Abstract
Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of Renewable Energy Sources (RES), the occurrence of the phenomenon of peak demand, as well as exceeding the voltage level above the nominal values in a smart grid makes it justifiable to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research is based on the Elastic Energy Management (EEM) algorithm for smart appliances (SA) using IoT (Internet of Things) technology. The data for the algorithm was obtained from a message broker that implements the Message Queue Telemetry Transport (MQTT) protocol. The complexity of selecting power settings for SA in the EEM algorithm required the use of a solution that is applied to the NP difficult problem class. For this purpose, the Greedy Randomized Adaptive Search Procedure (GRASP) was used in the EEM algorithm. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation.
Highlights
Modern low-voltage power distribution networks are used to supply electricity to the end user, and to receive electricity from local, prosumer Renewable EnergySources (RES) [1,2]
It was assumed that the Energy Management algorithm (EEM) algorithm would be designed to take action to lower or increase the load in the power grid when the ranges defined by the Distribution System Operator (DSO) are exceeded: UDSO_MI N ≤ USG
The concept of the system assumed the use of smart appliances (SA) devices to control the amount of energy consumption from the power grid
Summary
Modern low-voltage power distribution networks are used to supply electricity to the end user, and to receive electricity from local, prosumer Renewable Energy. This article will present the concept of the use and integration of smart home appliances and consumer devices in local low-voltage balancing of a distribution system in order to counteract the rise and fall of a network voltage. The article [27] proposes a new model, called the explicit-duration hidden Markov model with differential observations, for detecting and estimating the loads of individual household appliances on the basis of aggregated power signals collected by ordinary smart meters This model was used to manage energy demand in a residential environment. The main aim of the article is to present the concept of an electricity management system in a smart home using SAs. The proposed system will use a control algorithm called the Elastic Energy Management algorithm (EEM). The Message Queue Telemetry Transport (MQTT) protocol was created for the correct implementation of the algorithm on the Raspberry Pi 2 platform
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