Hasil untuk "Distribution or transmission of electric power"

Biao Zhang, Wan Jiang, Yang Yang et al.

A multi-magnetron microwave source, a metamaterial transmitting antenna, and a large power rectenna array are presented to build a near-field 2.45 GHz microwave power transmission system. The square 1 m2 rectenna array consists of sixteen rectennas with 2048 Schottky diodes for large power microwave rectifying. It receives microwave power and converts them into DC power. The design, structure, and measured performance of a unit rectenna as well as the entail rectenna array are presented in detail. The multi-magnetron microwave power source switches between half and full output power levels, i.e. the half-wave and full-wave modes. The transmission antenna is formed by a double-layer metallic hole array, which is applied to combine the output power of each magnetron. The rectenna array DC output power reaches 67.3 W on a 1.2 ohm DC load at a distance of 5.5 m from the transmission antenna. DC output power is affected by the distance, DC load, and the mode of microwave power source. It shows that conventional low power Schottky diodes can be applied to a microwave power transmission system with simple magnetrons to realise large power microwave rectifying.

Warren Farmer, Ulrich Minnaar

Walter Gil-Gonzalez, Oscar Danilo Montoya, Luis F. Grisales-Norena et al.

This study focuses on optimizing the efficient operation of standalone direct-current (DC) microgrids with photovoltaic (PV) sources using semi-definite programming (SDP) optimization. The PV source operation model is formulated as a nonlinear programming (NLP) problem with the objective of minimizing daily energy losses and reducing CO2 emissions compared to diesel generators. Transforming the NLP model into convex optimization involves a linear matrix model that combines positive semi-definite matrices with an affine space. This approach enhances robustness by incorporating uncertainties in demand and PV source power. The robust SDP model employs a min–max strategy for worst-case scenario energy management dispatch (EMD). Evaluating a 27-bus standalone DC microgrid, the SDP model outperforms random-based algorithms by achieving global optima in both objectives. Under uncertainties, the energy loss objective increases by 21.6706% with demand uncertainty, 0.3997% with PV source uncertainty, and 22.2009% with both. Meanwhile, the CO2 emissions objective increases by 11.9184%, 1.8237%, and 14.0045%, respectively. Additional simulations on an 85-node DC network confirm the efficacy of SDP in worst-case scenario EMD. All simulations utilized MATLAB’s Yalmip tool with the Mosek solver.

Jingwen Wang, Jiehui Zheng, Zhigang Li et al.

Abstract As more renewable energy generators and adjustable loads such as electric vehicles are being connected to the power grids, load modelling of the distribution network becomes more complicated. Therefore, this paper explores a dynamic equivalent modelling method for active distribution network that takes into account electric vehicle charging. First of all the combination of integrated ZIP loads and motors is adopted as an equivalent model for active distribution networks. Subsequently, a four‐layer, tri‐stage deep reinforcement learning approach is used to solve the relevant key parameters of the proposed equivalent model. The method proposed in this paper fully utilizes the superiority of reinforcement learning in decision making, while the method combines the excellent feature extraction capability of deep learning. The method utilizes measurements obtained at boundary nodes to obtain an active distributed network equivalent model after a series of calculations. At the same time, adjustable loads are identified in detail. On the other hand, this method introduces a prioritized empirical playback mechanism, log‐cosh loss function, and adaptive operator to improve the computational efficiency of the method. From the simulation results, the present method is effective.

Budanov Mykola P.

The problems of ensuring the management of energy security of the enterprise of the electric power system in the context of dynamic changes are considered. The article proposes a methodical basis for managing the energy security of enterprise, which allows taking into account the strategic tasks of managing the energy security of enterprise, the goals of the energy security management system of enterprise and its basic principles. It is proved that one of the elements of ensuring the management of energy security of enterprise is the spatial and dynamic monitoring of external and internal negative factors in the form of threats and risks, both for the energy enterprise itself as a whole and for its objects of generation, transmission, distribution and consumption of energy. This is an effective method of monitoring and control within the framework of ensuring the management of the energy security of the enterprise and the normal functioning of various energy objects and processes. The main directions of research and analysis of energy security on the basis of spatial-dynamic monitoring are allocated. The essence and substantive sequence of formation of the stages of spatial-dynamic monitoring of energy security of enterprise as an economic process in the current conditions of development of the electric power system at the macro-, meso- and micro-levels of the economy are studied, and an algorithm for implementing the stages of its conduct is proposed. The article proposes the implementation of the functions of spatial-dynamic monitoring on the basis of the use of integrated automated management systems of the energy enterprise, which allow in real time to generate management decisions, the effectiveness of which should be adjusted to reduce the impact or complete elimination of threats to the energy security of the enterprise. The selected instruments provide the integration of various data and their in-depth analysis, which will allow enterprises to effectively manage energy security in conditions of uncertainty and change. The use of modern technologies and methods will allow adapting to market dynamics and increasing the resilience of energy systems.

Osaka Rubasinghe, Tingze Zhang, Xinan Zhang et al.

Abstract Ancillary service provision and peak shaving (PS) play essential roles in the current day‐to‐day power system operation, which is challenged by the increasing renewable generation penetration. Providing these critical services using classical approaches such as peak load generators has been limited due to high operational costs and environmental impacts. The use of battery energy storage systems (BESS) is another popular method that is limited by high initial investment costs and high degradation rates. In this work, a novel approach to utilize industrial loads and BESS to provide multiple power system services in different stages is proposed. Industrial loads such as aluminium crushers are known for their intensive electricity consumption. Nevertheless, when applied in frequency regulation (FR), they perform poorly due to their discrete nature in operation. This drawback and the aforementioned BESS shortcomings are addressed by combining on‐site BESS with plant machinery to provide FR services and recover BESS related costs. Later, depending on the optimal capacity distribution, BESS usage is extended into the energy arbitrage market to provide PS services. This approach resulted in higher earnings for participating customers and network operators, as well as in less CO2 emissions, and minimal BESS degradations. An Australian case study of the South West Interconnected System, along with Worsley Alumina refinery data of Western Australia has been used to showcase the model performances.

Afshin Hasani, Hossein Heydari, Mohammad Sadegh Golsorkhi

Abstract Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real‐time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN‐based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.

G. M. Casolino, M. de Santis, L. Di Stasio et al.

The field measurement campaigns have revealed that voltage sags also occur as clusters and not only as rare phenomena. The clusters of sags represent a stochastic process due to their time dependence; the rare satisfy the requirements for a Poisson distribution process. To forecast both kinds of sags using the statistics of the measurements, different approaches are required. In this study, a general method for predicting both types of sags is proposed with a procedure that can be implemented automatically. The method uses intermittent indices to distinguish between the sites that have a prevalent number of rare sags and the sites where rare sags and clusters occurred. Based on this means of identification, the technique offers two distinct models for predicting each kind of sag. The final goal is to implement the procedure in a measurement system that can automatically pre-analyze the recorded sags and choose the best technique for prediction depending on the type of sag. The first results were satisfying with forecast errors reduced in comparison with those obtained without the proposed procedure.

Jiajia Huan, Xiaohui Zhang, Yuling He et al.

Abstract Numerous policies have been implemented to advance the growth of renewable energy. Nonetheless, certain policies have not yielded the anticipated impact on the progression of renewable energy development. In order to maximize the promotion effect of renewable energy policies, this study proposes a capacity allocation optimization method of wind power generation, solar power and energy storage in power grid planning under different policy objectives. First, based on the policy quantification, grey relation analysis (GRA) is used to calculate the correlation degree of the policy indicators on the planning capacity of renewable energy. Further, a multi‐objective capacity estimation model for wind, solar and energy storage is comprehensively presented. Some highly correlated policy indicators are transformed into the special constraints. And the economy and the stability of the power grid are integrated as the objective function. Meanwhile, the carbon trading and punishment for wind power and solar power abandonment are considered. Finally, the proposed model is solved by NSGA‐II‐PSO (particle swarm optimization) algorithm. The novelty of the algorithm is that the crossover operation of NSGA‐II is replaced by the position updating of particle swarm. The calculation result of the case study can effectively evaluate the optimal planning capacity of renewable energy under different policies, while ensuring the economic and the stability of the power system. The study can provide the reasonable basis and the valid analytical method for the policy formulation and the renewable energy development.

Sattar Rajabpour Sanati, Rasoul Jamshidi, Mostafa Rajabi Mashadi et al.

Modern cities increasingly rely on efficient electrical distribution. In recent years, efforts have focused on developing integrated infrastructures that include resilient power systems, renewable energy generation, secure communication, and real-time energy pricing. This integrated system, known as the smart grid, forms the backbone of smart cities. In essence, consumers play a significant role in smart grids, as their energy use accounts for a considerable proportion of overall demand. As a result, utility companies, government agencies, and environmentally conscious organizations aim to reduce and alter energy consumption patterns to achieve peak load reduction, load smoothing, and carbon emission reduction.In this survey paper, we provide an overview of approaches that engage smart grid consumers through mobile applications to achieve energy efficiency, primarily by providing them with information, motivation, and recommendations. We focus on discussing both recent research project outputs and commercial products, examining various design aspects, such as game mechanics, motivation techniques, and target audiences. Furthermore, we aim to share Iran's experience with gamification in the smart grid by introducing the "BAENERGY" application.

Pujing Yang, Guangyi Zhang, Yunlong Cai

Deep learning-based joint source-channel coding (JSCC) is emerging as a potential technology to meet the demand for effective data transmission, particularly for image transmission. Nevertheless, most existing advancements only consider analog transmission, where the channel symbols are continuous, making them incompatible with practical digital communication systems. In this work, we address this by involving the modulation process and consider mapping the continuous channel symbols into discrete space. Recognizing the non-uniform distribution of the output channel symbols in existing methods, we propose two effective methods to improve the performance. Firstly, we introduce a uniform modulation scheme, where the distance between two constellations is adjustable to match the non-uniform nature of the distribution. In addition, we further design a non-uniform modulation scheme according to the output distribution. To this end, we first generate the constellations by performing feature clustering on an analog image transmission system, then the generated constellations are employed to modulate the continuous channel symbols. For both schemes, we fine-tune the digital system to alleviate the performance loss caused by modulation. Here, the straight-through estimator (STE) is considered to overcome the non-differentiable nature. Our experimental results demonstrate that the proposed schemes significantly outperform existing digital image transmission systems.

Homayoun Berahmandpour, Shahram Kouhsari, Hassan Rastegar

Power system flexibility is an important characteristic in both power system planning and operation, which should be evaluated and maintained in the desired value. On the other hand, more renewable energy integration leads to increasing uncertainty and variability in the power system. Therefore, the power system should have the sufficient ability to overcome the adverse effects of uncertainty and variability named as flexibility, which should be improved by suitable tools such as adequate reserve, fast ramp up/down generation sources and suitable energy storage capacity. Power system flexibility evaluation is the main task that needs suitable indices to indicate the level of system flexibility correctly. In the current paper, a well-known system flexibility index named normalized flexibility index, which is used for power system planning horizon is modified to use for the operational planning time zone. In this concept, the flexibility index is separated into two components, each of them indicating the ability of the power system to withstand upward/downward net-load uncertainty and variability. In the further, this is shown these two components are the same as the upward/downward system reserve and can be converted to economic value simply. So, this concept facilitates the economic trade-off between operation cost and system flexibility, improving cost to achieve the best level of system flexibility.

Hadi Sadeghi, Mohammad Reza Salehizadeh, Masoud Rashidinejad

In recent decades, the welfare of human being is seriously threatened by climate change. As a result, numerous energy regulations have been put in place to encourage the expansion of investments in renewable energy. In this context, open questions remain regarding the impacts that these policies may have on generation expansion planning (GEP). This paper addresses this issue by applying three of the most widely adopted energy strategies, namely quota obligation, feed-in tariffs, and emission trade system, to the GEP problem, resulting in an integrated renewable-conventional generation expansion planning (IRCGEP) model with a properly modified cost function and extra constraints. To achieve this aim, first, the IRCGEP model is solved using general algebraic modeling system from a generation company (GENCO) perspective. Afterward, according to the obtained optimized expansion strategies, the policies impact on the social welfare terms including consumer surplus, GENCO profit, and environmental damages cost are investigated, while they are included on the Bergson-Samuelson social welfare function. Moreover, to assess the financing mechanism effect of the policies on consumer surplus, a suitable attribute known as the "virtual price" is put forth. Numerical studies shed light on the reactions of investment decisions and the social welfare to the energy policies.

Mahdi Ghotbi‐Maleki, Reza Mohammadi, Hamid Javadi

Abstract The fault‐induced delayed voltage recovery (FIDVR) and short‐term voltage instability (STVI) phenomena appear in networks with high penetration of induction motor loads because the increase in requested reactive powers of these loads prevents the voltages from quickly returning to their pre‐fault levels. Load shedding (LS) is one of the ways to deal with FIDVR and STVI and reduce the imbalance between the generation and demand of reactive power. Under‐voltage (UV) relays that disconnect loads during voltage drop cannot effectively deal with this phenomenon because of their inability to detect the effective loads on the reduction of FIDVR and STVI severities. Therefore, the operation of UV relays during these phenomena causes unnecessary load disconnection, and the interference of their operations with FIDVR and STVI must be avoided. This paper presents two wide‐area approaches based on network and loads parameters, the first of which deals with the most critical FIDVR, and the second tries to simultaneously handle critical FIDVRs of all buses. Also, bus prioritization for LS during STVI has been addressed. Simulation results revealed the better performance of the proposed approaches than the previous ones in terms of the amount of LS and the number of selected buses for LS.

Jiangjun Ruan, Xuezong Wang, Taotao Zhou et al.

Abstract The trip mechanism is a weakness in circuit breakers. Traditional fault identification based on the coil current is difficult to report early mechanical defects such as coil‐plunger jam. Here, the vibration signal during the trip process was extracted. Based on the coil current signal and vibration signal, the characteristics of the trip mechanism are analyzed. The phase space reconstruction (PSR) method is used to extract features from the vibration signal. Combined with the features from the coil current waveform, the feature set representing the health condition of the trip mechanism is proposed. The fault simulation tests are carried out and the variation of current vibration characteristics under fault conditions is studied. The fault identification model based on a support vector machine (SVM) is proposed and compared with the identification results when features are extracted from a single signal. When the power supply voltage is dispersed, the prediction accuracy of fault identification is 83.3% considering only the features of the current waveform or vibration signal. And the identification accuracy rises to 96.7% while using the feature set of current and vibration signals. On basis of the current signal, the method further combines the vibration signal so that the robustness of defect identification improves.

Marek Florkowski, Maciej Kuniewski

The endurance of medium- and high-voltage electrical insulation is a key reliability element in a broad spectrum of applications that cover transmission and distribution levels, the transportation segment, the industrial environment, and power electronics-based energy-conversion systems. The high electric-field stress and high-frequency switching phenomena as well as the impact of environmental conditions lead to the occurrence of partial discharges (PD) and the subsequent deterioration of electrical insulation. Partial discharges usually occur inside solid insulation materials in tiny voids that may either be located adjacent to the electrodes or in the bulk of dielectric material. This effect refers to both AC and DC systems; however, AC voltage is usually much more intensive as compared to DC voltage. This paper describes a novel combined approach based on surface-resistance and potential mapping to reveal the effects of internal processes and the deterioration of insulating material due to the actions of partial discharges. To realize the research objective, the following two-step approach was proposed. Multi-point resistance mapping enables us to identify the spots of discharge channels, manifesting a-few-orders-of-magnitude-lower surface resistance as compared to untreated areas. In addition, surface-potential mapping that was stimulated by corona-charge deposition reflects quasi-equipotential clusters and the related polarity-dependent dynamics of charge decay. A high spatial and temporal resolution allows for the precise mapping and tracing of decay patterns. Experiments were carried out on polyethylene (PE) and Nomex specimens that contained embedded voids. During PD events, the effective discharge areas are identified along with the memory effects that originate from the accumulation of surface charges. Long-term aging processes may drive the formation of channels that are initiated from the deteriorated micro clusters, in turn, penetrating the bulk isolation. The presented methodology and experimental results extend the insight into PD mechanisms and internal surface processes.

Hossein Panamtash, Shahrzad Mahdavi, Qun Zhou Sun et al.

This paper aims to forecast solar power in very short horizons to assist in real-time distribution system operations. Popular machine learning methods for time series forecasting are studied, including recurrent neural networks with Long Short-Term Memory (LSTM). Although LSTM networks perform well in different applications by accounting for long-term dependencies, they do not consider the frequency domain patterns, especially the low frequencies in the solar power data compared to the sampling frequency. The State Frequency Memory (SFM) model in this paper extends LSTM and adds multi-frequency components into memory states to reveal a variety of frequency patterns from the data streams. To further improve the forecasting performance, the idea of Fourier Transform is integrated for optimal selection of the frequency bands by identifying the most dominant frequencies in solar power output. The results show that although the SFM model with uniform frequency selection does not significantly improve upon the LSTM model, the proper selection of frequencies yields overall better performances than the LSTM and 27% better than the persistent forecasts for forecast horizons up to one minute. Furthermore, a predictive voltage control based on solar forecasts is implemented to demonstrate the superior performance of the proposed model.

Seonho Park, Pascal Van Hentenryck

Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems in milliseconds. Indeed, PDL-SCOPF remedies the limitations of supervised counterparts that rely on training instances with their optimal solutions, which becomes impractical for large-scale SCOPF problems. PDL-SCOPF mimics an Augmented Lagrangian Method (ALM) for training primal and dual networks that learn the primal solutions and the Lagrangian multipliers, respectively, to the unconstrained optimizations. In addition, PDL-SCOPF incorporates a repair layer to ensure the feasibility of the power balance in the nominal case, and a binary search layer to compute, using the Automatic Primary Response (APR), the generator dispatches in the contingencies. The resulting differentiable program can then be trained end-to-end using the objective function of the SCOPF and the power balance constraints of the contingencies. Experimental results demonstrate that the PDL-SCOPF delivers accurate feasible solutions with minimal optimality gaps. The framework underlying PDL-SCOPF aims at bridging the gap between traditional optimization methods and machine learning, highlighting the potential of self-supervised end-to-end primal-dual learning for large-scale optimization tasks.

C. Birk Jones, Cynthia J. Bresloff, Rachid Darbali-Zamora et al.

Limited access to transmission lines after a major contingency event can inhibit restoration efforts. After Hurricane Maria, for example, flooding and landslides damaged roads and thus limited travel. Transmission lines are also often situated far from maintained roadways, further limiting the ability to access and repair them. Therefore, this paper proposes a methodology for assessing Puerto Rico’s infrastructure (i.e., roads and transmission lines) to identify potentially hard to reach areas due to natural risks or distance to roads. The approach uses geographic information system (GIS) data to define vulnerable areas, that may experience excessive restoration times. The methodology also uses graph theory analysis to find transmission lines with high centrality (or importance). Comparison of these important transmission lines with the vulnerability results found that many reside near roads that are at risk for landslides or floods.

Thilini Hathiyaldeniye, Chandana Karawita, Bagen Bagen et al.

Leading to the enormous growth in renewable and power electronics technologies and the global drive towards environmental friendliness and sustainability, a significant number of renewable energy sources are being connected to the power system via inverter-based systems. The inverter-based generations (IBG) have no stored energy and less fault current injection capability compared to the conventional synchronous machines. Consequently, a large penetration of IBG creates challenges to maintaining the stability of the power system, especially the transient stability. The weaker the power system, the higher the significance of instability. Few solutions exist in the literature to improve the fault recovery of IBG connected to weak power systems. This paper considers the method of storing energy in sub-module capacitors of the Modular-Multi-level Converter (MMC) along with temporarily boosting the inverter’s current limit. Conversely, increasing the ratings of the inverter will result in high manufacturing costs. Hence an optimization strategy is proposed in this paper, for obtaining a robust set of inverter control parameters that enhances fault recovery without excessively increasing the manufacturing cost of MMC. A frequency scanning technique supplemented with Generalized Nyquist criteria is incorporated into the optimization methodology to constrain the search space for the optimization algorithm. This enables the optimization algorithm to converge to an acceptable solution with a reasonable computing time. Furthermore, validation of the resultant set of parameters for different system conditions is presented. Finally, IBG with optimized fault recovery controllers is integrated into a simplified real-world power system, and the applicability of the proposed optimized controllers is illustrated.