Optimal Operation Of Power System Research Articles

Enhanced thermal modeling of power transformers: A comparison of bond graph and IEEE methods considering external environmental factors

The optimal and secure operation of electrical power system is largely affected by the reliability of power transformers in operational and economic terms. The continuous monitoring of power transformer is crucial for ensuring the quality of electrical power supply. Hence, the thermal modeling of power transformer is very essential to prevent their failure. This paper aims to design thermal models of operating power transformer by considering the two external influencing parameters of solar irradiation and wind flow. This paper presents a Bond Graph (BG) method-based design of thermal models for power transformer under operating condition by comparison with the results obtained from IEEE Std. based thermal models and also with the practical readings. The models have been implemented on a 5MVA, 33/11 kV power transformer running at 33 kV Substation, Sardarpura, Jodhpur, by initially considering the input hourly dataset of load and ambient temperature. The designed thermal models were subsequently updated by integrating the impact of solar radiation and wind incident on the surface of power transformer. Comparative study of all the designed thermal models has been done that indicates improved accuracy of the model if the impact of solar radiation and wind flow are included. Also, the effectiveness of the BG based thermal models in accurately predicting the system behaviour validates their applicability for real world engineering applications.

Adaptive energy management strategy for optimal integration of wind/PV system with hybrid gravity/battery energy storage using forecast models

Hybrid energy systems, including hybrid power generation and hybrid energy storage, have attracted considerable attention as eco-friendly solutions to meet the increasing global energy demands while minimizing environmental impacts. The economic viability and resilience of hybrid energy system solutions depend on careful consideration of economic and reliability factors during the design phase. This paper explores the optimization and design of a wind turbine (WT)/photovoltaic (PV) system coupled with a hybrid energy storage system combining mechanical gravity energy storage (GES) and an electrochemical battery system. An adaptive energy management strategy linked to an optimization process has been proposed for the optimal integration of the WT/PV system with the hybrid Gravity/Battery storage system. Forecast models have been employed to predict solar and wind generation. Simulation models are developed for each component of the multi-source power plant to predict energy flow behavior based on real-world industrial load demand scenarios. The renewables contribution to 100 % reliability is approximately 18 % for photovoltaic and 92 % for wind turbine, primarily attributed to the great wind energy potential of the selected site. The optimal cost of energy is 0.28 €/kWh for a reliability of 100 %, which reduces to 0.03€/kWh when reliability level is decreased to 20 %. The incorporation of forecast models into the study and the hybridization of storage options improve the design accuracy and optimal operation of the multi-source power system leading to reliable and efficient use of renewable energy.

LoadNet: enhancing energy storage system integration in power system operation using temporal convolutional and recurrent models with self-attention

Introduction: In the context of the evolving energy landscape, the efficient integration of energy storage systems (ESS) has become essential for optimizing power system operation and accommodating renewable energy sources.Methods: This study introduces LoadNet, an innovative approach that combines the fusion of Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models, along with a self-attention mechanism, to address the challenges associated with ESS integration in power system operation. LoadNet aims to enhance the management and utilization of ESS by effectively capturing the complex temporal dependencies present in time-series data. The fusion architecture of TCN-GRU in LoadNet enables the modeling of both short-term and long-term dependencies, allowing for accurate representation of dynamic power system behaviors. Additionally, the incorporation of a self-attention mechanism enables LoadNet to focus on relevant information, facilitating informed decision-making for optimal ESS operation. To assess the efficacy of LoadNet, comprehensive experiments were conducted using real-world power system datasets.Results and Discussion: The results demonstrate that LoadNet significantly improves the efficiency and reliability of power system operation with ESS. By effectively managing the integration of ESS, LoadNet enhances grid stability and reliability, while promoting the seamless integration of renewable energy sources. This contributes to the development of a more sustainable and resilient power system. The proposed LoadNet model represents a significant advancement in power system management. Its ability to optimize power system operation by integrating ESS using the TCN-GRU fusion and self-attention mechanism holds great promise for future power system planning and operation. Ultimately, LoadNet can pave the way for a more sustainable and efficient power grid, supporting the transition to a clean and renewable energy future.

Analysis of System Operation Optimization In Steam Power Plants with the Lagrange Method

Steam Power Plant (PLTU) is a plant that relies on kinetic energy from hot steam to produce electrical energy. At the Paiton Power Plant, thermal energy is generated from burning a certain amount of coal. The use of coal-fired power plants still dominates most of the world's electricity supply. The optimal operation of electric power systems has grown in importance in recent years due to ever-increasing fuel costs. An electric power system basically consists of power generation units that aim to serve the needs of the load. Total production costs can be minimized by a combination of power loading on existing generating units so that an optimal load or more is obtained. This optimization process is called Economic Dispatch. Economic Dispatch has conducted a lot of research using various optimization methods. In this study, the optimization method to be used is the Lagrange method. Firefly algorithm and genetic algorithm methods are also used as performance comparisons. The results of this study show that the lagrange method can optimize generation costs with a difference of 243,227,475 $ / hour or 7,043% of the actual cost. While the firefly algorithm gets a difference of 243,227,471 $ / hour or 7.043% of the actual cost. And the genetic algorithm gets a cost difference of 242,119,792 $ / hour or 7,011% of the actual cost.

Comparison of power system flow analysis methods of IEEE 5-bus system

Load flow analysis is a crucial tool used by electrical engineers for simulating the power system. It is aimed at examining the most possible way of operating and controlling a power system and the exchange of power flow within the power system. For the economic and optimal operation of power systems, the most essential task is to find the most feasible solution technique suitable and efficient for the study of power generation, transmission, and distribution. There are various power flow study solution techniques, and for some solution techniques, the simulation of the system can take a long time, which prevents the simulator from attaining a higher accuracy result for the power flow simulation due to the interrupting rise and fall in power demand from the consumer, which also affects the power generation as well. This paper discusses the comparison of various techniques used in load flow studies with the assistance of a small power system with five buses. The numerical solution techniques used are the fast decoupled load flow solution technique, the Gauss-Seidel solution technique, and the Newton-Raphson solution technique for a power flow study solution on an IEEE 5-bus using MATLAB/Simulink.

Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an ε-based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems.

Frequency Control and Optimal Operation of Low-Inertia Power Systems With HVDC and Renewable Energy: A Review

With the integration of large-scale renewable energy sources (RES) and high voltage direct current (HVDC) based inter-regional transmission, modern power systems are facing low-inertia issues and poor frequency regulation capabilities. This paper conducts a comprehensive review on frequency control and optimal operation methods in this context. First, the frequency characteristic of a low-inertia power system is analyzed covering the whole frequency response process. Secondly, the impacts of RES and HVDC integration on system inertia and frequency performance are studied, and the system frequency characteristics considering RES and HVDC are analyzed. Then, the frequency response modes of various frequency regulation resources are compared from the perspective of “source-grid-load-storage”, including their basic control principles and frequency control strategies. Further, from the operation dispatch perspective, the frequency security-constrained power system optimal operation methods are reviewed. Finally, future research trends in this area are discussed.

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field.

Integration of wind farm, energy storage and demand response for optimum management of generation and carbon emission

AbstractThe optimal operation of power systems is one of the most critical issues for operators. Optimizing the operation of power systems while accommodating the increasing penetration of RES, owing to the intermittent and variable nature of these resources, stands as one of the most critical challenges for power systems. Numerous primary challenges, including fluctuations in load and nonlinear constraints, further complicate the issue, given its substantial dimensions. Therefore, this paper introduces an approach for improving the management of optimal generation and the associated carbon emissions costs of traditional power plants, which is achieved through integrating wind farms and incorporating battery energy storage. This enhancement is achieved by integrating wind farms and utilizing battery storage systems while considering the costs associated with traditional units using fossil fuels and the expenses related to carbon emissions. The simulation incorporates seven load customer types and five traditional generation sources integrated with wind farms and battery storage devices. The simulation results demonstrate that the new problem formulation has significantly improved the ability and performance of optimizing the scheduling of power plant generations.

Multi-objective cooperative optimization of communication base station and active distribution grid under dual carbon targets

To achieve “carbon peaking” and “carbon neutralization”, access to large-scale 5G communication base stations brings new challenges to the optimal operation of new power systems, but also provides new opportunities for the low-carbon development of distribution networks. This paper develops a method to consider the multi-objective cooperative optimization operation of 5G communication base stations and Active Distribution Network (ADN) and constructs a description model for the operational flexibility of 5G communication base stations. Based on this, a multi-objective cooperative optimization 5G communication base station operating model and active distribution network considering the system operation economy and minimum carbon emissions as the optimization objectives are established. In the above model, by encouraging 5G communication base stations to engage in Demand Response (DR), the Renewable Energy Sources (RES), and 5G communication base stations in ADN are concurrently scheduled, and the uncertainty of RES and communication load is described by using interval optimization method. Finally, the problem is solved by combining the equivalent transformation based on interval analysis and the non-dominated sorting genetic algorithm. The analysis results of the example show that participation in grid-side dispatching through the flexible response capability of 5G communication base stations can enhance the power system’s renewable energy consumption and usage efficiency, resulting in significant low-carbon benefits.

Modified Arithmetic Algorithms for Optimal Power System Operation Considering Uncertainties from Intermittent Renewable Energy and Electric Vehicles

Modified Arithmetic Algorithms for Optimal Power System Operation Considering Uncertainties from Intermittent Renewable Energy and Electric Vehicles

Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network

Precise anticipation of electrical demand holds crucial importance for the optimal operation of power systems and the effective management of energy markets within the domain of energy planning. This study builds on previous research focused on the application of artificial neural networks to achieve accurate electrical load forecasting. In this paper, an improved methodology is introduced, centering around bidirectional Long Short-Term Memory (LSTM) neural networks (NN). The primary aim of the proposed bidirectional LSTM network is to enhance predictive performance by capturing intricate temporal patterns and interdependencies within time series data. While conventional feed-forward neural networks are suitable for standalone data points, energy consumption data are characterized by sequential dependencies, necessitating the incorporation of memory-based concepts. The bidirectional LSTM model is designed to furnish the prediction framework with the capacity to assimilate and leverage information from both preceding and forthcoming time steps. This augmentation significantly bolsters predictive capabilities by encapsulating the contextual understanding of the data. Extensive testing of the bidirectional LSTM network is performed using multiple datasets, and the results demonstrate significant improvements in accuracy and predictive capabilities compared to the previous simpleRNN-based framework. The bidirectional LSTM successfully captures underlying patterns and dependencies in electrical load data, achieving superior performance as gauged by metrics such as root mean square error (RMSE) and mean absolute error (MAE). The proposed framework outperforms previous models, achieving a remarkable RMSE, attesting to its remarkable capacity to forecast impending load with precision. This extended study contributes to the field of electrical load prediction by leveraging bidirectional LSTM neural networks to enhance forecasting accuracy. Specifically, the BiLSTM’s MAE of 0.122 demonstrates remarkable accuracy, outperforming the RNN (0.163), LSTM (0.228), and GRU (0.165) by approximately 25%, 46%, and 26%, in the best variation of all networks, at the 24-h time step, while the BiLSTM’s RMSE of 0.022 is notably lower than that of the RNN (0.033), LSTM (0.055), and GRU (0.033), respectively. The findings highlight the significance of incorporating bidirectional memory and advanced neural network architectures for precise energy consumption prediction. The proposed bidirectional LSTM framework has the potential to facilitate more efficient energy planning and market management, supporting decision-making processes in power systems.

Evaluation of Economic Impact of Renewable Energy Sources in Multi-period Optimal Operation of Power Systems

Abstract: In order to operate the electrical network economically and effectively, generation from various sources must be appropriately scheduled. Integrated systems are more common today because of the increase in the cost of fossil fuels and the technological advancements made in the field of renewable energy Resources. To be able to determine the best settings, the optimal power flow problem is posed with all relevant system characteristics, including generator outputs. The network might include conventional fossil fuel generators and renewable energy sources like wind power generators and solar photovoltaic. The classic problem of optimal power flow is a complex non-linear problem with non-linear constraints. In this research, it is suggested how thermal plants with wind and solar integration operate economically using General Algebraic Modeling System (GAMS) software's which has used Non-linear programming to resolve the Dynamic Economic Dispatch (DED) which is a part of optimal power flow problem. The impact of wind and solar integration on the cost-effective operation of thermal units while taking into considering IEEE-24 bus test system has been researched. In this paper, the reduction of a thermal plant's fuel consumption with the integration of wind and solar power is the primary focus of study.

Comparative Analysis of the Particle Swarm Optimization and Primal-Dual Interior-Point Algorithms for Transmission System Volt/VAR Optimization in Rectangular Voltage Coordinates

Optimal power flow (OPF) is one of the most widely studied problems in the field of operations research, as it applies to the optimal and efficient operation of the electric power system. Both the problem formulation and solution techniques have attracted significant research interest over the decades. A wide range of OPF problems have been formulated to cater for the various operational objectives of the power system and are mainly expressed either in polar or rectangular voltage coordinates. Many different solution techniques falling into the two main categories of classical/deterministic optimization and heuristic/non-deterministic optimization techniques have been explored in the literature. This study considers the Volt/VAR optimization (VVO) variant of the OPF problem formulated in rectangular voltage coordinates, which is something of a departure from the majority of the studies, which tend to use the polar coordinate formulation. The heuristic particle swarm optimization (PSO) and the classical primal-dual interior-point method (PDIPM) are applied to the solution of the VVO problem and a comparative analysis of the relative performance of the two algorithms for this problem is presented. Four case studies based on the 6-bus, IEEE 14-bus, 30-bus, and 118-bus test systems are presented. The comparative performance analysis reveals that the two algorithms have complementary strengths, when evaluated on the basis of the solution quality and computational efficiency. Particularly, the PSO algorithm achieves greater power loss minimization, whereas the PDIPM exhibits greater speed of convergence (and, thus, better computational efficiency) relative to the PSO algorithm, particularly for higher-dimensional problems. An additional distinguishing characteristic of the proposed solution is that it incorporates the Newton–Raphson load flow computation, also formulated in rectangular voltage coordinates, which adds to the efficiency and effectiveness of the presented solution method.

Probabilistic Optimal Active and Reactive Power Dispatch including Load and Wind Uncertainties considering Correlation

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system’s reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded.

Optimal operation of new coastal power systems with seawater desalination based on grey wolf optimization

Seawater desalination is one of the effective means to efficiently consume renewable energy and to improve the flexibility of system control in the new power system. In the optimal operation of new power systems, seawater desalination is usually considered as an entirety, and a motor equivalent model is used to simulate the overall external characteristics of seawater desalination. The characteristics of multiple sets of motors with multiple equipment processes inside the seawater desalination are neglected, which makes operation control characteristics inaccurate. In order to solve the above-mentioned problems, this paper firstly studies a seawater desalination system composed of 12 motors, and introduces the specific parameters of these 12 motors in detail. Secondly, after comprehensively considering the constraints of water flow, water pressure, and other factors, three control strategies are proposed for the 12 motors, respectively. Then, a detailed mathematical model of seawater desalination units and an optimal operation model of a new power system with seawater desalination are established. The optimization model aims at the minimum operating cost of seawater desalination, and considers the constraints of practical problems comprehensively such as motor power requirements and water storage capacity. Finally, the grey wolf optimization algorithm (GWO) is used in this paper to solve the optimization problem. The simulation results show that the model proposed in this paper can precisely control each unit in the seawater desalination system and improve the economic benefits of the system. Compared with the Particle Swarm Optimization algorithm (PSO) and the Moth-flame Optimization algorithm (MFO), the algorithm used in this paper can find the global optimal solution, and has both faster convergence speed and wider practicability.

Research on Coordination Planning Model of Source-Grid-Load-Storage Considering Demand Response Uncertainty

With the integration of wind power, photovoltaic, and other new energy into the grid, the growth of carbon emissions has been effectively suppressed, which greatly contributes to the realization of the “Carbon peak, Carbon neutral’’. However, the randomness and instability of new energy generation also greatly affect the stable operation of the power grid. At present, the combination of demand response and power system can give full play to the maximum potential of new energy. This is an effective method to realize stable and optimal operation of power system. Based on this, this paper first constructs the SOC output characteristic model of energy storage and considers the DLC and time-of-use price as well as different demand response types. The robust optimization method is used to deal with the uncertainty of demand response. Secondly, a multi-objective function and constraint conditions are constructed to minimize the system planning cost, operation cost, and pollution emission. Finally, the genetic algorithm is used to analyze an example in a certain area of Fujian. In the example, the economy of planning schemes under different robust control parameters is compared and analyzed. The results show that the model and calculation method in this paper are beneficial to delaying the investment in power systems, and can provide decision-making reference for mining and utilizing demand response resources, reducing system planning costs and improving operation efficiency.

Coordination of Water Flow with Optimal Power System Operation

Energy and water are essential resources for modern society. Their increasing demands result in various social, economic, and environmental issues. While traditional approaches consider energy and water distribution systems as two uncouple systems, they are interdependent in reality, and this relationship is often known as the water-energy nexus. This paper proposes a new model to optimize both the water distribution network and the energy network which runs the water distribution system. Additionally, we propose a fully distributed algorithm based on the Alternating Direction Method of Multiplier (ADMM), which eliminates the need for a central coordinator and enhances the privacy and security of the system. The effectiveness of the proposed optimization scheme is verified by numerical simulations.

Optimal Operation of Electric Power System Incorporating Renewable Energy Source Based on Artificial Hummingbird Algorithm

Optimal Operation of Electric Power System Incorporating Renewable Energy Source Based on Artificial Hummingbird Algorithm

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.