Operation of surface water pumps are limited by surface water availability and delivery schedules with little inherent flexibility. Dwindling surface water sources has made operation of surface water pumps even less flexible as they are dictated by water availability and not the irrigation schedule, energy cost, and/or the grid needs. The most convenient form of energy storage on a farm is not a battery or similar technology but is in the form of water storage.Soil moisture is the most common form of water storage on a farm. Adjusting the soil properties can significantly increase soil’s moisture uptake capacity. More recently, farmers in California have started a new form of water storage by flooding their fields even outside of the irrigation season. This will recharge the groundwater aquifers during times of year when excess surface water is available . Another form of water storage that can be beneficial for DR participation is above groundwater storage . On farm water storage can act equivalently as a battery, smoothing the electricity demand for irrigation. Availability of water storage allows irrigation when needed, or when it makes the most economical sense and not when water is available. The speed of an alternating current electric motor in a pump system is directly proportional to the frequency of the power supply. A Variable Frequency Drive6 takes the electrical supply from the utility and changes the frequency of the electric current,cultivo del arandano azul which results in a change of motor speed .
VFDs are most commonly installed for energy saving purposes; however, improved process control is another reason for installing VFDs. VFDs, although promising for AutoDR can pose potential disadvantages such as damage to the motor bearing and other components if operated improperly. Considerations must also be given to VFD reliability, maintenance costs, and skills of available personnel [ CITATION USD141 \l 1033 ]. VFDs are not recommended for pumps with high static head or pumps that operate for extended periods under low flow conditions [CITATION NSW171 \l 1033 ]. Therefore, VFDs are not suited for pumps that pull water from deep groundwater wells, and are best fitted for smaller surface/booster and fertilizer pumps. Figure 11 summarizes the operational characteristics of a pump with a VFD under different speeds. Note that there is no static head present in Figure 4, only dynamic head . As discussed earlier, groundwater pumps can be easily coupled with water storage and be able to shift their operations to off peak hours. Therefore, VFDs with their drawbacks for high static head systems are not the best fit for groundwater pumps. However, VFDs would be ideal for tapping into the DR potential that booster and surface pumps can provide. Given that booster pumps need to maintain a minimum pressure on the irrigation system, VFDs can allow modulation of their power and allow such systems to provide DR services to the electric grid while meeting the operational requirements of a farm’s irrigation system. According to Figure 4, a low static head pump can reduce its power demand by a third without significant efficiency losses if operated correctly.Over 57,000 farm businesses and other farms were engaged in producing renewable energy such as solar, wind, and geothermal in 2012, more than twice as many as in 2007 . Solar energy production is the most prevalent from on farm renewable energy, with an estimated 82% of farms with renewable energy generation reporting solar electricity generation capacity . With dropping prices of solar energy, agricultural industry can benefit from dual land use for energy production. Solar panels installed on an irrigation ponds can reduce evaporative losses, and solar arrays installed elsewhere on a farm can provide shading for the livestock or the farming equipment .
On farm renewable energy production can also protect farms from volatile energy prices. This trend provides an added incentive for farmers to get a better handle on the timing of their energy use, and restructure their operations to utilize larger amounts of renewable energy such as wind and solar . Much of the infrastructure and technology that can be used for AutoDR enablement, can be used for helping farms in maximizing the use of the onsite generated renewable energy. The framework put forward in this paper also helps pave the path for policy development as farms transition from being net energy consumers to net energy generators. A high renewable penetration grid requires flexible loads in order to maintain its stability. Agricultural loads, with their large magnitude can provide that flexibility as energy markets move into a future of increasingly distributed and intermittent renewable generation. However, several on farm constraints, as well as lack of appropriate market mechanisms limit farms from taking advantage of more flexible energy and water use strategies that could benefit the grower, utility, and the grid. Agricultural demand management programs have proven to be unsuccessful in facilitating the needs of the farm and helping the utilities manage their demand and reduce cost. This indicates that current programs and tariffs do not adequately account for the needs of the grid and constraints that exist on farms. New technologies and market based approaches are needed to give utilities greater flexibility and their agricultural customers greater incentives to balance the grid and meet the high penetration of renewable sources in the coming decades. To address these barriers for DR adoption, researchers and Agricultural Technology companies have focused on topics such as scientific irrigation scheduling, real time irrigation prediction using sensor data, and remote scheduling of operations. In the meantime, utilities have done very little to tailor their DR programs to the needs of agricultural operations. A common misconception within the AgTech industry is that technology alone will inherently bring all the benefits. However, technology can lead to more complications if it is not coupled with improved management and training.
In recent years, an abundance of AgTech companies , has led to a surge of promising technologies but most lack scalability and impact on the field. For example, various models of water efficiency and environmental benefits have been developed, yet they are under utilized in irrigation scheduling; at most, they help retrospectively to evaluate seasonal approaches . Another example is of soil moisture sensors being ubiquitous on the market but are not easy to handle, lack reliability, and fail to provide adequate spatial data. The same situation applies to technologies geared toward managing energy and electricity demand on farms. Several years of agricultural DR research has identified that the DR and pathways through which a farm can be approved and enabled for DR, participate in DR events, and receive compensation are complex. Most farms lack the in house expertise for going through the entire process without the help of external consultants. Without an energy or sustainability manager, it is very challenging and intimidating for farms to even begin to approach DR enablement – unless they put all their faith in a utility or aggregator in spite of their own lack of understanding. The research for facilitating higher uptake of agricultural DR has been segmented by keeping farmers, grid operators, and utilities in silos with little thought to having them talk and understand each other’s needs. Moreover, much of AgTech sector’s focus has been on yield increase and crop quality improvement and little attention has gone towards other operational aspects of the farm including irrigation energy and water management. Ultimately,arándanos azules cultivo irrigated agriculture will need to adopt a new management paradigm based on an economic objective which not only includes yield but also takes into account water and energy . The framework put forward in this paper is unique as no similar farm to grid analysis framework has been identified in the extensive literature search in the field of agricultural water and energy. The discussion put forward in this paper can be summarized in a diagram similar to what is presented in Figure 13. With the information provided in previous sections, one should be able to connect various on farm electricity consuming equipment to the appropriate grid need using available market mechanisms. This framework will allow identification of missing market mechanisms for tapping into agricultural DR potential or can shed light on technology gaps that can facilities higher DR participation of agricultural farms. Figure 13 is intended to serve as a starting point for addressing the knowledge gap that hinders farms to provide valuable DR services to the grid and benefit from untapped revenue streams. The future work should be focused on data collection that will allow better mapping of farm equipment to various grid needs through existing mechanism or developing new ones. Even though this paper will not provide an end to end solution for DR enablement at farms, but it paves the way for widespread DR participation for all significant electricity users on farms. Figure 12 illustrates a hypothetical application of the proposed framework. In the example below, actual farm load profiles are disaggregated into various end uses . Based on the information collected regarding each end use , the relevant component of the load profile can then be mapped to the appropriate grid need based on its characteristics .The federal government’s under count of nonfatal occupational injuries and illnesses for all industries combined has received considerable research and popular press attention. A US General Accounting Office report addressed under counting and suggested remedies for all industries combined. This study extends previous research by focusing on agriculture, an industry that merits special attention for several reasons.
First, although estimates vary, agriculture employs roughly 2 to 4 million people, and includes the highest share of self-employed persons in any industry. Second, agriculture is among the most hazardous industries, especially for the self-employed. Third, agriculture employs many undocumented workers; for example, the most recent analysis from the National Agricultural Workers Survey estimated 53% of all hired crop workers were undocumented. Contentious debate surrounds whether undocumented workers should be granted citizenship and the impact this may have on workers’ subsequent use of Medicaid and workers’ compensation. Fourth, many farm workers are migrants; the NAWS estimated 42% of crop workers annually traveled 75+ miles to obtain jobs. Fifth, and most importantly, agriculture poses the greatest challenge of any industry for generating estimates of under counting because of the seasonal nature of employment, and predominance of small, family-run operations. We measured the injury and illness under count as the difference between estimates from the Bureau of Labor Statistics ’s Survey of Occupational Injuries and Illnesses and our own estimates. Unlike the SOII, we accounted for the self-employed and workers on small farms as well as willful and negligent under reporting by both employees and employers. We believe our estimates are conservative, in part because we use the same criteria as the BLS to qualify a case as an occupational injury or illness. We do not include, for example, estimates of job-related cancers, COPD, and circulatory disease that far exceed those recognized by the SOII. The under count has institutional and behavioral causes. Institutional causes pertain to deliberate reasons for excluding persons. Two of these institutional causes are the exclusions of self-employed farmers on all farms and workers on farms with <11 employees from the SOII. A third institutional cause is the government’s under count of employment of farm workers in virtually all government data sets. This employment under count is widely recognized owing to the fluid and part-time nature of farm work. BLS readily acknowledges the employment under count and estimates its magnitude in supplements to the Quarterly Census of Employment and Wages. There are two behavioral causes: negligence and willful under reporting. Despite the under count, the SOII is widely cited by researchers and journalists, in part, because it has been providing the only annual national estimates of nonfatal workplace injuries and illnesses for 40 years. There are three additional data sets with relevant information, but none as comprehensive as the SOII. The National Health Interview Survey provides information on injuries, but not illnesses, nor estimates within industries. The Census of Fatal Occupational Injuries provides information within agriculture, but only for injury fatalities. The National Agricultural Workers Survey contains data on injuries but only for crop, not animal farms.The 1970 Occupational Safety and Health Act requires very high percentages of firms to record qualifying work-related injuries and illnesses, i.e., those associated with death, loss of consciousness, lost or restricted work days, or medical treatment beyond first aid.