Stochastic Modeling and Optimization in a Microgrid: A Survey

Abstract

The future smart grid is expected to be an interconnected network of small-scale and self-contained microgrids, in addition to a large-scale electric power backbone. By utilizing microsources, such as renewable energy sources and combined heat and power plants, microgrids can supply electrical and heat loads in local areas in an economic and environment friendly way. To better adopt the intermittent and weather-dependent renewable power generation, energy storage devices, such as batteries, heat buffers and plug-in electric vehicles (PEVs) with vehicle-to-grid systems can be integrated in microgrids. However, significant technical challenges arise in the planning, operation and control of microgrids, due to the randomness in renewable power generation, the buffering effect of energy storage devices and the high mobility of PEVs. The two-way communication functionalities of the future smart grid provide an opportunity to address these challenges, by offering the communication links for microgrid status information collection. However, how to utilize stochastic modeling and optimization tools for efficient, reliable and economic planning, operation and control of microgrids remains an open issue. In this paper, we investigate the key features of microgrids and provide a comprehensive literature survey on the stochastic modeling and optimization tools for a microgrid. Future research directions are also identified.

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

“…In recent years, environmental concerns, fossil fuel resource depletion and advances in technology have resulted in the increase of distributed generation (DG) in distribution networks [1,2]. Reasonable application of DGs can bring many advantages, such as voltage profile improvement and pollutant emission reduction [3][4][5].…”

Smart Grid Cyber Security: An Overview of Threats and Countermeasures

“…The ability to run microgrids in so-called island mode provides high local reliability, though this causes difficulty in correlating its supplies with those of the main grid. This has been addressed through the use of stochastic modeling techniques by Liang and Zhuang [40]. These techniques implemented state evolution modeling in order to capture the trajectory of the evolution of operational states of devices within the microgrid and its connection to the main grid.…”

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

“…), while fast loads characterised by short and high power demand are paired with flywheels, supercapacitors or other technologies capable of reacting in a very short time, outputting relatively high power [1][2][3]. Usually, the variability of the load also depends on the time constant of the application: the power demand in regional power network fluctuations is periodical with peaks occurring at around the same time of the day and of the year, leading to optimal solutions for power management that account for the deviation from the typical daily or yearly profile [4][5][6][7][8]. When loads are limited to a short period of time (tens of seconds or less), the variability tends to be defined by three main factors: when the demand occurs, its intensity and duration.…”