Demand Side Management of Power for Time-of-Use Pricing Based on Particle Swarm Optimization

Chang, Jui-Chuan Della; Wu, Tsung-Hung

doi:10.1109/ickii50300.2020.9318854

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Another approach that aims to reduce cost of consumed electricity is presented by Faia et al [91] who formulate it as a problem of minimizing the energy bill and the cost associated with curtailment of power in a system with a battery and a photovoltaic system. The work in [88,[92][93][94]] also perform appliance scheduling to reduce cost of electricity. Appliance scheduling for smart homes has also been performed by Fatima et al [81] and Abid et al [80] considering a microgrid for homes where instead of optimizing data from single homes, the authors used data from connected smart meters to determine an optimized control scheme for appliances across the grid.…”

Section: Smart Homesmentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

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One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

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Section: Smart Homesmentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

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“…Their goals were to develop a plan to schedule household load, reduce energy costs based on demand response (DR) programs, and integrate RESs and BSS. A particle swarm optimization approach in HEMS is proposed to optimize load scheduling, energy demand, and distributed energy resources considering UC, RESs, BSS, and DR program [13]. In order to overcome user inconvenience, a heuristic-based algorithm is developed to create a user-friendly DSM approach.…”

Section: Introductionmentioning

confidence: 99%

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

et al. 2022

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A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO 2 in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user comfort improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in terms of average delay, indoor air quality, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy bill costs, reduce carbon emissions, mitigate peak loads, and improve user comfort.INDEX TERMS energy management controller, user-comfort, demand shifting, load scheduling, battery storage systems, demand response, solar energy, smart grid. NOMENCLATURE

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Particle Swarm Optimization in Smart Grid Load Management

Ramos

Casella

2022

Proceedings of the 7th Brazilian Technology Symposium (BTSym’21)

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Demand Side Management of Power for Time-of-Use Pricing Based on Particle Swarm Optimization

Cited by 6 publications

References 13 publications

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

Particle Swarm Optimization in Smart Grid Load Management

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