Day-Ahead Multi-Objective Energy Optimization of a Smart Building in a Dynamic Pricing Scenario

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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A New Energy Optimization Strategy for Pumping Operation in Water Distribution Systems

Volume 6: Energy, Parts A and B ◽

10.1115/imece2012-86463 ◽

2012 ◽

Author(s):

Dhafar Al-Ani ◽

Saeid Habibi

Keyword(s):

Distribution System ◽

Energy Cost ◽

Distribution Systems ◽

Water Distribution ◽

Water Distribution System ◽

Water Distribution Systems ◽

Energy Optimization ◽

Operating Conditions ◽

Optimization Strategy ◽

Multi Objective

As time goes on, more and more operating-modes based on changing demand profiles will be compiled to enrich the range of feasible solutions for a water distribution system. This implies the conservation of energy consumed by a water pumping station and improves the ability for energy optimization. Another important goal was improving safety, reliability, and maintenance cost. In this paper, three important goals were addressed: cost-effectives, safety, and self-sustainability operations of water distribution systems. In this work, the objective functions to optimize were total electrical energy cost, maintenance costs, and reservoir water level variation while preserving the service provided to water clients. To accomplish these goals, an effective Energy Optimization Strategy (EOS) that manages trade-off among operational cost, system safety, and reliability was proposed. Moreover, the EOS aims at improving the operating conditions (i.e., pumping schedule) of an existing network system (i.e., with given capacities of tanks) and without physical changes in the infrastructure of the distribution systems. The new strategy consisted of a new Parallel Multi-objective Particle Swarm optimization with Adaptive Search-space Boundaries (P-MOPSO-ASB) and a modified EPANET. This has several advantages: obtaining a Pareto-front with solutions that are quantitatively equally good and providing the decision maker with the opportunity to qualitatively compare the solutions before their implementation into practice. The multi-objective optimization approach developed in this paper follows modern applications that combine an optimization algorithm with a network simulation model by using full hydraulic simulations and distributed demand models. The proposed EOS was successfully applied to a rural water distribution system, namely Saskatoon West. The results showed that a potential for considerable cost reductions in total energy cost was achieved (approximately % 7.5). Furthermore, the safety and the reliability of the system are preserved by using the new optimal pump schedules.

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Multi-objective energy optimization in grid systems from a brain storming strategy

Soft Computing ◽

10.1007/s00500-014-1474-7 ◽

2014 ◽

Vol 19 (11) ◽

pp. 3159-3172 ◽

Cited By ~ 7

Author(s):

María Arsuaga-Ríos ◽

Miguel A. Vega-Rodríguez

Keyword(s):

Energy Optimization ◽

Grid Systems ◽

Multi Objective

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A hybrid non-dominated sorting genetic algorithm for a multi-objective demand-side management problem in a smart building

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i1.pp559-574 ◽

2020 ◽

Vol 10 (1) ◽

pp. 559

Author(s):

Zineb Garroussi ◽

Rachid Ellaia ◽

El-Ghazali Talbi ◽

Jean-Yves Lucas

Keyword(s):

Goal Programming ◽

Optimization Model ◽

Demand Side Management ◽

Computational Time ◽

Management Problem ◽

Demand Side ◽

Smart Building ◽

Multi Objective ◽

Decision Variables ◽

Feasible Solutions

One of the most significant challenges facing optimization models for the demand-side management (DSM) is obtaining feasible solutions in a shorter time. In this paper, the DSM is formulated in a smart building as a linear constrained multi-objective optimization model to schedule both electrical and thermal loads over one day. Two objectives are considered, energy cost and discomfort caused by allowing flexibility of loads within an acceptable comfort range. To solve this problem, an integrative matheuristic is proposed by combining a multi-objective evolutionary algorithm as a master level with an exact solver as a slave level. To cope with the non-triviality of feasible solutions representation and NP-hardness of our optimization model, in this approach discrete decision variables are encoded as partial chromosomes and the continuous decision variables are determined optimally by an exact solver. This matheuristic is relevant for dealing with the constraints of our optimization model. To validate the performance of our approach, a number of simulations are performed and compared with the goal programming under various scenarios of cold and hot weather conditions. It turns out that our approach outperforms the goal programming with respect to some comparison metrics including the hypervolume difference, epsilon indicator, number of the Pareto solutions found, and computational time metrics.

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