Hybrid PV-battery Systems Research Articles

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.

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.

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

Monitoring and Effectiveness Analysis of a Hybrid PV Battery System in Real Conditions

Monitoring and Effectiveness Analysis of a Hybrid PV Battery System in Real Conditions

Cost-Effective Hybrid PV-Battery Systems in Buildings Under Demand Side Management Application

In this paper, a sizing method is proposed for photovoltaic (PV) and battery energy storage systems (BESSs) for buildings with demand side management capability. Three objective functions (OF) are defined, describing the self-sufficiency of the building, the net-present value of the investment in PV and BESS, and their combination in a joint expression, respectively. The latter OF provides cost-effective solutions that are potentially more profitable in case of future high increase of the electricity cost. Two analysis methods are developed to find the optimal solution under a predefined load shifting strategy. The first one applies an exhaustive search by examining all possible combinations of PV and BESS capacity. The drawback of this method is the high computational burden due to unnecessary access to the solution space. The second method is implemented with a particle swarm optimization (PSO) variant, namely the unified-PSO. Under this approach, the analysis algorithm is guided in the solution space toward efficient solutions under lower computational time. The comparative assessment of the two methods in both residential and industrial sites verifies that via the PSO algorithm the optimal solutions are found under lower execution time.

Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture

On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. The proposed model is tested on dynamic electricity costs. Compared with existing energy systems, the proposed strategy and prediction model can handle more than half of the annual load need.

Design and Dynamic Modelling of a Hybrid PV-battery System for a House with an RO Water Desalination Unit in Iran

Energy crisis and power shortage are major concerns in Iran nowadays, where people experience several blackouts during the day. On the other hand, potable water scarcity is another trend in Iran. In this study, the design and dynamic modelling of a stand-alone hybrid PV-Battery-RO system are discussed for a house in Sinak village, Tehran, Iran. Site characteristics are analyzed in the first part to estimate the house load and deferrable RO load. In the second part, the system has been modelled in HOMER pro software to determine the size of the photovoltaic panels and battery. Moreover, complete electrical details of the system such as system autonomy, unmet load and excess energy have been described. In the third part, dynamic modelling of the small-scale RO unit based on a transfer function is described. The introduced transfer function correctly simulates the system's output flow rate in response to input water pressure variations. Electrical dynamic modelling of the PV- battery system has been designed in MATLAB/Simulink. The results prove that introduced model can simulate the system's behavior in four conditions: normal operating conditions, zero irradiance conditions, maximum irradiance conditions, and net-zero energy conditions. The battery supports the system, and PV arrays power the loads with a fixed and stable voltage and frequency in all the conditions.

Multi-objective optimisation of a grid-connected hybrid PV-battery system considering battery degradation

ABSTRACT Variable nature of solar energy leads to inconsistent performance of photovoltaic systems. Integration of photovoltaic systems with storage and grid mitigates variability issues and helps maximise self-consumption. This work presents a design methodology for a grid-connected photovoltaic system integrated with storage to meet an annual hourly average demand of 135 kWh of an establishment situated in northern India which receives a daily average solar insolation of 5.16 kWh/m2. System design has been carried out to meet the energy demand at minimised annualised cost. The annualised cost of the optimised system has been found to be US$115184.20/year. Analysis shows that the proposed model results in reduction in annualised cost by 32.27% and a net reduction in emissions by 610 MT as compared with the traditional grid power. Furthermore, multi-objective optimisation carried out to explore system design trade-offs between annualised cost, peak to average ratio of grid energy, and emissions, indicates that overall reduction in emissions by 15% could be obtained at 6.5% increased cost. It is also observed that reduction in grid-outages by 25% leads to reduction in AC by as high as 42.4%.

Techno-economic impacts of battery performance models and control strategies on optimal design of a grid-connected PV system

A battery storage has emerged as the most widely-used storage option, due to its flexible and complementary functionality for renewable energy systems such as solar PV and wind power. In order to ensure the efficient operation of batteries in energy systems, a proper battery model is essential in predicting realistic battery performance under various operating conditions. Accurate knowledge of the state of charge, state of power, and battery efficiency is a necessity for the development of advanced grid management applications. This paper investigates the techno-economic impacts of two battery modelling scenarios on the sizing and optimization of a grid-connected PV-battery system. Scenario 1 is based on a common simple battery model and control strategy which represents the battery status without reflecting dynamic behavior. By contrast, Scenario 2 is based on a complex battery model involving estimation of battery current-voltage characteristics under various operating conditions. A rule-based operational strategy linked to a non-dominated sorting genetic algorithm is further employed for the simulation and multi-objective optimization of a grid-connected hybrid PV-battery system. The battery life cycle cost and the self-sufficiency ratio are analyzed and optimized as objective functions, and battery capacity constitutes as a decision variable. The results show that in order to reach the same self-sufficiency ratio, the optimization of a hybrid energy system based on Scenario 1 leads to solutions with a higher life cycle cost and requiring bigger battery capacity, compared to that of Scenario 2. Moreover, under the same design parameters, the system optimization based on Scenario 2 delivers more power to the end-user, which leads to a higher self-sufficiency ratio compared to when the system is simulated based on Scenario 1. This study proves that an efficient battery model with sufficient accuracy is techno-economically more beneficial, and leads to more accurate battery sizing.

Adaptive robust control-based energy management of hybrid PV-Battery systems with improved transient performance

Energy management of hybrid photovoltaic (PV)-battery systems still serve as a challenging task owing to their complex and nonlinear characteristics, multicomponent structures, and the extensive range of environmental factors disturbing their nominal performance. The hybrid energy system developed in this study encompasses PV arrays, a battery component, one boost converter, and one bidirectional boost converter. In this paper, we propose a novel adaptive robust control framework for the optimal energy management of the PV-battery systems under many operating conditions and subject to unmodelled dynamics. An improved exponential-like adaptive integral sliding mode (EISM) control coupled to neural network approximator is introduced using a multi-rate convergence tweaking mechanism for the sliding surface to improve the transient performance of the closed-loop system. Furthermore, the entire dynamics of the hybrid energy system is considered unknown, unlike the previous studies that only assumed the parametric uncertainties. The global asymptotic stability of the system is guaranteed, and the effectiveness of this novel framework is compared to benchmark studies.

A super-twisting sliding mode observer for boost inverter-based hybrid photovoltaic -battery system control

This paper proposes an observer-based control scheme for a three-phase differential boost inverter in a hybrid PV-battery system. In a conventional control scheme for three-phase differential boost inverter (DBI)-based PV system, the measurements of input inductor current and voltage across output capacitors are required for obtaining the desired voltage at the load end. In a typical three-phase differential boost inverter operation, four voltage sensors and seven current sensors are required to achieve the desired AC voltage. In this paper, an observer-based strategy for elimination of these sensors without compromising power quality is reported. For estimation of boost inverter inductor current and output capacitor voltage, only DC-link capacitor voltage and the load currents are measured. A super-twisting sliding mode observer is used for estimation of the states of the boost inverter. A comparative study between the first-order sliding mode observer and super-twisting sliding mode observer is also presented. The simulation and experimental results show that the proposed observer-based control scheme works satisfactorily under various operating conditions such as sudden change of load, changes in solar insolation, and also under unbalanced load conditions.

A Hybrid PV-Battery System for ON-Grid and OFF-Grid Applications—Controller-In-Loop Simulation Validation

In remote locations such as villages, islands and hilly areas, there is a possibility of frequent power failures, voltage drops or power fluctuations due to grid-side faults. Grid-connected renewable energy systems or micro-grid systems are preferable for such remote locations to meet the local critical load requirements during grid-side failures. In renewable energy systems, solar photovoltaic (PV) power systems are accessible and hybrid PV-battery systems or energy storage systems (ESS) are more capable of providing uninterruptible power to the local critical loads during grid-side faults. This energy storage system also improves the system dynamics during power fluctuations. In present work, a PV-battery hybrid system with DC-side coupling is considered, and a power balancing control (PBC) is proposed to transfer the power to grid/load and the battery. In this system, a solar power conditioning system (PCS) acts as an interface across PV source, battery and the load/central grid. With the proposed PBC technique, the system can operate in following operational modes: (a) PCS can be able to work in grid-connected mode during regular operation; (b) PCS can be able to charge the batteries and (c) PCS can be able to operate in standalone mode during grid side faults and deliver power to the local loads. The proposed controls are explained, and the system response during transient and steady-state conditions is described. With the help of controller-in-loop simulation results, the proposed power balancing controls are validated, for both off-grid and on-grid conditions.

A Novel Constant Power Management Controller for Hybrid PV-Battery System

In grid-connected applications, the photovoltaic (PV)-based power generation is getting more attention these days due to several factors like pollution-free, single investment plant, etc. However, it has the drawback of interruption in power generation, which makes it undesirable to power system. In order to overcome this drawback, a constant power is supplied to the grid by integrating energy storage system like battery and/or ultra-capacitor with solar PV. This paper proposes a grid-connected PV-battery-based hybrid power system to deliver a constant power to the grid. Modeling and simulation of solar PV-battery-based hybrid system have been developed and the constant power management controller (CPMC) is ensured to inject constant power at the change in environmental conditions. The simulation result shows the performance of proposed control algorithm to PV-battery-based hybrid system is validated.

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

Environmental impacts of small-scale hybrid energy systems: Coupling solar photovoltaics and lithium-ion batteries

One of the benefits of hybrid solar PV-battery systems is that they can reduce grid dependency and help balance electricity supply and demand. However, their environmental impacts and benefits remain underexplored. This study considers for the first time life cycle environmental impacts of domestic-scale PV-battery systems in Turkey, integrating multi-crystalline PV and lithium-ion battery. The impacts were estimated for both individual installations and at the national level, considering different regions across the country and taking into account their insolation and other climatic differences. Electricity generation and storage were modelled on an hourly basis taking into account consumer behaviour. The results show that the system can meet between 12.5% and 18.4% of the household's annual electricity needs. On a life cycle basis, it generates 4.7–8 times more energy than it consumes. Solar PV is the major contributor to most impacts (75%–81%). An exception is human toxicity which is mainly due to the battery (66%). The hybrid system has 1.6–82.6 times lower impacts than grid electricity. Assuming a very modest uptake at the national level (2%–8%), the use of hybrid systems would save 558,000 t CO2-eq./yr compared to grid electricity. Thus, these results demonstrate clearly the environmental benefits of these hybrid systems. Together with the financial and energy security benefits for both the country and the consumer, this provides a strong impetus for their wider deployment. However, this will be difficult to achieve, as there are no incentives for battery storage. Therefore, it is recommended that relevant legislation be introduced to stimulate future uptake of hybrid PV-battery systems.

Optimal operation of hybrid PV-battery system considering grid scheduled blackouts and battery lifetime

In many developing countries where the energy supply capacity is less than the demand, energy companies distribute the available power according to a specific time schedule, leading to grid scheduled blackouts (GSBs) for the costumers. A suitable way to solve this problem for residential loads is to install a combined photovoltaic (PV) and battery storage system as a replacement of the grid during the blackout periods. However, it is well-known that the lifetime of battery storage will be considerably shortened, if it is not operated appropriately. This paper explores the potential benefits of applying economic model predictive control (EMPC) to optimize the operation of a hybrid PV-battery system to address the GSB problem. The aim of our control strategy is to cover the load as much as possible and meanwhile minimize the total cost of the energy consumed from the grid. In particular, the battery is operated in such a way that its lifetime will be prolonged. A comparison between different operation strategies for the system considering the seasonal variation in the load and the PV power generation is made. The results show that the proposed approach leads to a significant reduction of the total energy consumed from the grid while decreasing the curtailment of the generated power from the PV-array, and maximizing the battery lifetime.

Simulation and Evaluation of the Economic Merit of Cloud Energy Storage for Prosumers: The Case of Germany

This paper investigates the merits of a virtual aggregation of spare capacities from decentralized batteries installed in private households. To this end, we develop a simulation model that enables to take into account the prevailing grid- use tariffs, feed-in tariffs, and other parameters for an economic assessment of the viability of such an “energy storage cloud”. In the illustrative model application, we study the merits for four households in Germany over the period of one month by analyzing three distinct scenarios (one with no grid-use tariffs, one with unidirectional grid-use tariffs, and one with bidirectional grid-use tariffs). Three households are assumed to have hybrid PV-battery systems, whereas the fourth household aims at using the energy storage cloud for increasing its self-supply. We find that below a feed-in tariff of 10 €-ct/kWh, profits can indeed be made. The maximum net profit results in Scenario 1, where feed-in tariffs and grid-use costs are zero, and amounts to around €36 per month.

Employing Battery Storage to Increase Photovoltaic Self-sufficiency in a Residential Building of Sweden

Photovoltaic (PV) or hybrid PV-battery systems are promising to supply power for residential buildings. In this study, the load profile of a multi apartment building in Gothenburg and the PV production profile under local weather conditions are compared and analyzed. Three different types of batteries, including lead acid, NaNiCl (Sodium-Nickel-Chloride) and Lithium ion, are studied in combination with the PV systems. It is found that Lithium ion battery system is superior in achieving higher Self-Sufficiency Ratio (SSR) with the same Life Cycle Cost (LCC). Achieving high SSR with the hybrid PV-battery system is unrealistic because of the seasonal mismatch between the load and electricity production profile. The capacity match between the PV and battery to maximize SSR was investigated, showing different trends under system LCC range of 0.1-40 Million SEK (1SEK≈0.12USD). The system LCC should be lower than 10.6 Million SEK (at the SSR of 36%) in order to keep the payback time positive.