Economic analysis based on saline water treatment using renewable energy system and microgrid architecture

The growing demand from other important industries like agriculture and manufacturing places additional emphasis on the availability of water for home use. Water use in the agricultural sector alone might increase by almost 19% by 2050 (Dokhani et al. 2022). The demand for water on a global scale is increasing by 1% annually and may reach 120–130% of the current demand in 2050. On the other hand, due to climatic change and other factors, freshwater sources are diminishing (Jabari et al. 2022). Freshwater generation appears to be a solution to the rising water demand. Desalination of plentiful seawater has become a viable technique for producing freshwater. Around 1% of the world's population currently receives water from desalination plants, and more plants are being built every year (Sui et al. 2021). Due to its reduced energy usage, which ranges from 3 to 6 kWh/m³, reverse osmosis (RO) of membrane process is the most favoured approach for desalination. It should not come as a surprise that almost 60% of the desalination plants in the world employ RO technology. Reverse osmosis desalination (ROD) facilities have historically operated with traditional generators, which are polluting. Desalination utilizing renewable energy is becoming more and more important as environmental concerns resulting from pollution grow. The power generated by renewable sources is sporadic. To synchronize them, conventional generators and energy storage (ES) are typically also used. As a result, use of renewable energy resources (RERs) to generate electricity has increased. In comparison to conventional or traditional power sources, the power produced by RERs is thought to be more sustainable, affordable, and ecologically beneficial. When more than one renewable resource is included in generation mix, according to scientists who are working to integrate it into the network, there are various benefits, including a rise in power generation efficiency (Dong et al. 2022). As a result, it has been widely predicted that most nations’ electricity will be produced by hybrid renewable energy integrated systems. This is also because a large portion of the current electricity system relies on environmentally harmful and rapidly diminishing fossil fuels (coal). When it comes to allowing for acceptable voltage variation and system frequency in any particular electrical network, integrating RERs poses a variety of issues. Currently, smart grid (SG) technology offers a variety of approaches to address problems caused by the instability and fluctuation of RERs. Installation of a microgrid system (MG) is a crucial methodology that aids in the efficient usage of RERs. The goal of this strategy is to maximize system performance despite a variety of operational problems by establishing a peer-to-peer operation mode for electrical systems. Numerous RERs difficulties are thought to be resolved by energy storage systems (ESS). Energy storage acts as a dampening mechanism between energy demand and generation in the urban sector, where energy demand needs as well as RERs implementation issues are a concern (Hemmati et al. 2021).

The contribution of this research is as follows:

1. To propose a novel method in economic saline water treatment based on MG architecture integrated with a renewable energy system.

2. An effective and cost-effective water-energy system can be operated by using an optimization framework that simultaneously improves salty and freshwater water sources as well as decentralized renewable and conventional energy sources.

3. Salinity of water is analysed using a radial Boltzmann basis machine.

The literature contains several water and energy co-optimization models where costs, energy use, or load demands of water systems were reduced (Prathapaneni & Detroja 2020). All models, however, did not account for desalination procedures and assumed that freshwater was the only supply of water. A desalination plant's ability to operate economically depends on energy reduction. The high electrical power requirements of desalination systems are one of their biggest obstacles. In Alzahrani et al. (2022), the economic and reliability concepts of the MGs are investigated. In He et al. (2022), the optimal placement of distributed generation (DG) resources is proposed, and suggested strategies are assessed. The increase in use of combined heat and power (CHP) DGs is also described in Vitale et al. (2021), in which it is objected to improving the reliability of MGs. The short-term generation scheduling for MGs is also evaluated in Wang et al. (2022). In Harish et al. (2022), a multi-objective optimization is conducted to deal with the energy management of MGs while the economic and environmental restrictions are taken into account. Several techniques, including physical models, machine learning (ML), and (more recently) deep learning (DL), can be used to forecast (Jalilian et al. 2022). They are used, for instance, to forecast and optimize energy use in smart MGs, anticipate energy use in the production of wheat, enhance health services, boost wireless network performance, manage floods, and forecast hydrogen production. Energy management of an MG made up of photovoltaic (PV), wind turbine (WT), and electrical storage system was resolved by the authors in Moazeni & Khazaei (2021) while satisfying the constraints of the MG. In Cruz et al. (2019), the uncertainty of solar, as well as wind power units, was taken into account when solving energy management of an MG. Latin hypercube sampling is utilized to manage uncertainty (Wu et al. 2021). The performance of failures in an MG was studied by Jumare (2020) and Okampo et al. (2022) under both dynamic and static loads, such as static power loads, static impedance loads, and current static loads. The major objective of this work was to investigate how the kind of load affected the fault performance of the independent MG using the WT as an energy resource. Mazzoni et al. (2019), Ogbonnaya et al. (2021), and Shayan et al. (2022) provided a thorough analysis of hybrid renewable MG optimization strategies.

This section discusses novel methods in economic saline water treatment based on MG architecture integrated with renewable energy systems. To operate a water-energy system economically and efficiently, this study provides an optimization framework to simultaneously optimize salty and freshwater water sources as well as decentralized renewable and conventional energy sources. Utilizing a deep radial Boltzmann basis machine, salinity of water is examined. ES devices, loads, and distributed power sources typically make up MG. More and more deep row entrenchment (DRE) will be adopted as renewable energy technology advances. One of the efficient approaches to DRE linked to the electrical grid is MG.

(8)

The radiation of photovoltaic cell (TPV) cell's current density can be expressed as Equation (10):

(10)

Low level control: Power converters must be controlled to regulate DC bus and allow localised current to flow in both directions.

High level control: Method is designed to monitor the SoC (state of charge) and guarantee complete PHEV stability.

The bidirectional converter balances load output and consumption while maintaining a fixed voltage of 300 V.

Deep radial Boltzmann basis machine-based saline water analysis:

The saline water analysis based on radial basis fuzzy system (SRBFS) layer can linearly receive input signal vector X(t). It was believed that exponential sigmoid with fuzzy membership would make up the radial basis kernel function. After that, Equation (12) returns the output of the jth SRBFS:

(12)

Z_j(t) is kernel centre vector in jth Fuzzy radial basis network (FRBN). Morphological parameters a and c. The output of an FRBNN is a fuzzy linear weighted sum of outputs from hidden layer nodes. It is calculable using Equation (13):

(13)

In the deep belief model (DBM) with one visible layer and two hidden layers, h(1) and h, have a look at joint probability distribution of energy function E.

The machine used for the experiment has the following hardware components: an Intel Core i5 7200U processor, 8 GB of RAM, a 1 TB hard drive, and NVIDIA GTX 760MX graphics. Additionally, Python 3.5 environments were used to simulate how the suggested strategy might be put into practise. To establish the results of the offered technique, we carried out a statistical analysis by evaluating expected performance.

Dataset description: Datasets included following variables: feed flow rate (F = 400–600 L/h), permeate flux (P_flux (L/h m²)), condenser inlet temperature (T_cond = 20–30 °C), evaporator inlet temperature (T_evap = 60–80 °C), and feed salt content (S = 35–140 g/L). Permeate flux was the main output. Data has also been divided into three categories for training, validation, and testing to facilitate neural network (NN) training. Model parameters are normally obtained from the training division. Validation division verifies accuracy of continuous training while testing division verifies its performance to avoid overfitting.

Table 1 shows analysis based on water salinity composition. The salinity ranges analysed are S = 35–140 g/L, T_cond = 20–30 °C, T_evap = 60–80 °C in terms of accuracy, precision, recall, and specificity, computational cost, and kappa coefficient.

This research proposes a novel technique in economic saline water treatment based on MG architecture integrated with a renewable energy system. The salinity of water is analysed using a radial Boltzmann basis machine. To ease hydraulic coupling restrictions and guarantee the security of the water supply, a novel supply model that assesses the dependency between the major water saline supply (WSS) and various islanded subsystems, hydraulic stability, and desalination characteristics are constructed. When examining the location of the case, low-cost power-generating options were evaluated using renewable resources such as PV devices, wind turbines, and electricity from the grid. The proposed method achieved 88% accuracy, 65% precision, 59% recall, 65% specificity, 59% computational cost, and 51% kappa coefficient.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.