Bus Test System Research Articles

Interactive demand response and dynamic thermal line rating for minimizing the wind power spillage and carbon emissions

Spilling has already occurred as a result of rising the penetration of intermittent renewable generation, and it is anticipated that the level of renewable energy curtailment will continue to soar. This leads to an increase in operating costs, CO2 emissions, and not good utilization of renewable energy resources. A bi-level multi-objective optimization model is proposed in this paper to reduce wind power spillage (WPS) based on demand response (DR) and dynamic thermal line rating (DTLR). In the upper level, multiple objectives will be satisfied based on the optimal allocation and time of DR programs considering DTLR obtained in the lower level. The minimization of WPS, load shedding, power losses, and CO2 emissions are the objectives of this level. While the lower-level aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level problems that represent the market clearing conditions is used to constrain the upper-level. The effectiveness of the proposed algorithm is examined on a modified two-area IEEE 24-bus test system. Results depict that the suggested bi-level model enables considerable reductions in the WPS by up to 32.7 %. Also, there is an enhancement in load shedding, power losses, and CO2 emissions by 28.93 %, 23.07 %, and 13.9 % respectively. Finally, the social welfare increased by up to 36.6 %.

A frequency stability assessment framework for renewable energy rich power grids

The worldwide power grid is undergoing a paradigm shift due to the rise of renewable energy sources. Traditional rotating synchronous generators are being replaced by inverter-based renewable energy sources (RESs), and this trend is expected to persist in the years to come. As a result, the grid’s inertia is decreasing gradually, which presents significant obstacles to maintaining grid frequency stability. Moreover, due to the uncertainties associated with the generation of RESs and load demand, grid inertia has become a stochastic quantity. In this paper, considering the stochastic characteristics of the power generation, load demand profile and grid inertia, a framework to assess grid frequency stability with high penetration of RESs has been presented. This framework first quantifies grid frequency characteristics at different levels of RES penetration using Monte-Carlo simulation (MCS) considering the intermittent nature of RESs and load demand. This is followed by assessment of frequency stability of the power grid using proposed matrices that quantifies the amount of deviation in grid frequency and rate of change of frequency (RoCoF) outside the boundary prescribed by the grid operator. In addition, the proposed framework evaluates the sensitivity of grid frequency stability to RES penetration levels and quantifies the resiliency against contingency. Rigorous case studies on modified IEEE 39 bus test system have been presented to demonstrate the effectiveness of the proposed framework in assessing grid frequency stability.

Sustaining power distribution network submerged in plug-in hybrid CNG-electric vehicles for future green transportation: A risk-constrained analysis

The urgent need to address environmental issues, particularly the reduction of greenhouse gas emissions, has become a global priority. In this pursuit, various technologies are being explored to facilitate the promotion of sustainability. This study focuses on a virtual energy hub (VEH) integrated with Plug-in hybrid gas-electric vehicles (PHGEVs), various energy sources, and storage devices. However, the presence of fossil-based energy sources like combined heat and power (CHP), diesel generation (DG), boilers, and gas-based operation modes of PHGEVs, resulted in the release of carbon emissions. In this concern, the power-to-gas (P2G) technology, with its immense potential, is being considered to absorb and recycle captured CO2 emissions into methane gas, thereby enhancing economic and sustainability perspectives. Furthermore, uncertain parameters including the charge and discharge of PHGEVs, the price of the day-ahead market for heat and power, energy demands, and wind speed are considered in the VEH, the management of which is achieved through the downside risk constraint (DRC) method. The proposed model is implemented and tested on an IEEE 33-bus test system. Results show that P2G reduces the CO2 level by a mean of 3.7 %. Also, for the minimum of 3.2 % profit reduction, the risk level has fallen by nearly 100 %.

A novel stochastic multistage dispatching model of hybrid battery-electric vehicle-supercapacitor storage system to minimize three-phase unbalance

The unbalanced load distribution, the single-phase connection of renewable energy, and the uncoordinated charging of electric vehicles (EVs) will bring a severe issue corresponding to the three-phase unbalance in modern distribution networks. To deal with this issue, a novel multistage optimal dispatching model with the hybrid energy storage system (HESS), consisting of the battery energy storage system (BESS), EV, and supercapacitor (SC), is proposed in this paper. The first stage is conducted on the day-ahead stage, driven by minimizing the total operation cost and relieving power unbalance, the unbalanced penalty cost is introduced into the optimal dispatching model with the electricity purchasing cost and the degradation cost of HESS. Also, the chance-constraint stochastic programming (SP) model with the coordinated operation of HESS is accommodated to handle the diverse uncertainties of renewable energy, load demand, and EV users’ behaviors. In the intra-day operation stage, a rolling optimization-based dispatch model is formulated to re-optimize the operation of the SC for the rapid response of mitigating the real-time three-phase unbalance caused by renewable energy. The numerical experiments are executed on the IEEE 34-bus three-phase test system. Compared with the cases without considering SC and the degradation cost of HESS, the power unbalance in the proposed model is reduced by 3.27% and 3.62% per day at the real-time stage, respectively, while total operation cost is reduced by 46.77 US$ and 350.62 US$ per day, respectively. The results validated that the proposed model is efficient in reducing the three-phase unbalance under multi-time scales, improving the economic operation of the distribution network.

A hierarchical structure for harnessing the flexibility of residential microgrids within active distribution networks: Advancing toward smart cities

The increasing integration of Renewable Energy Sources (RESs) and the replacement of fossil fuel vehicles with electric types in distribution systems, while enhancing economic and environmental aspects, have introduced new challenges to network production-consumption balance due to the inherent randomness of these resources. This factor has magnified the significance of balancing markets in renewable-based distribution networks, as these markets address uncertainties and are vital for operational stability. Consequently, this paper introduces a two-stage multi-layer methodology for organizing local energy and balancing markets among Residential Microgrids (RMGs) within an Active Distribution Network (ADN). The AND operator organizes both energy and balancing markets.In the first stage, the intra-day energy market operates, engaging non-profit-seeking aggregators, RMGs, and ADN. Moving to the second stage, the intra-hour balancing market is conducted involving profit-seeking Flexible Load (FL) and Electric Vehicle (EV) aggregators, RMGs, and ADN. The ADN operator organizes both markets and can alter the network topology online within the energy market. The planning of profit-seeking aggregators is formulated using Karush-Kuhn-Tucker (KKT) conditions and Strong Duality Theory (SDT) in the form of Mathematical Programming with Equilibrium Constraints (MPEC), considering the correlation of aggregators' profit with the satisfaction index of end-users. The simulation results of the proposed methodology on a modified 118-bus IEEE distribution test system using GAMS indicate that this approach not only provides a significant share of balancing capacities through FLs and EVs but also ensures system stability against intra-hour fluctuations across various scenarios. The results also validate that the proposed methodology achieves a reduction of 24.8 % in power losses and 5.06 % in market costs, while simultaneously ensuring profitability for both aggregators and RMGs.

Optimal Allocation of Distributed Generation Using Improved Cuckoo Search Algorithm

This work presents an improved cuckoo search algorithm based on Success Rate (CSA-SR) for optimal allocation of Distributed Generation (DG). This is necessary in order to improve the power quality of the distribution system by minimizing power loss and improving voltage stability. Voltage Stability Index (VSI) is applied to identify restricted unhealthy buses/areas in the distribution networks. The work is implemented on standard IEEE 33 Bus test systems. Newton Raphson Power Flow (NRPF) technique is used in MATLAB R2019b environment for the power flow analysis. The overall IEEE 33 Bus system real power loss obtained from the simulation result is found to be 202.7071MW at the base case. Following the application of 3 DGs using CSA-SR, the real power loss is found to be 113.9630 MW which shows a reduction of 88.441MW which is equivalent to 43.77% as related to the base case. The results minimized the system losses while significantly enhancing the voltage profile of the bus voltages as a result of optimal siting and sizing of the DG. These show that the CSA-SR technique outperformed PSO, which was used to ascertain the effectiveness of the developed scheme.

Complex network theory and game theory-based partitioning decision-making of parallel restoration for resilient power grid

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method.

A differential amplitude variation based pilot relaying scheme for microgrid integrated distribution system

Abstract The inclusion of inverter-interfaced distributed generating sources (IIDGS) in the active distribution network offers operational and protection constraints. Dynamic fault behavior of these DGS results in changes like the radial distribution systems. Thus, conventional protection schemes fail to discriminate the in-zone and out-zone faults. Hence, this manuscript describes a new protection scheme based on current variation at the relay location used to discriminate the internal and external faults. This technique requires phasor information of the current signals of both ends and adjacent lines using micro phasor measurement units (µPMUs). Using the current phasor information two components named internal and external are computed at the relay location which is called differential amplitude variation index (DAVI), used to identify the internal and external faults. A modified two-feeder radial distribution network and modified IEEE-13 bus test systems are modeled using EMTDC/PSCAD to validate the proposed strategy. Numerous disturbance cases have been simulated and the feasibility and applicability attributes of the suggested method are verified from the results.

Micro-grid tri-level EPEC based model for finding equilibria in the day-ahead and balancing markets

This paper presents a model that explores the strategic bidding equilibrium within a microgrid and its interactions with other strategic and non-strategic rivals in a joint energy and balancing market. The model utilizes a tri-level mathematical program with equilibrium constraints (MPEC) to represent the behavior of each strategic producer. Upper level maximizes the profit of each strategic producer, including microgrid and another strategic rival. The first lower-level problem involves maximizing social welfare through the day-ahead market clearing process, while the second lower-level problem deals with the balancing market clearing process. These objectives form the core of the proposed model. To simplify the tri-level problem, duality theory and the Karush-Kuhn-Tucker (KKT) optimally conditions are employed to transform it into a mixed integer linear programming (MILP) problem. By simultaneously solving all MPECs, an equilibrium problem with equilibrium constraints (EPEC) is formulated. The resulting EPEC is then addressed using a diagonalization algorithm and game theory to obtain a market Nash equilibrium, which constitutes the secondary objective of this paper. The effectiveness of the model is evaluated using the 6-bus test system as a case study. The results indicate that, at the equilibrium point, both the microgrid and rivals experience reduced profits compared to the initial state.

Deep learning-based integrated attack detection framework to protect distance relays against cyberattacks

The cyber security of communication-assisted intelligent relays at the IEC61850-enabled modern digital substations has become a prime concern among power companies and regulatory bodies. Particularly, distance relay IEDs are easy to manipulate to send false trip command to circuit breakers through random false data injection and replay attacks. With these attacks evolving and becoming more sophisticated, existing intrusion detection techniques can be easily compromised, resulting in unwanted line outages. This work proposes a convolutional-based autoencoder and Siamese neural network deep learning integrated attack detection framework (IADF) to differentiate these attacks from actual faults. The framework works in conjunction with distance relay IEDs to issue genuine trip commands to circuit breakers. The autoencoder, trained on fault voltage and current signals, detects the false data injection attacks using reconstruction error, while the siamese neural network trained on similar and dissimilar pairs of the fault voltage and current signals detects the fully synchronized replay attacks using similarity estimation with fault data stored on database. The validity of the proposed framework is assessed using IEEE 39 and 118 bus test system simulated on PSCAD/EMTDC software. The validation results suggest that the proposed IADF can accurately discriminate the faults from attacks and is resilient to various system parameter changes.

Multi-objective configuration and evaluation of dynamic virtual inertia from DFIG based wind farm for frequency regulation

Modern power systems are gradually evolving into low-inertia systems due to the integration of high penetration of renewable energy and power electronic equipment. For the natural inertia on the rotor, doubly-fed induction generator (DFIG) based wind farms are expected to provide inertia support for systems. The paper proposed an analytical model of DFIG with df/dt inertia control for studying frequency dynamics, and investigates the optimal design of virtual inertia from wind farms, as well as its equivalent inertia contribution to system. The inertia response differences between DFIG and synchronous generator (SG) are discussed and analyzed for the first time, and then to accurately describe the frequency response with low cost, an analytical model of DFIG with inertia control is developed considering wind turbine aerodynamic characteristics. The explicit expression of virtual inertia provided by DFIG is derived according to the kinetic energy equilibrium, and the key factors that dominate the magnitude of virtual inertia are illustrated. Based on the system frequency response model including wind farms, taking the system frequency nadir (SFN) and the time of reaching nadir (ToN) as targets, a multi-objective optimal configuration model of virtual inertia is constructed and solved through MOPSO. The time domain simulations of a generic two machine four bus system and modified IEEE 9-bus test system are both performed in MATLAB/Simulink, the accuracy and efficiency of our proposed DFIG model on system frequency dynamics research are demonstrated, the pareto frontiers of virtual inertia configuration under different working conditions are optimized and analyzed as well as the impacts of different parameters on inertia evaluation.

A multilevel optimization approach for daily scheduling of combined heat and power units with integrated electrical and thermal storage

Renowned for their remarkable overall efficiencies ranging from 70% to 90%, combined heat and power systems stand as a pivotal strategy for optimizing energy consumption by capitalizing on the synergistic relationship between electricity and thermal energy production. However, achieving optimal performance in combined heat and power systems remains a formidable challenge due to the intricate interplay of numerous variables. This paper presents a novel approach to daily scheduling optimization for combined heat and power units, focusing on the integration of electrical and thermal storage systems and meticulous consideration of security constraints. The optimization of combined heat and power unit scheduling introduces a mixed-integer nonlinear programming challenge, replete with deterministic and random variables. Addressing this complexity requires resilient solutions. In this study, we employ a multilevel optimization technique, transforming the problem into a bilevel structure. The initial step involves mapping operating parameters and minimizing costs through a water cycle optimization algorithm, laying a robust foundation for combined heat and power unit operation. Subsequently, we immerse ourselves in the realm of stochastic contingency scenarios, acknowledging the myriad uncertainties inherent in real-world systems. To empirically validate the efficacy of our proposed algorithm, extensive simulations are conducted on IEEE 18-bus and 24-bus test systems that emulate practical power networks. The results unequivocally showcase the power of our approach in navigating the complexities of optimal CHP unit planning. This paper's contributions lie in its innovative multilevel optimization technique, adeptly addressing both deterministic and stochastic aspects, ultimately paving the way for increased energy efficiency and enhanced system reliability in practical applications.

Enhancement of ATC with FACTS Devices in Deregulated Power System Considering Various Contingency and Benefit Margins

The global electric power systems are under stressed condition because of increased per capita power consumption and ever-growing load demand. It is difficult to modify existing infrastructure of transmission systems to meet increasing load demand. The volume of electric power transmitted depends on the real and reactive power supply and the availability of margin in transmission system. Available Transfer Capability is an estimation of additional power transfer capability of the existing transmission system for a further market activity over and above the already committed power transactions. Factors such as weather, line and generator outages must be taken into account when computing the rigorous value of ATC. This paper focuses on enhancing the ATC limit of a system by employing STATCOM and Whale Optimization Algorithm for tuning system variables and to determine optimal size and location. An investigation has been carried out to understand the impacts of ATC by implementing line and generator outages in the test system. Additionally, an investigation has been done to realise the various benefit margins like ETC, TRM and CBM. The competence of the proposed methodology is validated by conducting different case studies on IEEE-30 bus test system using MATLAB for various bilateral and multilateral transactions.

Novel cyber-physical architecture for optimal operation of renewable-based smart city considering false data injection attacks: Digital twin technologies for smart city infrastructure management

Smart grid is considered a cyber-physical system, which is a combination of physical devices and computational processes. Since there are lots of interactions between the cyber layer and the physical layer, the operation, management, and security of the entire system are crucial topics that must be taken into account. In this regard, this paper tackles two problems in AC MGs, as a special case of smart cities. Firstly, a framework is proposed to solve the optimal scheduling problem in these systems. In this stage, the optimal management and scheduling of the system are structured and modeled as an optimization problem. The dragonfly is utilized as a powerful optimization technique to solve this optimization problem. It is worth noting that in this paper different renewable energy sources, as well as batteries are considered. Since cyber-attacks are among the greatest threats to the system and can cause disruption and outage in smart grids, in this paper a deep-learning-based method called long short-term memory along with the concept of prediction interval is utilized to develop a cyber-attack detection model for false data injection attacks on smart meters. The proposed cyber-attack detection model is first trained using historical data and then is used in real-time for detection. In order to investigate the effectiveness of the proposed optimal scheduling scheme, the modified IEEE 33-bus test system is utilized. Also, for testing the proposed cyber-attack detection model a real-world dataset is used. The simulation results show the great performance and effectiveness of both proposed methodologies. It should be noted that all model are simulated in digital twin simulator. The novel LSTM-based cyber-attack detection model enhances security in electric grids, demonstrating remarkable accuracy.

A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.

Optimal Allocation of Photovoltaic-Green Distributed Generation for Maximizing the Performance of Electrical Distribution Networks

Renewable energy sources provide an environmentally sustainable solution to meet growing energy demands. Consequently, photovoltaics (PV) is regarded as a promising form of green distributed generation (GDG). The penetration of PV-GDG into distribution networks (DNs) is crucial, presenting a significant opportunity to improve power grid quality and reduce power losses. In this study, a comprehensive investigation was conducted to determine the optimal location, number, and capacity of PV-GDG penetrations with DN to achieve these objectives. Therefore, employing the Newton–Raphson (NR) technique and particle swarm optimization (PSO) approach for case studies, the analysis focused on the IEEE 33 bus test system as a benchmark test and the Iraq–Baghdad DN at 11 kV and 0.416 kV as a real case study. The outcomes revealed that integrating 4 × 1 MW PV-GDG units in a centralized configuration at bus 13 of the 11 kV Rusafa DN in the first scenario significantly reduced power losses and alleviated voltage drops across the network. In contrast, the second scenario entailed the utilization of dispersed PV panels with a capacity of 10 kW installed on rooftops at all 400 consumer load points with a cumulative capacity of 4 MW. This approach exemplified the enhancement of DN performance by significantly maximizing the power loss reduction and minimizing the voltage drops across the buses, exceeding the results achieved in the first scenario. The software applications employed in the practical implementation of this study included the CYMDist 9.0 Rev 04 program, PVsyst 7.2.20 software, and MATLAB R2022b.

Ambient data-driven participation factors related to oscillation modes based on subspace dynamic mode decomposition

Analyzing the connection between the variables participating in the oscillation and modes in electromechanical oscillation is particularly important for maintaining system stability. This paper proposes an ambient data-driven method based on Subspace Dynamic Mode Decomposition (Sub-DMD) to extract the Participation Factors (PFs) related to system state and algebraic variables in electromechanical oscillation modes. This method uses ambient data to calculate the low-dimensional approximate matrix of the system, and the PFs is extracted by using eigen-decomposition and mode energy. Compare the extracted PFs with the results of power generation dispatch. The effectiveness of the proposed method is demonstrated by using simulation data from IEEE 4-generator 2-area test system and IEEE 16-generator 68-bus test system.

Stochastic scheduling of energy sharing in reconfigurable multi-microgrid systems in the presence of vehicle-to-grid technology

On the one hand, the inherent intermittency in demands and renewable energy sources (RES) frequently bring challenges such as overload or surplus generation within microgrids. On the other hand, electric vehicle aggregations (EVAs) have garnered substantial attention as a pivotal strategy to address climate change and serve as a sustainable substitute for petroleum-based vehicles. However, the uncoordinated deployment of EVAs within microgrids, especially in the face of the intermittent nature of RES, poses a potential threat to the secure operation of microgrid systems. To tackle the mentioned issues, this research concentrates on interconnecting a group of scattered microgrids to create a multi-microgrid system. In more detail, by developing an energy management strategy to reconfigure the interconnections among microgrids, the efficient exchange of power among these multi-microgrid systems is facilitated, addressing the variability in load demands amidst the stochastic generation patterns of RESs. Besides, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) concepts of EVAs are synchronized within the reconfigurable microgrid structure to enhance the flexibility of the model. To evaluate the model under realistic situations, a scenario-based method is also employed to reflect the effects of uncertainties on the model. The proposed approach, characterized by its mathematical convexity, allows for employing efficient solvers like CPLEX, ensuring the attainment of a feasible global solution within a finite timeframe. The effectiveness of the proposed method is demonstrated through its implementation on a modified 33-bus test system operated as multi-microgrid system. The results show the effectiveness of the proposed approach as a promising tool for optimizing the operation of reconfigurable multi-microgrid systems in the presence of EVAs, leading to operational cost reduction and voltage profile enhancement.

Online assessment of short-term voltage stability based on hybrid model and data-driven approach

With the continuously increasing integration of renewable energy sources into power grids, the dynamic response of a power system is becoming more complex. For example, the interaction between the dynamic loads and low-voltage ride-through of renewable energy generators makes the voltage response more rapid and unpredictable. Ensuring the accuracy and speed of traditional voltage stability assessment methods is difficult. This study developed a novel hybrid model and data-driven voltage stability assessment approach. First, the equivalent parameters of a power system were calculated based on the measured data, and the parameters were constantly modified based on the response data. To further improve the accuracy of the approach, a data-driven method was introduced to correct the assessment results using a Thevenin equivalent-based assessment. The difference between the Thevenin and system impedances, which better reflects the system stability, was included in the data-driven input data. Finally, by combining the clear physical mechanism of the model-driven method and high accuracy of the data-driven method, the final the assessment process was a serial combination of the model- and data-driven methods. The effectiveness of the method was verified using an IEEE New England 10-generator 39-bus test system and a 100-bus actual system in China. The results showed that the method developed was more accurate and had higher robustness under data loss and noise conditions than other methods.