Comparative study of metaheuristic algorithms for optimal sizing of standalone microgrids in a remote area community

Fathi, Mohammad; Khezri, Rahmat; Yazdani, Amirmehdi; Mahmoudi, Amin

doi:10.1007/s00521-021-06165-6

Cited by 25 publications

(10 citation statements)

References 32 publications

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“…Although the optimization model can be optimized using suitable optimization tools in MATLAB, particle swarm optimization (PSO) has been proven as a reliable tool for such a study and provide reliable solutions. This method has been efficiently applied for power system planning studies [10,18,19], and its results have been approved for optimal sizing compared to other methodologies [22,23]. It is notable that the optimization algorithm is not a contribution of this study.…”

Section: Optimization Modelmentioning

confidence: 99%

Optimal planning of solar PV and battery storage with energy management systems for Time‐of‐Use and flat electricity tariffs

Pan

Khezri

Mahmoudi

et al. 2022

IET Renewable Power Gen

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This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) with novel rule-based energy management systems (EMSs) under flat and time-of-use (ToU) tariffs. Four schemes are investigated based on the combinations of flat and ToU tariffs for buying and selling the electricity: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. For each scheme, two configurations are evaluated: (i) PV only, and (ii) PV-BES. The optimization of the grid-connected household is evaluated based on one-year realistic data. An uncertainty analysis is presented based on the variations of insolation, temperature, and load. Sensitivity analyses are implemented based on the average daily load, the grid constraint, and the costs of PV and BES. The operational analyses for 48 h in summer and winter are carried out to evaluate the dynamic performance of the systems for high and low solar insolation. The effectiveness of the proposed model is verified by comparing the results with that of common EMS based on the net metering scheme. It is found that the COE of the proposed EMS for a PV-BES house with ToU-Flat scheme (as the best option) is 2 ¢/kWh lower than that of the net metering scheme.

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Section: Optimization Modelmentioning

confidence: 99%

Optimal planning of solar PV and battery storage with energy management systems for Time‐of‐Use and flat electricity tariffs

Pan

Khezri

Mahmoudi

et al. 2022

IET Renewable Power Gen

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“…A connected local generation in such circumstance would be disconnected from the affected grid thereby operating in islanded mode to guarantee resilience to its local loads. Another factor that results in a standalone operation of localized generation is when its generation site is remote and thus too far away to accommodate the long distance to the utility site [11]. To evade extreme transmission costs and losses, local generations independently serve the residing loads in a coordinated fashion.…”

Section: A Localized Standalone Generationsmentioning

confidence: 99%

Adopting the Game Theory Approach in the Blockchain-Driven Pricing Optimization of Standalone Distributed Energy Generations

Okoye

Kim

2022

IEEE Access

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The importance of the distributed generations cannot be overemphasized. This ranges from the contribution to resilience down to the energy cost efficiency advantage at the consumers' end. The distributed generations in some localities, however, lack connection to the utility grid due to the remoteness of the generation site. Certain benefits are, however, threatened. Thus, where there is no energy price regulation policy, fluctuations in energy prices could be the order of the day. This paper, thus, focuses on the transaction price optimization of the standalone distributed generations using the game theory approach. First, blockchain technology is incorporated in the energy transaction arena to bind prosumers and their energy transactions to a common platform. Next, for electricity price prediction, the linear regression algorithm is used to obtain the fitting equation from the current transaction data stored by the blockchain network. Using the fitting equation as the objective function, the particle swarm optimization (PSO) algorithm is used to achieve the proposed energy transaction price minimization and profit maximization. Finally, the individual hourly optimization results are fitted by a decision tree algorithm for instant referencing purposes in making energy best price transaction decisions. The individual results show that it is capable of constantly updating the optimized energy price in real-time based on the subsequent transaction records updated by the blockchain network.

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“…In this study, the method chosen as the optimisation algorithm to complete this problem is particle swam optimisation (PSO). PSO has been successfully used previously in several studies for a similar power system optimisation problem type [2], [22], [28][29][30][31]. PSO is a relatively fast and simple way due to its suitable convergence without much dependency on the initial condition.…”

Section: Optimisation Proceduresmentioning

confidence: 99%

Optimal planning of solar photovoltaic and battery storage for electric vehicle owner households with time‐of‐use tariff

Merrington

Khezri

Mahmoudi

2021

IET Generation Trans & Dist

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This paper presents a practical optimal planning of solar photovoltaic (SPV) and battery storage system (BSS) for electric vehicle (EV) owner households with time of use (TOU) electricity pricing. The main aim of the optimisation problem is to minimize the Cost of Electricity (COE) while satisfying the design constraints over 20‐year project lifespan. Novel rule‐based home energy management systems are created to investigate the optimal sizing of two system configurations: (1) SPV‐EV, and (2) SPV‐BSS‐EV. The arrival and departure times of the EV's availability, as well as its initial state‐of‐charge are incorporated into the energy management system via stochastic functions. Sensitivity analyses of the feed in tariff, grid constraint, electricity demand, and available rooftop area are provided to investigate the variations of cost of electricity and capacities of SPV and BSS. From the study results, practical guidelines for grid‐connected households, based on TOU energy pricing, are provided to select the optimal capacity of SPV and BSS to accommodate the existing EV. The optimisation method is general and applicable to any household owning EV; however, in this study realistic data of solar insolation and temperature as well as household electricity demand and electricity prices are implemented for South Australia. Uncertainty analysis is investigated based on 10‐year real and stochastic data to prove the optimal results. It is found that the optimal configuration for a typical SA household utilising TOU pricing with an EV requires a 10‐kW SPV with a 10‐kWh capacity of BSS.

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Comparative study of metaheuristic algorithms for optimal sizing of standalone microgrids in a remote area community

Cited by 25 publications

References 32 publications

Optimal planning of solar PV and battery storage with energy management systems for Time‐of‐Use and flat electricity tariffs

Optimal planning of solar PV and battery storage with energy management systems for Time‐of‐Use and flat electricity tariffs

Adopting the Game Theory Approach in the Blockchain-Driven Pricing Optimization of Standalone Distributed Energy Generations

Optimal planning of solar photovoltaic and battery storage for electric vehicle owner households with time‐of‐use tariff

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