Search

Load modeling is one of the most important features in power system operation and stability studies. So far, various methods have been applied based on measuring the relevant load data, and obtaining the load models through the mathematical models. This paper presents a method for load modeling based on processing the recorded data by the measurement equipment with PSO algorithm, which is located in substations. In the proposed method, for improving the accuracy of the load model, some constraints are applied for eliminating the undesirable measured data. These constraints are applicable for any time periods, such as; hourly, daily, seasonally, and annually. Furthermore, a combination of the static and dynamic models is utilized. The performance of the proposed method is tested on two industrial power grids from the west regional electric company (WREC) network, and the results are thoroughly analyzed. The results have shown that the proposed method has an acceptable accuracy for load modeling in comparison with real model of the case study systems. [ABSTRACT FROM AUTHOR]

Stability studies in distribution smart grids (SGs) are very important due to the increased penetration of distributed energy resource making bidirectional flow of electric power. This paper presents two novel fast voltage stability indices (VSIs) applicable to the radial distribution SGs based on the real-time measured voltage data only, obtained by the SG infrastructures such as smart metering device. Each VSI depends on the system bus voltages and does not need additional calculations such as power flow solution, converting the actual distribution network into two-bus equivalent system etc. In other words, for a radial system with n-node, the voltage stability analysis is carried out by parallel n-microprocessor units independently. The concentrator units can collect and evaluate the computational results and decide about control actions directly or indirectly depending on the system requirements. The accuracy and efficiency of the proposed indicators are tested on a 41-node radial distribution system. Six load models including constant and composite types have been considered, and also SG in various states of operation and four types of distributed generation are analysed. The comparison results demonstrate that the proposed VSIs are fast and effective to identify the most sensitive node of the radial grids to the voltage collapse based on the smart infrastructures. [ABSTRACT FROM AUTHOR]

In this work; we suggest a new intelligent optimization method to solve the optimal energy management issue in the renewable micro-grids (MGs). The proposed method is constructed based on the harmony search (HS) algorithm as well as a novel adaptive modification method to solve the problem effectively. The proposed modification method consists of three sub-modifications to provide the algorithm with powerful tool to escape from the local optima of the problem with fast convergence. The feasibility and satisfying performance of the proposed method is examined on a typical MG including different types of renewable energy sources such as photovoltaic, fuel-cell, micro-turbine, wind turbine and battery as the storage device. Comparative studies demonstrate the consistent superiority of the proposed method over the other well-known algorithms in the area. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive model is proposed for long-term planning of various combined heat and power units in an integrated heat and electricity network. The proposed model takes into account the uncertain electric loads, and market price applying the information gap decision theory. Furthermore, the security of the power grid from the voltage stability viewpoint utilizing the L-index approach is considered. The model is based on the risk-averse multi-objective combined heat and power planning methodology, which maximizes the profit of combined heat and power owners and minimizes the system operator costs over the planning horizon in the presence of the environmental emissions cost. The best compromise solution is achieved via a fuzzy logic-based min-max method. The risk-averse strategy of Information gap decision theory is applied to the obtained solution, which demonstrates the impact of data uncertainty. The proposed mixed-integer non-linear programming model is solved using the general algebraic modeling system package and tested on the IEEE 14-bus standard system. The results indicate that the risk-averse strategy improves the robustness of the network against the uncertainty. Also solving the model using the multi-objective framework gives comprehensive results, and shows that the voltage stability constraints affect the planning decisions. • Presenting a comprehensive model for long-term planning of various combined heat and power units. • Investigating the uncertainty of electric loads, and market price using information gap decision theory. • Modeling the long-term security of the power grid from the voltage stability viewpoint. • Introducing a risk-averse multi-objective planning model. • Considering the environmental objectives in the combined heat and power investment. [ABSTRACT FROM AUTHOR]