Generation Rescheduling Research Articles

Islanded microgrid congestion control by load prioritization and shedding using ABC algorithm

The continued growth in load demand and the gradual change of generation sources to smaller distributed plants utilizing renewable energy sources (RESs), which supply power intermittently, is likely to strain existing power systems and cause congestion. Congestion management still remains a challenging issue in open access transmission and distribution systems. Conventionally, this is achieved by load shedding and generator rescheduling. In this study, the control of the system congestion on an islanded micro grid (MG) supplied by RESs is analyzed using artificial bee colony (ABC) algorithm. Different buses are assigned priority indices which forms the basis of the determination of which loads and what amount of load to shed at any particular time during islanding mode operation. This is to ensure as minimal load as possible is shed during a contingency that leads to loss of mains and ensure a congestion free microgrid operation. This is tested and verified on a modified IEEE 30-bus distribution systems on MATLAB platform. The results are compared with other algorithms to prove the applicability of this approach.

A Bayesian Learning Based Scheme for Online Dynamic Security Assessment and Preventive Control

In this paper, a two-stage Bayesian learning-based approach is proposed to enable online dynamic security assessment (DSA) and preventive control in power systems. To develop a reliable data mining-based model for DSA, the Bayesian neural network is learned by Bayes by Backprop to detect the security status in real-time and to inform the system operators with the confidence of the prediction. For dynamic insecurity prevention, the security-constrained optimal power flow (SCOPF) model incorporating the Bayesian neural network based security constraints is proposed, which is solved by the derivation-free Bayesian optimization. Case studies on the IEEE 39-bus system show that the Bayesian neural network is able to detect the security status of the power system correctly and reliably and is able to avoid the over-fitting problem compared with the traditional point estimation neural network. Also, Bayesian optimization is able to solve the non-analytical SCOPF model efficiently to mitigate the dynamic insecurity by preventive generation rescheduling.

RETRACTED ARTICLE: Multi objective dragonfly algorithm for congestion management in deregulated power systems

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.

A Remedial Action Scheme to Prevent Mid/Long-Term Voltage Instabilities

The main objective of this article is to propose a special protection system (SPS) to execute efficient remedial actions to prevent mid-term and long-term voltage instabilities. In this method, when the operating point (OP) leaves the normal operation state, the proposed SPS is initiated to execute the required corrective remedial actions to bring the OP back to a normal state and maintain bus voltages above prespecified thresholds. Considering the local nature of Volt/Var control and in order to execute fast remedial actions, in this approach, the required generation rescheduling or load shedding procedures were selected based on the electrical distance concept. This allows for remedial actions with the highest impact on volt/var control. In addition, in generation rescheduling procedures, the proposed method uses the ability of utility-scale photovoltaic resources, which can change their generation quickly. This plays a major role in maintaining stability of power systems. Efficiency of the proposed algorithm was validated through several scenarios performed in IEEE 39-bus, Nordic32, and PST 16 test systems using DIgSILENT PowerFactory software. Results of these dynamic simulations and their comparison with some previously published methods show the effectiveness of this method in timely executing appropriate remedial actions to maintain system stability.

Detecting bi-level false data injection attack based on time series analysis method in smart grid

Smart grid is a crucial Cyber-Physical system and is prone to False Data Injection Attack (FDIA). In this paper, we propose a novel detection mechanism for a new-type FDIA which targets at inducing generation rescheduling and load shedding. We exploit a signal processing method to recognize the behavior features of the estimated states under this FDIA and employ the captured features to train a time-series-analysis based detector. Before training the detector, an improved ELM method is proposed to eliminate the redundancies of the feature vectors. By doing so, our proposed detection mechanism can effectively detect the new-type FDIA by analysing the deviations between the feature vectors in both the spatial and temporal aspects. We assess the performance of the proposed mechanism with comprehensive simulations on IEEE 14- and 118-bus systems. The results indicate that the proposed mechanism can be performed in a real-time way with satisfactory detection accuracy.

Flexible Power System Defense Strategies in an Isolated Microgrid System with High Renewable Power Generation

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.

Preventive-Corrective Coordinated Transient Stability Dispatch of Power Systems With Uncertain Wind Power

This paper proposes a new coordinated method for preventive generation rescheduling and corrective load shedding to maintain power system transient stability under uncertain wind power variation. A two-step bi-level optimization model is proposed where generation rescheduling and load shedding are coordinated by a risk coordination parameter, which adjusts the total coordination cost in the upper level. Then, based on Extended Equal Area Criterion (EEAC) and trajectory sensitivities, the non-linear risk and stability constraints are converted into the linear form for generation rescheduling cost and load shedding cost optimization in the lower level, respectively. Uncertain wind power output is modeled as a small number of robust test scenarios. Finally, the golden section search is applied to solve the bi-level problem. The proposed method is validated on the New-England 39-bus system by using commercial grade software. The computational efficiency, economic optimality, and stability robustness under random wind power of the method are demonstrated.

Preventive control for power system transient security based on XGBoost and DCOPF with consideration of model interpretability

This paper proposes a new approach for online power system transient security assessment (TSA) and preventive control based on XGBoost and DC optimal power flow (DCOPF). The novelty of this proposal is that it applies the XGBoost and data selection method based on the 1-norm distance in local feature importance evaluation which can provide a certain model interpretability. The method of SMOTE+ENN is adopted for data rebalancing. The contingency-oriented XGBoost model is trained with databases generated by time domain simulations to represent the transient security constraint in the DCOPF model, which has a relatively fast speed of calculation. The transient security constrained generation rescheduling is implemented with the differential evolution algorithm, which is utilized to optimize the rescheduled generation in the preventive control. Feasibility and effectiveness of the proposed approach are demonstrated on an IEEE 39-bus test system and a 500-bus operational model for South Carolina, USA.

Protection of Transmission Line Based on the Severity Index Using Generation Rescheduling Strategy

Protection of Transmission Line Based on the Severity Index Using Generation Rescheduling Strategy

Voltage Security-Constrained Optimal Generation Rescheduling for Available Transfer Capacity Enhancement in Deregulated Electricity Markets

Modern utilities are forced to operate very close to their loadable limits (maximum capacity) due to geographical, economical and some technical reasons. The deregulation of the power industry, the competitive nature of modern electricity markets and the continuous quest for modernization of cities and hamlets all over the world has also led to fast increase in the load demand. The stability of power systems all over the world are threatened with recurrent occurrences of voltage stability issues. Hence, Inter-zonal energy transactions between willing supplier and buyers need to be done with adequate consideration for power system security. In this work, a voltage security-constrained optimal generator active and reactive power rescheduling is carried out using the IEEE 30 and IEEE 57 bus systems. The simultaneous maximization of available transfer capacity (ATC) and voltage stability margin (VSM), using the weighted sum approach, is the objective function. Credible optimal power flow and power system security constraints are considered. Three variants of particle swarm optimization in MATLAB® are used in this work for analyzing the results for objectivity. The technical and economic benefits of the optimal generator rescheduling on the system’s ATC, VSM, line losses, line flow and voltage profile are adequately analyzed.

Impact of Wind Units in Congestion Management for Hybrid Electricity Market

This paper presents the impact of Wind energy source in CM (congestion management) problem and CMP (congestion management approach) based on rescheduling of generators for a bilateral/pool electricity market considering secure bilateral transactions. The functioning constraints with and without Wind units are included in the optimization problem. Participation of Wind units in CM is dependent on the availability of wind during the congestion. The Weibull PDF is utilized for wind model and Monte Carlo simulation (MCS) is utilized for 24-hours wind speed sample. The multi line congestion cases with one/two/three lines have been considered and the selection of the lines is based on the sensitivity factors based on congestion distribution factors. The MINLP (mixed-integer non-linear programming) CM optimization problem has been solved. The results have been obtained for IEEE-24 bus Reliability Test System with modification adding wind generators.

Transient stability‐oriented assessment and application of preventive control action for power system

The modern power system is becoming more complex and dynamic because of increasing penetration of renewable energy resources, operating closer to system capacity for economic benefits. In order to maintain the system stability, the system operator is required to initiate appropriate preventive control action under severe contingencies while satisfying the system operating constraints. Real time transient stability assessment (TSA) of power system is proposed in this paper by using wide area measurement system (WAMS) and phasor measurement units (PMUs). An architecture based on least square support vector machine (LS-SVM) has been developed to identify the system stability state in real time. Also, this paper proposes a coherency based selection method to identify the appropriate members for generation rescheduling as preventive operation in insecure operating contingency. Rotor angle trajectories based transient stability index (TSI) is employed to classify the generators as either critical or non-critical generators in a power network. Accordingly preventive control action in the form of generation rescheduling can be initiated to achieve stability. The method has been demonstrated on the IEEE 10-machines, 39-bus system. The proposed methodology is effective and capable of handling complex power system models with multiple contingencies.

Demand response based congestion management of power system with uncertain renewable resources

In this paper, the combined approach of rescheduling of generators and demand-side management using various demand response programme (DRP) for congestion management is analysed. The uncertainties of wind and solar output power are modelled by Rayleigh and Beta distribution function. These uncertainties result in a large number of scenarios which increases the computational burden. The k-means clustering algorithm is applied to reduce the number of scenarios. This system is analysed by using Monte Carlo simulation. The impact of various DRPs on technical and economic characteristics of the load profile is investigated. The priority of DRPs for the reduction in rescheduling cost and a bill is derived. The result has shown that all DRPs are not profitable for minimising rescheduling cost compared to without DRPs. The best preferred DRP for congestion management is determined among all DRPs. The proposed approach is modelled in GAMS environment and solved by using CONOPT solver.

Generation rescheduling using multiobjective bilevel optimization

This paper presents a new multiobjective optimization method that can be used for generation rescheduling in power systems. Generation rescheduling in restructured power systems is performed by the system operator for different operations like congestion management, day-ahead scheduling, and preventive maintenance. The nonlinear nature of the equations involved and the constraints on decision variables pose a challenge to find the global optimum. In order to find the global optimum using a genetic algorithm, a bilevel optimization method is proposed. In the proposed multiobjective optimization method, the objectives are classified as primary and secondary based on their relative importance. The best solution is found using the secondary objective from the acceptable solutions of the Pareto-optimal front in the primary objective plane. As the financial feasibility and adherence to emission limits are of higher importance, the primary objectives considered are minimization of generation cost and emission. The secondary objective considered is reliability, to find the most reliable solution from the set satisfying the primary objectives. The proposed technique is validated on the IEEE 30-bus system and the results are presented.

Rescheduling of Generators with Pumped Hydro Storage Units to Relieve Congestion Incorporating Flower Pollination Optimization

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.

Preventive and Corrective Control Actions on Power Systems via Heuristic Optimization Methods with Consecutive Search Space Reduction

This paper represents a new methodology to improve the performance of population-based optimization algorithms designed for corrective and preventive control actions enhancing power system dynamic security. Unlike many adaptive approaches employing the parameters such as population size, crossover or mutation rates, the proposed method for the performance improvement is based on the reduction of the search space size. In the method, optimization algorithms run consecutively, while the size of the search space is reduced according to the objective function values attained during the optimization process. In this study, generation rescheduling combined with load curtailment is applied as a preventive control action, whereas load shedding is selected as a corrective control. Each of these control actions is determined through the formulation of a security constrained optimization problem and its solution via population-based optimization algorithms. The proposed methodology is successfully applied to differential evolution, particle swarm optimization, artificial bee colony optimization, and big bang big crunch optimization methods for solving the optimization problems in a 16-generator-68-bus system and the Turkish Transmission System with 750 generators and 2600 buses. It is demonstrated that the proposed method provides better solutions with lesser computational complexity than the ones obtained by using fixed search space sizes.

Congestion management using thyristor controlled series compensator employing Improved Grey Wolf Optimization technique

For reliable and secure operation of deregulated power systems, congestion management is a big task for system operators to control the line flows in the specified limits. Congestion may occur due to an increase in load demand or as a consequence of unexpected contingencies. Generator rescheduling and use of Flexible AC Transmission System devices are some of the approaches to lessen the flows in heavily loaded lines. This paper proposes to use an Improved Grey Wolf Optimization (IGWO) technique to solve congestion management problems. The improvement in Grey Wolf Optimization technique is made by a scheme which keeps an appropriate balance among the exploitation and exploration phases of the technique and a modified formula to determine the Grey Wolves’ positions throughout the course of iterations. The suggested IGWO technique is initially estimated by means of some typical test functions and comparative results are presented with original Grey Wolf Optimization, sine cosine algorithm, gravitational search algorithm, differential evolution and particle swarm optimization techniques to validate its advantage. The suggested IGWO algorithm is employed to control line power flows within limit in an overloaded modified IEEE 14 bus power system installed with a FACTS device by optimizing the generator real power settings, voltage settings, transformer tap settings, reactive power compensation setting and FACTS reactance settings.

Swarm intelligent-based congestion management using optimal rescheduling of generators

Congestion management (CM) refers to the controlling of transmission system such that the power transfer/flow limits are observed. In the restructured electrical system, the challenges of CM for th...

Swarm intelligent-based congestion management using optimal rescheduling of generators

Congestion management (CM) refers to the controlling of transmission system such that the power transfer/flow limits are observed. In the restructured electrical system, the challenges of CM for the system operator (SO) is to maintain the desired level of system reliability and security in the short and long terms, while improving the market/system efficiency. In this paper, the CM problem is tackled by using the centralised optimisation, i.e., optimal rescheduling of generators, which in turn is solved by using the Swarm intelligent techniques. Here, the CM problem is solved by using the particle swarm optimisation (PSO), fitness distance ratio-PSO (FDR-PSO) and fuzzy adaptive-PSO (FA-PSO). First, the generators are selected based on sensitivity to the over-loaded transmission line, and then these generators are rescheduled to remove the congestion in the transmission line. The suitability and effectiveness of the proposed CM approach is examined on the standard IEEE 30 bus and practical Indian 75 bus systems.

Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

This article proposes a new strategy for Congestion Management (CM) through Generation Rescheduling (GR) with Distributed Slack Bus (DSB) model using Improved Teaching Learning Based Optimization (ITLBO) algorithm. Most of the previous research mainly focuses on rescheduling the existing generators for CM and least concerned about the consequence of this rescheduling on the slack bus. As a result, the slack bus is supposed to carry the entire residual effect of all other generators which leads to increased Congestion Management Cost (CMC). This work proposes a distributed slack bus model for reducing the excess burden on the slack bus and a two-fold contribution is made to develop the proposed technique. First, is the selection of the participating generator for CM by means of Incremental Generator Sensitivity Factor (IGSF), which adopts the generator sensitivity as well as the bidding cost. Next, CM is formulated as an optimization problem with the objective function involving congestion management cost and solved using ITLBO algorithm, which incorporates the self-motivated learning concept with the basic TLBO operators. For evaluation, the standard IEEE 30-bus and IEEE 118-bus systems are used and the obtained results are compared with the other CM systems reported through literature.