scholarly journals Distributed multi-objective scheduling of power consumption for smart buildings

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.

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.

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

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.

scholarly journals Demand Side Management Technique to Optimally Schedule Electric Vehicle Loads in Smart Grid

2019 ◽  
Vol 8 (4) ◽  
pp. 10043-10046
Keyword(s):  
Electric Vehicles ◽  
Smart Grids ◽  
Peak Load ◽  
Demand Side Management ◽  
Management Technique ◽  
Demand Side ◽  
Load Curve ◽  
Load Variations ◽  
Vehicle Loads ◽  
Aggregated Load

Demand-side management (DSM) in smart grids helps the problem of reducing peak load of utilities during certain hourly periods. Based on DSM techniques, peak load hours can be equalized to non-peak load hours therefore users will have less bill payments. In this paper optimal scheduling of Electric Vehicles (EVs) is done based on an objective function formulated to minimize the load variations. Firstly, hourly consumption of load during a day at Koneru Lakshmaiah Education Foundation is considered, EVs load is assumed and flattened the aggregated load curve by optimally scheduling the EVs during off peak hours.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

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