Units In Grid Research Articles

Optimal IoT-based decision-making of smart grid dispatchable generation units using blockchain technology considering high uncertainty of system

This article proposes a blockchain-based architecture for the optimal scheduling of the dispatchable units in the smart grids considering the high penetration of renewable energy sources. The proposed method makes use of blockchain technology to secure the data exchange among the agents in the smart grid and avoid unauthorised access to the real data of the system. Considering the flexibility which is provided by the optimal switching in the smart grid, the reconfiguration problem is also considered in the model using some remote control switches. A stochastic framework based on the unscented transform is proposed to not only capture the independent randomness of the renewables but also to handle the correlated uncertainties among them. The problem formulation is constructed as a multi-objective optimization problem optimizing the total operating cost and the reliability indices. Due to the nonlinear and complex nature of the problem, a new optimization method based on the honey bee mating (HBM) algorithm is proposed to solve the problem without any simplification or linearization. Moreover, a correction approach based on local search is proposed to increase the algorithm capability. The appropriate performance of the proposed model is examined on a test smart grid. The data for the optimal decision is based on the internet of things (IoT) data.

General situation of heating transformation of thermal power units in Shandong Power Grid

This paper introduces the basic situation of thermal power units in Shandong power grid, the mode of unit heating transformation, the results of installed capacity and heating area after the transformation of heating, and the main work of Shandong power grid in ensuring people’s livelihood heating and promoting the peak load adjustment of thermal power units.

A Flexible Ensemble Algorithm for Big Data Cleaning of PMUs

With an increasing application of Phase Measurement Units in the smart grid, it is becoming inevitable for PMUs to operate in severe conditions, which results in outliers and missing data. However, conventional techniques take excessive time to clean outliers and fill missing data due to lacking support from a big data platform. In this paper, a flexible ensemble algorithm is proposed to implement a precise and scalable data clean by the existing big data platform “Apache Spark.” In the proposed scheme, an ensemble model based on a soft voting approach utilizes principal component analysis in conjunction with the K-means, Gaussian mixture model, and isolation forest technique to detect outliers. The proposed scheme uses a gradient boosting decision tree for each extracted feature of PMUs for the data filling process after detecting outliers. The test results demonstrate that the proposed model achieves high accuracy and recall by comparing simulated and real-world Phase measurement unit data using the local outlier factor algorithm and Density-Based Spatial Clustering of Application with Noise (DBSCAN). The mean absolute error, root mean square error and R2-score criteria are used to validate the proposed method’s data filling results against contemporary techniques such as decision tree and linear regression algorithms.

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Optimal Model Predictive Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Advanced learning-based energy policy and management of dispatchable units in smart grids considering uncertainty effects

In this paper, a new machine learning based framework is developed for the energy policy and operation management of the smart grids, utilizing advanced support vector networks in the renewable smart grids (RSGs), considering storage unit, wind and tidal systems and dispatchable units. The proposed system first develops a support vector regression (SVR) for prediction of the tidal and wind units output power with high accuracy. In the second step, an energy policy system is devised which forces the system operator to support renewable sources by guaranteeing the full purchase of their generation. In the third step, the optimal energy management framework is launched which optimizes the operation costs when considering the practical constraints. In the proposed novel framework, a new optimization method based on fuzzy dragonfly algorithm (FDA) is developed to enhance the search performance by creating adjusting fuzzy version this algorithm. In order to handle the uncertainty effects, a reduced scenario based approach is developed which shows high accuracy of 95% confidence level but with trivial computational time. The system quality is assessed on a test RSG system. The results prove the contributing claims of the research, clearly.

Cause analysis and verification method for output adjustment of Shandong Power Grid

Thermal power units account for a large proportion of the total installed capacity of Shandong power grid, so the output adjustment of thermal power units has a great impact on Shandong power grid. This paper analyzes all the output adjustment of thermal power units in Shandong power grid in November 2019, and puts forward a classification method of output adjustment for units with different capacity levels, gives the verification method of output adjustment, and puts forward the suggestions for output adjustment.

Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities

With the advent of advancements in the power sector, various new methods have been devised to meet modern society’s electricity needs. To cope with these large sets of electronic device’s current requirements, better energy distribution is needed. Smart Grid (SG) facilitates energy providers to distribute electricity efficiently to the user according to their particular requirements. Recent advancements enable SG to monitor, analyze, control and coordinate for the demand and supply of electricity efficiency and energy saving. SG also allows two-way real-time communication between utilities and customers using cloud and Fog enabled infrastructures. SG minimizes management and operations cost, electricity theft, electricity losses, and maximize user comfort by giving the user choice about their energy use. It also facilitates Renewable Energy Resources (RER) and electric vehicles. Blockchain is a promising technology, provides the necessary features to solve most of these issues. Current Issues include saving a large amount of data, deletion, tampering, and revision of data. It also eliminates the necessity of intermediaries. Inherent security, along with the distributed nature, makes it a perfect candidate for improving the overall services. The rules of the smart contract are automatically enforced upon execution. Smart contracts are enhanced in a way that per-unit price is calculated dynamically based upon RER and utilities generated energy units in the overall grid. The system is also automated in a way that electricity is transferred from one resident (or service) to another resident according to their requirements. The exchange of energy is done via a smart contract after checking the needs of each participant. Each participant defines their requirements at the time of the registration and can update these thresholds. The privacy protection scheme has higher security, shown by theoretical security analysis. The main contributions of our work are two-fold; Using smart contracts to automate the bidding process for transactions based upon supply and demand for energy in smart cities. Secondly, at the same time, using hyper ledger fabric and composer to leveraging Blockchain to uphold privacy, anonymity, and confidentiality at the same time giving the users ability to have dynamic pricing based on supply and demand.

Study on the Factors influencing the qualified Rate of Large Disturbances of the Primary Frequency Regulation on Heat Supply Units and the Improving Measures

At present, the major problem faced by heat supply thermal power units is not only to ensure the external heating demand, but also to meet the requirements of the power grid for the primary frequency modulation of units. The main types of heat supply units in Shandong power grid are introduced. And take a 600MW pure condensing heat supply unit as an example, the reasons that affect the test results of large disturbance of primary frequency regulation in the period of heat supply are analysed. The scheme of optimization and transformation is put forward, which has good effect and meets the requirement of primary frequency regulation of power grid.

Operational flexibility of electrified transport and thermal units in distribution grid

The decentralised infrastructure of the Danish thermal and electricity infrastructure has improved security, efficiency and reliability in energy transmission and consumption in each of the systems. To further improve this trend, this paper investigates the concept of decentralised coupling of electrified thermal and transportation system in low voltage residential network to identify their operational flexibility. In this paper, diagnosis based on actual data from the energy distributors and surveys is used to understand and improve the flexibility of an integrated energy system. The main contribution is a set up model with an autonomous control system that can assess the potential flexibility from thermal units (eg heat pumps and storages) and electric vehicles (EV) charging systems, in the low voltage distribution network as a multi-energy system. Each thermal system and EV charging has its respective individual controller. The proposed control technique manages to successfully operate and control the thermal units and EVs charging system within the recommended operating limits of grid voltage, and by sharing flexibility within the specific network integrated with multi-carrier energy systems. It has the capability of sensing local key control parameters like node voltage, state of charge of EV, temperature and level of hot water in the storage tank. These control parameters allow scheduling, re-scheduling, and decision making on the operation of individual thermal and EV charging-unit with operational priorities. This enhances the sharing of flexibility for proper coordination, control, and management of thermal and EV charging systems in low voltage (LV) distribution networks with mutual technical benefits. From the results of the steady-state analysis of power system, the application of the proposed control architecture is found to be effective to manage grid congestions and local voltage control, satisfying the thermal energy requirements of the customer as well as charging needs of EV.

Research on Optimal Control of PFC of Thermal Power Units in DC UHV Grid

By analyzing the change of grid frequency during direct current(DC) ultra high voltage(UHV) faults this year, it is found that the use of conventional primary frequency control(PFC) technology in thermal power units cannot suppress the occurrence of extreme frequency of the power grid, and the conventional PFC method can no longer adapt to the frequency control of the power grid. By analyzing the control signals and control logic of the unit participating in the frequency regulation of the power grid, the PMU(phasor measurement unit) frequency signals that are closely connected to the power grid are introduced, and the second-order differential feed-forward is used to achieve overshoot compensation, which makes the frequency modulation action faster and more accurate, ensuring the power grid’s safe and stable operation.

Advantage analysis of variable-speed pumped storage units in renewable energy power grid: Mechanism of avoiding S-shaped region

Pumped storage units (PSU) as the most efficient energy storage device (ESD) play an important role in absorbing renewable energy (RE) sources. Nevertheless, pump-turbines have inherent S-shaped characteristics caused by their reversible design which seriously threaten the operation stability of PSU. In recent years, the variable-speed pumped storage units (VSU) have been proposed as an alternative to the fixed-speed pumped storage units (FSU). Therefore, it is a meaningful subject to evaluate the operation advantage of VSU, which has not been adequately quantified. This paper aims to analyse the advantages of VSU in operation stability by revealing the mechanism of avoiding trapping in the S-shaped region. Firstly, a precise coupling model for VSU with nonlinear pump-turbine characteristics is established and the operation areas in turbine mode are analysed. Then, the mechanism of how the trap of S-shaped region could be avoided is revealed and the regulation performance of the control system is quantified by comparing with FSU. Based on a real Chinese VSU, three scenarios are studied, including turbine start-up process, power regulation and turbine closing process. The results indicate that the S-shaped region could be escaped effectively by operating the VSU. The periodic oscillations times and setting time index of rotational speed in turbine start-up process are 0.5 and 20.74 s respectively, which are much smaller than the values of FSU. VSU outperforms the FSU in the aspect of power regulation performance: much smaller average and standard deviation of power difference for wind and load disturbance. Moreover, VSU can also obtain a significantly higher assessment in closure process: the maximum water pressure deviation is only 40% of FSU.

Power load forecasting in energy system based on improved extreme learning machine

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

ОБҐРУНТУВАННЯ ДОЦІЛЬНОСТІ ВИКОРИСТАННЯ НАКОПИЧУВАЧІВ ЕНЕРГІЇ В ЕЛЕКТРОМЕРЕЖАХ МІСЬКОГО ТРАНСПОРТУ ТА ДОМОГОСПОДАРСТВ

The article deals with the methodology of business case for application of energy storage units in urban transport and home power grids while using dual- and three-zone pricing; in addition it provides an example of the methodology application. The object of study are electrical grids with energy storage units. The purpose of the work is to develop the business case for application of energy storage units in urban transport and home power grids while using dual- and three-zone pricing. Methods of investigations - mathematical modeling of processes. As a rule, the volumes of electricity production and its consumption do not coincide in time. Therefore, energy storage units are used to optimize energy flows. The drive is charged with an excess of energy and transfers the stored energy to the system at the moments of its absence. The development of such technologies can have a significant economic effect and is of great practical importance. In Ukraine, dual­zone and three-zone pricing are set for the population and businesses. When using the dual-zone pricing, the energy price coefficients for households are 0.5 at night from 11 p.m. to 7 a.m. and 1.0 in the daytime. The maximum operation of urban electric transport, as well as power consumption by enterprises, organizations and households, occurs at the time of day with the highest energy price. To reduce overall consumption, you can charge energy storage units at night and use the stored energy during the day. The methodology for business case for application of energy storage units by using dual-zone and three-zone pricing is proposed. We have obtained the criteria for this need. To calculate the payback period of the project, we use the method of expected current clean price is used. It is shown that at the existing prices of energy storage systems and electricity pricing in Ukraine, the use of such systems to save money due to low night pricing is impractical. It was determined that business case for application of energy storage units when using a dual-zone pricing in Ukraine can take place if only specific electricity pricing from available units is less than 0.2 ... 0.7 UAH/kWh. The results of the study can be used in the design of electrical systems with energy storage units. The predicted assumption about the development of the object of study is to determine business case for application of energy storage units in consumer power circuits using alternative sources of electricity. KEYWORDS: ELECTRICITY, STORAGE UNITS, PRICING, ELECTRIC TRANSPORT, HOUSING, INVESTMENT, ECONOMIC EFFECT, EXPECTED CURRENT CLEAN PRICE, PAYBACK PERIOD.

Voltage Quality and Power Factor Improvement in Smart Grids Using Controlled DG Units

The increased penetration of renewable energy sources in the electrical grid, due to the rapid increase of power demand and the need of diverse energy sources, has made distributed generation (DG) units an essential part of the modern electrical grid. The integration of many DG units in smart grids requires control and coordination between them, and the grid to maximize the benefits of the DG units. Smart grids and modern electronic devices require high standards of power quality, especially voltage quality. In this paper, a new methodology is presented to improve the voltage quality and power factor in smart grids. This method depends on using voltage variation and admittance values as inputs of a controller that controls the reactive power generation in all DG units. The results show that the controller is efficient in improving the voltage quality and power factor. Real data from an electrical network have been used in the simulation model in MATLAB Simulink to test the new approach.

Experimental evaluation of the performance of virtual storage units in hybrid micro grids

The work presented in this article proposes a new energy management algorithm for hybrid micro grids consisting of higher penetration of DC non-critical (NC) loads and renewable energy sources. The methodology suggested in this work is a rule based approach and tries to make the micro grids more autonomous. During generation deficits in the micro grids, the suggested control strategy proposes to make the hybrid micro grids self-dependent to a possible extent, without the incorporation of actual storage devices. Instead of using actual storage elements like the batteries or super capacitors, the projected approach uses virtual storage devices, like the DC electric springs for its functionality. The electric springs used in this work operate the DC NC loads in accordance with the voltage produced by the renewable sources, which in turn reduces the power import from the main grid during generation deficits in the micro grid. Further, the work presented in the due course only studies about the efficiency of the proposed algorithm by operating the DC NC loads as per the requirement, without intervening with the AC and DC critical loads operation. In order to test the robustness of the proposed methodology a scaled down hybrid micro grid is developed in the laboratory using dSPACE 1104 real time interface.

Solar energy and wind power supply supported by storage technology: A review

Solar energy and wind power supply are renewable, decentralised and intermittent electrical power supply methods that require energy storage. Integrating this renewable energy supply to the electrical power grid may reduce the demand for centralised production, making renewable energy systems more easily available to remote regions. Control systems optimise solar energy and wind power sources to supply renewable energy to the power grid. Vehicle to Grid (V2G) operations support intermittent production as battery storage. In V2G operations, electric power flows from the power grid to the battery storage and from the battery storage back to the power grid. The primary goal of this study is to improve the existing renewable energy supply to provide more reliable units in the power grid. We consider the V2G concept as an extension of the smart charging system allowing electric vehicles to be able to inject battery energy into the power grid, acting as distributed generators or energy storage systems. This review shows how parallel V2G storage and battery storage supports the power grid. Further, the review indicates that decentralised V2G battery storages will be included in future renewable energy systems.

Scheduling and value of pumped storage hydropower plant in Iran power grid based on fuel-saving in thermal units

Abstract One of the most considerable current discussions in many countries is the valuing of pumped storage hydropower plants (PSHPs) in power grids. Calculating the value of PSHP can provide an appropriate perspective in the pricing of these large-scale storage systems. In this paper, first, a method to determine the scheduling of the Siahbishe PSHP to have the maximum impact on peak shaving and valley filling, considering the daily generation scheduling program of the thermal units in Iran power grid is proposed. Then, a mechanism for estimating the value of the PSHP in Iran’s national grid based on modified efficiency indices of the thermal units and the daily equivalent fuel-saving concept is suggested. Other benefits of the PSHP such as providing ancillary services in the power grid are not taken into account in the valuing. Therefore, the actual value of the PSHP is expected to be higher than the estimated value. To demonstrate the effectiveness of the proposed mechanism, numerical studies are done based on the actual data from Iran power grid in a two-year period (April 2015-March 2017). According to the results, the Siahbishe PSHP plays an important role in reducing production costs with an approximate annual value of 93,793,000 $.

Fault Diagnosis Technique for Hydroelectric Generators using Variational Mode Decomposition and Power Line Communications

With the increasing proportion of Hydro generator units in the whole power grid and its aging infrastructure, it becomes increasingly important hot topic for our next generation smart grid to complement a real-time fault diagnosis in Hydroelectric Generators. In this paper, we propose a road-map towards this issue. To this end, Variational Mode Decomposition method and other signal processing algorithms, such as Multi-dimension Permutation Entropy and fuzzy C-means clustering algorithm and so on, are developed and power line communication technique is used. Computer simulations results show that our proposed diagnostics solutions successfully empower Hydroelectric Generators to independently predict and identify the three different most commonly faults.