Generation adequacy assessment for microgrid with ESS

Micky, R. Rohikaa; Lakshmi, R.; Sunitha, R; Ashok, S.

doi:10.1109/poweri.2016.8077182

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…It consists of nine (9) generation sources, each consisting of several generating units. The values of λ and µ are also obtainable from the IEEE RTS standard for each of the generating units via (4) and (5). The states' durations of each generating unit, i.e., TTF and TTR, are sequentially calculated as follows:…”

Section: The Generation Model Load Model and The Risk Modelmentioning

confidence: 99%

“…It did not consider the natural load characteristics of the electricity users. The adequacy assessment of microgrid generation is presented in [5], but it limited its assessment to the use of two reliability indices, namely; loss-of-load probability and expected demand not supplied. It did not consider the information regarding the duration of load loss during energy dispatch.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Reliability Assessment of Generation System

et al. 2020

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Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

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Section: The Generation Model Load Model and The Risk Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive Reliability Assessment of Generation System

et al. 2020

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“…where, VR is the variance produced by MC integration according to (11). As specified in (12), VR is composed of 2 parts, i.e., VR' and R 2 .…”

Section: E Multidimensional Integration Model Of Mg Reliabilitymentioning

confidence: 99%

A Novel Sequential Sampling Algorithm for Reliability Assessment of Microgrids

Song

Wang

et al. 2020

IEEE Access

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Due to the polymorphic uncertainties in microgrids (MGs), prohibitive computational burden is produced in reliability assessment. In this work, a novel sequential sampling algorithm (NSSA) compatible with sequential Monte Carlo (SMC) simulation is developed to overcome the computational burden. First, optimal probability density functions (PDFs) of random variables are worked out based on variation method. Then, optimal PDFs are employed to chronologically simulate the random states of microturbine (MT), photovoltaics (PV) and time varying load with improved computational efficiency. Therefore, the convergence of reliability assessment is accelerated accordingly. A series of case studies have been conducted, and the computational results show that NSSA provides a favorable sampling efficiency and adaptability to system conditions in reliability assessment of MGs. At last, based on optimal PDFs produced by NSSA, dominant joint PDF (DJ-PDF) is defined and employed to quantify the contributions of different scenarios to the reliability indices. Case studies have confirmed that DJ-PDF can provide detailed information for scenario-based reliability analysis. INDEX TERMSMicrogrid, sequential sampling, reliability assessment, computational efficiency, coefficient of variation NOMENCLATURE Random Variables u random variable sampled for random operation hours v random variable sampled for random repairing hours q random variable defined to model time varying load p random variable defined to model intermittency of PV Indices and Sets f p estimate of fp * (p) on the wp-th subinterval of [0, 1] FR(•) reliability test function in terms of u, v, q and p with uniform PDFs FR ' (•) reliability test function in terms of u, v, q and p with varying PDFs GR(•) reliability test function in terms of the hourly states of MT, PV and load FL[•] Functional in terms of fu(u), fv(v), fq(q) and fp(p) I. INTRODUCTION

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Energy management of a microgrid using demand response strategy including renewable uncertainties

Dayalan

Rathinam

2021

International Journal of Emerging Electric Power Systems

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Microgrid is an effective means of integrating multiple energy sources of distributed energy to improve the economy, stability and security of the energy systems. A typical microgrid consists of Renewable Energy Source (RES), Controllable Thermal Units (CTU), Energy Storage System (ESS), interruptible and uninterruptible loads. From the perspective of the generation, the microgrid should be operated at the minimum operating cost, whereas from the perspective of demand, the energy cost imposed on the consumer should be minimum. The main key in controlling the relationship of microgrid with the utility grid is managing the demand. An Energy Management System (EMS) is required to have real time control over the demand and the Distributed Energy Resources (DER). Demand Side Management (DSM) assesses the actual demand in the microgrid to integrate different energy resources distributed within the grid. With these motivations towards the operation of a microgrid and also to achieve the objective of minimizing the total expected operating cost, the DER schedules are optimized for meeting the loads. Demand Response (DR) a part of DSM is integrated with MG islanded mode operation by using Time of Use (TOU) and Real Time Pricing (RTP) procedures. Both TOU and RTP are used for shifting the controllable loads. RES is used for generator side cost reduction and load shifting using DR performs the load side control by reducing the peak to average ratio. Four different cases with and without the PV, wind uncertainties and ESS are analyzed with Demand Response and Unitcommittment (DRUC) strategy. The Strawberry (SBY) algorithm is used for obtaining the minimum operating cost and to achieve better energy management of the Microgrid.

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Generation adequacy assessment for microgrid with ESS

Cited by 3 publications

References 19 publications

Predictive Reliability Assessment of Generation System

Predictive Reliability Assessment of Generation System

A Novel Sequential Sampling Algorithm for Reliability Assessment of Microgrids

Energy management of a microgrid using demand response strategy including renewable uncertainties

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