Sizing of hybrid PMSG-PV system for battery charging of electric vehicles

Singaravel, M. M. Rajan; Daniel, S. Arul

doi:10.1007/s11708-015-0349-7

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…The CPP of the investment in the PV module and ESS is calculated by evaluating the net present value (NPV) of the system for each year of the project lifespan [41]:…”

Section: Energy Cost Analysis and Cash Payback Periodmentioning

confidence: 99%

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

et al. 2023

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Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in a smart home. The contradictory nature of these two objective functions (cost of energy and satisfaction level) requires a multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered the cost and satisfaction, but there is a lack of research that considers both these objectives simultaneously. Our work proposes an optimum home appliance scheduling method to obtain an optimum satisfaction level with a minimum cost of energy. To achieve this goal, first, an energy management system (EMS) is developed using a rule-based algorithm to reduce the cost of energy by efficient utilization of renewable energy resources and an energy storage system. The second part involves the development of an optimization algorithm for optimal appliance scheduling based on consumer satisfaction level, involving their time and device-based preferences. For that purpose, a multi-objective grey wolf accretive satisfaction algorithm (MGWASA) is developed, with the aim to provide trade-off solutions for optimal load patterns based on cost per unit satisfaction index (Cs_index) and percentage satisfaction (%S). The MGWASA is evaluated for a grid-connected smart home model with EMS. To ensure the accuracy of the numerical simulations, actual climatological data and consumer preferences are considered. The Cs_index is derived for six different cases by simulating (a) optimal load, (b) ideal load, and (c) base (random) load, with and without EMS. The results of MGWASA are benchmarked against other state-of-the-art optimization algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), multi-objective binary particle swarm optimization algorithm (MOBPSO), Multi-objective artificial bee colony (MOABC), and multi-objective evolutionary algorithm (MOEA). With the proposed appliance scheduling technique, a % reduction in annual energy cost is achieved. MGWASA yields Cs_index at 0.049$ with %S of 97%, in comparison to NSGAII, MOBPSO, MOABC, and MOEA, which yield %S of 95%, 90%, 92%, and 94% at 0.052$, 0.048$, 0.0485$, and 0.050$, respectively. Moreover, various related aspects, including energy balance, PV utilization, energy cost, net present cost, and cash payback period, are also analyzed. Lastly, sensitivity analysis is carried out to demonstrate the impact of any future uncertainties on the system inputs.

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“…The CPP of the investment in the PV module and ESS is calculated by evaluating the net present value (NPV) of the system for each year of the project lifespan [41]:…”

Section: Energy Cost Analysis and Cash Payback Periodmentioning

confidence: 99%

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

et al. 2023

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“…The voltage system of the equation noted (V abc s ) and magnetic flux system of the equation noted (Ø abc s ) can be visualized in Equations ( 15) and ( 16). All of these systems are created in a three-phase process (ABC) [27,28].…”

Section: Electrical Subsystemmentioning

confidence: 99%

“…Supposing that these two switches have the nomination of K 10 and K 11 . The relationship between these two parameters can be expressed in Equation (28).…”

Section: Convertersmentioning

confidence: 99%

Decreasing the Battery Recharge Time if Using a Fuzzy Based Power Management Loop for an Isolated Micro-Grid Farm

et al. 2022

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An isolated micro-grid has different requirements from the traditional power grids. Several energy sources may be linked for the purpose of sharing load demand without being linked to the grid. The isolated micro-grid is made up of at least one energy generator, an energy storage system, and a load portion. Because there are several energy sources and a range of models, the power flow must be managed to ensure the safety of all hardware. Monitoring the flow of power from multiple energy sources necessitates adherence to many parameters and other requirements. As a result, the goal of this work is to identify a worthwhile solution for providing the appropriate portions with the necessary power while also obtaining the necessary energy from other sources. The approach is based on the fuzzy logic controller, which is an intelligent technology. This regulator is used in an efficient process that tries to control all of the equipment in the isolated micro-grid under investigation. The MATLAB/Simulink platform is employed for simulating this proposed system, and then, the depicted results were discussed and compared. Showing the traditional relay control, the standard PI regulator, and a neural control combination process, the achieved results prove that it is possible to reduce the battery recharge time to half; if the proposed fuzzy controller is used. Then, the established controller specifications have been used for evaluating the energy performances of the hybrid energy system under a real case situation in a specific location in the world. Consequently, the obtained results prove that this proposal power management system will be largely beneficial for such energy storage applications and an energy yield can be assured during all climatic conditions and specifications.

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“…Furthermore, variations in quoted demand may cause changes in the participants' electricity bill. The difference in the electricity bill is referred to as the deviation cost, which can be calculated as given in (24).…”

Section: Priority-based Auction Strategy (Pas)mentioning

confidence: 99%

“…Furthermore, people are interested in battery storage in order to lower their power expenditure by fulfilling critical demands during peak times. Because the quantity of the power generated from renewable energy resources determines the decrease in electricity bill, the prosumers may size their renewable resources and batteries by considering the space availability and cheap installation cost [24]. Considering the lengthy time span for each trading may severely affect the reliability of the peer-to-peer energy market.…”

Section: Simulation Studymentioning

confidence: 99%

Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions

et al. 2022

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Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO.

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Sizing of hybrid PMSG-PV system for battery charging of electric vehicles

Cited by 9 publications

References 29 publications

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Decreasing the Battery Recharge Time if Using a Fuzzy Based Power Management Loop for an Isolated Micro-Grid Farm

Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions

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