scholarly journals A Mixed Binary Linear Programming Model for Optimal Energy Management of Smart Buildings

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1719 ◽  
Author(s):  
Zahra Foroozandeh ◽  
Sérgio Ramos ◽  
João Soares ◽  
Fernando Lezama ◽  
Zita Vale ◽  
...  
Keyword(s):  
Linear Programming ◽  
Energy Management ◽  
Programming Model ◽  
Electrical Power ◽  
Peak Load ◽  
Electricity Consumption ◽  
Residential Building ◽  
Electricity Sector ◽  
Smart Buildings ◽  
Binary Linear Programming

Efficient alternatives in energy production and consumption are constantly being investigated and conducted by increasingly strict policies. Buildings have a significant influence on electricity consumption, and their management may contribute to the sustainability of the electricity sector. Additionally, with growing incentives in the distributed generation (DG) and electric vehicle (EV) industries, it is believed that smart buildings (SBs) can play a key role in sustainability goals. In this work, an energy management system is developed to reduce the power demands of a residential building, considering the flexibility of the contracted power of each apartment. In order to balance the demand and supply, the electrical power provided by the external grid is supplemented by microgrids such as battery energy storage systems (BESS), EVs, and photovoltaic (PV) generation panels. Here, a mixed binary linear programming formulation (MBLP) is proposed to optimize the scheduling of the EVs charge and discharge processes and also those of BESS, in which the binary decision variables represent the charging and discharging of EVs/BESS in each period. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis are considered. The results point to a 65% reduction in peak load consumption supplied by an external power grid and a 28.4% reduction in electricity consumption costs.

scholarly journals A Mixed-Binary Linear Programming Model for Optimal Energy Management of Smart Buildings

2020 ◽  
Author(s):  
Zahra Foroozandeh ◽  
Sergio Ramos ◽  
Joao Soares ◽  
Fernando Lezama ◽  
Zita Vale ◽  
...  
Keyword(s):  
Linear Programming ◽  
Energy Management ◽  
Programming Model ◽  
Storage System ◽  
Peak Load ◽  
Residential Building ◽  
Electricity Sector ◽  
Pollutant Emissions ◽  
Smart Buildings ◽  
Binary Linear Programming

Efficient alternatives in energy production and consumption are constantly investigated by increasingly strict policies. In this way, the pollutant emissions that contribute to the greenhouse effect reduce and sustainability of the electricity sector increase. With more than a third of the world's energy consumption, buildings have great potential to contribute these sustainability goals. Additionally, with growing incentives in the Distributed Generation (DG) and Electric Vehicle (EV) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this work, an energy management system in SBs are developed to reduce the power demanded of a residential building. In order to balance the demand and power provided by the grid, microgrids such as Battery Energy Storage System (BESS), EVs and Photovoltaic Generation panels (PV) are used. Here, a Mixed Binary Linear Programming formulation (MBLP) is proposed to optimize the charge and discharge scheduling of EVs and also BESS. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis is considered. The results point a 65% reduction in peak load consumption supplied by grid and a 28.4% reduction in electricity consumption costs.

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

Processes ◽  
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.

Optimization of knowledge transferring costs in designing product portfolio: a fuzzy binary linear programming model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nahid Dorostkar-Ahmadi ◽  
Mohsen Shafiei Nikabadi ◽  
Saman babaie-kafaki
Keyword(s):  
Linear Programming ◽  
Programming Model ◽  
Fuzzy Model ◽  
Linear Programming Model ◽  
Product Portfolio ◽  
Ranking Functions ◽  
Content Type ◽  
Binary Linear Programming ◽  
Knowledge Based ◽  
Knowledge Based Economy

Purpose The success of any organization in a knowledge-based economy depends on effective knowledge transferring and then proper use of the transferred knowledge. As is known, optimizing the knowledge transferring costs in a product portfolio plays an important role in improving productivity, competitive advantage and profitability of any organization. Therefore, this paper aims to determine an optimal product portfolio by minimizing the konlwedge transferring costs. Design/methodology/approach Here, a fuzzy binary linear programming model is used to select an optimal product portfolio. The model is capable of considering the knowledge transferring costs while taking into account the human-hours constraints for each product by a fuzzy approach. Using fuzzy ranking functions, a reasonable solution of the model can be achieved by classical or metaheuristic algorithms. Findings Numerical experiments indicate that the proposed fuzzy model is practically effective. Originality/value The contributions of this work essentially consist of considering knowledge transferring costs in selecting an optimal product portfolio and using the fuzzy data which make the model more realistic.

scholarly journals The Carbon Footprint Estimation based on Campus Activities in ITERA (Institut Teknologi Sumatera)

2021 ◽  
Vol 1 ◽  
Author(s):  
Novi Kartika Sari ◽  
Rinda Gusvita ◽  
Deny Juanda Puradimaja
Keyword(s):  
Energy Management ◽  
Carbon Footprint ◽  
Food Waste ◽  
Organic Waste ◽  
Electricity Consumption ◽  
Electricity Sector ◽  
Waste Generation ◽  
Campus Activities ◽  
One Year ◽  
Indirect Source

ITERA (Institut Teknologi Sumatera) is one of young university situated in Lampung Province, Indonesia. In 2018, the average population of campus (students, faculties, and staff) was about 9584 persons. The objective of this paper is to inventory Green House Gasses (GHGs) and then to calculate carbon footprint using equation by UI Greenmetric Guideline 2018 except for fuel and LPG consumption, paper use, and organic waste generation by using GHGs Protocol. Three scopes of GHGs emission were used to classify based on both direct and indirect source(s). The first scope was presented by LPG consumption and fuel consumption of campus’s car. Electricity consumption became scope 2 while the scope 3 involved paper use, organic waste generation including food waste and Yard trimmings, and transportation activities of both motorcycles and cars. The estimated GHGs emission was about 2846.541 metric ton CO2eq during 2018-2019 (one year) with the portion of each scope resulting 10.2%, 62%, and 28.2%, respectively. Electricity usage was being the highest contributor of carbon footprint. The inventory of GHGs will help top management of campus to evaluate and determine some strategies for minimization, reduction, and mitigation notably in electricity sector by some strategies such as substituting electric devices into eco-friendly products, applying energy management ISO 50001, and others.

scholarly journals SCHEDULING OF REPETITIVE CONSTRUCTION PROCESSES WITH CONCURRENT WORK OF SIMILARLY SPECIALIZED CREWS

2020 ◽  
Vol 26 (6) ◽  
pp. 579-589
Author(s):  
Piotr Jaskowski ◽  
Slawomir Biruk
Keyword(s):  
Mathematical Model ◽  
Linear Programming ◽  
Programming Model ◽  
Idle Time ◽  
Work Zones ◽  
Repetitive Processes ◽  
Project Duration ◽  
Binary Linear Programming ◽  
Under Construction ◽  
Construction Works

The highest degree of construction works harmonization can be achieved when planning a repetitive project with processes replicated many times in work zones of identical size. In practice, structural considerations affect the way of dividing the object under construction into zones differing in terms of scope and quantity of works. Due to this fact, individual processes are being allotted to different non-identical zones. Most methods intended for scheduling repetitive processes were developed with the assumption that the work zones are identical and that a particular process cannot be concurrently conducted. To address this gap, the authors put forward a mathematical model of the problem of scheduling of repetitive processes that are repeated in different work zones with the following assumption: several crews of the same type are available, thus particular process can run simultaneously in different locations. The aim of optimization is minimizing the idle time of all crews under the constraint of not exceeding the contractual project duration. The proposed mixed binary linear programming model can be solved using software available in the market or developed into a dedicated system to support decisions. To illustrate the benefits of the model, an example of scheduling interior finishing works was provided.

DEVELOPMENT OF A REMOTE ACCESSIBLE ELECTRICAL POWER MEASURING SYSTEM FOR DOMESTIC CUSTOMER

2015 ◽  
Vol 77 (12) ◽  
Author(s):  
H. M. Yusoff ◽  
Y. Z. Arief ◽  
Z. A. Noorden ◽  
Z. Adzis ◽  
N. Bashir ◽  
...  
Keyword(s):  
Electrical Power ◽  
Electricity Consumption ◽  
Electricity Sector ◽  
Measuring System ◽  
Power Utility ◽  
Total Consumption ◽  
Large Power ◽  
Utility Company ◽  
Message Service ◽  
Distribution Sector

Tenaga Nasional Berhad (TNB) is a power utility company in Malaysia that performs three main thrust in the electricity sector. One of which is the distribution sector. The distribution sector involves other activity associated with the customer. Many initiatives have been introduced by TNB to satisfy all its customers. This research introduces a method of reading electric meter without sending meter readers to the premises of the customers. This method was introduced to large power customers (LPC) to ensure total consumption recorded is accurate. Since 2005, remote metering reading project has been using global packet radio service (GPRS) technology as a medium of communication. However, GPRS communication system at that time has not been widely used. Use of electronic meters is introduced to LPC because this meter can provide current information about electricity consumption. A total of 45,000 users of LPC involved in the installation of electronic meters and global system for mobile communication (GSM) modem in which this system can determine the amount of electric current consumption compared to manual meter reading is applied. In this project, the XBee radio module is used to send data to a GSM modem. From GSM modem, users can get the total electricity consumption through the SMS (short message service) system in their mobile phones. This project aim is to facilitate the customers so that they do not have to waste time going to the Kedai Tenaga (electricity payment counter) to find out the amount of energy consumed during the period. TNB can also collect the metering details without sending personnel to produce monthly bills.

Optimal Energy Scheduling Method under Load Shaping Demand Response Program in a Home Energy Management System

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
Sandeep Kakran ◽  
Saurabh Chanana
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Management System ◽  
Electricity Consumption ◽  
Electricity Sector ◽  
Energy Management System ◽  
Home Energy Management ◽  
Utility Companies ◽  
Consumption Cost

Abstract With the latest smart technologies in the electricity sector, the consumers of electricity got the opportunity to reduce their electricity consumption cost by participating in the demand response programs offered by the utility companies. In this paper, a model of energy management system is introduced for the energy scheduling at home. Residential automatic smart appliances of general use are selected for energy scheduling. The energy controlling device in the EMS model receives the real time electricity price signals from the utility company and schedule the appliances according to the user requirements in such a way so that the electricity consumption cost could be minimized. The appliances are scheduled under real time pricing combined with inclined block rate pricing scheme so that the peak to average ratio of power could be maintained in the satisfactory range. This helps the utility companies in maintaining the system reliability. For the solution of the scheduling problem, particle swarm optimization algorithm is used due to its effectiveness and easy implementation. Finally, the results have been compared and verified against the results achieved by genetic algorithm.

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