Hybrid simulation and optimization-based design and operation of integrated photovoltaic generation, storage units, and grid

Mazhari, Esfandyar; Zhao, J. Leon; Celik, Nurcin; Lee, Seungho; Son, Young Jun; Head, Larry

doi:10.1016/j.simpat.2010.08.005

Cited by 50 publications

(13 citation statements)

References 6 publications

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“…We consider the residential electricity demand of a single-family house by summing up all the active appliances at each time interval of a day as listed in Table 3 [3]. Among the residential elec- tricity usage, some are rigid in demand and can hardly be changed or shifted, such as lighting and cooking, while others are flexible loads including dishwashers, washing machines, clothes dryers and vacuum cleaners, which are the key factors to be considered in the demand-side management.…”

Section: Day-ahead Schedulingmentioning

confidence: 99%

“…Many studies have been reported on the modeling, control and optimization of hybrid energy systems from design to operation [3][4][5][6]. [7] comprehensively reviewed HRES for power generation in stand-alone applications and claimed that the high manufacturing and maintenance costs of wind turbines and photovoltaic (PV) panels are a major concern for renewable energy penetration into the grid.…”

Section: Introductionmentioning

confidence: 99%

“…[8] applied model predictive control to solve the economic/environmental dispatch problem for a regional electric power system with many intermittent resources. [3] demonstrated several simulation tools to size and operate an integrated solar generation system involving PV generators, compressed-air energy storage and super-capacitors. As there are several different stages in the life cycle of HRES [9], it is necessary to consider the system design and operation from a variety of time scales.…”

Section: Introductionmentioning

confidence: 99%

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Operational optimization and demand response of hybrid renewable energy systems

2015

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Section: Day-ahead Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operational optimization and demand response of hybrid renewable energy systems

2015

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“…[23,[25][26][27][28]. Energy and environment related consumer technology adoption has been a particular area of growth in the development and applications of ABM [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. Because the underlying components of the system and how they interact with each other are modeled explicitly in ABM, the processes that lead to observable emergent phenomena (such as the rate and pattern of adoption) can be altered through simulation experiments, creating virtual laboratories [24,45,46].…”

Section: Introductionmentioning

confidence: 99%

Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach

2015

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h i g h l i g h t sWe present an agent-based model of residential solar photovoltaic (PV) adoption. Model integrates social, behavioral, and economic elements of agent decision-making. Real-world, large-scale integrated dataset used for model validation and testing. We study the importance of using disaggregated empirical data on model performance. Social and attitudinal components are critical for spatial and demographic accuracy. a b s t r a c tEnergy technology adoption is a complex process, involving social, behavioral, and economic factors that impact individual decision-making. This paper uses an empirical, geographic information system (GIS)-integrated agent-based model of residential solar photovoltaic (PV) adoption to explore the importance of using empirical household-level data and of incorporating economic as well as social and behavioral factors on model outcomes. Our goal is to identify features of the model that are most critical to successful prediction of the temporal, spatial, and demographic patterns that characterize the technology adoption process for solar PV. Agent variables, topology, and environment are derived from detailed and comprehensive real-world data between 2004 and 2013 in Austin (Texas, USA). Four variations of the model are developed, each with a different level of complexity and empirical characterization. We find that while an explicit focus only on the financial aspects of the solar PV adoption decision performs well in predicting the rate and scale of adoption, accounting for agent-level attitude and social interactions are critical for predicting spatial and demographic patterns of adoption with high accuracy.

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“…Mazhari et al [29] studied the simulation and optimization of photovoltaic power generation when integrated with energy storage and a grid. The solution included the optimal combination of photovoltaic modules and storage to minimizing cost and ensuring reliability of the grid.…”

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confidence: 99%

Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies

Sharifzadeh

Lubiano-Walochik

Shah

2017

Renewable and Sustainable Energy Reviews

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Renewable energies can play an important role in mitigating the emissions associated with electricity power generation and diversification of the energy supply. The challenge is that renewable resources such as wind and solar have intermittent production patterns and their incorporation into conventional electricity grids will increase the degree of uncertainty. The present research proposes a comprehensive framework in which design and operation of the electricity grid are considered simultaneously and the uncertainties in the wind and solar generation as well as demand are systematically taken into account. The case of retrofitting the current UK electricity grid to include 50% renewable power generation by 2030 was posed as the demonstrating example. The research problem was formulated as a piece-wise linear mixed integer optimization under uncertainty and solved using CPLEX v12.0 in GAMS. The results suggested that it is possible to retrofit the electricity grid using renewable generators while optimizing the overall profitability and ensuring the secure supply of electricity. It was also observed that at the price of higher computational costs, stochastic optimization generates more realistic and robust solutions for the design and operation of the smart electricity grid. KeywordsIntegrated renewable energy systems, wind power, solar energy, stochastic mixed-integer optimization, uncertainty. , , ( ) Power generated by i natural gas power plant in scenario s at node n at time t , , ( ) Power generated by k nuclear power plant in scenario s at node n at time t , ( ) Wind power generated in scenario s at node n at time t , ( ) Solar power generated in scenario s at node n at time t , ( ) Power pumped from storage to node n in scenario s at time t , ( ) Power pumped to storage from node n in scenario s at time t cite this publication at: Mahdi Sharifzadeh, Helena Lubiano-Walochik, Nilay Shah. Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and

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Hybrid simulation and optimization-based design and operation of integrated photovoltaic generation, storage units, and grid

Cited by 50 publications

References 6 publications

Operational optimization and demand response of hybrid renewable energy systems

Operational optimization and demand response of hybrid renewable energy systems

Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach

Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies

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