Multimodal energy management system for residential building prosumers utilizing various lifestyles
Introduction
Smart grid technologies [1] developed for the management of distributed energy generation [2] force the power systems to a different delivery operation than the conventional one [3]. Alongside the power systems, consumers’ behavior patterns and energy profiles of buildings undergo significant transformations [4]. While conventional buildings play the sole role of passive consumers, modern ones progressively adopt a more active operation resulting in a hybrid role – concurrent active and passive operation – coined as prosumers. For instance, modern buildings’ operations extend to energy storage, retrieval of stored energy, generation, and sale of - usually excess - energy to the utility or other buildings nearby. Under this new role, modern buildings act as prosumers, i.e., sophisticated active agents, contributing significantly to the decision-making process of consumption and purchase of energy [3].
Regarding prosumer's energy management, new energy challenges are being imposed by the rapid diffusion of smart home technologies [5,6]. The smart home concept includes a plethora of interconnected electric-powered devices [7], such as hubs, routers, cameras, computer servers, and sensors with the goal of implementing home automation [8], [9], [10]. It also includes smart technologies such as lights, speakers, TV screens, electric vehicles (EVs), and touchscreens [11]. The aforementioned devices generate an additional energy demand [12] that leads to higher carbon emissions and electricity bills. Considering that occupants are not capable of continuously managing their energy consumption at every time instance, the need for use of autonomous energy management systems (EMS) inevitably arises. In particular, an effective decision-making system that manages the battery's state of charge (SOC) and is provided with data regarding the real-time electricity prices and the produced renewable energy can improve the consumption performance of the prosumer.
The current literature offers a diverse range of works regarding residential energy management having various operational optimization objectives, but it fails to focus on meeting multiple objectives including social conditions and lifestyle preferences. For instance, a large set of them aims at reducing the energy production costs and consequently greenhouse gas emissions such as [13] by maximizing the use of renewable energy. The objective in [14] is an increased resilience of the EMS with the use of an EV and Distributed Energy Generators (DEG) as emergent generation backup when a grid outage occurs. Similar works target energy cost reduction through various methods such as model-free deep reinforcement learning [15], and a mixed-integer linear programming approach [16]. Others aim at the extension of the life expectancy of the battery energy system (BES) such as in [17] which utilizes a fuzzy logic-based controller and [18] which targets the most optimal use of DEGs. Some EMSs aim at multiple objectives such as peak shaving [19] and simultaneously total electricity cost minimization, for instance [20,21]. There are also systems that incorporate privacy protection of the residential consumers by utilizing several local controllers under a central controller [22], or through minimization of information leakage at the expense of higher energy costs [23]. Furthermore, two main ways are being adopted to accomplish the aforementioned objectives, and are related to the existing controllable variables in a residential house system. The first way is managing the charging and discharging process of BES such as [24] aiming at reducing energy cost. The second way is the scheduling of the load demand in controllable appliances for instance by the use of fuzzy logic [25,26], or a rolling time framework-based decision-making approach [27]. This second way of scheduling load demand includes the control of the HVAC system which in the case of [28] it utilizes a dynamic demand response controller to change the set-point temperature. Prior to the deployment of the EMS, its performance needs to be tested via simulations and respectively validated. For this reason, real-world data or specialized software is utilized to simulate the residential buildings and include various energy sources and loads found in modern residential prosumers. For instance, [29] and [30] simulate a photovoltaic (PV) plant to reduce energy cost with linear programming and fuzzy logic respectively. On the other hand, the authors in [31] and [21] utilize real-world data to validate an adaptive stochastic controller and a neighborhood EMS respectively. Another work in [32] uses recorded data to simulate both solar and wind energy. Another part of simulating a residential building in an energy market is the electricity pricing schemas such as the dynamic prices used in [33] and [34]. Instead of time-based pricing, the work in [35] applies distribution locational marginal prices. Specifically, the EMS in an energy market can sell the renewable energy produced to the utility grid [36] or other consumers [37].
Although there is extensive literature on EMS that are capable of adapting to price and weather conditions input, little work has been done to address social conditions and personal lifestyle conditions such as an emergency, extra energy demand due to social celebrations, occupancy variability, and power-saving preferences of an occupant. The proposed multimodal management system combines the flexibility of fuzzy logic decision-making and the multiple objectives that coexist in hierarchical designed systems. Not only does it reduce the electricity bill but also satisfies the needs of the occupant regarding robustness during particular time periods. In detail, the proposed EMS is adaptable to energy emergencies, social celebrations, and occupant's power-saving preferences, thus allowing the implementation of various lifestyles that the resident may follow. This equips the proposed EMS with the capability of adapting to real-time prices, weather conditions, and social conditions.
The current work includes results of residential buildings simulation and a benchmark single-mode EMS. The main contributions of this paper are 1) Proposal of a multimodal fuzzy logic EMS that allows implementation of various lifestyles by being adjustable to social conditions such as emergencies, local celebrations, occupancy, and occupant's power-saving preferences, and 2) Testing and validation with extended simulation cases using the GridLAB-D software, which uses real-world weather data and real-time electricity prices to showcase the performance of the multimodal EMS.
This paper is structured as follows: In Section 2, the mechanism of the multimodal fuzzy logic EMS is presented and analyzed, where each operational mode of the EMS is fully explained. In Section 3, the simulation setup and the metrics used for benchmarking purposes are described. In Section 4, we present the results together with the main conclusions. The last section, i.e., Section 5, summarizes the main points of the paper.
Section snippets
Residential System Architecture
For our study, we developed the residential hybrid energy system depicted in Fig. 1. The arrows in Fig. 1 indicate the energy flow between its components, which are PVs, Wind Turbine Generators (WTGs), BES, the utility grid, and an EV that follows a probabilistic energy profile. It should be noted that the arrival and departure times of EV are determined by a probability distribution function, and more specifically the default probabilistic profile provided by the online tutorial (Module 5) of
Simulation Setup
Fig. 8 depicts the block diagram for the simulation setup of the residential building testing of the presented multimodal EMS. First of all, the building simulation requires weather data which we extracted from the online repository of the National Solar Radiation Database [40]. Specifically, we utilized weather data of three geographical regions located within the USA: (i) Tucson International Airport in Arizona, (ii) Block Island State Airport in Rhode Island, and (iii) Montpelier Airport in
Simulation Results
The house simulation and the execution of the two EMS, i.e. multimodal and single-mode systems, run for the years 2017, 2018, 2019 in the regions Arizona, Rhode Island, and Vermont. In Fig. 11, we present the power balance throughout a day (June 15th, 2019) for a better representation of our system's power dynamics. Specifically, the positive power values indicate the energy derived from different sources, i.e. solar, wind, BES discharging, and utility purchasing. The energy consumed is
Conclusion
The excess energy demands of the new smart home technologies impose new challenges to the building prosumers. Considering also the difficulty of energy consumption management by the occupants on a regular basis, the need for autonomous EMS arises. In particular, an effective decision-making system provided with real-time data can improve the consumption performance of the prosumer.
In this paper, a multimodal EMS is presented for residential prosumers. The proposed EMS is equipped with multiple
CRediT authorship contribution statement
Pantelis Dimitroulis: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Visualization. Miltiadis Alamaniotis: Conceptualization, Resources, Writing – review & editing, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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