Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case Study

Abrishambaf, Omid; Faria, Pedro; Vale, Zita; Corchado, Juan M.

doi:10.3390/en12203987

Cited by 9 publications

(11 citation statements)

References 33 publications

(41 reference statements)

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“…The method used for energy efficiency analysis [18,19,31,32] is based on the definition of direct and indirect energy inputs, calculation of the energy output for given plant production. Based on these data in wheat and sugar beet production specific energy input, energy output-input ratio, energy productivity and net energy gain were estimated as follows:…”

Section: Methodsmentioning

confidence: 99%

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Energy Use and Economic Analysis of Fertilizer Use in Wheat and Sugar Beet Production in Serbia

et al. 2020

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Increased demand for food production, influenced by the constant growth of population, resulted in the agricultural production systems that are more energy and economy intensive. The aim of this study was to evaluate the energetic and economic efficiency of sugar beet and wheat production. Attention was given to the fertilizer usage and its share in energy consumption since it can amount to 50%. Data show that energy input in wheat production was 5.84 MJ·kg−1 and in sugar beet it was 0.93 MJ·kg−1. The highest share of energy input both in wheat and sugar beet was observed for fertilizers, 52.45% and 46.70%, respectively. Economic analysis has shown that wheat production is a low profitable production with a net return of only 20.69 USD·ha−1, in comparison with sugar beet production with a net return of 513.53 USD·ha−1. Costs related to the fertilizer use prevailed in total variable and total production costs. Economic analysis has also shown that the benefit-to-cost ratio was higher in sugar beet production (1.33) compared to wheat production (1.03). Furthermore, it was determined that these economic indicators were less sensitive in sugar beet production than in wheat production regarding the variation of fertilizer.

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Section: Methodsmentioning

confidence: 99%

“…Today's agricultural practices still lack energy optimization measures, leading to high energy consumption [18]. One way to optimize energy consumption is to determine the efficiency of methods and techniques used [19].…”

Section: Introductionmentioning

confidence: 99%

Energy Use and Economic Analysis of Fertilizer Use in Wheat and Sugar Beet Production in Serbia

et al. 2020

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“…Moreover, a research study proposed an energy management model in farms to facilitate the use of solar systems and hydropower. 22 The study simulated a farm near a river to use the run-of-the-river hydroelectricity in the irrigation systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study evaluated the energy consumption of the irrigation systems to justify the cost‐effective usage of the suggested solar‐powered irrigation systems. Moreover, a research study proposed an energy management model in farms to facilitate the use of solar systems and hydropower 22 . The study simulated a farm near a river to use the run‐of‐the‐river hydroelectricity in the irrigation systems.…”

Section: Introductionmentioning

confidence: 99%

Operational scheduling of responsive prosumer farms for day‐ahead peak shaving by agricultural demand response aggregators

Golmohamadi

2020

Int J Energy Res

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This paper proposes a novel structure to optimize the operational strategies of responsive farms for day-ahead peak shaving. To achieve the aim, the modern irrigation system of farms, including groundwater, surface water, and booster water pumps, are modeled mathematically. To develop the demand response (DR) potentials of the farms, electrical storage systems, and self-generation facilities, including thermal distributed generations and on-farm solar sites, are addressed. In order to facilitate the integration of the agricultural DR programs into the electricity market, a mathematical formulation for agricultural demand response aggregator (ADRA) is suggested. The ADRA participates in the dayahead electricity market on behalf of the responsive farms. To overcome the price uncertainty of the electricity market, a robust optimization approach is addressed. This approach determines the robust decisions of farms in the worstcase realizations of the uncertain electricity price. Regarding on-farm solar sites located in rural areas, a data-driven approach is used to estimate the solar power generation of a significant number of sites without needing to install costly communication and measurement infrastructures. Finally, the proposed approach is implemented on agricultural lands in the northeast of Iran. The numerical results show that the suggested approach provides day-ahead peak shaving for the power systems meeting the crop's water requirements.

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“…In the same statement, it is shown that between 14% to 30% of these GHG emissions are related to the fossil fuels that have been used in this sector for the pump water and irrigation purposes. This shows the need for exchanging fossil fuels to Renewable Energy Resources (RERs) in the agriculture sector [8].…”

Section: Introductionmentioning

confidence: 99%

Emulation of Energy Scheduling for an Agriculture Irrigation System Considering Field Data

Abrishambaf

Faria

Vale

2020

2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Syst

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As the global population is daily soaring, the need for water and energy is also increasing. This makes the role of the agriculture sector more tangible. Water and energy efficiency in this sector is still very low, and the need for smart management and strategic planning are obvious. This paper proposes an autonomous approach to increase the efficiency of energy and water consumption in the agriculture irrigation process. The model contains local renewable energy resources to supply the irrigation electricity demand. In the previous works developed by the authors, the irrigation scheduling had been performed by a decision tree approach. however, in this paper, the model schedules irrigation according to the real-time field data as well as the availability of the local resources to minimize operational costs. The system also includes a laboratory demonstration to test the scheduling process under practical challenges. A case study is also shown to validate the model, and its results show a gap between expected and real results. Nevertheless, the results proved the functionality and applicability of the proposed model.

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Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case Study

Cited by 9 publications

References 33 publications

Energy Use and Economic Analysis of Fertilizer Use in Wheat and Sugar Beet Production in Serbia

Energy Use and Economic Analysis of Fertilizer Use in Wheat and Sugar Beet Production in Serbia

Operational scheduling of responsive prosumer farms for day‐ahead peak shaving by agricultural demand response aggregators

Emulation of Energy Scheduling for an Agriculture Irrigation System Considering Field Data

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