Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid

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.

Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers

“…The architecture of the system of the smart grid that powers smart homes constitute the generation of energy (either using renewable energy sources, traditional energy sources, and nuclear power), transmission of energy to households (including smart homes with their own battery energy storage and electric vehicles) as well as industry and production sector [32,33].…”

An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices

“…In EEM, after describing parameters (2) and taking into account dependencies (1), the need to use heuristic algorithms was established. The rationale behind this decision was the necessity to solve NP-hard problems [8]. The Greedy Randomized Adaptive Search Procedure (GRASP) heuristic algorithm allows the finding of the right solution (not necessarily the optimal one, but one acceptable due to the cost of calculations) and is suitable for solving difficult NP problems.…”

“…One of the conceptual approaches to using energy overproduction is to manage energy demand in such a way that its consumption is as high as possible at times of high generation. Local self-consumption should be increased during a large surplus of energy production from RESs, and self-consumption should be reduced during an energy shortage in the system [8]. There are many concepts employed in controlling the demand for electricity by the use of a smart appliances (SA) described in the scientific literature.…”

“…This information can be taken directly from an intelligent energy meter, solar inverter or other device providing information about the generation level of prosumer sources. Information about the amount of energy in the grid can also be obtained from the voltage level of the consumer grid [8,15]. When there is a need to increase energy consumption or reduce it, the central management unit sends a signal to distributed control devices (DCD) located in a given building or at a location.…”

“…The proposed algorithm for the Elastic Energy Management (EEM) is implemented in DCD. The DCD communicates with individual smart appliances in order to achieve the set goal of reducing or increasing energy consumption [8]. For the proper operation of the EEM algorithm, all necessary data on energy consumption or generation will be provided from an external MQTT broker.…”