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A key concern in a deregulated power market (DPM) scenario is congestion on electricity transmission lines, which is one of the most pressing problems. The independent system operator (ISO) manages congestion to ensure the power system framework operates reliably and securely. Considering the context of the restructured power system's inherent uncertainties, congestion management (CM) is critical in the operation and security of DPM. CM aims to ease congestion on the transmission lines while meeting all system constraints at the lowest possible congestion cost. This study employs a novel adaptive restarting genetic algorithm (ARGA) for rescheduling the generation to alleviate transmission congestion in the energy market. This work uses the proposed ARGA CM technique to reduce congestion costs by rescheduling generators' active power output most cost-effectively. The participation of generation units in the CM is made possible by considering the generator sensitivity factors (GSF). IEEE 57-bus and IEEE 118-bus test systems are employed to evaluate the suggested methodology's accuracy. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique's validity. The proposed algorithm was tested in Python, and power flow analysis was done using the PANDAPOWER tool. [ABSTRACT FROM AUTHOR]

Congestion of the power system is the most common challenge an Independent System Operator (ISO) faces in restructured electricity markets. It affects the efficiency of the market when transmission lines are congested causing transmission costs to rise. To prevent transmission line congestion, ISO needs to take the necessary steps. To solve these issues, this paper introduces a new method namely the Adaptive Red Fox Optimization algorithm (ARFOA) to compute the congestion cost considering the power losses in the transmission line system. Initially, all the generators in the system are selected to reschedule real power outputs. Second, by establishing a proposed optimization issue, ARFOA is employed to control transmission line congestion. The implementation of the proposed method is evaluated on the IEEE 30 bus system. The algorithm's adaptability is tested using several case studies involving the base case and line outages, also compared with the other existing techniques such as PSO, ASO, and GSO approaches. The simulation outcomes indicate that the proposed strategy outperforms existing techniques in terms of congestion cost, power loss, generation rescheduled power, and computational time. [ABSTRACT FROM AUTHOR]

The aim of this article is to apply Hybrid Nelder–Mead – Grey Wolf Optimizer (HNMGWO) in congestion management problem with optimal congestion cost by rescheduling generators in the power system. When the transmission power lines are overloaded in the power system, the following strategies are followed: generator rescheduling and installation of FACTS devices on the lines and load curtailment. But, load curtailment is not performed as the deregulated system encourages customer satisfaction. Thus, generator rescheduling is selected for problem solution as it does not involve the creation of any new infrastructure. Grey Wolf Optimizer (GWO) is one of the recent optimization techniques which work based on the leadership hierarchy and the hunting technique of grey wolves. The effective local search is performed by the Nelder–Mead (NM) method and the output is used to initialize the population for GWO, which searches global best value. In this paper, the HNMGWO is attempted based on the positive outcome of both NM and GWO. This proposed algorithm has been applied and results have been discussed related to the tasks of Independent System Operator. The proposed HNMGWO is applied in a standard IEEE 30 bus and IEEE 118 bus systems and thereby results are compared with standard GWO, Particle Swarm Optimization (PSO), fuzzy adaptive PSO, genetic algorithm, and bacterial foraging algorithms. The result shows that the proposed work is more effective by consuming minimum congestion cost in reduced rescheduled power and power loss. [ABSTRACT FROM AUTHOR]

Congestion management is one of the most critical issues in the operation of deregulated power systems. This research work proposes a Congestion Management (CM) approach considering the optimal real power rescheduling of power system generators. The Generator Sensitivity Factors (GSF) have been considered to select the most sensitive generators that would participate in CM. An Improved Crow Search Algorithm (ICSA) has been formulated to minimize the congestion cost. The stages of exploration and exploitation of the Crow Search Algorithm (CSA) have been modified with the incorporation of the dynamic awareness probability and Lévy​ flight approach for the formulation of ICSA in CM. The CM cost optimization problem was validated using the standard 39-bus new England system and the IEEE-118 bus system. A comparison analysis with other optimization techniques has also been carried over to further assess the performance of the proposed ICSA approach. The obtained results showed that the congestion cost achieved with ICSA has been reduced by 1.84%, 4.01%, 6.25%, when compared to the Differential Evolution (DE) Algorithm, Grey Wolf Optimization (GWO), and Crow Search Algorithm (CSA), respectively, for the 39-bus system. For the IEEE 118-bus system, the application of ICSA has curtailed the congestion cost by 2.50%, 8.55%, and 12.50%, when compared to the congestion cost achieved with DE, GWO, and CSA, correspondingly. It is observed that the performance of ICSA for CM cost has superseded the other approaches adopted for CM.

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.

Emergency control of the power system is performed via generator rescheduling or load shedding depending upon the availability of generation and demand. This paper presents a generator rescheduling method based on Krasovskii’s theorem for enhancing rotor speed regulation during multiple contingency conditions. The speed governor in automatic generation control (AGC) and automatic voltage regulator (AVR) are indispensable components of the control mechanism for deploying any control scheme. Contingencies may lead the security state of the power system towards a preventive, emergency, and restorative state. Generally, there is a violation of equality constraints of active and reactive power in the corrective security state. However, in this paper, the corrective security state considers a significant imbalance in active power with a generation capacity always greater than load. For rotor angle stability, active power delivery by the generator met the demands, whilst the turbine and automatic speed governor together balance it by changing the set point of the turbine inlet. This paper investigates the rotor stability by triggering a new set point by the rescheduling of the generator. The strategy proposed encompasses the broad domain of active power imbalance using Krasovskii’s theorem, which is an extensive form of the Lyapunov stability theorem for the nonlinear multivariable autonomous system. The proposed control scheme is deployed on an IEEE-10 machine 39-bus system for validation and feasibility detection. It is found that the proposed scheme is more accurate than the conventional turbine automatic speed governor close loop system. Moreover, there is turbine power-saving up to 400 MW. In addition, the rotor attains steady-state speed quicker up to 5–10 sec and attains synchronous speed in all the cases considered, i.e., single load outage; multiple load outage; area outage; and load increment. The MATLAB® and the CVX platform are used to validate the proposed concept.

Congestion management (CM) is becoming a challenging task in the power system due to the increase in the system complexities in the deregulated environment. The congestion causes grave damage to the power system components if the outages tend to occur more frequently. Hence, in the present power system scenario, CM is a complex task of an independent system operator (ISO). This proposal intends to introduce a novel optimization‐based CM model with advanced soft computing techniques. A new hybrid algorithm is introduced in this paper, which optimizes the generating power of added generators with the bus system. Thereby manages the congestion with minimized rescheduling cost. The proposed optimization algorithm termed Lion Algorithm with Moth‐based Mutation (LA‐MM) hybridizes the concepts of both Lion Algorithm (LA) and Moth Search Algorithm (MS). Subsequently, the experimentation is conducted in two test bus systems like IEEE 14 and 30 bus systems. The performance of the proposed model is compared over other state‐of‐the‐art models concerning cost analysis and convergence analysis, respectively. [ABSTRACT FROM AUTHOR]

This manuscript proposes a novel modified whale optimization algorithm-based solution approach for congestion controlling cost problem in power system framework. The integral objective of the proposed methodology is twofold: firstly, to identify the most sensitive bus in the power system framework using the Bus Sensitivity Factor for optimal positioning of the wind power source that will influence the power flow in the overburden line. Secondly, the selection of the most sensitive generators participating in the power rescheduling process is achieved considering the Generator Sensitivity Factor that are computed based on the congested line power flow status. Further, the proposed MWOA is implemented to achieve the minimum congestion cost with congestion alleviation. The notion of MWOA has been developed with the introduction of two correction factors that enhances the coordination between the exploration and exploitation processes which prevents it from premature convergence and aids in its searching capability towards the global optima. The objective function is subjected to the sequential execution of the proposed optimization technique. The potency of the proposed approach is justified considering the system parameters and constraints for the 39-bus New England test system and IEEE-118 bus system. MATLAB 2016(a) is utilized as the simulation platform. The simulation results obtained with the proposed approach shows a substantial curtailment in the congestion cost, improvement in the system losses and voltages, and provide a better convergence profile when compared to other existing optimization techniques. [ABSTRACT FROM AUTHOR]

Congestion in transmission corridors are the major bother for deregulated power system's operation. Generator rescheduling along with demand alteration is a traditional remedy for transmission line congestion. According to market clearing process, the system operator (SO) has to pay a certain amount of cost to the market participants for rescheduling the generation and demand. This kind of redispatch related congestion management (CM) procedure is mainly carried out to reduce the congestion cost, but they are failing to provide an attention in power systems security. The risky generator's power shifts may diminish the voltage and transient stability of the power system. So power system security should be included in the congestion management procedure. In this proposed multi objective congestion management procedure, rescheduling of active power is carried out to improve/retain the power systems security along with a congestion cost reduction. Voltage security margin (λ) and corrected transient energy margin (CTEM) provides a measure for power system security level. Multi Objective Dragonfly Algorithm (MODA) is employed to trace the non dominated solutions for three conflicting objectives. Fuzzy decision making principle is applied to select the best Pareto solution depends on the objective's significances. The goodness of the MODA optimization approaches is experimented in congestion alleviation of New England 39 bus systems and solutions are compared with some reputed methods. [ABSTRACT FROM AUTHOR]

In this paper, a study of multi-objective based congestion management is carried out by generator rescheduling with considering generator fuels cost. Due to the rapid growth of electrical load, the pressure in the transmission sector increases to provide transmission of power in a safe manner. But owing to the load growth, the transmission line power flow reaches beyond thermal limit which results in transmission congestion. The congestion management in the present work has been done in a multi-objective framework considering generator rescheduling method as one of the objectives. A Multi-objective Grasshopper Optimization Algorithm (MOGOA) is implemented to perform the optimization for elimination of congested line and minimizing the operational cost of the system. The efficacy of the proposed method has been compared and analyzed with different multi-objective algorithms in IEEE 30 bus test system. [ABSTRACT FROM AUTHOR]

In this paper, a study of multi-objective based congestion management is carried out by generator rescheduling with considering generator fuels cost. Due to the rapid growth of electrical load, the pressure in the transmission sector increases to provide transmission of power in a safe manner. But owing to the load growth, the transmission line power flow reaches beyond thermal limit which results in transmission congestion. The congestion management in the present work has been done in a multi-objective framework considering generator rescheduling method as one of the objectives. A Multi-objective Grasshopper Optimization Algorithm (MOGOA) is implemented to perform the optimization for elimination of congested line and minimizing the operational cost of the system. The efficacy of the proposed method has been compared and analyzed with different multi-objective algorithms in IEEE 30 bus test system.

This work develops an underfrequency preventive control strategy for an islanded power system with a high penetration of wind power generation. First, the preventive control strategy uses the frequency nadir forecasting module to analyze the frequency stability under largest diesel generator tripping (N-1) contingency events. If predicted frequency nadir is too low, four frequency support methods are then analyzed and used for preventing potential frequency stability problem. They include generator rescheduling (GR), the use of battery energy storage system (BESS), direct load control (DLC) and emergency demand response program (EDRP). In terms of the GR method, the optimal diesel generator dispatch is obtained, with sufficient frequency stability and minimal fuel cost and start-up cost. In the BESS method, the optimal instantaneous power output from BESS is obtained based on its frequency support capability. With the DLC or EDRP method, the optimal contract-based load-shedding or the load-reduction to provide frequency support is obtained, respectively. Then, the operating costs of each method to support frequency are analyzed. The research methods and simulation results are very useful to the low-frequency protection of actual power systems with high renewable power generation. This work proposed a complete defense strategy in a microgrid system. It combines GR, BESS, DLC and EDRP. Therefore, the system operators have many options to implement their defense strategies, based on the operating costs of various methods. In other words, the proposed defense strategy provides a more flexible solution for the protection of micro grids with a high renewable power penetration. Therefore, the solution considers the system safety and economical aspects. [ABSTRACT FROM AUTHOR]

This paper addresses the implementation of an advanced twin extremity chaotic map adaptive particle swarm optimization (TECM-PSO) algorithm to the nonlinear congestion management cost problem in deregulated power system. The goal of proposed approach is twofold: firstly, to identify accurate number of participating generators for rescheduling process using a robust upstream real capacity tracing method requiring less information of generator units, and secondly, to achieve minimum possible rescheduled generation cost function using TECM-PSO algorithm while alleviating all the line overloads. Further to preserve the diversity of the algorithm and to increase its near-global searching capability, the incursion of dynamic constraint handling has also been done in the algorithm to retrieve the feasible solutions in the search space. The objective function is solved for near-global optima by step-by-step execution of the proposed algorithm. Twin extremity chaotic maps have been generated by updating the equations governing the PSO algorithm in order to prevent the particle swarm optimization plugging into local minima with less convergence rate at later stages of iterations. The feasibility of the proposed algorithm is validated on various line outage cases of both the small and large test systems, namely modified IEEE 30-, IEEE 57- and IEEE 118-bus systems. Simulation results show a considerable reduction in net rescheduled generation cost, power losses and rescheduled generation amount, ensuring more secure and reliable operation of power system. [ABSTRACT FROM AUTHOR]

The initiative to manage congestion has gained interest in the current deregulated scenario. The principle commitment of the work in this article is to extend the gravitational search algorithm (GSA) as an efficient metaheuristic optimizing algorithm to diminish the rescheduling cost and efficiently attenuate the overloading of the line with the minimal deviation in the active power generation. The congestion management drive is accomplished by prioritizing the generators based on their sensitivity values. Thereafter, the GSA is introduced to optimally minimize the rescheduling cost along with the minimization of the total amount of active power output and system losses. The potency of the proposed method is tested on the 39-bus New England System and the IEEE 30-bus system and 118-bus system, and the outcomes achieved with the GSA outperform the results reported with other algorithms. [ABSTRACT FROM AUTHOR]

The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures. [ABSTRACT FROM AUTHOR]

This work develops an underfrequency preventive control strategy for an islanded power system with a high penetration of wind power generation. First, the preventive control strategy uses the frequency nadir forecasting module to analyze the frequency stability under largest diesel generator tripping (N-1) contingency events. If predicted frequency nadir is too low, four frequency support methods are then analyzed and used for preventing potential frequency stability problem. They include generator rescheduling (GR), the use of battery energy storage system (BESS), direct load control (DLC) and emergency demand response program (EDRP). In terms of the GR method, the optimal diesel generator dispatch is obtained, with sufficient frequency stability and minimal fuel cost and start-up cost. In the BESS method, the optimal instantaneous power output from BESS is obtained based on its frequency support capability. With the DLC or EDRP method, the optimal contract-based load-shedding or the load-reduction to provide frequency support is obtained, respectively. Then, the operating costs of each method to support frequency are analyzed. The research methods and simulation results are very useful to the low-frequency protection of actual power systems with high renewable power generation. This work proposed a complete defense strategy in a microgrid system. It combines GR, BESS, DLC and EDRP. Therefore, the system operators have many options to implement their defense strategies, based on the operating costs of various methods. In other words, the proposed defense strategy provides a more flexible solution for the protection of micro grids with a high renewable power penetration. Therefore, the solution considers the system safety and economical aspects.

In this paper, a Flower Pollination Algorithm (FPA) has been proposed for relieving congestion in the deregulated power electricity industry. Congestion in the power market is one the contemplative challenges to be overcome in the era of deregulation. The primary cause of congestion is due to the loss of the transmission line, an increase in load, or loss of generator(s). Hence, managing congestion is one of the issues which have to be tackled in the present scenario. There are several techniques to relieve congestion. It is quite well-known that the thermal limits of transmission lines in a power system are fixed. One of the methods to abate congestion is to reschedule the real power of the generators. The purpose of the present work is to benefit the Independent System Operator (ISO) in reliving congestion. (1) In order to meet this objective effectively, a FPA algorithm has been proposed for relieving congestion and is simulated on a modified IEEE 30-bus system initially. (2) Congestion cost, compared with and without the application of FPA, is computed. (3) To validate its effectiveness, the obtained results are compared with recent power system optimization algorithms present in the literature. (4) Further, the work has been extended with the incorporation of a Pumped Hydro Storage Unit (PHSU). Here an economic analysis of congestion cost reduction employing FPA before and after the incorporation of PHSU is investigated applying FPA. In comparison with other evolutionary algorithms, the uniqueness of generating a new population is attained in FPA by the levy flight procedure. It is one of the latest evolved algorithms and is suited for different power system problem due to fewer clear-cut tuning parameters in contrast with other algorithms. (5) Furthermore, the effects of other network parameters, including system losses and voltage, has been computed. The result obtained is tested in terms of congestion mitigation with and without the incorporation of PHSU, in terms of novel objective improvement, and with and without applying recently evolving FPA for the above application. Thus the objective-wise and algorithmic-wise innovative concept has been presented. This proves effectiveness of the algorithm in terms of minimized cost convergence and other parameter including system losses and voltage before and after the incorporation of PHSU as compared with other recent trendsetting reported optimization techniques.

In this paper, an efficient meta-heuristic satin bowerbird optimization (SBO) algorithm is presented for congestion management (CM) in the deregulated power system. The main objective of CM is to relieve congestion in the transmission lines using a generation rescheduling-based approach, while satisfying all the constraints with minimum congestion cost. The SBO is a nature-inspired algorithm, developed based on the ‘male-attracts-the-female for breeding’ principle of the specialized stick structure mechanism of satin birds. The proposed approach is effectively tested on small and large test systems, namely, modified IEEE 30-bus, modified IEEE 57-bus, and IEEE 118-bus test systems. The constraints like line loading, line limits, generator limits, and bus voltage impact, etc., are incorporated into this study. The proposed technique gives superior results with regards to congestion cost and losses compared with various recent optimization algorithms.

Owing to the variations of loads and sustainable energy, the power injections and the load increment direction are of uncertainties, which pose great challenges to the traditional models of power system operation. To determine the effects of those uncertainties on the reactive power reserve (RPR), an interval optimal RPR dispatch with generator rescheduling (GR) is formulated. To evaluate the RPR interval more comprehensively, two sub‐models, namely the optimistic and pessimistic sub‐models, are built. The proposed method is applied to several IEEE systems to illustrate its effectiveness. Case tests show that, proper GR schemes can enhance the RPR of power system by postponing the limit hitting of the generator reactive power. The obtained RPR scheme shows better robustness to the uncertainties of power injections compared with that of the conventional RPR dispatches, and the restriction on the RPR interval widths also suggests the operating risk reduction. [ABSTRACT FROM AUTHOR]

In this paper, a unified approach for transient stability enhancement following large disturbance is proposed through preventive or/and emergency control measures. Transient stability-constrained generation rescheduling is proposed as a preventive control strategy with participation of only few generators on the basis of the rotor angle swings. However, if generator rescheduling fails to regain the transient stability of the power system, heuristic real-time transient stability assessment scheme is proposed through synchrophasor measurements. If the system is predicted to be unstable in real-time, emergency control actions like generator tripping and load shedding are proposed with information about their initiation, location, type, and the magnitude. These emergency actions are purely based on the voltage magnitudes and phase angles obtained through phasor measurement units in real-time. The effectiveness of the proposed scheme is demonstrated on IEEE-39 New England test system, and the results obtained are promising. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A power system configuration undergoes frequent changes due to contingencies and/or disturbances. If the power system survives after the disturbance, it will be operating in a new steady state in which one or more transmission lines may be overloaded. A corrective control action must be taken to eliminate such overloads. The most practiced technique for overload alleviation is generator rescheduling and/or load shedding. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) based generation reschedule to alleviate transmission line overloads. The system parameters such as overload factor (OF), generation shift sensitivity factor (GSSF) and sensitivity of vulnerability index of generation system (SVIGS) are given to the ANFIS as inputs. The output (ANFISOUT) from the ANFIS gives the quantity of real power to be rescheduled. The effectiveness of the proposed approach has been tested for modified IEEE 30 bus system, 39 bus New England system and modified IEEE 57 bus system in MATLAB environment and their results are compared with fuzzy logic based approach. [ABSTRACT FROM AUTHOR]

The electric power system is a complex system, whose operating condition may not remain at a constant value. This necessitates the power system operator be alert to keep the system performance under normal condition. The various contingencies like, large load variations, outage of components (transmission lines, transformers, generators, etc.) are more common. Any of these conditions causes overloads and system parameters to exceed the limits thus resulting in an insecure system. Hence the operator has to maintain the security level by proper analysis and reschedule the system accordingly. The most practiced techniques for overload alleviation is generator rescheduling and/or load shedding. The conventional method is to solve an optimal power flow problem to find out the rescheduling for overload alleviation. But this will not give the desired speed of solution. This paper considers the application of fuzzy logic to dispatch the generator to relieve the overload in power system. A fuzzy rule-based controller is developed which reschedules the real power generation. The system parameters such as overload factor, generation shift sensitivity factor and sensitivity of vulnerability index of generation system are given to a fuzzy inference system (FIS) as inputs. The output from the FIS gives the quantity of generation to be rescheduled. The proposed approach is illustrated with 6-bus system and IEEE 30-bus system. The validity of the proposed method is done using MATPOWER. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a preventive control of transient stability with generation rescheduling based on coherency obtained using time domain simulations. The transient security dispatch finds a generation configuration with better transient stability behavior. One of the most practiced and an obvious technique of preventive control is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in rescheduling process. Selection of participating generators has been introduced using generator coherency for given contingency. The effectiveness of the proposed methodology is demonstrated on IEEE 145-bus 50-generator system for a three phase fault at different loading conditions with single and multiple line outage cases. [Copyright &y& Elsevier]

This paper presents an effective method of congestion management in power systems. Congestions or overloads in transmission network are alleviated by generation rescheduling and/or load shedding of participating generators and loads. The two conflicting objectives 1) alleviation of overload and 2) minimization of cost of operation are optimized to provide pareto-optimal solutions. A multiobjective particle swarm optimization (MOPSO) method is used to solve this complex nonlinear optimization problem. A realistic frequency and voltage dependent load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics is used to solve this problem. The proposed algorithm is tested on IEEE 30-bus system, IEEE 118-bus system, and Northern Region Electricity Board, India (NREB) 390-bus system with smooth as well as nonsmooth cost functions due to valve point loading effect. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a method of congestion management by generation rescheduling and load shedding. The sensitivities of the overloaded lines to bus injections and the costs of generation and load shedding are considered for ranking the generation and load buses. The new generation and load shedding schedule for these buses are then computed based on a simple method considering cost and sensitivity to line currents. The algorithm will help the system operator to generate the contingency plan quickly for secure operation of the system. Test results for three systems are presented. The results show that cost effective generation rescheduling and load shedding plans can be obtained to alleviate overloading of the transmission lines in a computationally efficient manner. [Copyright &y& Elsevier]

More and more extreme weather events now occur due to global warming, and the existing power systems are powerless to cope with such high-impact, low-probability events. In order to tackle such highly stochastic events with large state spaces during an extreme event, conventional model-based approaches establish optimization models to capture the impact on the power system. However, the sophisticated models cause high computational complexity and do not have the ability to learn. This paper formulates the distributed generator (DG) rescheduling problem as a discrete Markov decision process (MDP) without transition probability information. The uncertainties associated with component failures are considered. A model-free optimization framework based on deep reinforcement learning (DRL) is proposed to determine the optimal rescheduling strategy to improve the resilience of a distribution system. Deep neural networks (DNN) are applied to extract large stochastic state space features and form multivariate Gaussian distributions to handle high-dimensional continuous control actions. A conventional reinforcement learning (RL) algorithm and two DRL algorithms are tested and compared in the IEEE 9-bus, IEEE 39-bus and IEEE 123-bus systems, and the results illustrate the effectiveness of the proposed framework in both meshed and radial topologies. [ABSTRACT FROM AUTHOR]

In this paper, a Flower Pollination Algorithm (FPA) has been proposed for relieving congestion in the deregulated power electricity industry. Congestion in the power market is one the contemplative challenges to be overcome in the era of deregulation. The primary cause of congestion is due to the loss of the transmission line, an increase in load, or loss of generator(s). Hence, managing congestion is one of the issues which have to be tackled in the present scenario. There are several techniques to relieve congestion. It is quite well-known that the thermal limits of transmission lines in a power system are fixed. One of the methods to abate congestion is to reschedule the real power of the generators. The purpose of the present work is to benefit the Independent System Operator (ISO) in reliving congestion. (1) In order to meet this objective effectively, a FPA algorithm has been proposed for relieving congestion and is simulated on a modified IEEE 30-bus system initially. (2) Congestion cost, compared with and without the application of FPA, is computed. (3) To validate its effectiveness, the obtained results are compared with recent power system optimization algorithms present in the literature. (4) Further, the work has been extended with the incorporation of a Pumped Hydro Storage Unit (PHSU). Here an economic analysis of congestion cost reduction employing FPA before and after the incorporation of PHSU is investigated applying FPA. In comparison with other evolutionary algorithms, the uniqueness of generating a new population is attained in FPA by the levy flight procedure. It is one of the latest evolved algorithms and is suited for different power system problem due to fewer clear-cut tuning parameters in contrast with other algorithms. (5) Furthermore, the effects of other network parameters, including system losses and voltage, has been computed. The result obtained is tested in terms of congestion mitigation with and without the incorporation of PHSU, in terms of novel objective improvement, and with and without applying recently evolving FPA for the above application. Thus the objective-wise and algorithmic-wise innovative concept has been presented. This proves effectiveness of the algorithm in terms of minimized cost convergence and other parameter including system losses and voltage before and after the incorporation of PHSU as compared with other recent trendsetting reported optimization techniques. [ABSTRACT FROM AUTHOR]