium-proton antiporters and Na(+)-translocatingNADH-quinoneoxidoreductase (NQR). The latter protein uses the cell respiratory machinery to harness redox force and convert the energy for efflux of sodium. As the roots and their microbiomes are exposed to the same environmental conditions, it was previously hypothesized that understanding the soil and rhizospheremicrobiome response will shed light on natural processes in these niches. This study demonstrate how newly available tools can better define complex processes and their downstream consequences, such as irrigation with water from different qualities, and to identify primary cues sensed by the plant host irrigated with TWW. From an agricultural perspective, many common practices are complicated processes with many ‘moving parts’, and are hard to characterize and predict. Multiple edaphic and microbial factors are involved, and these can react to many environmental cues. These complex systems are in turn affected by plant growth and exudation, and associated features such as irrigation, fertilization and use of pesticides. However, the combination of shotgun metagenomics, microbial shotgun metatranscriptomics, plant transcriptomics, and physical measurement of soil characteristics provides a mechanism for integrating data from highly complex agricultural systems to eventually provide for plant physiological response prediction and monitoring. BARD Report

Hybrid electric vehicles (HEVs) represent one of the main technological options for reducing vehicle CO2 emissions, helping car manufacturers (OEMs) to meet the stricter targets which are set by the European Green Deal for new passenger cars at 80 g CO2/km by 2025. The optimal power-split between the internal combustion engine (ICE) and the electric motor is a challenge since it depends on many unpredictable variables. In fact, HEV improvements in fuel economy and emissions strongly depend on the energy management strategy (EMS) on-board of the vehicle. Dynamic Programming approach (DP), direct methods and Pontryagin’s minimum principle (PMP) are some of the most used methodologies to optimize the HEV power-split. In this paper two online strategies are evaluated: an Adaptive-RuleBased (A-RB) and an Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). At first, a description of the P2 HEV model is made. Second, the two sub-optimal strategies are described in detail and then implemented on the HEV model to derive the fuel-optimal control strategy managing the power split between the thermal and electric engine to satisfy the driver's power request, including the engine on/off operating mode and the best gear selection. Finally, the two proposed strategies are tested on different driving cycles and then compared to other commercial strategies available in literature, such as the Equivalent Consumption Minimization Strategy (ECMS) and a RuleBased (RB) strategy. The results show that the A-ECMS is more conservative in terms of state of charge (SoC) compared to the A-RB. In fact, in the A-ECMS the SoC is always within the admissible range with considerable margin from the upper and lower limits for tested cycles, while in the A-RB a deep discharge of the battery is allowed. This behavior leads to a better fuel consumption of the A-RB compared to the A-ECMS, both in the WLTC and in the FTP-75 cycle.

In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy - ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications.