Transmission Network Congestion Research Articles

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods.

Impact of Dynamic Thermal Rating on optimal siting and sizing of energy storage systems under Renewable Portfolio Standards requirements

Renewable Portfolio Standards (RPS), enforced as part of environmental regulations, are taking precedence worldwide to promote investments in renewable energy sources (RES). From the grid perspective, one of the bottle-neck factors that limits large-scale RES integration is the transmission network congestion. Dynamic Thermal Rating (DTR) is a smart grid technology that utilizes line condition and real-time weather conditions to alleviate network congestion by exploiting the transmission network inherent flexibility. Due to the intermittency of the RES, energy storage systems (ESS) have emerged as an essential component of power system that stores the excess generated energy by RES and pumps it back into the network at peak hours. However, despite the benefits of these technologies, the coordination of these two technologies with RPS regulations are not studied together. This paper presents a co-optimized model to evaluate the role of DTR in optimal siting and sizing of ESS units in connection with RPS requirements. The modeling framework co-optimizes bulk ESS expansion and DTR investment planning subject to constraints on RPS regulations and is formulated as a two-stage stochastic mixed integer linear programming (MILP) optimization problem. The model utilizes DTR to overcome the problem of network congestion and ESS to mitigate RES curtailment by saving the extra generated energy in non-peak hours. The efficacy of the proposed model in realizing impact of the DTR on ESS (location, energy capacity and power rating) in achieving high RPS targets is investigated on the modified IEEE 24-Bus system. Results demonstrate that DTR complements ESS under different RPS constraints by reducing the total system cost and ESS size significantly. • A stochastic MILP model is presented to model DTR implementation and ESS sizing simultaneously. • The model incorporates RPS policies and demand side management with various limits. • The impact of the DTR on investment cost, location and size of ESS units are discussed. • We find that the co-optimized model is noteworthy to study the power systems with high RPS targets.

A tri-level framework for distribution-level market clearing considering strategic participation of electrical vehicles and interactions with wholesale market

With the increasing penetration of distributed renewable energy sources in distribution networks, a large number of independent market entities such as load serving entities will participate in the distribution-level market, which provides a platform for transparent energy transactions among them. This paper proposes a novel tri-level optimization model for distribution system operator market clearing, fully considering the strategic participation of electrical vehicles and the interactions with the transmission system operator in the market environment. The upper-level model addresses the bidding strategy problem of charging stations and the randomness of renewable energy sources, the middle-level model performs the distribution-level market clearing, and the lower-level model focuses on the joint optimization of power and reserve for transmission network. Furthermore, the Stackelberg game model between distribution system operator and each charging station is converted into a mathematical program with equilibrium constraints formulation, and a decentralized approach based on analytical target cascading algorithm is developed to realize the decoupling of distribution and transmission network. The simulation results show that the proposed distribution-level market clearing method can alleviate the congestion in transmission network and enhance the economic benefits of load serving entities in distribution network.

Transmission network congestion control by DESS through interval computation and capacity optimization via hybrid DE‐PSO technique

Transmission congestion is one of major drawbacks of deregulated power system. Congestion gradually violates the physical, operational and policy constraints of the present electricity market. Congestion management in huge power system remains a challenging and tough task, which can be achieved by introducing one or more Distributed Energy Resources (DERs) such as Distributed Generation (DG), electric vehicles, modern micro grid, Energy Storage Systems (ESSs) etc. over congested lines. Consequently, this work proposes the hourly congestion management by optimal allocation of DG in ‘24 hours’ time frame. Further, Transmission Congestion Rent is used to determine the optimal and sub optimal locations for DG, whereas Differential Evolution and Particle Swarm Optimization based hybrid optimization technique is exploited to identify optimal DG size. In this work, solar based DG along with battery based ESS is considered to act as Distributed Energy Storage System ‘DESS. Subsequently, Interval computation method is introduced in this work to consider the intermittency of solar DG. Battery Energy Storage System ‘BESS’ is integrated with DGs due to its excess energy storage capability during off-load period and make it available at peak load conditions, thereby enhancing the overall efficiency of the network. The ‘24 hours’ solar irradiance and temperature data of Delhi, India is taken into account to mathematically model the power generated from Solar DG. The proposed methodology is tested on IEEE 30-bus system and ‘24 hours’ demand data is generated from the original IEEE load data. From the obtained results, it has been established that the hybrid optimization technique results into a better solution as compared to their individual enforcement in managing available generation resources and congestion problem.

Optimization of Grant-Free NOMA With Multiple Configured-Grants for mURLLC

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.

Incentive-Based Caching and Communication in a Clustered D2D Network

Caching at the network edge can reap significant advantages to improve service quality by reducing the transmission cost and network congestion. Edge caching with device-to-device (D2D) communication helps offload cellular traffic during a surge in network traffic. This article considers the classic clustering problem for D2D users, followed by an incentive mechanism designed for successful D2D communication. The proposed clustering scheme merges D2D users with similar interests into one cluster. Specifically, a hierarchical agglomerative clustering algorithm is applied, where clusters are formed based on the similarity in users’ social interests. The cache hit probability is then optimized for each cluster, and the performance is examined for a varying number of clusters. We then propose a monetary incentive-based mechanism based on a points system to increase user participation for successful D2D communication by keeping track of a user’s content-providing history. Using this approach, devices with a good participation rate in D2D communication are identified and rewarded for their performance. Through simulations, we show that the hit rate improves by more than 40% after ensuring incentive-based methods in the D2D network.

Transmission Congestion Management Using Generator Sensitivity Factors for Active and Reactive Power Rescheduling Using Particle Swarm Optimization Algorithm

Independent System Operators have difficulty in fulfilling all contractual power transactions in a competitive energy market due to transmission network congestion. As a result, applications of generator rescheduling become one of the antidotes in alleviating this difficulty in the consequence of ever-increasing numerous power transactions. The goal of this research is to lower the cost of active and reactive power of the generators by reducing the deviation of rescheduled active and reactive power from scheduled values. The inclusion of reactive power rescheduling and voltage stability in this paper is innovative, as compare to other existing methodologies solely examine active power rescheduling. This paper made the following contributions: formulated a multi-objective function for congestion control in an electric transmission network. Furthermore, formulated the generator sensitivity factors to identify overloaded lines and which generators will be involved in congestion management. Developed a particle swarm optimization (PSO) algorithm to solve the multi-objective function of the transmission congestion management system. In addition, the developed PSO method for CM approach was validated on three IEEE standard test system networks (14, 30, and 118). The simulation results prove that reduces active and reactive power, lowering the cost of generator rescheduling, and demonstrating the usefulness of developed PSO method for transmission network congestion. Furthermore, voltage stability and voltage profile improvements demonstrate the performance effectiveness of the PSO algorithm used in this work.

Fault Detection and Protection Schemes for Distributed Generation Integrated to Distribution Network: Challenges and Suggestions

The use of distributed generation has received wide attention due to low maintenance costs, reduced transmission line losses and network congestion, as well as minimal impact on climate change and global warming. However, the distributed generation integrated to the distribution network introduces various protection problems that cannot be solved by conventional protection systems. These obstacles include the bi-directional power flow and the variation in the fault current level during the topology change. Thus, appropriate fault detection and protection scheme are strongly recommended to increase safety and reliability of the distributed network. This paper presents a comprehensive overview of the distributed network fault detection and protection strategies incorporated with distributed generation. This review also investigates the various fault detection approaches concerning types, communication methods, operation mode, constraints and benefits. Additionally, numerous island detection techniques are explored, focusing on generation types, parameters, cost and advantages. Moreover, the review outlines the various protection schemes highlighting categories, operation, constraints, strength and shortcomings. The key issues and challenges are discussed along with selective proposals for future research. All the highlighted viewpoints of this research will hopefully be beneficial to power system engineers and researchers for the advancement of distributed network fault and protection strategies for suitable operation and management of future distributed generation systems.

A novel approach for optimal allocation of series FACTS device for transmission line congestion management

AbstractThe reformed electricity market structure has forced the transmission network to operate near its operational limits. Due to competition in electricity market and with increasing demand, the probability of occurrence of congestion in transmission network has also increased. This resulted in insecure operation of power system and inefficient operation of the electricity market. The main aim of this article is to achieve efficient management of congestion in terms of minimum cost of electricity. This is accomplished by optimally allocating thyristor‐controlled series capacitor (TCSC) in the system. Since flexible alternating current transmission system (FATCS) devices involve heavy installation cost, its proper allocation plays a vital role in achieving the electricity market economics. Therefore, in this article, a new sensitivity index called line flow sensitivity factor is proposed to find the optimal location of TCSC while its control parameter setting is optimally obtained with particle swarm optimization algorithm. The obtained results are validated through implementation of TCSC based on its minimum installation cost. Further, two different penalty factors for violation of system constraints are also introduced in the problem formulation to effectively consider the competitive nature of the electricity market. The proposed method is tested on IEEE 30‐bus system, IEEE 118‐bus system, and 33‐bus Indian network and the results are found to be encouraging. The performance of the optimization algorithm on different systems is also analyzed in this article.

Structural and behavioural evaluation of renewable energy power plants' impacts on transmission network congestion using an analytical approach

Renewable energy support schemes implementation increases the significance of renewable energy power plants (REPPs) in studying power market characteristics. In this study, an analytical approach is presented to investigate the impact of REPPs on transmission lines congestion. For this purpose, considering REPPs, social welfare maximisation problem corresponding to the spot electricity market should be solved by the independent system operator. A closed-form expression of the Lagrange multiplier (LM) of each congested line is presented to analytically demonstrate the role of the power system structure and strategic behaviour of conventional generation companies (GenCos) on the network congestion. The structural part of LM expression is decomposed into four components to distinguish the impact of GenCos from the impact of REPPs. The GenCos' strategic behaviour in the spot market bidding problem is simulated using well-known Q-Learning algorithm. The results confirm that REPPs deployment can remarkably affect both the structural and behavioural parts of the LM and emphasise the significance of the GenCos strategic behaviour on the occurrence of congestion. The results illustrate that since renewable energy support schemes may increase the possibility of congestion and GenCos' strategic behaviour, it is essential to take power system characteristics into account in their design.

Grid integrated renewable DG systems: A review of power quality challenges and state‐of‐the‐art mitigation techniques

Increased energy demands due to rapid industrialization, environmental concerns with fossil fuel–based generation, diminishing fossil energy resources, transmission network congestion, and technical performance deterioration are the motivations behind the integration of small renewable distributed generation (DG) units and turning the existing power systems into a restructured one. Optimizing the technical benefits offered by DG placement is a well-known challenge for distribution network operators (DNOs) for both fossil and renewable energy resource–based DGs, but renewable DG systems have several power quality (PQ) challenges associated additionally. Power quality is a very significant characteristic of renewable DG systems because today's loads are more sensitive to PQ disturbances and penetration of renewable energy as well as nonlinear loads is proliferating in distribution power networks. So the need for innovative power quality improvement (PQI) techniques becomes inevitable due to ongoing reformation in traditional distribution networks by the integration of renewable energy. This article presents a comprehensive analysis of power quality challenges with grid integration of renewable DG systems and current research status of associated mitigation techniques. Firstly, this paper puts emphasis on theoretically illustrating all the crucial power quality challenges associated with grid integration of renewable energy, and secondly, a thorough survey, of all PQI techniques introduced till date, is elaborated along with highlighting the opportunities for future research. Furthermore, all the crucial power quality issues, the impact of high penetration of renewable energy and mitigation techniques on power quality, are demonstrated also by simulating a grid integrated PV-based DG system in MATLAB/Simulink. This article is believed to be very beneficial for academics as well as industry professionals to understand existing PQ challenges, PQI techniques, and future research directions for renewable energy technologies.

Review of Congestion Management Methods for Power Systems

With the acceleration of China’s new power reform, the grid congestion problem has become more and more prominent. It has been a hot spot of power industry that studying on the appropriate grid congestion management method of electricity market in every country or region, for ensuring the safe and economic operation of the power system. In recent years, many great researches have been published on grid congestion management. This paper reviews the literature on grid congestion management through two aspects: transmission network congestion and distribution network congestion. Besides, dynamic congestion, congestion pricing and congestion allocation are described by summarizing various publications. Finally based on the reviews of the existing methods for grid congestion, recommendations for transmission and distribution congestion management methods that are suitable for Chinese electricity are suggested market in the end.

TEP Considering Wind Farms, Network Congestion, Line Repairs, and Reliability

This paper studies transmission expansion planning (TEP) problem considering effects of wind farms on transmission network congestion, power system reliability, line repair, and operational costs of wind and generating units in a deregulated environment. Transmission congestion is modeled using difference between local marginal prices (LMPs) of start and end buses of each line that is multiplied by its power flow. In this way, wind units can efficiently affect network congestion through decrease in lines loading and LMPs of buses. Transmission system reliability is modeled using load shedding (LS) index as well as outage rate and outage duration of a single line or simultaneous outage of a line and a wind unit. Also, generation reliability is formulated using loss of load, value of lost load, and LS criteria due to outage rates and outage durations of generating and wind units considering line limits. In addition, the effect of wind units on repair cost of transmission lines is modeled by the relationship among wind generation, line failure rates and lines loading. Operational cost includes cost of network losses, pollution penalty, fuel expenses, and generation cost of wind units. In order to more efficiently investigate the effect of wind power on TEP problem, the proposed model was tested on the IEEE Reliability Test System (IEEE RTS) for different scenarios, and the results were discussed.

Determination of available transfer capability (ATC) in a competitive electricity market using Nigerian 3-bus and 14-bus power network as case study

In the deregulated power system, competition arises in generation and distribution but the transmission corridor remains the same for transferring power. Every operator wants to maximize profit, as such the number of transaction increases. This may cause congestion in transmission network. To avoid this, every operator should know the value of available transfer capability (ATC) before every transaction. Determination of ATC is a complex task which has to be done at periodic intervals for each source and sink pairs. Though there are many methods available to assess ATC, the most accurate method is repeated power flow using newton raphson (RPFNR). In this research work, RPFNR approach was applied on both 3-bus and 14 bus network by modeling and simulation using Power World Simulator (PWS). ATC between nodes and areas for both networks were determined. The error difference between ATC obtained from both procedures was negligible. A 14-bus system was further modeled, and ATC between nodes and areas were determined.Keywords: ATC, RPFNR, PWS, bus, grid, deregulated , contingency

FJADA: Friendship Based JellyFish Attack Detection Algorithm for Mobile Ad Hoc Networks

TCP and UDP are considered the most popular and well known transport layer protocols to facilitate the end to end communication between two nodes in the network. TCP is used as the transport layer protocol in packet delivery and error sensitive applications, where packet loss cannot be compromised. However, low-rate TCP targeted Denial of Service (DoS) attacks exploit the retransmission timeout and congestion control features of TCP protocol. These low-rate TCP targeted Denial of Service (DoS) attacks are also called JellyFish (JF) attacks. These attacks perform the malicious activities either by delaying, or periodically dropping or mis-ordering the data packets on the route from source to destination node in the network, and cause severe degradation in end-to-end throughput in the network. JellyFish attack is further classified as JF-Delay Variance Attack, JF-Periodic Drop Attack and JF-Reorder Attack based on the type of the malicious activities being performed. JellyFish attack conforms to all existing routing and packet forwarding protocol specifications, and therefore it becomes very difficult to detect its presence in the network. In this paper, a Friendship Based JellyFish Attack Detection Algorithm (FJADA) is presented for Mobile Ad Hoc Networks, where the basic concept of friendship mechanism is added to the existing Direct Trust-based Detection (DTD) algorithm to save the valuable resources of a node in monitoring the activities of its one hop neighbours, through promiscuous mode. FJADA also minimizes the possibility of overestimating the malicious behaviour of innocent nodes due to radio transmission errors, network congestion or packet collisions. The results obtained throughout the simulation experiments clearly show the feasibility and effectiveness of the proposed detection algorithm.

A Fuzzy-Preconditioned GRBFN Model for Electricity Price Forecasting

This paper proposes a fuzzy-preconditioned ANN (Artificial Neural Network) model for electricity price forecasting. The deregulated electric power systems have been widely spread to trade electric power through electric power markets. The market players are interested in gaining the inside track to maximize the profits and minimize risks in advance. One of the most important tasks is how to forecast a complicated time series of electricity price that affects transmission network congestion. In practice, it is hard to forecast electricity price with the spikes due to the high nonlinearity. In this paper, GRBFN (General Radial Basis Function Network) of ANN is proposed for electricity price forecasting. It is an extension of RBFN of ANN in a way that the optimal parameters of the Gaussian functions are determined by the learning process in RBFN. To improve the model accuracy of GRBFN, FCE (fuzzy c-Elliptotypes) is introduced into GRBFN as a fuzzy precondition technique. FCE is an extension of FCM (fuzzy c-means) and plays a key role to classify input data into fuzzy clusters. The use of FCE contributes to the improvement of model accuracy for spikes of electricity price that bring about much higher price. This paper makes use of DA clustering to evaluate a better initial solution of the parameters of the Gaussian functions. Also, EPSO of evolutionary computation is used to evaluate better weights between neurons. The proposed method is successfully applied to real data of electricity price.

Approach for the solution of transmission congestion with multi‐type FACTS devices

This study presents gravitational search algorithm (GSA)-based optimisation technique for the optimum co-ordination of multi-type flexible AC transmission system (FACTS) devices with the existing reactive power sources present in a connected power network. Three types of FACTS devices: namely, unified power flow controller, static var compensator and thyristor controlled series capacitor are used in the present problem and IEEE-30 and IEEE-57 bus systems are taken as standard test systems. The existing reactive power sources are reactive generations of the generators and transformer tap setting arrangements. The main objective of the present study is to reduce active power loss, system operating cost including the cost of FACTS devices and congestion in transmission network. The result obtained with the proposed GSA-based approach is compared with the results obtained by genetic algorithm, differential evolution and particle swarm optimisation techniques. It is observed that by optimal placement of FACTS devices along with the optimal setting of the transformer tap arrangements and control of reactive generations of the generators line loss, system operating cost and transmission congestion is reduced significantly by the GSA-based method compared with other methods.

Firefly algorithm for congestion management in deregulated environment

In competitive electricity market, congestion is a serious economic and reliability concern. Congestion is a common problem that an independent system operator faces in open access electricity market. This paper presents a reliable and efficient meta-heuristic based approach to solve congestion problem. The proposed approach of the present work employs firefly algorithm (FFA) for alleviation of transmission network congestion in a pool based electricity market via active power rescheduling of generators. FFA is a new meta-heuristic approach based on flashing patterns and behavior of fireflies. Various important security constraints such as load bus voltage and line loading have been taken into account while dealing with congestion problem. The proposed methodology may help in removing the congestion of line with minimum rescheduling cost. The numerical results of modified IEEE 30- and 57-bus test power systems are illustrated.

Application of hybrid heuristic optimization algorithms for solving optimal regional dispatch of energy and reserve considering the social welfare of the participating markets

Abstract Market integration allows increasing the social welfare of a given society. In most markets, integration also raises the social welfare of the participating markets (partakers). However, electricity markets have complexities such as transmission network congestion and requirements of power reserve that could lead to a decrease in the social welfare of some partakers. The social welfare reduction of partakers, if it occurs, would surely be a hindrance to the development of regional markets, since participants are usually national systems. This paper shows a new model for the regional dispatch of energy and reserve, and proposes as constraints that the social welfare of partakers does not decrease with respect to that obtained from the isolated optimal operation. These social welfare constraints are characterized by their stochastic nature and their dependence on the energy price of different operating states. The problem is solved by the combination of two optimization models (hybrid optimization): A linear model embedded within a meta-heuristic algorithm, which is known as the swarm version of the Means Variance Mapping Optimization (MVMO S ). MVMO S allows incorporating the stochastic nature of social welfare constraints through a dynamic penalty scheme, which considers the fulfillment degree along with the dynamics of the search process.

Optimal power flow-based congestion management in restructured power systems

This paper deals with an important issue of transmission network congestion in deregulated power systems, where relieving transmission network congestion without disturbing the existing transactions. The thyristor controlled series capacitor (TCSC) is one of the alternatives to reduce the flow in heavily loaded lines, result in an increased loadability and fulfilled contractual requirement by controlling the power flow in the network. The distribution generation (DG) units are another alternative to compensate deficit power at load bus so that reduce power flow heavily loaded lines. Considerable cost of TCSC and maximum benefits with DG unit at load buses, it is necessary to find their optimal location in the system, a method to determine the optimal placement of these devices has been suggested real power flow and transmission line relief sensitivity index methods. The OPF-based congestion management (CM) strategy has been implemented to minimise production cost and alleviates transmission network congestion with TCSC/DG unit. The OPF-based CM problem is implemented on 26 bus power system network and solutions are obtained by three conventional methods. The experimental results prove that the interior point (IP) method is gives an optimum economical solution.