Towards an Optimal Residential Home Energy Management in Presence of PV Generation, Energy Storage and Home to Grid Energy Exchange Framework

Iqbal, M. Tariq; Sajjad, Intisar Ali; Manan, Abdul; Waseem, Muhammad; Ali, Amir; Sohail, Amir

doi:10.1109/icomet48670.2020.9073798

Cited by 20 publications

(12 citation statements)

References 28 publications

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“…They are adaptable and can return several solutions to a single problem in a single simulation run. Several well-known optimization techniques have attempted to overcome these issues, including: Genetic algorithm (GA) [25], moth swarm optimization algorithm (MSA) [26],differential evolution (DE) [27], [28],simulated annealing (SA) [29], particle swarm optimization (PSO) [30], [31], spider monkey optimization (SMO) [32], grey wolf optimizer (GWO) [33], gravitational search algorithm (GSA), [34],fire fly algorithm (FFA) [35], spiral optimization algorithm (SOA) [36], harmony search algorithm (HSA) [37], [38], harris hawks optimization (HHO) [39], squirrel search algorithm (SSA) [40], artificial bee colony (ABC) [41], sine-cosine algorithm (SCA) [42], differential evolution (DE) [43], bacterial forging algorithm (BFA) [44], Fluid search optimization (FSO) [45], improved ABC (IABC) [46], modified BFA (MBFA) [47], hybrid hierarchical evolution (HHE) [48], whale optimization algorithm (WOA) [49], chaos turbo PSO (CTPSO) [50], hybrid particle swarm gravitational search algorithm (PSOGSA) [51], multi-objective PSO (MOPSO) [52], new global PSO (NGPSO) [53], quantum inspired glowworm swarm optimization (QGSO) [54], multi-objective DE based PSO (MODE/PSO) [55], combination of continuous greedy randomized adaptive search procedure and modified differential evolution (CGRASP-MDE), combination of continuous greedy randomized adaptive search procedure and self-adaptive differential evolution (C-GRASP-SaDE) [56], successful history-based adaptive DE variants with linear population size reduction (L-SHADE) and improved L-SHADE (IL-SHADE) [57].…”

Section: Literature Reviewmentioning

confidence: 99%

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

et al. 2022

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This paper deals with the combined economic emission load dispatch (CEELD) problem with and without the integration of renewable energy sources (RESs), in some more rational test scenarios of single CEELD and multi-objective CEELD (MO-CEELD) optimization. Hence, an efficient and coherent approach is presented to minimize the generation and emission cost using one of the bio-inspired metaheuristic algorithms named flower pollination algorithm (FPA). The evolution of a power system along with the integration of RESs demands equal advancement in the operation and control algorithms of the power grid. Therefore, the proposed approach in this paper offers an evolutionary single and multiobjective optimization process based on a bio-inspired FPA. Further, it has been validated by achieving the best compromise solution (BCS) using the Pareto categorizing process and fuzzy membership function. Moreover, different study cases comprising eleven and fifteen thermal units with and without considering RESs are tested with the proposed technique. Finally, the effectiveness of the proposed approach is tested by comparing the simulation results with some already existing techniques in terms of overall fuel and emission cost. Significantly, it has been noticed from the results that it outperforms all the previously presented approaches like PSO, DE, GSA, AEO, BA, and dBA, thus justifying its applicability.INDEX TERMS Combined economic emission dispatch, fuzzy membership function, multi-objective flower pollination algorithm, Pareto categorizing process, price penalty factor approach.

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Section: Literature Reviewmentioning

confidence: 99%

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

et al. 2022

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“…To incorporate this feature into already existing PBO algorithm we devised a unique two-tier global search stage in which 1 bear among 30% of least fit bears is selected to undergo global search based on their sense of smell mimicking its scavenging capabilities in extreme food shortage. This behavior is modeled using Levi flight equation (18) taken from [55] as shown below.…”

Section: Improved Pbomentioning

confidence: 99%

“…The outcomes of these problems are beneficial to initiate different demand response actions and demand side flexibility assessment [7][8][9][10]. Several prominent optimizations algorithms that tried to solve these problems include: Genetic algorithm (GA) [11], simulated annealing (SA) [12], differential evolution (DE) [13,14], moth swarm optimization algorithm (MSA) [15], spider monkey optimization (SMO) [16], particle swarm optimization (PSO) [17,18], grey wolf optimizer (GWO) [19], gravitational search algorithm (GSA), fire fly algorithm (FFA) [20,21], harmony search algorithm (HSA) [22,23], spiral optimization algorithm (SOA) [24], squirrel search algorithm (SSA) [25], harris hawks optimization (HHO) [26], sine-cosine algorithm (SCA) [27], artificial bee colony (ABC) [28], bacterial forging algorithm (BFA) [29], flower pollination algorithm (FPA) [30], differential evolution (DE) [31], modified flower pollination algorithm (FPA) [32], , Fluid search optimization (FSO) [33], improved ABC (IABC) [34], modified BFA (MBFA) [35], whale optimization algorithm (WOA) [36], hybrid hierarchical evolution (HHE) [37], hybrid particle swarm gravitational search algorithm (PSOGSA) [38], chaos turbo PSO (CTPSO) [39], new global PSO (NGPSO) [40], multiobjective PSO (MOPSO) [41], multi-objective DE based PSO (MODE/PSO) [42] quantum inspired glowworm swarm optimization (QGSO) [43], combination of cont...…”

Section: Introductionmentioning

confidence: 99%

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

et al. 2021

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This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.

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“…The development of technologies related to the internet of things (IoT), artificial intelligence (AI), blockchain and big data encourages power system operators to modernize power grid and smart city development [10][11][12]. It is always significant to have an efficient power system as greater losses distress the overall economy [13][14][15]. An efficient power system that meets demand uncertainties is helpful to generate deferent flexible options to initiate demand response (DR) actions [16,17].…”

Section: Introductionmentioning

confidence: 99%

SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities

et al. 2021

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Renewable energy-based distributed generators (DGs) are gaining more penetration in modern grids to meet the growing demand for electrical energy. The anticipated techno-economic benefits of these eco-friendly resources require their judicious and properly sized allocation in distribution networks (DNs). The preeminent objective of this research is to determine the sizing and optimal placing of DGs in the condensed DN of a smart city. The placing and sizing problem is modeled as an optimization problem to reduce the distribution loss without violating the technical constraints. The formulated model is solved for a radial distribution system with a non-uniformly distributed load utilizing the selective particle swarm optimization (SPSO) algorithm. The intended technique decreases the power loss and perfects the voltage profile at the system’s nodes. MATLAB is used for the simulation, and the obtained results are also validated by the Electrical Transient Analysis Program (ETAP). Results show that placing optimally sized DGs at optimal system nodes offers a considerable decline in power loss with an improved voltage profile at the network’s nodes. Distribution system operators can utilize the proposed technique to realize the reliable operation of overloaded urban networks.

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Towards an Optimal Residential Home Energy Management in Presence of PV Generation, Energy Storage and Home to Grid Energy Exchange Framework

Cited by 20 publications

References 28 publications

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities

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