Optimal Generation Scheduling with Demand Side Management for Microgrid Operation

Wynn, Sane Lei Lei; Boonraksa, Terapong; Marungsri, Boonruang

doi:10.1109/ieecon51072.2021.9440356

Cited by 16 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing HBG with GA and BFA, PAR, and wait time can be decreased via HBG price. Ref [8] examined a community-driven method for optimizing energy consumption. By integrating renewable power resources, the paper was able to achieve a high level of satisfaction for consumers and less power consumption by utilizing particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Legal Implications of Smart Grid Energy Management on System: Sustainability, Privacy, Security, and Regulatory Challenges

Wen¹,

Xu²,

Dehghan³

2023

Preprint

View full text Add to dashboard Cite

The use of smart grids has enabled a number of planning methods to be developed to optimize energy costs, Peak to Average Ratios (PARs), and consumer satisfaction for load management in industrial, commercial, and domestic sectors. From a technical point of view, achieving optimal outcomes requires Demand Side Management (DSM). In smart grids, utility companies and electric users communicate two-way using digital technology to make a sustainable and economic system. This paper proposes a novel framework within which an Energy Management Controller (EMC) keeps track of each appliance, its operational time, and the costs associated with them. Customers of smart grids are motivated to shift their Off-Peak Hours (OPH) from Peak Hours by presenting incentives in OPH. The metering devices would also save customers costs by preventing load shifting between high- and low-cost periods. In addition, the study proposes the bacterial foraging algorithm and grasshopper optimization algorithm for lessening power price and PAR without compromising user comfort (UC) through appliance planning. The simulation results on a practical test system advocate the high effectiveness and reliable performance of the proposed model.

show abstract

Section: Introductionmentioning

confidence: 99%

Legal Implications of Smart Grid Energy Management on System: Sustainability, Privacy, Security, and Regulatory Challenges

Wen¹,

Xu²,

Dehghan³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [52] and [53] an online and real-time energy management controller is suggested for grid-connected sustainable smart home considering UC. The authors in [54] have proposed a strategy for local energy production management, RESs usage efficiency, and reducing fuel-based generation consumption considering demand scheduling during peak hours for both grid-connected and island-connected modes. For both the direct and indirect distribution modes, particle swarm optimization is used to schedule the generation of distributed energy resources.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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

show abstract

“…e former adopts the predicted data during every six hours, and the latter deals with the energy storage value of the prediction period. Unit commitment of DGs using the PSO has also been addressed in the literature [33]. In [34], a stochastic optimization models various uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Operation of the Microgrids Including Active Loads and Distributed Generation Constrained to Flexibility and Reliability Indices

Shahi

Nafar

Simab

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

This paper presents a flexible-reliable operation strategy for clean microgrids (MGs) consisting of power sources, energy storage systems (ESSs), and responsive loads. The proposed strategy attempts to provide the minimum expected operating cost for the MG, power sources, predicted pollutant emission, and expected energy not-supplied (EENS) because of an N − 1 event considering different applications. The objective function of the problem is subject to several constraints including AC optimal power flow equations, flexibility and reliability boundaries, and model of power sources and active loads. The Pareto optimization based on the weighted sum method is then incorporated to obtain a single-objective model. Probabilistic programming is also used to model the uncertainties of load, renewable energy, electrical energy price, and MG equipment accessibility. The ant-lion and crow search algorithms are merged to solve the problem and find a reliable optimal solution. A standard MG is employed to test the proposed strategy and indicate its capability in enhancing technical and economic indicators of the MG while ensuring that the MG is clean.

show abstract

Optimal Generation Scheduling with Demand Side Management for Microgrid Operation

Cited by 16 publications

References 9 publications

Legal Implications of Smart Grid Energy Management on System: Sustainability, Privacy, Security, and Regulatory Challenges

Legal Implications of Smart Grid Energy Management on System: Sustainability, Privacy, Security, and Regulatory Challenges

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

Probabilistic Operation of the Microgrids Including Active Loads and Distributed Generation Constrained to Flexibility and Reliability Indices

Contact Info

Product

Resources

About