Reliable Operation Of Power System Research Articles

Real-time energy optimization and scheduling of buildings integrated with renewable microgrid

Real-time energy optimization is essential for effective load scheduling, cost reduction, maintaining demand and supply balance, and ensuring reliable power system operations. However, real-time energy optimization is challenging due to the unpredictable nature of renewable energy sources (RES) and the behavior of electric loads. On this note, a rigid model is required that can deal with this dilemma. Thus, the Lyapunov optimization technique (LOT) emerged as a solution for the real-time energy optimization problem. This work investigates a smart home equipped with inflexible loads (TV, computer, light, etc.), flexible loads (EVs, HVAC, water heaters, etc.), and RES (photovoltaic and wind energy) in a grid-connected mode that ensures energy trading (purchasing and selling of energy). The aim is to optimize total cost, thermal discomfort cost, and batteries and EVs charging/discharging using LOT by real-time energy optimization, which does not require any system parameters to be anticipated. The proposed algorithm employs LOT for four queues to solve the real-time energy optimization problem. Simulations are conducted for different scenarios and varying weather conditions to endorse the effectiveness of the developed real-time energy optimization solution in various aspects of the performance metrics.

An Effective Method to Estimate the Aggregated Flexibility at Distribution Level

The exploitation of flexibility services provided by distributed energy resources (DERs) located in the distribution grid becomes increasingly important for the secure and reliable operation of power systems. These resources are typically small-scale and located close to end-users, however their aggregated flexibility can offset the imbalance caused by the stochastic variations of the renewable power sources. This paper proposes a methodology to estimate with increased accuracy the available flexibility of DERs in an Active Distribution Grid (ADG) at the points of interconnection with the transmission system. An optimization-based approach is used to estimate the available maximum active and reactive power flexibility at each TSO-DSO substation for specific directions formed by constant active to reactive power ratios. In order to keep calculations manageable and calculate the available flexibility with high accuracy, the search directions are sampled from a distribution that is iteratively updated at each step. Three variations are proposed for the angle sampling, both in the angle domain and in the p-q domain. Each variation is tested on a modified 18-bus radial distribution system and its effectiveness and convergence is compared.

A Scenario-Oriented Approach to Energy-Reserve Joint Procurement and Pricing

We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources. These issues have so far been largely addressed empirically. In this paper, we first develop a scenario-oriented energy-reserve co-optimization model, which directly connects reserve procurement with possible outages and load/renewable power fluctuations without the need for empirical reserve requirements. Accordingly, reserve can be optimally procured system-wide to handle all possible future uncertainties with the minimum expected system total cost. Based on the proposed model, marginal pricing approaches are developed for energy and reserve, respectively. Locational uniform pricing is established for energy, and the similar property is also established for the combination of reserve and re-dispatch. In addition, properties of cost recovery for generators and revenue adequacy for the system operator (SO) are also proven. Simulations validate the effectiveness of the proposed mechanism.

A Recent Review on Approaches to Design Power System Stabilizers: Status, Challenges and Future Scope

Modern power system networks are complex and subjected to several uncertainties. Due to the complexity and uncertainties involved in power system operation, the networks are prone to instabilities. Rotor-angle instability is one such issue that, if not addressed properly, may lead to system collapse. A power system stabilizer (PSS) is primarily a power oscillation damping (POD) controller used to dampen power oscillations, thereby improving rotor angle stability. The proper design of PSSs is a challenging task and is essential for the secure and reliable operation of power systems. In the past, many power system instability events occurred, resulting in cascading failures and even blackouts. In the literature, several methods are proposed for the efficient design of PSSs or POD controllers. In this contribution, a recent review on the design of PSS is presented, along with challenges and the future scope of research in this field. On the basis of this review, it can be concluded that there is ample scope for designing PSS in a more efficient and robust manner, considering several uncertainties that may occur during the operation of modern power systems.

Power transformer overload risk evaluation based on temperature rise simulation and fuzzy analytic hierarchy process

The evaluation of transformer overload risk and operational safety is a systematic process. Selecting an appropriate method to accurately assess its overload operation risk is an urgent need for flexible control and safe and reliable operation of power systems. According to the thermal characteristics and state of the transformer, a method for evaluating the overload risk of transformers with improved accuracy based on temperature rise simulation calculation combined with the Fuzzy Analytic Hierarchy Process (FAHP) is proposed. The weights are calculated by fuzzy set theory and FAHP, and the overload risk of the transformer is finally determined. The method comprehensively considers the possible information conflict between the working condition data and the thermal characteristics of the structure itself, and the calculation results are accurate and the process is clear. The calculation results after substituting the actual data show that the method can accurately evaluate the overload risk and operational risk of the transformer. This method is beneficial to identify the relative importance of transformer overload risk factors, calculate transformer overload risk coefficients, and formulate transformer risk control methods.

Characterizing Low-Data-Rate Power Line Communication Channels

Electric power lines could, in principle, provide a ubiquitous wired communication network linking electrical loads, power meters, and other devices. This communication infrastructure could unlock new sensing and control capabilities, improving the efficiency and reliability of power system operations. In practice, however, power line communication (PLC) is complicated by both known and variable difficulties related to background noise, signal attenuation, and unknown line impedances. This article develops instrumentation that characterizes and verifies line characteristics for PLC applications, especially for demand response and control applications. This instrumentation provides a window to key variables that affect communication on a local power distribution network. With these measurements, limits on PLC communication can be verified before an installation, and a PLC system may also be adaptively tuned to measurements of the current local environment. Field experiments in a challenging microgrid and a 24-floor apartment building demonstrate an appropriately tailored PLC system communicating reliably in locations where conventional PLC fails.

Two-Layer Generation Expansion Planning Based on Flexibility Balance

Reasonable planning of flexible power sources is crucial for the reliable operation of new power systems based on new energy sources. However, in the long-term planning, how to consider the short-term ramping capability of the flexible power supply to meet the capacity demand of the net load and the flexible ramping requirement at the same time is still an unsolved problem. Therefore, this paper conducts research on generation expansion planning based on flexibility balance. Firstly, the flexibility balance are modeled based on the flexible supply domain and the net load fluctuation domain, which are used as the flexible supply and demand constraints in the planning stage. Then, a two-layer expansion planning model is constructed, including two modules of investment decision and production simulation. Flexibility index is proposed to quantify the system’s ability to adjust at different levels of payload, and incorporated into the planning model for iterative optimization. Finally, an example is verified on the improved IEEE-39 node system. The test results show that the long-term generation expansion planning scheme based on the balance of flexible supply and demand can effectively meet the net load demand and its ramping demand.

Primeri ispitivanja energetskih transformatora nakon ispada iz pogona i loših rezultata hemijske analize uzorka ulja

Power transformers represent one of the most important components in terms of generation, transmission and distribution of electrical power. Precisely for this reason, it is necessary to pay some attention in terms of preventive electrical measurements that should be done in order to have an adequate insight into their condition, avoid unwanted breakdowns and thus ensure stable and reliable operation of the entire power system. However, there are situations when power transformers fail due to various factors. In such cases, depending on which protection has reacted and preemptively switched off the observed transformer, and especially in the case of an outage due to the differential protection of the transformer, it is necessary to carry out certain tests before possible reconnection in order to assess the reason for the outage and the continued ability of the transformer itself to work. Electrical tests should be preceded by sampling oil from the transformer (and/or gas and oil from the Buchholz relay) for chemical analysis by the gas chromatography method. The results of the chemical analysis of the oil sample can express doubts about possible irregularities that may be a problem for further normal operation of the transformer and such situations are followed by certain electrical tests whose results in most cases will give a better insight into the state of the transformer itself. This paper will give examples of the results of electrical measurements (most often insulation resistance and dielectric loss factor of windings, magnetizing currents and ohmic resistance of windings) made after power transformers were out of operation and measurements after bad results of chemical analysis of oil samples.

Comparison of Different Simulation Tools for Load Flow Analysis in Power Systems

The comparative analysis provides valuable insights into the capabilities and limitations of different simulation tools, helping to improve the accuracy and efficiency of load flow studies, ultimately contributing to the reliable and secure operation of modern power systems. In this paper load flow analysis of IEEE 9 bus system is performed on three different software tools i.e. Power world simulator (PWS), MATLAB, MATPOWER. The results of load flow analysis of all three tools are compared. The economic load dispatch problem is also resolved in PWS and MATPOWER software. The results are promising and efficient when compared with latest ones.

A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

Multi-objective energy optimization is pivotal for reliable and secure power system operation. However, multi-objective energy optimization is challenging due to interdependent and conflicting objectives. Thus, a multi-objective optimization model is needed to cater to conflicting objectives. On this note, a multi-objective optimization model is developed, where a non-dominated genetic sorting algorithm is employed to optimize objectives pollution emission, operation cost, and loss of load expectation (LOLE) considering renewable energy sources (RES). RES, like wind and solar, are intermittent and uncertain, which are modelled using a beta probability density function (PDF). The developed method’s effectiveness and applicability are analyzed by implementing it on the 30-bus system, and the results are compared for two cases. Findings reveal that the developed multi-objective optimization model minimizes operation cost, pollution emission, and LOLE by 59%, 7%, and 2.67%, respectively, compared to existing models.

Features of ensuring high-quality work of modern electrical power systems of unmanned vessels

Unmanned seagoing vessels are increasingly being used. In the paper the dump scow Rabochaya is considered as an example of an unmanned vessel. An extended marine control system, a video surveillance subsystem, a surveillance and search engine are additionally installed on the scow to carry out industrial tasks in unmanned mode. The correct operation of these systems and the whole vessel depends on the high-quality operation of the ship's electrical power system. The aim of the study is to identify abnormal modes of parallel operation of marine diesel generator sets and to develop methods and means for their elimination. The results of the experimental research and mathematical modeling have revealed the existence of power exchange oscillations when the ship generators operating in parallel. Power exchange oscillations can be detected with the help of generator current oscillations. It has been proposed to introduce a unit for eliminating power exchange oscillations (EPEO) into the automatic control system of ship electrical power systems for eliminating such oscillations. An algorithm for the operation of the EPEO unit has been developed, it controls the settings of the rotational speed regulators of diesel-generator sets. The proposed methods and tools reduce the amplitude of power exchange oscillations to an acceptable value, and therefore contribute to the high-quality operation of the ship's electrical power system and control systems in unmanned mode. The results obtained make it possible to ensure reliable and trouble-free operation of any autonomous electrical power system during the operation mode without maintenance personnel.

Transformer Implementation for Short Term Electricity Load Forecasting, Case Study: Bali, Indonesia

Short term load forecasting is a crucial process in ensuring optimal and reliable operation of electric power system which is critical in sustaining highly technological economies. Various approaches and methods have been implemented in forecasting electricity load of a system including statistical methods such as auto regression and machine learning methods such as support vector machine and also deep learning methodology as recurrent neural network methodology which gain popularity in electricity load forecasting nowadays. In this paper, Transformer as a deep learning methodology is used to forecast hourly electricity load in Bali Area. Three lookback days scenario and ten days of forecast period are used to evaluate the performance of the Transformer models. This study suggest that although higher lookback days will give more complicated model due to increasing number of parameters involved, the best overall prediction performance are given by transformer model with 1 day of lookback period. The three model in this study also tend to have low prediction performance in predicting electricity load for weekend or holiday period. Future study using multivariate transformer model is suggested to improve the prediction performance of the transformer model in predicting electricity load in Bali area.

Transmission Congestion Management in Deregulated Power System Using Adaptive Restarting Genetic Algorithm

Power systems in a deregulated environment have more intense and recurrent transmission line congestion than conventionally regulated power systems. With the help of generation rescheduling, this article shows how to effectively manage congestion in the day-ahead energy market by taking corrective measures to reduce congestion. The research employs an adaptive restarting genetic algorithm (ARGA) to provide an effective congestion management strategy in a deregulated power market (DPM). The study makes two significant contributions. First, the generator sensitivity factors (GSF) are calculated to choose re-dispatched generators. Second, the least congestion cost is calculated using the adaptive restarting genetic algorithm. Several different line outage contingency cases on IEEE 30 bus systems are used to examine the suggested algorithm’s implementation efficacy. The simulation results demonstrate a significant reduction in net congestion costs, resulting in a more reliable and secure power system operation. The proposed algorithm was tested in a python environment, and power flow analysis was done using the PANDAPOWER tool. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The ARGA technique gives a lower congestion cost solution than the particle swarm optimization (PSO), real coded genetic algorithm (RCGA), and differential evolution (DE) algorithm.

Model-and-data hybrid driven method for power system Operational reliability evaluation with high penetration of renewable energy

With the rising penetration of renewable energy, the fluctuation of operating conditions such as wind speed and light intensity affects the reliability of the power system. It leads to the repeated reliability evaluation of the power systems and increases the computational complexity. Based on the non-sequential Monte Carlo simulation (NMCS) and back propagation neural network (BPNN), a new model-and-data hybrid driven method for power system reliability evaluation is proposed. Firstly, NMCS is used to calculate the system reliability indices under different operating conditions to obtain the input data for neural network training. Then, the proposed Multiple BPNN (M-BPNN) is used to establish the highly nonlinear mapping relationship between operating conditions and system reliability indices, which can significantly reduce the calculation time of power system operational reliability evaluation with high penetration of renewable energy. The IEEE-RTS 79 system is used to verify the effectiveness and efficiency of the proposed method, and sensitivity analysis is performed.

Optimal scheduling of ancillary services provided by an electric vehicle aggregator

Massification of Electric vehicles (EVs) is becoming a worldwide reality as a means to combat climate change and local pollution. Considering that most of the time vehicles are in parking places, there is an opportunity for using EVs to provide some valuable services to the power network. In particular, EVs can provide ancillary services in electricity markets through an aggregating agent. To this end, EVs aggregators need to develop decision support tools to optimally allocate energy and regulation resources considering power network constraints. Unlike optimization models for EVs aggregators currently available in the literature, in this paper we propose an optimization approach for EVs aggregators that jointly considers the most important aspects influencing EVs profitability, such as uncertainty, drivers’ patterns, capacity constraints, state of charge constraints, regulation demand constraints, regulation offer constraints, regulation bounds constraints, and power-system security constraints. The optimization problem is formulated as a mixed-integer linear programming problem, thus ensuring global optimality. Results are presented in the form of the hourly allocation for charging/discharging power profiles, distinguishing between day-ahead energy and capacity/energy for regulation, and the profit that can be reached, while accounting for network constraints.The proposed model is illustrated through a case study, which allows us to show that EVs aggregators allow for leading to a more reliable power system operation, avoiding transmission lines congestion, while providing important profits for EV owners who are able to provide regulation services.

Nano-Optomechanical Resonators Based Graphene/Au Membrane for Current Sensing

The monitoring of electric current is essential to ensuring the reliable operation of power systems and electronic equipment. The present study demonstrates an optomechanical cavity-based resonator suitable for current sensing. The proposed device consists of a suspended graphene/Au membrane supported by a ceramic ferrule with a bore diameter of approximately 125 µm and incorporates optical actuation and detection processes. The present sensor exhibits high current sensitivities of approximately 7.91 and 18.04 Hz/mA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> when monitoring the resonant frequency at first and second-order vibrational modes. This optomechanical current sensor operates for the first time in the 100 mA range, an advance of more than two orders of magnitude on the sole previously reported cavity optomechanical current sensor. Moreover, the current sensor shows a short response time of ∼2 ms and provides the additional advantages of low power consumption and a compact structure. This device is expected to have applications in high-precision current and magnetic field sensing.

An Integrated Missing-Data Tolerant Model for Probabilistic PV Power Generation Forecasting

Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic operation of a modern power system. In practice, due to various faulty issues in the sensor, communication, or database system, the historical and online measurement data may not be always complete, and the missing data could dramatically degrade the forecasting model's accuracy. To solve this problem, this paper proposes an integrated missing-data tolerant model for probabilistic PV power generation forecasting. Taking historical PV generations as input, this model is based on a recursive long short-term memory network (Rec-LSTM), which can provide multi-step ahead forecasting of the probability distribution of PV generation. The unobserved input data will be imputed recursively based on the model output at the previous time step. During the training process, the imputations and forecasting values are iteratively updated by the negative log-likelihood loss function. As a salient advantage, this method can deal with data missing scenarios at both offline and online stages. Numerical experiments are conducted on two one-year datasets from Australia and Singapore, respectively. Probabilistic forecasting for both large-scale and small-scale building-level PV power generation is tested at the time resolution of 15 mins. Testing results show the proposed method can achieve superior probabilistic prediction accuracy as well as strong robustness under various data missing scenarios, compared to other state-of-the-art methods.

Discharge development process and flashover performance of snow-accreted insulators under natural environment

The safe and reliable operation of power system in China is significantly affected by snow-covered insulator. At present, many studies on snow-accreted insulator have been undertaken by domestic and foreign scholars in the laboratory. However, whether the laboratory test is equivalent to the field test and whether it can be directly used to guide the external insulation design of transmission line still need to be evaluated. In this regard, AC flashover test of snow-coated insulator was carried out in the laboratory and field in this paper, and then the characteristics of AC discharge path and the variation of leakage current of snow-accreted insulator with time were obtained, and its flashover performance was compared and analysed. The research results show that the amount of snow on the insulator gradually decreases from the stagnation point at the edge of the shed on the windward side to both sides, which is attributed to the uneven distribution of the local collision coefficient of snow crystal particles on the insulator surface. The arc is yellow during the flashover of snow-accreted insulator in the artificial climate chamber. While in the natural environment, the discharge arc appears blue and purple, and the phenomenon of arc levitation and snow layer shedding can be observed. For composite insulator FXBW-35/70 covered with different degrees of snowing in the natural environment, the flashover path develops along the creepage distance in case of the slight snowing, while the path propagates over the dry arc distance under severe snowing condition. Consequently, the flashover voltage decreases by 48.7%. There are many discrepancies between different types of the insulators, such as the creepage distance, dry arc distance and other structural parameters, and the number of air gap after snowing, which affect arc dynamic development properties. Hence, the electrical performance of composite insulator is better than that of glass insulator, and the snowing flashover voltage of the former is 5.56–9.47% higher than that of the latter.

Transmission congestion management through sensitivity based rescheduling of generators using improved monarch butterfly optimization

The emergence of competitive power markets in recent decades has paved the way for the enormous increase in the number of transactions between producers and consumers to satisfy the exponentially increasing load demand. These transactions usually occur through direct bilateral, multi-lateral agreements or some common platform. However, this operation puts additional strain on the transmission network, which serves as a conduit between producers and consumers leading to congestion. As a result, satisfying all the committed power transactions under congested transmission lines economically becomes infeasible. Independent System Operator (ISO) is an entity that is responsible for ensuring congestion-free power transactions to preserve the system's security and reliability. The objective of this work is to mitigate the congestion in the network while satisfying the system constraints resulting in more stable operation with reduced line flows and low system losses. The generator sensitivity factor (GSF) is utilized to single out the generating units that need to be rescheduled to alleviate transmission lines congestion. Further, the proposed variants of Monarch Butterfly Optimization (MBO) namely Improved MBO with linear weighting factor (IMBO-LW) and chaos-embedded MBO (CHMBO) are used to reschedule the generators with an aim to minimize the cost of rescheduling and the total change in power after rescheduling. Additionally, standard benchmark functions are used to validate the effectiveness of the proposed MBO variants and the same variants have been scrutinized on modified IEEE 30-bus and New England 39-bus test systems to alleviate the problem of congestion for secure and reliable operation of power system.

A Modified Multiparameter Linear Programming Method for Efficient Power System Reliability Assessment

Power systems face adequacy risks because of the high integration of renewable energy. It is urgent to develop efficient methods for power system operational reliability assessment. Conventional power system reliability assessment methods cannot achieve real-time assessment of system risk because of the high computational complexity and long calculation time. The high computational complexity is mainly caused by a large number of optimal power flow (OPF) calculations. To reduce the computational complexity, this paper transfers the optimal power flow model as a multiparameter linear programming model. Then, the optimal power flow can be obtained by linear calculations. Furthermore, this paper proposes a state reduction method considering the importance index of transmission lines for further improving the calculation efficiency. Case studies are carried out on IEEE standard systems and a provincial power grid in China. Compared with the conventional reliability assessment method, the reliability assessment efficiency of the proposed method increases by 10–40 times, and the assessment error is less than 1%.