A decentralized load control architecture for smart energy consumption in small islands

Croce, Daniele; Giuliano, Fabrizio; Bonomolo, Marina; Leone, G.; Musca, Rossano; Tinnirello, Ilenia

doi:10.1016/j.scs.2019.101902

Cited by 33 publications

(16 citation statements)

References 29 publications

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“…We refer to the community of buildings participating to the load control program as an Overgrid. In the present paper, we extend previous results on Overgrid in [1,2] by introducing a mechanism that limits the shared knowledge about user habits to preserve their privacy. The main contributions of the paper are:…”

Section: Introductionmentioning

confidence: 58%

“…In this paper, we consider DR schemes for a community of smart buildings, aiming at preserving the privacy related to the single user power consumption. The key idea is to extend Overgrid [1,2], a Peer-to-Peer (P2P)-based control scheme for distributed DR, to control a community of users with power consumption comparable to industrial utilities, but without dealing directly with the single residential user (whose contribution in terms of load reduction is limited anyway). Instead, we introduce Privacy-Preserving Overgrid (PP-Overgrid) which aims at controlling a group of smart buildings as a whole, protecting the privacy of the users through an innovative Secure Multi-Party Computation mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid

Croce

Giuliano

Tinnirello

et al. 2020

Sensors

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In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings.

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Section: Introductionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid

Croce

Giuliano

Tinnirello

et al. 2020

Sensors

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“…The result of this research was to offer households economic and/or ecological incentives to change consumption at specific times. Similar activities, but in relation to asmall group of households on a small island are presented in [24].…”

Section: Introductionmentioning

confidence: 95%

“…In many published research results, conducted in various countries, there are individual approaches to the application of DSM&R at the level of TSO, DSO [6][7][8][9][10][11][12][13][14][15][16] and the final consumer [13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Some of them relate to the narrow scope of DSM&R. For example, the paper [6] presents the impact of aggregators as important entities on the development of DSM&R in Great Britain.…”

Section: Introductionmentioning

confidence: 99%

Household Energy Management

et al. 2021

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Ensuring flexibility and security in power systems requires the use of appropriate management measures on the demand side. The article presents the results of work related to energy management in households in which renewable energy sources (RES)can be installed. The main part of the article is about the developed elastic energy management algorithm (EEM), consisting of two algorithms, EEM1 and EEM2. The EEM1 algorithm is activated in time periods with a higher energy price. Its purpose is to reduce the power consumed by the appliances to the level defined by the consumer. In contrast, the EEM2 algorithm is run by the Distribution System Operator (DSO) when peak demand occurs. Its purpose is to reduce the power of appliances in a specified time period to the level defined by the DSO. The optimization tasks in both algorithms are based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm. The EEM1 and EEM2 algorithms also provide energy consumer comfort. For this purpose, both algorithms take into account the smart appliance parameters proposed in the article: sections of the working devices, power reduction levels, priorities and enablingof time shifting devices. The EEM algorithm in its operation also takes into account the information about the production of power, e.g., generated by the photovoltaic systems. On this basis, it makes decisions on the control of smart appliances. The EEM algorithm also enables inverter control to limit the power transferred from the photovoltaic system to the energy system. Such action is taken on the basis of the DSO request containing the information on the power limits. Such a structure of EEM enables the balancing of energy demand and supply. The possibility of peak demand phenomenon will be reduced. The simulation and experiment results presented in the paper confirmed the rationality and effectiveness of the EEM algorithm.

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“…It would be consequently advantageous to increase the energy efficiency of the whole system via the use of local renewable energy sources (RES) at large scale or by implementing retrofit strategies at the end-user level (including building automation control, small RES plants, etc.) [7,8]. As Papadopoulos [9] affirmed, RES installation is a suitable action to decrease the dependency on fossil fuels and resulting environmental problems, because, in general, they are free from large emissions and locally available.…”

Section: Introductionmentioning

confidence: 99%

Solar and Heat Pump Systems for Domestic Hot Water Production on a Small Island: The Case Study of Lampedusa

et al. 2020

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The achievement of United Nations Sustainable Development Goals, related to energy and resource use, is a critical issue for small and insulated communities. In many minor islands, solar energy is not correctly exploited, and electrical heaters are connected to weak grids with very a high share of generation by fossil fuels. As a consequence, there is the necessity to assess the potential and the suitability of diffusion of alternative systems to avoid dependency on the electrical grid and reduce carbon emissions. This paper aims to evaluate the technical and economic performances of some alternative systems exploiting renewable energy for domestic hot water production. Four different systems were simulated and studied: a heat pump connected to the grid, a heat pump coupled with a photovoltaic plant, a heat pump combined with a solar thermal collector, and a solar thermal plant. Moreover, heat and electricity storages were studied for reducing impacts on the distribution network. The work presents data gathered for a study on energy-retrofit strategies on Lampedusa Island (Italy, 35°30′56″ north (N), 568 degree-days). Finally, to select the most cost-effective plant, an economic analysis of the chosen systems was carried out. This analysis shows that the best net present values are associated with the heat pump (HP) coupled with a stand-alone PV system and a small battery and solar thermal-assisted HP. The shortest payback time was calculated for the solar thermal system.

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A decentralized load control architecture for smart energy consumption in small islands

Cited by 33 publications

References 29 publications

Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid

Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid

Household Energy Management

Solar and Heat Pump Systems for Domestic Hot Water Production on a Small Island: The Case Study of Lampedusa

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