Hasil untuk "Distribution or transmission of electric power"

Franko Pandžić, Ivan Sudić, Tomislav Capuder et al.

Abstract The cost for covering active power losses makes a significant item in transmission system operators (TSO) annual budgets, and still it received limited attention in the existing literature. The focus of accurate power loss forecasting and procurement is of high increase during the past 2 years due to spikes in electricity prices, making the cost of covering the active power losses a dominant factor of TSO operational costs. This paper presents practical aspects of the highly accurate models for transmission loss forecast in the day ahead time frame for the Croatian transmission system. The contributions are two‐fold: 1) Practical insights into usable TSO data are provided, filling a critical research gap and a foundational literature review is established on transmission loss forecasting. 2) A novel method utilizing only electricity transit data as input which outperforms existing practices is presented. For this, several algorithms such as gradient boosted decision tree model (XGB), support vector regressors, multiple linear regression and fully connected feedforward artificial neural networks are developed, and implemented and validated on data obtained from the Croatian TSO. The results show that the XGB model outperforms current TSO model by 32% for 4 months of comparison and TSCNET's commercial solution by 25% during a year‐long testing period. The developed XGB model is also implemented as a software tool and put into everyday operation with the Croatian TSO.

Mohammad Ebrahim Hajiabadi, Mahdi Samadi, Mohammad Hassan Nikkhah et al.

Abstract One of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.

Yangwu Shen, Feifan Shen, Heping Jin et al.

Abstract Natural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution.

Lionel Leroy Sonfack, René Kuate‐Fochie, Andrew Muluh Fombu et al.

Abstract This manuscript proposes a robust excitation control strategy for synchronous generators using backstepping theory and an artificial neural network with a radial basis function to improve power system performance during disturbances and parametric uncertainties. The artificial neural network is used to estimate unmeasurable quantities and unknown internal parameters of a recursive backstepping control. Lyapunov theory is used to carry out the stability analysis and to deduce the online adaptation laws of artificial neural network parameters (weights, centres and widths). To validate the performance of this approach, simulations are performed on an IEEE 9 bus multi‐machine power system. Different test results, compared with those of an existing non‐linear adaptive controller, confirm the high robustness of the proposed method against disturbances and uncertainties.

Yuanshi Zhang, Wenyan Qian, Jun Shao et al.

Active power sharing and voltage regulation are two of the major control challenges in the operation of the voltage source converter based multi-terminal high-voltage DC (VSC-MTDC) system when integrating large-scale offshore wind farms (OWFs). This paper proposes two novel adaptive voltage reference based droop control methods to regulate pilot DC voltage and share the power burden autonomously. The proposed Method I utilizes DC grid lossy model with the local voltage droop control strategy, while the proposed Method II adopts a modified pilot voltage droop control (MPVDC) to avoid the large errors caused by the DC grid lossless model. Dynamic simulations of a five-terminal MTDC grid are carried out using MATLAB/Simulink SimPowerSystems /Specialized Technology to verify the proposed autonomous control methods under various types of disturbance and contingency. In addition, comparative study is implemented to demonstrate the advantages of the proposed methods.

Mohammad Tayyab, Adil Sarwar, Shadab Murshid et al.

Abstract In recent times, multilevel inverters (MLIs) have become very popular for commercial and industrial applications. Here, an eleven‐level inverter and its power flow control are presented. The presented topology has a lesser component count than other existing topologies, thus reducing the devices and overall cost of the inverter. This inverter comprises six bidirectional switches, two DC sources, one four‐quadrant switch, and two capacitors for the voltage divider circuit. The conduction modes and corresponding switching states of the presented eleven‐level inverter are shown in detail. Further, the apparent power control of the presented inverter under grid‐connected operation is discussed, which provides simultaneous active and reactive power control over the power injected into the grid. Switching and conduction losses are calculated for 3 and 6 kVA grid injected power at 0.8 power factor lagging. The obtained results show that the total harmonic distortion (THD) of the inverter output voltage and grid current is 12.10% and 0.23%, respectively, under 6 kVA power transfer conditions. The real‐time analysis is also carried out for 3 and 6 kVA power transfer conditions for the presented eleven‐level inverter to validate the active and reactive power flow control.

Jan Klusacek, Jiri Drapela, Roberto Langella

The presence of power generating plants owned by prosumers may lead to unbalanced bidirectional energy flows at the points of connection to the relevant distribution systems. This will impact future energy communities, where appropriate metering within the community is a crucial issue for billing purposes. This paper shows that the current metrics for active energy measurement and the registration of three-phase revenue meters may fail to fairly charge unbalanced prosumers for their use of the distribution system as an inherent phase-to-phase balancer. On the other hand, it is proven here that adopting metrics based on positive sequence power/energy measurement would lead to more fair billing within the community. A comparative study was performed using a simplified but realistic model of a distribution system feeding two prosumers (i.e., an archetype of an energy community). First, representative case studies were considered. Then, a more realistic simulation of a single day of operation was conducted. The main contribution of the paper is a detailed and systematic comparison of the methods used for measuring and sorting energy into registers in revenue meters to support the ongoing discussions about fair metering within future energy communities.

Xiangwu Yan, Chen Shao, Jiaoxin Jia et al.

Abstract With the rapid development of active distribution networks, the “petal”‐type distribution network has become the mainstream power supply structure. Power control methods for active distribution networks should be further studied to ensure the safe and reliable power supply of a distribution system. An electromagnetic rotary power flow controller (RPFC) is a feasible solution for controlling power in active distribution networks. However, when testing the effectiveness of the PQ decoupled control method for RPFC based on instantaneous reactive power theory, difficulties were encountered with the synchronous control of the rotor position angle of two rotating‐phase transformers, and the accuracy of power control was unsatisfactory. Given this condition, PQ control is improved in three ways. First, the system's periodic oscillation problem is solved via variable speed control. Second, the servo motor‐rotary phase‐shifting transformer synchronous rotation scheme, which reduces power control error and improves stability, is designed. Third, the overshoot phenomenon in power control is improved using the variable‐domain fuzzy proportional‐integral adaptive method. Experimental results show that the proposed advanced control scheme exhibits good dynamic and static performance in power control scenarios and achieves effective improvement in RPFC.

Xuan Deng, Yufan Hu, Yiyang Jia et al.

Abstract The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system.

Kun Liu, Feng Gao

Abstract With the breaking down of information barriers between energy flow with manufacturing flow, the coordination between them is an effective way to relieve the dual pressure from energy and environment industrial integrated energy system. The authors develop a scenario‐based coordination model for energy flow and manufacturing flow to make full use of the flexibilities of energy supply and production process to reduce energy cost. To capture the flexibility in the energy flow, the authors enhance an electricity‐steam‐product gas–gas storage coupling energy flow model considering multi‐uncertainties. The authors also develop a batch process model to formulate the flexibility in the production process. Based on the batch energy consumption constraints, the energy flow model and the batch process model are integrated as a coordination model. To ensure the feasibility of hard budget constraints under all possible random realisations, we add all‐scenario‐feasibility robust constraints which are infinite‐dimensional constraints into the model. To solve the model, a vertex scenario set based on the characteristics of convex optimisation is constructed to equivalently convert infinite‐dimensional constraints to finite‐dimensional constraints. In this way, the coordination model is transformed to a mixed integer linear programming and can be solved using CPLEX. Finally, numerical test based on a real iron and steel plant is analysed. The results show that coordination between energy with manufacturing flow is effective to reduce the energy cost and carbon emission. Compare with only optimising energy flow, the coordination model can reduce total cost about 221.6 thousand RMB and 304.44t coal every day.

Pascal A. Schirmer, Iosif Mporas

Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter in a household. Although when data from the same household are used to train a disaggregation model the device disaggregation accuracy is quite high (80% - 95%), depending on the number of devices, the use of pre-trained disaggregation models in new households in most cases results in a significant reduction of disaggregation accuracy. In this article we propose a transferability approach for Non-Intrusive Load Monitoring using fractional calculus and normalized Karhunen Loeve Expansion based spectrograms followed by a Convolutional Neural Network in order to generate device characteristic features that do not change significantly across different households. The performance of the proposed methodology was evaluated using two publicly available datasets, namely REDD and REFIT. The proposed transferability approach improves the Mean Absolute Error by 13.1% when compared to other transfer learning approaches for energy disaggregation.

Nitesh Funde, Sung‐Guk Yoon

Abstract Microgrids (MGs) have emerged as a key platform for integrating distributed energy resources into distribution systems. However, a high penetration of renewable energy resources in an MG causes an imbalance frequently between energy generation and load. One solution to this problem is internal energy trading among MGs, where MGs trade (i.e. buy or sell) energy with other MGs in the network. In internal trading, multiple buyer MGs may compete to obtain energy from seller MGs with surplus energy. By contrast, seller MGs also compete to sell their surplus to those confronted with a deficit. Considering this scenario, we propose a novel rank‐based energy scheduling strategy for networked MGs in a distribution system to solve the competition between buyer and seller MGs. The technique for order of preference by similarity to the ideal solution method, which is a multi‐criteria decision‐making approach, is applied to prioritise the MGs. The proposed strategy determines the amount of internal energy to trade for each seller/buyer MG. Different from existing mechanisms, the proposed rank‐based approach evaluates each MG with respect to four criteria: ratio of energy, forecasting accuracy, contribution of supply, and historical performance. The proposed strategy is compared with existing no‐rank‐based scheduling and absolute contribution‐based method. Moreover, the effect of each criterion on the cost of each MG is illustrated by considering and disregarding the criterion in the proposed strategy that demonstrates the effectiveness of the proposed strategy in reducing the cost of MGs according to their rank in the network.

Xiaolong Li, Guangkuo Zhang, Xin Lin

Abstract The surface charge characteristics of a ±200 kV gas‐insulated transmission lines spacer are studied to understand the influence of geometry, ion transportation, applied voltage, and gas pressure on the surface charge distribution. The simulation result indicates that the geometry simplification would affect the surface charge distribution. A millimeter‐level protrusion on the spacer causes an increase of 20 μC m−2 in charge density. A difference of 10 μC m−2 is found between the simulations based on the measured and calculated ion mobility. Moreover, it is found that the identical surface charge distribution cannot be achieved with the downsized model based on the identity of field strength, since ion transportation is affected by the scaling of geometry. The influence of gas pressure on the ion migration results in an initial excitation to the field variation, which further promotes the variation of surface charge via the dominative bulk conduction. Thus, with increasing pressure from 0.3 to 0.6 MPa, an increase of 8.4 μC m−2 is found on the convex surface, while a decrease of 5.5 μC m−2 is found on the concave surface. This investigation would be helpful for the simulation and experiment concerning the surface charge characteristics of spacers.

Syed A. Raza, Jin Jiang

With increased installation of single-phase rooftop PV systems, in-house battery storages, and high-power plug-in loads (i.e. EVs) at single-phase residential sites, it is prevalent that more and more distribution systems are becoming severely unbalanced causing power quality problems and thermal risks at distribution substations. To solve this problem, two main strategies have been developed previously by the authors, known as intra- and inter-phase power management strategies. The latter makes use of interconnecting back-to-back converters between the phases to mitigate the phase imbalance caused by the various single-phase DG units and loads. This paper devotes laboratory evaluation of the developed schemes and demonstrate key features experimentally on inter-phase power management. Two main experiments have been carried out to confirm that the developed technique can use the surplus power capacity from one phase to support the load demand in another phase to achieve dynamic power balance. From the point view of the substation transformer, the three-phases will always appear to be balanced despite the fact that different phases can have very different local generation and load profiles.

Negin Zarbakhsh, M. Saeed Misaghian, Gavin Mcardle

COVID-19 non-pharmaceutical interventions (NPIs) are changing human mobility patterns; however, the effects on power systems remain unclear. Previous loads and timings along with weather features are often used in literature as input features in load forecasting, but these may be insufficient during COVID-19. As a result, this paper proposes an analytical framework to assess the impact of COVID-19 on power system operation as well as day-ahead electricity prices in Ireland. To improve peak demand forecasting during pandemics, we incorporate mobility, NPIs, and COVID-19 cases as complementary input features and representative of human behaviour changes. By defining different combinations of these explanatory features, several Machine Learning (ML) algorithms are applied and their performance is compared with the baseline scenario currently used in the literature. Using SHapley Additive Explanations (SHAP), we interpret the best performing model, Light Gradient Boosted Machine, to determine the influence of each feature on the predicted outcomes. We discover that typical load forecasting features still influence ML outcomes the most, but mobility-related changes are also significant. Our finding shows that NPIs impact human behaviour and electricity consumption during times of crisis and can be used in the context of load forecasting to assist policymakers and energy distributors.

Mehrnaz Ahmadi, Mehdi Khashei

Abstract Wind power is one of the most important renewable energy sources that is widely used in many developed and developing countries. However, it is generally stated in the literature that providing accurate forecasts for large‐scale planning purposes is not a simple task, especially by single models. It is the main reason for this fact that why researchers in recent years have sought to propose hybrid models for increasing the accuracy of predictions. In general, choosing the appropriate type and the number of components, as well as the proper type of hybridization methodology, are the most effective factors in the performance of the developed hybrid models. Although in the literature, numerous attempts have been made in order to answer these questions, there is no general consensus on this matter. For this reason, the main idea of this paper is to concurrently combine different hybrid methodologies as well as different single models in order to benefit from the advantages of these models and methodologies, simultaneously. In this way, three well‐known and widely used hybrid methodologies, including the preprocessing, the series, and the parallel methodologies, are combined together by incorporating the linear/nonlinear and certain/uncertain components. In addition, in the proposed model, a new process is proposed based on the complex/uncertain modelling to model the preprocessing phase residuals, which have been ignored in the modelling procedures. In this way, in the first stage of the proposed model, the data is preprocessed by the Kalman filter as a preprocessing approach in order to divide data into two groups of trend and residual patterns. The trend data provided in the previous step, with the original data, are simultaneously considered input data of an autoregressive integrated moving average as the certain linear model and a multilayer perceptron as the certain nonlinear model for certain linear and nonlinear modeling of patterns. This step is repeated for the residual data by the series hybridization of models in the previous stage by the fuzzy models for the uncertain linear and nonlinear modelIng of patterns. Finally, each component's weight is optimally calculated by the least square algorithm, and then the results are combined together in a parallel process. Empirical results of two benchmarks of wind domain indicate that the proposed method has averagely improved the performance of its component used separately, parallel‐based hybrid models, and series‐based hybrid models 46%, 22%, and 19%, respectively, for predicting wind power time series.

Mohammad Jafarian, Alireza Soroudi, Andrew Keane

Abstract The widespread integration of intelligent electronic devices has facilitated the employment of data mining methods in evaluating the operating condition of distribution systems. This possibility comes to prominence in active networks, where distributed energy resources can cause unforeseen dynamics that requires an effective monitoring infrastructure and a fast‐track procedure to convey the system operating condition in a comprehensible manner to the operator. To this end, a data‐driven approach is proposed to assess the status of system operating constraints by presenting each constraint as a classification problem. Afterwards, by exploiting the propounded presentation of the system operating condition, the measurement placement problem in distribution systems is addressed as selecting a set of features that have the most contribution to evaluating the system operating status . To do so, first, the effectiveness of the measurement units is identified through their contribution to the classification process, and then a procedure is proposed to pinpoint the measurement units with redundant information. Monte–Carlo simulations are performed to provide a comprehensive training set. Receiver operating characteristic analysis and time‐series power flows demonstrate the effectiveness of the proposed approaches.

Antonin Demazy, Tansu Alpcan, Iven Mareels

Distributed Energy Resources (DER), mainly residential solar PV, are embedded deep within the power distribution network and their adoption is fast increasing globally. As more customers participate, these power generation units cause Reverse Power Flow (RPF) at the edge of the grid, directed upstream into the network, thus violating one of the traditional design principles for power networks. The effects of a single residential solar PV system is negligible, but as the adoption by end-consumers increases to high percentages, the aggregated effect is no longer negligible and must be considered in the design and configuration of power networks. This article proposes a framework that helps to predict the RPF intensity probability for any given scenario of DER penetration within the distribution network. The considered scenario parameters are the number and location of each residential DERs, their capacity and the daily net-load profiles. Classical simulation-based approach for this is not scalable as it relies on solving the load-flow equations for each individual scenario. The framework leverages machine learning techniques to make fast and precise RPF prediction within the network for each scenario. The framework enables the Distribution Network Service Providers (DNSPs) to assess DERs penetration scenarios at a granular level, derive and localise the RPF risks and assess the respective impacts on the installed assets for network planning purpose. The framework is illustrated with scenario analysis conducted on an IEEE 123 bus system and OpenDSS and shown that it can lead to multiple orders of magnitude savings in computational time while retaining an accuracy of 94% or above compared to classical brute force simulations.

Sreten Davidov, Jurij Curk

This study presents a novel framework for investment prioritisation in a distribution network by performing a flexibility needs assessment with regards to power quality parameters. Power range and time instances of activation are identified with a view of maintaining normal operation. A step forward is made by incorporating the importance of the flexibility needs assessment in investment prioritisation as part of the network expansion planning. The proposed framework consists of three procedures: input data preparation, operational calculations and the post‐processing of results, which are used to quantify the flexibility needs and propose an investment prioritisation list. A bus node, which forms a part of the existing distribution network in Slovenia, is used to demonstrate the general applicability of the framework. As a result, an investment prioritisation list was compiled by assessing the flexibility needs. The secure power supply buffer of the node is determined, while the quantification of the flexibility power range and time instances of activation are also provided in order to further mitigate network constraints. Apart from providing valuable information to network operators, the newly proposed framework lays the foundation for aggregators and market players to plan and self‐balance their portfolio and position when providing flexibility market services.

Shin’ya OBARA

In order to make an electric power of renewable energy source increase, development of a new transmission network is effective. However, it is because installation of a new transmission network takes a huge investment, operating technique regarding improvement of the utilization factor of a transmission network is investigated. On the other hand, stabilization of distribution electric power by hydrogen energy careers, such as ammonia or methyl-cyclohexane (MCH) is expected to contribute to the drastic increase in utilization factor of the transmission network. Moreover, because these hydrogen energy careers can store electric power, it is effective in making the reliability of the transmission network increase. The energy flow of the system accompanied by use of each energy career of ammonia or MCH is clarified, the examination method of the utilization factor of the transmission network with the electric power leveling by the hydrogen career was proposed. The proposal analysis method is applied to a transmission network of Wakkanai, the relation between the amount of introduction of renewable energy and the utilization factor of the transmission network, and the utilization factor of the transmission network using a hydrogen career were clarified.