Power System Model Research Articles

Hybrid Power System Design and Dynamic Modeling for Enhanced Reliability in Remote Natural Gas Pipeline Control Stations

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator.

Analysis of Characteristics on a Compressed Air Power System Generating Supercavitation Drag Reduction for Underwater Vehicles

An unmanned underwater vehicle (UUV) powered by a compressed air power system is proposed to address challenges for battery/motor-powered vehicles under high-speed navigation, long endurance, and high mobility. These vehicles actively utilize supercavitation drag reduction by the exhausted gas from the compressed air power system. MATLAB/Simulink and FLUENT are used to establish theoretical models of the compressed air power system and ventilation supercavitation. The relationship between system power and navigation resistance is examined with different air flows, along with a comparison of endurance of different power vehicles at various speeds. The issue of the endurance-enhancing effect of supercavitation at high speed is investigated. The results demonstrate that increasing the air flow leads to higher power and reduced navigation resistance, and there is a balance between them. Furthermore, compared to the battery-powered vehicles with equal energy storage capacity, the compressed air power system shows 210.08% to 458.20% longer endurance times at speeds of 30 kn to 60 kn. Similarly, considering equal energy storage mass, it achieves 42.02% to 148.96% longer endurance times at high speeds (30 kn to 60 kn). The integration of supercavitation and air-powered systems can greatly enhance the endurance and maneuverability of the vehicle at high speeds while ensuring a compact system structure. The investigations could offer valuable ideas for the development and application of compressed air power systems for UUV at 30 kn to 60 kn or higher maneuvering.

Research on optimal power flow of AC/DC hybrid system for civil more electric aircraft

Modern civil more electric aircraft often use AC/DC systems instead of AC systems to reduce the weight of the power system. Compared with the AC/DC system of the land power grid, the aircraft power system needs to pursue economic operation mode under more complex security constraints. This paper establishes the optimal power flow model of the AC/DC hybrid system of more electric aircraft, which takes minimizing the active power loss of the line as the optimization objective, including the steady-state operation constraints and security constraints of the AC/DC system. The interior point method and prediction correction interior point method are used to solve the model. Then, based on the simplified steady-state model of the B787 power system, the model and algorithm are verified by experiments. The experimental results show that by optimizing the power flow distribution of the power system, the power utilization efficiency of the system has been significantly improved, and its stability and reliability can also be better guaranteed.

A single-objective optimization model for power systems with the introduction of line-heavy load rate index constraints

The increasing penetration of new energy sources in new power systems has exacerbated the volatility and complexity of power system currents. Grid planning is facing higher challenges. Established grid planning models usually use the upper and lower limits of grid transmission capacity as constraints, but this may lead to long-term heavy load operation of certain lines and increase system risks. Therefore, this paper designs a line-heavy load rate constraint and establishes a single-objective optimization model for grid planning based on this constraint. Example analyses verify the effectiveness of adding line-heavy load rate constraints in planning and the reasonableness of the single-objective optimization model.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

Research on interval optimization of power system considering shared energy storage and demand response

Considering the low utilization rate of energy storage system under uncertainty of source-load and the coarse demand response mechanism, an interval optimization model of power systems based on shared energy storage and refined demand response is proposed. The dual-side uncertainty of source-load is expressed by interval numbers, and the refined demand response mechanism and shared energy storage optimization model for different building load are analyzed. Then, the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed. In order to improve the accuracy of the optimization results, sensitivity analysis is performed on the linearized segmentation number of the objective function under dual-side uncertainty of source-load. Finally, by comparing the simulation with the traditional centralized energy storage system, the results show that the dual-side uncertainty of source-load can be effectively handled using interval optimization. Considering shared energy storage and demand response, it can effectively improve the energy storage utilization rate and system operation economy, and realize the source-grid-load-storage synergistic interaction.

Sustainable Development Strategies in Power Systems: Day-Ahead Stochastic Scheduling with Multi-Sources and Customer Directrix Load Demand Response

Increasing the installed capacity of renewable energy sources (RESs) in the power system is significant for advancing sustainable development. As the proportion of RESs rapidly increases in power systems, the inherent stochasticity and variability of renewable energies significantly reduce the regulatory capacity of generation resources. To compensate for the lack of power system flexibility, it is necessary to coordinate the participation of load-side resources in demand response (DR). Therefore, this paper proposes a solution to the diminished flexibility of power systems. It introduces a day-ahead stochastic scheduling model for an integrated thermal-hydro-wind-solar system. This model relies on customer directrix load (CDL) to efficiently absorb RES output. CDL represents an ideal load profile shape. Firstly, the stochastic scenario sets of RES output were modeled using Monte Carlo simulations, and the complementary characteristics between wind and solar output are considered using Copula theory. Then, CDL is introduced into day-ahead scheduling model, which considers relevant demand-side responsive load constraints. Secondly, customer-side DR effectiveness model is proposed to obtain the shaping load profile after DR, based on quantitative customer response effectiveness evaluation metrics. Lastly, system-side stochastic scheduling model of high-proportion RES power system is proposed based on the shaping load profile. Case studies were conducted on a modified IEEE-6 bus system. These studies show that the model effectively addresses the uncertainty of RES. It improves the power system’s regulation capability. Additionally, it promotes the absorption of RES.

Electric vehicle participation for optimized load frequency control in a multi-area restructured power system

ABSTRACT Electric vehicles (EVs) can be employed intelligently in Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes for Load Frequency Control (LFC) in the power grid. Due to the rapid variation of charging or discharging power, EVs can perform frequency regulation tasks and serve as distributed energy storage devices. In this paper, a linearized model of an interconnected multi-area power system is formulated in the restructured environment to include Electric Vehicle Aggregators (EVA). The DISCO Participation Matrix (DPM) is used to model Poolco and bilateral contracts in restructured multi-area power systems along with the participation of EVs. The faster dynamic response of EVs can effectively control the deviations in area frequencies imposed by the DISCOs load demand. The gains of the integral controller employed in each area are tuned by Real Coded Genetic Algorithm (RGA) by minimizing the integral square error function for area frequency deviations. The efficacy of power support from the EVs for load frequency control is validated by simulating different Poolco and bilateral transactions. It is shown that the EVs have a significant impact on obtaining optimal transient response for area frequencies, tie-line powers and power generation of conventional thermal generating units for any type of transaction in a restructured environment.

Cluster and Representative Models for Generation Units of Flexible Grids with Small Modular Reactors

The analysis of existing mathematical models for power systems with large shares of electricity production by NPP and renewable energy sources generation was carried out. The low level of adequacy is shown for power system models used in the problems of long-term development planning for generating plants with nuclear fuel and renewable energy sources. In order to plan the development of power systems at the current stage of technological changes in the electric power industry, new models of load modes for NPP units and generating equipment with renewable energy sources are proposed.&#x0D; Dispatching models with a cyclic forecast period were used to ensure the adequacy unit commitment models for NPP units and energy storage systems. Representative models of solar and wind power plants are presented, which, together with cluster models of NPP units and energy storage systems, solve the issue on the adequacy of power system modeling in current conditions of their development. The results of computational experiments with the proposed models for generating equipment of the flexible grid are presented. The adequacy of cluster mathematical models of NPP units and energy storage systems, as well as representative models of solar and wind power plants generating equipment is experimentally confirmed.

Design and Analysis of a Low Voltage Simulink Model (LVSM) of IEEE 57 Bus

The present power system is gaining momentum towards designing equivalent circuit models. The Ward and REI methods involve the admittance reduction method as well as being merged with the EMS model to derive boundary parameters, but these methods are limited and valid for a predefined condition. Therefore, it is required to design an equivalent circuit that adopts the real power system for analysis. In this paper, a new method is proposed to design a scaled-down power system model without changing the impedance of components. In this regard, a Low Voltage Simulink Model (LVSM) of the IEEE 57 bus network was designed in MATLAB/ Simulink so that it could be useful for laboratory model design purposes. The main objectives of this paper are to propose a mathematical procedure to scale down the network parameters and design a 3-phase LVSM of an IEEE 57 bus power system network within the Simulink platform. The performance of LVSM was analyzed with no-load, balanced load, and unbalanced load models. These simulation studies were validated and compared with the theoretical results to prove that the proposed LVSM modeling has good mathematical accuracy, robustness, and validity for practical model implementation.

A Cost-Effective Earthquake Disaster Assessment Model for Power Systems Based on Nighttime Light Information

The power system is one of the most important urban lifeline engineering systems. Identifying the damage to the power system is an important task in earthquake disaster assessments. Considering the importance of timeliness and accessibility, a hyperparameter optimization model is proposed to address the assessment of disaster losses in power systems on earthquakes. The power system vulnerability on earthquakes, PSVE, is assessed by the hyperparameter optimization model based on nighttime light information. Through the utilization of the computational resources provided by Google Earth Engine, the accuracy of the baseline model has been significantly improved to 87.9%; meanwhile, the cost-effectiveness in the evaluation process is maintained. The PSVE-based damage evaluation has the potential to aid in assessing earthquake damage to cities’ energy supply, power infrastructure, and lighting. Furthermore, the PSVE-based damage evaluation can provide valuable guidance for prioritizing and efficiently allocating resources for rapid repair and reconstruction efforts.

Super capacitor and SSSC-based coordinated strategy for performance improvement of diverse source interconnected power system

In this work, the dynamical behavior of the dual area thermal-hydro-wind (DATHW) model of the interconnected power system (IPS) is investigated. For analysis, the area-1 of the DATHW system is laid with a disturbance in step load (SLD) of 10%. The investigation is carried out on the DATHW system under the supervision of the fractional order proportional-integral-derivative-filter (FOPIDN) controller optimized with the water cycle algorithm (WCA). However, the dominance of FOPIDN in regulating the DATHW system performance deviations is validated by other regulators. To get the research near practicality, the DATHW system is deliberated with communication time delays (CTDs). The CTDs is the inherit nature of the communication peripherals that are employed in the IPS network for providing the data exchange among various devices. Further, the supplementary control approach of super capacitors (SC) and static synchronous series compensators (SSSC) is adopted with the DATHW system for performance improvement. The supplementary control is implemented by integrating the SC in the control areas and the SSSC with the tie-line. Finally, we conducted the sensitivity test to reveal the efficacy of the suggested controller mechanism in this work.

Comparative modeling of cost-optimal energy system flexibility for Swedish and Austrian regions

This study develops a reproducible method for estimating the cost-efficient flexibility potential of a local or regional energy system. Future scenarios that achieve ambitious climate targets and estimate the cost-efficient flexibility potential of demonstration sites were defined. Flexible potentials for energy system assessment are upscaled from the demonstration sites in Eskilstuna (Sweden) and Lower Austria (Austria). As heat pumps (HPs) and district heating (DH) are critical for future heat demand, these sites are representative types of DH networks in terms of size and integration with the electricity grid. In both regions a TIMES model is used for energy system optimization, while for upscaling, Eskilstuna uses the building-stock model ECCABS, whereas Lower Austria uses a mixed integer linear programming optimization model, and the BALMOREL power system model. According to the modeling, HPs will dominate Eskilstuna’s heating sector by 2040. In Lower Austria, DH becomes more prevalent, in combination with wood biomass and HPs. These findings are explained by the postulated technological-economic parameters, energy prices, and CO2 prices. We conclude that future electricity prices will determine future heating systems: either a high share of centralized HPs (if electricity prices are low) or a high share of combined heat-and-power (if electricity prices are high). Large-scale energy storage and biomass can be essential solutions as may deliver increased cost-effectiveness, if available and under certain conditions.

Security load frequency control model of interconnected power system based on deception attack.

The interconnected power system connects the power grids of different regions through transmission lines, achieving power interconnection and resource sharing. However, data is transmitted through open power networks and is more susceptible to network attacks. To improve the stability of interconnected power systems under deception attacks, three scenarios of system security load frequency control were studied. Based on the construction of a dynamic model of load frequency control, an event-triggered strategy was used to reduce the communication frequency between nodes, resulting in a reduction in the amount of network transmission data. A sliding mode controller was constructed to solve the problem of event-triggered sliding mode security load frequency control. Elastic event-triggered sliding mode load frequency control for interconnected power systems under mixed attacks. The simulation results showed that using the load frequency control model triggered by events, the load frequency deviation of the interconnected power system can be stabilized at around 12 seconds, effectively saving the cost of network resources. Under the regulation of the load frequency control model based on sliding mode control, the interconnected power system stabilized in 10 seconds, reducing the load of network transmission. The elastic event-triggered sliding mode load frequency control model can ensure stable transmission of power data under various attacks and has good anti-interference performance. The results of this study have played an important role in achieving the stability of power resource supply. Compared with previous studies on individual power systems, this study solves the attack problem of interconnected power systems and considers the frequency control problem of system security loads under mixed attacks, enabling the system to recover stability faster.

Dynamic state estimation of power system using hybrid whale-tunicate optimized unscented Kalman filter based on wide area measurement systems

The Energy Management Systems (EMS) utilizes Dynamic State Estimation to monitor the operating condition of the power grid. For a synchronous generator, the speed and angle of a rotor are the most important dynamic states. These states are responsible for producing uncertainty in the power system models. Extended Kalman Filter (EKF) and Unscented Kalman Filters (UKF) are utilized to estimate the dynamic states of the power system when it perceives significant disturbances or noise. The conventional Unscented Kalman Filter (UKF) has limitations in terms of slow convergence speed and its lack of positive semi-definiteness. This prohibits its ability to accurately track the transients of the system. The scaling parameters of UKF (alpha and beta) are optimally tuned to overcome these issues. In this paper, a novel Hybrid Whale-Tunicate-based UKF (HW-TO-UKF) is proposed for tuning the scaling parameters of UKF for reducing the mean difference between the real state and the measured state. During the validation of the proposed method, better robustness and statistical efficiency are achieved by defining the objective function as the minimization of the error difference between the actual and measured states. The simulation is carried out on the WSCC-3 and NPCC-48 machine systems to demonstrate the performance of the proposed HW-TO-UKF over the existing models.

Single-level reduction of the hydropower area Equivalent bilevel problem for fast computation

For inclusion in large-scale power system models, various aggregations and simplifications in the modeling of relevant actors are needed. This paper focuses on reduced models of hydropower, so called area Equivalent models. They use a simplified topology but are not a direct aggregation of the real hydropower system. Instead, the area Equivalent is constructed to mimic the simulated power production of a more detailed hydropower reference model. Here, this goal is fulfilled by formulating a bilevel problem minimizing the difference in simulated power production between the area Equivalent and its reference. Solving this can be computationally heavy. Thus, for a fast solution of this bilevel problem, a single-level reduction is done, which is then solved using two methods. The first method includes McCormick envelopes to form a linear single-level problem. Second is a modified Benders with a relaxed sub-problem to handle the non-convex single-level. These are then also compared to Particle Swarm Optimization. Moreover, six new upper-level objective functions are investigated for a case study of hydropower in northern Sweden. The method using McCormick envelopes is fast (2–5 min), but the area Equivalent shows lower average performance. The modified Benders finds a solution in 5–31 min with good performance.

Impact of locational pricing on the roll out of heat pumps in the UK

This paper investigates the impact of locational pricing on the roll out of heat pumps in the UK. Qualitative assessment of proposals set out for electricity market reform in the UK identified locational pricing as potentially having an impact on heat pump running costs. Energy system modelling was used to assess the economics of both individual heat pumps and system-wide heat pump roll out under both unified pricing and locational pricing for the UK for 2020 and 2035. PyPSA-GB, a future power system model, was employed to simulate unified and locational wholesale prices and the Octopus Agile tariff was used to calculate indicative retail tariffs applicable to domestic heat pumps. The research highlights that locational pricing can create market conditions which better reflect the true cost of generating and delivering electricity, however, it can also lead to higher heat pump operating costs in regions with projected high heat demand. Key findings reveal that locational pricing could result in significant geographical disparities in heat pump operating costs due to varying electricity prices across different zones. Further work is required to develop policy to alleviate high operating costs and to promote flexible operation of heat pumps.

Static-Voltage-Stability Analysis of Renewable Energy-Integrated Distribution Power System Based on Impedance Model Index

Static-voltage stability has become one of the most significant risks faced by large-scale renewable energy integration. However, traditional methods for static-voltage-stability analysis are often overly complex. This paper constructs an equivalent impedance model for renewable energy-integrated distribution power systems, proposing a static-voltage analysis method for renewable energy-integrated distribution power systems based on an impedance model index. This method has been verified to be applicable not only to a renewable energy single-infeed system but also to a multi-infeed system. Furthermore, an analysis is conducted on the influence of the integration capacity, location of renewable energy, and the topology of networks on the impedance model index, indicating that a higher impedance model index corresponds to greater static-voltage-stability margins in the system. Hence, during the planning of renewable energy integration, the plan with the highest impedance model index should be selected. Finally, the accuracy of the analysis method and conclusions in this paper was validated based on the IEEE 14-node system.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

Fractional-Order Load Frequency Control of an Interconnected Power System with a Hydrogen Energy-Storage Unit

Modern power systems are confronted with widespread concern on the frequency stability issue due to the widespread integration of randomly fluctuating renewable resources. To address the above concern, this work introduces a load-frequency-control (LFC) scheme based on a parameter tuning strategy for fractional-order proportional–integral–derivative (FOPID) controller. Firstly, a two-area interconnected power system (IPS) model, including thermal, hydro, solar, wind, and gas power generator and a hydrogen-based energy-storage unit, is established. Then, a FOPID controller is designed for this IPS model, and an improved gradient-based optimizer (IGBO) is developed to adaptively regulate the parameters of the FOPID controllers. Finally, the effectiveness of the offered LFC scheme is tested through load disturbance and renewable energy fluctuations test scenarios and provides a comparison and robustness analysis among different schemes. The test results validated that the offered LFC scheme can effectively suppress the frequency fluctuations of the IPS and has excellent robustness.