Operation Of Electric Power Systems Research Articles

From interconnections of local electric power systems to Global Energy Interconnection

The interconnections of electric power systems are developed for the economic benefits and in order to increase the overall power supply reliability and quality level. Development of power industry shows the positive effects in operation of the country-wide electric power systems and international interconnections. Creation of World Energy System or, by the other words, Global Energy Interconnection is objective trend on the way of expansion of international and intercontinental electric power interconnections. Several important aspects of above mentioned problems are discussed in this paper.

The Hybrid Real-Time Dispatcher Training Simulator: Basic Approach, Software-Hardware Structure and Case Study

AbstractThe article presents the Hybrid Real-Time Dispatcher Training Simulator (HRTDTS). The main advantages of this simulator are the adequate simulation of a single spectrum of processes in the object for training – any real electric power system (EPS), as well as the real-time control of the equipment circuit-mode states in modes of the EPS operation. This is achieved by the HRTDTS development within the concept of hybrid simulation, which allows to surpass the modern widely-used simulators based purely on numerical simulation, which do not comprehensively reproduce processes in the EPS and limit the training of certain dispatchers skills. The dispatcher simulator structure and the developed specialized HRTDTS software are demonstrated. The developed dynamic dashboards for monitoring and operating, implementing the circuit-mode states visualization of the equipment or the EPS districts that correspond to specific objects for training, are presented. The special panels that display extended information about the processes in the EPS are also shown in the article. The comparison of simulation results with the data, obtained via RTDS, for the HRTDTS validation was carried out. The practical emergency scenario for the training of dispatching personnel, including the assignment of an emergency situation and the actions of dispatchers to eliminate it, was created for testing and demonstration of the HRTDTS capabilities.

Scheduling of electricpower system operation in the light of amodern whole sale market

Scheduling of electricpower system operation in the light of amodern whole sale market

Development of a mathematical model for cost distribution of maintenance and repair of electrical equipment

The research is devoted to the development of a model for cost distribution of maintenance and repair of electrical equipment when making decisions on the management of the electric power system state. The decrease in the reliability of electric power system operation, caused by the objectively existing aging of electrical equipment, requires consideration of equipment significance when planning the maintenance and repair. For this purpose, it is proposed to use the theory of fuzzy sets, Saati’s method and Boolean programming method. The result of solving the optimization problem of multicriteria analysis is a vector of the best alternatives, built on the principle of dominance. The developed algorithm of complex simulation of the electric power system state and cost distribution of maintenance and repair for making decisions on the determination of priority of electrical equipment out of service allows for effective decision­making. The results of probabilistic and statistical simulation of electric power system states using the Monte Carlo method allow us to take into account the probabilistic nature of emergency situations in the electric power system when determining its weakest elements that require priority replacement. The advantage of the proposed approach is taking into account the technical condition of electrical equipment for risk assessment of the electric power system emergency situation. A comparative analysis of ranking results of electrical equipment based on the emergency risk assessment of the electric power system confirmed the high efficiency of the planning of electric power system states when solving the problems of preventive control. The developed model will be used for further research and development of the algorithm for making effective decisions regarding the operation strategy of the electrical equipment and preventive control of the subsystem operation of the electric power system. The obtained results of complex simulation of the electric power system state and technical condition of the electrical equipment give grounds to assert the possibility of software implementation of operation risk analysis of the electric power system for power supply companies

Optimization model for charging infrastructure planning with electric power system reliability check

This paper presents a significantly improved optimization model for the planning of the charging infrastructure for electric-drive vehicles, where the optimization objective function is the minimization of overall (installation, maintenance, operation) placement costs of charging stations with regards to a charging technology. The constraints involve the electric power system reliability check, ensuring charging reliability and the required quality of service of the charging infrastructure. In ensuring the charging reliability, at least one candidate location must be selected within the driving range of electric vehicles and suitable charging technologies placed to accommodate the disposable charging times of electric vehicle users for the requested quality of service. The proposed optimization model presents an upgrade of an existing optimization formulation since it includes a power system reliability check based on a DC power flow model. To show the general applicability and significance of the model, a test 10 × 10 grid road network and a standard six-bus test power system are considered. Numeric results illustrate the optimal charging stations placement layout and overall costs placement for different driving ranges and the required quality of service level by including a power system reliability check, to serve both the charging infrastructure investors and electric power system operators.

Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium

This article discusses a new method for the sizing of operating reserves by electric power system operators. Operating reserves are used by system operators to deal with unexpected variations of demand and generation, and maintain a secure operation of the system. This becomes increasingly challenging due to the increasing share of renewable generation based on variable resources. This paper revisits the current sizing method applied in Belgium, which is based on a static approach that determines the required capacity once a year. The presented dynamic sizing method determines the required capacity on a daily basis, using the estimated probability of facing a system imbalance during the next day. This risk is estimated based on historical observations of system conditions by means of machine learning algorithms. A proof of concept is presented for the Belgian system, and demonstrates that the proposed methodology improves reliability management while decreasing the average capacity to be contracted. The method is compliant with European market design, and the corresponding regulatory framework, and is of particular interest for systems with a high share of renewable generation. For these reasons a gradual implementation in Belgium towards 2020 has been decided based on the results of this study.

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

Optimal joint strategy of wind battery storage unit for smoothing and trading of wind power

Wind power is intermittent and causes problems to the operation of electric power systems. This leads to the need for flexible battery storage units to smooth the wind power output. However, the volatile profile of wind power output affects the operational performance of battery units. We propose here an optimisation model to define the optimal hourly commitment of a wind battery storage unit while also including relevant operational constraints for the battery and modelling its degradation. Results show that although the wind farm can have reduced revenue in the shorter-term because of these constraints, the revenue of the wind farm increases in the longer-term and the lifetime of the battery is also extended.

A Multi-Objective Demand Response Optimization Model for Scheduling Loads in a Home Energy Management System.

Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Belém—PA, Teresina—PI, Cuiabá—MT, Florianópolis—SC and São Paulo—SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina—PI; with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers.

Very short term load forecasting peak load time using fuzzy logic

One of the important things to do in electric power system operation is load forecasting. Load forecasting consists short term load forecasting and very short term forecasting. The very short-term load forecasting is predicting electrical loads in every 30 minutes. This forecasting is done to decide which plant to operate. The capacity of the plant to be operated adjusts to the load plan to be supplied the next day One method utilized in this research is Fuzzy Logic. This method has been applied for short-term load forecasting and will be employed for very short-term forecasting peak load time. Fuzzy logic expected has a small MAPE (0,6244%).

Application of functional safety to electrical power equipment and systems in process industries

In process industries, the application of functional safety in preventing major incidents is a well-established practice. The functional safety standard IEC 61511 (IEC, 2016) is applied to the safety instrumented system (SIS) protection layers to avoid the undesired events or reduce the likelihood of the events or impacts due to failures in the process, process equipment, or its control system including human interactions. However, there are risks of catastrophic incidents due to electrical equipment failures as well. Therefore, one should not underestimate the importance of the management, design, installation, operation, and maintenance of electrical power systems and protection devices. Regulatory authorities, in some countries, require the owners or operators to address the risks that arise from electrical equipment failure.The risk-based assessment, allocation of safety functions to protection devices, the establishment of integrity requirements, design, installation, operation, and maintenance of electrical protection devices must be managed like the protection layers for the process units. This paper focusses on the application of IEC 61511 to the protection of electrical equipment and systems, available industry guidelines, and the unique challenges in implementing the functional safety standards. The paper guides the electrical engineers with an example risk assessment, identification of protection device and its safety integrity level (SIL), verification of the reliability of the protection device and establishing a maintenance and operation program.

Superresolution Reconstruction of Electrical Equipment Incipient Fault

With the rapid development of industry and technology, the electrical power system becomes more complex and the electrical equipment becomes more diverse. Defective equipment is often the cause of industrial accidents and electrical injuries, which can result in serious injuries, such as electrocution, burns, and electrical shocks. In some cases, electrical equipment fault may result in death. However, in some special situation, some fault is very small even invisible, such as equipment aging, holes, and cracks, so the detection of these incipient faults is difficult or even impossible. These potential incipient faults become the biggest hidden danger in the electrical equipment and electricity power system. For these reasons, this paper proposes a superresolution reconstruction method for electrical equipment incipient fault to ensure complete detection in electrical equipment, which aims to guarantee the security of electrical power system operation and industry production. Experimental results show that this method can get a state-of-the-art reconstruction effect of incipient fault, so as to provide reliable fault detection of electrical power system.

Research and application of a hybrid model based on Meta learning strategy for wind power deterministic and probabilistic forecasting

Wind power forecasting is becoming an increasingly significant part in the operation and programming of electric force and power systems. However, highly precise wind power forecasting is still a difficult and challenging issue owing to the randomisation and transience of the wind series. In this paper, a novel Meta-learning strategy is proposed for adaptively combining heterogeneous forecasting models that were selected from a constructed candidate model bank. This study first implements the Box–Cox transformation to the wind speed and wind power sequence. Subsequently, the wind power as well as wind speed, which are decomposed by adopting the wind direction, are regarded as the inputs of the individual models. They are used to train a base-level forecasting learner to model the forecasting values of the wind power series. Finally, models with poor performances are dynamically trimmed and combining the remaining individual models are combined by adopting the random forest algorithm for the subsequent deterministic and probabilistic forecasting task. The wind power data from a wind farm located in northwestern of China are adopted to illustrate the forecasting effectiveness of the developed approach. The simulation in three experiments demonstrated the following: (a) the proposed Meta-learning based model is suitable for providing accurate wind power forecasting; (b) the proposed Meta-learning based hybrid model exhibits a more competitive forecasting performance than the individual models by extract advantage of each models; (c) the proposed model not only improves the accuracy of the deterministic forecasts but also provides more probabilistic information for wind power forecasting.

Assessment of the Impact of Geomagnetic Disturbances on Korean Electric Power Systems

Geomagnetic disturbances have the potential to impact the operation of electric power systems, and thus the assessment of their impacts is required as the first step for secure power system operations. While the effects of the disturbances have been observed primarily at higher latitudes, geomagnetic problems are also observed at mid and low latitude locations, in particular including neighboring countries to Korea such as China and Japan. This paper deals with the assessment of impact of geomagnetic disturbances on Korean electric power systems. For the assessment, the geoelectric fields induced by the geomagnetic disturbances are calculated using geomagnetic data measured over the past 20 years in order to quantify the strength of geomagnetic events in Korea. Then, the geomagnetic currents on the grid driven by the geoelectric fields are computed. Finally, the increased reactive power absorption in high voltage transformers is analyzed and accordingly the change of system voltage magnitudes is identified to evaluate whether the system maintains the voltage stability. The systematic study concludes that during a strong geomagnetic disturbance, the Korean electric power system satisfies the associated standards in the U.S. and maintains system stability.

Stochastic wind speed modelling for estimation of expected wind power output

Increased wind energy penetration causes problems to the operation and system balancing of electric power systems. This in turn leads to the need for more detailed wind power modelling. The modelling and management of wind power involves two stages, neither of which is analytically tractable. In particular, the first stage involves stochastic variations in wind speed; wind speed typically presents noisy short-term variations, plus cyclicality over periods of 24 h and longer. The second stage refers to stochastic variations of the resulting wind power output, a non-linear function of wind speed. This paper proposes and tests an Ornstein-Uhlenbeck Geometric Brownian Motion model in continuous time to represent the wind speed, while including its longer-term daily cycle. It also illustrates a partial differential equation model of the wind speed and of the resulting wind power output, aiming at computing both their statistics. The proposed stochastic model has the potential to be used for various applications where wind speed or wind power are stochastic inputs, such as the optimal valuation of energy storage or system balancing. We verify by statistical tests that the results from the proposed model for the wind speed and the wind power match those from the empirical data of a wind farm located in Spain.

Very Sort Term Load Forecasting Using Interval Type - 2 Fuzzy Inference System (IT- 2 FIS) (Case Study: Java Bali Electrical System)

One of the important things to do an electric power system operation is load forecasting. Load forecasting consists of short-term forecasting and short-term forecasting. The very short term load forecasting are required for regulating electrical energy generation, maintenance arrangements and regulating the labor involved. This forecasting is done to decide which plant to operate. The capacity of the plant to be operated adjusts to the load plan to be supplied the next day. The very short-term load forecasting is predicting electrical loads with time intervals every 30 minutes for the next day. In this study using Interval Type-2 Fuzzy Inference System (IT-2FIS) because it delivers a high flexibility that can be developed using other methods (hybrid). Laying out the footprint of uncertainty (FOU) membership function of the Interval Type-2 Fuzzy Inference System (IT-2FIS). This method has been applied for short- term load forecasting and will be employed for very short-term forecasting. In very short-term load forecasting IT-2 FIS has Mean Average Percentage Error (MAPE) arround 0,729%.

Optimal Power Flow Using a Novel Metamodel Based Global Optimization Method

Optimal power flow (OPF) is one of the important task in the operation and control of electric power system. In this paper, a novel metamodel-based global optimization approach has been proposed and applied to the OPF problems. The approach use limited “expensive” sample data points from the original, computationally expensive optimization model to introduce the surrogate models or metamodels, and to effectively use “cheaper” sample points from the metamodel to speed up the search of global optimum with much reduced computation time and limited number of original model simulations, thereby effectively reducing the calculation amount and greatly improving the efficiency of the optimization search. The simulation verification has been carried out on the IEEE 30-bus test system and by comparing with the conventional population based global optimization methods, the numerical results have shown the effectiveness and feasibility of the proposed method.

Power Transformer Interruption Analysis Based on Dissolved Gas Analysis (DGA) using Artificial Neural Network

The power transformer is an important component in the power system, as it is directly related to the reliability of the electric power system operation. Therefore, the diagnosis of disturbances in power transformers is important for device safety as well as electrical system stability. Dissolved Gas Analysis (DGA) is a disruptive diagnostic technique in power transformers that has been recognized effectively, because it provides knowledge of the state of the transformer based on the dissolved gas content in the transformer oil. The DGA test results can be represented by different methods such as Doernenburg ratio, Rogers ratio, IEC ratio, Duvals triangle, and key gases. The problem presented here is that two methods namely Doernenburg ratio and Rogers ratio, for the same data inputs give two different results from the error diagnosis to know the actual state of the transformer. In this paper, a combination method is proposed to solve the problem of conflict between Doernenburg ratio and Rogers ratio by utilizing multi-layer artificial neural network perceptron to localize and identify the error on the transformer and to select the most appropriate method.

Analysis of the Influence of Distributed New Energy Access on Relay Protection

Access to new energy large scale has changed our traditional characteristics produced during operation of electric power system, relay protection of our traditional and automatic safety devices bring higher requirements and challenges [1-2]. This paper introduces the situation of new energy power generation connected to a power grid, analyzes the operation risk of new energy access on distance protection and safety device and puts forward some suggestions and measures to resist the risk of power grid relay protection and self-installation. Research and analysis on relay protection and safety device for distributed new energy access to a regional power grid.

Two layer hydropower plant dynamic mathematical modelling using synchronized measurements

The operation and control of electric power system highly depends on knowledge of dynamic conditions. Power plants, especially hydropower plants, have the highest impact on overall power system dynamics. Therefore, dynamic mathematical model of hydropower plant was developed in order to conduct a theoretical research of the dynamic conditions of the transmission power system during normal and abnormal operating conditions. To build the mathematical model of the hydropower plant, synchronized phasor measurements from the Phasor Measurement Units were used for a dynamic picture of a part of power system near hydropower plant. Relevant events recorded by WAM (Wide Area Monitoring) System were used for fine tune-up of the dynamic mathematical model of hydropower plant. Additional validation of the mathematical model was made by using MatLab calculations. An example of the proposed dynamic modelling was performed on reversible hydropower plant.