Energy Cost Research Articles

Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.

Is stochastic thermodynamics the key to understanding the energy costs of computation?

The relationship between the thermodynamic and computational properties of physical systems has been a major theoretical interest since at least the 19th century. It has also become of increasing practical importance over the last half-century as the energetic cost of digital devices has exploded. Importantly, real-world computers obey multiple physical constraints on how they work, which affects their thermodynamic properties. Moreover, many of these constraints apply to both naturally occurring computers, like brains or Eukaryotic cells, and digital systems. Most obviously, all such systems must finish their computation quickly, using as few degrees of freedom as possible. This means that they operate far from thermal equilibrium. Furthermore, many computers, both digital and biological, are modular, hierarchical systems with strong constraints on the connectivity among their subsystems. Yet another example is that to simplify their design, digital computers are required to be periodic processes governed by a global clock. None of these constraints were considered in 20th-century analyses of the thermodynamics of computation. The new field of stochastic thermodynamics provides formal tools for analyzing systems subject to all of these constraints. We argue here that these tools may help us understand at a far deeper level just how the fundamental thermodynamic properties of physical systems are related to the computation they perform.

Enhancement of a heat pump's efficiency COP operating on the water-soil principle

AbstractGeothermal energy is increasingly promising for residential use due to rising energy costs and environmental awareness. This work experimentally examines the impact of pipe distribution on the heat pump's performance at varying temperatures for both the incoming water and the ground. The pipes are buried in the soil, distributing them on layers of varying depths up to a depth of 2 m, separated by insulating layers. The quantities of heat gained in the evaporator and released in the condenser are calculated to determine the coefficient of performance of the heat pump. It was found that at the same temperature, the total heat loss in the soil is larger in the case of water entering from the bent pipe towards the great depth compared to water entering from the direct pipe, but the heat gain is larger in the case of water entering from the direct one.

Research on Intelligent Energy System and Power Metering Optimization Based on Multi-objective Optimization Decision Algorithm

In this paper, the intricate problem of optimizing power metering within an intelligent energy system, utilizing a multi-objective optimization decision-making algorithm, is thoroughly explored. Given the current energy landscape, achieving efficient energy utilization and environmental sustainability has become a focal point of research. As a pivotal aspect of future energy management, the precision and optimization of power metering in intelligent energy systems directly influence the effectiveness and cost of energy consumption. To begin, this paper delves into the fundamental principles and application backdrop of intelligent energy systems, highlighting the significance of power metering in such systems. Subsequently, addressing the multi-objective optimization challenges in power metering, a novel optimization method based on a multi-objective optimization decision algorithm is introduced. This algorithm achieves comprehensive optimization of power metering, encompassing multiple objectives such as power cost reduction, enhanced energy efficiency, and environmental protection. The experimental results underscore the remarkable performance of this algorithm, which not only elevates the precision of power metering but also achieves substantial savings in energy costs and significantly boosts energy efficiency. Furthermore, the algorithm exhibits robust adaptability, making it capable of addressing power metering optimization challenges across diverse scenarios. Finally, this paper discusses the practical application prospects of the optimization algorithm in intelligent energy systems, and points out the direction of future research. The research in this paper provides new ideas and methods for the optimization of power metering in intelligent energy systems, and has important theoretical and practical significance for promoting the intelligence and refinement of energy management.

Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries

Lithium batteries have revolutionized energy storage with their high energy density and long lifespan, but challenges such as energy density limitations, safety, and cost still need to be addressed. Crystalline materials, including Ni-rich cathodes and lithium anodes, play pivotal roles in the performance of high-energy-density lithium batteries. Understanding the micro-scale behavior and degradation mechanisms of these materials is crucial for improving macro-scale battery performance. Simulation methods, particularly machine learning (ML) techniques, have become indispensable tools in elucidating these intricate processes because of great efficiency and acceptable accuracy. ML methods depend on descriptors, which bridge the gap between crystal structures and input matrices of models. These descriptors encode essential atomic-level details in crystal structures, enabling predictions of material properties and behaviors relevant to lithium batteries. This paper reviews and discusses the diverse array of descriptors employed in the simulation of crystalline materials for lithium batteries with high energy density. Case studies highlight the effectiveness of different descriptors in simulating cathode behaviors such as Li/Ni disordering, screening of stable LiNi0.8Co0.1Mn0.1O2 (NMC811) configurations, and lithium deposition behaviors at the anode interface. The discussed descriptors can also be applied to other crystalline cathode, anode, and electrolyte materials in lithium batteries and advance the development of lithium batteries with superior energy density.

Synthesis and Characterization of an Alkali-Activated Binder from Blast Furnace Slag and Marble Waste.

This study reports an alkali-activated binder including blast furnace slag (BFS) together with marble waste (MW). Cement is an industrial product that emits a significant amount of CO2 during its production and incurs high energy costs. MW is generated during the extraction, cutting, and processing of marble in production facilities, where dust mixes with water to form a settling sludge. This sludge is an environmentally harmful waste that must be disposed of in accordance with legal regulations. In this study, a substantial amount of MW, a by-product with considerable environmental and economic impacts worldwide, was utilized in the production of a binder through the alkaline activation of BFS. In doing this, different experimental parameters were tested to obtain the best binder samples according to workability and mechanical properties. Then, some experiments such as drying shrinkage determination, strength testing, and microstructure analyses were fulfilled through samples with the best values. The findings supported the improvement of the rapid-setting property of BFS by means of the addition of MW. MW reduced the time-dependent drying shrinkage values of BFS by 55%, especially in slag alkaline activation systems with a low or moderate alkali activator content. The substitution of MW (≤50%) in BFS increased flexural and compressive strengths (4.5 and 61.7 MPa), while a reference sample contained BFS only. Although the use of MW did not create a new phase, it contributed to a C-S-H bonding structure during the alkali activation of BFS in a microstructure analysis.

General Industrial Process Optimization Method to Leverage Machine Learning Applied to Injection Moulding

ABSTRACTThe development of machine learning (ML) technologies is driving changes across many sectors. In industrial settings, this is called the fourth industrial revolution and encompasses several technologies pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial injection moulding machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process, while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines K‐nearest neighbours classification and nearest neighbours optimization methods to optimize an injection moulding process effectively. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and ML methods in a real‐world setting to improve production quality, production time and energy cost.

Water–Energy Nexus-Based Optimization of the Water Supply Infrastructure in a Dryland Urban Setting

Managing water supply systems is essential for developing countries to face climate variability in dryland settings. This is exacerbated by high energy costs for pumping, water losses due to aging infrastructures, and increasing demand driven by population growth. Therefore, optimizing the available resources using a water–energy nexus approach can increase the reliability of the water distribution network by saving energy for distributing the same water. This study proposes a methodology that optimizes the Water Distribution Network (WDN) and its management that can be replicated elsewhere, as it is developed in a data-scarce area. Indeed, this approach shows the gathering of WDN information and a model to save energy by optimizing pump schedules, which guarantee water distribution at minimal operational costs. The approach integrates a genetic algorithm to create pumping patterns and the EPANET hydraulic simulator to test their reliability. The methodology is applied for a water utility in the dryland urban setting of Lodwar, Turkana County, Kenya. The results indicate a potential reduction in energy costs by 50% to 57% without compromising the supply reliability. The findings highlight the potential of WEN-based solutions in enhancing the efficiency and sustainability of data-scarce water utilities in dryland ecosystems.

Grey correlation analysis of the main influencing factors of wax deposition of waxy crude oil and determination of pigging period based on dynamic analysis

Normal temperature gathering and transportation has significant advantages in reducing energy consumption and cost. Due to the high wax content of crude oil in carbonate reservoirs, obvious wax deposition will occur in the process of gathering and transportation at room temperature. In this paper, according to the component analysis results of crude oil on site, the HEATANALOGY wax deposition model was used to calculate the wax deposition of different gathering pipelines. The main influencing factors of wax deposition were analyzed by grey correlation method. Finally, according to the pigging mechanical balance model, the safe pigging period is derived, and the relationship diagram of different pipe diameters, throughput and ground temperature is drawn. The results show that the wellhead temperature has the greatest influence on the initial wax deposition position and the peak wax deposition position of the pipe section. The flow rate has the greatest influence on the maximum wax deposition thickness of the tube wall. According to the relationship between the working conditions and the pigging cycle, it can be found that with the increase of the throughput, the pigging cycle decreases first and then increases. With the increase of ground temperature, the pigging cycle increases.

Why aren't we teaching this? Smart local energy systems and the young person's perspective

AbstractWith the UK government's target of Net Zero by 2050, alongside the rising cost of energy in the UK, it is imperative that public opinion aligns with and promotes affordable, greener energy systems. Within this dialogue, young people's voices and lived experience are needed to deepen the impact of energy policy intervention strategies. This article stresses the importance of a formal curriculum that informs future generations on the subjects of Smart Local Energy Systems (SLES), and the digitalisation advances of energy systems. This paper explores the development of a toolkit to educate and engage young people in these subjects and reviews young people's attitudes and understanding from three schools in England, UK. We find through an interpretive qualitative analysis of both images drawn by the young people, and insights gathered from focus groups, that (i) for these young people, SLES are a useful innovation regarding energy management and distribution, (ii) young people recognise that the education system as it currently stands is not aware of, or informed by smart energy technologies and (iii) that despite being a generation that is the most integrated with personal assistance technology, these young people have significant concerns over the reliability and safety of AI in SLES. These findings are set alongside calls for widespread education in key stages 4 and 5 (ages 14–18) around the subject of SLES, and their benefits to wider society, thereby enabling a future that empowers young people to make change, encourage engineering and computer science‐based career options and secure a fairer, cleaner, sustainable energy transition in the future.

Theoretical Study of the Influence of Electroconvection on the Efficiency of Pulsed Electric Field (PEF) Modes in ED Desalination

Pulsed electric field (PEF) modes of electrodialysis (ED) are known for their efficiency in mitigating the fouling of ion-exchange membranes. Many authors have also reported the possibility of increasing the mass transfer/desalination rate and reducing energy costs. In the literature, such possibilities were theoretically studied using 1D modeling, which, however, did not consider the effect of electroconvection. In this paper, the analysis of the ED desalination characteristics of PEF modes is carried out based on a 2D mathematical model including the Nernst–Planck–Poisson and Navier–Stokes equations. Three PEF modes are considered: galvanodynamic (pulses of constant electric current alternate with zero current pauses), potentiodynamic (pulses of constant voltage alternate with zero voltage pauses), and mixed galvanopotentiodynamic (pulses of constant voltage alternate with zero current pauses) modes. It is found that at overlimiting currents, in accordance with previous papers, in the range of relatively low frequencies, the mass transfer rate increases and the energy consumption decreases with increasing frequency. However, in the range of high frequencies, the tendency changes to the opposite. Thus, the best characteristics are obtained at a frequency close to 1 Hz. At higher frequencies, the pulse duration is too short, and electroconvective vortices, enhancing mass transfer, do not have time to develop.

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Comprehensive methodology for the integrating of the organic rankine Cycle-ORC with diesel generators in off-grid areas: Application to a Colombian case study

Efficient fuel management in diesel generator sets for power generation in non-grid areas is a persistent concern. In this context, the use of an Organic Rankine Cycle (ORC) to recover heat from exhaust gases from diesel generator sets represents a promising route for additional power generation. To undertake such projects, it is necessary to understand the critical parameters related to diesel engines, including the specific fuel consumption, mass flow, and exhaust gas temperature. These parameters are fundamental to the sizing process of the ORC heat recovery system. This study introduces an innovative methodology for evaluating the operation of diesel-ORC systems based on the load demand of off-grid communities. The proposed system is a viable solution for the generation of additional power with the objective of improving the overall efficiency and meeting higher energy demands in isolated areas. Four organic fluids were selected for the ORC: R245fa, benzene, cyclopentane, and toluene. This selection was made based on many criteria, including global warming potential (GWP), ozone depletion potential (ODP), and safety classification (ASHRAE 34). In addition, the exergy behaviors of these fluids were reviewed. A comparative analysis was subsequently conducted for a non-interconnected region of Colombia to evaluate the performance of a diesel generator operating independently and in conjunction with the diesel ORC system. The key indicators employed were fuel consumption savings (L/year), energy produced (MWh/year), levelized cost of energy (LCOE, USD/kWh), and payback period (years). The results demonstrated that, over the course of a 1-year simulation period, the benzene ORC system exhibited the highest overall energy efficiency, achieving a value of 41.38%. The exergy analysis indicated that toluene had lower irreversibilities, achieving an exergy efficiency of 44.78%, followed by benzene (43.5%. Furthermore, the diesel-ORC system using benzene demonstrated a notable decrease in the specific fuel consumption from 0.282 to 0.247 L/kWh, signifying a 10.52% reduction in the annual CO2eq emissions. The cost of electricity generation decreased by 4.3%, with investment payback periods not exceeding 14 years.

Long-term price guidance mechanism for integrated energy systems based on gated recurrent unit - vision transformer prediction and fractional-order stochastic dynamic calculus control

Against the backdrop of global climate change, renewable energy (RE) performs significant effects in reducing carbon emissions. Nevertheless, volatile RE sources disrupt the energy supply-demand balance (ESDB) in the energy market. Flexible energy (FE) can effectively mitigate the problem with high flexibility and controllability. FE sources are widely distributed in integrated energy systems (IESs). However, existing methods guide scattered FE in IESs with insufficient accuracy. To accurately guide FE, this study proposes an improved long-term price guidance mechanism (ILPGM) based on gated recurrent unit (GRU)- vision Transformer (ViT) prediction and fractional-order stochastic dynamic calculus (FSDC) control. The ILPGM combines GRU and ViT for baseline load forecasting and applies the FSDC method. The ILPGM can optimize the energy consumption of IESs and promote the ESDB while decreasing energy costs. The ILPGM is applied to the IES at Arizona State University-Tempe. The experimental results indicate that: the average coefficient of determination of electrical and heat energy under ILPGM is 97.69 %, realizing flexible regulation of energy consumption; the ILPGM can save 26.36 % actual cost and 94.62 % potential cost savings for the IES; compared to the previous long-term price guidance mechanism, the ILPGM provides an additional 5.87 % total cost savings and 28.65 % potential savings.

Rationalization of Energy Expenditure: Household Behavior in Poland

Background: The implementation of the EU climate and energy policy, along with changes in the legal environment, has led to a significant increase in energy prices in Poland. Consequently, energy expenditures are now a larger part of household budgets. These rising energy costs and the evolving legal landscape are compelling households to invest in energy-saving solutions and modify their energy consumption habits. This article aims to identify the activities of households in Poland regarding the rationalization of energy expenditures. It formulates the following research hypothesis: households invest in energy-saving appliances to rationalize energy expenditures and/or change their behaviors to reduce energy consumption. Methods: The paper is based on primary research conducted using an online questionnaire survey on a sample of 331 respondents in Poland in March and April 2023. Results: A classification tree algorithm was used to identify the level of investment activities and behavioral changes made by households to reduce energy expenditures. The authors found that low-income households and people who fear further energy price increases are the first of all to change their behaviors for more energy-efficient ones. Medium- and high-income households take investment measures. They replace household appliances with more energy-efficient ones and install heat pumps and photovoltaic panels. These investments are motivated by responsible consumption, environmental protection, cleanliness, and the ease of use of the appliances.

The application of ozone within the food industry, mode of action, current and future applications, and regulatory compliance.

The food industry faces numerous challenges today, with the prevention and reduction of microbial contamination being a critical focus. While traditional chemical-based methods are effective and widely used, rising energy costs, the development of microbial tolerances, and growing awareness of the ecological impact of chemical biocides have renewed interest in novel biocides. Ozone, in both its gaseous and aqueous forms, is recognized as a potent disinfectant against bacteria, viruses, and fungi due to its high oxidation potential. Our review highlights several studies on the applications of ozone within the food industry, including its use for surface and aerosol disinfection and its capacity to reduce viable Listeria monocytogenes, a pertinent foodborne pathogen harbouring environmental and biocide stress tolerances and biofilm former. We also explore the use of ozone in food treatment and preservation, specifically on blueberries, apples, carrots, cabbage, and cherry tomatoes. While ozone is an effective disinfectant, it is important to consider material incompatibility, and the risks associated with prolonged human exposure to high concentrations. Nevertheless, for certain applications, ozone proves to be an efficacious and valuable alternative or complementary method for microbial control. Compliance with the biocide products regulation will require ozone device manufacturers to produce proven efficacy and safety data in line with British standards based on European standards (BS EN), and researchers to propose adaptations to account for ozone's unique properties.

Metamodeling the deposition process in oil preprocessing to optimize heat exchanger cleaning: a deep neural network approach

The accumulation of deposits in heat exchangers during oil preprocessing, known as fouling, compromises thermal efficiency and increases maintenance and energy costs. This study aimed to develop effective strategies for managing these deposits to optimize heat exchanger operations. Metamodeling combined with Artificial Intelligence was employed, using machine learning models to process sequential data, which is crucial for understanding the evolution of deposits over time. Two machine learning models, Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM), were trained and evaluated. The DNN achieved a Mean Squared Error (MSE) of 0.05345, outperforming the LSTM, which had an MSE of 0.8763. The results suggest that the DNN was more effective in predicting fouling, although both models showed signs of overfitting. Advanced monitoring technologies, such as IoT and remote sensing, as well as predictive maintenance, were found to be essential for improving operational efficiency and reducing costs, highlighting the importance of digitalization and automation in oil preprocessing.

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

With a target of 35 % renewable energy in the country’s energy mix by 2030, the goal of this research is to support the Libyan government’s plan to maximize the use of environmentally friendly and sustainable energy sources. This will be accomplished by making effective use of the region’s plentiful natural resources. The feasibility of wind and solar energy has been established by local research, and the presence of highlands that can store pumped hydropower (PHS) makes hybrid renewable energy systems (HRES) even more feasible. These systems might offer a sizable edge over competitors in the regional energy market. In this study, PHS was utilized to stabilize electricity production in addition to storing energy. A hydropower turbine provides electricity to the load, and the PHS system is powered by a PV/wind supply. By lowering the volatility of wind and photovoltaic power generation, the suggested system could offer a more dependable renewable energy source without requiring complicated control mechanisms. This strategy could encourage a wider adoption of renewable energy, which could be especially advantageous for developing nations. Under particular load and climate conditions, the energy production of different solar PV array and wind turbine farm configurations was estimated using the System Advisor Model (SAM) software. The ideal HRES configuration consists of a 290 MW wind farm, a 154 MW solar PV field, and a 508 MW PHS system. This configuration will provide 100 % of the annual electrical demand of 513 GWh, plus a 20 % safety margin. The levelized cost of energy (LCOE), estimated at $211.65/MWh, is reduced by this configuration. It is anticipated that the $929.02 million initial investment will be recovered in 11 years, and the system will turn a profit in the following 9 years. Also, the planned HRES will stop 532.17 tons of CO2 emissions from being released each year.

Design of photovoltaic and battery energy storage systems through load demand characterization: A case study in Thailand

The integration of photovoltaic (PV) system at behind the meter has gained popularity due to the growing trend toward environmentally friendly energy solutions. Coupling PV systems with battery energy storage systems (BESS) addresses the uncertainties of PV energy production while enhancing energy management. Load characteristics have influence on PV and BESS design both in technical and economic aspects. This paper presents a comprehensive analysis of load demand characterization methodologies tailored for the design of PV and BESS. The fundamental load properties such as daily maximum power, minimum power, and load energy, categorizing loads based on their daily and annual variation degrees are investigated. The techno-economic performance of PV-only and PV+BESS systems is analyzed, focusing on metrics such as excess energy, peak limiting performance, and economic feasibility using the levelized cost of energy (LCOE). The findings of this study reveal three distinct load characteristics based on daily variation: moderate (50–70 %), moderate to high (70–80 %), and high (80–90 %). Load profiles with moderate variation (50–70 %) demonstrate economic feasibility when sized with a PV-only system matching maximum power demand. With BESS prices reduced to less than 140 USD/kWh, these loads could also integrate a 1C-BESS sized at half capacity under the same control strategy, resulting in lower excess energy and LCOE. Conversely, loads with higher variation (>70 %) and lower self-consumption rates (<70 %) face fewer viable options for both PV-only and PV+BESS configurations, especially if BESS costs remain high. These loads require custom-designed solutions and more extensive load analysis. Through analysis, this study establishes the relationship between load characteristics and system design suitability for the loads studied. The findings underscore the importance of incorporating load demand characterization techniques in the design systems, highlighting the need for tailored approaches based on specific load characteristics and economic viability.

Reliable cost-efficient integration of pumped hydro storage in islanded hybrid microgrid for optimum decarbonization

The employment of renewable energy-based resources is growing rapidly, necessitating the use of energy storage technologies to effectively manage their intermittent power generation. This study proposes an optimal planning for various distributed generators in a microgrid system. The objective is to size and operate a reliable hybrid islanded microgrid with minimum total system operational cost. To determine the optimal energy management and size of each unit, the problem is formulated applying Particle Swarm Optimization methodology utilizing sets of historical data such as wind speed, solar irradiation, and load demand on an hourly basis. With the proposed methodology, the capacities of photovoltaic, wind turbine, pumped hydro storage, and fuel cell are optimized as 1300 kW, 1125 kW, 400 kW, and 1590 kW, respectively. The obtained results concluded that optimum size reduces 14.85 % and 22.6 % in the fuel cell consumption in summer and fall respectively. Furthermore, the amount of CO2 emission is reduced by 10.6 ton in those mentioned seasons. The results obtained indicate that the proposed system having cost of energy (COE) of 0.2961 ($/kWh), and an excess energy of 196.208 MWh. An effectiveness analysis highlights the significant impact of incorporating pumped hydro storage, increasing the reliability index by 50.9 %.