Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden

Azaza, Maher; Wallin, Fredrik

doi:10.1016/j.energy.2017.01.149

Cited by 164 publications

(47 citation statements)

References 45 publications

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“…Azaza and Wallin [57] studied energy management in a MG with a hybrid system consisting of wind turbines, photovoltaic panels, diesel generator, and battery storage. A multi-objective particle swarm optimization is used, which evaluates the probability of losing energy supply over a time horizon/period of 6 months each during summer and winter.…”

Section: Energy Management Based On Metaheuristic Methodsmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

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Section: Energy Management Based On Metaheuristic Methodsmentioning

confidence: 99%

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

2019

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“…Various research has been conducted on optimum component sizing using various optimization techniques to evaluate a cost-effective hybrid microgrid configuration such as PV/biodiesel/BESS using simulated annealing [37], Supercapacitors/BESS/WT/Fuel using Non-dominated Sorted Genetic Algorithm [6], diesel/PV/WT using multi-objective self-adaptive differential evolution algorithm [38], PV/WT/BESS using cuckoo search algorithm [39], MOPSO [40], GA-PSO and MOPSO [41], and more. However, it is observed from the research trends in the literature that in order to ascertain the maximum techno-economic benefits for any microgrid configuration and investment, the flexibility requirements of the system must be factored into its design, i.e., reliability based on adequate system flexibility provision must be prioritized alongside the planning and capacity sizing.…”

Section: Research Motivationmentioning

confidence: 99%

Multi-Objective Optimal Capacity Planning for 100% Renewable Energy-Based Microgrid Incorporating Cost of Demand-Side Flexibility Management

et al. 2019

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The need for energy and environmental sustainability has spurred investments in renewable energy technologies worldwide. However, the flexibility needs of the power system have increased due to the intermittent nature of the energy sources. This paper investigates the prospects of interlinking short-term flexibility value into long-term capacity planning towards achieving a microgrid with a high renewable energy fraction. Demand Response Programs (DRP) based on critical peak and time-ahead dynamic pricing are compared for effective demand-side flexibility management. The system components include PV, wind, and energy storages (ESS), and several optimal component-sizing scenarios are evaluated and compared using two different ESSs without and with the inclusion of DRP. To achieve this, a multi-objective problem which involves the simultaneous minimization of the loss of power supply probability (LPSP) index and total life-cycle costs is solved under each scenario to investigate the most cost-effective microgrid planning approach. The time-ahead resource forecast for DRP was implemented using the scikit-learn package in Python, and the optimization problems are solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm in MATLAB R . From the results, the inclusion of forecast-based DRP and PHES resulted in significant investment cost savings due to reduced system component sizing.Keywords: demand response program (DRP); photovoltaic system (PV); pumped heat energy storage (PHES); critical peak pricing (CPP) DRP; time-ahead dynamic pricing (TADP) DRP; loss of power supply probability (LPSP); energy storage system (ESS); Multi-Objective Particle Swarm Optimization (MOPSO)

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“…The formula for output power of an individual wind turbine can be expressed as 26,27 However, plenty of literature estimates the output power of wind turbines without considering variation in wind speed at different vertical heights.…”

Section: Power Output Model Of An Individual Wind Turbinementioning

confidence: 99%

“…The power output curve, wind speed, and tower height are the three main factors determining the output power of wind turbines. The formula for output power of an individual wind turbine can be expressed as 26,27…”

Section: Power Output Model Of An Individual Wind Turbinementioning

confidence: 99%

Implementation of repowering optimization for an existing photovoltaic‐pumped hydro storage hybrid system: A case study in Sichuan, China

Cao

et al. 2019

Int J Energy Res

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Summary For a remote area or an isolated island, where the grid has not extended, a standalone hybrid energy system can provide cheap and adequate power for local users. However, with the development of society, the load demand will increase and the original system cannot completely meet the load demand. This situation occurs in Xiaojin, Sichuan, China. The existing photovoltaic‐pumped hydro storage (PV‐PHS) hybrid system in this area as the original system cannot completely meet the load requirements at present. The term “repowering” aims to maximize the reliability of power supply and the utilization of the PV‐PHS hybrid energy system that differs from traditional planning optimization to build all components. The repowering strategy is to integrate wind turbines (WTs) and battery into the original system. For the repowering system, a power management strategy is proposed to determine the operating modes of the PHS and battery. Three objectives, which are minimizing percentage of the demand not supplied, levelized cost of energy, and curtailment rate of renewable energy, are considered in the optimization model. Simulation is conducted by single‐objective, biobjective, and triobjective particle swarm optimization (PSO) techniques. For the single‐objective optimization, the comparison of PSO and genetic algorithm (GA) is made. For the double‐objective optimization, multiobjective PSO (MOPSO) is compared with weighted sum approach (WSA), and fuzzy satisfying method is utilized to find the win‐win solution. The results reveal that the repowering strategy can help to achieve maximum reliability of power supply after load demand increases significantly, and the battery plays an important role in such a hybrid system.

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Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden

Cited by 164 publications

References 45 publications

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

Multi-Objective Optimal Capacity Planning for 100% Renewable Energy-Based Microgrid Incorporating Cost of Demand-Side Flexibility Management

Implementation of repowering optimization for an existing photovoltaic‐pumped hydro storage hybrid system: A case study in Sichuan, China

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