Abstract
The advancement of renewable energy (RE) represents a pivotal strategy in mitigating climate change and advancing energy transition efforts. A current of research pertains to strategies for fostering RE growth. Among the frequently proposed approaches, employing optimization models to facilitate decision-making stands out prominently. Drawing from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems (RES) from 1990 to 2023 within the Web of Science database, this study reviews the decision-making optimization problems, models, and solution methods thereof throughout the renewable energy development and utilization chain (REDUC) process. This review also endeavors to structure and assess the contextual landscape of RES optimization modeling research. As evidenced by the literature review, optimization modeling effectively resolves decision-making predicaments spanning RE investment, construction, operation and maintenance, and scheduling. Predominantly, a hybrid model that combines prediction, optimization, simulation, and assessment methodologies emerges as the favored approach for optimizing RES-related decisions. The primary framework prevalent in extant research solutions entails the dissection and linearization of established models, in combination with hybrid analytical strategies and artificial intelligence algorithms. Noteworthy advancements within modeling encompass domains such as uncertainty, multienergy carrier considerations, and the refinement of spatiotemporal resolution. In the realm of algorithmic solutions for RES optimization models, a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven optimization. Furthermore, this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps, expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors.
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Abbreviations
- AI:
-
Artificial intelligence
- AHP:
-
Analytic hierarchy process
- APSO:
-
Adaptive particle swarm optimization
- BESS:
-
Battery energy storage system
- BP:
-
Bilevel programming
- CCHP:
-
Combined cooling, heating, and power
- CDPSO:
-
Chaotic Darwinian particle swarm optimization
- CVaR:
-
Conditional value-at-risk
- DDRO:
-
Data-driven robust optimization
- DE:
-
Differential evolution
- DEA:
-
Data envelopment analysis
- DL:
-
Deep learning
- DNP:
-
de Novo programming
- DP:
-
Dynamic programming
- DPPO:
-
Distributed proximal policy optimization
- DR:
-
Demand response
- DRG:
-
Distributed renewable generation
- DRL:
-
Deep reinforcement learning
- DROCCP:
-
Distributed robust optimization chance constraint programming
- DSM:
-
Demand-side management
- DSO:
-
Distribution system operator
- EFI:
-
Ecological footprint index
- ELECTRE:
-
Elimination et choice translating reality
- EV:
-
Electric vehicle
- FCP:
-
Fuzzy compromising
- FIT:
-
Feed-in tariff
- FL:
-
Fuzzy logic
- FMCDA:
-
Fuzzy multicriteria decision analysis
- GA:
-
Genetic algorithm
- GEP:
-
Generation expansion planning
- GHG:
-
Greenhouse gas
- GP:
-
Goal programming
- GRG:
-
Generalized reduced gradient
- GTEP:
-
Generation and transmission expansion planning
- HRES:
-
Hybrid renewable energy system
- IoT:
-
Internet of Things
- KKT:
-
Karush?Kuhn?Tucker
- LCA:
-
Life cycle assessment
- LCOE:
-
Levelized cost of electricity
- LP:
-
Linear programming
- LPSP:
-
Loss of power supply probability
- MADM:
-
Multiattribute decision making
- MC:
-
Markov chain
- MCDM:
-
Multicriteria decision making
- MCS:
-
Monte Carlo simulation
- MILP:
-
Mixed integer linear programming
- MINLP:
-
Mixed integer nonlinear programming
- ML:
-
Machine learning
- MO:
-
Multiobjective
- MODM:
-
Multiobjective decision making
- MOGA:
-
Multiobjective genetic algorithm
- MOGSO:
-
Multiobjective glow-worm swarm optimization
- MOGWO:
-
Multiobjective gray wolf optimizer
- MOPs:
-
Multiobjective optimization problems
- MOPSO:
-
Multiobjective particle swarm optimization
- MOWDO:
-
Multiobjective wind-driven optimization
- MPEC:
-
Mathematical program with equilibrium constraints
- NLP:
-
Nonlinear programming
- NPV:
-
Net present value
- NSGA:
-
Nondominated sorting genetic algorithm
- OPF:
-
Optimal power flow
- PDF:
-
Probability distribution function
- PPO:
-
Proximal policy optimization
- PSO:
-
Particle swarm optimization
- QPP:
-
Quadratic programming problem
- RE:
-
Renewable energy
- REDUC:
-
Renewable energy development and utilization chain
- RES:
-
Renewable energy system
- RL:
-
Reinforcement learning
- RO:
-
Robust optimization
- RPS:
-
Renewable portfolio standard
- SAE:
-
Stacked autoencoder
- SAIFI:
-
System average interruption frequency index
- SD:
-
System dynamics
- SP:
-
Stochastic programming
- SQP:
-
Sequential quadratic programming
- SVR:
-
Support vector regression
- TEP:
-
Transmission expansion planning
- TOPSIS:
-
Technique for order of preference by similarity to ideal solution
- UC:
-
Unit commitment
- WOS:
-
Web of Science
- WPM:
-
Weighted product model
- WSM:
-
Weighted sum model
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This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 72293572, 72174188, and 31961143006) and Hubei Natural Science Foundation, China (Grant No. 2019CFA089).
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Yu, S., You, L. & Zhou, S. A review of optimization modeling and solution methods in renewable energy systems. Front. Eng. Manag. 10, 640–671 (2023). https://doi.org/10.1007/s42524-023-0271-3
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DOI: https://doi.org/10.1007/s42524-023-0271-3