Generation Rescheduling Research Articles

A Novel Hybrid Harris Hawk Optimization–Sine Cosine Algorithm for Congestion Control in Power Transmission Network

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

Power System Transient Stability Preventive Control via Aptenodytes Forsteri Optimization with an Improved Transient Stability Assessment Model

Transient stability preventive control (TSPC), a method to efficiently withstand the severe contingencies in a power system, is mathematically a transient stability constrained optimal power flow (TSC-OPF) issue, attempting to maintain the economical and secure dispatch of a power system via generation rescheduling. The traditional TSC-OPF issue incorporated with differential-algebraic equations (DAE) is time consumption and difficult to solve. Therefore, this paper proposes a new TSPC method driven by a naturally inspired optimization algorithm integrated with transient stability assessment. To avoid solving complex DAE, the stacking ensemble multilayer perceptron (SEMLP) is used in this research as a transient stability assessment (TSA) model and integrated into the optimization algorithm to replace transient stability constraints. Therefore, less time is spent on challenging calculations. Simultaneously, sensitivity analysis (SA) based on this TSA model determines the adjustment direction of the controllable generators set. The results of this SA can be utilized as prior knowledge for subsequent optimization algorithms, thus further reducing the time consumption process. In addition, a naturally inspired algorithm, Aptenodytes Forsteri Optimization (AFO), is introduced to find the best operating point with a near-optimal operational cost while ensuring power system stability. The accuracy and effectiveness of the method are verified on the IEEE 39-bus system and the IEEE 300-bus system. After the implementation of the proposed TSPC method, both systems can ensure transient stability under a given contingency. The test experiment using AFO driven by SEMLP and SA on the IEEE 39-bus system is completed in about 35 s, which is one-tenth of the time required by the time domain simulation method.

A novel congestion mitigation key of power system via generator rescheduling using Jaya algorithm

ABSTRACT In the contemporary power system, congestion is a big hazard to system security. Congestion reduces the capability of the power system to work under secure limits of thermal and voltage profiles. Implementing FACTS devices or rescheduling the generator’s active power output are the most prevalent ways to mitigate congestion in the current scenario and hence this paper presents generator rescheduling for congestion management under the N-1 contingency condition. The generator sensitivity factor (GSF) is used to select the generators’ participation in the rescheduling process. The minimization of active power rescheduled by the selected generators is the key to success. JAYA algorithm is used first time in this work to reduce the congestion cost of the system. Furthermore, the JAYA algorithm is applied to a modified IEEE 30 bus system and the results obtained are validated by comparing them with other popular heuristic algorithms as available in the literature for diverse case studies.

A Novel Transient Stability Control Strategy for an AC/MTDC Grid Under $N-k$ Contingency Induced by Typhoon Extreme Weather

Due to the severe global warming, the frequency and intensity of typhoon extreme weather events have increased around the world. These events would cause serious <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-k$</tex-math></inline-formula> contingency of coastal AC/multi-terminal high voltage DC (MTDC) power grid, resulting in a high possibility of transient instability. To address this problem, this paper develops a novel two-stage resilient control strategy. In the first stage, an optimization model is established to obtain a control scheme that involves generator rescheduling, load shedding and MMC operation point adjustment after <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-k$</tex-math></inline-formula> contingency. In the second stage, an active power modulation method of modular multi-level converters (MMC) based on the model predictive control algorithm is proposed to improve the transient stability of the coastal AC/MTDC grid. The proposed control strategy makes full use of the flexible control resources of MMC-MTDC, thereby increasing the transient stability margin of the coastal AC/MTDC grid. The effectiveness of the proposed strategy has been validated on an AC/MTDC power grid built on MATMTDC that is an open source software based on MATLAB. Simulation results demonstrate that the proposed two-stage resilience control strategy exhibits promising performance in enhancing the transient stability of the coastal AC/MTDC power system when suffering a typhoon event.

Generation Rescheduling Based Contingency Constrained Optimal Power Flow Considering Uncertainties Through Stochastic Modeling

The generation rescheduling is described as the power generation shifting from one or more generators to one or more other generators as a preventive action to improve and maintain the security of the power system. Since, there is a direct link between security improvement and the lines overload under contingencies, by rescheduling generation, the transmission lines become more flexible and thus, the overload can be relieved. In this paper, contingency constrained optimal power flow (CCOPF) problem based on generation rescheduling by considering the uncertainty of photovoltaic (PV), wind turbine (WT), and plug-in hybrid electric vehicle (PHEV) have been addressed. Water cycle algorithm (WCA) using its potential in finding optimal solution has been used in order to reschedule the generators and optimize the total fuel cost, power losses under contingency scenario, and system security. Moreover, stochastic approach has been proposed for taking into account the uncertainty of PV, WT, and PHEV. Overall performance index including the power and the voltage severity indices have been provided for determining overloaded transmission lines due to the lines’ outage and consequently elimination of overloaded lines. The efficiency of the proposed algorithm has been evaluated on two IEEE-30 and IEEE 118-bus systems. The results are compared with the results of other classical and metaheuristic optimization algorithms. The simulations reveal that the WCA outperforms the other reported optimizers, and is more efficient and effective in improving security for power systems. In addition, the obtained numerical results show that renewable energy sources can significantly reduce fuel costs.

Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment

The technical issue of congestion, which is predominantly found in deregulated power systems, is caused by the failure of transmission networks to satisfy load power demands. This failure is primarily caused due to an increase in loads or loss of transmission lines or generators in modern restructured power networks. This work introduces a CM approach using Deep Convolution Neural Network (DCNN) for minimizing congestion and supporting Independent System Operators (ISOs). The purpose of the work is to generate enhanced prediction outputs for congestion management with reduced error values. These objectives were achieved through the actual power rescheduling of generators. The proposed work adopts DCNN which is optimized using an Improved Lion Algorithm (LA) and aids in providing significant outcomes for congestion management with reduced error. By implementing customized IEEE 57-bus, IEEE 30-bus, and IEEE 118-bus test systems, the suggested approach has been successfully verified for its performance on test systems of varied sizes. This analysis incorporates restrictions such as line loads, bus voltage influence, generator, line limits, etc. The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. When compared to several contemporary optimization techniques, the suggested technique performs better in terms of congestion cost and losses by generating improved prediction outputs with reduced errors.

An Efficient Model for Mitigating Power Transmission Congestion Using Novel Rescheduling Approach

In the electricity business, power evacuation from source to load is a challenging task when there is contingency and agencies have to swirl around for solutions in terms of rescheduling generator dispatch, load slashing, adding of network, etc. The more challenging scenario arises when the independent operator wants to re-dispatch a generator during the contingent situation. So, in this paper, we focus on a new generator rescheduling technique with congestion price and transmission security as an intervention. The significant intention of this paper involves restricting the load thereby dispatching a particular generator or a set of generators with low cost and secure transmission line. In addition to this, the network jamming is unpredictable and does not follow any pattern, but power supply in some zones is disrupted due to hidden reasons. Therefore, a macroscopic or holistic approach is adopted for congestion forecasting through demand schedule, gathering, minimum as well as maximum drawls. Here, two significant factors namely the transmission utilization charges as well as transmission congestion charges for predicting the congestion of a transmission line are evaluated. Finally, the experimental analysis to determine transmission utilization charges, transmission congestion charges, cost function and generation supply as well as demand balance with congestion optimization.

Cascaded deep NN‐based customer participation by considering renewable energy sources for congestion management in deregulated power markets

AbstractContinuously varying loading conditions and the cost‐based operation of a competitive power market lead to the problem of congestion as one of the most crucial issues. In day‐ahead power market operation (PMO), customer participation (CP) and generation rescheduling (GR) are the most effective techniques preferred by the system operator to eliminate congestion. In this paper, a cascaded Deep Neural Network (DNN) module has been presented for estimating customer participation and power generated by Wind Energy Source (WES) as on‐site generation (OSG) to manage congestion. The proposed module is a cascade combination of Artificial Neural Network (ANN) as a filtering module (FM) and DNN as a congestion management (CM) module. The CM module estimates the customer participation for all receptive costumers, power supplied by wind energy sources under uncertain conditions and generation rescheduling of all generators with minimum cost for all unseen congested power system loading patterns. The proposed CM approach provides an instant and efficient solution to manage congestion with minimum cost. The developed module has been examined on IEEE 30‐bus power system. The maximum error found in the testing phase is 1.1865% which is very less and within the acceptable limit.

A Sequential Generation Redispatch Algorithm to Ensure Power System Small Signal Stability under Low-Frequency Oscillations

Generation rescheduling is a major action against abnormal system conditions such as small signal instability. The operating point of the power system impacts the small signal stability; therefore, this article proposes a novel sequential generation rescheduling model as a preventive control against low-frequency fluctuations. An optimal power flow (OPF) problem is utilized to determine the base operating point. Then, in the process of improving the small signal stability, the modal analysis specifies the system stability status in each step, and the proposed second-order convex redispatch model defines the optimal direction of generation rescheduling. Small signal stability is considered a constraint in this model. Using a sensitivity analysis, system generators and system buses are divided into two increasing and decreasing groups, which are included in the model according to their contribution coefficients in damping improvement. Finally, the alternating current (AC) power flow analysis provides the subsequent operating points. The redispatch model is solved using the quadratic programming algorithm, and the Newton algorithm is used to manage the nonlinear and nonconvex characteristics of the power flow model. The proposed method is simulated over the IEEE 9, 39, and 118 bus test systems. It is shown that the proposed method increases the damping of the power system from the unstable state to the desired condition by controlling the operating point.

Methodology for Assessment of Congestion Problem

Abstract: Due to increasing in load demand, blackout or unexpected failure in any component of power system including generator, transformer, transmission lines, lead to congestion and over loading in power system this leads the power system to reach on its verse of emergency state ,therefore there should be a proper management or control action for that condition. Several methods are there to control congestion management including Generator Rescheduling (GR),Nodal Pricing, Particle Swarm Optimizer(PSO),Genetic Algorithm (GA) ,Grey Wolf Optimizer (GWO),This paper presents proper congestion management based on Grey Wolf Optimizer (GWO) for reducing load shedding, improve voltage profile, voltage stability, active power loss, this shows optimal load shedding is effective control action for congestion management. The algorithm applied on IEEE 30 bus system considering (n-1) contingency.

Transmission Congestion Management in Deregulated Power System Using Adaptive Restarting Genetic Algorithm

Power systems in a deregulated environment have more intense and recurrent transmission line congestion than conventionally regulated power systems. With the help of generation rescheduling, this article shows how to effectively manage congestion in the day-ahead energy market by taking corrective measures to reduce congestion. The research employs an adaptive restarting genetic algorithm (ARGA) to provide an effective congestion management strategy in a deregulated power market (DPM). The study makes two significant contributions. First, the generator sensitivity factors (GSF) are calculated to choose re-dispatched generators. Second, the least congestion cost is calculated using the adaptive restarting genetic algorithm. Several different line outage contingency cases on IEEE 30 bus systems are used to examine the suggested algorithm’s implementation efficacy. The simulation results demonstrate a significant reduction in net congestion costs, resulting in a more reliable and secure power system operation. The proposed algorithm was tested in a python environment, and power flow analysis was done using the PANDAPOWER tool. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The ARGA technique gives a lower congestion cost solution than the particle swarm optimization (PSO), real coded genetic algorithm (RCGA), and differential evolution (DE) algorithm.

A fast sequential transient stability preventive control approach driven by model interpretation

Transient stability preventive control (TSPC), regarded as generation rescheduling, plays an important role in maintaining secure and economic dispatch of power systems. To accommodate the increasing uncertainty of operation conditions, online TSPC is urgently required in real power systems. To this end, a new sequential online TSPC strategy driven by transient stability assessment (TSA) model interpretation is proposed in this paper. In this strategy, two data-driven TSA models, namely full-feature-trained and controllable-feature-trained models, are deployed for online TSA and generation rescheduling, respectively. To identify the control generators for TSPC, an instance-based model interpretation tool, Local Interpretable Model-agnostic Explanations (LIME), is introduced to interpret the unstable operation detected by online TSA. In addition, a pair-wise generator regulation strategy and a continual prediction method are used to quickly search the TSPC operating point while ensuring the power shift balance of the system during regulation. The adaptability of the strategy to the online applications is demonstrated on the IEEE 3-machine 9-bus system and the IEEE 10-machine 39-bus system. • Sequential framework speeds up transient stability preventive control (TSPC). • TSA model trained by controllable features locates the origin of system instability. • Model interpretation breaks up the opacity of TSA model’s predictions.

A novel congestion management method through power system partitioning

• Presenting a novel real time partition based CM method through power tracing and sensitivity analysis. • Introducing a new congestion index (CI) that simultaneously considers the contributions of each element in the congested line(s) and the effectiveness of the elements in alleviating the congestion. • Developing a new zonal CM based rescheduling and load shedding procedure. Congestion management (CM) is inevitable in today's competitive power markets. A CM method should be fast, fair, effective, and motivational. Moreover, in critical congestions, the system simplification and congestion clearing time are also of considerable importance. All the mentioned features can be found in an intelligent zonal CM. In this paper, using sensitivity analysis and power tracing techniques, a new CM model is developed based on power system partitioning. By the proposed model, a congestion index (CI) is introduced by which a candidate zone(s) is specified including some elements (generators and loads) with the highest CIs which means that they have high participation in congestion creation and simultaneously high effectiveness in congestion alleviation. Using the proposed method, power system operators can put their focus on the candidate zone(s) and alleviate the congestion effectively by some remedial measures such as generation rescheduling and load shedding. The proposed methodology is applied on IEEE 39-bus New England test system including 46 transmission lines. Results show the effectiveness and practicality of the proposed model, so that the congestion at the critical lines is alleviated well and their available transfer capabilities (ATCs) are increased to the reliable amounts.

Congestion Management in Deregulated Power System Using Different Control Technique

Abstract: In today’s scenario there is a challenge for power companies to meet the expected demand load due to continuous increases in load demand causes unpredictable failure in the components of power system including transmission line, generator, transformer and various other equipment this leads to over loading in the power system and the line become congested if this failure not removed on time the system reaches to emergency state, therefore we are using various techniques to control or manage these situations these methods includes Generator Rescheduling(GR),Load Shedding, Particle Swarm Optimizer(PSO), Grey Wolf Optimization (GWO),Harmony search algorithm etc. Optimal load shedding is effective control action for congestion management. The various algorithm applied on IEEE 30 bus system.

Flexibility potential of aggregated electric vehicle fleets to reduce transmission congestions and redispatch needs: A case study in Austria

Redispatch measures are a key instrument of the Central European electricity market design to prevent congestion of several transmission lines after the market clearing. In addition to generation rescheduling, demand-side flexibility can reduce this redispatch need. This paper mainly aims to analyse the flexibility demand application of electric vehicle fleets in the redispatch market. The developed European electricity market model minimises dispatch- and subsequently redispatch costs whilst using electric vehicle fleets as flexible demand. A novel modelling approach allows the integration of this flexibility into large-scale linear programming models without losing their essential parameters. This case study is examined in Austria with a remarkably high share of electricity from renewable energy sources and different market penetrations of electromobility. Results show that integrating this system as a redispatch measure leads to a reduced curtailment of renewable energies (up to 25%), whilst less additional thermal power plant usage is needed. Furthermore, redispatch cost and the associated CO2 emissions are reduced by 3.3% to 13.9%. By contrast, using this flexibility as a market-based charging strategy raises CO2 emissions and redispatch costs drastically by 186% if 2 million electric vehicles are considered. Especially with the high electrification rate of the transport sector, the provided flexibility potential significantly impacts the electricity market. Further efforts could address the influence of demand-side flexibility not only on redispatch within one control area but also on cross-border redispatch and counter-trading.

Synchronized-ambient-data-driven participation-factor-based generation rescheduling strategy for enhancing the damping level of interconnected power systems

Participation factor (PF) is widely used in assisting the selection of critical rescheduled generators in re-dispatching issues. The conventional PF relies heavily on the detailed system model and is commonly limited to quantifying the participation degree of system state variables in electromechanical mode. In this paper, the PF with respect to generator active power is presented by employing LQ-decomposition based recursive sub-DMD (Rsub-DMD) from synchrophasor ambient data. The proposed participation factor directly quantifies the relationship between the mode damping and generator outputs with the ability to track system operation conditions, thereby identifying the most effective re-dispatching generators online. Then a general online generation rescheduling strategy integrated with the proposed PF is applied for enhancing critical damping. The proposed PF-assisted rescheduling strategy is ambient data oriented, and the developed scheme enables online damping enhancement of critical mode. The effectiveness of the PF-assisted selection of rescheduled generators and the proposed rescheduling strategy has been demonstrated via numerical simulation and simplified China Northern system model cases.

Transmission congestion management through sensitivity based rescheduling of generators using improved monarch butterfly optimization

The emergence of competitive power markets in recent decades has paved the way for the enormous increase in the number of transactions between producers and consumers to satisfy the exponentially increasing load demand. These transactions usually occur through direct bilateral, multi-lateral agreements or some common platform. However, this operation puts additional strain on the transmission network, which serves as a conduit between producers and consumers leading to congestion. As a result, satisfying all the committed power transactions under congested transmission lines economically becomes infeasible. Independent System Operator (ISO) is an entity that is responsible for ensuring congestion-free power transactions to preserve the system's security and reliability. The objective of this work is to mitigate the congestion in the network while satisfying the system constraints resulting in more stable operation with reduced line flows and low system losses. The generator sensitivity factor (GSF) is utilized to single out the generating units that need to be rescheduled to alleviate transmission lines congestion. Further, the proposed variants of Monarch Butterfly Optimization (MBO) namely Improved MBO with linear weighting factor (IMBO-LW) and chaos-embedded MBO (CHMBO) are used to reschedule the generators with an aim to minimize the cost of rescheduling and the total change in power after rescheduling. Additionally, standard benchmark functions are used to validate the effectiveness of the proposed MBO variants and the same variants have been scrutinized on modified IEEE 30-bus and New England 39-bus test systems to alleviate the problem of congestion for secure and reliable operation of power system.

A Novel Improved Crow Search Algorithm to alleviate congestion in power system transmission lines

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.

Constrained economic dispatch following generation outage for hot spinning reserve allocation using hybrid grey wolf optimizer

The paper presents a methodology for hot spinning reserve allocation (HSRA) following expected disconnection of big generators during peak demand. The proposed method includes generation rescheduling, optimal reactive power allocation, and load curtailment to prevent system cascaded outages. Hybrid grey wolf optimization (HGWO) and Marine Predators Algorithm (MPA) are implemented to solve the economic HSRA problem with total costs minimization. The conversion process of HGWO is enhanced utilizing two internal loops based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The effectiveness of the optimization algorithms is conducted on a 66-bus three-area test system for cost minimization following the outage of the biggest generator in each area. The optimal solution maximizes the social benefits of participating consumers, keeping other consumers' reliability at minimum total cost. The percentages of load curtailment in the three cases are 6.9%, 8% and 6.7% of system load, respectively, where there was no solution for the optimal power flow without shedding some loads to satisfy the network constraints. HGWO and MPA are used to determine the optimal solution. The optimal solution depends on the energy price of load curtailment, system configuration as well as the cost coefficients of generators.

An intelligent hybrid technique for optimal generator rescheduling for congestion management in a deregulated power market

Congestion not only affects the power flow, but also leads certain issues, like market power, market inefficiency and security. When the transmission line exceeds their limits congestion is occurred (voltage, thermal, stability). Congestion management is a technique that helps to deal the issue corresponding to congestion. Many methods have been developed to manage congestion, and also several countries execute various strategies for the smooth functioning of their network. In this manuscript, the rescheduling of congestion management in a deregulated environment using DA-MRFO is proposed. The proposed hybrid technique is the combined execution of both the dragonfly algorithm (DA) and manta ray foraging optimization (MRFO). Dragonfly algorithm is enhanced using Manta ray Foraging optimization (MRFO), hence it is named DA-MRFO technique. The proposed method is used to alleviate transmission grid congestion on group-based electricity market via reprogramming active power of generators and also to reprogram the generator power. Congestion is the major Independent System Operator (ISO) concern on deregulated electricity market that is traditionally controlled by reprogramming generator output power. However, the effects of changes in the generator output power on the overloaded line flow are not identical. All the generators do not represent a desirable approach for congestion management. Here, a generator sensitivity factor is adapted for supporting the optimal generator selection in a congestion management (CM). In a congestion relief process, it is provided at the lowest possible cost. The reduction of power flow with collection of congested lines is probable through coordinated response of reactive energy dispatch as wind farms. The proposed approach is executed in modified IEEE 30 bus system and IEEE 57 bus system, then the efficiency is compared with the various existing optimization approaches.