Uncertainty Of Distributed Generation Research Articles

A Robust Measurement Placement Method for Active Distribution System State Estimation Considering Network Reconfiguration

The influences of network reconfiguration and distributed generations (DGs) on distribution system state estimation (DSSE) and measurement placement should not be ignored in active distribution systems. In this paper, considering network reconfiguration and the output uncertainties of DGs, a robust measurement placement method for active distribution systems is proposed based on measurement saturation analysis and heuristic algorithm. First, the saturation number determined by measurement saturation characteristic is chosen as the measurement number. Then, the impacts of different network topologies are represented by various weights in the robust measurement placement model, which are computed by Markov chain and analytic hierarchy process. In addition, Gaussian mixture model is applied to approximate the power fluctuations of DGs. Uncertainties caused by measurements are considered by Monte Carlo simulations. The accuracy of DSSE in different network topologies can be guaranteed by the proposed robust measurement placement method. Simulation results based on the IEEE 33-bus and 119-bus distribution systems demonstrate the effectiveness of the proposed method.

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.

An optimal allocation of distributed generation and voltage control devices for voltage regulation considering renewable energy uncertainty

Distributed generation (DG) may result in voltage fluctuation by changing line flow and reactive power injection, especially DG that generates power from renewable energy resources. To cope with this problem, this paper proposes an optimization process to optimally regulate the system voltage profile to lie close to the desired values by using the adaptive Tabu search (ATS) algorithm. The system voltages will be regulated by using dispatchable DG and voltage control devices, i.e. voltage regulator and capacitor. Moreover, probabilistic load flow calculation by using Monte Carlo simulation is chosen to evaluate the uncertainty of DG powered by renewable energy resources. The number of switching operations of the voltage regulator and capacitor are also accounted for in the optimization constraints, as excessive frequent switching operations can damage these devices. The optimal sizes and locations of dispatchable DGs and capacitors are considered as the optimization variables. The proposed method is demonstrated in an IEEE 34‐bus distribution test system and a modified 21‐bus Provincial Electricity Authority (PEA) system (Thailand). © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Multistage expansion planning for active distribution networks under demand and Distributed Generation uncertainties

This paper presents a methodology for active distribution networks dynamic expansion planning based on Genetic Algorithms, where Distributed Generation integration is considered together with conventional alternatives for expansion, such as, rewiring, network reconfiguration, installation of new protection devices, etc. All aspects related to the expansion planning problem, such as multiple objective analysis, reliability constraints, modeling under uncertainties of demand and power supplied by Distributed Generation units and multistage planning, which are usually dealt with separately, are considered in an integrated model. Uncertainties are represented through the use of multiple scenario analysis. Multiple stages are incorporated by an algorithm based on the pseudo-dynamic programming theory. Results obtained with a test system and with an actual large scale system are presented and demonstrate the flexibility of applying the model for different purposes active network planning.