Determining the Spinning Reserve in Power Systems by Corrected Recursive PJM Method

Security for grids in the electric industry is very important to the power gridsystem. Dispatching a consistent high quality to customers is the main goal. There is aneed for balance in power generation and power consumption.Consumption forecasts do not meet load amounts and grid system toleratesconsumption loads beyond actual amounts to be sure the spinning reserve is necessarynot only in order to be secure but also because of need for accurate calculating.In this research the amount of the Spinning Reserve needed in an instantsystem using the PJM method will be determined. Then, with an innovative recursivemodel, optimize and correct the determined spinning reserve

Adaptive Voltage and Current Control to Improve Stability and Restoration of Operation after the Occurrence of a Transient Fault in The Inverter-based Standalone Microgrid

339-348Small signal stability analysis and control strategies are well provided for conventional power systems. Still, in inverter-based microgrids, it is necessary to determine the model features and control specific oscillation modes. In this paper, inverter-based standalone microgrid performance modelling, including voltage and current control, has been developed. This model has inverter control loop details but does not provide switching performance. A simple adaptive controller controls the microgrid voltage and current, with only three parameters, including two negatively stable feedbacks and positive unstable feedback. Advantages of this controller over a Proportional Integral Derivative (PID) controller include proper adaptation to system changes, flexibility in setting up, and a robust deal with sudden changes in the system. Finally, by making drastic changes in the operation of a sample microgrid, such as loading and short circuit faults, we prove the effectiveness of the proposed adaptive control scheme in improving the stability and restoration of operation after the occurrence of a transient fault in the microgrid

Local and Remote Voltage and Reactive Power Control in the Presence of Induction Generators

Voltage and reactive power control in distribution networks is one of the most basic tasks of a distribution network operator. This is usually controlled by on-load tap-changer (OLTC), substation capacitors, and feeder capacitors. This paper first investigates a local voltage and reactive power control in a distribution system and then how the presence of induction machine based DG affects it. In the following a proper coordination among OLTC and capacitors to minimize losses in the distribution system, with and without DG, is formulated. The results showed that in the presence of induction generator, local control was not optimal, and sometimes operating constraints are violated. Finally three methods for controlling voltage and reactive power in the presence of induction generator to reduce losses and comply with the limitations operation during the day are proposed. Induction generator can be both fixed and variable be evaluated. Simulation studies on a distribution network of 10 bus with 70/10kV voltage has been made

Weighted ensemble learning for real‐time short‐term voltage stability assessment with phasor measurements data

Abstract Voltage stability assessment based on machine learning has become an important challenge in power systems. This paper presents real‐time short‐term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data and machine learning (ML). The database is created through time series of measurement data to involve system time‐temporal and dynamics. Then multiple operating states of the power system are classified through the calculation of the Lyapunov exponent and dynamic voltage index according to the database. This paper presents a weighted combination of random forest (RF) and LightGBM (LGBM) classifiers to train a time‐series database. One of the main advantages of this paper is using the gradient concept in data preprocessing, which has enhanced performance metrics and reduced the defect of data noise. Also, hyperparameter optimization is conducted to improve machine performance. Studies on the IEEE 118bus and a real local grid (RLG) demonstrate that the proposed method improves the performance metrics such as accuracy and F1‐score. Also, this approach is robust against PMU data noise and topology changes in the network

Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

Transmission Network Expansion Planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI) tools such as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Artificial Neural Networks (ANNs) are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs) and Harmony Search Algorithm (HSA) was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network

Considering the load uncertainty for solving security constrained unit commitment problem in presence of plug-in electric vehicle

In this paper, the load uncertainty for solving Security constrained unit commitment (SCUC) problem in presence of plug-in electric vehicle is studied. SCUC is one of the most important research areas in power systems which enables commitment scheduling table of the generation unit commitment in order to maximize the security and minimize the cost, beside satisfaction of the constraints of the system and units, during a specified time period. Recent advances in technology, caused power system operators to focus more on renewable energy resources beside thermal units. Vehicle to grid (V2G) technology is one of these new resources of energy. On this aim, V2G by reducing the dependence of the power generation system to small and expensive thermal units, can play an important role in reducing operation cost and load fluctuation management. According to the probability of error existence in load forecasting, the system planner must appoint the generation pattern in a way that beside of load satisfaction, the reserves been provided and the error of load forecasting been compensated. In this paper, by two-stage stochastic programming, the uncertainty of load forecasting while solving SCUC problem in a short period (24 hours) is investigated. Nowadays one of the most effective strategies for evaluating the influences of V2G connection in an efficient operation of generation system, is SCUC performing on the power systems in which V2Gs are connected to it in different buses. Therefore, this issue is considered in this paper, in a way that results of numerical studies on independence presence of V2G, and the improvement of operation indices are illustrated

Adaptive Voltage and Current Control to Improve Stability and Restoration of Operation after the Occurrence of a Transient Fault in The Inverter-based Standalone Microgrid

Small signal stability analysis and control strategies are well provided for conventional power systems. Still, in inverter-based microgrids, it is necessary to determine the model features and control specific oscillation modes. In this paper, inverter-based standalone microgrid performance modelling, including voltage and current control, has been developed. This model has inverter control loop details but does not provide switching performance. A simple adaptive controller controls the microgrid voltage and current, with only three parameters, including two negatively stable feedbacks and positive unstable feedback. Advantages of this controller over a Proportional Integral Derivative (PID) controller include proper adaptation to system changes, flexibility in setting up, and a robust deal with sudden changes in the system. Finally, by making drastic changes in the operation of a sample microgrid, such as loading and short circuit faults, we prove the effectiveness of the proposed adaptive control scheme in improving the stability and restoration of operation after the occurrence of a transient fault in the microgrid

Resilient energy management incorporating energy storage system and network reconfiguration: A framework of cyber‐physical system

Abstract Due to increasing the intricacies of cyber‐physical systems (CPSs) and the severity of natural phenomena, upgrading network planning is vital to reduce the vulnerability of these systems. This study develops a novel preventive‐corrective resilient energy management strategy (PC‐REMS) for a CPS in two stages, exploiting the network reconfiguration (NR) and energy storage systems (ESSs) capacity. The first stage of the proposed PC‐REMS follows preventive actions based on contingency faults. In contrast, the second stage applies corrective measures for improving the CPS resilience to cope with natural physical disasters. Vulnerability assessment data is sent to the physical power system daily through the communication network. The first stage of preparing the CPS for predictable faults focuses on pre‐scheduled ESSs and preventive NR to minimise the expected energy curtailment cost. The second stage involves the network recovery in real‐time through corrective NR to minimise energy curtailment cost after the faults. Three resistance, recovery, and resilience indices are introduced for evaluating the effectiveness of the model. The proposed model is examined by performing multiple simulations on the 33 and 118‐bus radial test systems. The simulation results show the efficiency of the proposed PC‐REMS model in dealing with predictable disasters to improve the CPS resilience