Real Power Generation Research Articles

Three network design problems for community energy storage

AbstractIn this article, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid configurations are modeled using a unifying mixed‐integer linear programming formulation. These configurations represent three different business models, namely: the island model, the interconnected model, and the Energy Service Companies model. The proposed mathematical formulations determine the optimal households' aggregation as well as the location and sizing of CES. To overcome the computational challenges of treating operational decisions within a multi‐period decision making framework, we also propose a decomposition approach to accelerate the computational time needed to solve larger instances. We conduct a case study based on real power consumption, power generation, and location network data from Cambridge, MA. Our mathematical models and the underlying algorithmic framework can be used in operational and strategic planning studies on smart grids to incentivize the communitarian distributed renewable energy generation and to improve the self‐consumption and self‐sufficiency of the energy sharing community. The models are also targeted to policymakers of smart cities, utility companies, and Energy Service Companies as the proposed models support decision making on renewable energy related projects investments.

Fast distributed co-optimization of electricity and natural gas systems hedging against wind fluctuation and uncertainty

The synergistic operation of integrated electricity and natural gas systems (IEGS) in the presence of wind power necessitates a distributed optimization framework that ensures information privacy. However, incorporating wind power penetration leads to distributed optimization problems including uncertainty and unstable distributed algorithm’s convergence. With the increasing proportion of wind power in IEGS, fluctuating wind power penetration highly affects the convergence of distributed optimization solutions, resulting in uncontrollable optimization time of the distributed algorithm. Accordingly, this paper investigates the impact of wind fluctuation and uncertainty on distributed IEGS optimization and proposes a novel fast distributed co-optimization framework. Specifically, an adaptive alternating direction method of multipliers (ADMM) is developed to accommodate wind fluctuation. Based on designed rules, the penalty parameter is updated in every step to maximize the gradient of the optimization objective. The experiments are conducted using real wind power generation data sourced from a wind farm in Australia and a classical IEGS framework composed of the IEEE 24-bus electricity system and a 12-node natural gas system. Compared to original ADMM and residual balancing ADMM, the proposed framework achieves an average reduction of 0–91.4% in the number of iterative steps for multiple solution iterations across various scenarios, with a corresponding decrease in the standard deviation by 13.8%–93.0%.

Predictive Maintenance Framework for Fault Detection in Remote Terminal Units

The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory.

Detection of False-Reading in Smart Grid Net-Metering System

Malicious consumers may hack their smart meters (SMs) in the smart grid to report fake readings in order to make unlawful financial profits. Since the reported readings are used for energy management, the utility suffers significant financial losses as a result, and grid performance may suffer as a result. The net metering system, in which one SM is used to report the difference between the power consumed and the power generated, is the subject of this research, which is the first study to look into the issue. First, we process real power consumption and generation information to create a benign dataset for the net-metering system. The data was then examined, and time correlations between the net meter readings and correlations between the measurements and pertinent information from reliable sources, such as temperature, were discovered. Based on the data analysis, we suggest a single-pole double-throw detector to detect erroneous readings. In addition to data from reliable sources, our detector is trained on net meter readings from every customer in order to improve its performance by discovering correlations between them. We also create a relationship between the customer and the grid. Since the only billing system available nowadays is online, this project includes an app that may be used to solve a variety of problems, including electrical outages. Overall, with this, we improve customer and grid communication and simplify their lives.

A computational intelligence approach for solar photovoltaic power generation forecasting

This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed predictions enable the implementation of efficient planning, management, and distribution strategies for the generated power, ultimately enhancing the performance and efficiency of the system. The study analyzes and compares artificial neural network approaches for a specific case study using real solar photovoltaic power generation data from Uruguay in the period 2018 to 2022. Several artificial neural network architectures are evaluated for forecasting. The main results indicate that the approach applying a combination of Encoder-Decoder and Long Short Term Memory artificial neural networks is the most effective method for the addressed forecasting problem. The approach yielded promising results, with an average mean error value of 0.09, improving over the other artificial neural network architectures. Even better results were obtained for sunny days. The generated forecasts hold significant value for its application in planning and scheduling processes, aiming to enhance the overall quality of service of the electricity grid.

Short-Term Photovoltaic Power Generation Prediction Model Based on Improved Data Decomposition and Time Convolution Network

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research.

Stability Analysis of Autonomous Microgrid Operated at Constant-Frequency with the Consideration of Reactive Power Balance

Large-scale autonomous microgrids have potential application values as they can increase renewable energy penetration level without compromising the stability of the existing large power systems. Before their widespread implementation, critical issues like stability analysis etc need to be solved. This paper analyses the stability in an autonomous microgrid operated at constant frequency with the consideration of reactive power balance. Difference equations of reactive power for the grid-forming generator are constructed separately from those for the grid-supporting and grid-feeding generators while the difference equations of the real power for all the generators are the same. For the voltage source inverter with its current controlled by proportional resonant controller, at the fundamental frequency, its output current is disentangled from its terminal voltage and is controlled to trace its reference accurately, namely . Therefore, each inverter can be modelled as an equivalent current source and the equivalent circuits for d-component and q-component can be separated from each other. Then, the nodal equations in matrix form for the microgrid system can be established readily. With these, the system level state-space equations are built to study the distribution of eigenvalues. By choosing proper coefficients for real power and reactive power reference generations and controller’s parameters, one can make all the eigenvalues falling in the left-hand-side of the complex plane. Therefore, the system is stable. Such a research paves the way for systematically searching good sets of coefficients and controller’s parameters which make system operate safely away from unstable region with necessary margin.

A survey on voltage stability indices for power system transmission and distribution systems

Voltage stability is a critical aspect of power system operation, ensuring the reliable and efficient delivery of electrical energy to consumers. Recently, voltage stability in the power system has received much attention. The primary cause of voltage instability is the lack of real and reactive power generation to cope with the continuous demand increment. Maintaining voltage stability while planning, controlling, and assessing the system’s security is a difficult task for power system engineers. From knowledge of past incidents, a lack of reactive power is identified as the primary cause of voltage instability, which may further lead to the total collapse of the system. The importance of voltage stability assessment is essential to maintain the integrity of power systems. It helps prevent voltage instability, which can lead to cascading failures and widespread outages. This paper uses various parameters, including load demand, generator capacity, and system impedance, to develop voltage stability indices (VSIs). The paper mainly focuses on the idea of VSIs that have been developed in order to monitor and control the criticality of the power system. The voltage stability indices for transmission and distributed systems, as well as their subclasses, are thoroughly reviewed in this study. Furthermore, traditional voltage stability methods, as well as various software tools for monitoring the voltage stability problems, are also discussed. In addition, the development of VSIs and its related concepts are clearly described in this paper. This comprehensive survey provides a decent groundwork for future work in this area, and assists professionals in selecting the optimal VSI for various applications. Moreover, it provides a concise overview of methods and the importance of voltage stability assessment in both transmission and distribution systems.

Approaches for smart linear regression in a difficult quasi economic dispatch problem

<span lang="EN-US">Traditional methods indispensably necessitate monotonically increasing characteristic for fuel cost of generators in a thermal power plant. However, in medium and large thermal power plants, this condition is a dream to accomplish. So, to meet out these exigencies heuristic methods like swarm optimization technique, genetic algorithm technique and bee colony based hybrid solar thermal technology (BHSTT) are used to realize the practical nonlinearities associated with valve point loading emanated out of multi-valving effect, associated with power station. However, the heuristic methods too face challenges arising out of bulky thermal power plants adopting cubic cost functions and possessing stringent non-convex economic dispatch problem following multi-valving and erratic behavior of nonlinear loads at the load center. So, at its favor function evaluation method dealing with cubic cost function is attempted in this dissertation to yield a satisfactory optimal solution for economic dispatch problem. This method deals with the real power generation of producing units as well as the complex power of units, as well as dealing with severe nonlinear stringent fuel cost characteristics that are prevalent in today’s bulky thermal power plants. In comparison to previous approaches, the findings achieved are highly encouraging.</span>

New technique for improving transient stability of standalone microgrid using superconductor fault current limiter

This study proposes a new insertion topology for Superconductor Fault Current Limiter (SFCL) to maintain and enhance the transient stability of a wind generation system in a standalone microgrid. The proposed topology is inserting the SFCL in series with the faulty phase only. The new insertion topology performance is compared with the traditional insertion method. The proposed insertion topology of the SFCL displays excellent performance compared to the traditional method. The overall performance of the Micro-Grid is improved. The performance of the wind generator is improved dramatically after employing the proposed controller. The proposed insertion topology suppresses the fluctuations of the wind generator speed (from ± 6% to less than ± 1%) as well as the post fault wind generator real power and imaginary power. For a single line to ground fault, the wind generator active power fluctuates between + 80 kW, and − 40 kW with traditional SFCL insertion topology. When using the proposed insertion topology, the wind generator active power fluctuates between + 27 kW, and + 12 kW. Also, the proposed SFCL insertion topology mitigates the fluctuations in voltage, real power and imaginary power not only at the generator bus but also at all other Micro-Grid buses (PV bus, and storage batteries bus). The voltage at all buses recover to steady state values fast (0.01 s) compared with the recovering time when using the traditional method (nearly 1 s). The proposed method is very simple and low cost because there is no additional hardware is required. This fact makes the proposed method highly attractive from the economical and implementation point of views.

An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation

This paper describes the use of an improved version of the Salp Swarm Algorithm, known as iSSA, to address Optimal Power Flow (OPF) issues in power system management. The iSSA is applied to OPF problems involving stochastic solar power generation, with the goal of optimizing control variables such as real power generation, voltage magnitude at generation buses, transformer tap settings, and reactive power compensation. The optimization aims to achieve three objectives: minimizing power loss, minimizing cost, and minimizing combined cost and emissions from power generation. The iSSA's performance was tested on a modified IEEE 30-bus system and compared to other recent algorithms, including SSA. The simulation results show that the iSSA outperformed all compared algorithms for all objective functions that have been derived in this study.

Eradicating Homelessness and Improving the Living Standards of the Poorest of the Poor in Greater Port Harcourt City, Nigeria

Homelessness and poverty are significant challenges facing Greater Port Harcourt City in Nigeria. This paper discusses these issues and proposes a solution through the development of an inclusive and sustainable agropolitan residential housing project. The project aims to provide affordable housing for young unmarried, newly married, and married individuals with young children, falling within the income groups of 'No income,' 'Low Income,' and 'Lower Medium Income' as defined by the 2012 National Housing Policy Document (NHPD) of Nigeria. It will create sustainable, mainly agro-based livelihoods through integrated farming and other activities, such as real estate, power generation, waste management, and recycling. This will enable beneficiaries to take care of their housing and other needs while generating employment and revenue. The proposed housing project aligns with the Greater Port Harcourt City Master Plan and will complement the city's proposed beautiful townscape. The paper concludes that eradicating homelessness and improving the living standards of the poorest in Greater Port Harcourt City requires sustainable and inclusive housing solutions that provide access to employment and income-generating activities. A comprehensive and collaborative approach involving various stakeholders such as the government, private sector, and non-profit organizations is necessary to address this complex issue.

Tracking and control of power oscillation dampings in transmission lines using PV STATCOM

This paper proposed a new control of PV solar system as a FACTS device STATCOM, called PV-STATCOM, for power oscillation damping (POD) in transmission systems. In this proposed control, as soon as power oscillations due to a system disturbance are detected, the solar farm briefly discontinues its real power generation function and makes its entire inverter capacity available to operate as a STATCOM for POD. As soon as power oscillations are damped, the solar farm restores real power output to its pre-disturbance level while keeping the damping function activated. This results in much faster restoration than that specified in grid codes. During the nighttime, the solar farm performs POD with its entire inverter capacity. The proposed control system provides a significant increase in power transfer capacity on a 24/7 basis in systems that exhibit both local inertial and inter-area oscillatory modes. The developed PV-STATCOM is about 50–100 times cheaper than an equivalent STATCOM for providing POD at the same location. This new control method provides more savings for transmission utilities and opens up a new revenue-making opportunity for solar farms

Quantum behaved artificial bee colony based conventional controller for optimum dispatch

<p>Since a multi area system (MAS) is characterized by momentary overshoot, undershoot and intolerable settling time so, neutral copper conductors are replaced by multilayer zigzag graphene nano ribbon (MLGNR) interconnects that are tremendously advantageous to copper interconnects for the future transmission line conductors necessitated for economic and emission dispatch (EED) of electric supply system giving rise to reduced overshoots and settling time and greenhouse effect as well. The recent work includes combinatorial algorithm involving proportional integral and derivative controller and heuristic swarm optimization; we say it as Hybrid- particle swarm optimization (PSO) controller. The modeling of two multi area systems meant for EED is carried out by controlling the conventional proportional integral and derivative (PID) controller regulated and monitored by quantum behaved artificial bee colony (ABC) optimization based PID (QABCOPID) controller in MATLAB/Simulink platform. After the modelling and simulation of QABCOPID controller it is realized that QABCOPID is better as compared to multi span double display (MM), neural network based PID (NNPID), multi objective constriction PSO (MOCPSO) and multi objective PSO (MOPSO). The real power generation fixed by QABCOPID controller is used to estimate the combined cost and emission objectives yielding optimal solution, minimum losses and maximum efficiency of transmission line.</p>

Detection, location, and diagnosis of different faults in large solar PV system—a review

Abstract Over the past decade, the significance of solar photovoltaic (PV) system has played a major role due to the rapid growth in the solar PV industry. Reliability, efficiency and safety of solar PV systems can be enhanced by continuous monitoring of the system and detecting the faults if any as early as possible. Reduced real time power generation and reduced life span of the solar PV system are the results if the fault in solar PV system is found undetected. Therefore, it is mandatory to identify and locate the type of fault occurring in a solar PV system. The faults occurring in the solar PV system are classified as follows: physical, environmental, and electrical faults that are further classified into different types as described in this paper. Once a fault is located and detected, an appropriate diagnosis method needs to be used to rectify it. In this paper, a comprehensive review of diverse fault diagnosis techniques reported in various literature is listed and described. This paper helps the researchers to get an awareness of the various faults occurring in a solar PV system and enables them to choose a suitable diagnosis technique based on its performance metrics to rectify the fault occurring in solar PV systems.

Photovoltaic power prediction based on dilated causal convolutional network and stacked LSTM.

Due to the crucial role of photovoltaic power prediction in the integration, scheduling and operation of intelligent grid systems, the accuracy of prediction has garnered increasing attention from both the research and industry sectors. Addressing the challenges posed by the nonlinearity and inherent unpredictability of photovoltaic (PV) power generation sequences, this paper introduced a novel PV prediction model known as the dilated causal convolutional network and stacked long short-term memory (DSLSTM). The methodology begins by incorporating physical constraints to mitigate the limitations associated with machine learning algorithms, thereby ensuring that the predictions remain within reasonable bounds. Subsequently, a dilated causal convolutional network is employed to extract salient features from historical PV power generation data. Finally, the model adopts a stacked network structure to effectively enhance the prediction accuracy of the LSTM component. To validate the efficacy of the proposed model, comprehensive experiments were conducted using a real PV power generation dataset. These experiments involved comparing the predictive performance of the DSLSTM model against several popular existing models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), stacked LSTM and stacked GRU. Evaluation was performed using four key performance metrics: Mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R2). The empirical results demonstrate that the DSLSTM model outperforms other models in terms of both prediction accuracy and stability.

A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems

• A hybrid model is developed, blending ELM and Levenberg-Marquardt. • Particle swarm optimization-based weighted averaging ensemble is developed. • Energy margin and time margin are calculated for better application of techniques. • Hybrid ELM is found most suited technique for online DSA application. This manuscript develops a new hybrid-extreme learning machine (ELM) based ensemble model for real-time dynamic security assessment (DSA) of power systems. In order to boost the forecasting accuracy of ELM algorithm, a Levenberg-Marquardt (LM) backpropagation algorithm is used. The Ensemble strategy takes advantage of complementary information to improve the generalization capability of model. To realize an accurate ensemble model, particle swarm optimization (PSO) based weighted averaging is used to combine the individual forecasts. Transient energy function (TEF) terms, generator real and reactive power outputs and fault location are taken as input features for classification as well as prediction of transient energy margin (TEM) and time margin (TM) for a given operating condition and fault location. The potential of the proposed technique is validated on New England 39-bus and 68-bus test power systems. The results demonstrate that the developed model outperforms other state-of-the-art methods.

An Optimization Method of Active Distribution Network Considering Time Variations in Load and Renewable Distributed Generation

Network optimization is one of an effective ways to enhance the performance of an active distribution network (ADN). Aiming to improve the operation and power quality of the ADN considering time variations in load and renewable distributed generation (RDG) power, a multi-time period optimization model and its dynamic solution method are proposed. Considering the real time load demand and power generation variation of RDG versus input parameters like wind speed and solar irradiance, the time variation models of load and RDG power output are developed. The minimum power loss and maximum absorption of RDG power are served as the optimization indexes to construct the dynamic muti-time period optimization model. A hybrid particle swarm optimization (HPSO) algorithm is presented based on integer coding and random coding technique, which can find the most satisfactory solutions for the proposed dynamic model. Considering the time variation of load and RDG power of ADN, the optimal network structure and RDG allocation scheme at any time interval are determined by analyzing the obtained solutions. Additionally, two ADNs with time variation in load and RDG are tested to verify the effectiveness and superiority of the proposed dynamic optimization model and HPSO algorithm. The simulation results show that the proposed method can improve the operation performance and RDG optimal utilization of the ADNs through muti-time period dynamic optimization.

A Spiking Neural Network Based Wind Power Forecasting Model for Neuromorphic Devices

Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error of 2.84 percent for one-step-ahead wind power forecasts. The study illustrates the plausibility of the development of neuromorphic devices aligned with the specific demands of the wind energy sector.

Optimal rating and placing of numerous distributed generators in distribution network applying spider monkey optimization

The optimal location and rating of distributed generators in the distribution network is important role in the incremental consumer load. Proposed research determines a novel approach for the optimal placement rating of distributed generators. To achieve the multi-objectives of expanded work is to minimize the power loss, reduce operating cost, and development of magnitude output voltage. At present, the objectives are achieved by using spider monkey optimization algorithm (SMOA). Design and prescribed optimization is considered various constraints such as voltage limit, dg (Distributed Generator), real power generation limit, current limit, and power equality constraint estimated. The losses sensitivity factor involves the best selection of the optimal locations to fix dg units. In this context proposes to determine the optimal rating of dg units based on the searching behavior of spider monkeys. IEEE (Institute of Electrical and Electronics Engineering) 33-bus, and IEEE 69-bus distribution systems with various load levels are tested the validation of presented method. The entire work is done by MATLAB/Simulink program, and the SMOA is also compared with familiar algorithms to show the efficiency for an optimal rating of the power system.