Cost‐effective sizing of an AC mini‐grid hybrid power system for a remote area in South Australia

Combe, Matthew; Mahmoudi, Amin; Haque, M.H.; Khezri, Rahmat

doi:10.1049/iet-gtd.2018.5657

Cited by 48 publications

(40 citation statements)

References 20 publications

(24 reference statements)

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“…Power dispatch strategy of island microgrid consists of diesel generator, PV and battery energy storage system presented in [46] to minimize operation cost and optimization problem was formulated as MINLP. Capacity of PV/WT/DG/FC/BES in island hybrid system was determined using particle swarm optimisation to minimise net present cost [47]. Dispatch of PV/DG/ BES in isolated microgrid was presented in [48] to minimise annual system cost.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Murty

Kumar

2020

Prot Control Mod Power Syst

208

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Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixedinteger linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it's impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO 2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it's effectiveness.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Murty

Kumar

2020

Prot Control Mod Power Syst

208

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“…Different optimization techniques have been used to solve the MGEM problem. These techniques includes robust optimization, evolutionary approach, linear programming, nonlinear programming, dynamic programming, stochastic programming, multi‐period imperialist competition, Lyapunov optimization, multi‐objective cross entropy, distributed algorithm, nondominated sorting genetic algorithm (GA), Particle Swarm Optimization (PSO), model predictive control, heuristic approach, fuzzy logic, multistep hierarchical, chance constrained programming, artificial intelligence, tabu search, graph theory, SOC‐based control strategy, MATPOWER, GA, flexible time frame, column and constraint generation algorithm, chaotic group search optimizer, Whale Optimization Algorithm (WOA), water cycle algorithm (WCA), Moth‐Flame Optimizer (MFO), and hybrid Particle Swarm‐Gravitational Search Algorithm (PSO‐GSA) . MGEM problem has been studied in conjunction with demand response (DR) program .…”

Section: Introductionmentioning

confidence: 99%

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

Murty

Kumar

2020

Int Trans Electr Energ Syst

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Summary Integration of renewable energy sources provides energy security, substantial cost savings, and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management (MGEM) is a challenging task for microgrid operator (MGO) for optimal energy utilization with penetration of renewable energy sources, energy storage devices, and demand response. In this paper, optimal energy dispatch strategy is established for grid‐connected and stand‐alone microgrids integrated with photovoltaic, wind turbine, fuel cell, microturbine, diesel generator, and battery storage system. Techno‐economic benefits are demonstrated for the hybrid power system considering uncertainty of load and generation. So far, MGEM problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and emission‐related objectives need to be addressed for effective MGEM. In this paper, MGEM is formulated as mixed‐integer linear programming, and a new multi‐objective solution is proposed for MGEM. Demand response is also included in the optimization problem to demonstrate its impact on optimal energy dispatch and techno‐commercial benefits. Simulation results are obtained for the optimal capacity of each component, charging/discharging scheduling, state of charge, power import from main grid, net present cost, cost of energy, initial cost, operational cost, fuel cost, and cost of emissions taking care of seasonal load variation throughout the year. The results show that CO2 emission in stand‐alone microgrid is reduced by 41.74% compared with traditional system with diesel generator. Simulation results obtained with the proposed method has been compared with various evolutionary algorithms to verify its effectiveness.

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“…This study is a continuation of an earlier work 38 where the focus was only on the economic analysis. The detailed optimization process, optimization algorithm, operational results, and comparison of results with other optimization algorithms were not described.…”

Section: Contribution and Paper Organizationmentioning

confidence: 93%

“…The proposed off-grid hybrid systems studied in this paper consist of various combinations of wind generators, solar PV systems, BBs, FWs and DGs with associated FTs. Detailed models and equations of each component are presented in Combe et al 38 Since this study is not a real-time application, the communication infrastructure is not considered. All components including the consumer loads and dump load are connected to a common AC bus and thus called as AC-coupled system.…”

Section: Proposed Hybrid Standalone Power Systemmentioning

confidence: 99%

“…The dynamic reserve depends on the available extra power of the online DGs, the battery's available output power, and the power of DGs on standby which supported by FW, after feeding the load. 38 Since most of the load is supplied by the DGs, no criterion is considered in the dynamic reserve for the portion of load covered by RE resources.…”

Section: Proposed Hybrid Standalone Power Systemmentioning

confidence: 99%

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AC‐coupled hybrid power system optimisation for an Australian remote community

Combe

Mahmoudi

Haque

et al. 2020

Int Trans Electr Energ Syst

Self Cite

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Summary This paper investigates the operation of various optimised AC‐coupled hybrid power systems for an isolated community in South Australia. In addition to diesel generators, various combinations of distributed generators and storage technologies are considered to form different hybrid power systems. Optimal design of the hybrid power systems is implemented using particle swarm optimisation (PSO) algorithm. The performance of the PSO in optimizing the above hybrid systems is discussed in detail. Whilst meeting the power system operational constraints, the minimum levelised cost of electricity (LCOE) of all systems, with various combinations of system components, are calculated. Simulation results obtained for each optimized system are carefully described and analysed. The annual operations of various components in each system are then compared and discussed in detail. A sensitivity analysis by varying the cost of wind turbines and the photovoltaic is also conducted for all hybrid systems. It has been found that the inclusion of energy storage systems (battery and flywheel) can reduce the LCOE and CO2 emission significantly compared to the inclusion of renewable generations (solar and wind) alone. The diesel‐wind‐solar‐battery system is the eco‐friendliest with an annual CO2 emission of 451 t compared to the reference diesel system (1064 t). In addition, the hybrid system consisting of diesel, wind, solar, battery and flywheel is found to be the most economic with a LCOE of 58.94 ¢/kWh compared to the diesel system with a LCOE of 67.17 ¢/kWh.

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Cost‐effective sizing of an AC mini‐grid hybrid power system for a remote area in South Australia

Cited by 48 publications

References 20 publications

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

AC‐coupled hybrid power system optimisation for an Australian remote community

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