https://orcid.org/0000-0003-4469-2638
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ABSTRACT
In this study, a weighted multi-objective mixed-integer linear programming (WMO-MILP) model considering both economic and environmental factors is proposed for the optimal sizing of the grid-connected hybrid renewable energy systems without storage (HRES-WS). The proposed model is capable of designing the system including several different types of renewable energy generation units to meet the demands of various consumption points. One of the significant values of the model is that it holistically combines the operational, technical, physical and/or capacity constraints which are rarely considered in an integrated way in the literature. Another contribution of the model is its ability to evaluate the tradeoff between the cost-related and CO2 related conflicting objectives by allocating them various weights resembling the decision-maker’s cost-based, environmental-based, or partially cost- and environmental-based priorities. A case study is utilized to demonstrate the value of the model. In order to take into consideration the stochastic nature of the modeling environment, the Monte Carlo simulation is used to predict weather data and load demand based on the historical data. The findings indicate that the combined effect of environmental and cost-related objectives influences the demand to be met by RES at acceptable cost and CO2 emission level. For example, focusing only on the environmental objective, the annual amount of CO2 emission decreases by 14% and the total installed capacity increases by 41%, and therefore the system cost increases by 205% as compared to the base case in which the weight of each objective function is assumed to be equal. The proposed model has the potential to significantly support decision-making process when evaluating a grid-connected HRES-WS both economically and environmentally.
Abbreviations
| AC | = | Annual cost |
| AEPC | = | Average electricity production cost |
| CC | = | Capital cost |
| CO2 | = | Carbon dioxide |
| CRF | = | Capital recovery factor |
| DEF | = | Diesel energy fraction |
| DG | = | Diesel generator |
| DPSP | = | Deficiency of power supply probability |
| EENS | = | Expected energy not served |
| EIA | = | Energy information administration |
| EIR | = | Energy index of reliability |
| ELF | = | Equivalent loss factor |
| FE | = | Fuel emission |
| GA | = | Genetic algorithm |
| GAMS | = | General algebraic modeling system |
| GWO | = | Grey wolf optimization |
| HOGA | = | Hybrid optimization by genetic algorithms |
| HOMER | = | Hybrid optimization of multiple electric renewables |
| HRES | = | Hybrid renewable energy systems |
| HRES-WS | = | Hybrid renewable energy systems without storage |
| HS | = | Harmony search |
| IC | = | Initial cost |
| IP | = | Integer programming |
| LCC | = | Life cycle cost |
| LCOE | = | Levelized cost of energy |
| LLP | = | Loss of load probability |
| LOLE | = | Loss of load expectation |
| LOLF | = | Loss of load frequency |
| LPSP | = | Loss of power supply probability |
| MC | = | Maintenance cost |
| MILP | = | Mixed integer linear programming |
| MO | = | Multi-objective |
| NPV | = | Net present value |
| NREL | = | National renewable energy laboratory |
| NSGA-II | = | Non-dominated sorting genetic algorithm II |
| OC | = | Operation cost |
| O&MC | = | Operation and maintenance cost |
| PB | = | Power balance |
| PE | = | Pollutant emission |
| PSO | = | Particle swarm optimization |
| PV | = | Photovoltaic |
| QP | = | Quadratic programming |
| RER | = | Renewable energy ratio |
| RES | = | Renewable energy sources |
| RC | = | Replacement cost |
| SAM | = | Sampling average method |
| SB | = | Storage battery |
| SNPV | = | System’s net present value |
| SOC | = | State of charge |
| SPEA 2 | = | Strength pareto evolutionary algorithm 2 |
| SPPW | = | Single payment present worth |
| SSR | = | Self-sufficiency ratio |
| STC | = | Standard test conditions |
| SV | = | Salvage value |
| TAOC | = | Total annual operation cost |
| TC | = | Total cost |
| TCGB | = | Total cost for purchasing energy form the grid |
| TCGS | = | Total cost for selling energy to the grid |
| TIC | = | Total investment cost |
| TNPC | = | Total net present cost |
| TOMC | = | Total operation and maintenance cost |
| TR | = | Total revenue |
| TRC | = | Total replenishment cost |
| TSV | = | Total salvage value |
| WT | = | Wind turbine |
Acknowledgments
The authors also wish to acknowledge the Denizli Meteorological Service and the energy company located in Denizli for their help in gathering the necessary data used in the case study. The authors are also indebted to the editor and the anonymous referees and for their helpful comments and suggestions, which substantially improved the paper.
Additional information
Notes on contributors
Ozan Capraz
Ozan Capraz received his M.Sc. degree from the Department of Industrial Engineering at Pamukkale University and he is currently a Ph.D. student in the same department. He is also working as a research assistant in the Department of Industrial Engineering at Tekirdağ Namık Kemal University. His research areas are multi-criteria decision-making, optimization, meta-heuristics applications in product recovery and renewable energy.
Askiner Gungor
Askiner Gungor is a full professor of Industrial Engineering in the Faculty of Engineering of Pamukkale University in Turkey. He received his Ph.D. degree from Northeastern University (USA) by introducing “the disassembly line balancing problem” to the literature. His research has been published in international respected journals, books and several conferences. He has been an editorial board member of International Journal of Business Performance and Supply Chain Modeling, International Journal of Advanced Operations Management and Journal of Industrial Engineering (Turkish). His research interests include design, planning and operational issues in green supply chains, environmentally conscious manufacturing, logistics, product recovery and disassembly.
Ozcan Mutlu
Ozcan Mutlu is an associate professor in the Department of Industrial Engineering at Pamukkale University. He received his Ph.D. degree from West Virginia University, USA in 1999. His research interests include optimization, artificial intelligent, ergonomics and their applications to industrial problems.
Aysun Sagbas
Aysun Sagbas is a professor in the Department of Industrial Engineering at the Faculty of Çorlu Engineering of Tekirdağ Namık Kemal University in Turkey. She received her Ph.D. degree from Çukurova University in 2003. Her research interests include operations research, multi-criteria decision-making, experimental design, modeling and optimization with applications.