Grid-connected PV-battery System Research Articles

Modelling and Sizing a Grid-connected PV-Battery System Using DIgSILENT for Powering UTeM Main Campus

Modelling and Sizing a Grid-connected PV-Battery System Using DIgSILENT for Powering UTeM Main Campus

A Strategy for Grid-Connected PV-Battery System of Mongolian Ger

One of the main sources of energy utilized in the Mongolian Gers is coal and wood mainly for the purpose of heating and other domestic use. This heavily increases the air pollution levels. A viable solution for handling the air pollution is switching to renewable energy sources (RES). Grid-connected photovoltaic (PV) systems with battery back-up provide a reliable solution to the problem addressing the energy demand and pollution control. This paper proposes a grid-connected PV–second-life battery system and its operation strategy. A single Ger, which consists of a PV array, battery energy storage system (BESS), and an electric heater (EH), is modeled and tested. The trading coefficient and selling unit price are calculated based on variables such as loan, selling price, and purchasing price. The advantages of the proposed strategy are its simple design and easy implementation. The economic result shows that there is a significant reduction in the electricity bill during winters, while the bill can be reduced to zero during summers. Furthermore, the annual profit from the proposed system is USD 15. The CO2 emissions are reduced from 32 to 7 tCO2.

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.

An integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems

Smart home energy management (SHEM) with residential photovoltaic (PV)-battery systems is a complicated issue with different facets. An integrated SHEM model covering the essential functions is missing. Meanwhile, residential PV-battery systems' optimal operations with renewable energy exchanges and imperfect forecasts are still open challenges. In this study, the research activities in SHEM are firstly organized by a pyramid with four functional layers: (i) Monitoring; (ii) Analyzing and forecasting; (iii) Scheduling; and (iv) Coordinating, which can serve as a standard pathway for developing SHEM. Second, guided by the pyramid taxonomy, an integrated SHEM model is developed for residential houses with PV-battery systems. Assuming a perfect Monitoring layer, we obtain the probabilistic load/PV forecasts and user preference vectors of shiftable appliances based on historical data. Then, we develop a two-stage stochastic programming model for optimal scheduling of single houses with a grid-connected PV-battery system, incorporating the probabilistic forecasts and user preference vectors. A retail electricity market with day-ahead (DA) and real-time (RT) markets is employed for leveraging imperfect forecasts. Finally, we design a distributed coordinating algorithm - Asynchronous Scheduling and Iterative Pricing for PV power-sharing among multiple prosumers based on the single-house scheduling model. Numerical simulations based on realistic loads and PV generation data validated the two-stage stochastic programming model's economic superiority and the distributed PV power-sharing approach compared with the rule-based dispatching and selfish scheduling strategies. We concluded that 1) the modeling of load/PV forecast uncertainties is valuable than averaging or ignoring them, 2) the two-stage stochastic programming model and the DA-RT retail electricity market are beneficial for utilizing imperfect forecasts, and 3) coordinating multiple prosumers could benefit each household by sharing PV and battery investments for revenue or trading with local small prosumers for cost reductions.

Implementation of a novel hybrid BAT-Fuzzy controller based MPPT for grid-connected PV-battery system

The paper develops a control scheme for a hybrid energy system, including a photovoltaic (PV) panel and a battery energy storage (BES) system. The capital cost of PV systems is considerably high today and it has been continuously trying to mitigate the manufacturing cost and also, several research works have been devoted to developing effective methods to maximize the power output of these generation systems. In this respect, the most efficient technique is named maximum power point tracking (MPPT) for solar PV systems. To this end, this paper utilizes the fuzzy controller and BAT algorithm (BA) to find the optimal values of the parameters of the controller for the MPPT. The presented framework is known as BAT-Fuzzy. Using the mentioned method, the membership functions of the input and output, which are triangular, are autonomously set. The results obtained from simulating the proposed model on a test system shows the superiority of the BAT-Fuzzy technique in terms of the preciseness and convergence rate, while taking into consideration altering values of solar irradiance and temperature. It also performs better, considering the efficiency of the system in practical cases. Besides, the impact of temperature variations and also, different states of the partial shading conditions (PSC) have discussed. The PV system alone does not produce power over some hours of the day and it should be operated jointly with a storage system to generate the required power over all hours of the day. The presented method is simulated using a test system and the results are compared to some well-known methods, verifying that the developed framework is associated with a fast response during transients with enhanced stability.

Optimal energy management for a grid connected PV-battery system

The increase demand for electricity and the non-renewable nature of fossil energy makes the move towards renewable energies required. However, the common problem of renewable sources, which is the intermittence, is overcome by the hybridization of complementary sources. Thus, whenever the load demand is not fully covered by the primary source, the second one will absolutely support it.Furthermore, the production, the interaction with the grid and the storage system must be managed by the grid-connected hybrid renewable energy system, which is the main objective of this paper. Indeed, we propose a new system of a grid-connected PV-battery, which can manage its energy flows via an optimal management algorithm. The DC bus source connection topology in our proposed hybrid architecture tackles the synchronization issues between sources when the load is powered. We consider in this work that choosing a battery discharge and charge limiting power provides an extension of the battery life. On the other hand, we simulated the dynamic behavior of the architecture’s various components according to their mathematical modeling. Following this, an energy management algorithm was proposed, and simulated using MATLAB/SIMULINK to serve the load. The results have shown that the load was served in all cases, taking into account the electrical behavior of the inhabitants as well as the weather changes on a typical day. Indeed, the load was served either by instant solar production between sunrise and sunset, or the recovery from sunset to 10pm, which could be a stored or injected energy without exceeding the 1000W per hour.

Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.