Renewable Energy Resources Power Research Articles

Capacity planning for fossil fuel and renewable energy resources power plants

Electricity industry is changing by organizational and legislative evolutions dealing with economical and environmental affairs. Besides rapid development in energy generation decreased the related variable costs, considering environmental issues. In this study energy generation technologies are evaluated both from economical and environmental views. Applying Analytical Hierarchy Process (AHP) which is extremely applicable and known as one of the efficient methods to analyze complicated multi criteria problems it is achieved interesting results from evaluation. The main goal of this evaluation which is presented in this essay is to extract total preference weights for generation technology. Total preference weights are determined considering four criteria in fossil fuel power plants and renewable energy resources power plant using this evaluation pattern. For three power plant of gas, steam and combine cycle the total preference weights are calculated by considering seven criteria. For the next eight power plants of wind, small hydro, photovoltaic, solar thermal, micro turbine, biomass, geothermal and fuel cell total preference weights is considered according to seven criteria. Optimum capacity is calculated and determined. Sensitivity analysis is made on two most important criteria of this model named 1- operation and investment cost, and 2-fossil resources scarcity for power plants. 1- Multiple criteria of objective function, 2-possibility of sensitivity analysis on criteria weights and 3-concurrent inspection of qualitative and quantitative criteria in optimization of objective function are three advantages of the proposed model comparing common applying method such as (mono- objective software WASP)

PSO tuned interval type-2 fuzzy logic for load frequency control of two-area multi-source interconnected power system

Nowadays, most of modern power systems integrate concentrated renewable energy resources power plants like solar and wind parks in addition to central conventional plants. The output power from these concentrated renewable energy resources varies continuously according to weather conditions like solar irradiance value or wind speed and direction, the variation for their output power may be in mega watts. In this work, Robust secondary load frequency controller (LFC) based on one of artificial intelligent technique which called interval type-2 fuzzy logic controller (IT2FLC) has been proposed for two-area multi-source interconnected power system with central solar park power plants in each area while considering non-linearities in the power system. IT2FLC has accommodated vagueness, distortions and imprecision for the power system input signals which caused by weather fluctuations and system non-linearities. In addition to LFC, another controller based also on IT2FLC has been proposed to control the output power from the central solar parks in each area of generation during cloudy periods instead of maximum power point tracking method (MPPT) in order to enhance the stability for the power system during disturbance periods. In order to enhance the performance of the proposed LFC, particle swarm optimization technique (PSO) has been utilized to optimize the proposed LFC gains to minimize the steady state error, over/under shooting value, settling time and system oscillation for the investigated power system frequency. The performance and the superiority of the proposed PSO tuned IT2FLC is evaluated and compared with another LFC based on PSO tuned cascaded PID controller while applying severe demand load and solar irradiance changes. the simulation has been carried out using matlab/simulink program.

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

Management of Fluctuating Output Power of Renewable Energy Resources by Optimum Charge/Discharge Scheduling of Electric Vehicles in Smart Parking Lots

امروزه آلودگی‌های زیست محیطی و کاهش منابع تجدیدناپذیر از جمله سوخت‌های فسیلی، موجب شده است تا تولیدات پراکنده برق گسترش قابل توجهی یابد. از طرفی با گسترش روز افزون خودروهای الکتریکی، بررسی حضور خودروها بسیار با اهمیت است. زیرا حضور بدون برنامه خودروهای الکتریکی و شارژ همزمان آن‌ها تأثیرات مخربی بر پارامتر‌های شبکه توزیع برق خواهد داشت. لذا با مدیریت مناسب شارژ و دشارژ خودروهای الکتریکی در پارکینگ‌های هوشمند می‌توان از خودروهای الکتریکی در راستای بهبود پارامترهای شبکه و همچنین گسترش حضور منابع انرژی تجدیدپذیر استفاده نمود. از همین رو در این مقاله از پارکینگ‌های هوشمند خودروهای الکتریکی برای کاهش نوسانات توان تولیدی یک مجموعه منابع انرژی تجدیدپذیر استفاده می‌شود. در این راستا دو هدف اصلی این مقاله عبارت است از پاسخگویی به توان مبادله‌ای مورد نیاز برای کاهش نوسانات توان تولیدی منابع انرژی تجدیدپذیر و رسیدن سطح شارژ خودروها به سطح مناسب. این مدل پیشنهادی به صورت برنامه‌ریزی خطی آمیخته با اعداد صحیح مدل‌سازی شده و با استفاده از نرم‌افزار متلب حل می‌شود. نتایج شبیه‌سازی بر روی داده‌های پارکینگ شهر نمونه تهران نشان می‌دهند که مدیریت بهینه و مناسب خودروها علاوه بر آنکه سبب می‌شود خودروها در هنگام خروج به سطح شارژ مطلوب خود برسند، سبب کاهش قابل توجهی در تعداد باتری‌های مورد نیاز و در نتیجه هزینه بالای خرید و نصب باتری‌ها می­گردد.

Portfolio optimization of generic energy storage-based virtual power plant under decision-dependent uncertainties

Distributed energy resources (DERs) can be integrated into a smart and aggregated entity, namely a virtual power plant (VPP). This integration is beneficial to facilitate their flexibility utilization and optimally allocate their capacity to participate in competitive electricity markets. While various types of uncertainties exist in the market operation of VPP, such as the stochastic power of renewable energy resources (RES), etc., the literature generally overlooked the decision-dependent uncertainties (DDUs) with the operation of generic energy storage (GES). To be risk-hedging, this manuscript proposes a novel approach for risk-averse portfolio optimization of VPP with special attention to DDUs. To achieve tractable optimization, we propose a two-stage chance-constrained stochastic optimization model and three reformulations of different types of uncertainties. Furthermore, a modified and iterative progressive hedging algorithm is further proposed to handle the conditional value at risk (CVaR) and DDU items and efficiently solve the problem with large-scale GES portfolios. Exhaustive case studies based on real-world data demonstrate that VPP has to face the consequence of a significant real-time penalty (6.36 % loss in average profit) and losses in GES power (about 6.5 MW) when overlooking the DDUs' consequences. Moreover, it is more beneficial for greedy decision-makers to incorporate the DDUs' impact into their less risk-aversion strategy. Additionally, comparative results prove the validity of the proposed algorithm and its superiority in solving problems with large-scale GES portfolios and high-performance computing systems (over 30 % CPU time saved).

Reliability assessment and improvement of distribution system with virtual energy storage under exogenous and endogenous uncertainty

Exploiting flexibility of low-cost but uncertain demand-side resources attracts great attention in the recent research of the distribution system with special respect to reliability assessment and improvement. While various kinds of uncertainties and risks exist, such as the stochastic power of renewable energy resources (RES), etc., the literature overlooked the exogenous and endogenous uncertainties along with the utilization of these resources denoted as virtual energy storage (VES). Instead, this manuscript proposes a novel approach for reliability assessment and improvement of the distribution system in special consideration of the endogenous uncertainties in VES operations, also denoted as decision-dependent uncertainties (DDUs). The modified and unified probabilistic model of VES is first presented to reveal the dynamic process and human behavior of VES. Additionally, we extend our recent work on probabilistic optimization under DDUs to the reliability assessment problem which addresses the optimal load curtailment with the support of VES. Moreover, reliability indices are newly introduced to (1) calculate the response losses of VES and the consequence of overlooking these uncertainties, and (2) explain the difference between theoretical and practical reliability. Case studies based on the ground truth data verify that the available capacity of VES will gradually reduce (maximum of 40 %–60 %) because of DDUs and it reveals the overlooked risks by using “greedy” strategies in the previous models, while the best practical reliability performance (increased 5 %) is witnessed via the proposed methods. We further investigate four key factors affecting the reliability performance, i.e., probability level of chance constraints, DDUs levels, dispatch time period, and locations of VES. Eventually, reasonable suggestions are provided for better flexibility utilization of VES, and the system's reliability is further improved by around 30 %.

Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach

The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches.

Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources’ power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.

HOW COULD RENEWABLE ENERGY RESOURCES BRIDGE THE GAP IN NIGERIA’S ENERGY SECTOR?

This paper presents renewable energy resources development as a means of abating the shortfall in electricity generation in Nigeria. Levellized Bus-bar Cost Estimation method is used to carry out cost analysis of various power generation technologies by comparing their levellized costs. Carbon Emissions Pricing theory is also utilized to show that the cost per mega watt of renewable energy resources power plants can be comparable to their fossils counterpart when price is assigned to the emissions that result from using fossil fuels. Finally, government’s participation in time bound renewable energy projects, as well as financial and fiscal incentives are identified as actions that will encourage private sector investments in renewable energy development, which has a stimulating effect on the larger economy.

Improving Solar and PV Power Prediction with Ensemble Methods

Estimation of the generated power of renewable energy resources is in general important for planning operations as well as demand balance and power quality. This paper addresses the problem of the estimation of the short-term (3-hour ahead) and medium-term (1-day ahead) generated power of a photovoltaic plant. Firstly, the design of day-ahead solar radiation predictors is investigated with different setups of time series models, and with their combinations with the weather forecast services using ensemble methods. Support Vector Machine methods are also adopted in this stage, to cluster data. Secondly, under a similar ensemble framework, the generated power prediction is investigated. The whole generated power and solar radiation prediction tasks are then implemented on a low-cost, embedded mini PC module Raspberry Pi 3. As an application, the prediction is employed in the control system of a typical microgrid settings focusing on energy management problem. The impact of the quality of generated power prediction on the performance of the controller is also evaluated in this paper.

Risk evaluation and retail electricity pricing using downside risk constraints method

Electricity in the retail market has a different value for different types of consumers. Therefore, different retail prices are usually determined for various consumers in the retail market. However, imposed risks from uncertain parameters are a big challenge in the real-time retail market pricing process. This paper proposed a real-time pricing (RTP) framework for various users including residential, commercial, and industrial consumers by the electricity retailer. In addition, uncertainties of various input parameters such as output power of renewable energy resources, electricity demand, and pool market price are modeled using scenario-based stochastic approach while downside risk constraints method is proposed to model risk associated with uncertainties. By implementing this method, electricity retailer will be able to select various risk-based strategies. Furthermore, numerical results illustrate the various risks versus various profits by the occurring of each scenario which helps the retailer for decisions-making in different scenarios. According to obtained results, retailer by choosing of zero risk strategy can reduce its risk by 100% while expected profit is reduced by 2.07%. In addition, offered RTP by the retailer is higher for industrial, commercial, and residential customers, respectively. Finally, risk-averse and risk-neutral strategies of electricity retailer are determined in the power procurement problem.

Performance Analysis of Isolated Hybrid System Model on Dynamic Load State (Pre-Generator, Generator and Post-Generator)

A Hybrid Power Plant Diesel Generator with Renewable Energy Resources Power Plant is one of the solutions for supply electric demand to isolated area. The energy sources that can be used for hybrid system are such as photovoltaic, wind, and biomass or biogas, because these sources are almost available in every isolated area. This research used a model of hybrid system from diesel generator and 1.5 kWp photovoltaic power plants. The reliability and some of power quality issues of this system tested by 1300VA house hold daily load characteristic effectively 24 hour. Power quality and some electricity parameters during transition mode will be analyze. Furthermore the power quality analyze will be conducted and evaluated base on Electrical Engineers’ Association (EEA).

Optimal sizing and location of renewable energy based DG units in distribution systems considering load growth

In recent years, scientists have extensively focused on renewable energy resources such as wind power and solar units to reduce the consumption of fossil fuels as the main source of environmental pollutions. One of the main challenges for the production of wind and solar energies is that they often involve uncertainties due to the stochastic natures of wind speeds and solar radiation. Therefore, the existing uncertainties in the resources of wind and solar units must be considered in the planning procedure of distribution systems for having a reliable performance. To consider uncertainties in the output power of renewable energy resources, a new formulation is proposed to determine the optimal location and sizing of solar and wind units in the distribution system. To achieve this, Particle Swarm Optimization (PSO) algorithm as a robust technique is employed to find the best location and sizing of wind, solar and fuel cell units in the distribution system. The main objective of the present work is to minimize the Total Harmonic Distortion (THD), the total power losses, the total cost of DG units (including investments, replacement, operation and maintenance costs) and greenhouse gas emissions. Different types of loads such as linear and non-linear loads as well as load growth are also considered in the distribution system. To demonstrate the effectiveness of the proposed algorithm, 31-bus test system is considered as the case study. Our findings show that the use of renewable energy resources significantly reduces the greenhouse gas emissions in the mentioned system. Furthermore, the simulation results reveal that DG units must be added at some years to keep the voltage within its allowable limits.

Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs

The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO’s risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

Coordination Dispatch of Electric Vehicles Charging/Discharging and Renewable Energy Resources Power in Microgrid

A large number of distributed powers access to the distribution network have a multifaceted impact on the power grid, while microgrid is an effective way to solve the impact of distributed generation on the power grid. In microgrid, the proportion of renewable energy is large, while it's output is random. Besides, electric vehicles(EVs) play an important role in microgrid and a high penetration level of EVs could bring about a large impact on microgrid. Therefore, considering the coordination between renewable energy sources and EVs, a optimization model is proposed in this paper. In the optimization model, the minimum variance of equivalent load as the objective function, considering the electric vehicle driving behavior and electric vehicle capacity constraints, EVs charging/discharging power in each scheduling period is obtained. Simulation result shows the effectiveness of the model. The results show that charging/discharging of EVs can be dispatched to reduce the equivalent load and increase absorptive capacity of renewable energy.

직류형 마이크로그리드의 전운전영역을 고려한 협조제어

Recently, one of the main research on the power distribution system is the microgrid. The microgrid is a combination of power sources and loads, which is controllable and has separable connection. The main objective of microgrid is the deployment of the renewable clean energy and the enhancement of load-side reliability. The modern power sources and loads have DC I/O interfaces, which is the major advantage of DC microgrid compared to the conventional AC grid. The components in the microgrid have diverse features, so there is need of proper coordination control. For achieving economic feature, the active power of renewable energy resources is regarded as major control parameter and the whole operation modes of DC microgrid are defined, and the proper operations of each component are described. From the inherent characteristics of DC, there are two control variables: voltage and active power. Through analysis of operation modes, it is possible to determine exact control objectives and optimized voltage & power control strategy in each mode. Because of consideration of whole operation modes, regardless of the number and capacity of components, this coordination control method can be used without modification. This paper defines operation mode of DC microgrid with several DC sources and suggests economic and efficient coordinated control methods. Simulation with PSCAD proves effectiveness.

MPC Based Approach for Reliable Power System Energy Management with High Penetration Level of Renewable Energy Resources

This paper discusses using the battery energy storage system (BESS) to mitigate intermittency and sustain stability of distribution system integrating high penetration level of renewable energy resources (RER). The goal of the control is to have the BESS provide as much smoothing as possible, so that the RER power can be dispatchable in some kind and reliable. The effectiveness of model predictive control (MPC) based approach proposed in this paper have been tested by detail case study, also compared with the day ahead control strategy, load following strategy , and normal situation without energy storage which are usually used before. The result shows that the proposed MPC based approach is more practical, and more robust.

Control Scheme of Three-Level NPC Inverter for Integration of Renewable Energy Resources Into AC Grid

This paper presents a multiobjective control scheme based on the dynamic model of three-level, neutral-point-clamped voltage source inverter for integration of distributed generation (DG) resources based on renewable energy resources to the distribution grid. The proposed model has been derived from the abc/αβ and αβ/dq transformation of the AC system variables. The proposed control technique generates the compensation current references and by setting appropriate references of DG control loop, the DG link not only provides active and reactive currents in fundamental frequency, but also it can supply nonlinear load harmonic currents with a fast dynamic response. Simulation results and mathematical analysis have achieved a reduced total harmonic distortion, increased power factor, inject maximum power of renewable energy resources via a multilevel converter as an interface to the AC grid. It also compensated the active and reactive powers of linear and nonlinear loads. The analyses and simulation results show the high performance of proposed control scheme in the integration of renewable energy resources to the AC grid.