Renewable Energy Sources System Research Articles

Interfacing Standalone Loads With Renewable Energy Source and Hybrid Energy Storage System Using a Dual Active Bridge Based Multi-Port Converter

Batteries used for providing backup power in renewable energy sources (RES) fed standalone power systems often suffer from a limited lifetime because of high charging and discharging currents arising from sudden changes in load and/or generation. To prevent this, supercapacitors (SCs) are used to supply the high-frequency (HF) power surges. In this article, a new way of interfacing hybrid energy storage systems (HESSs) (Battery + SC) with RES and loads by employing a current fed dual active bridge (DAB) converter based multi-port converter (MPC) is proposed. The proposed approach enables the MPC to regulate the load voltage while tracking the maximum power point (MPP) of the RES and protecting the battery from transients arising from variations in load and/or renewable generation. The SC is used for tracking MPP so that the transient changes in RES generation do not cause a sudden rise/fall in the battery current. The employed MPC structure is designed in such a way that most of the power processing takes place in one stage only resulting in lower losses. The number of switches is reduced by merging the power conversion stages, which helps in keeping the cost low. Experimental results on a laboratory prototype under various static and dynamic conditions are presented.

Power Quality of Renewable Energy Source Systems: A New Paradigm of Electrical Grids

The power quality delivered by the distribution companies to consumers has always been a relevant issue, especially to industrial consumers, where power quality is directly related to productivity [...]

Hybrid solar photovoltaic thermal systems in Nearly-Zero Energy Buildings: the case of a residential building in Greece

ABSTRACT The continuous increase in energy demand has led to the gradual depletion of fossil fuels and the corresponding environmental degradation. As a result, the implementation of energy efficiency measures, as well as the integration of Renewable Energy Sources systems, are a challenge on the road towards Nearly-Zero Energy Buildings. Within the framework of this study, an extensive literature review was carried out on the existing Photovoltaic/Thermal collector technologies, their energy, environmental and economic efficiency. Furthermore, a solar combi system with photovoltaic/thermal collectors installed in a residential building was studied. The system was evaluated for its energy and environmental efficiency. Finally, the results in terms of thermal efficiency and carbon footprint were compared with those of a solar combi system with a flat plate collector.

A Multilevel Inverter Topology With an Improved Reliability and a Reduced Number of Components

The reliabilities of the renewable energy source systems are affected by their inverter structures. So, an inverter topology with higher reliability reduces the maintenance cost of the system. In this article, a multilevel inverter structure is proposed with improved reliability and reduced component count. The proposed structure produces 7 and 11 voltage levels in symmetrical and asymmetrical configurations, respectively. This inverter topology utilizes six unidirectional switches and one bidirectional switch. The proposed inverter structure can be extended to generate any number of voltage levels with the inherent property of producing both positive and negative voltage levels. Various comparisons including the total number of switches, the total number of switch drivers, and the total blocking voltage are done to show the effectiveness of the proposed structure in the reduction of the component counts. In addition, the reliability of the proposed structure is analyzed and comparisons with other counterpart structures are studied. The comparison results show the reliability improvement of the proposed inverter structure. Finally, the simulation results, extracted by MATLAB/Simulink, and the experimental results, obtained from a laboratory prototype, are provided to prove the feasibility of the proposed multilevel inverter (PMI) structure.

Home Energy Management and Control System With Renewable Energy Source And Energy Storage System

Home Energy Management and Control System With Renewable Energy Source And Energy Storage System

Innovative Control of the Development of an Ergatic Electric Power System Based on Fuzzy Logic

This article deals with the search for a palliative optimal solution to the optimization of the electric power problem. A mathematical model of planning by the development of a hierarchical ergatic electric power system is proposed to be presented as an optimization problem of minimizing vectors. An innovative direction in modeling the control system of an ergatic electric power system based on artificial neural networks is also considered. It is noted that intelligent control systems for power supply systems are robust. The innovative concept of a smart power system is due to the integration into a hierarchical power system of renewable energy sources based on modern computer technologies. It is noted that during the operation of an intelligent electric power system, control systems must provide adaptive qualities in relation to the topology of the power supply system. It is proposed to investigate the solution to the optimization of electric power problems using methods of fuzzy mathematical programming.

Design optimization of multi energy systems for domestic hot water uses on the building sector

The burden that has come upon the environment, combined with the ever-declining fossil fuel reserves, has led to the need of reducing the conventional energy consumption in building sector and to the promotion of systems based on Renewable Energy Sources (RES). This paper deals with the optimization of multi energy systems in order to cover the needs of hot water in domestic use. In particular, integer linear programming models are formulated and the optimal solutions regarding the degree of participation of the multi energy systems are explored; economic, energy, and environmental criteria are assumed. The respective mathematical programming approaches include linear objective functions, multiple objective functions that either do or do not use weights, as well as goal programming-based ones. The modeling and solution of the problems is done with the General Algebraic Modeling System (GAMS). The case study refers to residential use; both conventional and RES systems are selected for the respective energy demand coverage. The time step of the analysis is 1 hour, in the context of annual operation. According to the results, in the case of the energy criterion, biomass predominates, or heat pumps, when biomass is not included, with an increase in participation of solar thermal collectors when the environmental criterion is introduced. The participation of solar thermal factor is also reinforced in the case of goal programming, because of the relaxation of the initial targets. The analysis demonstrated that the existing integer linear programming methodological tools can be used for investigating problems of multiple energy systems or comparing subsystems.

Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN

This paper presents a multi-agent Double Deep Q Network (DDQN) based on deep reinforcement learning for solving the transmission network expansion planning (TNEP) of a high-penetration renewable energy source (RES) system considering uncertainty. First, a K-means algorithm that enhances the extraction quality of variable wind and load power uncertain characteristics is proposed. Its clustering objective function considers the cumulation and change rate of operation data. Then, based on the typical scenarios, we build a bi-level TNEP model that includes comprehensive cost, electrical betweenness, wind curtailment and load shedding to evaluate the stability and economy of the network. Finally, we propose a multi-agent DDQN that predicts the construction value of each line through interaction with the TNEP model, and then optimizes the line construction sequence. This training mechanism is more traceable and interpretable than the heuristic-based methods. Simultaneously, the experience reuse characteristic of multi-agent DDQN can be implemented in multi-scenario TNEP tasks without repeated training. Simulation results obtained in the modified IEEE 24-bus system and New England 39-bus system verify the effectiveness of the proposed method.

Power Quality Improvement in HRES Grid Connected System with FOPID Based Atom Search Optimization Technique

An intelligent control strategy is proposed in this paper which suggests the Optimum Power Quality Enhancement (OPQE) of grid-connected hybrid power systems with solar photovoltaic, wind turbines, and battery storage. Unified Power Quality Conditioner with Active and Reactive power (UPQC-PQ) is designed with Atom Search Optimization (ASO) based Fractional-order Proportional Integral Derivative (FOPID) controller in the proposed Hybrid Renewable Energy Sources (HRES) system. The main aim is to regulate voltage while reducing power loss and reducing Total Harmonic Distortion (THD). UPQC-PQ is used to mitigate the Power Quality (PQ) problems such as sag, swell, interruptions, real power, reactive power and THD reductions related to voltage/current by using ASO based FOPID controller. The developed technique is demonstrated in various modes: simultaneous to improve PQ reinforcement and RES power injection, PRES > 0, PRES = 0. The results are then compared to those obtained using previous literature methods such as PI controller, GSA, BBO, GWO, ESA, RFA, and GA and found the proposed approach is efficient. The MATLAB/Simulink work framework is used to create the model.

Frequency Regulation of Multi Area Renewable Energy Source system with Practical Constraints under Fractional-order Fuzzy Controller

This paper introduces a novel control strategy of fractional order (FO) fuzzy (F) PID (FOFPID) controller optimized with the latest soft computing technique of seagull optimization algorithm (SOA) for power system frequency regulation. Initially, a simple and widely accepted power system of dual area photovoltaic (PV) and reheat thermal (RT) (PVRT) system is perceived and named as test system-1 in this paper. The performance of FOFPID fine-tuned with SOA mechanism is tested on PVRT system for a step load disturbance of 10% (SLD) on area-2 along with other controllers reported in the literature. Dynamical analysis of the PVRT system reveals the potency of the proposed controller over others. Further, the SOA based FOFPID controller is extended to frequency regulation of multi-area system with hybrid generating sources (MAHS) named as test system-2 in this paper for 10% SLD on area-1. MAHS system is constituted with realistic constraints to conduct research close to realistic practice. The potentiality of SOA based FOFPID is demonstrated by comparing it with traditional controllers of PID/FPID/FOPID on MAHS system. Finally, robustness analysis is perpetuated to reveal the presented control scheme robustness.

Converter-Driven Stability Analysis of Power Systems Integrated with Hybrid Renewable Energy Sources

Renewable energy sources such as wind power and photovoltaics (PVs) have been increasingly integrated into the power system through power electronic converters in recent years. However, power electronic converter-driven stability has issues under specific circumstances, for instance, modal resonances might deteriorate the dynamic performance of the power systems or even threaten the overall stability. In this work, the integration impact of a hybrid renewable energy source (HRES) system on modal interaction and converter-driven stability was investigated in an IEEE 16-machine 68-bus power system. In this paper, firstly, an HRES system is introduced, which consists of full converter-based wind power generation (FCWG) and full converter-based photovoltaic generation (FCPV). The equivalent dynamic models of FCWG and FCPV are then established, followed by linearized state-space modeling. On this basis, converter-driven stability analysis was performed to reveal the modal resonance mechanisms between different renewable energy sources (RESs) and weak grids in the interconnected power systems and the multi-modal interaction phenomenon. Additionally, time-domain simulations were conducted to verify the effectiveness of dynamic models and support the converter-driven stability analysis results. To avoid detrimental modal resonances, a multi-modal and multi-parametric optimization strategy is further proposed by retuning the controller parameters of the multi-RESs in the HRES system. The overall results demonstrate the modal interaction effect between the external AC power system and the HRES system and its various impacts on converter-driven stability.

An interval scheduling method for the CCHP system containing renewable energy sources based on model predictive control

Scheduling strategies are crucial for combined cooling, heating, and power-containing renewable energy source (RES-CCHP) systems to realize cost-effective operation. However, the prediction error generated by renewable energy sources (RES) and load leads to mismatch between the scheduling plan and actual demand. To address this problem, an interval scheduling method is proposed for the RES-CCHP system based on the model predictive control method. A genetic algorithm to optimize a back propagation (GA-BP) neural network combined with historical and weather data is used to predict the multistep RES and load to improve prediction accuracy. An interval optimization model (cost optimization) is then constructed based on RES and load prediction data to achieve an output consistent with the actual situation. Finally, feedback correction is used to compensate the RES and load prediction data online. The prediction error and interval are reduced, thereby improving the performance of the interval optimization method in achieving an optimal solution. This research is applicable to living spaces such as residential areas, schools, and hospitals. In this study, a hospital was considered as a case study to verify the effectiveness of the proposed method. A comparison of the results of the proposed method with those obtained in the following electrical load and following thermal load modes in a typical summer day revealed a cost reduction of 16.21% and 16.92%, respectively. The proposed method can improve the prediction accuracy of the RES and load of the RES-CCHP system and help decision makers to develop reasonable operation schemes and maximize profits.

Research Advancement of Energy to Molecules and Materials (E2M2) at Idaho National Laboratory

Integrated Energy System (IES) is a core initiative at Idaho National Laboratory where energy to molecules and materials (E2M2) pillar plays a critical role, due to the widely available distributed feedstock, energy input (renewable and/or nuclear energy associated electricity and excess heat) as well as the resulting industrial electrification. These factors offer new opportunities to those technologies with smaller scale, modularized/distributed, and driven by an integrated system of renewable and other energy sources that can show promise for increased productivity with decreased energy, capital, and operating costs. Intermediate temperature electrochemical processing through the solid oxide cells is a promising strategy because it can efficiently utilize electrons and heat while overcoming and/or mitigating the challenges of their lower and higher temperature counterparts, including 1) the use of precious metal; 2) sluggish kinetics; 3) fast degradation; 4) costly interconnect materials. Despite of these promising features, this technology still faces many challenges that include but not limited to immature process, relatively low catalyst activity, and unproven operation durability. At Idaho National Laboratory, we have made significant achievements on electrosynthesis of intermediates, fuels and chemicals using natural gas, steam, carbon dioxide and nitrogen through protonic ceramic membrane reactors. The research outcomes have the potential to establish a globally competitive U.S. industry for converting the Nation’s abundant supply of feedstock into conversion-ready intermediates and/or high demand fuels and chemicals.

Recent Advances on Materials for Lithium-Ion Batteries

Environmental issues related to energy consumption are mainly associated with the strong dependence on fossil fuels. To solve these issues, renewable energy sources systems have been developed as well as advanced energy storage systems. Batteries are the main storage system related to mobility, and they are applied in devices such as laptops, cell phones, and electric vehicles. Lithium-ion batteries (LIBs) are the most used battery system based on their high specific capacity, long cycle life, and no memory effects. This rapidly evolving field urges for a systematic comparative compilation of the most recent developments on battery technology in order to keep up with the growing number of materials, strategies, and battery performance data, allowing the design of future developments in the field. Thus, this review focuses on the different materials recently developed for the different battery components—anode, cathode, and separator/electrolyte—in order to further improve LIB systems. Moreover, solid polymer electrolytes (SPE) for LIBs are also highlighted. Together with the study of new advanced materials, materials modification by doping or synthesis, the combination of different materials, fillers addition, size manipulation, or the use of high ionic conductor materials are also presented as effective methods to enhance the electrochemical properties of LIBs. Finally, it is also shown that the development of advanced materials is not only focused on improving efficiency but also on the application of more environmentally friendly materials.

Performance improvement of hybrid renewable energy sources connected to the grid using artificial neural network and sliding mode control

The main purpose of this paper to compare and analyze three types of controllers in the three phases DC–AC inverters in hybrid renewable energy source (HRES) systems. To achieve this, two modern controllers are developed and compared based on sliding mode control (SMC) and artificial neural network techniques. The HRESs comprise photovoltaic (PV), wind turbines, battery storage systems, and transmission lines connected to infinite bus bars via a step-up transformer. The developed controllers at the inverter side utilize both voltage control and current regulation. A DC–DC boost converter is employed to set up a voltage demand at the point of common coupling (PCC). Then, the formulation of an HRES with the developed controllers is presented. The developed controllers are considered to operate under various solar radiations, temperatures, and wind speed loading conditions. The HRESs with the developed controllers are simulated via MATLAB/Simulink to verify the effectiveness of the developed controllers. The obtained results demonstrate that adaptive SMC and artificial ANN control techniques give better results in terms of input power, output power, current, and voltage when compared to classic PI control.

A review of optimization methods for economic and emission dispatch considering PV and wind energy

The power generation system has fossil fuels as their major electricity generation source. This source is not reliable and environmentally friendly so, the alternative source such as renewable energy (RE) is getting much attention in the development of a power generation system. To achieve optimal power output for the power system there is a necessity to solve the economic and emission dispatch (EED) problem considering the operational system constraints. Numerous types of research on optimization of fossil fuel cost and emission roles have been focused to solve the concerned power dispatch issue. This paper represents a comprehensive study on combined economic and emission dispatch (CEED) problem with respect to conventional and unconventional energy sources. To optimize the power dispatch problem, the existing algorithm consisting of optimization methods and objective functions in integrated renewable energy sources (RES) system is tested and reviewed. This paper presented various optimization methods applied to different test systems to minimize emission and cost for power dispatch with PV and wind. Future trends and necessary steps next in the field of power dispatch are also been deliberated.

Computer application for studies of potentials of renewable energy sources

Computer science's technologies have come to revolutionize the current era, the distributed generation and the introduction of new alternatives in the conception of the energy production, it is a factor that seen from the energy planning it allows to introduce the renewable energy sources as an element in the development of energetically sustainable territories. The space distribution of the natural resources that they generate, it has made necessary to develop advanced systems for the studies at the space level. For the necessities of printing an analysis space and territorial of the renewable energy sources studies, a geographical information system of renewable energy sources was designed using free software that offers information of the renewable potentials in the territory. This system is a tool that allows an appropriate way to offers information of the place (longitude and latitude) and the potentials (solar, hydric, wind, and biomass) that are generated in them.

Two cases studies of Model Predictive Control approach for hybrid Renewable Energy Systems

<abstract><p>This work presents a load frequency control scheme in Renewable Energy Sources(RESs) power system by applying Model Predictive Control(MPC). The MPC is designed depending on the first model parameter and then investigate its performance on the second model to confirm its robustness and effectiveness over a wide range of operating conditions. The first model is 100% RESs system with Photovoltaic generation(PV), wind generation(WG), fuel cell, seawater electrolyzer, and storage battery. From the simulation results of the first case, it shows the control scheme is efficiency. And base on the good results of the first case study, to propose a second case using a 10-bus power system of Okinawa island, Japan, to verify the efficiency of proposed MPC control scheme again. In addition, in the second case, there also applied storage devices, demand-response technique and RESs output control to compensate the system frequency balance. Last, there have a detailed results analysis to compare the two cases simulation results, and then to Prospects for future research. All the simulations of this work are performed in Matlab®/Simulink®.</p></abstract>

Profit Maximization for EVSEs-Based Renewable Energy Sources in Smart Cities With Different Arrival Rate Scenarios

This paper proposes a profit maximization model for a Decentralized Electric Vehicle Supply Equipment (D-EVSE) equipped with a renewable energy sources system such as a solar energy system. We acknowledge a power connection to the central power grid when necessary. We allow EV to offer its surplus power as discharging power processes. We design a Decentralized Profit Maximization Algorithm (DPMA) to help D-EVSEs take profit from the electricity price variation during the day when selling or buying electricity respectively to EVs or from the grid or EVs as discharging processes. Finally, numerical simulations with MATLAB are conducted to prove the effectiveness of our proposed solution.

Adaptive Robust Control Based on System Identification in Microgrid Considering Converter Controlled-Based Generator Modes

Microgrids with installation of converter controlled-based generations (CCGs), i.e. renewable energy source (RES) and battery energy storage system (BESS), may lead to a lack of system inertia. Moreover, CCG modes dominated by the CCGs may result in new control issues. Accordingly, optimal-fixed controllers designed at an operating point may not be sufficient to deal with the nonlinearity of such microgrids. This paper presents an adaptive robust control strategy of a future microgrid considering the CCG modes. Without requiring any microgrid parameters, the proposed control using a subspace-based state-space identification is used to 1) monitor microgrid changes along with moving window, 2) identify microgrid model, 3) assess stability indices, and 4) robustly design controllers of RES and BESS. Characteristic and sensitivity of the new CCG modes are analyzed. Effectiveness of the proposed control method is verified in a microgrid with 100% CCGs under various RES outputs and load patterns.