Optimal Scheduling for Campus Prosumer Microgrid Considering Price Based Demand Response

Muqeet, Hafiz Abdul; Ahmad, Aftab

doi:10.1109/access.2020.2987915

Cited by 72 publications

(54 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An optimal system is adopted for the campus microgrid by calculating 850$/kWh as a daily electricity cost which is an optimal solution for summer, whereas, 1161.9$/kWh is an optimal case in case of winter. However, savings analyzed with the previous case scenarios [5] that 38.3% savings analyzed for summer, comparatively 3.3% higher than the previous case scenario and 31.3 % saving analyzed for the winter case, comparatively 2.3% higher than the previous case. It is analyzed that the optimal BESS solution with the incorporation of WT system, savings are analyzed comparatively higher which is best possible solution for the university campus microgrid.…”

Section: % Yearmentioning

confidence: 59%

“…The campus µG considered rated power of wind to be same as compared to the summer season. The LCOE calculated for the wind to be 0.0703 $/kWh which is 31.3 % less as compared to the 29% [5] for the winter season. It is analyzed with the integration of WT system with ESS, Solar PV, Diesel Generator and the grid connection, 2.3 % saving increased in the electricity net cost for the UET Taxila campus.…”

Section: Winter Season Casementioning

confidence: 71%

“…Various kinds of energy management systems require secure interaction among prosumer and conventional grid to operate the control devices intelligently [4]. However, the distribution network includes a group of µGs wherein every µG acts as a selfgoverning distribution node, consequently, µGs consist of onsite DGs, energy storage systems, and DR programs which may play a significant role in minimizing the network overloading and electricity cost [5], [6]. The aforementioned benefits are greatly reported for multiple µGs with excessive loads.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Scheduling And Sizing of Campus Microgrid Considering Demand Response And Economic Analysis

Pansota

Javed

Muqeet

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Current energy systems face multiple problems related to inflation in the energy prices, reduction of fossil fuels, and greenhouse gas emissions in disturbing the comfort zone of energy consumers and affordability of power for large commercial customers. This kind of problem can be alleviated with the help of optimal planning of Demand Response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of green DGs. To this end, a real-time-based campus is considered that is currently providing its loads from the utility grid only. Yet, according to the proposed given scenario, it contains the solar panels and wind turbine as a non-dispatchable DG while a diesel generator is considered as a dispatchable DG. It also incorporates the energy storage system with the optimal sizing of BESS to tackle with multiple disturbances that arise from solar radiations. Results: The resultant problem of linear mathematics has been simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of EMS minimizes the grid electricity costs by 31% in case of summer and 38% in case of winter respectively, while the reduction of GHG emissions per day is 780.68 and 730.46 kg for the corresponding summer and winter seasons. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs is also observed. Conclusion: The substantial environmental and economic benefits compared to the present case prompt campus owners to put investment in the DGs and to install large-scale energy storage.

show abstract

Section: % Yearmentioning

confidence: 59%

Section: Winter Season Casementioning

confidence: 71%

mentioning

confidence: 99%

See 1 more Smart Citation

Scheduling And Sizing of Campus Microgrid Considering Demand Response And Economic Analysis

Pansota

Javed

Muqeet

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Different types of batteries are used in microgrid operation for smooth function. As compared to other types of batteries the initial cost of lithium-ion batteries is high but more reliable with a competitive number of life cycles [30].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Muqeet

Munir

Ahmad

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

show abstract

“…The large-scale penetration of DG, and more generally of distributed energy resources, into conventional electricity systems has posed numerous challenges [4], for operators involved in the operation and maintenance of modern grids, e.g., islanding detection [5], voltage and frequency regulation [6], harmonic distortion [7], electromagnetic interference [8], optimal demand side management of prosumers [9], as well as low-environmental impact routing of overhead power lines for the connection of renewable energy plants [10]. Yet, a large number of opportunities are offered by future proactive [11] and transactive [12] energy systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

et al. 2020

View full text Add to dashboard Cite

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this paper is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks.

show abstract

Optimal Scheduling for Campus Prosumer Microgrid Considering Price Based Demand Response

Cited by 72 publications

References 53 publications

Scheduling And Sizing of Campus Microgrid Considering Demand Response And Economic Analysis

Scheduling And Sizing of Campus Microgrid Considering Demand Response And Economic Analysis

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

Contact Info

Product

Resources

About