Integration Of Renewable Energy Resources Research Articles

Mapping RO-Water Desalination System Powered by Standalone PV System for the Optimum Pressure and Energy Saving

Brackish water desalination is widely used to supply fresh water; reverse osmosis (RO) desalination units are considered as the most widespread technology used for this purpose due to the advantage of low power consumption. On the other hand, renewable energy resource integration into the power systems is an important trend, which serves energy supply especially in rural areas and non-stable power supply places. RO units powered from Photovoltaic (PV) systems are considered one of the reliable solutions in places where both water and energy demands are issues to be improved. In this research, the idea of storing energy in water salinity is introduced and discussed to reduce conventional battery storage banks. This concept depends on changing the pressure of the RO unit based on solar profiles to get high distilled water at high solar radiation times (high pressure applied) and low distilled water at low radiation times (low pressure values). Then, the produced water is mixed to get an acceptable salinity in the produced water. This research was applied on a small-scale RO testing unit with a pressure that changed from 40 to 60 bar, and, as a result, the water conductivity changed from 1.7 to 1.1 mS/cm. This was the base line to investigate the possibility of curtailing the battery storage system of the selected plant. Following the variable pressure scenarios, energy storage capacity was reduced by a factor of 20%.

Integrating Renewable Energy Systems into Urban Furniture for Recreational Spaces: A Design Proposal for Konya Adalet Park

Recreational areas are designed to promote physical activities as well as provide opportunities for relaxation. Recreational open spaces improve the long-term environmental and social sustainability of cities. These areas offer natural beauty in and around cities while sustaining the environmental equilibrium of city life. They also serve as a socialization hub for people to make contact with each other. They can also be promoting areas for sustainability. Introducing the use of technology in public areas will promote its use in daily life also. One area of technological advances that needs to be promoted for establishing sustainable communities is the integration of renewable energy resources for our daily needs. This study focuses on difficulties and strategies for the integration of renewable energy in recreational areas and evaluates a recreational open space; i.e. Adalet Park in Konya in terms of possible applications of renewable energy sources. Since solar energy potential is high in Konya PV integration is the best option for the area. On the other hand, wind power is not sufficient for producing energy with wind turbines, hence, wind belts and small scale wind mills can be integrated into the park furniture. To this end, the design of various urban furniture with integrated renewable energy systems was prepared for the Karatay Adalet Park in Konya; and the amount of energy that can thus be produced was determined. According to calculations using wind and solar data and a software, it is possible to produce 50 MWh/a of electrical energy with these installations.

Impact of the Weather on the Combined Economic and Emission Optimization Problem of MG

The high integration of renewable energy resources and utilizing of storage devices with possible exchange power with the utility grid make the optimization problem of microgrid (MG) significantly difficult comparing with the conventional systems. This paper quantifies the impact of weather conditions on the combined economic and emission optimal operation of the MG. The optimization problem is formulated as mixed integer quadratic program (MIQP), where the unit commitment (UC) is taken into consideration in the optimization problem. In addition, realistic constraints are taken into account to make the problem close to the real scenario. The proposed optimization approach is applied to the low voltage MG, which includes distributed generators (DGs), renewable energy sources (wind, solar) and storage battery. The results reveal that the abundant of wind and solar energies leads to reduce the operation and emission cost.

An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting

Due to high penetration of distributed energy resources, integration of intermittent renewable energy resources and deployment of demand-side management, highly accurate short-term load forecasting becomes increasingly important. This paper proposes a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The full wavelet packet transform model is used to decompose the load profile and various features into several components with different frequencies and these components are used to train the neural networks. To perform load forecasting, the full wavelet packet transform model decomposes features into various components that are fed into the trained neural networks, and the outputs of the neural networks are constructed as the forecasted load. The proposed model is applied for load prediction in the electric market of Ontario, Canada. Simulation results show that the proposed approach reduces the mean absolute percentage error (MAPE) by 20% in comparison with the traditional neural network method. The proposed approach can be used by utilities and system operators to forecast electricity consumption with high accuracy, which is highly demanded for renewable energy integration, demand-side management and power system operation.

Improved virtual synchronous generator control to analyse and enhance the transient stability of microgrid

In recent years, the integration of renewable energy resources (RESs) into the power system is growing rapidly, and it is necessary to analyse and evaluate the effect of RES on transient stability of the power system. In this study, centre of inertia (COI) concept is implemented to analyse and evaluate the integration effects of an auxiliary damping control-based virtual synchronous generator (VSG) consisting an improved governor. The impact of VSG integration is divided into synchronous generator (SG) linked parts and COI associated parts. Due to VSG integration into the power system, the significant elements which disturb the COI dynamic motion and rotor dynamics of SG are examined in detail. Different cases are considered to evaluate the effectiveness of the proposed method, i.e. VSG's different integrating location and different power capacities. It is observed in simulation results that COI dynamic motion and rotor dynamics of SG are positively affected by VSG integration, and transient stability improves significantly.

Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions

Energy storage systems with Li-ion batteries are increasingly deployed to maintain a robust and resilient grid and facilitate the integration of renewable energy resources. However, appropriate selection of cells for different applications is difficult due to limited public data comparing the most commonly used off-the-shelf Li-ion chemistries under the same operating conditions. This article details a multi-year cycling study of commercial LiFePO4 (LFP), LiNixCoyAl1−x−yO2 (NCA), and LiNixMnyCo1−x−yO2 (NMC) cells, varying the discharge rate, depth of discharge (DOD), and environment temperature. The capacity and discharge energy retention, as well as the round-trip efficiency, were compared. Even when operated within manufacturer specifications, the range of cycling conditions had a profound effect on cell degradation, with time to reach 80% capacity varying by thousands of hours and cycle counts among cells of each chemistry. The degradation of cells in this study was compared to that of similar cells in previous studies to identify universal trends and to provide a standard deviation for performance. All cycling files have been made publicly available at batteryarchive.org, a recently developed repository for visualization and comparison of battery data, to facilitate future experimental and modeling efforts.

Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives.

Power flow solution in direct current grids using the linear conjugate gradient approach

The Colombian power system is being modified by the large-scale integration of renewable energy resources and energy storage systems, in conjunction with the microgrid concept that originates the possibility of alternating and direct current grids or hybrid between them. Here, we propose a classical gradient conjugate method to solve linear algebraic equations without matrix inversions, to address the power flow problem in electrical direct current networks with constant power loads, to contribute with the paradigm of microgrids operated in direct current. This methodology can be applied to the power flow equations since the admittance matrix is positive definite and diagonal dominant which guarantees convergence of the power flow problems. Numerical simulations evidence the applicability of the gradient conjugate method to solve power flow problems in direct current networks with radial and mesh topologies. All the simulations are conducted in MATLAB software version 2017a licensed by the Universidad Tecnológica de Bolivar, Colombia.

On the nonlinear control of a single-phase current source converter for sinusoidal voltage generation

Colombian power system is being transformed for the large-scale integration of renewable energy resources and energy storage systems; all of these are integrated by power electronic converters controlled by voltage and current sources. In addition, for regulatory policies it is required that the alternating current networks that feeds linear and nonlinear loads, can provide pure sinusoidal voltage and current forms in terms of frequency and amplitude. To guarantee it, in this paper the design of a nonlinear controller for a single-phase current source converter is explored, which is switched through a pulse-width modulated signal for providing sinusoidal voltages on linear loads. It is applied a feedback nonlinear control design on the dynamical model of the converter by using its averaged representation. The proposed control strategy allows guaranteeing asymptotic stability in the sense of Lyapunov for closed-loop operation. In steady state conditions the voltage behavior on the linear load evidences a sinusoidal form with an estimation error lower than 0.667%, which can be considered negligible for any practical power system application. All the simulations are conducted via MATLAB software 2017b licensed by Universidad Tecnologica de Bolívar, Colombia.

Prospective expansion of the ENTSO-E transparency platform to include TSO–DSO interaction and wider market participation

The increasing integration of renewable energy resources connected to the distribution networks has led to an increased installed capacity of distributed generation. In this context, new technical and operational challenges have arisen concerning data exchanges between transmission system operators (TSOs) and distribution system operators (DSOs), requiring scalable, secure and standardised data exchange between them at different timeframes. The aim of this study is to investigate prospects for wider usage of the transparency platform developed by the European Network of Transmission System Operators for Electricity and critically evaluate them with its information and data requirements. The evaluated prospects are based on the designed use cases of Work Package 2 in the TDX-ASSIST project. The proposed approach in this study has the potential to provide a wider range of stakeholders and participants as data providers to energy market-related information and data.

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

Robust control method to improve microgrid frequency stability in islanded operation

Islanded microgrids have low inertia due to their small size as well as integration of inverter-based renewable energy resources. Hence frequency stability is one main concern regarding their operation. Load step pre-announcement and a bang–bang controller were proposed and applied in a test islanded microgrid, consisting of a conventional generator, a photovoltaic (PV) generator and a lumped load, to improve the frequency response. In this study, a possible control architecture for the proposed method is illustrated. A sensitivity analysis is conducted to determine the influence of two parameters, preset and total time, on the performance of the proposed control method. Furthermore, the effectiveness of the implementation of load step pre-announcement and bang–bang controller in the test microgrid with different shares of PV is evaluated through simulation results.

Solution of the optimal power flow problem in direct current grids applying the hurricane optimization algorithm

The Colombian electrical power system is being transformed by the large-scale integration of energy storage systems and renewable energy resources to the power system. These technologies can be integrated using alternating current or direct current technologies via power electronic converters. Here we analyze the direct current paradigm by proposing a master-slave optimizer for solving the problem of optimal power flow considering nonlinear loads. The master stage covers the dispatch of all distributed generators using the hurricane optimization algorithm. In the slave stage, a power flow method based on successive approximations is used to determine the voltage variables and evaluate the objective function of the problem, which is the minimization of power loss. A 69-nodes direct current network is used as a test case to compare the numerical performance of the hurricane optimization algorithm with a nonlinear optimization package and a metaheuristic approach called black hole optimizer. All simulations are performed using MATLAB software version 2017a licensed by Universidad Tecnológica de Bolívar, Colombia.

Grid Integration Challenges of Wind Energy: A Review

The strengthening of electric energy security and the reduction of greenhouse gas emissions have gained enormous momentum in previous decades. The integration of large-scale intermittent renewable energy resources (RER) like wind energy into the existing electricity grids has increased significantly in the last decade. However, this integration poses many operational and control challenges that hamper the reliable and stable operation of the grids. This article aims to review the reported challenges caused by the integration of wind energy and the proposed solutions methodologies. Among the various challenges, the generation uncertainty, power quality issues, angular and voltage stability, reactive power support, and fault ride-through capability are reviewed and discussed. Besides, socioeconomic, environmental, and electricity market challenges due to the grid integration of wind power are also investigated. Many of the solutions used and proposed to mitigate the impact of these challenges, such as energy storage systems, wind energy policy, and grid codes, are also reviewed and discussed. This paper will assist the enthusiastic readers in seeing the full picture of wind energy integration challenges. It also puts in the hands of policymakers all aspects of the challenges so that they can adopt sustainable policies that support and overcome the difficulties facing the integration of wind energy into electricity grids.

Influence of Phase-Shifted Square Wave Modulation on Medium Frequency Transformer in a MMC Based SST

This article proposes an approach to identify intrinsic power losses of the medium frequency transformer that interfaces two modular multilevel converters (MMC) operated with phase-shifted square wave modulation (PS-SWM) to form a DC solid state transformer (DC SST) module. Cascaded MMC module based DC SST architecture is an excellent choice for HVDC to MVDC conversion for DC grid application as well as for renewable and distributed energy resource integration into the DC grid. Operation with PS-SWM allows reduced cell capacitor size and lower total semiconductor device ratings. However, this modulation generates a unique quasi-square waveform at the SST internal medium frequency (MF) AC interface, which differs from other known square-wave modulation types. This produces a similarly unique transformer flux density, resulting in a transformer core loss that cannot be predicted by existing methods which makes it challenging to identify. In the interest of presenting a useful and optimizable MF transformer design methodology, this article develops a tool to predict the loss of the MF transformer subjected to PS-SWM. The proposed loss estimation technique is validated through an experimental testbed operated in two different configurations and is demonstrated to provide a valid basis for the use of the proposed MF transformer design methodology. Furthermore, this article provides an analysis of the load-dependent MF transformer core loss, which demonstrates a secondary usefulness of the proposed loss prediction tool in identifying load-dependent converter losses

Large-Scale Grid Integration of Renewable Energy Resources with a Double Synchronous Controller

This paper provides virtual inertia and mechanical power-based double synchronous controller (DSC) for power converters based on the d- and q-components of the converter current to assure the stable operation of the grid with the penetration of large-scale renewable energy resources (RERs). The DSC is projected based on emulating both the inertia and mechanical power variables of the synchronous generators (SGs), and its performance is compared with a non-synchronous controller (NSC) that is without these emulations. The main contributions of the DSC are providing a large margin of stability for the power grid with a wide area of low and high values of virtual inertia, also improving significantly power grid stability (PGS) with changing properly the embedded virtual variables of inertia, mechanical power, and also mechanical power error. Also, decoupling features of the proposed DSC in which both d and q components are completely involved with the characteristics of SGs as well as the relationship between the interfaced converter and dynamic models of SGs are other important contributions of the DSC over the existing control methods. Embedding some coefficients for the proposed DSC to show its robustness against the unknown intrinsic property of parameters is another contribution in this paper. Moreover, several transfer functions are achieved and analyzed that confirm a more stable performance of the emulated controller in comparison with the NSC for power-sharing characteristics. Simulation results confirm the superiority of the proposed DSC in comparison with other existing control techniques, e.g., the NSC techniques.

Storing electricity as thermal energy at community level for demand side management

Direct electrical energy storage can play a pivotal role in the efficient grid integration of renewable energy resources and compensating temporary power surpluses and shortages, however it is needed to move beyond the single sector focus and electricity’s conversion to other storable forms of energy should not be neglected in efforts to find the optimal solutions for maintaining the momentary balance between electricity supply and demand. This paper discusses the benefits of storing electricity as thermal energy over direct electricity storage. A community located in a hot climate region is considered as a case study and the performance of cold thermal storage and direct electricity storage are compared for it. For this purpose, the hourly performance of the storage systems during each year is modeled and particle swarm method is used to solve the optimization problem. Results show that storing electricity as thermal energy shows a much better performance over direct electricity storage. It requires much less investment, has a higher net present value and results in a flatter load profile. The paper also investigates the effects of electricity pricing structure, economies of scale and battery cost reduction on the economic performance of both thermal and electricity storage systems.

Output-feedback based robust controller for uncertain DC islanded microgrid

The integration of renewable energy resources to DC microgrid has captured the attention of the researchers in recent years. One of the active field of application of DC distribution is the islanded DC microgrid (DC ImG). The DC ImG present numerous challenges to researchers. Among many challenges, the regulation of voltage and stability of the system is indispensable to efficient operation. The voltage stability problem becomes more prominent when the system is exposed to disturbances and possess uncertainties in parameters. However, challenges can be overcome by skilful design of a robust controller for the system. Therefore, in this paper, an output-feedback based centralized robust control scheme is proposed. The proposed controller is designed to maintain good control performance in the presence of parametric uncertainties and exogenous disturbances. The uncertainties of the DC microgrid is modelled as a linear time-varying state-space system. The upper and the lower bounds of the time-varying parameters are determined by a Lebesque-measurable matrix. To attenuate the exogenous disturbances of the system [Formula: see text] based output-feedback controller is designed. The system stability is assured by the Lyapunov function candidate. The output-feedback controller needs only the voltage measurement; therefore, it requires less communication bandwidth as compared to the state-feedback. To obtain the controller parameters linear matrix inequality constraints are formulated and solved. The performance of the proposed controller is verified via simulations and compared with the existing schemes.

Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). The proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.

Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources

Standalone renewable energy system holds the most promising solution to the electrification of remote areas without utility grid access as well as to reduce fossil fuel consumption and environmental pollution. However, the random volatility and unpredictability of renewable energy are key factors to restrict its large-scale accommodation. In the present study, a multi-objective mean-semi-entropy model is proposed for a standalone micro-grid with photovoltaic-wind-battery-diesel generator hybrid system, with the aim of providing a trade-off solution between maximum profits and minimum risk in consideration of photovoltaic and wind uncertainties. Then, the preference-inspired co-evolutionary algorithm, along with Pareto optimality concept, is used for the system techno-economic optimization, i.e., to maximize the profits defined as the mean value of the return and to minimize the risk defined as the semi-entropy simultaneously. Subsequently, the preference ranking organization method is used for decision making to determine the optimal trade-off dispatch solution. Simulation results show that the multi-objective mean-semi-entropy model is well applicable to deal with standalone micro-grid operation, considering the integration of uncertain renewable energy resources.