30 research outputs found
Hybrid AC/DC Transmission Expansion Planning Considering HVAC to HVDC Conversion Under Renewable Penetration
In this paper, a dynamic (i.e. multi-year) hybrid model is presented for
Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating
Current (HVAC) and multiterminal Voltage Sourced Converter (VSC)-based High
Voltage Direct Current (HVDC) alternatives. In addition to new HVAC and HVDC
lines, the possibility of converting existing HVAC transmission lines to HVDC
lines is considered in the proposed model. High shares of renewable resources
are integrated into the proposed hybrid AC/DC TEP model. Due to the
intermittency of renewable resources, the planning of large-scale Energy
Storage (ES) devices is considered. In order to accurately estimate the total
TEP costs and hence capturing the scenarios of load and renewable generation
uncertainty, using a clustering approach, each year of the planning horizon is
replaced with four representative days. The proposed model is formulated as a
Mixed-Integer Linear Programming (MILP) problem. Using Benders Decomposition
(BD) algorithm, the proposed model is decomposed into a Master investment
problem to handle the decision variables, and Sub-problems to check the
feasibility of master problem solution and optimize the operation and ES
investment cost. Three test systems are used as case studies to demonstrate the
effectiveness of the proposed hybrid AC/DC TEP model
Decision making under uncertainty in energy systems: State of the art
The energy system studies include a wide range of issues from short term (e.g. real-time, hourly, daily and weekly operating decisions) to long term horizons (e.g. planning or policy making). The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. In this paper, a new standard classification of uncertainty modeling techniques for decision making process is proposed. These methods are introduced and compared along with demonstrating their strengths and weaknesses. The promising lines of future researches are explored in the shadow of a comprehensive overview of the past and present applications. The possibility of using the novel concept of Z-numbers is introduced for the first time
Fault Detection in Distribution Networks in Presence of Distributed Generations Using a Data Mining Driven Wavelet Transform
Here, a data miningâdriven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems
Scenario-Based Selection of Pilot Nodes for Secondary Voltage Control
Owing to the local nature of voltage and reactive power control, the voltage control is managed in a zonal or regional basis. A new comprehensive scheme for optimal selection of pilot points is proposed in this study. The uncertainties of operational and topological disturbances of the power system are included to provide the robustness of the pilot node set. To reduce the huge number of probable states (i.e. combined states of load and topological changes), a scenario reduction technique is used. The resulted optimal control problem is solved using a new immune-based genetic algorithm. The performance of the proposed method is verified over IEEE 118-bus and realistic Iranian 1274-bus national transmission grids
Immuned based Selection of Pilot Nodes for Secondary Voltage Control
International audienc
An Immune-Based Algorithm for Voltage Stability onstrained Load Shedding
International audienc
Adaptive Under-Voltage Load Shedding Scheme using Model Predictive Control
International audienc
Secure expansion of energy storage and transmission lines considering bundling option under renewable penetration
This paper presents a multi-stage expansion model for the co-planning of transmission lines, battery energy storage (ES), and wind power plants (WPP). High penetration of renewable energy sources (RES) is integrated into the proposed model concerning renewable portfolio standard (RPS) policy goals. The possibility of bundling existing transmission lines to uprate power flow capacity is considered. Renewable energy curtailment and load shedding are included in the model to assess the system operation more precisely. Battery ES devices are co-planned to defer transmission expansion and renewable management. To make the time complexity of the problem tractable and capture the uncertainties of load and RES in an hourly resolution, a chronological time-period clustering algorithm is used to extract the representative hours of each planning stage. Additionally, the flexible ramp reserve is utilized to handle the uncertainty of RES. An accelerated Benders dual decomposition (BDD) algorithm is developed to solve the proposed model mixed-integer linear programming (MILP) formulation. The N-1 security criterion is evaluated by considering a designed contingency screening (CS) algorithm to identify higher risk contingencies. The effectiveness of the proposed co-planning model is evaluated using IEEE RTS 24-bus and IEEE 118-bus test systems.Intelligent Electrical Power Grid