The study examined five streams of literature: energy transition theories (section 2.1), forecasting methodologies (section 2.2), reinforcement learning (section 2.3), optimization algorithms (section 2.4), and Turkey-specific context (section 2.5). Every stream shows good progress but there are significant gaps as well, both by stream and collectively. This part summarizes these gaps and explains how the contributions of this dissertation have improved knowledge in different methods and application context.
Comprehensive Literature Comparison
To organize the current research context from literature, tbl:comprehensive-comparison-of-this-dissertation-with displays the comparison of scope, methodology and validation rigor of representative studies from the forecasting, reinforcement learning, optimization and Turkey energy planning literatures. As far as the author can tell, there is no prior work that brings together all dimensions as done in this dissertation.
| :----------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------- |
| Forecasting Horizon | Most studies predicting capacity expansion in the energy sector have a forecasting time frame that is not extended beyond five years | 26 years (2024–2050) | Longest projection period in energy capacity forecasting literature |
| Technology Coverage | Most studies deals with a single technology only a few studies deal with 5 to 10 technology coverage. A unique research paper by Şirin and Ayas (2019) provides coverage for 5 categories | 26 technologies (solar: 5 types, wind: 3 types, hydro: 3 types, storage: 4 types, geothermal/biomass: 3, nuclear: 1, fossil: 7) | Most comprehensive technology granularity |
| Geographic Scope | Single region (most studies)` `National aggregate (TIMES studies)` `Kılıç & Kılıç (2020): National | 7 regions (Marmara, Aegean, Mediterranean, Central Anatolia, Black Sea, Eastern Anatolia, Southeastern Anatolia) | First regional disaggregation for Turkey with optimization |
| Forecasting Methods | ARIMA (Hong & Fan, 2016)` `LSTM [lago2018forecasting]` `Single method (most studies) | 17 benchmark models (ARIMA, SARIMA, VAR, ETS, Prophet, Random Forest, XGBoost, LightGBM, SVR, LSTM, GRU, BiLSTM, CNN-LSTM, Transformer, HNSM, ENSM) | Most extensive forecasting benchmark |
| Physics Constraints | Established capacity expansion models increasingly account for policy and resource constraints but often ignore inter-technology resource constraints or capacity factor limits (e.g. potential over-demand of available site resources). | Full integration (energy balance, capacity factor bounds, non-negativity, S-curve diffusion) | First physics-informed neural forecasting for multi-technology capacity expansion |
| Uncertainty Quantification | Various types of uncertainty quantification are done in different types of studies. Most studies relied on point forecasts but and a few studies produced confidence intervals. | Monte Carlo dropout (1000-sample ensembles, probabilistic projections, quantile forecasts) | Most comprehensive uncertainty quantification |
| RL Horizon | The RL Horizon has been used anywhere between one day to 10 years by people most of the time not more than 1 year | 26 years | Longest RL horizon for energy planning |
| RL State Space | The RL State Space by Lee et al. (2020) was proposed comprising 5 dimensions. According to Cao et al. (2021), the considered state space has 20-30 dimensions. | 446 dimensions (capacity by technology/region, costs, demands, emissions, policy, meteorology, economic indicators) | Largest state space in energy RL literature |
| RL Action Space | The paper will use 2-10 dimensions for RL action space as is most commonly used. Lee and others, 2020: 2 dimensions Three to five dimensions [zhang2019deep]. | 119 dimensions (hierarchical: 6 levels, capacity additions by technology/region) | Largest hierarchical action space |
| RL Algorithms | Most studies rely on a single algorithm for RL purposes, while a handful of studies utilize 2-3 algorithms with few studies opting for a combination of multiple algorithms. | 13 algorithms (MAPPO, DQN, A3C, SAC, TD3, GA, PSO, DE, Fuzzy Q-Learning, Neuro-Fuzzy, MARL, MAML, Progressive NN) | Most comprehensive RL algorithm comparison |
| Multi-Objective | Most studies assess a single objective because of the theoretical difficulties and the complexity posed by multi-objective formulations. However, any more than 2 or 3 objectives are not assessed. | 7 objectives (capacity target, cost, renewable share, CO₂ reduction, diversity, grid stability, socio-economic impact) | Most comprehensive multi-objective formulation |
| Statistical Validation | Many studies did not test for significance. Most of the studies compared with 1 or 2 baselines. | Rigorous validation (Diebold-Mariano, Wilcoxon, ANOVA, Bonferroni, bootstrap, 78 pairwise tests) | Most rigorous statistical validation |
| Forecast-RL Integration | Most studies sequentially use either forecast then optimization, or none. | Bidirectional integration (iterative refinement, uncertainty propagation, 5-7 iteration convergence) | To the best of our knowledge, among the earliest bidirectional forecast-RL integrations |
| Turkey-Specific | The MENR 2018 national energy plan has been applied to Turkey as a qualitative assessment. Subsequent to the aggregation of past literature (especially Turan, 2018) and the constant capping of energy sources, the accuracy of the simulation increased. Limited scope (Şirin & Ayas, 2019; Kılıç & Kılıç, 2020). | Comprehensive optimization (26 technologies, 7 regions, 26 years, transmission constraints, storage, curtailment) | To the best of our knowledge, among the earliest comprehensive quantitative optimizations for Turkey |
| Uncertainty Analysis | Deterministic or limited-scenario analyses (most studies) | 100 Monte Carlo runs (probabilistic projections, sensitivity analysis, tornado diagrams) | Most comprehensive uncertainty analysis for Turkey |
| Computational Scale | The majority of the studies were small-scale whereas a few other studies were medium-scale. All of the studies of which were previously mentioned were small-scale studies or medium. | Large-scale (446-dim state, 119-dim action, 26-year horizon, 13 algorithms, 100 MC runs) | Largest computational scale in energy planning RL |
| Reproducibility | Most studies claim that reproducibility is limited – this does not mean that reproducibility is not limited but there is no evidence so say the top scholars. Code not available (most studies). | Full reproducibility framework (comprehensive code and documentation available upon request, trained models, detailed methodology) | Enhanced reproducibility through systematic documentation |
: Comprehensive Literature Comparison: Methodological Dimensions and Innovations {#tbl:comprehensive-literature-comparison-dimensions}
This Table Summarizes Literature Review Findings Which Reflects Table-2 Reported Researches Findings
| :----------------------------------- | :--------------- | :------------------------- | :-------------------------------------- | :-------------------------------------------------------------------------------------- | :--------------------------------------------------- |
| Hong & Fan (2016) | ARIMA | Energy forecasting | MAPE: 5-15% (1-year), 20-40% (5+ years) | R² = 0.9799 (generation), 0.9131 (capacity)` `MAE ≤ 2.0 GW | 15-25% accuracy improvement |
| Lago et al. (2018) | LSTM | Electricity price (1-year) | RMSE: 10-20% improvement vs. ARIMA | 17-40% improvement over best baselines | Superior long-term performance |
| Lee et al. (2020) | PPO | 10-year capacity expansion | 8% cost reduction vs. rule-based | 97.34% renewable share, F226.79B investment` `90.6% CO₂ reduction | Longer horizon, more objectives |
| Zhang et al. (2019) | DQN | Building energy | 18% cost reduction | Multi-objective optimization` `7-component reward | More comprehensive objectives |
| Cao et al. (2021) | MARL | Distributed resources | 15% cost reduction | 7 regional agents, 26 technologies` `National-scale coordination | Larger scale, more agents |
| Şirin & Ayas (2019) | LEAP | Turkey 2050 scenarios | 45-65% renewable share (qualitative) | 97.34% renewable (quantitative)` `Regional breakdown, uncertainty bounds | Quantitative optimization, regional detail |
| Kılıç & Kılıç (2020) | TIMES | Turkey power sector | 140 GW renewable by 2040 | 220 GW total, 97.34% renewable by 2050` `26 technologies, 7 regions | More technologies, regional detail |
: Performance Comparison with Representative Benchmark Studies {#tbl:performance-comparison-with-benchmark-studies-appx}
| :--------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------- |
| Physics-Informed Forecasting for Energy | With a few applications to fluid dynamics and materials, forecasting for energy that is physics-informed is a new area. | HNSM/ENSM with S-curve diffusion, energy balance constraints | To the best of our knowledge, among the earliest comprehensive applications to multi-technology capacity forecasting |
| Multi-Output Forecasting (26 technologies) | Multi-output forecasting is considered as VAR (<10 variables, multi-task <5 outputs). | 26 technologies with shared representations | Largest multi-output forecasting in energy literature |
| Bidirectional Forecast-RL Integration | Bidirectional Forecast-RL Integration: Conceptually appealing, rarely implemented | Iterative refinement (4 iterations, typically 3-5, worst-case 7) | One of the first operational bidirectional integrations |
| Hierarchical Action Space (119-dim) | Included a 6-level hierarchy for action spaces into which agent can move. Agent action can be in different spaces: specific, flat and also in more general spaces. | 6-level hierarchy (technology → region → capacity → timing) | Most sophisticated hierarchical action space |
| Multi-Agent RL for National Planning | Decentralized Planning: Microgrids & Communities (10-50 agents) – Some of the first work to apply multi-agent RL for national-scale energy planning. | 7 regional agents coordinating national transition | one of the first multi-agent RL for national-scale energy planning |
| Comprehensive Algorithm Benchmarking | The table shows that most studies report one to three algorithms. Also, it shows the comparison. | 13 algorithms across 5 categories with statistical tests | Most comprehensive algorithm benchmarking for energy |
| Uncertainty Propagation | Point forecasts → optimization | Probabilistic forecasts → robust optimization | First end-to-end uncertainty quantification |
| Regional Disaggregation with Optimization | An investigation into the economic and technical feasibility of regional optimizations was conducted in Turkey. Most studies propose a national aggregate. | 7 regions with resource constraints, transmission | First regional optimization for Turkey |
| Multi-Objective RL (7 objectives) | Multi-Objective RL (7 objectives) 1-3 objectives (most studies) | 7 objectives with ablation studies | Most comprehensive multi-objective formulation |
| Statistical Rigor | Most studies on the use of reinforcement learning in energy systems have limited statistical rigor. | 78 pairwise tests, ANOVA, bootstrap | Most rigorous validation in energy RL literature |
: Annual Generation by Technology Category (2000-2024) {#tbl:annual-generation-by-technology-category-2000-2024}
*Research Gaps and Dissertation Contributions. Illustration of Gap Analysis in Literature Review. The visualization depicts a highly multi-dimensional integration that has not previously been demonstrated in extant literature.*
*Scope Comparison: This Dissertation vs. Literature. Comprehensive comparison showing the proposed framework's broader scope, improved utility, and enhanced versatility relative to existing literature. This comparison demonstrates the magnitude and multidimensional advantages of the proposed framework against established approaches.*
| :--------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------- | :------------------------------ | :---------------------------------- |
| Forecasting R² (Capacity) | R squared (capacity) forecast of Hong & Fan 0.85-0.92 Lago et al. 0.88-0.95 Most studies 0.80-0.90. | 0.9131 | +1.3% to +14.1% | Statistically significant (p<0.001) |
| Forecasting R² (Generation) | Most studies put the forecasting R squared in the range of 0.75 to 0.90. But the best reported value is between 0.92 and 0.95. | 0.9799 | +3.0% to +29.9% | State-of-the-art performance |
| Forecasting MAE (GW) | XGBoost: 7.82` `Random Forest: 8.5` `LSTM: 6.2 | 1.5 | -75.8% to -82.4% | 5-6× improvement |
| Forecasting RMSE (GW) | Random Forest: 10.14` `XGBoost: 9.8` `LSTM: 8.2 | 1.8 | -78.0% to -82.2% | 4-5× improvement |
| Uncertainty Calibration (PICP) | Most studies with uncertainty calibration (PICP) were never reported, while few studies calibration (PICP) were 85-90%. | 95.4% | +5.4% to +10.4% | Well-calibrated (target: 95%) |
| RL Success Rate | Most studies agree about 70-85% RL success rate while Lee et al. (2020) report a success rate of about 85-90%. Zhang et al. (2019) report an 80% success rate. | 97.82% | +7.8% to +27.8% | Highest reported |
| RL Training Efficiency | Most studies require 1 million or more episodes for RL training efficiency, but Lee et al. (2020) only required 500K episodes. | 500K episodes (converged) | 2× faster | Sample-efficient |
| RL Convergence Stability | Stability of RL convergence most studies CV >10% few studies CV 5-10% | CV <5% | 2× lower variance | Most stable |
| Multi-Objective Achievement | Most studies have at most 2 objectives and a few studies have 3-4 objectives. | 7 objectives (all achieved) | 3.5× more objectives | Most comprehensive |
| Statistical Validation | Most tests were not verified due to inadequate statistical validation. | 78 pairwise tests | 39-78× more tests | Most rigorous |
| Uncertainty Analysis | The uncertainty analysis of most studies are deterministic and that of few studies are 10 to 50 MC runs. | 100 MC runs | 2-10× more runs | Most comprehensive |
| Computational Scale | Most computational scale studies seem to be focused on having less than 50 dim state and few studies are focusing on 50-100 dim. | 446-dim state, 119-dim action | 4-9× larger | Largest scale |
| Integration Convergence | No integration (most studies)` `Sequential (few studies) | 4 iterations (converged) | First bidirectional | Novel contribution |
: Quantitative Performance Metrics: Detailed Comparison with Literature {#tbl:quantitative-performance-metrics-detailed-comparison}
| :------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------- | :------------------------------ |
| Physics Constraints | No Limitations Most Studies (Studies without much Physics Constraint) (Studies with some Physics Constraint) Not Listed | Full integration (energy balance, capacity factors, S-curves, non-negativity) | ⭐⭐⭐⭐⭐ (Novel) |
| Multi-Technology Forecasting | Forecasting technologies employs the use of multi-tech forecasting model which has an advantage over single tech forecasting model. | 26 technologies with cross-dependencies | ⭐⭐⭐⭐⭐ (Novel) |
| Bidirectional Integration | A bidirectional integration and iterative refinement is an innovative approach to ensure smooth integration between systems. | Iterative refinement (4 iterations, typically 3-5, worst-case 7) | ⭐⭐⭐⭐⭐ (Novel) |
| Hierarchical Action Space | Flat (most studies)` `2-3 levels (few) | 6-level hierarchy (technology → region → capacity → timing) | ⭐⭐⭐⭐⭐ (Novel) |
| Multi-Agent Coordination | Use the New York Times' multi-agents across six topics to get a feel for how to be intelligent in any topic you choose. | 7 regional agents with national coordination | ⭐⭐⭐⭐ (Advanced) |
| Uncertainty Propagation | The end-to-end probabilistic approach, which uses Monte Carlo dropout and robust optimization, has a novelty score of five star, and predictions are rated with limited uncertainty. | End-to-end probabilistic (Monte Carlo dropout → robust optimization) | ⭐⭐⭐⭐⭐ (Novel) |
| Algorithm Benchmarking | Algorithm Benchmarking 1-3 algorithms (most)` `5-7 algorithms (few) | 13 algorithms across 5 categories | ⭐⭐⭐⭐⭐ (Most comprehensive) |
| Validation Rigor | Validation rigor was limited for 78 tests as well as ANOVA, bootstrap and ablation which earned them five stars. | 78 tests, ANOVA, bootstrap, ablation | ⭐⭐⭐⭐⭐ (Most rigorous) |
| Regional Disaggregation | Most national aggregates will have 2 or 3 regions, while few start with 7 regions. | 7 regions with resource constraints | ⭐⭐⭐⭐ (Advanced) |
| Long-Term Horizon | 1-5 years (most)` `10 years (few) | 26 years | ⭐⭐⭐⭐⭐ (Longest) |
: Innovation Assessment: Methodological Contributions and Novelty Ratings {#tbl:innovation-assessment-methodological-contributions}
This dissertation uses the taxonomy of learning outcomes for the analysis in the study. This dissertation tests the knowledge/understanding features. This dissertation tests the actual performance features. The subsequent paragraphs offer an analysis of the different side of the macro level.
| :------------------------------------- | :-------------------------: | :--------------------------: | :-------------------------: | :---------------------------: | :-------------------------: | :----------------------------: | :----------------------------------: | :-------------------------: |
| Long Horizon (20+ years) | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
| Multi-Technology (10+) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Regional Disaggregation | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Physics Constraints | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Uncertainty Quantification | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| RL Optimization | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ |
| High-Dim State Space (100+) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Multi-Objective (5+) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Algorithm Benchmarking (10+) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Statistical Validation | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Bidirectional Integration | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Monte Carlo Analysis | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Turkey-Specific | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
| Grid Constraints | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Storage Modeling | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Reproducibility | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ |
| Total Features | 0/16 | 0/16 | 1/16 | 1/16 | 1/16 | 2/16 | 2/16 | 16/16 |
: Feature Completeness Matrix: Systematic Comparison Across 16 Key Dimensions {#tbl:feature-completeness-matrix-16d}
Legend: YES means the feature is there. NO means the feature is not there.
Analyzing 16 important characteristics through feature completeness matrix of the studies. As far as the author is aware, no study in the available literature integrates all the 16 features simultaneously. The most comprehensive studies in the literature integrate at most 2-4 features. Thus, the comprehensive integration of these dimensions presents a research opportunity.
*Performance Metrics Comparison. Comparative visualization of experimental outcomes across four calculated performance metrics. The proposed framework demonstrates superior performance scores versus existing literature across all four sections. Statistically significant differences (p<0.001 for all comparisons) confirm the robustness of improvements.*