Grid-connected Hybrid Renewable Energy Research Articles

Smart multi-level energy management algorithm for grid-connected hybrid renewable energy systems in a micro-grid context

The intermittency of single renewable energy sources affects significantly their reliability, and hence, hybrid renewable energy systems (HRESs) are introduced. HRESs are notably used to meet the needs of residential loads. Nevertheless, power surplus and shortages still represent issues that must be handled by energy management systems. In this paper, a novel method for optimal energy management of a grid connected micro-grid composed of five smart Moroccan prosumers is presented. The proposed energy management strategy is organized using three levels, namely, HRES level, load-scheduling level, and communication-sharing level between HRESs. The first two levels of energy management are processed at the local level, i.e., inside each smart home. For the third level, our paper demonstrates the feasibility of interactions and communication between the five different houses interconnected using an optimized infrastructure topology as a smart neighborhood grid. Each of these houses is empowered by their own hybrid renewable energy system made of possible system hybridization based on PV (Photovoltaic), wind turbine, and battery storage system. This helps each house to manage its surplus and to deal with energy shortage through the proposed multi-level system's energy management algorithm. The application of information and communication technologies enables the set of smart homes connected in a micro-grid framework to contribute to the emergence of the concept of the internet of sustainable green things. Simulations conducted by the MATLAB platform validate the proposed methodology, which relies on micro-grid surplus injection and neighborhood storage during extreme surplus, else load dynamics thereafter micro-grid-injected energy recovering during hybrid energy deficit. The proposed system manages the energy produced by HRESs and reduces the energy demanded by the utility grid using the proposed topology that connects houses and ensures interactions between the micro-grid and the utility. The results show that the frequency of micro-grid deficiency was significantly reduced due to the established micro-grid with the five houses according to the proposed topology and the developed multi-level management system.

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO2 emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.

Optimum utilization of grid connected hybrid renewable energy sources using hybrid algorithm

An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm plus tabu search, called AWOTS. The main objective is the RES optimum operation for decreasing the electricity production cost by hourly day-ahead and real time scheduling. Here, the load demands are predicted using AWOTS to develop the correct control signals based on power difference between source and load side. Adaptive whale optimization algorithm searching behaviour is adjusted by tabu search. The proposed technique is executed in the MATLAB/Simulink working platform. To test the performance of the proposed method, the load demand for the 24-hour time period is demonstrated. By then the power generated in the sources, such as photovoltaic, wind turbine, micro turbine and battery by the proposed technique, is analyzed and compared with existing techniques, such as genetic algorithm, particle swarm optimization and whale optimization algorithm. Furthermore, the state of charge of the battery for the 24-hour period is compared with existing techniques. Likewise, the cost of the system is compared and error in the sources also compared. The comparison results affirm that the proposed technique has less computational time (35.001703) than the existing techniques. Moreover, the proposed method is cost-effective power production of smart grid and effective utilization of renewable energy sources without wasting the available energy.

Impact of taxes and investment incentive on the development of renewable energy self-consumption: French households’ case study

Mutations in the energy sector are shifting the production and distribution systems from a centralized to a decentralized model. Within this context, we address the economics of grid-connected Hybrid Renewable Energy Systems (HRES) in relation with energy management strategies by considering taxes and economic regulation frameworks. Therefore, we propose a model to perform economic analysis of such HRES formulated as a MILP (Mixed Integer Linear Programming) model in the context of the French regulation. We test it on the case study of an integrated PV installation for French households’ self-consumption, comparing two household sizes: a two persons household with a consumption of 4000 kWh/year and a five persons household consuming 8500 kWh/year; and two solar exposures: one of 1400 h of equivalent maximum production and one of 1060 h. The main results is that the current regulation framework for incentive calculus does not encourage citizens to install a maximum of PV panels as well as it does not guarantee a uniform development of self-consumption infrastructure. Alternatively, we propose a new regulation framework in order to reverse those tendencies.

Grid Connected Hybrid Renewable Energy System with Various Controller Implementation

In power industry due to fast industrialization the generation system has upswing towards strongly procuring energy from various non-conventional energy sources (RES). Persistent work is carried out in order to use additional energy obtained from the renewable sources and limiting the dependence on the conventional energy sources. The amalgamation of various Hybrid Renewable Energy Sources (HRES) i.e. Solar, Wind and Fuel cell including load forms a Micro grid, the realistic management of energy from these renewable sources to accommodate the demand at the consumer end with proper efficiency is necessary. This paper proposes a hybrid system comprising of three energy sources PV, Wind and Fuel Cell and is connected to the grid by using power electronic converters using MATLAB/SIMULINK. A control circuit is designed by using PI controller and fuzzy logic based controller for providing gate signals to the inverter. The voltage profile when connected to a load by using various controllers is studied. A comparison study and behavior of source voltage, source current, load voltage and load current is studied by using PI controller and fuzzy logic controller.

Optimal sizing of a grid-connected hybrid renewable energy systems considering hydroelectric storage

ABSTRACTInternational policies encourage increasing penetration of renewable energy sources (RES) into the electrical grid in order to reduce greenhouse gas emissions and cover ever-increasing demands. However, the high variance of RES complicates their integration into the power system and complicates their transition from central to distributed energy sources. On the other hand, increasing the penetration of RES in electrical networks stimulates the demand for large capacity for energy storage. This paper presents a new approach to optimize the size of grid-connected renewable energy systems integrated to pumped-storage system using Salp Swarm Algorithm (SSA). This approach allows the examination of various energy sources and their combination to touch the base in the optimal configuration of the hybrid system. The impact of the system under study on the network utility is examined according to the power exchange values between the system and the network. Moreover, different scenarios are offered for optimal operation. The simulation results indicate that these hybrid systems can reduce power exchange with the grid. Furthermore, the results indicate the technical feasibility of seawater hydroelectric power plants in increasing the capacity of the electric grid to allow for high penetration of RES.

Techno-Economic Feasibility of a Grid-Connected Hybrid Renewable Energy System for a School in North-West Indonesia

Background: Schools typically have high diurnal fluctuation in electricity demand, with peak loads during daylight hours, which could be adequately met through harnessing solar renewable resources. This study demonstrates the strength of techno-economic assessment in selection and optimization of a grid-connected hybrid renewable energy system (HRES), utilizing local renewable resources to fulfil the daytime electricity demand for a school in northwest Indonesia. Methods: Three different scenarios are developed for optimizing the HRES configurations, comprising of PV panels, Wind turbine, Battery and Inverter. The following optimization parameters are used—one, technological performance of the HRES, in terms of their energy output to fulfil the energy deficit; two, economic performance of the HRES, in terms of their net present cost (NPC) and payback periods. Results: A clear trade-off is noted between the level of complexity of the three HRES, their renewable electricity generation potentials, NPC and payback periods. Scenario II, comprising of Solar PV and Inverter only, is found to be the most feasible and cost-effective HRES, with the optimized configuration of 245 kW PV capacity and 184 kW inverter having the lowest initial capital cost of US$ 51,686 and a payback time of 4 years to meet the school’s annual electricity load of 114,654 kWh. Its NPC is US$ −138,017 at the 20th year of installation. The negative value in year 20 is achieved through the sale of 40% of the renewable energy back to the grid. Conclusions: Techno-economic assessment can provide useful decision support in designing HRES relying on solar energy to serve predominantly daytime school electricity requirements in tropical countries.

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.

Optimal sizing of hybrid solar/wind/hydroelectric pumped storage energy system in Egypt based on different meta-heuristic techniques

Providing access to clean, reliable, and affordable energy by adopting hybrid power systems is important for countries looking to achieve their sustainable development goals. This paper presents an optimization method for sizing a hybrid system including photovoltaic (PV), wind turbines with a hydroelectric pumped storage system. In this paper, the implementation of different optimization techniques has been investigated to achieve optimal sizing of grid-connected hybrid renewable energy systems. A comprehensive study has been carried out between Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA), Salp Swarm Algorithm (SSA), and Grey Wolf optimizer (GWO) to validate each one. Moreover, the optimal sizing of the system's components has been studied using real-time information and meteorological data of Ataka region located in Egypt. The purpose of the optimization process is to minimize the cost of energy from this hybrid system while satisfying the operation constraints including high reliability of the hybrid power supply, small fluctuation in the energy injected to the grid, and high utilization of the photovoltaic and wind complementary properties. MATLAB software package has been used to evaluate each optimization algorithm for solving the considered optimization problem. Simulation results proved that WOA has the most promising performance over other techniques.

A New Approach for Grid-Connected Hybrid Renewable Energy System Sizing Considering Harmonic Contents of Smart Home Appliances

Even renewable energy sources provide several advantages, especially from an environmental point of view, where the world has faced great challenges in the last few decades; several negative issues also exist regarding the integration of renewable resources-based power production units in electric power systems. One of the main problems related to pivotal renewable energy resources such as solar, wind, etc., is their stochastic and uncontrollable nature in terms of power production. Therefore, this stochasticity in the supply side of the power system may pose many challenges for system operators. This issue is also problematic for smaller applications where the stochastic production by a main resource, such as a roof-top photovoltaic system, and load demand may not match perfectly at each time instant and therefore should be compensated by additional resources such as battery-based energy storage systems. Herein, the economic considerations to ensure minimum costs for such a hybrid system design are vital so as to increase the penetration of such systems. Therefore, the optimal sizing and planning of hybrid systems have recently gained increasing importance to enhance power system operation in the context of the smart grid paradigm. From a different perspective, harmonics are one of the most important power quality problems in system operations caused by widespread integration of power electronic loads with non-linear characteristics that should be considered. Thus, a new approach for grid-connected hybrid renewable energy system sizing is provided. In order to determine optimal capacities for photovoltaic (PV) and energy storage system (ESS) units for covering residential consumer demand, a mixed integer linear programming (MILP)-based formulation is presented. The main objective is minimizing total costs of the system consisting of investment, capital and maintenance cost functions. A daily power curve is created accurately with real measurements of non-linear loads considering harmonic contents of smart home appliances in Yildiz Technical University, Istanbul, Turkey. In addition, real radiation and temperature values are used in PV production as well as dynamic pricing schemes for realistic evaluations. Moreover, optimal sizing results are compared for both the harmonic-based power curve and rated power curve in terms of satisfying objective function.

Adaptive power management strategy-based optimization and estimation of a renewable energy storage system in stand-alone microgrid with machine learning and data monitoring

This work is carried out with the optimum design of a stand-alone hybrid energy storage system (HESS) based on solar, wind and super capacitor (SC) with battery storage system which is effectively optimized in the grid power system. The distribution of the power source is mainly considered on the Hybrid renewable energy power sources. This discourse presents an adaptive power optimizing the three-phase inverter and grid-connected hybrid renewable energy resources efficiently. In this analysis, the similar parameters are taken for the compensation such as voltage fluctuation, harmonics and Frequency imbalance by implementing Adaptive Power Management Strategy (APMS) and the obtained issues are synchronized by inverter control. All these comparative activities of the inverter are done either discretely or combined to stabilize the unbalanced impacts of a wide range of adjusted, uneven, power loss at the circulation level. A battery and SC energy management are essential for maintaining the energy sustainability in renewable energy system. Combination of solar and wind with the battery and SC is used to test the proposed stand-alone grid management. The proposed hybrid power system is designed to work under classical-based energy management and this performance is monitored with the help of the Internet of Things (IoT) and machine learning based on Polynomial Linear Regression Algorithm. The focus of the suggested HESS is reduced by the loss in stand-alone grid system with an economic performances.

Modified Instantaneous Power Theory and Fuzzy Logic Based Controller for Grid-connected Hybrid Renewable Energy System with Shunt Active Power Filter Functionality

Modified Instantaneous Power Theory and Fuzzy Logic Based Controller for Grid-connected Hybrid Renewable Energy System with Shunt Active Power Filter Functionality

Integration and energy management of a hybrid Li-VRB battery for renewable applications

Energy storage systems are becoming more and more important for integrating higher levels of varying and intermittent renewable power into the grid. As we have energy storages with different characteristics and applications, hybrid storage solutions combining various technologies to meet different applications are fast developing. In this paper, an efficient energy management strategy is proposed for a VRB and Li-ion hybrid battery system to be integrated with a grid-connected wind-PV Hybrid Renewable Energy System (HRES). The operation of the generating system and the storage batteries are evaluated using Matlab/Simulink and techno-economic analysis is carried out. The results demonstrated successful implementation of the hybrid system with improved deliver ability and storage lifetime.

Optimal control strategies for a hybrid renewable energy system: an ALANN/RNN technique

In this paper, optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the parallel execution of both the ant lion optimization with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. Here, the HRES is made out of photovoltaic, wind turbine, fuel cell, and battery which is associated with the DC link and can adjust the real and reactive power. The proposed ALANN/RNN technique predicts the required control gain parameters of the HRES to maintain the power flow based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy accessibility and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. In the HRES unit, the power flow management of grid is accomplished while controlling the PI controller for producing the optimal control pulses of the DC/DC converter. The proposed method is actualized in MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques such as SPC, GA, PSO and BAT technique. The comparison results demonstrate that the occurrence of proposed approach confirms its ability for controlling the power flow in the HRES system. An optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the joined execution of both the ant lion optimization (ALO) with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. ALO is utilized to minimizing the power variations of the HRES units. ANN is used for real power prediction, and RNN is utilized for reactive power prediction.

A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization

Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy systems usually hinges on different economic and environmental criteria. This paper used the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimally size ten grid-connected hybrid blocks selected amongst Photo-Voltaic (PV) panels, onshore wind turbines, biomass combustion plant using sugarcane bagasse, Battery Energy Storage System (BESS), and Diesel Generation (DG) system as backup power, to reduce the supply deficit in Sierra Leone. Resource assessment using well-known methods was done for PV, wind, and biomass for proposed plant sites in Kabala District in Northern and Kenema District in Southern Sierra Leone. Long term analysis was done for the ten hybrid blocks projected over 20 years whilst ensuring the following objectives: minimizing the Deficiency of Power Supply Probability (DPSP), Diesel Energy Fraction (DEF), Life Cycle Costs (LCC), and carbon dioxide (CO 2 ) emissions. Capacity factors of 27.41 % and 31.6 % obtained for PV and wind, respectively, indicate that Kabala district is the most feasible location for PV and wind farm installations. The optimum results obtained are compared across selected blocks for DPSP values of 0–50% to determine the most economical and environmentally friendly alternative that policy makers in Sierra Leone and the region could apply to similar cases.

Power Flow Management of the Grid-Connected Hybrid Renewable Energy System: A PLSANN Control Approach

This paper clarifies the optimal control strategy of Hybrid Renewable Energy System (HRES), which has the parallel execution of Lightning Search algorithm with Artificial Neural Network and Recurrent Neural Network (PLSANN). The projected system is composed of Photovoltaic (PV), Wind Turbine (WT), Fuel Cell (FC), and Battery, which can associate DC link and competent to balance the real and reactive power. The introduction of wind/PV power within an electric grid origin the PQ troubles are considered. At this point, the recompense policy of DC/DC converter is examined by PLSANN/RNN technique to provide the optimal power flow management of HRES. Here, LSA is exploited for the optimization process of real power and the Recurrent Neural Network (RNN) is utilized for the optimal reactive management. The projected process recognizes the finest control pulses of the DC/DC converter derived from the foundation part and load part limitation. The projected system is competent to establish the active power into the grid and additionally it is capable of enhancing the PQ. With proper control, HRES significantly enhance the dynamic security of the power system. The proposed method is implemented in MATLAB/Simulink working platform and analyzed their performances. The statistical analysis of proposed method is analyzed in terms of mean, median and standard deviation factors. In order to prove the effectiveness, the proposed method is compared with current techniques such as Combined Modified Bat Search algorithm–artificial neural network (CMBSNN) technique, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and PI controller.

Power flow control of grid connected hybrid renewable energy system using hybrid controller with pumped storage

This paper presents a grid-connected HRES using a hybrid controller with PHS for optimal power flow control and minimizing the production cost. The novelty of the proposed approach is the joined execution of the SSA and CSA named as SSA-CS are apparently a very new metaheuristic algorithm. Moreover, the proposed method is the cost-effective power production of the microgrids and effective utilization of renewable energy sources without wasting the available energy. Here, the energy sources in particular PV system, WT, MT and battery with PHS are utilized to generate the power of the MG system. In the proposed approach, the required power demand of the energy system is predicted by the ANN technique. After that, the production cost minimization is done in view of the anticipated load demand by utilizing the optimization approaches to be a specific SSA-CS algorithm. The result of the proposed approach is actualized in the MATLAB/Simulink working platform. The performance of the proposed approach is examined by comparing the current methodologies such as SSA and PSO with the proposed SSA-CS approach. The simulation results show that the proposed method generates maximum power and furthermore the proposed framework has less production cost in light of the power demand.

Analysis of grid connected hybrid renewable energy system

This paper proposes a DC-linked hybrid renewable energy system (HRES) connected to the grid through a three level neutral point clamped converter. The proposed structure consists of a wind generator (with Maximum Power Point Tracking (MPPT)), power limitation, and pitch control modes) with a flywheel energy storage system, a photovoltaic generator (MPPT and power limitation modes) with a battery, and a super-capacitor connected to each capacitor of the DC link of the three level converter. With the aim of managing the power flow between the HRES, the grid, and the variable load demand, we designed a power management strategy according to the global state of charge of the storage system, the weather conditions, and the load demand. In order to show the efficiency of the proposed structure and management algorithm, numerical simulation was performed using MATLAB/Simulink.

Modified Farmland Fertility Optimization Algorithm for Optimal Design of a Grid-connected Hybrid Renewable Energy System with Fuel Cell Storage: Case Study of Ataka, Egypt

In this paper, a Modified Farmland Fertility Optimization algorithm (MFFA) has been presented for optimal sizing of a grid connected hybrid system including photovoltaic (PV), wind turbines and fuel cell (FC). The system is optimal designed for providing a clean, reliable and affordable energy by adopting hybrid power systems. This system is very important for countries looking to achieve their sustainable development goals. MFFA is proposed in order to reduce the processing implementation time. The optimization method depends on the high reliability of the hybrid power supply, small fluctuation in the injected energy to the grid and high utilization of the wind and solar complementary characteristics. Moreover, MFFA is applied to minimize the cost of energy while satisfying the operational constraints. A real case study of a hybrid power system for Ataka region in Egypt is introduced to evaluate the performance of the proposed optimization method. Moreover, a comprehensive comparison between the proposed MFFA optimization technique and the conventional Farmland Fertility Algorithm (FFA) has been presented to validate the proposed MFFA.

Optimal sizing of hybrid PV and wind energy system with backup of redox flow battery to postpone grid expansion investments

In this study, economical options to meet the load demand of Hormoz Island as a green Island in future years, located in Iran, are investigated. For this purpose, construction of a new undersea transmission line (UTL) and design of a grid-connected hybrid renewable energy system (RES) are selected as two possible options. Therefore, the optimization problem is modeled as a minimization of cost function including the investment, replacement, operation, and maintenance costs of components, carbon tax, and cost of purchased power from the upstream network as well as the cost of loss of load. It is notable that the grid-connected hybrid RES consists of photovoltaic (PV) and wind energy systems with consideration of redox flow battery storage. The optimization problem is simulated for a 20 year planning horizon subject to technical and geographical constraints of RES. To simulate the problem, the annual hour-based data of wind, solar radiation, and load demand of Hormoz Island are used. Also, wind energy potential of the region is used for the selection of the best turbine for this island. Moreover, an imperialist competitive algorithm and an improved particle swarm optimization algorithm are applied to find the best size and design of the hybrid RES and also the best time to use the RES and the new UTL. Three different scenarios for optimum hybrid RES designing are determined to consider the effects of using the hybrid system to postpone the grid expansion investments. Two of these scenarios have 20 year planning horizon, and one of them optimizes the system for a special year with a specific predicted load demand. The obtained results revealed that the wind/PV/vanadium redox flow battery hybrid system is the best configuration, where the new UTL will be constructed in 2020 with zero load energy shortage.