Coordination of pumped-storage unit and irrigation system with intermittent wind generation for intelligent energy management of an agricultural microgrid

This study aims to provide a comprehensive review about the configurations, operation, and integration of multiple energy sources for microgrid (MG) system. The applications of renewable and non-renewable energy sources have been discussed and analysed. Similarly, the several control strategies, protection schemes, and communication networks used for MG have been defined to provide a unique and distinct perspective for an optimal and real-time project. During the review analysis, different used software tools and techniques were observed and applied broadly. However, the most frequently used software and technique are hybrid optimization model for electric renewables (HOMER) Pro and particle swarm optimization algorithm. Additionally, the total global installed capacity for renewable sources is enhanced up to 2588 GW, including 627 GW of PV source, along with the global investment of USD 316.7 Billion on clean energy sources. Furthermore, the least levelized cost of energy for on-grid and off-grid hybrid systems using HOMER Pro was found as $0.072 and $0.145/kWh, respectively. Finally, the work has provided thorough study and future directions about MGs to assist engineers and researchers for their upcoming prospects in the field of renewable energy.

Section: Agriculturementioning

confidence: 99%

Microgrids: A review on optimal hybrid technologies, configurations, and applications

Shaikh

Memon

et al. 2021

“…First of all, in 2018, a research study presented a novel coordination approach to optimize the operation of the pumped-storage unit, water irrigation system, and intermittent wind turbines in an agricultural microgrid. 16 Afterward, an energy management framework was suggested to supply the irrigation systems by the joint solar and diesel power generators. 17 Besides, a research study investigated the practicality of using solar power-based irrigation systems for the agricultural sector.…”

Section: Literature Reviewmentioning

confidence: 99%

“…u i,j and v i,j are the startup and shutdown decision binary variable of DG. In addition, the relationship between μ i,j , ν i,j , and U i,j is described in Equations (15) and (16). Inequality (17) limits the power generation of DGs to lower and upper thresholds.…”

Section: Thermal Generation Unitsmentioning

confidence: 99%

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

Golmohamadi

2020

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.

“…Power generation and storage scheduling optimization is aimed by using two‐point estimate method. Ghasemi 81 also used wind turbines and pumped‐storage unit in an agricultural MG. With the same method, they optimized the day‐ahead scheduling of power exchange regarding the wind power forecasts besides the load demand, the water demand, and the market prices, they aimed to optimize day‐ahead scheduling of power exchange. Whereas, Hong et al 82 studied the same subject using the robust optimization and column generation algorithms.…”

Section: Reviewed Studies and Analysesmentioning

confidence: 99%

Review and clustering of optimal energy management problem studies for industrial microgrids

Bektas

Kayakutlu

2020

The optimal energy management problem of microgrids is an agenda item for the energy field. Especially industrial microgrids, whose energy demand is high and uninterruptible, need to be managed by a holistic view of optimization. Technical researches in this field are to be enriched with the literature review that looks through various angles. In the scope of this paper, the research literature is reviewed deeply and analyzed with the aim of presenting the gaps. Articles written in English, in the period of 2010 to 2019 have been revised thoroughly. By eliminating the search results, 105 relevant articles have been determined. Objective(s), criteria for analysis, processes included, power source(s), storage system, other system components and methodology of each article have been studied. The literature has been summarized, it has been seen that solar, wind, and diesel sources are used in about 82%, 55%, and 16% of articles, respectively. Then, they have been clustered using self-organizing maps. Nine clusters have been obtained which separate the articles clearly. Especially, three of them which have fewer data have been analyzed. Clustering results have been interpreted and utilized to show the research openings. Achievements of this study will lead the scientific research that will fill in the gaps.