Power Energy Storage System Research Articles

Optimal dispatch of an electricity-heat virtual power plant based on Benders decomposition

Recently, wind energy has been developed as an important technology to address the energy crisis. However, due to an unreasonable energy structure, wind power curtailment is becoming increasingly severe. Combined heat and power dispatch (CHPD) provided a solution for wind accommodation by utilizing the flexibility resources of district heating systems. Because of the imperfect dispatch methods and CHPD platforms, many wind power heating projects have not effectively linked the use of abandoned wind and heating. The virtual power plant (VPP) achieves the reasonable combination of controllable power sources, distributed energy, controllable loads, and energy storage systems within a certain area. Thus, we propose a VPP model based on combined dispatch of wind power and heat energy, which integrates wind turbines, thermal turbines, CHP units, etc., into a whole to join in the grid operation. Besides, to preserve the privacy of energy agents, Benders decomposition algorithm is adopted to solve the proposed model in this paper. The validity and efficiency of the proposed VPP model and Benders decomposition algorithm are verified via numerical cases.

ENHANCING EFFICIENCY AND SUSTAINABILITY: GREEN ENERGY SOLUTIONS FOR WATER SUPPLY COMPANIES

Water supply enterprises significantly impact local communities by providing essential services. These companies face high electricity costs for water extraction, purification, and transportation, affecting efficiency and reliability. Therefore, implementing innovative technologies to enhance sustainability in the water supply sector is crucial. This article explores the opportunities and strategies for renewable energy transition at water supply companies. Using investment, sensitivity, and strategic analyses, as well as a case study approach, the research examines technologies, conceptual frameworks, mechanisms, and approaches to integrate green power into water supply operations, addressing high energy costs and promoting sustainability. The article identifies solar, wind, hydroelectric, biomass, and geothermal energy technologies as the most prominent renewable power solutions for water supply enterprises. The developed conceptual framework for implementing these technologies includes needs assessment and goal setting; resource assessment and technology selection; system design and integration; financing and investment; regulatory compliance and permitting; stakeholder engagement and capacity building; and monitoring, evaluation, and continuous improvement. The mechanisms and approaches to integrate green energy solutions within the developed framework can involve on-site renewable energy generation, power purchase agreements, energy storage and microgrid systems, energy efficiency and demand management, and collaborative and community-based models. As a case study, the article examines a 120-kW solar power plant project for a water supply enterprise, demonstrating profitability with a net present value of 60,370 USD and an internal rate of return exceeding 21%. The project's payback period is estimated at 8.38 years, acceptable within industry standards. Sensitivity analysis indicates the project's financial resilience. Increasing electricity prices will boost profitability, justifying the solar power plant investment amid inflation and economic instability. Additionally, the project ensures reliable water transport and environmental benefits by reducing CO2 emissions through solar energy use. Thus, transitioning to renewable energy at water supply enterprises is feasible and essential for long-term sustainability, transforming operations to be more resilient, efficient, and environmentally friendly.

On ways to reduce the energy supply costs in Russia

Purpose: is to research of ways to reduce the energy supply costs for Russian consumers.Methods: the analysis of the transformation of the electric power industry was carried out using the tools of tectology and system economic theory.Results: the key reason for the constant increase in the cost of energy supply has been identified, which is the transition from a systematic approach to energy development aimed at increasing general economic efficiency to maximizing the economic performance of independent energy companies. The need to adjust the basic principles laid down in the concept of electric power industry development is substantiated.Conclusions and Relevance: the result of the successful completion of the reform of the Russian electric power industry is a steady influx of significant investments into the industry over the course of decades. But despite the increase in power system capacity, which is outstripping the dynamics of electricity demand, the need for the most expensive peak power sources and energy storage systems is growing, which leads to an increase in electricity prices in addition to the investment component. A positive feedback has been identified that reduces the structural stability of the energy sector, namely: between rising electricity prices; reduction of electricity consumption from the network as a result of the installation of their own generation by some consumers; increasing costs of generating and network companies; more intensive growth in electricity prices and increased motivation to install their own generation for that part of consumers who continue to use grid electricity. The ways are proposed and the solutions are considered aimed at forming new connections between participants in the single technological process of production, transmission and consumption of energy resources, with the aim of reducing the energy supply costs and improving the specific performance indicators of the energy system, and in the future reducing the costs of integrating renewable energy sources into it.

A method for selecting the type of energy storage for power systems with high penetration of renewable energy with multi-application scenarios

Energy storage (ES) configurations effectively relieve regulatory pressure on power systems with a high penetration of renewable energy. However, it is difficult for a single ES type to satisfy the complex regulatory demands of a power system. There has been little research on the selection methods for multiple types of ES that meet the demands of multiple application scenarios of power systems. This study introduces a method for the selection of ES types for power systems with a high penetration of renewable energy to determine the optimal ES types for multi-application scenarios. First, a generation method for ES schemes is proposed based on K-means clustering and combination theory. Then, considering the demands of peak shaving, frequency regulation and reserve of power systems with high penetration of renewable energy, an optimisation model of the expected indices of the ES is constructed to meet the demands of multi-application scenarios. Finally, the comprehensive evaluation method for ES schemes based on the error mean and the selection method for optimal ES types based on projection technology are proposed. Numerical studies show that for the power system in the example whose demand coefficients for peak shaving, frequency regulation and reserve are 30.59 %, 26.29 % and 43.11 %, respectively, a combination of pumped storage and colloidal battery, lithium iron phosphate battery, or lithium titanate battery is the most suitable ES type for the power system. This study offers a reference for the selection of ES types when an ES is configured.

Optimal allocation of autonomous PV-powered fast charging stations for electrified transportation considering traffic dynamics

In response to the imperative to reduce greenhouse gas emissions, global efforts are focusing on electrifying transportation and adopting renewable energy sources. This necessitates establishing reliable fast-charging stations (FCSs) for the widespread integration of electric vehicles (EVs). Indeed, the arbitrary distribution of FCSs can detrimentally affect the efficiency of traffic movement. Thus, this paper presents a planning methodology aimed at optimizing the allocation of FCSs within large-scale transportation networks. The energy system proposed includes an autonomous photovoltaic (PV)-powered FCS based on a DC microgrid, supplemented by a stationary battery energy storage system for backup power. The behavior of EVs within the traffic flow is modeled using a realistic traffic simulation software called Dynamic Network Assignment Simulation for Advanced Road Telematics (DYNASMAT). The conflicting objectives of minimizing total annual costs and EV average waiting time at stations are addressed through multi-objective optimization. To solve this optimization problem, the GAMS software is employed. A transportation network with 92 nodes and 284 links is utilized to test the proposed approach. The proposed autonomous PV-powered FCS is compared with Natural Gas-fired Distributed Generators (NGDG)-powered FCS. The simulation results show that while the NGDG-powered FCS exhibits higher emissions, the autonomous PV-powered equivalent operates emission-free. However, the annual cost of the autonomous PV-powered FCS is higher than that of the NGDG-powered FCS.

Studying the Intelligent Power Distribution and Consumption of the Power Grid and its Cost Structure Using Big Data Analysis

Based on the analysis of big data, this paper studies the impact of user behavior response on the cost structure of the microgrid system in the power grid system. The article first conducts in-depth research on distributed power generation and energy storage systems, focusing on the principles and output characteristics of smart power distribution and utilization in power grids, researches smart power distribution and utilization systems in power grids, and makes a more comprehensive discussion of the current situation. Secondly, a big data analysis platform was built, and distributed storage and computing were studied. The platform was used to perform distributed storage and regulation of electricity consumption data, and the electricity consumption information data was divided into the important load, controllable load and transferable load, constructed a microgrid system model based on electricity consumption behavior response, and analyzed a calculation example. After that, a micro-grid system was simulated based on HOMER software, and the optimal capacity configuration of the system was performed. Under this configuration, the micro-grid system has the highest economic efficiency. At the same time, a demand-side load control system was built. Introduced distributed power as a controllable load, integrated new energy access and load control technology, coordinated the contradiction between the power grid and distributed power, and completed a cost-benefit analysis. Finally, for demand-side management electricity price response, peak-valley time-of-use price, the most important implementation method, according to the cost structure theory, the peak-valley period is divided according to the membership function in fuzzy mathematics, and the user's response model to peak-valley time-of-use price is established. The experiment uses the original data to simulate, find the user response model parameters based on the load transfer rate, and complete the comparative analysis of the effect under the peak-valley time-of-use electricity price, which is of great significance to the implementation and improvement of the peak-valley time-of-use electricity price project. The analysis results of the calculation examples show that the method constructed in this paper can effectively realize the power quality analysis of the distribution network in the big data environment. The research results provide technical support for the management of the rural grid voltage deviation of the power company, and lay the foundation for improving the safe operation and management of the power grid.

Research on virtual inertia of energy storage systems under different grid-forming control strategies

Abstract To reasonably evaluate the support capability of grid-forming energy storage in power systems characterized by “double high” characteristics, it is essential to investigate the factors influencing the virtual inertia of grid-forming energy storage. This paper delves into the relationship between two control strategies of grid-forming energy storage and virtual inertia from an energy perspective. Initially, the operational principle and technological advantages of grid-forming energy storage are analyzed. Subsequently, based on the principles of two grid-forming control strategies, mathematical models for both types of grid-forming energy storage are established, elucidating the physical significance of virtual inertia in grid-forming inverters and revealing the adjustability of virtual inertia. Finally, a microgrid simulation system is constructed in PSCAD to validate the theoretical analysis.

Optimizing Distributed Generation in DC Microgrids: A Comprehensive Study

This thorough examination offers a critical analysis of the intricate relationship between Distributed Generation (DG) and DC microgrids. It provides a thorough analysis of basic ideas, sophisticated control techniques, technological developments, and useful applications in actual situations. In the framework of a paradigm shift towards decentralized energy solutions, this study investigates the efficacy of Direct Current (DC) microgrids in integrating and optimizing diverse distributed generation sources. The study conducts a critical analysis of the challenges and possibilities related to various distributed generation technologies and renewable energy systems. For scientists, engineers, and policymakers engaged in the challenging task of incorporating dispersed energy into current systems, it offers crucial perspectives. This review is an important tool that advances our knowledge of distributed generation in DC microgrids. It contributes significantly to the development of discussions on resilient and sustainable energy solutions. In addition to discussing the challenges associated with DG integration, this study highlights real-world examples that highlight the adaptability and efficiency of DC microgrid designs. Because it includes a thorough examination of power electronics, energy storage, and advanced control systems, this study is an invaluable resource for stakeholders hoping to take advantage of DC microgrids. Through the integration of analytical analysis and pragmatic application, this review provides insightful analysis and field assistance. The entire study contributes significantly to the advancement of distributed generation (DG) integration, which is necessary to establish a sustainable and resilient energy environment. It offers the fundamental knowledge required to accomplish successful integration. This review paper offers an in-depth analysis of DG integration in DC microgrids. This review is to provide a comprehensive overview of the dynamic landscape where distributed energy generation and DC microgrids interact, starting with the foundational ideas and moving on to a close examination of the difficulties, innovations in technology, and useful applications.

Mathematical Analysis of Power Quality Disturbances in Electrical Distribution Systems: Detection, Classification, and Mitigation Strategies

Maintaining a high-quality power source is an important part of modern electricity distribution systems for making sure that many industrial, business, and domestic uses work reliably and efficiently. Power quality disruptions, on the other hand, make this goal much harder to reach. This essay gives a thorough statistical study of power quality problems, focused on how to find them, classify them, and find ways to fix them. The first part of the study talks about how to find power quality problems, which means finding changes in voltage, current, or frequency that don't make sense. Different signal processing methods, like Fourier analysis, wavelet transform, and advanced machine learning algorithms, are tested to see how well they can find and describe different kinds of problems. Second, the paper talks about how to classify power quality problems, which is important for figuring out what causes them and choosing the best ways to fix them. There are different kinds of disturbances, such as voltage sags, swells, interruptions, harmonics, and transients. Each type needs a different way of being analyzed and dealt with. Researchers are looking into whether statistical methods, pattern recognition tools, and artificial intelligence can correctly group events based on their unique features. Lastly, the paper talks about ways to lessen the bad effects of power quality problems on electrical distribution systems and loads that are related to them. These methods include both preventative steps, like making equipment better designed and rearranging networks, and defensive ones, like using active power filters, voltage controls, and energy storage systems. Coordinating spread energy resources with smart grid technologies is also being looked into as a way to make the system more resistant to disruption. In general, the mathematical analysis in this paper gives us useful information about how complicated power quality problems are and gives us a way to find them, sort them into groups, and fix them. Electrical companies, equipment makers, and system workers can come up with better ways to make sure that modern distribution systems have a stable and high-quality power source by using advanced mathematical methods and new technologies.

Numerical simulation of a thermal energy storage system using sunrise and sunset transient temperature models

The temperature of the sun was modeled in this study using two transient solar temperature equations for sunrise and sunset that were developed for designing a latent heat thermal energy storage (LHTES) system for a concentrated solar power (CSP) plant. The derivation of the equations was based on the existing solar hour angle and the fundamental periodic function equations. Ansys' computational fluid dynamics code was used to investigate numerically the transient response of the conjugate melting and solidification of a phase change material (PCM) in a cylindrical shell and helical heat pipe (HHP) thermal system. The models for both equations were applied as user-defined functions (UDFs). The heat transfer medium was air. The predicted liquid and solid fractions provide quantitative data on the temperature and the stored solar energy. The effectiveness of both models in enhancing heat transfer and their suitability as real-time transient solar temperature models in heat transfer engineering is demonstrated by comparisons between their outputs. With high agreement, experimental data from the open literature was used to validate the numerical model's predictions. The solar temperature models aim to contribute to heat transfer enhancement for a reduced PCM energy storage time in designing a high-temperature solar thermal storage that is adequate to maintain a steady supply of electricity and energy for domestic and commercial applications and to accelerate the global transition to low-carbon energy.

A comprehensive investigation of DG integration with DC microgrid

This comprehensive review critically analyses the complex correlation between DC microgrids and the incorporation of Distributed Generation (DG). It offers a full evaluation of fundamental principles, advanced control strategies, technology advancements, and practical implementations in real-world scenarios. This research examines the ability of Direct Current (DC) microgrids to effectively integrate and optimize various DG sources within the context of a paradigm shift towards decentralized energy solutions. The paper critically examines the obstacles and opportunities associated with renewable energy systems and different DG technologies. It provides essential insights for researchers, engineers, and politicians who are involved in the complex process of integrating distributed energy into existing systems. This review serves as a crucial resource that contributes to the understanding of DG in DC microgrids. It plays a significant role in developing conversations surrounding energy solutions that are both resilient and sustainable. This paper not only discusses the difficulties related to the integration of DG, but also emphasizes practical examples that demonstrate the flexibility and effectiveness of DC microgrid designs. This study serves as a crucial resource for stakeholders who aim to use the potential of DC microgrids, since it encompasses a comprehensive analysis of power electronics, energy storage, and sophisticated control systems. By combining critical evaluation with practical implementation, this review offers valuable insights and guidance in the field. The complete investigation plays a significant role in advancing the integration of DG, which is essential for creating a resilient and sustainable energy environment. It provides basic information that is needed for achieving effective integration. This review paper provides a comprehensive examination of the integration of DG within DC microgrids. From laying the groundwork with fundamental principles to scrutinizing challenges, technological advancements, and practical applications, this review aims to offer a holistic understanding of the dynamic landscape where DC microgrids intersect with distributed energy generation.

Stock price forecast model for CATL based on BP neural network regression

With the introduction of the "Dual Carbon" policy and the increasing environmental awareness among residents, the new energy vehicle industry is experiencing positive growth momentum. New energy vehicles use non-traditional energy sources as their power supply, effectively reducing carbon emissions, enhancing energy efficiency, and contributing to the improvement of China's existing energy landscape, thus supporting environmental protection and the early realization of "carbon peak" and "carbon neutrality" goals. Contemporary Amperex Technology Co., Ltd. (CATL), a prominent and competitive player in China's emerging clean energy industry, focuses on researching, developing, manufacturing, and marketing power battery and energy storage systems specifically designed for new energy vehicles. Moreover, in recent years, machine learning and deep learning have gained wide application in various domains, including stock price prediction and financial investment. This paper constructs a stock price prediction model for CATL based on a BP neural network regression, considering factors related to traditional energy, carbon trading, environmental aspects, and industry-specific factors.

Performance Enhancement of Droop Controlled DC Microgrid Using Solar Fed Hybrid BIFRED Converter with RBFNN

Electric power system’s structure is shifting from a generator to a converter-based, posing the risk of reduced system inertia. This transition is due to the increased penetration of power electronics-based renewable energy sources (RESs) and Energy Storage Systems (ESS). The microgrid concept supports their accommodation into electric networks; however, it requires appropriate control to solve the voltage stability issues created by reduced inertia. The proposed DC microgrid aims to maintain voltage stability in DC-link comprising wind, solar, and ESS, irrespective of changes in operating conditions. An improved Z source- Boost Integrated Fly Back Rectifier/Energy Storage DC–DC (BIFRED) converter is proposed to increase the output voltage of photovoltaic (PV) system to achieve better DC-link voltage stability. Radial basis function neural network (RBFNN) approach is used to harvest the maximum power possible from the PV panel and to improve the conversion efficiency. The proposed DC microgrid also includes Wind Energy Conversion System (WECS) powered by a Doubly Fed Induction Generator (DFIG). It integrates droop control with virtual inertia and damping control and effectively handles the voltage stability issues brought out by RESs and loads. Artificial neural network (ANN) optimized droop-controlled bidirectional converter (BDC) is implemented to integrate ESS and resolve the imbalance created by ESS. The work is validated by MATLAB simulation model and a laboratory prototype.

Dual Timescales Voltages Regulation in Distribution Systems Using Data-Driven and Physics-Based Optimization

A large number of electric vehicles (EVs), distributed solar and/or wind turbine generators (WTGs) connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming (QP) for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators (DGs) and static VAR compensators (SVCs) is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process (MDP) is developed, in which the charge/discharge power of energy storage systems (ESSs), statuses/ratios of switchable capacitors reactors (SCRs), and voltage regulators (VRs) are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient (DDPG) deep reinforcement learning (DRL) algorithm. The capabilities of the proposed method are validated with IEEE 33-bus balanced and 123-bus unbalanced distribution systems.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems, we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system. First, we employ a strategy that restricts long- and short-term power output deviations to smoothen wind power fluctuations in real time. Second, we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise (SW-ICEEMDAN) strategy to achieve real-time decomposition of the energy storage power, facilitating internal power distribution within the hybrid energy storage system. Finally, we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components. Through simulation validation, we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power. Compared with traditional empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition strategies, the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal. Additionally, the state-of-charge of energy storage components fluctuates within a reasonable range, enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Frequency response of microgrids with PV power generation and energy storage system (battery and supercapacitor)

Since renewable energy sources (RES) have almost little inertia, an increase in their electricity might harm the power system's ability to run steadily and dependably. Numerous solutions to the issue mentioned above are offered. This paper aims to assess the technological possibility of using energy storage system (ESS) devices built from batteries and supercapacitors to enhance the interia response of sources in microgrids with a large amount of PV power penetration. The microgrid's inertia was altered by varying the penetration level of RES. To obtain a rigid microgrid, batteries and supercapacitors are suggested in this study to enhance frequency stability and droop control is utilized to complete this assessment. The model of the on-grid power network was designed using Simulink in MATLAB to evaluate the high level of RES penetration impact on the frequency stability of the system. Results verify that the microgrid stiffness is significantly enhanced when the suggested storage elements are incorporated. The findings show that the rate of change of frequency (RoCoF) is reduced when the size of the ESS increases and vice versa. The supercapacitor energy storage system (SCESS) can increase the stability of the system's frequency more effectively than the battery energy storage systems (BESS) with a slower time response.

Analysis of power dispatching decisions with energy storage systems using the optimal probability distribution model of renewable energy

Effectively managing the inherent unpredictability and fluctuation attributes of renewable energy sources within scheduling systems has emerged as a critical matter demanding immediate attention. The incorporation of energy storage technology offers notable advantages by mitigating fluctuations in wind power generation and curtailing peak shaving costs in scheduling systems through economical utilization of energy storage. This study employs the adjustment of energy storage’s reserve capacity function to modify the economic gains and losses arising from fixed spinning reserve. This approach tailors the reserve capacity function by formulating the uncertainty inherent in wind power generation. The paper presents an enhanced selection method for calculating optimal bandwidth within the framework of least squares approximation probability density function scenarios. The fusion of least squares approximation and optimal bandwidth computation permits precise refinement of bandwidth in non-parametric kernel density estimation approaches. Consequently, the study puts forth a comprehensive model for coordinated scheduling and regulation of a multi-scale energy storage power system. Finally, the stability and economy of the proposed method in the scheduling operation process were verified through simulation, and the research results can provide a theoretical basis for future uncertain wind power scheduling.

Fully Parallel Algorithm for Energy Storage Capacity Planning Under Joint Capacity and Energy Markets

Energy storage (ES), with its flexible characteristics, has been gaining attention in recent years. The ES planning problem is highly significant to establishing better utilization of ES in power systems, but different market regulations impact the ES planning strategy. Thus, this paper proposes a novel ES capacity planning model under the joint capacity and energy markets, which aims to minimize the total cost for power consumers. The great challenge is that the ES planning model has a large number of time periods, which significantly increases the problem dimensionality. In order to alleviate the computational burden, a fully parallel algorithm is proposed to temporally decompose the original problem into a series of small sub-problems, which can be solved in parallel. Moreover, we find that the corresponding analytical solutions to the sub-problems remarkably accelerate the calculation speed while ensuring accurate results. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Energy storage (ES) has become more and more essential to guaranteeing power balance in power and energy systems by shifting peak loads to valley loads. However, investors may face challenges to ES capacity planning due to the lack of business models. To address this challenge, price tariffs should be carefully investigated. In the practical power system, the market price should consider both the energy price and capacity price for industries and big companies. In the energy market, investors can gain a profit by selling energy at the peak load (high price) and buying energy in the valley (low price). It should be noted that the energy market cannot recover the ES investment cost, but investors can, in fact, reduce their capacity cost since ES can reduce the peak load. Consequently, we have designed a new business model for ES planning under joint capacity and energy markets to analyze the profits via the two market regulations. The computational burden is another challenge for the proposed multi-period convex optimization model. The model must consider a long-term simulation, potentially containing thousands of time periods, which can be difficult to solve. In order to alleviate the computational burden resulting from the long-term market simulation, we further propose a fully parallel algorithm to solve the proposed business model for ES quickly. To sum up, the proposed model and method have been tested on a practical company with a practical price tariff in China to show their effectiveness.

Assessing the impact of power dispatch optimization and energy storage systems in Diesel–electric PSVs: A case study based on real field data

This research paper presents an in-depth analysis of diesel–electric power systems in offshore Platform Supply Vessels (PSVs). The main contribution is the use of real data obtained from a PSV to produce and validate computational models subsequently used to accurately calculate fuel consumption and emissions, with representative load demands, considering different configurations of non-hybrid and hybrid power systems with the addition of lithium-ion batteries to provide a reliable analysis. Power dispatch optimization had a significant impact, achieving fuel and CO2 reductions of around 7%–12% in non-hybrid systems, depending on the generator loading limits. Hybridization and battery integration further enhanced reductions, with up to 10% improvements in fuel consumption and CO2 emissions. Particulate matter (PM) emissions and running hours were substantially reduced, but NOx varied. Validity checks accounted for biases, and the study provided reliable assessments of trends. The differences between monthly averages and robust approaches were minimal. Overall, the strategies presented achieved reductions of up to 15% in fuel consumption and CO2 emissions, 20% in NOx emissions, and 68% in PM emissions for the complete mission.

A comprehensive and comparative study of an innovative constant-pressure compressed air energy storage (CP-CAES) system

Electricity and potable water are two vital resources for the world's population. A pioneering green energy storage system for power and potable water production has been introduced and investigated in this context. The innovative system integrates compressed air, pumped hydro, and thermal energy storage, along with multi-effect desalination. This combination enables the generation of power and potable water without exerting any adverse impact on the environment. This technology manages the start-up time of the desalination unit by using a low thermal energy storage unit and allows potable water to be generated throughout the day. By conducting a comprehensive analysis including energy, exergy, economic, and exergoeconomic, the performance of the system has been carefully examined and the effect of various parameters has also been considered. Finally, the advantages of the proposed system are compared to the conventional system. According to the study, round trip efficiency and total cost rate have been calculated as 62.18% and 618.6 USD/hr, respectively. Based on a case study conducted in the coastal areas of the state of California, the proposed system has the potential to generate 138 m3 of potable water and 88.11 MWh of electricity per day. This could lead to a total profit of 27.65 million dollars with a payback period of 3.82 years.