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Abstract In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.

Abstract The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.

IntroductionPhotovoltaic systems offer immense potential as a future energy source, yet maximizing their efficiency presents challenges, notably in achieving optimal voltage due to their nonlinear behavior. Operating current and voltage fluctuations, driven by temperature and radiation changes, significantly impact power output. Traditional Maximum Power Point Tracking (MPPT) methods struggle to adapt accurately to these dynamic environmental conditions. While Artificial Intelligence (AI) and optimization techniques show promise, their implementation complexity and longer attainment times for Maximum Power Point (MPP) hinder widespread adoption.MethodThis paper proposes a hybrid MPPT technique that integrates the Pelican Optimization algorithm (POA) with the Perturb and Observe algorithm (P&O) for a grid-connected photovoltaic system (PV). The proposed technique consists of two loops: PO as the reference point setting loop (inner loop) and POA as a fine-tuning (outer)loop. The combination of inner and outer loops minimizes oscillations by adjusting the perturbation direction and enhancing the convergence speed of the MPPT.Results and DiscussionTo validate the efficacy of the proposed MPPT technique for different environmental conditions, a comprehensive comparison is conducted between the proposed hybrid pelican and perturb and observe (HPPO) technique and other MPPT algorithms. The proposed technique has optimized PV and grid outputs with an MPPT efficiency of 99%, best tracking speed, and total harmonic distortion (THD) for all conditions below 5% agree with IEEE 519 standards.

Big cruise ships travel for months in the sea, requiring electrical power to meet their needs. The reliability of maritime ships depends abundantly on renewable energy resources (RERs), and one of their reasons is to curtail greenhouse gas emissions through reduced reliance on conventional sources in marine power networks. Consequently, advanced control methods are necessary to address the intermittent characteristics of RESs. This article proposes a methodology for load frequency control in an independent system. This paper presents an efficient control scheme for the frequency control of a maritime shipboard, which is suited for its low inertia and intermittent power penetration into the shipboard microgrid system. We present a two-stage cascaded design with fractional order proportional integral (FOPI) control and one plus tilt derivative (1 + TDN) controller to counter the frequency fluctuations in the microgrid system. This keeps the frequency constant in the face of different factors like renewable energy penetration, load variation, and uncertainty. The FOPI-1 + TDN is optimized using the jellyfish search optimizer (JSO) algorithm, which has demonstrated efficient results in tuning the proposed controller’s parameters. In the last evaluation, the performance of the FOPI-(1 + TDN) for a maritime hybrid microgrid (MHM) demonstrated efficient results when compared to other recently applied control techniques. The proposed controller has shown frequency regulation ability in a time of 1.5 sec, 1.7 sec, and 2.9 sec for different load-changing conditions.

The rapid growth in the popularity of Electric Vehicles (EVs) requires accurate driving range predictions to minimize range anxiety and optimize trip planning, especially in real-world driving conditions where diverse factors affect range. This study addresses the challenges of EV range prediction by presenting a novel deep learning technique that uses a Deep Neural Network (DNN) model optimized with the RMSProp optimizer. This approach leverages a unique real-world dataset that reflects varied driving environments, leading to superior performance. The model achieves an R2 score of 0.99, a Mean Absolute Percentage Error (MAPE) of 2.01%, a Mean Absolute Error (MAE) of 6.81 km, and a Root Mean Squared Error (RMSE) of 9.32 km. These results significantly outperform conventional machine learning techniques like support vector machines and linear regression, demonstrating the practicality and reliability of the proposed model for reducing EV range anxiety and improving trip planning in real-world scenarios. This enhancement supports the broader adoption of EVs, ultimately contributing to a sustainable transportation ecosystem

This study aims to investigate the potential of rooftop solar photovoltaic systems for commercial buildings. Helio-Scope software is utilized to perform simulations to determine the ideal rooftop area for photovoltaic panels. The efficiency of photovoltaic systems is impacted by the shading effects of photovoltaic modules installed in parallel rows. To enhance energy output, the optimal distance between rows is determined, and it is found that 5-feet inter-row spacing provides the best results. The simulation results indicate that with 5-feet inter-row spacing, photovoltaic system has an energy generation of 371.6 MWh, specific yield of 1508.0 kWh/kWp, performance ratio of 82.1%, solar access rate of 98.9%, total solar resource fraction of 96.3% and a total irradiance of 1655.9 kWh/m2. The annual nameplate energy is 425.1 MWh, output energy at irradiance levels is 423.1 MWh, optimal DC output is 378.5 MWh, inverter output is 373.5 MWh, and total energy delivered to the national power grid is 371.6 MWh. The average daily DC inverter input power is 158881.5110 W and the average daily AC inverter output power is 152231.6311 W, showing an inverter efficiency of approximately 95.93%. Moreover, detailed testing of the installed PV system is performed on-site to make sure that equipment’s performance guarantees are achieved, the system is properly installed and its configuration is suitable for commercial operations. The maximum daily output energy generation of an installed photovoltaic (PV) system is 1.33 MWh, and its average energy generation is 1.09 MWh. The voltage of all strings is within the rated range of the inverter, with a maximum voltage of 835 V and a minimum of 698 V, as tested by PV string open-circuit voltage. The inverter efficiency test is also performed, with a maximum efficiency of 98.83% and fill factors ranging from 81.37% to 82.34%. The payback period of a photovoltaic system is 4.22 years and LCOE is 0.0229$/kWh. PV system saved 215569.818 metric tons of CO2 in the first year and a total of approximately 5068976.99 metric tons in 25 years.

Effective energy management in grid-connected renewable energy systems is essential for achieving cost-efficiency and reliability. This work presents a versatile control technique to tackle power system difficulties in grid-connected and grid-failure modes. The concept employs a digital iterative approach to optimize the operation of a DC-DC converter, guaranteeing the attainment of maximum power output and stability. During a grid breakdown, the system injects carefully adjusted active power into the grid to ensure that voltage and current levels are maintained at appropriate levels, hence preventing overloading. The proposed strategy exhibits a 99% enhancement in system responsiveness and a 96% decrease in overshoot when compared to conventional methods, thus substantiating its efficacy in practical scenarios.

Pakistan is facing energy crises due to localized shortages, market manipulation, infrastructure disruption, rising demand, governance issues, climate and geopolitical events. In this situation Demand Side Management (DSM) is a promising solution to overcome the problem of energy crises. DSM strategy helps to manage consumer demand through energy conservation rather than to addition of new power capacity. In this study, Low Emissions Analysis Platform (LEAP) develops an energy model for Pakistan for the period 2022–2050. Three scenarios has been to constructed namely Baseline (BAS), Green Energy Policy (GEP), and Energy Efficiency (ENE) to predict the future energy demand, production, carbon emissions and the investment cost which covers capital, operational and maintenance costs. The model results suggest that DSM targets should be achieved through the implementation of ENE scenario. Predicted energy production and consumption under the ENE scenario are substantially less than those under the BAS scenario. The country can meet its 635.83,000 GWh energy demand with its 747.15,000 GWh energy production. Non-renewable sources produce 171.27,000 GWh, whilst renewable sources produce 575.88,000 GWh. According to this scenario, by 2050, CO2 emissions will be produced around 93.16 million metric tons, requiring an investment cost of $46.80 billion for building new power capacity. The study provides a roadmap with a suggestive optimal balance between energy saving with DSM approach and utilizing renewable energy production to meet energy demand for different sectors of the economy.

Electrification of transport industry in China presents several new prospects to fulfil requirements, which are necessary to encounter increasing issues of energy security, air quality, and to lower the dependence on fossil fuels. The Chinese government is paying significant attention in increasing EV market penetration and consumer adoption through numerous demonstration programs/plans with attractive transportation policies. In this study, key factors included in adoption barriers and EV policies are comprehensively reviewed, which can enhance consumer intention to adopt EVs. This research study extensively demonstrates the significant and positive impact of two distinguished types of EV policies including financial policies and preferential policies on consumer's intention to adopt EVs by implementing an extended and improved version of Theory of Planned behavior (TPB). A case study in Shanghai is performed through survey of 314 respondents, which is further evaluated by structural equation modelling (SEM) to assess the aspects of EV policies on consumer adoption. In particular, construct items of TPB including attitude, subjective norm (SN), and perceived behavioral control (PBC) are investigated in detail to present their joint impact on purchasing intention of consumers. The confirmatory factor analysis (CFA) and structural equation modelling are performed in AMOS for assessment of survey findings. The research findings from survey analysis reveal that financial and preferential EV policies have a considerable impact towards shaping the attitude of consumers and significantly related to adoption intention of EVs. However, for Shanghainese, preferential policies are more positively associated with consumer purchase intention in comparison with financial policies. Consequently, preferential policies are playing a crucial role in controlling the adoption intention of EVs in China. The principal policy suggestions for various aspects provide multifaceted perceptions for stakeholders to envision electrified transportation.

In power systems, load frequency control (LFC) matters significantly to achieve stability. Dealing with the fluctuations in the frequency of a multi-area power system becomes more challenging by incorporating additional energy resources. In this research, a multi-area power system is built by integrating thermal power systems with photovoltaic (PV) cells, wind turbines, and electric vehicles (EV). The addition of an electric vehicle “to a thermal power system which is integrated with a renewable energy source (RES)” increases the system productivity but also increases the system complexity, making it more problematic for LFC. Looking at the stability criteria for LFC, frequencies in two areas (Area-1 & Area-2) and tie-line power are considered for measurements. For the tuning of the proposed cascaded (1+PI)-PID controller, a new approach Bald Eagle Sparrow Search Optimization (BESSO) algorithm is implemented which is strongly inspired by nature. BESSO is a combination of bald eagle and sparrow searching techniques and performs comparatively better for fast convergence due to their strong food-seeking natural behavior to find the best solution for controller gains. Controller effects on multi-area systems are compared with the addition of PV, wind, and EV and resulting measurements meet the stability criteria with high accuracy even with the complexity of the system and also undertake a stability analysis to prove the performance by minimizing undershoot, overshoot, steady-state error and settling time for system frequencies and tie-line power. Simulation results are examined at different load-changing conditions. In contrast with similar combinations of PID controller with proposed cascaded (1+PI)-PID controller, it is claimed that the effect of the proposed controller is much finer and more reliable, even with electric vehicles to avoid system blackout caused by frequency fluctuations in interconnected power system.

In this paper, the reference tracking and switching loss minimization problem of grid-tied inverters has been formulated as an integer least square (ILS) problem. It is considered an NP-hard and combinatorial problem due to the binary nature of switching status. A novel modification to the traditional non-recursive sphere decoding algorithm is being proposed in this research to address the implementation of long-horizon control with reduced computational burden. The proposed algorithm demonstrates a significant reduction in computation cost for long horizons resulting in energy minimization. The proposed method exploits the state-space model of the system to predict the sector in the space vector two-dimensional plane in which reference voltage could exist, thereby reducing the search space and time to search for the optimal solution. The proposed algorithm optimizes the utilization of controller resources compared to the conventional approach. The algorithm’s performance has been validated through simulations and Hardware in loop verification, for the performance optimization of a three-phase grid-tied inverter coupled using RL filter.

Abstract The challenge of controlling frequency becomes greater as the complexity of a power network increases. The stability of a power system is highly dependent upon the robustness of the controller. This paper presents automatic generation control (AGC) of a four-area interconnected power system along with integrated renewable energy sources of PV and wind energy. The designed model is a challenge given the increased penetration levels of PV and wind along with a thermal-hydropower system. The addition of a hydropower system as a fourth type results in the pole of the open loop system of the hydropower system being located at the right half side of the s-plan. This demands a robust control. A novel MPC-(1 + PIDN) is designed for high-order interconnected areas (HOIA) to stabilize the frequency in a robust way. The salp swarm algorithm is adopted to optimize the parameters of the PIDN controller. The performance of the proposed controller under HOIA is tested in a unbalanced load environment with uncertainty in the power system. The proposed controller can effectively handle the frequency disruption by stabilizing it in $$0.86 s$$ 0.86 s for Area-1, $$1.08 s$$ 1.08 s for Area-2, $$0.81 s$$ 0.81 s for Area-3, and $$0.84 s$$ 0.84 s for Area-4 with an average time of $$0.89 s$$ 0.89 s for all the areas, whereas the average time for GWO: PI-PD, MPC/PI and GA-PI is $$3.48 s$$ 3.48 s , $$10.36 s$$ 10.36 s and $$18.47 s$$ 18.47 s , respectively. The results demonstrate the effectiveness of the controller when compared to other controllers.

The proposed work addresses the modeling, control, energy management and operation of hybrid grid connected system with wind-PV-Battery Energy Storage System (BESS) integrated with Fuel Cell (FC) and Electrolyzer. A hybrid PV-Wind-FC with electrolyzer consisting of BESS with the least number of control loops and converters has been proposed. The proposed hybrid system presents a cost efficient solution for integrating PV into a hybrid system by eliminating the PV converter. This includes the design of controllers for grid-connected hybrid systems with a renewable distributed generator (Wind and PV) as a primary source, BESS as a secondary source and FC with Electrolyzer as a tertiary source. In addition, the lead compensator along with integrator is used for obtaining enough phase margin and removing steady state error completely. It increases the stability of the controller and adds phase shift $\varphi _{s}$ at a cross gain frequency ( $\omega _{cut}$ ). The Grid Side Controller (GSC) is capable of providing frequency support to the utility grid, when it is linked to the grid. In the proposed configuration, PV power is maximized and injected into grid through GSC. Rotor Side Converter (RSC) and GSC ensure the support for sharing the burden of the grid station. Moreover, the proposed controller of BESS with coordination of FC eliminates the effect of intermittency of power generated from wind and PV. Excess power production by renewable distribution generation is used by Electrolyzer to generate hydrogen. This hydrogen is further used by FC when there is not enough power generation due to unfavorable weather conditions. The energy management has been presented to fulfill the load profile, avoid BESS overcharging and to minimize the intermittency and fluctuation of Wind and PV sources. This method guarantees steady power flow and service continuity. The Simulink model of the proposed system results validate the efficiency of the proposed hybrid system as compared to the conventional hybrid system reported in the literature. The modeling of the proposed system and analysis has been demonstrated using the MATLAB Simulink model. Lastly, the energy management of the system has also been examined and compared with the conventional power system.

A power system that comprises a photovoltaic (PV), wind energy system (WES), and fuel cell (FC) with a battery energy storage system (BESS), is collectively called a hybrid power system. The research shows discrete-time modeling of fuzzy tuned fractional-order proportional derivative (F-FOPDμ) Controller for load frequency control (LFC) of this hybrid power system. Under varying load conditions, the gains of the fuzzy tuned fractional-order proportional-derivative (F-FOPDμ) will be tuned automatically by a fuzzy logic controller. A diesel generator (DG) with zero-input power, is running as a synchronous condenser to provide the reactive power balance and voltage stability to the WES for automatic generation control (AGC). The hybrid scheme is tested under changing system parameters, load fluctuations, and a worst-case scenario which is the occurrence of three-phase symmetrical faults with the opening of circuit breaker (CB) with a delay of 5 cycles. The proposed controller enhances the optimization of the power system as well as decreases the transients. In case of surplus power availability, the secondary load (SL) absorbs the additional power. The control strategy ensures that the error in frequencies remains zero. The proposed scheme of control not only minimizes the frequency oscillation but also attains the minimum steady-state error in case of dynamic load conditions.

This paper presents a detailed comparison of eliminated and non-eliminated harmonics for 3-level, 5-level and 13-level cascaded multilevel inverter. For this research, three different types of cascaded H-Bridge multilevel inverters are considered where each cascaded multilevel inverter generates a different output voltage, but the number of notch angles in all the three cases are kept identical. A unified approach is adopted to derive the mathematical model of each multilevel inverter output voltage waveform, RMS of output voltage, RMS of fundamental component, selective harmonic elimination system of equations for the low order harmonics elimination and output voltage total harmonic distortion. It is shown that since selective harmonic elimination system of equations can be expressed as a function of the notch angles, hence notch angles are determined while taking in account only those harmonics to be eliminated. The Newton-Raphson method is proposed to resolve the nonlinear transcendental equations of selective harmonic elimination for the three multilevel inverter cases. Consequently, once the notch angles are determined, all three multilevel inverters are analyzed by comparing Total Harmonic Distortion and Zero-Sequence Harmonics Factor. The elimination of triplen-harmonics by evaluating them in the non-linear transcendental system of equations is a significant contribution. The theoretical and simulated results are presented to verify the validity of the proposed work.

In this study, we address the optimization of the direct power control of a doubly fed induction generator within a wind conversion system under actual wind conditions. The primary objective is to enhance the dynamic response of the wind energy conversion system (WECS) while minimizing the impact of wind fluctuations on power generation. To achieve this goal, we introduce a novel control methodology based on the super-twisting algorithm (STA). This approach allows for effective regulation of both reactive and active power output in the WECS. We employ comprehensive simulations using a detailed model of the WECS and real wind profiles to evaluate the efficacy of the STA-based control strategy. Our simulations demonstrate that the adopted STA-based control strategy successfully tracks the desired power set-point and effectively mitigates the adverse effects of wind power fluctuations and uncertainties on the WECS power output. Specifically, it exhibits superior performance in managing transients and rejecting disturbances compared to a conventional approach employing a switching table and hysteresis controller. These results suggest the practical viability and potential applications of the STA-based control strategy in real-world wind energy systems.

Abstract The advancement in technology has proceeded exceptionally that the stability of the power system becomes a major challenge. The mismatch of generation and demand creates frequency perturbation that need to be curbed under the limits to avoid power breakdown. Owing to this, the paper focuses to counter the ever‐increasing challenges to the power system much like load and fault disruption that causes frequency deterioration and breakdown. A novel master‐slave (MS) control design is formulated for a hybrid interconnected power system in which fractional‐order‐proportional‐integral‐derivative (FOPID) controller act as a master and model predictive controller (MPC) act as a slave controller. The gain parameters of the master controller are tuned by salp swarm algorithm (SSA) which act as a secondary loop controller, while slave act as a primary loop controller. The MS formation has resultantly removed the steady‐state error associated with MPC and help to mitigate the LFC problem. Furthermore, the superiority of the controller is tested under load and fault disruption in a multi‐area power system. The designed controller is tested under four states (1) abrupt load variation, (2) sinusoidal load deviation, (3) sensitivity analysis and (4) IEEE 39‐bus test system analysis. The disclosed simulation results depicts that the proposed control scheme has a dynamic performance as compared to other controllers.

Coordinated operation of hydrothermal scheduling with HVDC links considering network constraints becomes a vital issue due to their remote location and recent induction in the existing power system. The nonlinear and complex nature of the problem introduces many variables and constraints which results in a heavy computational burden. A widespread approach for handling these complexities is to reformulate the problem by several linearization methods. In this paper, a Lagrange multipliers-based method is proposed for the solution of hydrothermal economic scheduling including HVDC link. This method solves equality constraint optimization problems. The linear programming approach is embedded with the Lagrange method to consider both equality and inequality constraints. The proposed technique has been used on piecewise linear variables and constraints of the system considering generation, water volume, and line power flow limits. The formulated method efficiently minimizes the operational cost of thermal units and maximizes the utilization of hydro units while meeting all generation, water volume, and the HVDC link constraints. The method was successfully implemented in two scenarios of a case study. In the first scenario, hydrothermal scheduling was performed on the typical network without an HVDC line limit and equal nodal prices were found with minimal thermal generation cost of $278,822.3. In the second scenario, the proposed method optimally dispatches units to meet the HVDC line limit and minimizes thermal generation cost to $279,025.4 while satisfying hydro, thermal, and other operating constraints. Both scenarios are implemented for a 24 h period. The results have been presented to illustrate the performance of the proposed method.

The demand for energy is increasing at an abrupt pace, which has highly strained the power system, especially with high share of power generation from renewable energy sources (RES). This increasing strain needs to be effectively managed for a continuous and smooth operation of the power system network. Generation and demand exhibit a strong correlation that directly creates an impact on the power system frequency. Fluctuations and disruptions in load frequency can manifest themselves as over-voltages and physical damages in the power grid and, in the worst case, can lead to blackouts. Thus, this paper proposed an effective solution to mitigate the load frequency problem(s), which is initiated by the changing load demand under high penetration of RES. This paper presented an improved cascaded structure, the proportional integral with a fractional operator coupled with proportional derivative PI−FOP+PD. The proposed FOP+PD modifies the (1+PD) controller by introducing fractional properties that improve its tracking efficiency and mitigate frequency fluctuations taking minimal time. The introduction of FOP β diversifies its tracking and overall controlling ability, which translates it as a significant controller. The controller optimal parameters are extracted by deploying a dragonfly search algorithm (DSA). The study of the results illustrates that the proposed design displays efficient performance under any disturbance or uncertainty in the power system.

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments.

In a grid, the choice of the point of common coupling (PCC) does not solely rely on the voltage level alone but also conjointly depends on the grid strength for many explicit purposes. Nowadays, the affinity of low SCR grid connections has become a crucial thought once it involves the integration of wind generation plants (WPPs). Since the quality of wind resources is a critical issue, these plants are usually placed in remote areas with a sophisticated potential of wind flow. These remote areas are typically less inhabited, where the grid does not perpetually always have to be sturdy. Moreover, the exceeded power demand loading and higher wind penetration affect the generation, transmission, and distribution utilities by permitting the flow of unbalanced voltages and currents in the power system. Therefore, the quality of transmitted power is becoming a crucial facet of distributed energy generation units. In this paper, a permanent-magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. It discusses a solution, which provides the low voltage ride through (LVRT) provision by the suppression of DC link overvoltage and active power limitation during an asymmetrical grid fault. With improved back-to-back converter control, the machine side converter (MSC) is employed to control the DC-link voltage. Furthermore, the grid side converter (GSC) is used to implement the active/reactive current injection according to the outlined limits. The need for external hardware is eventually avoided, which is typically required to dissipate the additional energy generated during a grid fault. Hence, it is proven to be an affordable solution.