Power System Optimization Research Articles

Modified effective butterfly optimizer for solving optimal power flow problem

The optimal power flow (OPF) problem remains a popular and challenging work in optimizing power systems. Although researchers have suggested many optimization algorithms to solve this problem in the literature, their comparison studies lack fairness and transparency. As these studies increase in number, they deviate from a standard test system, considering a common security and technical constraints., there is a growing trend away from a standard test system. Different studies used different search ranges for the same decision and constraint parameters, different than the standard ranges suggested by IEEE systems. This caused many unfair comparisons in literature. Furthermore, these studies are generally not transparent enough so that their results cannot be verified. This has resulted in numerous infeasible solutions in the literature, violating the limits of constraint parameters. The recent incorporating of renewable energy sources in OPF studies has made this situation more complicated. Sorting through the literature and identifying those OPF applications having exactly the same test conditions is a challenging process. The main contribution is this paper adapts the modified effective butterfly algorithm (MEBO) to solve OPF problem under the common parameter constraints and sufficient transparency. The focus is on a transparent comparison with works in the literature with the same constraint values. This paper compares the performance of the proposed algorithm with other state-of-the-art algorithms in the literature, focusing on the wind energy and without wind energy IEEE 30-bus and IEEE 57-bus systems and the most commonly used constraints. The results demonstrate the efficiency and superiority of the proposed algorithm. For instance, in the 30-bus test system, compared to the initial case, fuel cost has been reduced by 11.42 %, emission by 14.33 %, L-index by 45.10 %, active power losses by 51.60 %, and voltage deviation by 92.70 %.

Efficiency and mass optimization for space nuclear power systems with closed Brayton cycle loops

Space nuclear power systems should be strictly limited in mass and size. Lack of detailed designs, mass estimation in the general design phase usually relies on simple and empirical models that leave too much design margin from the limits. Accurate mass estimation of space nuclear power systems is key to minimize the design margin and maximize the system performance in the general design phase. In this paper, new mass estimation models are proposed for the main components of space nuclear power systems, including nuclear reactor, shield, turbine, compressor, heat exchanger, and thermal radiator. The mass estimation models are more accurate by incorporating principles of nuclear physics, thermal hydraulics, and structural mechanics to the component designs. Combined with thermodynamic models and genetic algorithm, the MEGREZ code is developed to optimize the system performance by setting efficiency and mass as dual objectives. The code verification results indicate that the deviations between and the Jupiter Icy Moons Orbiter project parameters and the code calculated values are only 0.4% for system efficiency and 2.1% for system mass. The optimization for the Jupiter Icy Moons Orbiter project shows that the optimal molecular weight of helium-xenon mixture is the minimum value for single-stage turbomachinery limits. Additionally, the impacts of potential technological degradation, such as increased heat exchanger thermal resistances, higher frictional factors, and decreased turbomachinery efficiency, on system performances are discussed. The results show that turbomachinery efficiency presents the strongest influence on the system-specific mass; a 0.04 reduction in the polytropic efficiency of the compressor or turbine results in more than a 10% increase in system-specific mass.

Feasibility of Scaling up the Cost-Competitive and Clean Electrolytic Hydrogen Supply in China

Scaling up clean hydrogen supply in the near future is critical to achieving China’s hydrogen development target. This study established an electrolytic hydrogen development mechanism considering the generation mix and operation optimization of power systems with access to hydrogen. Based on the incremental cost principle, we quantified the provincial and national clean hydrogen production cost performance levels in 2030. The results indicated that this mechanism could effectively reduce the production cost of clean hydrogen in most provinces, with a national average value of less than 2 USD·kg−1 at the 40-megaton hydrogen supply scale. Provincial cooperation via power transmission lines could further reduce the production cost to 1.72 USD·kg−1. However, performance is affected by the potential distribution of hydrogen demand. From the supply side, competitiveness of the mechanism is limited to clean hydrogen production, while from the demand side, it could help electrolytic hydrogen fulfil a more significant role. This study could provide a solution for the ambitious development of renewables and the hydrogen economy in China.

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

Dynamic knowledge graph approach for modelling the decarbonisation of power systems

This paper presents a dynamic knowledge graph approach that offers a reusable, interoperable, and extensible framework for modelling power systems. Domain ontologies have been developed to support a linked data representation of infrastructure data, socio-demographic data, areal attributes like demand, and models describing power systems. The knowledge graph links the data with a hierarchical representation of administrative regions, supporting geospatial queries to retrieve information about the population within the vicinity of a power plant, the number of power plants, total generation capacity, and demand within specific areas. Computational agents were developed to operate on the knowledge graph. The agents performed tasks including data uploading, updating, retrieval, processing, model construction and scenario analysis. A derived information framework was used to track the provenance of information calculated by agents involved in each scenario. The knowledge graph was populated with data describing the UK power system. Two alternative models of the transmission grid with different levels of structural resolution were instantiated, providing the foundation for the power system simulation and optimisation tasks performed by the agents. The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

Dual-layer optimization planning for offshore wind farms considering power system security

The vigorous development of offshore wind farms (OWFs) has further promoted the utilization of wind energy. Owing to the demand for low-carbon environmental protection, the increase in the scale of OWF constructions has also had a significant impact on the power system, which cannot be ignored. Specifically, the uncertainty and volatility of wind-power generation should be considered in power system optimization planning schemes. Therefore, this study proposes a probabilistic power-flow analysis index construction method that employs the static-security region theory to optimize the OWFs–grid-connection topology, including the number, location, and capacity of the public connection points connected to the onshore power grid. This method can be used to calculate the probability indicators of power grid insecurity for OWFs–grid-connected systems with different network topologies. Furthermore, a dual-layer multi-objective programming model that considers the economics and stability for integrated planning of electrical collector and multiterminal direct current transmission systems is established. The proposed model comprises two inner-layer models and one outer-layer model. The inner-layer models are used to construct the collection and transmission topologies of the OWFs–grid-connected system that minimize the probability of grid insecurity and the planning cost of the outer-layer model. Finally, the effectiveness of the proposed OWFs planning model was verified using an improved IEEE 24-bus system that can be used for topology planning of electrical collector and transmission systems while ensuring grid stability.

Optimizing power system stability: A hybrid approach using manta ray foraging and Salp swarm optimization algorithms for electromechanical oscillation mitigation in multi‐machine systems

AbstractThis paper emphasizes the significance of ensuring adequate damping of electromechanical oscillations in power systems to ensure stable operation. Power System Stabilizers (PSSs) are influential in enhancing system damping and refining dynamic characteristics during transient conditions. However, the efficacy of PSSs is notably contingent on parameter values, particularly in the case of lead‐lag PSSs. In response to this challenge, the paper introduces a Tilt‐Integral‐Derivative (TID)‐based PSS, optimized through a novel optimization algorithm called Hybrid Manta Ray Foraging and Salp Swarm Optimization Algorithms (MRFOSSA). The MRFOSSA algorithm demonstrates robustness and enhanced convergence, validated through benchmark function tests, and outperforms competing algorithms. These superior characteristics of MRFOSSA were employed in optimal tuning of TID‐PSSs to uphold the stability of multi‐machine power systems. The MRFOSSA algorithm demonstrates robustness and enhanced convergence, outperforms competing algorithms in the optimal tuning of TID‐PSS within the Western System Coordinating Council (WSCC)‐3‐machines 9‐bus test system. In summary, the proposed TID‐PSS, coupled with the MRFOSSA algorithm, presents a promising avenue for enhancing power system stability.

Optimizing power system operations: integrating FACTS and ESS through advanced algorithms

Optimizing power system operations: integrating FACTS and ESS through advanced algorithms

Optimizing power systems and microgrids: A novel multi-objective model for energy hubs with innovative algorithmic optimization

This article delves into the dynamics of power system and microgrid planning, emphasizing the emerging significance of energy hubs. The study introduces a pioneering multi-objective model that integrates economic considerations and microgrid pollution mitigation. The focus is on renewable sources such as wind and solar, coupled with advanced energy storage systems like E-fuel. Sophisticated probabilistic modeling techniques address uncertainties in renewable resources, complemented by a demand-side management program for cost reduction during peak hours. Transforming into an optimization problem, an innovative algorithm inspired by African vulture foraging behaviors provides a distinctive approach to energy landscape optimization. In a comparative analysis with established models, this approach demonstrates proficiency in simulating market behavior and providing optimal strategies for profit maximization. In a simulation analysis of four scenarios, the impact of risk assessment, a novel electronic fuel storage market, and an innovative energy storage solution on operational costs and pollution emissions is scrutinized. Notably, the proposed method excels in Case 4, showcasing a significant reduction in both operational costs and emissions. Case 4 demonstrates a substantial reduction in operational costs to $1745.732 and pollutant releases to 10753.621 kg, outperforming existing models with lower operational costs and pollutant releases.

Power equalization and optimization of photovoltaic module based on forward-flyback converter

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

A novel derivative search political optimization algorithm for multi-area economic dispatch incorporating renewable energy

Multi-area economic dispatch (MAED) incorporating renewable energy has become an important issue in the power system optimization. Existing intelligent optimization algorithms often suffer from poor solution accuracy or slow convergence when dealing with MAED problems. In this paper, a novel derivative search-based political optimization (DSPO) algorithm is proposed to handle the MAED problem incorporating renewable energy including wind and solar energy. In the renewable energy modeling, the Weibull and log-normal probability density functions are used to calculate available wind and solar power respectively. In order to improve the search performance, DSPO adopts two strategies: leader guide strategy and derivative search mechanism. The former adds the leader’s global optimal information which can direct candidate solutions to more promising regions and speed up convergence. The latter derives neighborhood solutions around some high-quality solutions to improve the exploitation ability. The DSPO algorithm is applied to solve four MAED problems which take into account valve point effect, prohibited operating zone, power loss and so on. The simulation results show that the DSPO algorithm achieves the overall best results in terms of convergence speed, solution accuracy and stability when compared with several well-established algorithms.

A machine learning approach to analysis of Canadian provincial power system decarbonization

Canada committed to reducing national greenhouse gas emissions by 40–45 % by 2030 and achieving net-zero by 2050, relying on low-carbon electrification across economic sectors. This study investigates the machine learning’s potential to analyze least-cost optimization of Canadian provincial power systems to 2050, to identify robust pathways for net-zero electricity system greenhouse gas emissions, and to provide policy insights respecting wind and solar generation, reducing emissions from natural gas generation, and expanding interprovincial transmission. Surrogate modeling is applied as sensitivities to generation technology capital costs, electricity demand, and carbon pricing to results from an existing capacity expansion mixed-integer linear optimization model. Under almost all future scenarios, six provinces develop new wind generation, with substantial deployment in Alberta (mean 27.3 GW, max. 62.7 GW), Ontario (mean 6.8 GW, max. 19.3 GW), Saskatchewan (mean 3.6 GW, max 8.9 GW) and Manitoba (mean 1.5 GW, max 3.5 GW). With the potential exception of BC, where higher-cost wind generation results in solar generation becoming the primary low-carbon electrical energy source (mean 25.2 GW), future solar generation across Canada is more limited (mean 37.1 GW) and less certain than wind generation in all other Canadian provinces. Existing natural gas generation is maintained in almost all scenarios in all provinces, providing support for maintaining these resources for reliability and reserve requirements. Very limited natural gas is developed under any scenarios, with the exception of British Columbia (mean 4.3 GW) where more than 80 % of scenarios include new natural gas capacity. Additional transmission transfer capacity is developed in most scenarios between BC-Alberta (∼60 % of scenarios), Saskatchewan-Manitoba (∼85 %), and Ontario-Quebec (∼75 %). Carbon prices, along with wind and solar capital costs, act as primary determinants of least-cost decarbonization pathways. These findings offer insights for Canadian policymakers into net-zero pathways for provincial electricity systems that remain robust despite variability in inputs.

Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy

Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.

Research on optimization of improved short-term load composite forecasting model based on AM–CNN–Bi–LSTM

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

Design and research of an electric unmanned aerial vehicle power system

An Electric power test system of UAVs is presented, which can be applied to validate the efficiency of UAV propulsion systems and provide reliable verification for propeller numerical calculations. The design proposal, working principle, test equipment and test experiment is introducted. Reliability of various functions of the system is examined and assessed through the multiple comparisond tests, which indicates that, when subjected to identical conditions, the numerical solutions for propeller thrust, torque, and motor efficiency exhibit average errors of 1.02%, 10%, and 10.04%, respectively. The results indicate that the power system test platform effectively validates numerical calculation results, providing reliable engineering application value for general UAV propulsion system research. The findings of this study offer support for optimizing power systems of the UAV, providing valuable references for future engineering applications and development.

Improved Mayfly Algorithm for Optimizing Power Flow with Integrated Solar and Wind Energy

Across the globe, the transition towards sustainable energy systems necessitates seamless implementation of Renewable Energy Sources (RES) into traditional power grids. Such RESs include solar and wind power. The current research work intends to overcome the challenges associated with Optimal Power Flow (OPF) problem in power systems in which the traditional operation parameters ought to be optimized for effective and trustworthy integration of the RESs. The current study proposes an innovative nature-inspired approach by enhancing the Mayfly algorithm on the basis of mating behaviour of mayflies. The aim of this approach is to tackle the complexities introduced by dynamic and discontinuous nature of solar and wind power. The improved Mayfly algorithm aims at minimizing power losses, emission, optimize voltage profiles, and ensure reliable integration of solar and wind power. The current study outcomes provide knowledgeable insights towards power flow optimization in power systems with high penetration of renewable energy. The application results reveal that the improved mayfly algorithm achieved better efficacy compared to the classical mayfly algorithm and the rest of the optimization algorithms.

Design of information management model based on multiobjective optimization algorithm in intelligent electric financial system

The electric power infrastructure is the cornerstone of contemporary society’s sustenance and advancement. Within the intelligent electric power financial system, substantial inefficiency and waste in information management persist, leading to an escalating depletion of resources. Addressing diverse objectives encompassing economic, environmental, and societal concerns within the power system helps the study to undertake a comprehensive, integrated optimal design and operational scheduling based on a multiobjective optimization algorithm. This article centers on optimizing the power financial system by considering fuel cost, active network loss, and voltage quality as primary objectives. A mathematical model encapsulates these objectives, integrating equations and inequality constraints and subsequently introducing enhancements to the differential evolutionary algorithm. Adaptive variation and dynamic crossover factors within crossover, variation, and selection operations are integrated to optimize algorithm parameters, specifically catering to the multiobjective optimization of the electric power system. An adaptive grid method and cyclic crowding degree ensure population diversity and control the Pareto front distribution. They experimentally validated the approach and the comparisons conducted against AG-MOPSO, INSGA-II, and NSDE algorithms across standard test functions: ZDT1, ZDT2, ZDT3, and DTLZ4. The convergence evaluation indices for this study’s scheme on ZDT1 and ZDT2 are 0.000938 and 0.0034, respectively. Additionally, distribution evaluation indices on ZDT1, ZDT2, ZDT3, and ZDT4 stand at 0.0018, 0.0026, 0.0027, and 0.0009, respectively. These indices indicate a robust convergence and distribution, facilitating the optimization of electric power financial information management and the intelligent handling of the electric power financial system’s information, thereby enhancing the allocation of material and financial resources.

Optimisation Methods Based on Soft Computing for Improving Power System Stability

In order to keep electrical grids running reliably and efficiently, power system stability is essential. The optimisation of power systems is fraught with complexity and uncertainty, yet soft computing approaches have been shown to be an excellent tool for dealing with these issues. In order to improve the reliability of the power system, this article presents a summary of optimisation techniques that rely on soft computing techniques. , the methods of evolutionary computing, such as particle swarm optimisation (PSO) and genetic algorithms (GA), are discussed. By efficiently searching for optimum solutions in huge solution spaces, these algorithms are used to optimise power system parameters and control techniques. In general, optimisation techniques based on soft computing provide effective and flexible ways to enhance power system stability. These technologies improve grid operation by using swarm intelligence, evolutionary computing, fuzzy logic, and neural networks to tackle the issues of power systems, which are dynamic and unpredictable.

Optimization algorithm of 500kVGIS circuit breaker electric field simulation based on GPU acceleration

It proposes an optimization approach for electric field simulation in 500kV gas-insulated switchgear (GIS) circuit breakers that use GPU acceleration. Accurate modelling of electric field distribution within GIS circuit breakers is critical for assuring the dependability and safety of high voltage power systems. However, the computational complexity of simulating these systems presents substantial obstacles to established approaches. The goal of this work is to improve the speed, efficiency, and scalability of electric field simulations by leveraging GPU parallel processing capabilities. They methodically build and refine the simulation algorithm, utilizing techniques such as parallel algorithm design, GPU architecture optimization, and efficient memory management. Through rigorous performance studies, they demonstrate significant increases in computational efficiency relative to CPU-based techniques, with an average speed up of 15x. Furthermore, the scalability of the GPU-accelerated method is evaluated, demonstrating near-linear scalability up to 8 GPUs. Validation against experimental observations and analytical models indicates that the simulation findings are accurate and robust. This study helps the progress of electric field simulation tools for GIS circuit breakers by providing insights into electric field behaviour and aiding design optimization and performance evaluation in high voltage power systems.

Research on Power Optimization for Energy System of Hydrogen Fuel Cell Wheel-Driven Electric Tractor

Hydrogen fuel cell tractors are emerging as a new power source for tractors. Currently, there is no mature energy management control method available. Existing methods mostly rely on engineers’ experience to determine the output power of the fuel cell and the power battery, resulting in relatively low energy utilization efficiency of the energy system. To address the aforementioned problems, a power optimization method for the energy system of hydrogen fuel cell wheel-driven electric tractor was proposed. A dynamic model of tractor ploughing conditions was established based on the system dynamics theory. From this, based on the equivalent hydrogen consumption theory, the charging and discharging of the power battery were equivalent to the fuel consumption of the hydrogen fuel cell, forming an equivalent hydrogen consumption model for the tractor. Using the state of charge (SOC) of the power battery as a constraint, and with the minimum equivalent hydrogen consumption as the objective function, an instantaneously optimized power allocation method based on load demand in the energy system is proposed by using a traversal algorithm. The optimization method was simulated and tested based on the MATLAB simulation platform, and the results showed under ploughing conditions, compared with the rule-based control strategy, the proposed energy system power optimization method optimized the power output of hydrogen fuel cells and power batteries, allowing the energy system to work in a high-efficiency range, reducing the equivalent hydrogen consumption of the tractor by 7.79%, and solving the energy system power distribution problem.