Probabilistic model for microgrids optimal energy management considering AC network constraints

A new probabilistic approach for microgrids (MGs) optimal energy management considering ac network constraints is proposed in this paper. The economic model of an energy storage system (ESS) is considered in the problem. The reduced unscented transformation (RUT) is applied in order to deal with the uncertainties related to the forecasted values of load demand, market price, and available outputs of renewable energy sources (RESs). Moreover, the correlation between market price and load demand is taken into account. Besides, the impact of the correlated wind turbines (WT) on MGs’ energy management is studied. An enhanced JAYA (EJAYA) algorithm is suggested to achieve the best solution of the considered problem. The effective performance of the presented approach is verified by applying the suggested strategy on a modified IEEE 33-bus system. It can be observed that for dealing with probabilistic problems, the suggested RUT-EJAYA shows accurate results such as those of Monte Carlo (MC) while the computational burden (time and complexity) is lower.fi=vertaisarvioitu|en=peerReviewed

Optimal distributed generation and load shedding scheme using artificial bee colony- hill climbing algorithm considering voltage stability and losses indices

Around the world, the demand is increasing due to industrial activity and advances in both developing and developed countries. This situation has pushed many power system operators to operate their system closer to the voltage stability limits. Increase in power consumption can cause serious problems in electric power systems, such as voltage instability, frequency instability, line overloading, and power system blackouts.Voltage stability index (VSI) is a tool for detecting voltage stability related problems. This work proposes an index of the line voltage stability limits based on Thevenin’s Theorem, which is referred to as the Maximum Line Stability Index (MLSI). The function of MLSI is to estimate the voltage stability condition and determine sensitive lines in power system. To increase voltage stability and improve other aspects of power quality, many power system operators are considering the idea of integrating distributed energy resources into the existing power system. Another part of this work focuses on enhancing the stability of the power system using distributed generator (DG). The proposed solution is based on the optimization method developed from a combination of the Artificial Bee Colony and Hill Climbing algorithms (ABC-HC) to give the optimal placement and sizing of DG units to be deployed in the system. Under severe contingency conditions, such as increase in demand and loss of transmission lines, frequently the problem cannot be solved by just using the DG, the possible solution is to consider load shedding as to reduce the congestion in order to maintain voltage stability in the system. To solve this problem, an optimal load shedding approach, integrated with optimal DG sizing is proposed using the ABC-HC algorithm. This technique can find the load location to be shed, as well as the size of DG. The performance and effectiveness of each proposed solution was tested on IEEE test systems. The simulation results showed that the MLSI index has strong sensitivity to detect the overloaded line in the system and as reliable as other voltage stability indices. Meanwhile, the proposed ABC-HC optimization technique shows its ability to identify the bus location and the optimal active energy injection from the DG with a substantial power loss reduction. Finally, under severe contingency condition, the optimization of DGs and load shedding shows the system able to maintain its voltage stability

Distribution system state estimation-a step towards smart grid

State estimation (SE) is well-established at the transmission system level of the electricity grid, where it has been in use for the last few decades and is a most vital component of energy management systems employed in the monitoring and control centers of electric transmission systems. However, its use for the monitoring and control of power distribution systems (DSs) has not yet been widely implemented because DSs have been majorly passive with uni-directional power flows. This scenario is now changing with the advent of smart grid, which is changing the nature of electric distribution networks by embracing more dispersed generation, demand responsive loads, and measurements devices with different data rates. Thus, the development of distribution system state estimation (DSSE) tool is inevitable for the implementation of protection, optimization, and control techniques, and various other features envisioned by the smart grid concept. Due to the inherent characteristics of DS different from those of transmission systems, transmission system state estimation (TSSE) is not applicable directly to DSs. This paper is an attempt to present the state-of-the-art on DSSE as an enabler function for smart grid features. It broadly reviews the development of DSSE, challenges faced by its development, and various DSSE algorithms. Additionally, it identifies some future research lines for DSSE

Distribution system state estimation-a 1 step towards smart grid

State estimation (SE) is well-established at the transmission system level of the electricity grid, where it has been in use for the last few decades and is a most vital component of energy management systems employed in the monitoring and control centers of electric transmission systems. However, its use for the monitoring and control of power distribution systems (DSs) has not yet been widely implemented because DSs have been majorly passive with uni-directional power flows. This scenario is now changing with the advent of smart grid, which is changing the nature of electric distribution networks by embracing more dispersed generation, demand responsive loads, and measurements devices with different data rates. Thus, the development of distribution system state estimation (DSSE) tool is inevitable for the implementation of protection, optimization, and control techniques, and various other features envisioned by the smart grid concept. Due to the inherent characteristics of DS different from those of transmission systems, transmission system state estimation (TSSE) is not applicable directly to distribution systems. This paper is an attempt to present the state-of-the-art on distribution system state estimation as an enabler function for smart grid features. It broadly reviews the development of DSSE, and challenges faced by its development, and various DSSE algorithms, as well as identifies some future research lines for DSS

Covariance Analysis of LAV Robust Dynamic State Estimation in Power Systems

In power system state estimation, the robust Least Absolute Value robust dynamic estimator is well-known. However, the covariance of the state estimation error cannot be obtained easily. In this paper, an analytical equation is derived using Influence Function approximation to analyze the covariance of the robust Least Absolute Value dynamic state estimator. The equation gives insights into the precision of the estimation and can be used to express the variances of the state estimates as functions of measurement noise variances, enabling the selection of sensors for specified estimator precision. Simulations on the IEEE 14-bus, 30-bus and 118-bus systems are given to illustrate the usefulness of the equation. Monte-Carlo experiments can also be used to determine the covariance, but many data points are needed and hence many runs are required to achieve convergence. Our result shows that to obtain the covariance of the state estimation error, the analytical equation proposed in this paper is four orders of magnitude faster than a 10,000-run Monte-Carlo experiment on both the IEEE 14-bus and 30-bus systems.National Research Foundation Singapor

Game-theoretic modeling of curtailment rules and network investments with distributed generation

Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of sev