Optimization Of Energy Systems Research Articles

Improving wind power forecast accuracy for optimal hybrid system energy management

Abstract Due to its renewable and sustainable features, the wind energy is growing up around the word. However, the wind speed fluctuation induces the intermittent character of the generated wind power. Thus, wind power estimation, through wind speed forecasting, is very inherent to ensure an effective power scheduling. Four wind speed predictors based on deep learning networks and optimization algorithms, were developed. The designed topologies are the multilayer perceptron neural network, the long short-term memory network, the convolutional short-term memory network and the bidirectional short-term neural network coupled with the Bayesian optimization. The models performance was evaluated through evaluation indicators mainly, the root mean squared error, the mean absolute error and the mean absolute percentage. Based on the simulation results, all of them show a considerable prediction results. Moreover, the combination of the the long short-term memory network and the optimization algorithm is more robust on wind speed forecasting with a mean absolute error equal to 0.23 m/s. The estimated wind power was investigated for an optimal Wind/PV/Battery/Diesel energy management. The handling approach lies on the continuity of the load supply through the renewable sources as priority, the batteries on second order and finally the diesels. The proposed management strategy respects the designed criteria with satisfactory contribution percentage of the renewable sources equal to 71%.

Accelerating Conjugate Heat Transfer Simulations in Squared Heated Cavities through Graphics Processing Unit (GPU) Computing

This research develops an innovative framework for accelerating Conjugate Heat Transfer (CHT) simulations within squared heated cavities through the application of Graphics Processing Units (GPUs). Although leveraging GPUs for computational speed improvements is well recognized, this study distinguishes itself by formulating a tailored optimization strategy utilizing the CUDA-C programming language. This approach is specifically designed to tackle the inherent challenges of modeling squared cavity configurations in thermal simulations. Comparative performance evaluations reveal that our GPU-accelerated framework reduces computation times by up to 99.7% relative to traditional mono-core CPU processing. More importantly, it demonstrates an increase in accuracy in heat transfer predictions compared to existing CPU-based models. These results highlight not only the technical feasibility but also the substantial enhancements in simulation efficiency and accuracy, which are crucial for critical engineering applications such as aerospace component design, electronic device cooling, and energy system optimization. By advancing GPU computational techniques, this work contributes significantly to the field of thermal management, offering a potential for broader application and paving the way for more efficient, sustainable engineering solutions.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Optimization and performance enhancement of renewable energy microgrid energy system using pheasant bird optimization algorithm

The main aim of this research work is to design and select an optimal renewable energy resource based microgrid (MG) system for rural area electrification of India. MG system consists of diesel generator (DG), battery bank (BB), wind turbine (WT), biomass generator (BMG) and solar photovoltaic (PV) modules with seven different configurations. The optimal system is selected based on technological, economic, environmental, social and reliability parameters. For this objective, a novel algorithm called pheasant bird optimization algorithm (PBOA) is presented and compared with particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), cuckoo search algorithm (CSA) and HOMER Pro. Results show that PV/WT/BMG/BB/DG is an optimal configuration with sizes of 1148 units of PV, 42 units of WT, 20 units of BMG, 36 units of BB and 1 unit of DG. PBOA has determined the optimal resource size for the MG system. In terms of social and reliability parameters for the PV/WT/BMG/BB/DG system, PBOA generated values of 93.58 %, for RF, 0.08754 for EGJC and 0.01 % for LPSP. For the optimal configuration, PBOA estimated NPC, COE, CE values are ₹66185779, ₹9.3 and 650901 kg/yrs respectively. PBOA reaches the minimal NPC of ₹66185779 in just 180 iterations, whereas PSO, ACO, GA, and CSA required 360, 200, 360 and 250 iterations respectively. PBOA also achieved minimal COE at a rate of 1.52, 1.11, 1.11 and 1.94 times faster than PSO, GA, CSA and ACO respectively. The contribution of PV and WT sources to the total generated electricity is approximately 50 % and 35 % respectively. PBOA exhibits faster convergence time and iteration compared to the PSO, ACO, GA and CSA algorithm. The comparison results indicate that the optimal PV/WT/BMG/BB/DG configuration select for rural areas. This research provides valuable information for policy makers and investors on the implementation of MG system in rural regions.

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement.

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

Techno-economic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand

As New Zealand moves towards net zero carbon emissions by 2050, multiple factors must be considered including increased electrical load due to electrification, variability of renewable energy generators, required storage capacity, system economics and limitations on grid transmission capacity. Complex and region-specific interactions between the various design choices involved are likely to require an understanding of a range of optimal and near-optimal designs for proposed micro-grid systems as opposed to a single optimal point. This work develops a novel multiscale optimization algorithm for optimization from a univariate capacity optimization approach to a multivariate one. This enhanced algorithm is applied to a grid-connected hybrid energy system consisting of local wind and solar generation, battery storage, and a limited grid connection for industrial and residential loads. This analysis is repeated for current 2023 and forecasted net zero 2050 grid conditions. Development of local generation allows for a 36.8% reduction of levelized cost of electricity in the 2023 case and a 38.6% reduction in the 2050 case. This results in a projected reduction of 19920 tonnes of CO2/yr. The algorithm and methodology developed are broadly applicable to optimization of next-generation energy grids.

Application of Renewable Energy in Green Buildings and Energy Consumption Optimization

INTRODUCTION: With the increasing global awareness of sustainable development and environmental protection, green building has become one of the important development directions in the construction industry. The application of sustainable type energy in the construction industry is of great significance in reducing building energy consumption and environmental pollution. This study aims to explore the application of sustainable types of energy and conduct research on energy consumption optimization. OBJECTIVE: The aim of this study is to analyze the current situation of the application of sustainable types of energy in the construction industry, to explore its impact on the energy consumption of buildings, and to propose corresponding optimization strategies in order to achieve the goal of sustainable development of green buildings in China. METHODS: This study adopts a combination of literature review and case study; firstly, a literature review on the application of sustainable types of energy, sorting out its technical characteristics and application effects; then, several typical cases are selected to analyze its energy application and energy consumption in buildings; finally, relevant strategies and suggestions for optimizing the energy consumption are put forward by combining the results of the literature review and the case study. RESULTS: Through the literature review and case analysis, it is found that sustainable types of energy, such as solar energy and wind energy, have been widely used in buildings and achieved certain energy-saving effects. However, there are also some problems, such as inefficient energy utilization and high cost. To address these problems, this study proposes a series of optimization strategies, including suggestions for optimizing energy system design, improving energy utilization efficiency, and reducing energy costs. CONCLUSION: This study concludes that the application of sustainable types of energy in green buildings is an important way to optimize building energy consumption and sustainable development. Through measures such as optimizing energy system design and improving energy utilization efficiency, building energy consumption can be further reduced, environmental pollution can be reduced, and the development of the construction industry can be promoted. However, further research and practice are still needed to continuously improve relevant technologies and policies to promote the application and development of sustainable types of energy in buildings.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

LVRT Enhancement of DFIG-based WECS using SVPWM-based Inverter Control

Abstract: Across many countries, wind turbine generation systems (WTGS) have been established over the past few decades. In this paper, we augment the low voltage ride-through (LVRT) enrichment facility of driving a DFIG-based wind energy conversion system (WECS) using space vector pulse width modulation (SVPWM)-based inverter control. The proposed technique employs an SVPWM-based control algorithm to regulate the voltage and frequency of the output power during grid faults, thereby enhancing the WECS's ability to remain connected to the grid and provide power. The study focuses on decreasing transient current throughout the instant of fault. Modeling and control approaches were also discussed in this study. The performance of the proposed technique is evaluated using MATLAB/Simulink simulations, and the results demonstrate that the technique effectively improves the LVRT capability of the DFIG-based WECS. Background: Due to the variation in wind speed, the power generated by wind turbines is inconsistent. The power generated and the losses in wind turbines change correspondingly with changes in wind speed. The only type of machine that can generate power at speeds below the fixed speed is the doubly-fed induction generator (DFIG). But DFIG is oversensitive to network faults, which makes the bidirectional converters and DC link capacitor fail due to high inrush current and over-voltage. Methods: The converters connected to DFIG consist of an AC-to-DC converter, a boost converter, and a space vector pulse width modulation (SVPWM)-based DC-AC converter. The performance of the SVPWM controller is analyzed during symmetrical and unsymmetrical fault conditions. Results: The anticipated control provides adequate reactive power support to the network through the time of the fault and improves voltage and current waveform. The reactive power flow is also analyzed, and the effectiveness of the proposed controller is verified using MATLAB and Simulink. Conclusion: SVPWM (Space Vector Pulse Width Modulation)-based inverter control is an effective technique for wind energy conversion systems (WECS). The use of SVPWM can provide accurate and precise control of the AC voltage generated from the DC voltage source, resulting in improved system efficiency and reduced harmonic distortion in the output waveform. : The comparative analysis of THD suggests that SVPWM is a superior technique compared to other inverter control techniques such as sine-triangle pulse width modulation (SPWM) and carrier-based pulse width modulation (CPWM). SVPWM can help to reduce the distortion in the output waveform, leading to improved system efficiency, reduced wear on the system components, and overall better performance of the WECS. Furthermore, SVPWM offers several advantages over other inverter control techniques, including better utilization of DC voltage, improved voltage control, and better utilization of switching devices. These advantages make SVPWM a valuable tool for optimizing the operation of WECS and improving the reliability and performance of renewable energy systems. The value of THD for SVPWM inverter control in WECS is 1.53 under symmetrical fault and 1.34 for unsymmetrical fault, respectively. In summary, the use of SVPWM-based inverter control for WECS is an effective way to improve the efficiency and performance of the system while reducing the distortion in the output waveform and providing adequate reactive power support. The advantages of SVPWM over other inverter control techniques make it a valuable tool for the development and optimization of renewable energy systems.

Carbon and economic prices optimization of a solar-gas coupling energy system with a modified non-dominated sorting genetic algorithm considering operating sequences of water-cooled chillers

To promote the continuous operating of solar energy systems and increase the proportion of renewable energy consumption, solar devices are often integrated with conventional energy systems to reduce greenhouse gas emissions. In this study, a solar-natural gas coupling system is constructed, utilizing solar photovoltaics, thermal collectors, and high-efficiency energy conversion devices. After modelling the energy system, the operating modes are illustrated considering working sequences of water-cooled devices. A modified genetic algorithm, which incorporates reverse learning, controlled elite, and dynamic crowding distance theory, is constructed to optimize the configurations of the proposed energy system, with the aim of minimizing carbon and economic prices of the products. The performance of the energy system is compared with different algorithms and cases, and the results demonstrate that the proposed optimization algorithm addresses the limitations of conventional methods, albeit with increased optimization times. Moreover, the proposed operating mode (Case 1) exhibits a stable performance, leading to a higher environmental and economic performance compared to conventional modes, with carbon and economic prices of 0.242 kg/kWh and 0.067 $/kWh, respectively. This study provides new insights into carbon footprints and economic performance evaluation of high-efficiency energy systems, offering valuable directions for future research in this field.

Optimization of off-grid renewable energy systems using a hybrid version of golden search algorithm

Currently, the development of HRESs (hybrid renewable energy systems) in remote areas is of great importance and popularity. However, measuring and optimizing the capacity of these systems faces a difficult challenge. Multiple works had been reported in the literature to optimize such systems, all of which aim to achieve an optimal configuration with minimum annual net cost. Therefore, the significance of providing off-grid electrification to remote areas through HRESs can be highlighted as a crucial case for sustainable growth. Accordingly, the study proposes a modified metaheuristic approach, known as the Hybrid Golden Search Algorithm (HGSA), for long-term application planning and optimization of the off-grid HRES. The aim of this algorithm is to minimize the amount of net cost which is used annually; to reduce the probability of power supply interruption. In order to assess the effectiveness of the proposed algorithm, a simulation study over a long period on a remote area was conducted. From the results, increasing the reliability level from 1 % to 3 % causes a decrease in the total net annual cost by around 7.3 % under the proposed HGSA and also results in a reduction in component size (around 6 % and 21 % reductions for the wind turbine area and storage tanks, respectively). Further, the HGSA technique obtains the lowest value of fitness function for the hybrid system at a reliability level of 3 %, which is 31,539,810$. This result demonstrates that the efficiency of HGSA outperforms Fuzzy Logic and Optimization, Artificial Bee Colony (ABC), and GSA techniques. Based on this, the proposed HGSA could lead to more promising results than the other comparative algorithms. Hence, the proposed HGSA can be a valuable tool for long-term application planning and optimization of off-grid HRES, which can contribute significantly to achieving sustainable development goals.

Open energy system modelling for low-emission hydrogen roadmap planning: The case of Colombia

Climate change is driving the need for new approaches to energy production and utilization globally, contributing to the mitigation of greenhouse gas (GHG) emissions. Low-emission hydrogen is recognized as a key energy vector for decarbonization, and numerous countries are currently evaluating strategies and roadmaps for its integration. Colombia, with its abundant renewable resources, is poised as a potential player in hydrogen production. This study presents a techno-economic assessment of hydrogen pathways and their cross-sectoral interactions within the Colombian energy system. By employing an open energy system optimization framework (OSeMOSYS), we modelled a detailed supply-demand hydrogen chain and analysed a series of scenarios from 2021 to 2050, including a sensitivity analysis. Our results show that hydrogen pathways will require 2–6.4 Mt/year in hydrogen production, 8–27 GW in new installed capacity, and US$50–80 billion in investments, with favourable impacts in GHG mitigation, energy imports, and socioeconomic benefits. We use these findings to suggest an update to the targets of the current National Hydrogen Roadmap focused on a higher ambition to deploy a full hydrogen economy. Our open methodology provides a valuable tool for conducting further assessments in developing countries interested in defining low-emission hydrogen roadmaps.

Optimal scheduling of multi-regional energy system considering demand response union and shared energy storage

In the current context of the scarcity of fossil energy and the large-scale development and utilization of new energy sources, the power system is developing in the direction of multi-source synergy and interconnection. Demand response technology and energy storage technology have become important adjustment means of integrated energy system because of their efficient coordination ability and flexible adjustment ability. However, in the system where multiple energy systems operate cooperatively, there are still some limitations in time and capacity scales. Therefore, in order to enhance the demand-side response capability in multi-energy systems and give full play to the function of energy storage power stations, this paper proposes an optimal scheduling model for multi-area energy systems that considers joint demand response and shared energy storage. First, the system energy coupling matrix is constructed based on energy hubs, and the modeling framework for joint demand-side response and shared energy storage is established. Second, the multi-objective optimization problem is solved using NSGA-II algorithm solution with the optimization objectives of minimizing the total operating cost of the system and maximizing the net environmental impact. Finally, the simulation analysis is carried out. The simulation results show that the addition of joint demand response and shared energy storage can guide the scheduling optimization of multiple energy sources in each region in time and space, and realize the energy complementarity and mutual assistance of multi-regional energy systems. Compared with the traditional multi-regional energy system optimization scheduling scheme, the operation cost of the scheme is reduced by 4.2 %, and the environmental protection is improved by 41.9 %, which proves the feasibility of the optimal scheduling model proposed in this paper.

Complementary assessment and design optimization of a hybrid renewable energy system integrated with open-loop pumped hydro energy storage

The intermittent nature of renewable energy resources is a major obstacle in utilizing such resources for power generation despite their abundance. In this paper, a dynamic analysis of an integrated system based on photovoltaic (PV) panels and wind turbines coupled with pumped hydro energy storage is presented. When the power generation exceeds the power demand, surplus electricity is used to pump the water to the upper reservoir. The natural inflow of the river obviates the necessity for oversized reservoirs and saves costs. A recently introduced multi-objective equilibrium optimizer algorithm is developed to find the optimal size of the integrated hybrid energy system. The objectives are to minimize the levelized cost of electricity and maximize the reliability of the proposed system. The results show that the correlation between solar and wind power generation in the selected location is mildly strong with a value of 0.3. The total energy generation from hydro, solar, and wind resources are 77,508.7 kWh, 103,323 kWh, and 47,375.4 kWh, respectively. Comparing the results with other algorithms indicates that the equilibrium optimizer has a better performance in finding the optimal design.

Optimal planning and designing of microgrid systems with hybrid renewable energy technologies for sustainable environment in cities.

Although hybrid wind-biomass-battery-solar energy systems have enormous potential to power future cities sustainably, there are still difficulties involved in their optimal planning and designing that prevent their widespread adoption. This article aims to develop an optimal sizing of microgrids by incorporating renewable energy (RE) technologies for improving cost efficiency and sustainability in urban areas. Diverse RE technologies such as photovoltaic (PV) systems, biomass, batteries, wind turbines, and converters are considered for system configuration to obtain this goal. Net present cost (NPC) is this study's objective function for optimal sizing microgrid configuration. For demonstration, we assess the technical, economic factors, and atmospheric emissions of optimal hybrid renewable energy systems for Putrajaya City in Malaysia. The required solar radiation data, temperature, and wind speeds are collected from the NASA surface metrological database. From the quantitative analysis of simulations, the biomass-battery-based system has optimal economic outcomes compared to other systems with an NPC of around 1.07M$, while the cost of energy (COE) is 0.118 $/kWh. Moreover, environmentally safe nitrogen oxide emissions, carbon monoxide, and carbon dioxide concentrations exist. The grid-tied RE technology boasts cost-effectiveness, with an NPC of 348,318 $ and a COE of 0.0112 $/kWh. This study aids decision-makers in formulating policies for integrating hybrid RE systems in urban areas, promoting sustainable energy generation.

Integrated Energy System Optimization for Electric Vehicles and Demand Response within Carbon Trading Mechanism

Integrated Energy System Optimization for Electric Vehicles and Demand Response within Carbon Trading Mechanism

Reinforcement Learning Techniques in Optimizing Energy Systems

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization.

Power sector decarbonisation in developing and coal-producing countries: A case study of Indonesia

Global efforts to transition away from coal within a decade encounter significant challenges, particularly in developing and coal-producing countries, e.g. Indonesia. Most current studies do not discuss the impact of coal phase-out on the upstream sector, which is important for coal-producing countries. This study aims to analyse Indonesia's power sector decarbonisation impact on the power and fossil upstream sectors. This analysis used VEDA-TIMES energy system optimisation using three scenarios: least-cost system (BAU), decarbonisation without coal phase-out (PA 1.5), and decarbonisation with coal phase-out (PA 1.5 PO). In all cases, decarbonisation will reduce the amount of coal used in energy systems. However, in a technology-agnostic transition, coal still constitutes 16% of total capacity in 2060. By this time, the coal-fired power generating assets have also transitioned to high efficiency, low emissions (HELE) technologies. CCS plays an important role in Indonesia's decarbonisation. A key implication of the coal phase-out scenario is the stranding of $9 bn worth of existing assets and an increased role for renewable energy and imported natural gas in 2035–2050, posing a notable threat to gas security within the industrial sector. In addition, the potential loss due to untapped coal reserves is estimated at $63 bn, while the cumulative net loss in the coal upstream sector amounts to $799 bn (0.26% of Indonesia's GDP and 3.7% of the coal-producing GDRP). Nevertheless, the total investment and electricity production costs exhibit negligible differences in the rapid VRE expansion, but it will reduce total investment by 24% in the low VRE buildrate.