Real Power Generation Research Articles

Enhanced Thermoelectric and Mechanical Properties in Yb0.3Co4Sb12 with In Situ Formed CoSi Nanoprecipitates

AbstractFilled Skutterudites are one group of the most promising thermoelectric materials in real power generation applications. Herein, homogeneously dispersed multiscale CoSi nanostructures are successfully embedded into grains of the classic skutterudite system, Yb0.3Co4Sb12, by the in situ precipitation method. Such nanoprecipitates contribute much to the synergistic enhancement of thermoelectric and mechanical properties. On one hand, by the fine deployment of multiscale CoSi nanoparticles, the lattice thermal conductivity is significantly depressed almost to the theoretical limit because of the disrupted propagation of the heat‐carrying phonons at phase boundaries. On the other hand, low‐energy electrons are effectively screened due to the energy filtering effect by the interfacial potential barrier between the CoSi nanoprecipitate and the matrix, resulting in an enhanced power factor. Taken together, an enhanced peak ZT value of ≈1.5 at 873 K for the Yb0.3Co4Sb12/0.05CoSi composite is obtained with a high average ZT ≈0.95 between 300 and 873 K through decoupling the electrical and thermal transport parameters. Moreover, such a microstructure with multiscale CoSi nanoparticles shows significantly improved mechanical properties owing to particle hardening, making it more competitive for practical applications.

PV Solar System Control as STATCOM (PV-STATCOM) for Power Oscillation Damping

This paper presents a novel control of photovoltaic (PV) solar system as a FACTS device STATCOM, termed PV-STATCOM, for power oscillation damping (POD) in transmission systems. In the proposed control, as soon as power oscillations due to a system disturbance are detected, the solar farm discontinues its real power generation function very briefly (few tens of seconds) and makes its entire inverter capacity available to operate as a STATCOM for POD. As soon as power oscillations are damped, the solar farm restores real power output to its pre-disturbance level in a ramped manner, while keeping the damping function activated. This results in much faster restoration than that specified in grid codes. During nighttime, the solar farm performs POD with its entire inverter capacity. It is shown from EMTDC/PSCAD simulations that the proposed control provides significant increase in power transfer capacity on a 24/7 basis in systems that exhibit both local inertial and interarea oscillatory modes. The proposed PV-STATCOM is about 50–100 times cheaper than an equivalent STATCOM for providing POD at the same location. This novel control can potentially bring large savings for transmission utilities and open up a new revenue making opportunity for solar farms for providing POD.

Evaluating smart grid renewable energy accommodation capability with uncertain generation using deep reinforcement learning

Due to environment-friendliness, renewable energy like solar power and wind power is more and more introduced to energy systems all over the world. Simultaneously, high penetrations of wind and solar generation also have brought severe curtailment of wind and solar. How to alleviate curtailment of wind and solar is a crucial problem in evaluating accommodation capability of renewable energy, which reflects the extent of utilization of renewable energy and economic benefits. The uncertainty of renewable energy brings challenges to precisely describe renewable generation, which leads to difficulty in designing effective mechanisms for accommodation capability of renewable energy. Existing work suffers from high computation overhead from frequently updated data, and low precision of describing renewable energy, which leads to less effective policies for renewable energy accommodation and underestimated accommodation capability. To make the most of renewable energy, an algorithm AccCap-DRL based on deep reinforcement learning is proposed. AccCap-DRL partitions a distribution into segments by time intervals, employs WGAN to describe distributions of renewable energy data, and employs DDPG to obtain approximate policies for renewable energy accommodation in different scenarios. Simulation results from real power generation and users’ demand data show high effectiveness of the proposed algorithm, and high efficiency of evaluating accommodation capability.

Control strategies for frequency regulation in microgrids: A review

The electric power generation over the past decade has moved from conventional fossil fuel-fired thermal power plants to tiny-scale system generating power through distributed generation units. A group of such distributed generation units and loads are termed as microgrids. Microgrids can be located near the load centers to supply the load without any loss of power. Frequency regulation in a microgrid operating in autonomous mode is critical because of the intermittent nature of the renewable sources employed. To maintain the frequency regulation within a tolerance limit in a microgrid, proper control schemes have to be adopted in order to increase or decrease the real power generation. Hence, this article explores and presents a critical review of different types of control strategies employed for frequency regulation in microgrids.

Application of Modified ALO to Economic Load Dispatch for Coal Fired Stations

This manuscript explores the novel Modified Ant Lion (MALO) Optimization to resolve the load dispatch problem optimally. The primary role of Optimal Economic Load Dispatch (OELD) is to obtain an efficient and economical operation of a power system network. The main aim of performing the OELD is for minimizing the fuel cost of the real power generation when the losses are neglected. An Modified Ant Lion (MALO) Optimization is a narrative nature-inspired algorithm and apes the tracking mechanism of ant lions in life. The random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are the five main steps involved in the hunting process. The principal objective of OELD is to curtail the total generation cost while honoring effective constraints of accessible generating resources. The anticipated Modified ALO technique is utilized on three and six unit test system to various load demands for solving the load dispatch problem economically. A statistical result clarifies that the anticipated MALO method has lowest fuel cost and superior in quality of solution than other optimization techniques accounted in most latest literature.

Evolutionary Algorithm for Optimal Scheduling of Thermal Units in an Integrated Power System

Interconnection of power system is a key one to route power plant operation optimally with a diminishing rate of active power generation, in encountering the growing demand. Reduction of fuel consumption is one of the objective for the steam run power plants by assigning source power to every component (economic dispatch) focus to equal and unequal limitations optimally. This importance is due to power savings. ELD is the utmost significant issues to be resolved in the process and forecasting of a electrical system. This work presents an approach of Genetic Algorithm (GA) for finest load scheduling of steam run power plants to catch the universal optimum dispatch clarification. The genetic set of rules centered best approach explains the ELD problem. The core indication behind genetic set of rules is, all units should allocate the essential power load at lowest coal cost, though sustaining the coordination limitations. Simulation is executed on assessment system with six generators using Mat-lab. The results are shown for 24 hour loads with optimal generation of 6 generators including transmission losses and cost of real power generation.

A Two-Level Hierarchical Controller to Enhance Stability and Dynamic Performance of Islanded Inverter-Based Microgrids With Static and Dynamic Loads

This paper proposes phasor measurement unit (PMU) supported two-level hierarchical controller to enhance stability and dynamic performance of islanded inverter-based microgrids with static and dynamic loads. The proposed hierarchical controller consists of a local decentralized controller for each inverter-interfaced distributed generation (IIDG) unit at the primary level helped by a multi-input multioutput centralized controller at the secondary level. The primary-level decentralized controller incorporates the addition of auxiliary control terms to the conventional droop controller. The auxiliary control terms are based on the total real and reactive power generation information acquired from wide area measurement system through PMUs and the desired power sharing of each IIDG unit. The secondary-level centralized controller, on the other hand, enhances the performance of each local decentralized controller by compensating the voltage and frequency deviations caused by the load disturbances. The performance of the proposed hierarchical controller has been demonstrated through eigenvalue analysis and time-domain simulation results carried out in MATLAB/SIMULINK environment. Further, in order to have a fair comparison, the performance of the proposed hierarchical controller, based on modal-based extended linear quadratic Gaussian (LQG) approach, has been compared with that of hierarchical controller based on state-based extended LQG approach. Simulation results revealed the capability of the proposed hierarchical controller to mitigate the unstable oscillations as well as to settle quickly at different operating points under the application of small-signal disturbances.

Very Short-Term Wind Power Prediction Interval Framework via Bi-Level Optimization and Novel Convex Cost Function

Substantial challenges in power systems operation and control as a result of the intermittent and stochastic nature of wind power generation can be significantly alleviated by proficient very short-term wind power prediction interval (WPPI) models. In WPPI models, minimization of cost functions is conducted to train prediction engines and consequently tune their parameters. The prevalent cost functions of prediction engines in WPPI models are mainly non-differentiable and non-convex, and therefore the training process becomes problematic. To transcend such a crucial barrier, this paper addresses a new very short-term WPPI framework based on a bi-level formulation and benefiting from a differentiable and convex cost function. The prediction engine is trained by classical global optimization of the cost function in the lower-level problem, while hyperparameters that control the quality of the WPPIs are injected thereto from the upper-level problem. The hyperparameters can be tuned such that the most useful WPPIs are constructed from the lower-level problem depending on the power system operator's preferences. Lessening the need to heuristically tune a large number of prediction engine parameters is the foremost contribution of this work to the WPPI literature. The superior performance of the proposed WPPI is verified in the multistep ahead prediction of real wind power generation data in comparison to well-tailored benchmark models.

Efficacy of a Smart Photovoltaic inverter as a virtual detuner for mitigating Network Harmonic Resonance in Distribution Systems

As the grid is becoming smarter, more challenges are encountered with the integration of Photovoltaic plants in distribution systems. The real power generation varies based on the insolation from the sun. As a result, the fluctuations in voltages are seen as transient adversaries in the system causing the Photovoltaic inverters to trip. Smart Photovoltaic inverters nowadays are equipped with specialized controllers for exchanging reactive power with the grid based on the available capacity of the inverter, after the real power generation. With the ability of operation in VAR mode, Smart Photovoltaic inverters could regulate the voltage based on volt/VAR control, facilitate the power factor correction of the local loads and provide VAR support to ride through the voltage deviations during faults. Although the research that are undergoing are pertaining to all the aforesaid applications of Smart Photovoltaic inverters, employing a smart Photovoltaic inverter for resonance mitigation is a novel approach. U.S. based standard IEEE 519 for power quality describes the network resonance as a major contributor that has an impact on the harmonic levels. This paper proposes a new application in utilizing a Smart inverter to act as a virtual detuner in mitigating network resonance, based on the VAR mode control strategy with anti-windup and droop characteristics scheme that are incorporated with the design of Smart inverters. This novel application would further serve as a pioneering approach for researchers and planning engineers working in distribution systems as the grid gets more and more smarter.

Forecasting of Wind Power Generation with the Use of Artificial Neural Networks and Support Vector Regression Models

The stochastic character of wind power generation suggests limitations on the increased shares of wind energy in electricity systems and challenges market integration of wind power, mainly due to the fact that nowadays, new wind parks are set to cope with more dynamic pricing mechanisms. In this new environment, where advanced bidding strategies need to be adopted from wind power actors, the introduction of novel elements to support wind power generation and address the inherent impact of variability is thought to be a prerequisite. To this end, the current study expands the work of previous studies by examining different methods of prediction with regards to wind power forecasting. More specifically, both Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) models are trained and tested on the basis of different prediction horizons, using as case study real wind speed and wind power generation measurements from a wind park operating in the Greek territory. Models are trained using an in-house forecasting tool, with results obtained reflecting the better fit of the SVR method overall, especially for time horizons longer than 6 hours ahead. At the same time, an effort is made in order to optimize prediction of wind power generation through the combination of both prediction approaches via clustering of prediction areas. This novel approach results in an improvement of the predictions obtained, despite the fact that the SVR method already performs sufficiently even for 24 hours ahead.

Kalman Filtering Technique for Rooftop-PV System Under Abnormal Grid Conditions

This paper presents a robust extended complex Kalman filter based control for multifunctional double stage grid-connected solar photovoltaic array (SPVA) system. The main contributions this paper, include the following: first, real power generation from SPVA fulfills the requirement of the connected loads and it provides the surplus power to the distribution network, second, it behaves as a distribution static compensator, which performs various functionalities, such as grid currents balancing, harmonics mitigation, unity power factor operation, finally, it is capable to operate under polluted grid scenarios, such as undervoltage, overvoltage, and imbalanced grid voltages. The SPVA feed-forward term is incorporated in the control technique to enhance the dynamic response of the system. The real coded particle swarm optimization based metaheuristic approach is used for optimal tuning of the dc link voltage proportional integral controller. Simulation results illustrate the effectiveness of the presented control algorithm under variations in point of common coupling voltage. Test results illustrate the behavior of the grid-connected SPVA system subjected to various perturbations, such as undervoltages, overvoltages, distorted and imbalanced grid voltages, etc. The total harmonic distortions of grid currents are achieved well within recommended limits.

Congestion management using thyristor controlled series compensator employing Improved Grey Wolf Optimization technique

For reliable and secure operation of deregulated power systems, congestion management is a big task for system operators to control the line flows in the specified limits. Congestion may occur due to an increase in load demand or as a consequence of unexpected contingencies. Generator rescheduling and use of Flexible AC Transmission System devices are some of the approaches to lessen the flows in heavily loaded lines. This paper proposes to use an Improved Grey Wolf Optimization (IGWO) technique to solve congestion management problems. The improvement in Grey Wolf Optimization technique is made by a scheme which keeps an appropriate balance among the exploitation and exploration phases of the technique and a modified formula to determine the Grey Wolves’ positions throughout the course of iterations. The suggested IGWO technique is initially estimated by means of some typical test functions and comparative results are presented with original Grey Wolf Optimization, sine cosine algorithm, gravitational search algorithm, differential evolution and particle swarm optimization techniques to validate its advantage. The suggested IGWO algorithm is employed to control line power flows within limit in an overloaded modified IEEE 14 bus power system installed with a FACTS device by optimizing the generator real power settings, voltage settings, transformer tap settings, reactive power compensation setting and FACTS reactance settings.

Real coded genetic algorithm based transmission system loss estimation in dynamic economic dispatch problem

Estimation of transmission loss is vital in scheduling, optimization and planning of power systems. The conventional transmission loss evaluation methods used in power system scheduling problems are not accurate as the transmission network parameters in the system operator database are erroneous and not updated periodically. The conventional techniques rely on the precise network model. Moreover, loss evaluation gains significant importance as it affects the revenues of several utilities. In this context, this article proposes a method to evaluate transmission losses in a scheduling problem without relying on the network model. The proposed method uses samples of real power generation, consumption and losses collected at various operating conditions. From these data, genetic algorithm based loss coefficients (GALCs) are obtained by minimizing the mean absolute error between actual and calculated loss values using real coded genetic algorithm. Then, GALCs are used to evaluate losses in a dynamic economic dispatch problem and its performance is compared with conventional loss estimation techniques. The proposed GALC is validated on the IEEE 30 bus system and using the real time data of the Ontario power system. The performance analysis is also carried out for change in system operating conditions, transmission network modifications and outages.

PV-STATCOM: A New Smart Inverter for Voltage Control in Distribution Systems

This paper presents a novel smart inverter PV-STATCOM in which a photovoltaic inverter can be controlled as a dynamic reactive power compensator-STATCOM. The proposed PV-STATCOM can be utilized to provide voltage control during critical system needs on a 24/7 basis. In the night-time, the entire inverter capacity is utilized for STATCOM operation. During a critical system disturbance in the daytime, the smart inverter discontinues its real power generation function temporarily (for about a few seconds), and releases its entire inverter capacity for STATCOM operation. Once the disturbance is cleared and the need for grid voltage control is fulfilled, the solar farm returns to its predisturbance real power production. The low voltage ride through (LVRT) performance of the PV-STATCOM is demonstrated through both EMTDC/PSCAD simulations and laboratory implementation using dSPACE control. This proposed PV-STATCOM with a response time of 1-2 cycles can provide an equivalent service as an actual STATCOM in a given application and possibly seek revenues for providing this service.

An enhanced particle swarm optimization algorithm for improving the renewable energy penetration and small signal stability in power system

In power systems, increasing the renewable energy penetration with small signal stability is one of the demanding and critical tasks in recent days. This research work aims to develop a multistage optimization technique, namely particle swarm optimization (PSO), for improving both the energy penetration and small signal stability. Here, the wind and solar power sources are considered, and its penetration is maximized by satisfying the grid requirements such as the bus voltage, line flows, and real and reactive power generation within the limit. This work includes two stages: in the first stage, PSO algorithm is implemented for maximizing the renewable energy penetration to the test systems. Then, in the second stage, the small signal stability of the systems is improved with maximum renewable energy penetration in which the best locations for connecting the wind farm are identified by using the calculation of wind farm placement index and solar generation is fixed by considering voltage and bus load absorption capability. During simulation, the proposed method is tested and validated by using IEEE 14-bus standard system, and the 220 kV Kerala (India) grid practical system with the solar and wind power. Moreover, various measures such as power generation, load and bus voltage are evaluated for two different case studies. In this evaluation, it is proved that the renewable energy sources are safely integrated with the power system with increased energy penetration and improved small signal stability.

Cogeneration of power and methanol based on a conventional power plant in Germany

Abstract The global increase in electricity production by wind and photovoltaic plants poses challenges for local electricity grids as well as for electricity markets. These renewable energy sources rely heavily on current weather conditions, by that producing electricity in a fluctuating manner. This results in temporary electricity surpluses causing temporarily low or even negative electricity prices. Additionally, a stable operation of the electricity grid must be maintained even locally. To solve these problems, the intelligent coupling of the electricity, gas and heat sectors could be a promising approach. So called “Power-To-X” concepts such as Power-to-Heat (P2H) or Power-to-Fuel (P2F) cover technologies that aim to convert electrical energy into material energy storages, synthetic fuels and energy-intensive chemical raw products. For conventional power plants, the production of methanol is currently the most discussed option in Germany. In this paper a techno-economic analysis of the implementation of a Power-to-Methanol plant (P2MeOH) into a conventional power plant is presented. This study is based on real power generation data of the real hard coal-fired power plant “Westphalia E” in Germany in 2017. It takes into account parameters such as P2MeOH invest costs, costs for carbon dioxide emission as well as electricity, methanol and oxygen market prices. The required mass and energy flows for the techno-economic analysis are calculated by a custom implemented thermodynamic model. This study shows that an additional revenue could be generated under current market situations and that this possible revenue sums up to 5.2 million € per year. In ecological regards, around 150,000 tons CO2 could be saved per year. The calculated P2MeOH invest costs amount to 137.5 million €. Additionally, it could be obtained that by far most costs are caused by the electrolyser. As further technical progress in that field is expected, this could lead to higher economic benefits in the future.

Economic viability of concentrated solar power under different regulatory frameworks in Spain

Between 2004 and 2013, Spain applied a series of regulations that prompted a rapid expansion of the CSP sector. Most of this capacity was based on a proven technology that involved limited technical and financial risks (50 MWe, parabolic trough, synthetic HTF, 7.5 h thermal energy storage and wet cooling). This paper provides an introduction to CSP technology and a detailed review of these regulatory frameworks. This information has been used to evaluate the economic viability of a CSP plant representative of those deployed in Spain. The results evidence the limited competitiveness of this form of CSP, which is attributable not only to its elevated capital costs but also to high fixed operating costs per unit of output and limited revenues from power sales. Although generation capacity may be increased through hybridization with NG, this is also a loss making activity due to the limited efficiency of single cycle technology. With exceptions, the policy strategy followed in Spain had limited success at promoting technology advances with potential to achieve higher generation capacity, improved revenues, reduced costs and increased dispatchability. This form of CSP may still be attractive in isolated locations, where real power generation costs may be significantly higher.

Linear Decision Rules for Control of Reactive Power by Distributed Photovoltaic Generators

The revised IEEE 1547 Standard together with the grid codes from several European countries require the contribution of photovoltaic inverters to the reactive power and voltage control of distribution networks. This paper presents a decentralized approach for controlling reactive power from a photovoltaic (PV) inverter through a linear decision rule that is in terms of the PV generated real power, conforming to standard characteristic curves in the German grid code. The linear decision rules are computed using an affinely adjustable robust counterpart of an optimal power flow type formulation, with the PV real power generation specified in an uncertainty interval. The robust solution, which is computed via convex quadratic programming, guarantees that both the voltage deviations and power loss will remain within their optimized bounds for all realizations of PV real power sampled from the uncertainty set. The proposed linear decision rules are compared with a local control method that seeks the same type of multiobjective optimization. Monte Carlo simulation results on meshed distribution networks with up to 830 nodes show the superiority of the proposed approach.

Assess and Predict Automatic Generation Control Performances for Thermal Power Generation Units Based on Modeling Techniques

Automatic generation control(AGC) is a key technology to maintain real time power generation and load balance, and to ensure the quality of power supply. Power grids require each power generation unit to have a satisfactory AGC performance, being specified in two detailed rules. The two rules provide a set of indices to measure the AGC performance of power generation unit. However, the commonly-used method to calculate these indices is based on particular data samples from AGC responses and will lead to incorrect results in practice. This paper proposes a new method to estimate the AGC performance indices via system identification techniques. In addition, a nonlinear regression model between performance indices and load command is built in order to predict the AGC performance indices. The effectiveness of the proposed method is validated through industrial case studies.

On Variability of Renewable Energy and Online Power Allocation

As electric power system operators shift from conventional energy to renewable energy sources, power distribution systems will experience increasing fluctuations in supply. These fluctuations present the need to not only design online decentralized power allocation algorithms, but also characterize how effective they are given fast-changing consumer demand and generation. In this paper, we present an online decentralized dual descent (OD3) power allocation algorithm and determine (in the worst case) how much of observed social welfare can be explained by fluctuations in generation capacity and consumer demand. Convergence properties and performance guarantees of the OD3 algorithm are analyzed by characterizing the difference between the online decision and the optimal decision. We demonstrate validity and accuracy of the theoretical results in the paper through numerical experiments using real power generation data.