PV-battery System Research Articles

Robust Control System Tuner and Cascaded Adaptive Vectorial Filter-Based PV-Battery System With a Dual-Loop ANF-PLL

Robust Control System Tuner and Cascaded Adaptive Vectorial Filter-Based PV-Battery System With a Dual-Loop ANF-PLL

Efficient Peak Current Limit Strategy and Active Power Oscillation Reduction in a Three-Phase Grid-Interfaced PV-Battery System (GIPVBS) with Low Voltage Ride-Through Control

Efficient Peak Current Limit Strategy and Active Power Oscillation Reduction in a Three-Phase Grid-Interfaced PV-Battery System (GIPVBS) with Low Voltage Ride-Through Control

Sustainable Logistics: Synergizing Passive Design and PV–Battery Systems for Carbon Footprint Reduction

As more companies strive for net-zero emissions, mitigating indirect greenhouse gas emissions embedded in value chains—especially in logistics activities—has become a critical priority. In the European logistics sector, sustainability and energy efficiency are receiving growing attention, given the sector’s intersectional role in both transportation and construction. This transition toward low-carbon logistics design not only reduces carbon emissions but also yields financial benefits, including operational cost savings and new market opportunities. This study examines the impact of passive design strategies and low-carbon technologies in a Swedish logistics center, assessed using the low-carbon design criteria from the BREEAM International standard, version 6. The findings show that passive energy-efficient measures, such as the installation of 47 skylights for natural daylighting, reduced light power density in accordance with AHSHARE 90.1-2019 and the integration of free night flushing, contribute to a 23% reduction in total energy consumption. In addition, the integration of 600 PV panels and 480 batteries with a capacity of 268 ampere-hours and 13.5 kWh storage, operating at 50 volts, delivers a further 56% reduction in carbon emissions. By optimizing the interaction between passive design and active low-carbon technologies, this research presents a comprehensive feasibility analysis that promotes sustainable logistics practices while ensuring a future-proof, low-carbon operational model.

Towards decarbonizing large-scale industries: A decision support framework for optimizing grid-connected PV-battery energy systems planning – Case study of an OCP mining site, Morocco, North Africa

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

Modified control approach for MPP tracking and DC bus voltage regulation in a hybrid standalone microgrid

This paper proposes modified MPPT control technique for hybrid standalone microgrid. Standalone PV based microgrid has emerged as potential solutions to electricity challenges in the region where grid is not available. The battery energy storage system (BESS) is integrated into the system to facilitate synchronized load control and power flow. Variation in solar irradiation, load demand and fluctuation in battery SOC results in DC voltage fluctuation and maximum power point tracking challenges. Hybrid PV-Battery systems therefore must require control algorithms that can both flexibly adjust power flows as well as stabilize bus voltages. Proposed control technique is successful in regulating DC bus voltage and balancing the power flow in system under various operating conditions. A bidirectional converter is employed to maintain the nominal DC voltage conditions by charging/discharging the battery due to intermittent PV generation. It regulates the voltage of the DC bus to the maximum power point of the PV array. The main goal of this research article is to implement modified MPPT technique which includes Incremental conductance algorithm with double closed loop controller to track maximum power and to regulate the DC bus voltage of the system under different type of dynamic and atmospheric conditions.

Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems

This paper presents a mathematical optimization framework for the strategic placement of quasi-dynamic wireless charging (QWC) stations within road networks to address the charging needs of battery electric buses (BEBs). This study evaluates two scenarios for powering the buses. In the first scenario, a grid-connected system is considered. The optimization aims to minimize annual costs related to capital, operation, and energy losses of the electric bus fleet. This involves determining the optimal locations for QWC stations, the length of power transmitters, and the corresponding battery capacities for the BEBs. Using MATLAB-based optimization tools Casadi and Yalmip, with solvers Bonmin and Fmincon, the optimal configuration includes a 13 kWh battery capacity and a 300 m power transmitter distributed across five bus stop areas. The second scenario employs a chance-constrained optimization approach for an isolated solar photovoltaic (PV) and battery energy storage system (BESS). This system is designed to reliably meet the BEBs' energy requirements throughout the day, considering different seasonal data (winter, summer, all seasons/year-round). The optimization results for the PV and BESS capacities vary with the seasons: 394.247 kW and 2012.6 kWh using summer data, 1762.1 kW and 2738.2 kWh using winter data, and 1610.8 kW and 2741.9 kWh using year-round data. Additionally, the paper examines the impact of varying bus fleet sizes on the optimal battery size and power transmitter combination using a real-world example of the bus route between Khalifa City and Abu Dhabi Downtown in the UAE. The findings suggest that larger batteries with fewer or no charging stations are more economical for smaller fleets. Conversely, as the fleet size increases, a combination of smaller battery sizes and a greater number (and length) of QWC (power transmitters) becomes more cost-effective. This research offers significant insights into the efficient deployment of QWC stations and the integration of renewable energy and energy storage for sustainable urban electric bus networks. The proposed optimization models provide a systematic approach to designing and operating charging infrastructure, contributing to sustainable urban transportation systems. Moreover, the study highlights the influence of seasonal data on PV system sizing and costs.

Effects of energy sharing and electricity tariffs on optimal sizing of PV-battery systems for grid-connected houses

This paper investigates the optimal sizing of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected houses based on mutually-agreed energy sharing prices by considering the flat and time-of-use (TOU) tariffs. The grid-tied house with PV-BES, referred to house 1 (H1) in the paper shares electricity with house 2 (H2) whenever needed with mutually-agreed electricity tariffs. The main objective function of the study is to minimize the cost of electricity (COE) for the H1 while decreasing COE for H2. Eight different schemes are investigated with the combination of flat and TOU tariffs for buying, selling, and mutually-agreed rates, respectively. For each scheme, optimal sizing of components and COE for both houses are evaluated. Realistic hourly-arranged annual data of temperature, solar irradiation, and load consumption of two houses are used as the input data. For each scheme, the results are compared with the situation when H1 does not have PV-BES and there is no electricity sharing. Sensitivity, operational, and uncertainty analyses are provided for the scheme with the lowest COE.

Enhanced Third Order Generalized Integrator-Based Seamless Control for Grid-Tied PV-Battery System

Enhanced Third Order Generalized Integrator-Based Seamless Control for Grid-Tied PV-Battery System

Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates Gene Expression Programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient energy management in industrial settings.

Hybrid PV-Battery System Evaluation in Baghdad: ICOE and NPV Analysis using SAM Software

In this study, The System Advisor Model (SAM) simulation software has been used to analyze a Hybrid PV-Battery System in a residential cite in Iraq-Baghdad. The proposed system is 5kW which is affordable and applicable from the cost and required area points respectively. The monthly averaged electrical load is approximately calculated. The performance ratio for the system is 0.75 and about 6834 KWh is generated by the system per year. The results showed that the initial capital cost is 4,167.01$ which is returned after six years, LCOE Levelized cost of energy real is 7.66 ¢/kWh and Net present value is $4,649 made the system are very economical/no maintenance project and can be hooked to any the peak time load.

Performance analysis of drone sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions

In recent years, growing consumer demand for sustainable energy is driving down costs and paving the way for more clean energy in the future. Since these energies are sporadic, and the load pattern does not correlate to their generation pattern, a battery storage system is required. Operating dispersed alternative energy sources, connected to the grid in this situation makes energy control an unavoidable task. The difficulty is brought on by things like intermittent sources, daytime costs, solar panel and battery size restrictions, and limitations in battery charging and discharging rates. Moreover, integrating energy storage devices into the power system network is one of the recommendations made in this work to increase the performance and dependability of these systems. Compared to the existing works reported in the literature, this study offers enhanced control techniques for hybrid Photovoltaic and Battery Energy Storage System under fluctuating atmospheric scenario. Here, an innovative approach called Drone Squadron Optimisation, which tracks the Global Maximum Power Point, is used get maximum power. Further, suggested approach is tested with various partial shading instances and the uniform irradiance condition to validate it. This research also investigates photovoltaic models and the status of the battery storage device for improved energy management in the system. Finally, the proposed algorithm assists in quickly identifying the potential benefits of a grid-connected, battery-powered rooftop solar system.

Weak Grid Integrated Improved Power Quality PV-Battery System With Controlled Power Flow and Grid Assistive Capabilities

Weak Grid Integrated Improved Power Quality PV-Battery System With Controlled Power Flow and Grid Assistive Capabilities

Techno-economic analysis of grid-connected PV and second-life battery systems for net-zero energy houses

Net-zero energy houses (ZEHs) rely on energy-efficient building design and the incorporation of distributed generation and battery energy storage units. Nevertheless, two primary concerns arise: high investment cost of these units and harmful environmental impact of batteries. Using second-life batteries can overcome these concerns by reducing the cost of photovoltaic (PV)-battery systems and mitigating the adverse environmental effects of battery supply chain. Therefore, this study examines the techno-economic feasibility of utilizing second-life batteries for PV storage in grid-connected ZEHs in two provinces (Antalya and Istanbul) of Türkiye. First, two ZEHs with air-to-water heat pumps are designed using BEopt software. Next, the optimal PV-battery capacity in the ZEHs is determined using HOMER Grid software. Finally, the economic feasibility of using three types of batteries (new lead acid, new Li-ion, second-life Li-ion) in ZEHs is compared. The optimal design for a typical ZEH comprises a 5.92 kW PV and an 8.96 kWh second-life Li-ion battery in Istanbul (northern Türkiye), yielding an NPV of $10,906, and a 7.54 kW PV and an 11.52 kWh second-life Li-ion battery in Antalya (southern Türkiye), yielding an NPV of $16,402. The results indicate that using second-life Li-ion increases the NPV of PV-battery systems by 15 % in Istanbul and by 21 % in Antalya. The feasible system configuration categories for Türkiye's economic and climatic conditions are ranked as: PV-second-life Li-ion > PV-no battery ≅ PV-new Li-ion > PV-new lead acid. Incentivizing the use of second-life batteries due to their environmental contribution could result in an even higher NPV increase.

Assessment of renewable power generation applied in homestay hotels: Energy and cost-benefit considering dynamic occupancy rates and reservation prices

Increasing the revenue from hotel operations and decreasing the stress on utility grids from hotel energy consumption are two priorities for the modern tourism industry. Dynamic occupancy rate and hotel reservation prices influence their operations revenue, while occupancy rate also impacts real hotel energy consumption. Renewable power generation as an effective measure can benefit hotels not only by decreasing electricity purchases but also increasing some extra income from selling electricity generated from renewable sources. This study investigated the effects of renewable power generation on energy and the cost-benefit performance of hotel operation prices under different occupancy rates and reservation prices. A two-stage energy and cost-benefit performance assessment and a novel comprehensive cost-benefit assessment model were developed to examine these research issues. The impact of the renewable power generation system (i.e., a photovoltaic-battery system installed on the building rooftop) was considered on the hotel energy and cost-benefit performance. It was found that the novel cost-benefit assessment model accurately quantified the overall hotel profit by calculating its energy consumption cost with the PV-battery system under different occupancy rates and reservation prices. Three typical homestay hotels located in Sanya were tested and assessed. The results showed that PV-battery systems effectively decreased the amount of electricity purchased by hotels. Besides, the renewable energy penetration was above 57.98% in various cases. The overall hotel profit increased by a maximum of 31.4% since renewable power generation was used under different occupancy rates and hotel reservation prices.

Optimizing Renewable Energy Integration: A Case Study of Standalone PV-Battery Systems in IZTECH Campus

In this research paper the feasibility of renewable-powered, self-sufficient university campuses was explored by conducting a technoeconomic analysis of standalone PV-Battery systems for the buildings of Izmir Institute of Technology (IZTECH) in Izmir, Turkey. Given the high energy demand and dependence on fossil-based grids by universities, integrating renewables becomes important for minimizing carbon footprints. In this study the campus's solar potential was focused and the techno-economic feasibility of grid-independent operations provided by PV-battery systems was evaluated. Four scenarios were investigated: (i) maximum PV installation for each building (MPVB), (ii) maximum PV installation for the entire campus (MPVC), (iii) necessary PV installation for self-sufficiency of each building (NPVB), and (iv) necessary PV installation for self-sufficiency of the whole campus (NPVC). The first two scenarios considered the maximum achievable rooftop PV installation while the latter two included additional PV installation to cover all electricity needs. For all scenarios both lead-acid and Li-ion batteries were considered. Mathematical models were developed using PVSol and TRNSYS software, and technoeconomic analysis was conducted using Levelized Cost of Energy (LCOE) and Net Present Value (NPV) methods. It was found that the NPVC scenario with lead-acid batteries is the most favorable, as it minimizes battery utilization by enabling more PV installation and facilitating energy transfer between buildings. Additionally, the research showed that off-grid PV-battery systems are economically less feasible compared to on-grid counterparts, primarily due to the high cost of batteries.

Modular Open Source Solar Photovoltaic-Powered DC Nanogrids with Efficient Energy Management System

Initially the concept of a DC nanogrid was focused on supplying power to individual homes. Techno-economic advances in photovoltaic (PV) technology have enabled solar PV stand-alone nanogrids to power individual devices using device-specific architectures. To reduce costs and increase accessibility for a wider range of people, a modular open-source system is needed to cover all applications at once. This article introduces a modular PV-powered nanogrid system, consisting of a do it yourself (DIY) PV system with batteries to allow for off-grid power. The resultant open-source modular DC nanogrid can deliver DC power to loads of different voltage levels, which is possible because of the efficient and parametric energy management system (EMS) that selects modes of operation for the grid based on DC bus voltage and state of charge of batteries. Simulation results verify the coordination between the EMS and the PV-battery system under varying PV power generation and load conditions. This EMS has potential to enable easy personalization of a vast area of applications and expand appropriate technology for isolated communities. A thorough stability analysis has been conducted, leading to the development of an LQR (Linear Quadratic Regulator) controller as a replacement for the conventional PI (Proportional - Integral) controllers for better transient stability of the system.

Techno-economic comparative analysis of PV third–party ownership between customer and developer sides in Japan

The adoption of renewable energy sources (RES) offers a significant opportunity for nations across the world to achieve regional decarbonization. Nevertheless, the substantial penetration levels of RES can cause grid overcapacity and operational challenges. In Japan, photovoltaic (PV) third-party ownership (TPO) has emerged as the primary implementation approach aimed at bolstering collective energy self-consumption. However, economic uncertainties raise concerns about the financial feasibility of TPO for both developers and customers. This work presents a techno-economic analysis of rooftop PV-Only and PV-Battery system implementation, assessing the economic feasibility of TPO based on compensation mechanisms for project developers and five customer types at the regional scale. The results indicate that the TPO model is economically feasible, as the levelized cost of energy (LCOE) of developers is from 8.98 to 9.90 ¢/kWh. Additionally, the power purchase agreement (PPA) strategy provides up to 19.12 % higher benefits than the lease strategy (commercial building scenario). On the other hand, the economic performance of the energy storage strategy is inferior to that of the net energy metering strategy, as it relies on fluctuations in market electricity tariffs. This study provides valuable insights for regional decarbonization policies and helps different stakeholders make informed investment decisions.

Investigation of a simple energy management system of a hybrid PV-battery system

Investigation of a simple energy management system of a hybrid PV-battery system

Enhanced Third-Order Sinusoidal Signal Integrator-Based Seamless Control for Single-Phase Grid-Tied PV-Battery System

Enhanced Third-Order Sinusoidal Signal Integrator-Based Seamless Control for Single-Phase Grid-Tied PV-Battery System

Power Bill Optimization for Grid-tie Solar PV-Battery House

This study focuses on the optimization of power bills for a house equipped with a grid-tie solar PV-battery system. Rather than adhering to conventional load scheduling practices or minimizing grid power usage at each time interval, a novel approach is adopted wherein the optimization is performed for the entire 24-hour period simultaneously. By directly incorporating time-of-use rates into the cost function, an absolute optimal solution is attained. The findings indicate that compared to single time step optimization, the proposed method results in a reduction of the power bill ranging from 6% to 10%, depending on load-generation variations. Furthermore, if the utility or government enforces the summer tariff consistently throughout the year, the savings escalate to a range of 15% to 22%. Introducing a more intelligent tariff structure can thus serve as an effective means to expedite the transition towards renewable energy by incentivizing individual investments in solar PV, battery systems, and smart home energy management.