Hybrid Simulated Annealing–Tabu Search Method for Optimal Sizing of Autonomous Power Systems With Renewables

Katsigiannis, Yiannis A.; Georgilakis, Pavlos S.; Karapidakis, Emmanuel

doi:10.1109/tste.2012.2184840

Cited by 199 publications

(114 citation statements)

References 30 publications

(20 reference statements)

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“…This paper addresses the generation expansion planning involving wind-turbine generators (WTG), photovoltaic (PV), diesel generators, and energy storage (ES) for small standalone power systems to meet the restriction of fuel emissions [1,2]. Many works have addressed the generation expansion planning in small standalone power systems [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Existing methodologies fall into three categories: reliability, optimization-, and enumeration-based methods.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables

et al. 2015

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This paper explores real power generation planning, considering distributed generation resources and energy storage in a small standalone power system. On account of the Kyoto Protocol and Copenhagen Accord, wind and photovoltaic (PV) powers are considered as clean and renewable energies. In this study, a genetic algorithm (GA) was used to determine the optimal capacities of wind-turbine-generators, PV, diesel generators and energy storage in a small standalone power system. The investment costs (installation, unit and maintenance costs) of the distributed generation resources and energy storage and the cost of fuel for the diesel generators were minimized while the reliability requirement and CO2 emission limit were fulfilled. The renewable sources and loads were modeled by random variables because of their uncertainties. The equality and inequality constraints in the genetic algorithms were treated by cumulant effects and cumulative probability of random variables, respectively. The IEEE reliability data for an 8760 h load profile with a 150 kW peak load were used to demonstrate the applicability of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables

et al. 2015

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“…This combination is referred to as hybrid techniques. Examples of such techniques are SA-Tabu search; Monte Carlo simulation (MCS)-PSO; hybrid iterative/GA; MODO (multiobjective design optimization)/GA; artificial neural fuzzy interface system (ANFIS); artificial neural network/GA/MCS; PSO/DE (differential evolution); evolutionary algorithms and simulation optimization-MCS which have been used in several studies for optimizing HRESs [38][39][40][41][42][43][44][45][46][47]. Although hybrid techniques enhance the overall performance of the optimization, they may suffer from some limitations.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Optimizing Hybrid Renewable Energy Systems: A Review

Ghofrani¹,

Hosseini²

2016

Sustainable Energy - Technological Issues, Applications and Case Studies

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With the fast progression of renewable energy markets, the importance of combining different sources of power into a hybrid renewable energy system (HRES) has gained more attraction. These hybrid systems can overcome limitations of the individual generating technologies in terms of their fuel efficiency, economics, reliability and flexibility. One of the main concerns is the stochastic nature of photovoltaic (PV) and wind energy resources. Wind is often not correlated with load patterns and may be discarded sometimes when abundantly available. Also, solar energy is only available during the day time. A hybrid energy system consisting of energy storage, renewable and nonrenewable generation can alleviate the issues associated with renewable uncertainties and fluctuations. Large number of random variables and parameters in a hybrid energy system requires an optimization that most efficiently sizes the hybrid system components to realize the economic, technical and designing objectives. This chapter provides an overview of optimal sizing and optimization algorithms for hybrid renewable energy systems as well as different objective functions considered for designing such systems.

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“…Data for calculation of the incremental loss percentage per cycle for Li-Ion battery is taken from [40] and fitted into an exponential curve. Combined with (24), this yields the incremental percentage capacity loss per cycle for a typical Li-Ion battery:…”

Section: A Impact Of Cycling To a Battery Technologymentioning

confidence: 99%

“…They also briefly explain the applicability of other optimization routines, including the graphic construction method, the probabilistic approach, iterative techniques and artificial intelligence methods. Recent publications which were not covered by this review describe the calculation of the optimal capacity of pumped hydro storage with known wind turbine ratings and a diesel generator [20], a stochastic approach to a similar problem, but for a general ESS [21], studies on wind battery hybrid systems [22], [23], a simulated annealing algorithm [24] and a biogeography-based optimization algorithm [25]. However, none of these techniques takes advantage of the features of robust optimization nor considers the gradual loss of battery capacity over its lifespan.…”

Section: Introductionmentioning

confidence: 99%

Capacity Optimization of Renewable Energy Sources and Battery Storage in an Autonomous Telecommunication Facility

Dragičević

Pandžić

Škrlec

et al. 2014

IEEE Trans. Sustain. Energy

114

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Abstract-This paper describes a robust optimization approach to minimize the total cost of supplying a remote telecommunication station exclusively by renewable energy sources (RES). Due to intermittent nature of RES, such as photovoltaic (PV) panels and small wind turbines, they are normally supported by a central energy storage system (ESS), consisting of a battery and a fuel cell. The optimization is carried out as a robust mixed-integer linear program (RMILP), and results in different optimal solutions, depending on budgets of uncertainty, each of which yields different RES and storage capacities. These solutions are then tested against a set of possible outcomes, thus simulating the future operation of the system. Since battery cycling is inevitable in this application, an algorithm that counts the number of cycles and associated depths of discharges (DoD) is applied to the optimization results. The annual capacity reduction that results from these cycles is calculated for two types of battery technologies, i.e. valve-regulated lead-acid (VRLA) and lithium-ion (Li-Ion), and treated as an additional cost. Finally, all associated costs are added up and the ideal configuration is proposed.Index Terms-Autonomous power facility, batteries, energy storage system, renewable energy sources, robust mixed-integer linear programming. (t) electricity discharged form the battery (kW), NOMENCLATUREwind turbine generation (kW),curtailed RES output, n hyd number of hydrogen storage replacements,consumed hydrogen (l), x ch bat (t) binary variable equal to 1 if battery is being charged at time period t, and 0 otherwise, x dis bat (t) binary variable equal to 1 if battery is being discharged at time period t, and 0 otherwise.

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Hybrid Simulated Annealing–Tabu Search Method for Optimal Sizing of Autonomous Power Systems With Renewables

Cited by 199 publications

References 30 publications

Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables

Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables

Optimizing Hybrid Renewable Energy Systems: A Review

Capacity Optimization of Renewable Energy Sources and Battery Storage in an Autonomous Telecommunication Facility

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