Enhance Power System Reliability Research Articles

Pricing balancing ancillary services for low-inertia power systems under uncertainty and nonconvexity

In renewable-rich power systems, declining rotational inertia and unpredictable power fluctuations make the system vulnerable to contingencies. Recently, this issue has garnered significant attention in practice and academia, aiming to enhance power system reliability through market mechanisms. This paper proposes a day-ahead joint market that integrates energy, reserve, inertia, and frequency response services. A frequency-constrained unit commitment (UC) model is developed employing a distributionally robust chance-constrained (DRCC) approach to account for the uncertainties in wind farm outputs. Additionally, a convex hull pricing (CHP) model is introduced to minimize uplift payments associated with the nonconvex nature of commitment variables. Case studies conducted on a 5-bus and a 118-bus power system demonstrate the efficacy of the proposed market design in reducing operational costs and promoting renewable energy utilization. Furthermore, the results highlight the effectiveness of the CHP model in minimizing the uplifts and enhancing market transparency.

Enhancing power system reliability: Hydrogen fuel cell-integrated D-STATCOM for voltage sag mitigation

The focus of this study is to investigate the critical matter of voltage sags in power systems, which have a substantial adverse effect on both the functionality of equipment and the quality of power. Central to this investigation is the integration of hydrogen fuel cells and a D-STATCOM system, which is suggested as a substitute approach for mitigating voltage fluctuations. The hydrogen fuel cell serves as the pivotal component, renowned for its ability to significantly improve power quality through the efficient mitigation of voltage sag concerns. The paper provides a comprehensive analysis of the contribution that hydrogen fuel cell integration with D-STATCOM makes to the reliability of power systems. A systematic methodology is employed, supported by simulations and modeling, to emphasize the critical significance of hydrogen fuel cells in surmounting obstacles related to voltage injection and enhancing power quality. By utilizing a Type-3 Fuzzy system, the research evaluates the dependability of the control system and proposed model. The effectiveness of this method, which is focused on hydrogen fuel cells, is verified by means of MATLAB simulations, which demonstrate its superior performance in comparison to conventional PI and ANFIS controllers.

Analyzing the Impact of Renewable Energy Integration on Power System Reliability

The integration of renewable energy sources is pivotal in addressing climate change and ensuring sustainable energy provision. Reliability management in power systems plays a crucial role in this endeavor, ensuring a consistent supply of clean energy while reducing carbon emissions. Extensive national and international research has demonstrated the effectiveness of modeling techniques, analytical methods, and management strategies in mitigating the volatility and uncertainty inherent in renewable energy. Reserve capacity planning, intelligent control systems, and energy storage technologies are instrumental in enhancing power system reliability. Case studies and empirical analyses offer valuable insights for other regions dealing with renewable energy challenges. With continuous technological advancements and policy support, the obstacles of renewable energy integration can be surmounted, moving us closer to a sustainable energy future. Ongoing research and international cooperation provide further opportunities and solutions to achieve clean energy objectives.

Multiple distributed generators islanding detection using GBDT-JS techniques

Photovoltaic arrays and wind Distributed Generators (DG) have become integral to our renewable energy landscape. However, when a DG disconnects from the grid, the risk of islanding arises, necessitating detection within the stringent of two-second timeframe mandated by IEEE standards. Machine-level algorithms play a pivotal role in enhancing power system reliability and safety by swiftly identifying and isolating isolated segments, thereby preventing potential hazards and ensuring efficient grid operation. This study introduces an algorithm-based islanding detection approach for distributed generating systems employing both Solar Photo Voltaic (SPV) and wind systems. The GBDT-JS algorithm, a combination of Gradient Boosting Decision Trees and Jelly Fish Techniques, emerges as an intelligent solution. The focus of this technique is based on the Rate of Change in Phase Angle (RCPP) at the target DG position, offering a characterized approach to islanding detection. In addressing the major difficulties and challenges, the GBDT-JS method proves instrumental in categorizing islanding situations and grid disturbances. This classification aids in determining the system’s adoptability based on various loading and switching capabilities. The achievements in overcoming these challenges lie in the algorithm’s ability to provide a comprehensive solution, ensuring the reliability and safety of distributed generating systems.

Optimisation of generation unit commitment and network topology with the dynamic thermal rating system considering N-1 reliability

The electrical grid is a vital component of modern society that requires significant and cost-effective investments to ensure its reliability. The dynamic thermal rating system (DTR) and network topology optimization (NTO) aim to increase the capacity of existing networks and provide flexible options for enhancing power system reliability. The co-optimization framework for generation unit commitment and network switching is formulated with for N-1 reliability, a gap that has not been previously addressed. This is important as it ensures: (1) economic efficiency in power system despatch, (2) optimal operation of both generation and transmission systems, and (3) power grid operation that can withstand the loss of any single component without causing a cascading failure. The reliability impacts of these new proposed operating strategies are also tested on complex, large-scale power systems under long-term, multi-area weather conditions. This proposed work is useful for power system planners, as it helps them identify ways to improve the reliability and efficiency of power systems under various weather conditions. This paper shows that using the DTR-NTO combination improves EENS by 50% in both test cases compared to the original condition. Additionally, it also makes the reliability assessment more realistic when taking into account multi-area weather conditions.

Hybrid optimization to enhance power system reliability using GA, GWO, and PSO

Abstract An optimization approach is described in the research study that deals with the issue of reconfiguration networks built with certain conditions of power loss reduction and reliability. Furthermore, the reconfigured networking system seeks optimization based on criteria affecting the limitations. This study optimises specific network faults subjecting resources with no supply during reconfiguration to avoid the effect and possess through active power losses. These goals were met using the mathematical method of the optimisation process. The mathematical formulation is generated first in the system development process. As a result, a comprehensive methodology using genetic algorithm, Grey Wolf optimization (GWO), and particle swarm optimization (PSO) was developed. Finally, intended methodologies were estimated. Based on the results, it is clear that the proposed hybrid GWO-PSO approach outperforms all other methods in terms of node voltage, reliability, line currents, and computational duration. Furthermore, when optimally sized distributed generations are placed in optimal locations, total loss is reduced by up to 63% and voltage profiles improve.

Optimal battery energy storage system deployment from perspectives of private investors and system operators for enhancing power system reliability

Due to their intermittent nature, high penetration of renewable energy resources (RES) can deteriorate power system reliability. A battery energy storage system (BESS) offers an opportunity to reduce the uncertainty associated with RES and hence improve power system reliability. In the last decade, the cost of BESS has declined so its deployment has become increasingly feasible, but the decision ultimately depends on its Net-Benefit to the investor. Depending on the perspective of the investor, the deployment decision involves discovering the most optimal location, capacity, and technology of BESS. A sensitivity analysis based on the Z-bus matrix is used for selecting the optimal location. In this paper, from the narrow perspective of a private investor, the Net-Benefit function is the energy arbitrage value of BESS minus its overall cost. However, the system operator additionally values BESS because it enables more reliable system operation at reduced operating costs. After examining multiple power system reliability indices, the product of Expected Unserved Energy (EUE) and Value of Lost Load (VoLL) is incorporated into the Net-Benefit function of the system operator. MATPOWER Optimal Scheduling Tool (MOST) is used for testing the proposed Net-Benefit functions on a standard reliability test system (RTS) known as RTS-GMLC. Three diverse case studies are examined in three exploration phases; the first phase helps determine search space whereas the second one narrows it down and the third phase finds the optimal BESS solution. Presented research results demonstrate that, over a period of 15 years, optimal BESS deployment can offer as much as $M164.8 and $M33.6 Net-Benefits to the system operator and private investor respectively.

Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Aperiodic two-layer energy management system for community microgrids based on blockchain strategy

Regulatory changes in different countries regarding self-consumption and growing public concern about the environment are encouraging the establishment of community microgrids. These community microgrids integrate a large number of small-scale distributed energy resources and offers a solution to enhance power system reliability and resilience. This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles, mimicking the most typical layout of cities. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid, without requiring interaction with the distributed system operator. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.

Mathematical modeling of polymer dielectric strength considering filling concentration

Abstract Enhancement of Dielectric strength is an important issue to enhance power system reliability. Insulation filling such as Alumina Trihydrate (ATH) and Silicon dioxide (Sio2) are used to enhance the dielectric strength of polymer insulator. The paper proposes a mathematical model of dielectric strength considering different temperatures and filling percentages using neural network. Moreover, comparison of filling materials is carried out through this model. The comparison shows the superiority of ATH in enhancing the dielectric strength of the polymer. Also, the obtained model will help in getting the best filling concentration without needing to repeat the tests which can be considered as a techno-economical solution to enhance the dielectric strength. Finally, the curve fitting is used to get the dielectric strength model not only as a function of filling concentration percentage but also as a function of temperature.

Оптимизация расположения и мощности возобновляемых распределенных источников энергии с использованием модифицированного метода роя частиц

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

Assessing Impact of Optimally Placed Power Factor Correction Capacitors Reckoning Transient Switching Events

Installing capacitors to correct the power factor at particular locations is one way to enhance power system reliability. This paper offers a new formulation to address the issue of optimal placing capacitors. The proposed formulation considers reliability impact, in addition to the transient switching events. This is reflected in the cost minimization objective function, where the reliability calculations are considered to gauge the impact when capacitors are installed in the power system. The formulated problem is solved using genetic algorithm (GA), to minimize costs of capacitor installation, energy loss and failure impact. The new formulation has been examined on a test network for evaluating how effective the proposed approach is. The solution also considers the impact of uncertainties as well as future growth in the system, to ensure that it is optimal. The results evidence that the proposed approach is robust and effective in enhancing system reliability in the face of uncertainties and for future growth.

Efficient optimization technique for multiple DG allocation in distribution networks

In the last few decades, interest in the integration of Distributed Generators (DGs) into distribution networks has been increased due to their benefits such as enhance power system reliability, reduce the power losses and improve the voltage profile. These benefits can be increased by determining the optimal DGs allocation (location and size) into distribution networks. This paper proposes an efficient optimization technique to optimally allocate the multiple DG units in distribution networks. This technique is based on Sine Cosine Algorithm (SCA) and chaos map theory. As any random search-based optimization algorithm, SCA faces some issues such as low convergence rate and trapping in local solutions during the exploration and exploitation phases. This issue can be addressed by developing Chaotic SCA (CSCA). CSCA is mainly based on the iterative chaotic map which used to update the random parameters of SCA instead of using the random probability distribution. The iterative chaotic map is applied for single and multi-objective SCA. The proposed technique is validated using two stranded IEEE radial distribution feeders; 33 and 69-nodes. Comprehensive comparison among the proposed technique, the original SCA, and other competitive optimization techniques are carried out to prove the effectiveness of CSCA. Finally, a complete study is performed to address the impact of the intermittent nature of renewable energy resource on the distribution system. Hence, typical loads and generation (represented in PV power) profiles are applied. The result proves that the CSCA is more efficient to solve the optimal multiple DGs allocation with minimum power loss and high convergence rate.

From demand response to transactive energy: state of the art

This paper reviews the state of the art of research and industry practice on demand response and the new methodology of transactive energy. Demand response programs incentivize consumers to align their demand with power supply conditions, enhancing power system reliability and economic operation. The design of demand response programs, performance of pilot projects and programs, consumer behaviors, and barriers are discussed. Transactive energy is a variant and a generalized form of demand response in that it manages both the supply and demand sides. It is intended for a changing environment with an increasing number of distributed resources and intelligent devices. It utilizes the flexibility of various generation/load resources to maintain a dynamic balance of supply and demand. These distributed resources are controlled by their owners. However, the design of transaction mechanisms should align the individual behaviors with the interests of the entire system. Transactive energy features real-time, autonomous, and decentralized decision making. The transition from demand response to transactive energy is also discussed.

양전원 송전선로의 고장점 표정 알고리즘

In order to service restoration and enhance power system reliability, a number of impedance based fault location algorithms have been developed for fault locating in a transmission line. This paper presents an advanced impedance-based fault location algorithms in a two-ended sources transmission line to reduce the DC offset error effects. This fault location algorithm uses of the GPS time synchronized voltage and current signals from the local and remote terminal. The algorithm uses an advanced DC offset removal filter. A series of test results using ATPdraw simulation data show the performance effectiveness of the proposed algorithm. The proposed algorithm is valid for a two-end sources transmission network.

Reliability-based nodal evaluation and prioritization of demand response programs

SUMMARY Demand response (DR) programs are effective tools to relieve power systems operational risks and will play key role in future smart grids because of emerging advanced communication and metering technologies. Enhancing power system reliability is one of the main goals of employing DR programs. This paper presents a nodal viewpoint for reliability-based planning of DR programs. It aims to demonstrate the superiority of nodal evaluation and prioritization of DR programs to improve reliability over the global approach. Here, a sequential state-enumeration-based method is presented for composite system operational reliability assessment, and a multi-segment optimal power flow approach is developed to evaluate the effects of DR programs. In addition, analytic hierarchy process is employed for global and nodal prioritizing of different DR programs. The results show that employing DR programs based on nodal prioritization can lead to higher reliability, lower costs, and greater assurance on programs implementation. Copyright © 2014 John Wiley & Sons, Ltd.

Incorporating Demand Response With Spinning Reserve to Realize an Adaptive Frequency Restoration Plan for System Contingencies

Demand response (DR) is considered to be one of the most incentive smart grid functions to enhance power system reliability and control. For the purpose of system security, DR has been utilized to deal with contingent events. In this way, load shedding conducted by the DR program is no longer sorely considered as the last defense line to save system frequency, it should be considered as part of the available fast response resources to cooperate with spinning reserve for balancing generation and demand under some specific conditions. This paper proposes an overall frequency restoration plan considering the DR and spinning reserve. The frequency restoration is designed using the DR as the first option to intercept frequency decline for a large disturbance, followed by the scheduled spinning reserve to raise frequency back to the pre-disturbance level. The proposed scheme is especially developed to intelligently deploy DR for coordinating with different spinning reserve constraints and contingency scenarios. Tests of the proposed frequency restoration scheme are evaluated by simulation where the system data was retrieved from frequency events in a utility. Test results show that the proposed spinning reserve and DR deployment could effectively restore frequency under various contingency scenarios.

UPFC for Enhancing Power System Reliability

This paper discusses various aspects of unified power flow controller (UPFC) control modes and settings and evaluates their impacts on the power system reliability. UPFC is the most versatile flexible ac transmission system device ever applied to improve the power system operation and delivery. It can control various power system parameters, such as bus voltages and line flows. The impact of UPFC control modes and settings on the power system reliability has not been addressed sufficiently yet. A power injection model is used to represent UPFC and a comprehensive method is proposed to select the optimal UPFC control mode and settings. The proposed method applies the results of a contingency screening study to estimate the remedial action cost (RAC) associated with control modes and settings and finds the optimal control for improving the system reliability by solving a mixed-integer nonlinear optimization problem. The proposed method is applied to a test system in this paper and the UPFC performance is analyzed in detail.

Benefits of Power Electronic Interfaces for Distributed Energy Systems

With the increasing use of distributed energy (DE) systems in industry and its technological advancement, it is becoming more important to understand the integration of these systems with the electric power systems. New markets and benefits for DE applications include the ability to provide ancillary services, improve energy efficiency, enhance power system reliability, and allow customer choice. Advanced power electronic (PE) interfaces will allow DE systems to provide increased functionality through improved power quality and voltage/volt-ampere reactive (VAR) support, increase electrical system compatibility by reducing the fault contributions, and flexibility in operations with various other DE sources, while reducing overall interconnection costs. This paper will examine the system integration issues associated with DE systems and show the benefits of using PE interfaces for such applications.

Application assessment of system protection schemes for power network congestion management

In order to enhance power system reliability and manage network congestions, many power utilities around the world have deployed various forms of system protection schemes (SPS) in their systems. In a deregulated environment, SPS could offer immediate solutions to network congestion that are beneficial to both utilities and customers. This paper presents a framework for quantitative risk and benefit assessments of SPS operations. The risk of SPS operations and its effect to the system security are evaluated by a probabilistic risk assessment approach. The benefits obtainable from power market and the customer interruption costs resulting from SPS operations are appraised. The proposed methodology can be used by power system operators and planners to assess the applications of SPS for system reliability enhancement and compare different alternatives for managing network congestion. Numerical results obtained from an actual event-based SPS example are presented.