Abstract

New generation electricity network called Smart Grid is a recently conceived vision for a cleaner, more efficient and cheaper electricity system. One of the major challenges of electricity network is that generation and consumption should be balanced at every moment. This paper introduces a new concept for controlling the demand side by the means of automatically enabling/disabling electric appliances to make sure that the demand is in match with the available supplies, based on the statistical characterization of the need. In our new approach instead of using hard limits we estimate the tail probability of the demand distribution and control system by using the principles and the results of statistical resource management.

Highlights

  • The main issue in electricity networks is keeping an almost perfect balance between electricity generation and consumption all the time

  • We investigated the following aspects of the Consumption Admission Control (CAC) algorithm: - Relation of Quality of Service (QoS)(pp) and empirical probability of overconsumption (p p) in the case of different Large Deviation Theory (LDT) bounds; - Model complexity of load time series; - Load shape modification made by CAC; - Number of enabled appliances in the case of different LDT bounds and Central Limit Theorem (CLT); p~ p~

  • We present our investigation regarding the relation of predefined QoS and empirical probability of overconsumption.The ratio of predefined QoS and empirical probability of overconsumption will be denoted by p p kk = pp

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Summary

Introduction

The main issue in electricity networks is keeping an almost perfect balance between electricity generation and consumption all the time. Balance between demand and supply is crucial since oversupply means waste of energy, while undersupply causes performance degradation of the grid parameters (e.g. phase, voltage level, etc.). Demand Side Management (DSM) means a new kind of challenge: system operators should control the power grid in local scale, which is possible by installing intelligent measurement devices (smart meters). The residential sector accounts for about 30 % of total energy consumption [1] and contains time shiftable appliances in high number. In average cars are parked in Europe for more than 90 % of the time [2]; batteries of electric vehicles can serve as an extra storage capacity for the power grid. The algorithm enables/disables shiftable appliances and Tehnički vjesnik 24, 1(2017), 199-207 reshapes the probability density function (pdf) of the aggregate consumption

Related work
Problem formulation and system model
Estimation of the probability of overconsumption
Results and discussion
Relation of QoS and empirical probability of overconsumption
Model complexity of load time series
Load shape modification made by CAC
Number of enabled appliances in the case of different LDT bounds and CLT
Conclusions and future work
Full Text
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