Simulation and optimisation of renewable energy systems

Mitchell, Keith O; Nagrial, M.; Rizk, Jamal

doi:10.1016/j.ijepes.2004.10.001

Cited by 74 publications

(21 citation statements)

References 7 publications

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“…PV panels generated power changes throughout the year. Figures 1 and 2 show the average hourly solar distribution, and the electrical loading for residential customers [1,2]. It is clearly shown that the generated power requires energy storage to support the house daily (24 hours period) electrical consumptions.…”

Section: Theoretical Studymentioning

confidence: 99%

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Stand Alone PV House Design for Maximum Power Output in Sydney Australia Metropolitan Area

et al. 2018

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Abstract-Nowadays, a strong link exists between human comfort and electrical power. Abundant doings are not possible without the support of the electrical energy. Driven by the human obligation to save the environment, the installation of PV solar system increased dramatically in the past numerous years. The installation can be grid connected or standalone system. These micro-grid situations are installed world-wide and currently are contributing to the local electrical network. To maximize the use of the generated solar system, numerous system installed energy storage in the form of batteries. These energy storages harvest the sun power during daylight to support the household electrical demand after sunset. This paper linked the house roof design to the PV solar system for maximum generation condition. Furthermore the paper contains important information on the optimum location for the solar panel to ensure continuous and reliable system is achieved. An actual real case study is presented.

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Section: Theoretical Studymentioning

confidence: 99%

“…Taking into consideration the fact that PV micro-grid produces maximum power during the midday period on a sunny day. The house peak demands hours are located in the morning and late in the afternoon/evening [1,2]. Based on the mentioned information, the micro-grid generated power doesn't support the house critical electrical requirements.…”

Section: Introductionmentioning

confidence: 99%

Stand Alone PV House Design for Maximum Power Output in Sydney Australia Metropolitan Area

et al. 2018

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“…At the same time, a number of optimization modeling methods were proposed for supporting the management of GHG emission mitigation particularly through the adoption of renewable energies [54,[125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143]. In the middle of the 1970s, Duff (1975) presented an optimization model to design solar thermal energy systems in accordance with the minimum system cost [144].…”

Section: Optimization Of Ghg Emission Mitigationmentioning

confidence: 99%

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

et al. 2011

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Abstract:Energy is crucial in supporting people's daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in OPEN ACCESSEnergies 2011, 4 1625 uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.

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“…With flexible operational mode and environment-friendly features, more and more distributed generations are integrated into the distribution network, which brings benefit on the carbon reduction of distribution system [20]. As two basic methods of loss of expected energy (LOEE) for the distribution system with DG, the studies of analytical method [21][22][23][24][25][26][27][28][29] and Monte Carlo method [30][31][32][33][34][35][36] have made some progress. These studies indicate that the adoption of DG is actually equals to increasing the number and capacity of the backup of distribution system, thus improving the carbon reduction of the distribution system.…”

Section: Introductionmentioning

confidence: 99%

Low-carbon benefit analysis on DG penetration distribution system

Liu

et al. 2015

J. Mod. Power Syst. Clean Energy

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In response to the severe energy and environmental issues, CO 2 emission reduction and low-carbon development are inevitable. China has become the biggest CO 2 emitter in the world since 2006. As a major CO 2 emission source in China, the power industry is facing greater pressure for carbon emission abatement. By applying various low-carbon power technologies and mechanisms, the potential for CO 2 emission reduction in power systems is considerable. This paper proposes a pseudo-sequential Monte Carlo simulation method for the low-carbon benefit evaluation of distribution system including distributed wind turbines, solar array and battery energy storage systems. The hourly sequential models and state variation models for wind turbines, solar array and battery are established. The non-power components are sequentially sampled and the components of the wind turbine, the solar array and the battery are non-sequentially sampled. The failure modes and effect analysis (FMEA) procedure for the system are discussed and the heuristic load shedding approach is used, then the low-carbon benefit evaluation procedure is illustrated. Based on this, the system state transition sampling method for calculating the loss of expected energy index with high DG penetration is proposed. The state transition models for non-active elements and DGs are established. The validity of the proposed method is demonstrated by a study case. The method can be used for the low-carbon benefit analysis on DG penetration distribution networks.

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Simulation and optimisation of renewable energy systems

Cited by 74 publications

References 7 publications

Stand Alone PV House Design for Maximum Power Output in Sydney Australia Metropolitan Area

Stand Alone PV House Design for Maximum Power Output in Sydney Australia Metropolitan Area

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

Low-carbon benefit analysis on DG penetration distribution system

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