Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

ElAzab, Heba-Allah I.; Swief, R.A.; El-Amary, Noha H.; Temraz, H.K.

doi:10.3390/en11051140

Cited by 13 publications

(11 citation statements)

References 28 publications

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“…Results synopsis for GSA and Dynamic Programming (DP) (implicating emission cost and losses of the system). Considering losses of IEEE 30 bus system and comparing the obtained results in Table 7 with Table 8 in Reference [31] without considering transmission line losses, it is found that:…”

Section: Scenario 4 (Rers Pevs and The Three Coal-fired Units)mentioning

confidence: 99%

FPGA Eco Unit Commitment Based Gravitational Search Algorithm Integrating Plug-in Electric Vehicles

et al. 2018

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Smart grid architecture is one of the difficult constructions in electrical power systems. The main feature is divided into three layers; the first layer is the power system level and operation, the second layer is the sensor and the communication devices, which collect the data, and the third layer is the microprocessor or the machine, which controls the whole operation. This hierarchy is working from the third layer towards first layer and vice versa. This paper introduces an eco unit commitment study, that scheduling both conventional power plants (three IEEE) thermal plants) as a dispatchable distributed generators, with renewable energy resources (wind, solar) as a stochastic distributed generating units; and plug-in electric vehicles (PEVs), which can be contributed either loads or generators relied on the charging timetable in a trustworthy unit commitment. The target of unit commitment study is to minimize the combined eco costs by integrating more augmented clean and renewable energy resource with the help of field programming gate array (FPGA) layer installation. A meta-heuristic algorithm, such as the Gravitational Search Algorithm (GSA), proves its accuracy and efficiency in reducing the incorporated cost function implicating costs of CO2 emission by optimally integrating and scheduling stochastic resources and charging and discharging processes of PEVs with conventional resources power plants. The results obtained from GSA are compared with a conventional numerical technique, such as the Dynamic Programming (DP) algorithm. The feasibility to implement GSA on an appropriate hardware platform, such as FPGA, is also discussed.

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Section: Scenario 4 (Rers Pevs and The Three Coal-fired Units)mentioning

confidence: 99%

FPGA Eco Unit Commitment Based Gravitational Search Algorithm Integrating Plug-in Electric Vehicles

et al. 2018

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“…WCA is one of the promising heuristic optimization techniques especially in power system area. WCA has been introduced to solve optimal microgrids integration considering emission cost, reliability and loss minimization [11][12][13][14]. Gravitational Search Algorithm is another heuristic-based optimization technique that has been implemented for scheduling, sizing and citing of DGs [15][16][17][18][19][20].…”

Section: Pgmentioning

confidence: 99%

“…Gravitational Search Algorithm is another heuristic-based optimization technique that has been implemented for scheduling, sizing and citing of DGs [15][16][17][18][19][20]. Compared to the previously reported work which only considers either RERs [10] or PHEVs [13] in the scheduling problem, this paper augments the RER with PHEVs, together with the impact of weekly variation of loads, RER profile and SOC of PHEVs. As such, this paper presents a comprehensive unit commitment model compared to previous work.…”

Section: Pgmentioning

confidence: 99%

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

2020

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This paper introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB ® Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.

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“…The Cuckoo search algorithm will determine the size and location of the DGs. [15] The algorithm is done on "MATLAB© R2017b coding". Work is done on an A1278 Macbook 2.0 GHz Core 2 Duo (P7350) 2GB RAM, 256 MB VRAM 160GB GHH storage and 8X DL "SuperDrive"…”

Section: Load Flowmentioning

confidence: 99%

Minimizing Losses on Distribution System by PV and Compensating Capacitors

El-Mahdy

Megahid

Seleym

et al. 2020

Journal of Petroleum and Mining Engineering

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Electric power has become an essential measure of the structure of modern society with most of today's daily activity based on the proposition that the desired electric power constantly exists. In the Electrical Distribution System (EDS), each component is essential to the process of distributing power from the site where it is generated then supplied to the customer who utilizes it. Distribution system plays an utmost role in providing the electrical energy from its generation point to the consumers' end by the use of transmission system. Since the R/X -Where R is the Resistance and X denotes for the impedance-ratio in the distribution network is high, the losses in the distribution system is more. In other words, since Electricity is so important, In addition, since there is losses in the networks where electricity is generated and transmitted to the end users, so it is typically wise to think of solutions to reduce such losses. One of the solutions is installing Distributed Generators (DGs) in the network. The DGs employed in this work is a mix of Photovoltaic panels (PVs) for active power compensation and capacitors for reactive power compensation. Location, range and type of capacitors used in the system manipulate the amount of compensation provided by the capacitors. To decide the location and size of the DGs, a load flow will be used and losses in the radial network are considered. The backward-forward sweep load flow technique is used on the IEEE 69 bus standard network. Also, the Genetic algorithm, antlion technique and Cuckoo search algorithm (CS) are used as optimization techniques. The results are tested on the IEEE 69-bus standard test network to validate the proposed algorithms. The proposed algorithms showed enhanced results compared to the genetic algorithms.

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Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

Cited by 13 publications

References 28 publications

FPGA Eco Unit Commitment Based Gravitational Search Algorithm Integrating Plug-in Electric Vehicles

FPGA Eco Unit Commitment Based Gravitational Search Algorithm Integrating Plug-in Electric Vehicles

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

Minimizing Losses on Distribution System by PV and Compensating Capacitors

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