Power System Optimization Research Articles

Dual-weight decay mechanism and Nelder-Mead simplex boosted RIME algorithm for optimal power flow

The increasing demand for electricity presents substantial challenges in power system planning, particularly optimizing the Optimal Power Flow (OPF) problem. The OPF problem entails establishing the best settings for control variables in a power system to reduce objectives such as generating cost and transmission losses while meeting operational restrictions. This research introduces an upgraded RIME optimization algorithm (WDNMRIME) to address these challenges. WDNMRIME integrates a dual-weight decay mechanism and the Nelder-Mead simplex (NMs), enhancing population diversity and mitigating the risk of local optima. Additionally, NMs expedites convergence by refining the population's optimal solution set. Experimental validation on the IEEE 30-bus test system demonstrates that WDNMRIME achieves a generation cost of $806.00298 per hour and reduces total power loss from 1.43 MW to 1.39 MW. These results surpass the performance of the original RIME algorithm, showcasing a 15% improvement in convergence speed. The algorithm effectively optimizes multiple concurrent Flexible Alternating Current Transmission Systems (FACTS) devices, even under the uncertain nature of wind energy resources modeled using the Weibull probability density function. These findings highlight WDNMRIME's significant contribution to improving OPF optimization in dynamic power systems.

Comparative framework of representative weeks selection methods for the optimization of power systems

Comparative framework of representative weeks selection methods for the optimization of power systems

Effective Application of IoT Power Electronics Technology and Power System Optimization Control

Effective Application of IoT Power Electronics Technology and Power System Optimization Control

Research on Economic Environment Scheduling Optimization of Power System Based on Multi-objective Particle Swarm Optimization

Research on Economic Environment Scheduling Optimization of Power System Based on Multi-objective Particle Swarm Optimization

Leveraging AI and Machine Learning Models for Enhanced Efficiency in Renewable Energy Systems

Abstract. Artificial intelligence (AI) and machine learning (ML) have great potential in the management and optimization of renewable energy systems, through more in-depth AI-assisted analysis of store demand in the energy system, and through active learning models for energy optimization and scheduling. Among them, artificial intelligence and active learning can be combined to improve the utilization efficiency of renewable energy in a high range. In particular, recurrent neural network RNN and long- and short-term memory network LSTM in active learning can be used to predict energy and power demand, so as to achieve more effective intelligent power management and system optimization. Two of these models have their own advantages. LSTM and other active learning models can optimize the long-term dependence of traditional time series models and provide more efficient and accurate prediction model results for wind power forecasting and management and other renewable energy forecasting tasks. This study shows that compared with the traditional model, the LSTM model has a high advantage in processing long-term relationships and complex time series data, and the prediction results are more accurate and obvious. Through the dual combination of artificial intelligence and machine learning for renewable energy, this study provides theory, support and certain experimental guidance for the innovation of global energy systems, so as to make certain contributions to the development of renewable energy.

TID-FOPIDN controller for frequency control using Lemur optimization in two-area power systems

TID-FOPIDN controller for frequency control using Lemur optimization in two-area power systems

Simultaneous sizing and scheduling optimization for farmhouse PV-battery systems with a multi-structured power system model

Increasing the proportion of photovoltaic (PV) power in energy supplies is effective in decarbonizing energy use in buildings. Optimization model analysis is essential for the design and operation of PV-battery systems. The optimization models developed in previous studies are mainly suitable for one specific scenario, and there is a lack of research on the suitability of different types of PV-battery systems applying in various scenarios. This study proposes a multi-structured power system optimization model for various rural PV-battery systems, compares the optimal sizing and performance of three commonly used PV-battery systems, and quantifies the impacts of system capacity on system performance. The optimization model was constructed using the improved simulated annealing algorithm with the self-consumption rate and economy as the objective functions, while the system node power balance was the constraint. The sensitivity analysis shows that increasing the PV capacity will reduce the PV self-consumption rate and payback period of the system while increasing the battery capacity will increase the PV self-consumption rate and payback period of the system. For every 1 kW·h increase in battery capacity, the payback period of the system increases by 0.5 years. The spatial optimization model simulates the operation strategies of typical farm houses in different climate zones in China, and obtains payback periods for rural PV-battery systems in different regions of China. This study provides a theoretical basis for capacity sizing for rural PV-battery systems. The payback period of farmhouse PV systems in Gansu, Ningxia, Qinghai, Tibet, and Yunnan regions is <7 years, while the payback period of farmhouse PV systems in Guangdong, Guangxi, Jiangsu, Zhejiang, and Chongqing regions is higher than 10 years.

An Open-Source Julia Package for RMS Time-Domain Simulations of Power Systems

This paper presents RMSPowerSims.jl, an open-source Julia package for the time-domain simulation of power systems. The package is designed to be used in conjunction with PowerModels.jl, a widely used Julia package for power system optimization. RMSPowerSims.jl provides a framework for the simulation of power systems in the time domain, allowing for the study of transient stability, frequency stability, and other dynamic phenomena. The package is designed to be intuitive and flexible, allowing users to easily define custom models for network components and disturbances, while also providing a range of pre-constructed models for common power system components. RMSPowerSims.jl simplifies the process of performing RMS simulations on power system models developed using the PowerModels.jl ecosystem, and provides an easy-to-use modeling that reduces the barrier to entry for new users wishing to perform RMS simulations. The accuracy of the package is verified against DIgSILENT PowerFactory for short-circuit and load-increase disturbances, using the New England 39-bus system. The active power generation delivered by several generators in the network, and the voltage magnitudes of selected busbars are analyzed and noted to be in close agreement with those obtained using PowerFactory. The computational performance of the package is compared to that of PowerFactory and is found to be comparable for load-step simulations; however, PowerFactory is found to be considerably faster for short-circuit simulations. As computational performance is not a priority at this stage of development, this is expected, and speed optimization is planned for future work. RMSPowerSims.jl is available under an open-source license and can be downloaded from GitHub.

Advanced Short-Term Load Forecasting with XGBoost-RF Feature Selection and CNN-GRU

Accurate and efficient short-term load forecasting (STLF) is essential for optimizing power system operations. This study proposes a novel hybrid forecasting model that integrates XGBoost-RF feature selection with a CNN-GRU neural network to enhance prediction performance while reducing model complexity. The XGBoost-RF approach is first applied to select the most predictive features from historical load data, weather conditions, and time-based variables. A convolutional neural network (CNN) is then employed to extract spatial features, while a gated recurrent unit (GRU) captures temporal dependencies for load forecasting. By leveraging a dual-channel structure that combines long- and short-term historical load trends, the proposed model significantly mitigates cumulative errors from recursive predictions. Experimental results demonstrate that the model achieves superior performance with an average root mean square error (RMSE) of 53.29 and mean absolute percentage error (MAPE) of 3.56% on the test set. Compared to traditional models, the prediction accuracy improves by 28.140% to 110.146%. Additionally, the model exhibits strong robustness across different climatic conditions. This research validates the efficacy of integrating XGBoost-RF feature selection with CNN-GRU for STLF, offering reliable decision support for power system management.

Retrofitted CCS technologies enhance economy, security, and equity in achieving carbon zero in power sector

Retrofitting existing fossil fuel power plants with carbon capture and storage (CCS) technology could reduce carbon emissions while avoiding stranded asset, which will be important in facilitating a just transition of the global power sector. Although some studies have explored the cost-effectiveness and abatement potential of retrofitted CCS technologies, elaborate system modeling of full-chain retrofitted CCS technologies considering multiple technology types requires further research. Here, we developed an hourly-resolution intertemporal dynamic power system optimization model that elaborately considers three retrofitted CCS technologies for coal-fired and gas-fired power plant in addition to eleven power generation technologies and two energy storage technologies and applied it to evaluate the role of retrofitted CCS technologies in achieving carbon neutrality in China's power sector. The results show that, compared with no retrofitted CCS power system, the high development of retrofitted CCS can reduce the future installed capacity and power generation demand of China's power sector by up to 605GW or 10.5 % (in 2040) and 0.17 PWh or 0.9 % (in 2060), respectively. The cumulative system decarbonization costs and electricity supply costs will decrease 6.2–8.2 % and 2.1–2.6 % by 2060, respectively, due to the savings in related costs of newly built plants and reduction in potential power shortages, in addition to avoidance of large coal-fired power stranded assets. The developed model could be a reference for other countries, and in China and perhaps in other economies with coal-dominant power systems, policies advocating the development of retrofitted CCS should be strengthened.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Improved state transition algorithm for hybrid power system reactive power optimization using filter techniques

Abstract Reactive power optimization in power systems is crucial for their normal and efficient operation. However, with the increasing prevalence of active current and direct current (AC-DC) hybrid power grids, existing optimization methods face issues such as poor adaptability, low efficiency, insufficient accuracy, and inadequate convergence. To address these problems, this paper proposes a reactive power optimization model based on an improved state transition algorithm (STA) utilizing filter techniques. The optimization model considers operational costs associated with adjusting transformer tap positions, switching reactive power compensation capacitors, regulating generator terminal voltages, and adjusting active power in DC transmission. Simulation results demonstrate that this method can reduce system losses and voltage control costs while ensuring stable system operation, thus improving the reactive power distribution in AC-DC hybrid transmission systems.

Optimizing Power System Wide-area Damping Controllers under Communication Time Delays and Model Reduction Errors

Optimizing Power System Wide-area Damping Controllers under Communication Time Delays and Model Reduction Errors

Optimizing power system efficiency and costs in smart buildings with renewable resources

The increasing industrial development and energy demand have necessitated the maximization of available energy use and the deployment of renewable resources. Effective energy management, optimal modeling, and efficient planning are essential to transform the power system into a high-efficiency, optimal model. This study focuses on the initial step of modeling smart buildings (SBs) equipped with non-responsive devices and renewable photovoltaic sources. A comprehensive energy management (EM) plan is formulated for these buildings, incorporating the KNX protocol for solar energy system management. Batteries are integrated into the building model to store energy during periods of low consumption and serve as generators during peak load conditions, with the primary goal of minimizing power system losses and related costs. To address the complexity of this model, whale optimization algorithm (WOA) is employed for optimization. Optimal candidate buses are selected for interconnected building management based on a suggested sensitivity analysis to minimize losses. Cost performance is then assessed, considering energy production and sales. The findings indicate that substantial control of operating costs can be achieved through strategic management of battery charging and discharging, as well as the utilization of photovoltaic units. The proposed model is evaluated across various scenarios using a test system comprising 30 modified system, demonstrating its effectiveness in enhancing energy efficiency and management.

Investigating Intelligent Forecasting and Optimization in Electrical Power Systems: A Comprehensive Review of Techniques and Applications

Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future.

Optimization of a Biomass-Based Power and Fresh Water-Generation System by Machine Learning Using Thermoeconomic Assessment

Biomass is a viable and accessible source of energy that can help address the problem of energy shortages in rural and remote areas. Another important issue for societies today is the lack of clean water, especially in places with high populations and low rainfall. To address both of these concerns, a sustainable biomass-fueled power cycle integrated with a double-stage reverse osmosis water-desalination unit has been designed. The double-stage reverse osmosis system is provided by the 20% of generated power from the bottoming cycles and this allocation can be altered based on the needs for freshwater or power. This system is assessed from energy, exergy, thermoeconomic, and environmental perspectives, and two distinct multi-objective optimization scenarios are applied featuring various objective functions. The considered parameters for this assessment are gas turbine inlet temperature, compressor’s pressure ratio, and cold end temperature differences in heat exchangers 2 and 3. In the first optimization scenario, considering the pollution index, the total unit cost of exergy products, and exergy efficiency as objective functions, the optimal values are, respectively, identified as 0.7644 kg/kWh, 32.7 USD/GJ, and 44%. Conversely, in the second optimization scenario, featuring the emission index, total unit cost of exergy products, and output net power as objective functions, the optimal values are 0.7684 kg/kWh, 27.82 USD/GJ, and 2615.9 kW.

Advanced smart grid controller: a next-gen alternative to Fuzzy-logic for UPFC

A very valuable component of flexible AC transmission systems (FACTs) is the Unified Power Flow Controller (UPFC). By simultaneously regulating both active and reactive power on transmission lines, it maintains voltage stability on linked buses. This study devised a thorough control strategy for the Unitary Power Flow Controller (UPFC), combining traditional and artificial intelligence (AI)-based methods. To see how the system would react in different situations and how well artificial intelligence control worked compared to more traditional proportional-integral power control, we ran a lot of simulations. The study found that the control strategy based on artificial intelligence (AI) was better than the proportional-integral (PI) control at managing power flow, keeping the voltage stable, and responding to changes in the environment. The results of this study emphasize the capacity of artificial intelligence methods to improve the performance and stability of FACT components such as the UPFC. The research provides important insights into power system control and optimization, indicating that strategies based on artificial intelligence could greatly enhance power generation. In order to enhance the AI control strategy, future research should focus on its practical application in actual power systems.

Review on reactive power optimization of high proportion renewable energy power system

Review on reactive power optimization of high proportion renewable energy power system

Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba

As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.

A comparative study of evolutionary algorithms and particle swarm optimization approaches for constrained multi-objective optimization problems

Many real-world optimization problems contain multiple conflicting objectives as well as additional problem constraints. These problems are referred to as constrained multi-objective optimization problems (CMOPs). Many meta-heuristics for solving CMOPs, called constrained multi-objective meta-heuristics (CMOMHs) have been introduced in the literature, including those using particle swarm optimization (PSO)(Kennedy and Eberhart, 1995), genetic algorithms (GAs)(Man et al., 1996), and differential evolution (DE)(Storn and Price, 1997). CMOMHs can be grouped into four different classes: classic CMOMHs, co-evolutionary approaches, multi-stage approaches, and multi-tasking approaches. An extensive comparative study of twenty different CMOMHs on a wide variety of test problems, including real-world CMOPs in the fields of science and engineering, is conducted. A multi-swarm PSO approach called constrained multi-guide particle swarm optimization (ConMGPSO) is introduced and compared to the best-performing previous approaches according to the comparative study. The performance of each algorithm was found to be problem dependent, however the best overall approaches were ConMGPSO, paired-offspring constrained evolutionary algorithm (POCEA)(He et al., 2021), adaptive non-dominated sorting genetic algorithm III (A-NSGA-III)(Jain and Deb, 2014), and constrained multi-objective framework using Q-learning and evolutionary multi-tasking (CMOQLMT)(Ming and Gong, 2023). ConMGPSO and POCEA had the best performance on the CF benchmark set, which contains examples of bi-objective and tri-objective CMOPs with disconnected CPOFs. The CMOQLMT approach had the best performance on the DAS-CMOP benchmark set, which contain additional difficulty in terms of feasibility-, convergence-, and diversity-hardness. For the selected real-world CMOPs, A-NSGA-III had the best performance overall. ConMGPSO was shown to have the best performance on the process, design, and synthesis problems, and had competitive performance for the power system optimization problems.