Connecting to Smart Cities: Analyzing Energy Times Series to Visualize Monthly Electricity Peak Load in Residential Buildings

This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.

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Using Forecasted Daily Maximum Temperatures to Control a Chiller Thermal Storage System

Volume 6B: Energy ◽

10.1115/imece2018-88307 ◽

2018 ◽

Author(s):

Christopher Baldwin ◽

Cynthia A. Cruickshank

Keyword(s):

Storage Tank ◽

Residential Buildings ◽

Storage System ◽

Peak Load ◽

Cost Savings ◽

Thermal Storage ◽

The United States ◽

Daily Maximum ◽

Cooling Load ◽

Control Scheme

Residential buildings in Canada and the United States are responsible for approximately 20% of secondary energy consumption. Over the past 25 years, air conditioning has seen the single largest increase of any residential end use. This load currently places a significant peak load on the electrical grid during later afternoon periods during the cooling season. One method to reduce or eliminate this peak load being placed in the grid is the use of a chiller coupled with a thermal storage system. The chiller operates during off-peak periods, predominately over-night to charge the thermal storage tank, and the stored cooling potential is realized to meet the cooling loads during peak periods. In previous studies, the use of a chiller has seen a reduction in annual operating costs, however a significant increase in energy occurs as a result of decreased performance of the chiller. To improve system performance, a new control scheme was developed, which uses the forecasted daily high for the next day to predict the cooling load for the day during peak periods for the day. The predicted cooling load is then used as the set-point for the cold thermal storage tank, allowing the peak cooling load to be met using stored cooling potential. This control scheme was implemented into a modelled house located in each of the 7 major ASHRAE zones, with a storage tank with a previously found optimal tank volume. Across each of the locations, a reduction in annual utility costs and overall energy required to meet the building loads observed, with the total cost savings between 0.3% and 1.5% and total electricity required to meet the cooling demand decreasing by as much as 10.2%.

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Numerical Simulation of Phase Change Material Composite Wallboard in a Multi-Layered Building Envelope

Volume 6: Energy, Parts A and B ◽

10.1115/imece2012-89526 ◽

2012 ◽

Author(s):

Stephen D. Zwanzig ◽

Yongsheng Lian ◽

Ellen G. Brehob

Keyword(s):

Energy Consumption ◽

Boundary Conditions ◽

Phase Change ◽

Latent Heat ◽

Residential Buildings ◽

Peak Load ◽

The United States ◽

Building Envelope ◽

Latent Heat Storage ◽

Exterior Boundary

Residential buildings account for a large portion of total energy consumption in the United States. Residential energy usage can be dramatically reduced by improving the efficiency of building envelope systems. One such method is to incorporate thermally massive construction materials into the building envelope. This benefits building operation by reducing the energy requirement for maintaining thermal comfort, downsizing the AC/heating equipment, and shifting the peak load from the electrical grid. When impregnated or encapsulated into wallboard or concrete systems, phase change materials (PCMs) can greatly enhance their thermal energy storage capacity and effective thermal mass. In this work we numerically study the potential of PCM on energy saving for residential homes. For that purpose we solve the one-dimensional, transient heat equation through the multi-layered building envelope using the Crank-Nicolson discretization scheme. The latent heat storage of the PCM was accounted for with a phase fraction in a latent heat source term. Using this code we examine a PCM composite wallboard incorporated into the walls and roof of a typical residential building across various climate zones. The PCM performance was studied under all seasonal conditions using the latest typical meteorological year (TMY3) data for exterior boundary conditions. Comparisons were made between different PCM wallboard locations. Our work shows that there is an optimized location for PCM placement within building envelope surfaces dependent upon the resistance values between the PCM layer and the exterior boundary conditions. We further identified the energy savings potential by comparing the performance of the PCM wallboard against the performance of a building envelope without PCM. Our study shows that PCM composite wallboard can reduce the energy consumption in summer and winter and can shift the peak electricity load in the summer.

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Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization Approach

Processes ◽

10.3390/pr9020257 ◽

2021 ◽

Vol 9 (2) ◽

pp. 257

Author(s):

Zahra Foroozandeh ◽

Sérgio Ramos ◽

João Soares ◽

Zita Vale

Keyword(s):

Linear Programming ◽

Energy Management ◽

Residential Buildings ◽

Peak Load ◽

Electricity Consumption ◽

Mixed Integer ◽

Total Energy Consumption ◽

Smart Building ◽

Multi Objective ◽

Consumption Cost

Generally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction.

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Home Energy Management System (HEMS): Coupled Flexible Load Management in Homes

10.1115/detc2021-71680 ◽

2021 ◽

Author(s):

Yilin Jiang ◽

Li Song ◽

Janet K. Allen ◽

Farrokh Mistree

Keyword(s):

Decision Support ◽

Energy Management ◽

Management System ◽

Residential Buildings ◽

Peak Load ◽

Cost Savings ◽

Load Management ◽

Energy Management System ◽

Home Energy Management ◽

Home Energy Management System

Abstract Electricity suppliers have introduced time-of-use (TOU) metering and pricing in residential buildings in recent years. By increasing the price of electricity during on-peak hours (e.g., 2 pm to 7 pm in summer months), suppliers expect to regulate the energy usage from homeowners when the grid is near capacity. Therefore, homeowners are motivated to shift the load by moving their home electricity use from on-peak hours to off-peak hours for utility cost savings. However, peak load management is another factor that needs to be considered, since a higher peak load might cause other penalties, such as making suppliers change their current tariff policy in the next paying period since the grid needs to fulfill a higher demand. In this paper we explore the Home Energy Management System (HEMS) Strategy for homeowners who are considering saving money by reducing/avoiding the on-peak hour electricity usage while reducing peak load. A multi-goal scheduling problem is solved by constructing a coupled compromise decision support problem in which a water heater is coupled with flexible, non-thermal appliances such as a washing machine. To address these multiple goals, we use Decision Support Problem (DSP) construct. A use case simulation shows that our scheduler can make a reasonable tradeoff between two conflicting goals, helping the homeowner save money while maintaining low peak demand.

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A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances

Sustainability ◽

10.3390/su12229686 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9686

Author(s):

Bilal Naji Alhasnawi ◽

Basil H. Jasim ◽

Maria Dolores Esteban ◽

Josep M. Guerrero

Keyword(s):

Energy Management ◽

Power Factor ◽

Demand Response ◽

Energy Demand ◽

Smart Cities ◽

Residential Buildings ◽

Electrical Energy ◽

Base Station ◽

Smart Devices ◽

Demand Response Programs

There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day.

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Distributed multi-objective scheduling of power consumption for smart buildings

Energy Informatics ◽

10.1186/s42162-019-0080-4 ◽

2019 ◽

Vol 2 (S1) ◽

Author(s):

Marvin Nebel-Wenner ◽

Christian Reinhold ◽

Farina Wille ◽

Astrid Nieße ◽

Michael Sonnenschein

Keyword(s):

Power Consumption ◽

Smart Grids ◽

Residential Buildings ◽

Cost Optimization ◽

Peak Load ◽

Load Management ◽

Management Technique ◽

Smart Buildings ◽

Multi Objective ◽

The Cost

Abstract Load management of electrical devices in residential buildings can be applied with different goals in the power grid, such as the cost optimization regarding variable electricity prices, peak load reduction or the minimization of behavioral efforts for users due to load shifting. A cooperative multi-objective optimization of consumers and generators of power has the potential to solve the simultaneity problem of power consumption and optimize the power supply from the superposed grid regarding different goals. In this paper, we present a multi-criteria extension of a distributed cooperative load management technique in smart grids based on a multi-agent framework. As a data basis, we use feasible power consumption and production schedules of buildings, which have been derived from simulations of a building model and have already been optimized with regard to self-consumption. We show that the flexibilities of smart buildings can be used to pursue different targets and display the advantage of integrating various goals into one optimization process.

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A DIY Approach for the Design of Mission-Planning Architecture Using Autonomous Task–Object Mapping and the Deployment Model in Mission-Critical IoT Systems

Sustainability ◽

10.3390/su11133647 ◽

2019 ◽

Vol 11 (13) ◽

pp. 3647 ◽

Cited By ~ 6

Author(s):

Shabir Ahmad ◽

Faisal Mehmood ◽

Do-Hyeun Kim

Keyword(s):

Smart Cities ◽

Peak Load ◽

Semantic Knowledge ◽

Mission Planning ◽

Time Behavior ◽

Task Mapping ◽

Critical System ◽

Mission Critical ◽

Object Mapping ◽

Load Conditions

Recently, the World Economic Forum (WEF) highlighted mission-critical Internet of Things (MC-IoT) applications as one of the six enablers of sustainable development of smart cities. MC-IoT refers to systems which exacerbate properties like availability, reliability, safety, and security in an application environment of heterogeneously connected physical things and virtual things whose failure could lead to severe consequences such as life loss. The sole characteristic of the mission-critical system is its compliance with real-time behavior. As a result of the critical nature of these systems, it is essential to design the system with sufficient clarity so that none of the requirements is misinterpreted. For this, the involvement of non-technical stakeholders and policymakers is crucial. Previous studies on mission-critical structures mainly focus on the communication overheads, and overlook the design and planning of them. Therefore, in this paper, we present an architecture which enables mission planning on a do-it-yourself plane. We present a task–object mapping and deployment model where different tasks are mapped onto virtual objects and deployed on physical hardware in a task–object pair. The system uses semantic knowledge for autonomous task mapping and suggestions to further aid the orchestration of the process. The tasks are autonomously mapped onto the devices based on the correlation index; this is computed based on the attribute similarities, thus making the system flexible. The performance of the proposed architecture is evaluated with different key performance indicators under different load conditions and the response time is found to be under a few seconds even at peak load conditions.

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Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass

Energy ◽

10.1016/j.energy.2015.02.011 ◽

2015 ◽

Vol 82 ◽

pp. 1057-1067 ◽

Cited By ~ 56

Author(s):

W.J.N. Turner ◽

I.S. Walker ◽

J. Roux

Keyword(s):

Residential Buildings ◽

Peak Load ◽

Thermal Mass ◽

Electric Load ◽

Load Shifting

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Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction

Energies ◽

10.3390/en13040862 ◽

2020 ◽

Vol 13 (4) ◽

pp. 862 ◽

Cited By ~ 2

Author(s):

Ah-Yun Yoon ◽

Hyun-Koo Kang ◽

Seung-II Moon

Keyword(s):

Demand Response ◽

Residential Buildings ◽

Peak Load ◽

Electric Utility ◽

Hvac Systems ◽

Commercial Buildings ◽

Pricing Strategy ◽

Load Reduction ◽

Level Model ◽

Photovoltaic Generators

Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.

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