Overload Alleviation Research Articles

A multi-layer scheduling framework for transmission network overload alleviation considering capabilities of active distribution networks

As distributed generations and flexible loads are widely connected to distribution networks, traditional distribution networks become “active”, and the response of the active distribution network (ADN) to the transmission network is receiving increasing attention. In this paper, the congestion problem of transmission network is studied from a demand-side perspective, and a multi-layer scheduling framework considering capabilities of ADNs is developed to reduce unnecessary load shedding operations in transmission network. The proposed framework consists of three layers. In the first layer, ADN’s power flow is linearized by the second-order cone programming, based on which a reactive power optimization scheduling model is constructed to minimize network loss in ADNs. When the network loss reduction in ADN cannot meet the requirements for congestion mitigation, a bi-level self-cycle model based on analytical target cascading is constructed in the second layer, which contributes to dispatch distributed generators’ outputs and further curtail load from the ADN side. When the second layer fails to satisfy the required load curtailment, a demand response model considering user acceptance and rejection is proposed to obtain the optimal shedding strategy for interruptible loads, and ultimately alleviate the overload problem. A case study with varying degrees of overload in the transmission network is conducted to demonstrate the proposed framework.

On Line Algorithm for Line Overload Alleviation.(Dept.E)

On Line Algorithm for Line Overload Alleviation.(Dept.E)

LONP1 Regulates Mitochondrial Accumulations of HMGB1 and Caspase-3 in CA1 and PV Neurons Following Status Epilepticus.

Lon protease 1 (LONP1) is a highly conserved serine peptidase that plays an important role in the protein quality control system in mammalian mitochondria. LONP1 catalyzes the degradation of oxidized, dysfunctional, and misfolded matrix proteins inside mitochondria and regulates mitochondrial gene expression and genome integrity. Therefore, LONP1 is up-regulated and suppresses cell death in response to oxidative stress, heat shock, and nutrient starvation. On the other hand, translocation of high mobility group box 1 (HMGB1) and active caspase-3 into mitochondria is involved in apoptosis of parvalbumin (PV) cells (one of the GABAergic interneurons) and necrosis of CA1 neurons in the rat hippocampus, respectively, following status epilepticus (SE). In the present study, we investigated whether LONP1 may improve neuronal viability to prevent or ameliorate translocation of active caspase-3 and HMGB1 in mitochondria within PV and CA1 neurons. Following SE, LONP1 expression was up-regulated in mitochondria of PV and CA1 neurons. LONP1 knockdown deteriorated SE-induced neuronal death with mitochondrial accumulation of active caspase-3 and HMGB1 in PV cells and CA1 neurons, respectively. LONP1 knockdown did not affect the aberrant mitochondrial machinery induced by SE. Therefore, our findings suggest, for the first time, that LONP1 may contribute to the alleviation of mitochondrial overloads of active caspase-3 and HMGB1, and the maintenance of neuronal viability against SE.

Transmission Lines Overload Alleviation: Distributed Online Optimization Approach

The risk of transmission lines overload in the power grid is increasing with the large-scale integration of fluctuating distributed renewable energies. Meanwhile, the requirement for accommodating more distributed resources in the future smart grids promotes the transition from the current highly centralized control structure toward a distributed one. In this article, we propose a distributed corrective control approach to mitigate line overloads in a real-time and close-loop manner. Unlike the conventional centralized approach, only simple computation and local information exchange are required to update the local corrective control action. This makes it possible to mitigate line overloads timely and adapt to the topology changes of grids. Furthermore, the introduction of system measurements and security constraints in the proposed algorithm ensures an effective and secure corrective control without new line overloads. The performance of the proposed distributed approach is demonstrated in the IEEE 14-Bus and 118-Bus systems.

Model Predictive Control Based AC Line Overload Alleviation by Using Multi-Terminal DC Grids

The increasing power/load demand is becoming a new concern for the grid operators as it can potentially overload transmission lines. Among advantages of a multi-terminal dc (MTDC) grid, its high controlability makes it a promising asset for ac line overload alleviation. This paper proposes a model predictive control based strategy, which regularly updates the power setpoints of the MTDC converter stations and ac generators to alleviate ac line overloads. To compute and update the power setpoints, necessary measurements are received by the controller within regular time intervals. The proposed controller ensures that subsequent to any ac line overload, changing the power setpoints of the MTDC converter station does not cause any operation violation in the MTDC grid. Furthermore, it ensures that the ac voltage limits and voltage stability criteria are met to avoid voltage instability. Performance and effectiveness of the proposed controller are evaluated by simulation studies on two systems, i.e., the 39-bus New England system integrated with a 5-bus MTDC system and the IEEE 118-bus system connected to a 6-bus MTDC system. Simulation results confirm that the MTDC grid can play a major role in controlling active powers of most of the ac transmission lines.

Alleviation of overloads in transmission network: A multi-level framework using the capability from active distribution network

With the fast development of technologies of renewable generations and distributed energy resources, the distribution grids become more and more active, and the coordinated transmission and distribution management (CTDM) becomes more important. Under the CTDM framework, this paper proposes a multi-level overload relief method for transmission network using the capability from an active distribution network (ADN). When an overload event occurs at the transmission network, an appropriate operational scheme for ADN is selected by the proposed multi-level method. Then, the ADN takes the decided operational scheme and adjusts its operational status (i.e., reconfiguring its network topology, transferring loads to Microgrids and shedding the controllable loads) to contribute to load curtailment from the ADN side and further alleviate overload in the transmission network. Numerical case studies show that the proposed method can alleviate the overload of transmission network and maintain the normal operation of the ADN, which demonstrates a promising application for CTDM.

Automatic Alleviation of Overloads in Transmission Network by Generation Curtailment

For economic reasons, expansions of transmission networks are based on a compromise between capital cost and security of power system operation. At the same time, the power system must withstand typical highly likely contingencies. Various emergency state control systems, also called special protections, prevent emergencies following less likely contingencies. This paper describes overloads emergency state control (OLEC) used for the alleviation of overload in transmission networks near a power plant. Such control uses the functions of automated power reduction of generating units and has been implemented for a large steam power plant. The OLEC presented here uses different methods of generation curtailment for steam generation units. A case study illustrates the efficiency of the proposed control method.

Transmission Lines Overload Alleviation by Generation Rescheduling and Load Shedding

AbstractIn this paper, transmission lines overload alleviation for the Sudan National Grid has been performed. Resizing the transmission system components is not suitable during the operation. On the contrary, in that case, alleviation of the overloads by generation rescheduling is suitable and effective. Decreasing the generation station output relieves transmission line overload, but to maintain the power equilibrium, the output of other generation stations must be increased, making sure no additional transmission lines are being overloaded. A simple, efficient, fast, and accurate technique for the alleviation of line overloads by corrective generation rescheduling and load shedding is applied to alleviate the overloads. The method ensures that alleviation of the existing violations does not create any new violations. The load shedding is used as a last choice in cases when generation rescheduling has not completely alleviated the overloads. An overload that cannot be relieved by only rescheduling the g...

Static security enhancement using fuzzy particle swarm optimization

Purpose – The electric power system is a complex system, whose operating condition may not remain at a constant value. The various contingencies like outage of lines, transformers, generators and sudden increase of load demand or failure of equipments are more common. This causes overloads and system parameters to exceed the limits thus resulting in an insecure system. The purpose of this paper is to enhance the power system security by alleviating overloads on the transmission lines. Design/methodology/approach – Fuzzy logic system (FLS) with particle swarm optimization based optimal power flow approach is used for overload alleviation on the transmission lines. FLS is modeled to find the changes in inertia weight by which new weights are determined and their values are applied to particle swarm optimization (PSO) algorithm for velocity and position updation. Findings – The proposed method is tested and examined on the standard IEEE-30 bus system under base case and increased load conditions at different contingency. This method gives better results in terms of optimum fuel cost and fast convergence under base case and could alleviate the line overloads at different contingency with optimum generation cost, when compared to adaptive particle swarm optimization (APSO) and PSO. Originality/value – FLS is modeled in MATLAB environment. The effectiveness of the proposed method is tested and examined on the standard IEEE-30 bus system and their results are compared with APSO and PSO under MATPOWER environment. The results show that the proposed algorithm is capable of improving the transmission security with optimum generation cost.

Spautin-1 Ameliorates Acute Pancreatitis via Inhibiting Impaired Autophagy and Alleviating Calcium Overload.

Acute pancreatitis is characterized by zymogen pre-activation. Severe inflammation caused by zymogen activation can eventually lead to multiple organ dysfunctions, which contributes to the high mortality rate of severe acute pancreatitis. However, there is no specific treatment available for acute pancreatitis therapy. Here, we show that spautin-1, which effectively inhibits autophagy flux, ameliorated the pathogenesis of acute pancreatitis induced by cerulein or L-Arginine. CaMKII phosphorylation due to cytosolic calcium oeverload was revealed in this paper. It was also demonstrated that autophagic protein aggregates degradation blockade accompanying with impaired autophagy correlated positively to intra acinar cells digestive aymogen activation sitimulated by cerulein or L-Arginine. The role of spautin-1 in ameliorating acute pancreatitis was shown here to be associated with impaired autophagy inhibition and Ca2+ overload alleviation. We provided a promising therapy for acute pancreatitis here through targeting both impaired autophagy and increased cytosolic calcium.

Comparison of Ontological Subscription and Blocking and Rich Site Summary for Blog Posts

Information overload and privacy protection become critical issues with blogs. This paper presents the ontological subscription and blocking system (OSBS) using policies, ontologies, and rules to subscribe interesting posts and block unwanted posts. This system also uses rules for automatic conflict detection between polices. The effectiveness of two subscription systems, OSBS and RSS (rich site summary) reader, is compared. The experimental results reveal that the perceived privacy benefits on the novel OSBS are better than the existing RSS reader. The novel OSBS is as good as existing RSS reader from perceived information overload alleviation perspective.

Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

Abstract This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

Anticipatory load shedding for line overload alleviation using Teaching learning based optimization (TLBO)

This paper presents a load shedding algorithm for alleviating line overloads employing Teaching learning based optimization (TLBO). The buses are selected for load shed based on the sensitivity of severity index with respect to load shed. Load shed is based on the next interval predicted load which could cause emergency situation from thermal limit consideration. Line flow constraints have not only considered for next predicted interval but in present base case loading conditions also. Optimum load shed at the selected buses have been obtained for 30-bus, 39-bus standard test systems. Further another technique modified version of bare bones particle swarm optimization known as (BBExp) has been used to validate the algorithm for load shedding.

Coordinated Control of Distributed and Bulk Energy Storage for Alleviation of Post-Contingency Overloads

This paper presents a novel corrective control strategy that can effectively coordinate distributed and bulk energy storage to relieve post-contingency overloads. Immediately following a contingency, distributed batteries are implemented to provide fast corrective actions to reduce power flows below their short-term emergency ratings. During the long-term period, Pumped Hydro Storage units work in pumping or generation mode to aid conventional generating units keep line flows below the normal ratings. This problem is formulated as a multi-stage Corrective Security-constrained OPF (CSCOPF). An algorithm based on Benders decomposition was proposed to find the optimal base case solution and seek feasible corrective actions to handle all contingencies. Case studies based on a modified RTS-96 system demonstrate the performance and effectiveness of the proposed control strategy.

Overload alleviation in electric power systems using fuzzy logic-based generation rescheduling

Overload alleviation in electric power systems using fuzzy logic-based generation rescheduling

Overload Alleviation in Electric Power Systems using Fuzzy Logic-Based Generation Rescheduling

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.

Cascade BPN based transmission line overload prediction and preventive action by generation rescheduling

This paper is a modest attempt to address an intelligent technique based on Cascade Back Propagation Neural network (CBPN) for detection of line overloads, prediction of overloading amount and alleviation of overloads using generation rescheduling with N-1 contingency in restructured power system. In the present competitive power market, power transmission congestion has become more intensified and recurrent than the vertically bundled system. Transmission line congestion initiates the cascading outages which forces the system to collapse. Accurate prediction and alleviation of line overloads are the suitable corrective actions to avoid network collapse. The most obvious technique for network Congestion Management (CM) is rescheduling the power output of generators. The proposed CBPN comprises three Artificial Neural Networks (ANNs) in cascade. ANN1 is used as a classifier, which classifies the system state as secure or insecure. ANN2 is used for the prediction of overloading amount in the overloaded lines and ANN3 gives optimal generation rescheduling values for alleviating the overloads. The real power and reactive power of loads at all the load buses and the line outage number are considered as input variables to ANN1, ANN2 and ANN3. The effectiveness of the proposed approach is tested for various contingencies in the IEEE30 Bus System. The experimental results show that the proposed approach is computationally fast, reliable and efficient, in restoring the system to the normal state after a contingency with minimal control actions.

Overload Alleviation And Determination of Cost of Rescheduling In An Open Acess Power System

This paper presents an approach for alleviation of network over loads in the day-to-day operation of power systems. The method used for over load alleviation is real power generation rescheduling based on relative electrical distance (RED) concept. The method estimates the relative location of load nodes with respect to the generator nodes. First congestion is observed, and then each generator’s contribution to the congested line is found out. Based on RED method desired generation rescheduling is obtained to relieve overloaded line. Cost is also a key factor which has to be considered in real power rescheduling. A case studied is carried out for modified IEEE 39-bus New England system, where power flow is found by Newton Rapson’s method and compared with operational load flow method.

Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto‐optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

Line overload alleviation through corrective control in presence of wind energy

With the growth of wind energy for power generation, several transmission system operators (TSOs) have increasing difficulties to forecast congestions due to the unpredictable nature of the energy source. This paper proposes to enhance congestion management using a real time supervisor. This supervisor performs automatic and dynamic re-dispatchings using both variable speed wind generators and conventional generators. In order to minimize production constraints, the real time congestion management system is based on an indicator of the efficiency of a re-dispatching on the power flowing in an overloaded line. This approach leads to reduced re-dispatching costs and increased network reliability. The simulation of the IEEE 14-bus test power system enhanced by the supervision system is done using the software “EUROSTAG”. Grid integration of renewable generation is therefore increased through renewable production maximization while ensuring network security.