Abstract
Humanity is leaving an age behind which could be summarized as the industrialization of nations based on fossil fuels i.e. conventional energy resources which have also brought an environmental burden along with themselves. While the world leadership has been arguing about the emission rights and seemingly reaching a non-consensus, economies have been hit by an unexpected pandemic and this global health crisis which has deep environmental roots has alerted decision-makers once more that the already dying fossil energy resources has to be quickly replaced by their environmentally sustainable counterparts: renewable energy systems. As a general term, renewable energy systems may refer to many systems of different compositions and scales which can produce and dispatch power from renewable energy resources. In order to be in a state of full preparedness for a future without fossil fuels, human civilization needs a better understanding of how renewable systems work and how they can be operated and maintained more effectively and efficiently. In order to achieve this multi-paradigm and interdisciplinary challenge, more powerful and robust approaches are needed. In this paper, we have investigated the most obvious cases of renewable energy installations which are usually classified under the category of Microgrids, and the management systems they rely on called “smart energy management systems” (SEMS). The approach exploited here, can be summarized as finding a common ground for comparing computational frameworks employed within these systems and determining the advantages of SEMS which can operate effectively and efficiently in the context of power generating cost and environmental cost.
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Notes
- 1.
Source CDIAC. Carbon Dioxide Information Analysis Center. Historical CO2 Records from the Law Dome. DE08, DE08 2, and DSS Ice Cores. Available online: https://cdiac.ess-dive.lbl.gov/trends/co2/lawdome.html. (Accessed on 10 October 2020).
- 2.
Source https://crudata.uea.ac.uk/cru/data/temperature/. (Accessed on 6 December 2020).
- 3.
REN21.Renewables 2019 Global Status Report; REN21 Secretariat: Paris, France, 2019; ISBN 978–3-9,818,911–7 1. Available online: http://www.ren21.net/gsr-2019/ (accessed on 6 September 2020).
- 4.
U.S. Energy Information Administration, Monthly Energy Review, Appendix D.1, April 2020.
Abbreviations
- AC:
-
Absorption chiller
- BESS:
-
Battery energy storage system
- BMG:
-
Biomass generator
- CC:
-
Compression chiller
- CHPG:
-
Combined heat and power generator
- CSS:
-
Cooling storage system
- DE:
-
Differential evolution
- DG:
-
Diesel generator
- EC:
-
Electric chiller
- EMS:
-
Energy management system
- FC:
-
Fuel cell
- GB:
-
Gas boiler
- GE:
-
Gas engine
- GT:
-
Gas turbine
- HEG:
-
Hydro electric generator
- HSS:
-
Heating storage system
- MT:
-
Micro turbine
- NG:
-
Natural gas
- NPCE:
-
Net present cost of electricity
- PAFC:
-
Phosphoric acid fuel cell
- PV:
-
Photovoltaics
- RES:
-
Renewable energy sources
- RPL:
-
Real power loss
- SG:
-
Synchronous generator
- TCE:
-
Total cost of electricity
- TE:
-
Total emission
- TEC:
-
Total emission cost
- TESS:
-
Thermal energy storage system
- TPC:
-
Total production cost
- UG:
-
Utility grid
- VSI:
-
Voltage stability Index
- WT:
-
Wind turbine
- ABC:
-
Artificial bee colony
- ACO:
-
Ant colony optimization
- AMPSO:
-
Adaptive mutation particle swarm optimization algorithm
- BA:
-
Bat algorithm
- CSA:
-
Cuckoo search algorithm
- CSOA:
-
Chicken swarm optimization algorithm
- DEGL:
-
Differential evolution with global and local neighborhoods
- DEMPSO:
-
Differential evolutionary (De) and modified Pso
- DENGMS:
-
Differential evolution and the niche guided mating selection strategy
- DPLP:
-
Dynamic programming combined with the linear programming
- GA:
-
Genetic algorithms
- GAMS:
-
General algebraic modeling system
- GOAPSNN:
-
Particle swarm optimization aided artificial neural network and grasshopper optimization algorithm
- GWO:
-
Grey wolf optimization
- HBB-BC:
-
Hybrid big bang-big crunch
- HPEMG-HGPO:
-
Heuristic-based programmable energy management controller with hybrid genetic particle swarm optimization
- IBA:
-
Improved bat algorithm
- ICA:
-
Imperialist competitive algorithm
- ISA:
-
Interior search algorithm
- JAYAGM:
-
Jaya algorithm with the gradient method (Gm)
- SFS:
-
Stochastic fractal search algorithm
- LOHAWEC:
-
Lexicographic optimization and hybrid augmented-weighted epsilon-constrain
- MHS:
-
Modified harmony search
- MILP:
-
Mixed-integer linear programming
- MINLP:
-
Mixed-integer nonlinear programming
- MOALO:
-
Multi-objective ant lion optimizer
- MOFEPSO:
-
Multi-objective feasibility enhanced particle swarm optimization
- MOPSO:
-
Multi-objective particle swarm optimization
- NSGA:
-
Non-dominated sorting algorithms Ga
- PAFC:
-
Phosphoric acid fuel cell
- PSO:
-
Particle swarm optimization
- PSOFMN:
-
Particle swarm optimization with fuzzy max–min technique
- SOGSNN:
-
Squirrel optimization with gravitational search–aided neural network
- TSKFS-RBFNN:
-
Takagi–Sugeno–Kang fuzzy system under radial basis function neural network
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İcan, Ö., Çelik, T.B. (2021). A Review on Smart Energy Management Systems in Microgrids Based on Power Generating and Environmental Costs. In: Dorsman, A.B., Atici, K.B., Ulucan, A., Karan, M.B. (eds) Applied Operations Research and Financial Modelling in Energy. Springer, Cham. https://doi.org/10.1007/978-3-030-84981-8_4
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