Battery Energy Storage System Research Articles

DEVELOPMENT OF ULTRA CAPACITOR BASED REGENERATIVE BRAKING SYSTEM FOR ELECTRIC VEHICLES

Regenerative braking is a technique for converting a moving vehicle's kinetic energy into usable form while slowing it down. When an automobile brakes violently, it usually emits heat. If we could conserve energy rather than waste it, that would be amazing, right? That's where regenerative braking technology comes in handy, especially for electric vehicles. It stores the wasted energy in a battery or ultra-capacitor, so it can be used later to start or accelerate the vehicle. In our project, we looked at how effective regenerative braking is during both braking and starting modes. We compared starting the motor with a battery, ultra-capacitor, or a combination of both (HESS), and also analyzed how storing energy during braking works with these systems. Although not all of the regenerated energy is reinvested in the battery, the motor acts as a generator, charging the vehicle's battery. To move the vehicle forward, regenerative braking uses the kinetic energy it temporarily stores during deceleration. We used a lithium-ion battery and ultra-capacitor module energy storage system coupled to a BLDC motor via a converter. These brakes help batteries last longer without needing to be charged externally. They enable efficient power transfer during regenerative braking and acceleration. Our project discusses whether regenerative braking increases or decreases efficiency. of state of charge of battery and ultra- capacitor when starting and braking applied in discharging(in running of motor) and charging(in regenerative braking of motor). And also BLDC motor speed control in starting and braking mode. The model is simulated using MATLAB Simulink software. Keywords: Regenerative braking, kinetic energy, electric vehicles, energy storage, battery, ultra-capacitor, starting mode, braking mode, Hybrid Energy Storage System (HESS), efficiency, motor generator

Power Quality Improvement in Hybrid Power System using STATCOM :

Meeting the escalating electricity demands posed by population growth and industrial expansion presents a significant challenge. Utilizing renewable energy sources like wind, biomass, and hydro co-generation has become imperative to satisfy energy requirements. In this proposed framework, a model featuring a grid-integrated wind energy generation system and nonlinear load is developed using MATLAB/Simulink. Integrating wind energy into the power grid impacts electricity quality, notably manifesting in voltage fluctuations, harmonics, flicker, and poor power factor at the source. The efficacy of a wind turbine and, consequently, electricity quality are assessed. To enhance power quality, the project advocates for the implementation of a STATCOM control strategy in grid-connected wind power systems. A bang-bang controller, based on hysteresis current control, is devised for STATCOM. Integrating STATCOM with the Point of Common Coupling (PCC) mitigates power quality issues. In this envisioned scheme, the FACTS device STATCOM is linked with battery power storage (BESS) at the PCC to alleviate power quality concerns. Incorporating Battery Energy Storage Systems aids in stabilizing the real power source during wind power fluctuations and facilitates the rapid injection of reactive power at the PCC. Keywords: Wind energy, Solar Energy, STATCOM, Electric grid, IGBT, PWM.) etc.

Small-Scale Battery Energy Storage System for Testing Algorithms Aimed at Peak Power Reduction

Small-Scale Battery Energy Storage System for Testing Algorithms Aimed at Peak Power Reduction

Improved rule-based power distribution algorithm for hybrid battery storage systems and real-world validation

Large-scale battery energy storage systems (BESS) can serve many applications and are already widely used for grid services. The rapidly growing BESS market and the recent interest in their deployment accentuate the need for safe, reliable, and highly available energy management systems (EMS) for automated control. However, the EMS and their integrated power distribution algorithms (PDA) can still be optimized to adapt various characteristics of the BESS. This study investigates a new version of a PDA with a particular focus on battery aging and system efficiency. The rule-based PDA has been validated on a 6 MW/7.5 MWh BESS system with five battery technologies providing frequency containment reserve to the German power grid. The results underline the PDA's capability to exploit the individual strengths of each battery technology. The PDA sets objectives for the state of charge, energy throughput, and power of the batteries to extend battery life. The distribution of energy throughputs among batteries can be selected in advance through the new implementation of the PDA. At the same time, the inverters are significantly less often activated and used in the optimal efficiency range, increasing the overall system efficiency to approximately 82 %. The optimized switching behavior leads to less frequent power switching between individual battery units and longer phases with more constant power. In addition, the operational efficiency of BESS can be improved by the choice of battery technology and the overall system layout on the hardware side. The improvements on the software side are only possible by increasing the overall power requests through multi-use operation by about 6 % compared to our benchmark test. The results can be used by BESS operators to increase operational profits due to longer battery life and fewer efficiency losses.

Partial-Power Conversion for Increased Energy Storage Capability of Li-Ion Battery Energy Storage System

Full-power converters are used in battery energy storage systems (BESS) because of their simple structure, high efficiency and relatively low cost. However, cell-to-cell variation, including capacity, state of charge (SOC), and internal resistance, will decrease the available capacity of serially connected battery packs, thereby negatively affecting the energy utilization rate (EUTR) of BESS. This article proposes a novel BESS scheme that combines a modular converter with partial power conversion architecture to make a modular partial power converter (MPPC) that addresses the issue. The MPPC consists of input-serial and output-paralleled (ISOP) isolated phase-shift full-bridge (PSFB) sub-modules. It processes only the serial compensation power, about 1/3 of the full power. Consequently, the MPPC shrinks the converter capacity, which can reduce the cost and power loss. Furthermore, this article develops a BESS model considering cell-to-cell variations to analyze the energy storage capability of the MPPC-BESS compared with the existing full-power BESS. To test the model, we run a simulation using parameter values from 100 real retired batteries. Our simulation results show that the MPPC can significantly alleviate the reduction of EUTR as the voltage level increase. Finally, we construct a 36V/720W MPPC-BESS prototype with two battery packs and PSFB sub-modules to verify the bidirectional operating stability and energy storage capability.

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

AbstractContemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology.

Optimizing Energy Arbitrage: Benchmark Models for LFP Battery Dynamic Activation Costs in Reactive Balancing Market

This study introduces a novel benchmark model for lithium iron phosphate (LFP) batteries in reactive energy imbalance markets, filling a notable gap by incorporating comprehensive operational parameters and market dynamics that are overlooked by conventional models. Addressing the absence of a holistic benchmark for energy-storage systems in electricity markets, this research focuses on the integration of LFP batteries, considering their unique characteristics and market responsiveness. Regression and regularization techniques, coupled with temporal cross-validation, were employed to ensure model robustness and accuracy in predicting energy trading outcomes. This methodological approach allows for a nuanced analysis of battery degradation, power capacity, energy content, and real-time market prices. The model, validated using Belgium’s system imbalance market data from the 2020–2023 period, incorporates both capital and operational expenditures to assess the economic and operational viability of LFP battery energy-storage systems (BESSs). The findings reveal that considering a broader range of operational parameters in energy arbitrage, beyond just the usual energy prices and round-trip efficiency, significantly influences the cost-effectiveness and performance benchmarking of energy storage solutions. This paper advocates for the strategic use of LFP batteries in energy markets, highlighting their potential to enhance grid stability and energy trading profitability. The proposed benchmark model serves as a critical tool for energy traders, providing a detailed framework for informed decision making in the evolving landscape of energy storage technologies.

Comparative study of controllers in battery energy storage system integrated with doubly fed induction generator-based wind energy conversion system for power quality improvement

This study presents a comparative analysis of controllers in a Battery Energy Storage System (BESS) integrated with a Doubly Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS) aimed at improving power quality. The research background introduces the challenges associated with integrating renewable energy sources into the grid, emphasizing the intermittent nature of wind energy and its impact on power quality. Existing detailed problems, including voltage and frequency fluctuations, and grid instability, are outlined, highlighting the need for effective control strategies to mitigate these issues. The paper's contribution lies in evaluating and comparing different control techniques for BESS integration, including Proportional-Integral (PI) control, Fuzzy Logic (FL), and Artificial Neural Network (ANN) control. Through simulation studies conducted in MATLAB/Simulink, the effectiveness of each controller in regulating rotor current, rotor and stator power, voltage stability, state of charge (SoC) and grid synchronization is assessed. The findings provide valuable insights into the optimal controller selection for enhancing power quality in DFIG-based WECS with integrated energy storage systems, contributing to the advancement of renewable energy integration and grid stability.

Investment Decision for Long-Term Battery Energy Storage System Using Least Squares Monte Carlo

The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea’s fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations.

Battery electricity storage as both a complement and substitute for cross-border interconnection

Cross-border interconnectors play a critical role in fulfilling the growing demand for cleaner and more affordable electricity. They are integral to completing the single energy market in Europe, so the European Union has set ambitious targets for the coming decade. Many challenges affect and delay the development of transmission infrastructure, and the deployment of non-wires alternatives such as battery energy storage systems may threaten the financial viability of these investments. We evaluate the potential impact of storage deployment on the profitability of cross-border interconnectors using the European electricity market model “EuroMod”. We find that higher battery penetration than projected in ENTSOE's TYNDP in 2030 significantly reduces transmission surplus (by €267 m to €506 m) under different battery uptake scenarios. To maintain the expected total surplus as projected by TYNDP in 2030, the continent-wide transmission capacity must decrease by 4.1–7.0%, potentially superseding 19–33% of planned transmission capacity expansions to 2030. Although cross-border interconnectors are of strategic importance for energy security and flexibility, the results highlight the pressing need for regulators, policymakers, and private investors to account for the rapid uptake of storage and integrate both storage and interconnectors in system planning.

Techno-economic feasibility of integrating hybrid-battery hydrogen energy storage in academic buildings

Green hydrogen production and storage are vital in mitigating carbon emissions and sustainable transition. However, the high investment cost and management requirements are the bottleneck of utilizing hybrid hydrogen-based systems in microgrids. Given the necessity of cost-effective and optimal design of these systems, the present study examines techno-economic feasibility of integrating hybrid hydrogen-based systems into an outdoor test facility. With this perspective, several solar-driven hybrid scenarios are introduced at two energy storage levels, namely the battery and hydrogen energy storage systems, including the high-pressure gaseous hydrogen and metal hydride storage tanks. Dynamic simulations are carried out to address subtle interactions in components of the hybrid system by establishing a TRNSYS model coupled to a Fortran code simulating the metal hydride storage system. The OpenStudio-EnergyPlus plugin is used to simulate the building load, validate against experimental data, according to the measured data and monitored operating conditions. Aimed at enabling efficient integration of energy storage systems, a techno-enviro-economic optimization algorithm is developed to simultaneously minimize the levelized cost of the electricity and maximize the CO2 mitigation in each proposed hybrid scenario. The results indicate that integrating the gaseous hydrogen and metal hydride storages into the photovoltaic-alone scenario enhances 22.6% and 14.4% of the annual renewable factor. Accordingly, the inclusion of battery system to these hybrid scenarios gives a 30.4% and 20.3 % boost to the renewable factor value, respectively. Although the inclusion of battery energy storage into the hybrid systems increases the renewable factor, the results imply that it reduces the hydrogen production rate via electrolysis. The optimized values of the levelized cost of electricity and CO2 emission for different scenarios vary in the range of 0.376–0.789 $/kWh and 6.57–9.75 ton, respectively. The multi-criteria optimizations improve the levelized cost of electricity and CO2 emission by up to 46.2% and 11.3%, with respect to their preliminary design.

Optimizing battery energy storage prototypes for improved resilience in commercial buildings: Gaussian mixture modeling and hierarchical analysis of energy storage potential

The increasing energy demand in commercial buildings has led to the adoption of photovoltaic (PV) systems as a viable solution. However, the gap between energy consumption and PV capacity in commercial buildings remains uncertain, and there is limited research on the potential for energy storage in different commercial building types. This study utilizes Gaussian mixture modeling to extract energy consumption and PV capacity patterns from diverse commercial buildings, using datasets provided by the Department of Energy. By employing hierarchical analysis, we investigate the energy storage potential of various commercial buildings based on the disparities between energy consumption and PV capacity patterns. Moreover, this study introduces innovative battery energy storage system (BESS) prototypes tailored to the specific needs of different commercial building types. These prototypes determine the optimal BESS capacity and charging schedules, providing an effective means of improving electrical resilience in commercial buildings. The findings reveal that medium offices, warehouses, and secondary schools require BESS capacities of 410kWh, 838kWh, and 349kWh, respectively, and recommend charging time windows of 9:00–17:00, 8:00–18:00, and 9:00–14:00, respectively. Additionally, the cost recovery period for Li-ion phosphate and ternary Li-ion batteries ranges from approximately 2.5 to 7.5 years and 4 to 8.2 years, respectively. The developed BESS prototypes based on energy storage potential offer resilience to commercial buildings during power outages caused by natural disasters and emergencies, helping to improve their electrical resilience. This study provides valuable insights into the potential for energy storage in commercial buildings and promotes the wider implementation of BESS in the commercial sector, contributing to the achievement of effective demand-side management.

Optimal design and operation of battery energy storage systems in renewable power plants to reach maximum total electric sale revenues

Optimal design and operation of battery energy storage systems in renewable power plants to reach maximum total electric sale revenues

Optimizing size and economic feasibility assessment of photovoltaic and energy storage setup in residential applications

Battery energy storage systems (BESSs) are essential in enhancing self-sufficiency, sustainability, and delivering flexibility services. However, adoption of this technology in residential applications is constrained, predominantly due to its suboptimal economic performance. A proper selection of design parameters and optimal resource utilization can significantly enhance performance and establish economic feasibility. This research introduces a photovoltaic (PV)-BESS optimization framework, formulated to ascertain optimal infrastructure sizing, and maximize economic performance. The proposed tool considers various elements, such as energy storage state of health status, renewable profiles, residential load profiles, and prevailing energy market conditions. A novel rule-based, non-linear optimization method is developed, with a focus on maximizing revenue while considering energy storage (ES) degradation to project more accurate and realistic scenarios and payback periods. Employing the Estonian energy market as an illustrative case study, the model probes the potential of integrating ES and PV technology in North European residences. The model achieved the shortest payback period of five years in our case study, underscoring the emerging potential of such technologies in behind-the-meter applications.

The role of flexible demand to enhance the integration of utility-scale photovoltaic plants in future energy scenarios: An Italian case study

Photovoltaic (PV) will have a prominent role in the future energy mix to achieve the energy and climate targets. However, its variability requires that its exploitation must be improved to minimize its integration costs. The objective of this research is to provide a methodology applicable to any energy system modelling tool for assessing the benefits of flexible demand as an option to better manage the PV variability and to reduce its integration costs. Two strategies are used to shift the flexible demand within three different scenarios based on the Italian energy system and its planned evolution to the year 2030. The effects of flexibility are investigated through an expansion capacity optimization of battery energy storage systems (BESS) and powerlines transport capacities in three different scenarios. Results suggest that flexible demand allows to reduce the BESS capacity of around 2 %–6 %, and powerlines capacity in the range of 2%–10 %, thus the resulting VRES avoided integration costs varies from 758 M€ to 8611 M€. However, it has negligible effects in boosting RES penetration and lowering the CO2 emissions. The estimated DSM remuneration opens up interesting possibilities for applications in the context of energy communities and electricity market aggregators.

Moth Flame Algorithm to Optimize the Size and Location of Energy Storage Units in a Radial Distribution Network to mitigate Power Loss

The ideal placements and capacities for Battery Energy Storage Systems (BESS) sources in a distributed generation (DG) environment have an impact on power losses in the distribution network. Therefore, engineers are quite concerned with DG and BESS. The output from DG is, however, typically unpredictable and inconsistent. The secure and stable functioning of the power system will be impacted to varying degrees when it is connected to various sites, capabilities, and power grids. To provide a safer, more stable, more dependable, as well as more effective power grid operation, power grid planners must take the impact of capacity, type, and location into account while choosing the best BESS accessibility method. The objective is to choose bus stops with the right potential and minimal loss. This study demonstrates how to use a Moth Flame Optimization Algorithm (MFOA) to establish the appropriate battery energy storage system (BESS) sizing in a hybrid solar and wind energy conversion system (WECS) generation integrated in a radial distribution network. This research investigates the optimal capacities and allotment of the BESSs within a 33-bus and 69-bus radial network of distribution for the maximum power dissipation reduction. This research also provides a framework for carrying out conservation voltage reduction (CVR) when distributed energy resources like solar photovoltaic (PV) systems, WECS, and energy storage systems (ESSs) are connected into distribution networks. The simulation has been conducted using Matlab code and the suggested method's output is compared with Firefly Algorithm (FA), Genetic Algorithm (GA) and Ant Colony Algorithms (ACA) and evaluated against other results.

Inertia Energy-Based Required Capacity Calculation of BESS for Achieving Carbon Neutrality in Korean Power System

Frequency response performance in power systems is becoming vulnerable due to the transition toward the higher penetration of renewable energy such as achieving carbon neutrality. In particular, reducing power system inertia energy as the asynchronous generation increases could result in violating the frequency stability constraint when a disturbance occurs in the power systems. In order to control the rapidly fluctuating frequency of the power system with low inertia, it is necessary to introduce fast frequency response resources such as a Battery Energy Storage System (BESS). This paper proposes a method to calculate the required capacity of BESS for compensating the frequency control performance of the power system using inertia energy. For calculating the required capacity of BESS, the inertia energy in the critical power system, where frequency control performance marginally satisfies frequency stability constraint, should be calculated. Also, the inertia energy in the evaluated power system having deficit inertia energy should be calculated. By comparing power systems that respond with different dynamics when the same disturbance occurs, the proposed calculation corresponds to the ratio of inertia energy deficiency based on critical power system inertia energy within the power imbalance. Through various case studies employing Korean power systems, the effectiveness of the inertia energy-based calculation method for the required BESS is verified by the fact that the BESS integrated power system marginally satisfies the frequency stability constraint. In these study cases, it is found that the instant response of BESS is very effective for compensating the frequency control performance of the low inertia power system. By applying the proposed method, it is also found that about 840 MW of BESS can achieve carbon neutrality in the Korean power system.

Electrochemical Supercapacitor Application of Electrochemically Produced Chlorine-Doped Graphene Oxide with Cobalt Sulfide-Based Compounds Produced from Recycling of Spent Li-Ion Batteries

Abstract Recycling of LiCoO2 (LCO) based Li-ion batteries for energy storage systems is crucial both environmentally and economically. Reusing active species of LCO cathodes minimizes waste and conserves resources, promoting sustainability in energy storage. We have investigated repurposing cobalt from spent LiCoO2 (LCO) type Li-ion batteries into a cobalt sulfide-based compound (CS), which was then employed as an electrode material in asymmetric supercapacitors. Initially, the LCO cathode compound underwent leaching, resulting in the precipitation of CS utilizing the sulfur source derived from cobalt ions present in the solution. Furthermore, chlorine-doped graphene oxide (Cl-GO) was synthesized via the chronoamperometric method utilizing a 5 M perchloric acid solution. Produced CS and Cl-GO were characterized by using spectroscopic and microscopic techniques. The resulting CS and Cl-GO powders were combined to form the composite positive electrode of coin cell type asymmetric supercapacitors (CCTAS), with graphite powder (GP) utilized in the preparation of the negative electrode. CCTAS were also characterized by using electrochemical techniques such as cyclic voltammetry, electrochemical impedance spectroscopy and galvanostatic charge-discharge. The highest areal capacitance, recorded as 101 mF.cm-2 at a scan rate of 10 mV.s-1, was achieved in the CS/Cl-GO%15//GP CCTAS, with a capacitance retention of approximately 94% observed after 1000 cycles.

Numerical assessment and optimization of photovoltaic-based hydrogen-oxygen Co-production energy system: A machine learning and multi-objective strategy

Green hydrogen production technology based on photovoltaic (PV), battery energy storage system (BESS) and proton exchange membrane (PEM) water electrolysis plays a crucial part in the transition process of energy to zero carbon technology. But few studies have focused on achieving all-day continuous hydrogen production, which is the key to large-scale hydrogen utilization. In this study, we developed an off-grid PV-BESS-PEM system for hydrogen and oxygen production. Firstly, the PV, BESS and PEM water electrolysis components were modeled and model validation was performed. Secondly, an energy management strategy (EMS) is proposed to achieve the goal of continuous and stable hydrogen production. Then, a surrogate model between design variables and optimization objectives is established based on machine learning methods, and a multi-objective optimization algorithm is used to drive the optimization process. Finally, a case study of the PV-BESS-PEM system is presented based on solar irradiation data in Lhasa, where there is sufficient solar resources and oxygen demand. The results demonstrate that the EMS can satisfy the purpose of all-day continuous and stable hydrogen production. The number of cells in the electrolyzer and the water temperature have a significant effect on the hydrogen production and the electrolysis efficiency. And the optimal system has increased the hydrogen production by 16.80 % and the electrolysis efficiency of PEM by 12.08 %, respectively. Herein, the methodological framework is expected to provide theoretical guidance for large-scale water electrolysis hydrogen production applications based on solar energy.