Renewable Power System Research Articles

Optimal planning and electricity sharing strategy of hybrid energy system for remote communities in Nigeria

In recent times, renewable energy-based power systems are being used to address energy poverty or shortage that is experienced in developing countries. To improve these systems’ applicability, they are used to design a hybrid energy system. It is against this backdrop that this paper focuses on how hybrid energy systems can be designed optimally to address electricity sharing between domestic and productive use in remote communities. This paper, therefore, proposes a mixed-integer multi-objective optimization model for electricity sharing between domestic and productive use in remote communities. The model considered the number of solar photovoltaic (PV) systems acquisition, the total cost of energy utilized, and the cost of CO2 emissions avoided. A genetic algorithm was used to optimize these decision variables. The proposed model evaluation was carried out using data from three remote communities in South-West Nigeria. The results obtained from the model show that the number of installed solar PV systems in the first, second, and third communities is 74, 76 and 73 solar PV systems, respectively. For 25 years planning period, the first community required 29,554.05 kWh, the second community required 28,280.20 kWh, and the third community required 28,608.70 kWh. The average values for productive use of electricity from the conventional energy sources for the first community was 0.358, for the second community was 0.338, and for the third community was 0.348. In terms of the maximum productive use of electricity from the solar PV system, the first and second communities had the same value (0.39), while the third community's maximum had a value of 0.40. The developed model will be useful for evaluating the expected number of functional solar PV systems required, managing the quantity of electricity supply from the national grid and the generators, and planning electricity sharing for domestic and productive use within the selected communities.

Renewable Energy System on Frequency Stability Control Strategy Using Virtual Synchronous Generator

This study constructs a novel virtual synchronous generator system based on a transfer function, and optimizes the parameters of the model by using the improved whale algorithm to improve the frequency control ability of virtual synchronous generator. Virtual synchronous generator technology helps to solve the problem that the integration of large-scale renewable energy generation into the power system leads to the deterioration of system frequency stability. It can maintain the symmetry of grid-connected scale and system stability. The virtual synchronous generator technology makes the inverter to have the inertia and damping characteristics of a synchronous generator. The inverter has the inertia characteristics and damps to reduce the frequency instability of high penetration renewable energy power system. The improved whale algorithm is efficient to find the best combination of control parameters and the effectiveness of the algorithm is verified by microgrid and power system. The results show that the proposed frequency coordination control scheme suppresses the frequency deviation of power system and keep the system frequency in a reasonable range.

Planning for a 100% renewable energy system for the Santiago Island, Cape Verde

Ensuring the supply of affordable energy, improving energy efficiency and reducing greenhouse gas emissions are some of the priorities of the governments of several countries. The pursuit of these energy goals has triggered interest in the exploration and usage of Renewable Energy Sources (RES), which can be particularly appropriate for island systems as is the case of Cape Verde. This work proposes a generation expansion planning model for Cape Verde considering a 20 years’ period. Different scenarios were analysed, each one representing a possible RES contribution for electricity production, reaching a 100% RES share. The results demonstrate that the increase of the RES in the system will lead to an increase of the total cost of energy. However, both CO2 emissions and external energy dependency of the country significantly decrease. The seasonality of the RES resources, and in particular of wind power is shown to be one of the most important challenges for the effective uptake of such a renewable power system. While the proposed model allowed already to present some useful scenarios, it becomes also evident the need to expand the analysis, and the paper concludes with directions for future research.

Impact of Energy Storage on Renewable Energy Utilization: A Geometric Description

The high penetration of volatile renewable energy challenges power system operation. Energy storage units (ESUs) can shift the demand over time and compensate real-time discrepancy between generation and demand, and thus improve system operation flexibility and reduce renewable energy curtailment. This paper proposes two parametric optimization models to quantify how the power (MW) and energy (MWh) capacity of ESU would impact renewable energy utilization from two aspects: renewable energy curtailment and system flexibility for uncertainty mitigation. The two indicators are characterized as multivariate functions in the capacity parameters of ESUs. A severity ranking algorithm is suggested to pick up critical scenarios of fluctuation patterns from the uncertainty set; consequently, the proposed models come down to multi-parametric mixed-integer linear programs (mp-MILPs) which can be solved by a decomposition algorithm. The proposed method provides analytical expressions of the two indicators as functions in MW and MWh capacity. Such a characterization delivers abundant sensitivity information on the impact of ESU capacity parameters, and provides a powerful tool for visualization and useful reference for storage sizing. Case studies verify the effectiveness of the proposed method and demonstrate how to use the geometric information.

The near-optimal feasible space of a renewable power system model

Models for long-term investment planning of the power system typically return a single optimal solution per set of cost assumptions. However, typically there are many near-optimal alternatives that stand out due to other attractive properties like social acceptance. Understanding features that persist across many cost-efficient alternatives enhances policy advice and acknowledges structural model uncertainties. We apply the modeling-to-generate-alternatives (MGA) methodology to systematically explore the near-optimal feasible space of a completely renewable European electricity system model. While accounting for complex spatio-temporal patterns, we allow simultaneous capacity expansion of generation, storage and transmission infrastructure subject to linearized multi-period optimal power flow. Many similarly costly, but technologically diverse solutions exist. Already a cost deviation of 0.5% offers a large range of possible investments. However, either offshore or onshore wind energy along with some hydrogen storage and transmission network reinforcement appear essential to keep costs within 10% of the optimum.

Design, analysis and optimal sizing of standalone PV/diesel/battery hybrid energy system using HOMER

Now that the world’s most important problems are the lack of fossil fuel reserves, we need to find a solution to preserve non-renewable resources and replace them with other renewable resources such as solar power. The next focus is on the rising level of undesired gases, such as carbon dioxide, etc. There is a need to regulate the impact of such greenhouse gases. This paper mainly intentional to situate optimal scheduling and to meet the power demand profile by the utilization of available solar energy resources. Considering the above data we have put forth an attempt to scrutinize the leverage of distributed Generation considering solar energy, alone can vary, when associated together they give a dependable wellspring of vitality as a DG is utilized as reinforcement unit for the crisis. So consolidating a solar powered energy source with a diesel generator provides reliable generation, utilizing independent sunlight based renewable power system for a chose area in Vellore region, Tamil Nadu, India. The principle goal of this paper is to decide the optimal sizing dependent on different designs, moreover reduce the cost parameters such as, total net preset Cost (TNPC), system cost of Energy (COE), unmet electric load, CO2 emissions by utilizing HOMER Software. It is found that the PV/Diesel/converter combination provides optimal results which providing vitality with 0% unmet load at the minimum electricity cost, which is diminished from$ 0.672 to $0.319/Kwh.

Policy Decision Support for Renewables Deployment through Spatially Explicit Practically Optimal Alternatives

Summary Designing highly renewable power systems involves a number of contested decisions, such as where to locate generation and transmission capacity. Yet, it is common to use a single result from a cost-minimizing energy system model to inform planning. This neglects many more alternative results, which might, for example, avoid problematic concentrations of technology capacity in any one region. To explore such alternatives, we develop a method to generate spatially explicit, practically optimal results (SPORES). Applying SPORES to Italy, we find that only photovoltaic and storage technologies are vital components for decarbonizing the power system by 2050; other decisions, such as locating wind power, allow flexibility of choice. Most alternative configurations are insensitive to cost and demand uncertainty, while dealing with adverse weather requires excess renewable generation and storage capacities. For policymakers, the approach can provide spatially detailed power system transformation options that enable decisions that are socially and politically acceptable.

Effect of static liquid Galinstan on common metals and non-metals at temperatures up to 200 °C

Liquid metal Galinstan (GaInSn) is corrosive in nature against other solid metals as its base component is gallium. This study experimentally investigated the compatibility of GaInSn with eight common metals at temperatures up to 200 °C for 2000 h, including aluminum, copper, brass, ferritic and austenitic stainless steels (E-brite, SS304L, SS316L), and nickel-chromium alloys (Inconel and Hastelloy). This assessment aims to assist in design and material selection of a liquid metal magnetohydrodynamics system that houses Galinstan for power generation by low temperature natural heat sources or industrial waste heat. Design and fabrication of this renewable power system required assurance of material compatibility with common construction and instrumentation materials. The most severe corrosion effects of GaInSn on the metal alloys were observed on aluminum, copper, and brass, which confirms the results of previously conducted studies. No obvious corrosion on stainless steel or nickel–chromium alloys were observed by this study, which reveals that stainless steel has a good resistance to attack by GaInSn up to 200 °C. Six non-metals were also evaluated, including acronitrile butadiene styrene (ABS), acrylic, nitrile rubber (Buna N), nylon, polyvinyl chloride (PVC), and Teflon, which were deemed to be compatible with GaInSn up to the temperatures tested.

Enhancing the operation of fuel cell-photovoltaic-battery-supercapacitor renewable system through a hybrid energy management strategy

It is necessary to have an energy management system based on one or more control strategies to sense, monitor, and control the behavior of the hybrid energy sources. In renewable hybrid power systems containing fuel cells and batteries, the hydrogen consumption reduction and battery state of charge (SOC) utilizing are the main objectives. These parameters are essential to get the maximum befits of cost reduction as well as battery and hydrogen storage lifetime increasing. In this paper, a novel hybrid energy management system (HEMS) was designed to achieve these objectives. A renewable hybrid power system combines: PV, PEMFC, SC, and Battery was designed to supply a predetermined load with its needed power. This (REHPS) depends on the PV power as a master source during the daylight. It uses the FC to support as a secondary source in the night or shading time. The battery is helping the FC when the load power is high. The supercapacitor (SC) is working at the load transient or load fast change. The proposed energy management system uses fuzzy logic and frequency decoupling and state machine control strategies working together as a hybrid strategy where the switching over between both strategies done automatically based on predetermined values to obtain the minimum value of hydrogen consumption and the maximum value of SOC at the same time. The proposed HEMS achieves 19.6% Hydrogen consumption saving and 5.4% increase in SOC value compared to the results of the same two strategies when working as a stand-alone. The load is designed to show a surplus power when the PV power is at its maximum value. This surplus power is used to charge the battery. To validate the system, the results were compared with the results of each strategy if working separately. The comparison confirms the achievement of the hybrid energy management system goal.

Control of battery banks for wind energy conversion system

In this paper, the essential methodology to control the battery banks for a renewable power system is explained. The power converter used for the above is a step-down voltage regulator also known as Buck converter. It can be used to garner power from a small wind turbine. The paper also explains the charging process of the battery to track the point where maximum power output is obtained in a wide range of speed variations and proper circuit parameter design. Smart Management of battery charging and control is crucial since the lack thereof, would affect the battery’s performance. This method helps avoid the overcharging and overvoltage and also helps build systems that are economic and efficient. This process helps improve the charging efficiency of the battery, also its performance in terms of degradation rate and lifetime. Simple circuit and high reliability are the key factors in the usage of Buck DC/DC converter. A PMSG Stand-alone Wind Turbine System is also modelled and simulated using MATLAB.

Robust optimal utilization with a grid‐connected microgrid using EFO‐GBDT based controller technique with integrated converters

AbstractThis paper proposes the model and design of optimal utilization of grid connected Micro Grid (MG) system with Enhanced Fruit fly Optimization with Gradient Boosting Decision Tree (EFO‐GBDT) based controller technique. The integration (modeling and design) of high performance converter topology is formulated with the consideration of enhanced converter efficiency, effective implementation, source of renewable energy and reduced swapping loss. In the proposed technique the control scheme is used to consolidate the performance of both an EFO algorithm and also GBDT. In the proposed technique, the EFOA plays out an estimate method to initiate an exact control signal systems and accumulates the control signal database, an offline way of light in power variety between the source side (source of renewable energy and battery) and load side. The performance of dataset used to work as the GBDT mode for an online way and it leads to the control procedure in less performance time. In this proposed technique, an objective function is determined by the system, data subject to be an equality and inequality restrictions. The restrictions are the sources of renewable energy, power require the storage of an charged elements. A battery is implemented as source energy and it is used to permit and balance the renewable power system units and to keep running at a steady and balance output power. At that point the proposed model is performed based on MATLAB/Simulink, its working platform and the performance is assessed based on the existing techniques.

Hybrid photovoltaic/small-hydropower microgrid in smart distribution network with grid isolated electric vehicle charging system.

The increasing integration of renewable generation and power system innovations toward smartness have made microgrid a platform through which sources of energy could be annexed for efficient network operation. However, the sources must be carefully selected for a synergy to minimize intermittent challenges for productive output. This paper presents a grid-connected load-following hybrid solar photovoltaic and small-hydro microgrid with a grid isolated electric vehicle charging system. A decentralized multi-agent smart voltage network reactive power compensation dynamically regulates and monitors the network limits based on nodes' local measurements. The solar system supports hydropower during the peak load demand and solar storage is charged up by hydropower when irradiation is at the lowest threshold. The energy balance during excess production is configured for individual electric vehicle charging as load points. The photovoltaic-hydropower/electric vehicle microgrid is incorporated with maximum power point tracking and excitation control respectively as a means of control. The detail performance analysis using a time series evaluation over a 24 h daily simulation period is done on standard IEEE 33 and 118-bus radial distribution networks. Consequently, improved voltage regulation, dynamic energy reserve for electric vehicle’ charging and a better reduction in power loss are ensured in the research work.

A Novel Event- and Non-Projection-Based Approximation Technique by State Residualization for the Model Order Reduction of Power Systems With a High Renewable Energies Penetration

The fast development of renewable energies leads to an increase in the power electronics penetration in transmission systems. This is particularly true if one considers a 100% renewable energies power system, where wind and solar photovoltaic energies, which make most of the renewable energies connections today, are connected to the grid through power electronics converters. Because power electronics-based generators and synchronous machines-based generators have very different dynamic behaviours, some approximations usually used to run transient simulations of transmission systems might not be optimal for future grids. The systematic use of the phasor approximation applied to the power lines, implemented in most of the Transient Simulation Programs, is indeed not relevant any more. To avoid the use of Electromagnetic Transient simulations, that are resource-demanding and time-consuming, this paper presents a novel Model Order Reduction technique for large grids with a high penetration of power electronic devices. Compared to the classical phasor approximation, this event-based state residualization approximation allows accurate transient simulations and at the same time an optimal reduction of the number of the system’s state variables depending on the observed variables, the considered events and the tolerated approximation error.

Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.

IGDT-Based Robust Optimization for Multicarrier Energy System Management

In this paper, the information-gap decision theory (IGDT)-based robust problem model for multicarrier energy system (MES) management is presented. It is noted that in the proposed problem, electrical and gas energies are inputs of MES or energy hub, and electrical and heating energies are outputs of MES. The output electrical energy of hub provided by renewable energy sources (RESs), storage system, combined heat and power (CHP) system is managed by hub manager, and they do not participate in the market and input electrical energy. Also, it is considered that input electrical energy is bought from the PoolCo and bilateral contract markets. Moreover, the CHP is only connecting element for different energies. Therefore, the deterministic problem model expresses in the first step, where its objective function is minimized of hubs energy cost. Also, the constraints of problem are power flow equations and indexes limit in electrical, natural gas and district heating networks, PoolCo and bilateral contract market models, RES, storage system and CHP equations. This problem is as mixed integer nonlinear programming, where in the next step, the equivalent mixed integer linear programming model is presented because this new model can obtain the global optimal solution with low calculation error and time. In addition, the different loads, energy price in PoolCo market and RESs output power are uncertain parameters. Hence, this paper for considering uncertainty parameters presents the IGDT-based robust optimization for proposed problem. Finally, this problem is applied to standard test network with GAMS software, and thus the capability of the proposed problem investigates.

Evaluation of Wind Energy Accommodation Based on Two-Stage Robust Optimization

Large-scale renewable energy integration brings unprecedented challenges to electric power system planning and operation. The paper aims at economic dispatch and the safe operation of high penetration renewable energy power systems. According to the principle of power system dispatchability, the assessment of wind energy accommodation is formulated into a two-stage robust optimization problem with a min-max-min structure. Based on the benders algorithm, the intractable robust optimization problem is transformed into the form of sub-problem and master problem. Strong duality theory and big-M method are used to recast the sub problem into a mixed integer linear programming. The envelope of wind energy accommodation can be obtained by using commercial software to solve the master problem and sub problem alternately. For the realization of arbitrary wind power within the envelope, the amount of wind energy leakage and load shedding in power system operation are acceptable. An example of modified IEEE 39-bus test systems is used to verify the effectiveness and practicability of the evaluation method.

Design and analysis of a renewable energy power system for shale oil exploitation using hierarchical optimization

Abstract The exploitation of shale oil resources has attracted the wide attention. Using traditional fossil fuel as power supply for shale oil in-situ electric heating mining has an impact on the environment. In this paper, we propose a renewable energy microgrid system as a power supply for in-situ heating mining system of shale oil. A hierarchical optimization model was developed to optimize the capacity allocation of the microgrid components, which employs HOMER to initial optimization, then an improved multi-objective particle swarm algorithm with the lowest the economic cost function and the annual load shortage rate index of the system is implemented. A hierarchical hybrid strategy game method is used to screen the generated Pareto set to meet the different preferences of decision-makers. The sensitivity analysis of system reserve capacity, wind resources, and wind turbine prices was carried out. The simulation results using data from a demonstration project in China verified that the proposed method optimized the use of renewable energy, reduced the operating costs and produced positive environmental benefits. The results of this study provide a useful reference for decision-makers to plan the power supply scheme of shale oil extraction by in-situ electric heating.

Design of Renewable Energy System for a House in St. John’s, Canada

During this period of rising energy demand, utility companies are at a certain point in time, unable to satisfy the overall requirements of their entire consumer population. During this kind of situation, the system, which is, at a micro-scale, can also refer to a zero-energy building, which can also be very prominent in solving this problem. Another thing is that cries of non-renewable energy sources and most of the utility companies are majorly dependent on that kind of energy source, and it keeps along with issues of global warming. A renewable energy-based power system can solve this issue. In this paper, the solution to this problem by introducing the microscale installation of a renewable energy source at the residential level has been presented. For that here, the area selected for this project is located in St. John's, Newfoundland and Labrador, CANADA. Newfoundland is an island; hence, the proposed system would be beneficial here. Building this kind of system is the process of designing, selecting, and calculating the energy demand of equipment and, at last, synchronizing it with the grid to make it as zero energy building. This process depends upon a range of variables, including geographical location, load requirement, and solar irradiation. The required demand, system modeling, simulation, and techno-economic analysis are carried out by BEopt, HOEMR, and MATLAB software.

A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration

Abstract The increasing growth of renewable energy resources (RESs) in microgrids and the uncertainty associated with their generation have led to problems in the management of smart active distribution networks. One of the main challenges in the planning for the operation of renewable power systems is net electrical load forecasting, therefore, increasing the accuracy of net-load forecasting is a vital issue. In this study, a deep neural network model is used to forecast the net-load. The structure of the deep neural network used in the proposed forecasting model has been constituted by several autoencoders and a cascade neural network. The net-load forecasting has been done in the presence of uncertainties arising from wind and photovoltaic (PV) generation and electrical load consumption. To improve the net-load forecasting precision, the wavelet transform has been applied to the inputs of the proposed model. The net-load forecast in different scenarios is conducted on an open dataset from 37 central European countries, from the perspective of different types of forecasting strategies and changes in the neural network architecture. The simulation results significantly confirm the accuracy of the proposed forecasting model by different indices.

Retracted: An Intelligent Data-Driven Approach for Precise Tidal Unit Modeling and Prediction

Modeling and prediction of tidal units are necessary for the optimal and practical deployment of these renewables in power systems. Unfortunately, tidal units output power depends highly on the tidal waves' characteristics which are quite random in the nature. Therefore, a very precise and powerful model is needed to make sure that this task would be achieved. This article aims to propose a novel intelligent data-driven method for modeling and prediction of tidal currents including the wave current speed and direction. To this end, a hybrid method based on auto regressive integrated moving average (ARIMA) and deep learning is proposed which can predict the tidal wave behavior based on a linear-nonlinear procedure. The proposed deep learning model makes use of two adversarial neural networks to make sure that the final model would be the most optimal one considering the limited tidal data. It is an adversarial game in which two networks try to deceive each other through a recursive training process. A novel evolutionary optimization method is also proposed to improve the adjusting parameters of the model. The experimental results show the appropriate performance of the proposed model compared to other methods in the area.