A queueing-based framework for packetized energy in renewable storage systems

Maintenance scheduling optimization in renewable and conventional power systems: A review

Fractional-order control and energy storage strategy for frequency stability in renewable power systems

Sailing yachts can achieve operationally zero-emission propulsion by integrating solar photovoltaic (PV), wind and hydrokinetic generation with battery-electric drive systems. This study applies a systems engineering modeling framework to quantify the environmental, operational-energy and techno-economic performance of a renewable-electric retrofit for a 12 m cruising monohull, evaluated against diesel and battery-electric alternatives. An ISO 14040/14044-consistent life-cycle assessment (LCA) implemented in an Excel toolchain is coupled with time-resolved energy-balance simulations and a retrofit-oriented cost model (baseline year 2025). Over the functional unit (20 years or 20,000 nautical miles), the diesel baseline produces ∼ 65 t CO 2 -eq, while the battery-electric case yields ∼ 28 t CO 2 -eq under a moderately clean grid and the renewable-electric configuration achieves ∼ 10–12 t CO 2 -eq ( ∼ 80%–85% reduction versus diesel). In both electric cases, onboard operational CO 2 emissions are eliminated, while life-cycle impacts persist due to manufacturing, replacement and end-of-life processes. Energy simulations show that integrated PV, wind and hydro generation can supply >80% of combined hotel and propulsion demand under representative cruising profiles, with storage buffering variability and an energy management strategy prioritizing real-time renewable utilization. The principal constraint is prolonged motoring in low-renewable conditions: a 30 kWh usable battery provides approximately 4 h at 5–6 kn ( ∼ 2.6–3.1 m/s). Sensitivity results emphasize that life-cycle outcomes are strongly influenced by electricity carbon intensity, battery production impacts, recycling rates and renewable availability. Overall, the study provides a transparent, replicable framework for designing and evaluating renewable-electric propulsion in recreational and small-scale marine craft within the broader scope of green technologies and sustainability. • Renewable-electric yachts cut life-cycle emissions by up to 85%. • Solar, wind, and hydro meet over 80% of yacht energy demand. • Life-cycle model links vessel design, operation, and end-of-life impacts. • Adaptive energy management improves autonomy and battery lifespan. • Techno-economic analysis shows feasible payback under real sailing use.

ABSTRACT While adaptive façades have been extensively studied, computational platforms for designing nearly-zero energy buildings with optimally controlled adaptive façades remain limited. This study introduces a two-stage optimization platform to achieve thermo-visual comfort with minimal energy consumption in the first stage, followed by the optimal sizing of a renewable energy system to meet the corresponding energy demand in the second stage. The performances of the Opossum and deep reinforcement learning algorithms are compared for controlling façade openness to maintain indoor thermo-visual comfort and reduce energy demand. Once the first-stage optimization converges, a black box model is used to optimally size a standalone rooftop solar system or a hybrid solar/wind energy system. The proposed platform was validated through simulations of an office building using real-world data. Results indicate that controlling the façade with a deep reinforcement learning algorithm enhanced the annual daylight glare probability and the predicted percentage of dissatisfied by 5.9% and 2.01%, respectively, compared to Opossum. However, the off-grid solar and hybrid systems designed using the Opossum solver achieved 13.73% and 11.11% lower levelized costs of electricity, respectively, compared to those designed by the deep reinforcement algorithm.

Green hydrogen storage based two-stage optimal peak shaving for high-penetration renewable power system

A stochastic accommodation rate-constrained robust scheduling for renewable power systems

Static and rotary transformers for renewable energy storage systems

Hierarchical control framework for offshore hybrid AC/DC microgrid integrating renewable energy resources and hybrid energy storage system

Next-generation control for electrolyzers: a review of GPT-based AI frameworks in renewable hydrogen systems

Exploring the optimal size of grid-forming energy storage in an off-grid renewable P2H system under multi-timescale energy management

The energy storage systems are under pressure in terms of finding smart charging systems that are safe and fast, there is more demand of the high-performance and long life of the system. Constant-current constant-voltage (CC-CV) charging, though popular, is known to encourage high temperature and extreme rapidity in the losses of capacity in highly dynamic charging. To address this problem, this paper will introduce a Machine Learning (ML) Based Optimization Framework of adaptive battery charging that will maximize the charging rate and the battery will not be damaged. This framework has been used to combine deep learning and Reinforcement Learning (RL) to actively regulate the charging current and voltage, based on the current values of the battery state, e.g., State of Charge (SoC), State of Health (SoH), temperature, and internal resistance. A multi-objective rewarding process has been created to optimalize the working time, thermal stability and degradation rate as a combination. Some comparative analysis was conducted among different methods including Conventional CC -CV, Rule-Based fast Charging, LSTM-RL and the developed Transformer-RL model. The results indicate that Transformer-RL controller allowed decreasing the total time of charge by about a quarter of 39 percent, top temperature by less than 41 o C, and cycle life by an average of 35 percent comparing to the traditional methods. The convergence of rewards analysis supported the stable learning and good trade-off between the performance and the safety. The findings report the potential to expand Transformer-RR optimization on building health-conscious, intelligent, and self-adaptive charging protocols of the next-generation electric vehicles and renewable energy storage systems. It relies on the approach of real-time launching of smart charging systems which can balance energy efficiency and long-term battery endurance.

To address heightened source–load uncertainty and strengthened spatiotemporal dependence under high-penetration wind and photovoltaic integration, and to support a low-carbon and sustainable transition of power systems without compromising reliability, this study aims to develop a practical framework that converts spatiotemporally correlated uncertainty into actionable inputs for adequacy evaluation and reliability-constrained capacity-compensation decisions. First, a spatiotemporally correlated joint source–load forecasting model is established to generate statistically consistent joint uncertainty scenarios for operational risk analysis. Second, system adequacy is quantified using Loss of Load Probability and Expected Energy Not Served, and the computational burden is reduced through typical-day/representative-scenario construction with probability weighting, enabling efficient yet risk-preserving adequacy assessment. Finally, a risk-driven unified capacity-compensation clearing model is formulated that incorporates resource marginal costs and an unserved-energy penalty, while enforcing explicit reliability constraints to obtain economically optimal compensation decisions. Case studies demonstrate that the proposed framework effectively mitigates loss-of-load risk and improves both the economic performance and computational efficiency of compensation clearing. These results can support system operators and market operators in scenario-based adequacy studies and reliability-constrained clearing, and provide regulators and planners with quantitative evidence for designing capacity-remuneration mechanisms that facilitate secure renewable integration and sustainable power system operation.

The rapid integration of inverter-based renewable energy sources (RES), particularly solar photovoltaic (PV) and wind power plants (WPPs), together with the large-scale deployment of battery energy storage systems (BESSs) is fundamentally reshaping modern power systems. While these technologies are essential for decarbonization, their converter-dominated and variable characteristics introduce new challenges for grid stability, operational security, and regulatory compliance. As a result, grid codes are being continuously revised to define advanced technical requirements, including fault ride-through (FRT) capability, reactive power support, frequency response, voltage control, and active power management for RESs and energy storage systems (ESS). This study presents a systematic comparative assessment of international grid codes, examining the technical and operational requirements imposed on inverter-based resources (IBR) and ESSs across multiple jurisdictions. In parallel, the current Turkish Grid Code is evaluated from a future-oriented perspective, and recommendations that can improve the existing regulatory framework are proposed, particularly regarding high-voltage ride-through capability, synthetic inertia provision, fast frequency response (FFR), hybrid power plant (HPP) coordination, and ESS-specific performance criteria. Based on the comparative analysis, the study proposes targeted amendments to the Turkish Grid Code aimed at enhancing system resilience under high renewable penetration levels. Furthermore, field-testing methodologies, model-based validation practices, and emerging digitalized compliance monitoring architectures are investigated to assess their applicability to next-generation power systems. By integrating international best practices with country-specific recommendations, this work contributes to the development of transparent, adaptive, and technically robust grid code compliance frameworks, supporting both academic research and practical grid modernization efforts.

The integration of grid-forming (GFM) converters into strong AC grids introduces new stability challenges, particularly low-frequency oscillations, emphasizing the need for real-time small-signal stability monitoring. The critical short-circuit ratio (CSCR) is a widely adopted metric for assessing small-signal stability in renewable power systems, but its dependence on grid operating conditions and converter control parameters requires online evaluation under diverse scenarios. This paper proposes an online CSCR prediction model for GFM grid-connected systems, based on a hybrid support vector regression (SVR) and particle swarm optimization (PSO) approach, enabling real-time stability margin assessment. First, a detailed 12th-order state-space model is established, incorporating both grid-side dynamics and converter control dynamics. Impedance-based sensitivity analysis is performed to examine the impact of key control parameters on CSCR. Next, an SVR-PSO prediction model is developed, trained on data generated from the state-space model. Experimental results demonstrate that the SVR-PSO model achieves superior accuracy in estimating CSCR compared to conventional methods. Using the predicted CSCR, this paper derives a small-signal stability margin index and validates its effectiveness through detailed case studies. Simulation results confirm the model’s high-fidelity prediction capability and its applicability for online stability assessment in strong grid conditions. This work provides a data-driven, computationally efficient framework for real-time stability monitoring in GFM-integrated power systems, offering practical insights for grid operators to ensure stable grid operation with high renewable penetration.

Abstract Electric-power consumption (EPC) and renewable-energy generation (REG) exhibit high variability, impacting grid stability and power quality. To address these challenges, this paper proposes a deep learning driven real-time demand side management (DSM) controller for smart buildings integrated with renewable energy sources (RES) and energy storage systems (ESS). The system employs an Internet of things (IoT) enabled architecture to monitor, forecast, and optimize energy usage at high temporal resolutions of five minutes. A bidirectional long short-term memory (B-LSTM) network is utilized to forecast short-term EPC and photovoltaic (PV) generation, surpassing LSTM, GRU, and other baseline models in terms of RMSE, MAE, and R 2 . The DSM controller applies dynamic incentive and penalty schemes to shift curtailable loads from peak to off-peak periods, reducing peak-to-average ratio (PAR) and lowering electricity costs for both prosumers and utility operators. Real-time decisions are enabled through dynamic pricing integration and IoT-based ESS control. The proposed framework provides a scalable, intelligent, and sustainable solution for future smart grid energy management.

In the near future, most of the electrical power generation will rely on renewable sources with zero carbon emissions. When these renewable sources are connected to the grid in parallel, they only need synchronization controllers. This paper examines a standalone renewable system comprising a PV plant, a wind generator, and a Battery Energy System (BES) module for testing. All these sources are integrated to meet the local load demand. The PV plant and wind generator require MPPT modules for maximum power extraction, and the BES is connected at the common DC link. To achieve this, SMC-P&O MPPT is used for stable renewable power extraction, and inverter control is adopted with APC based AWPI controller to reduce harmonics. In further modification, the SMC MPPTs of the PV plant and wind generator are updated with a Fuzzy module for increased power extraction and faster response to variable solar irradiation. In the interfacing inverter control, the AWPI controller is replaced with a Dual Sliding Mode-Proportional Integral (DSM-PI) controller for reduced THD in the inverter voltages and currents. A comparative analysis is conducted to compare both the test systems with SMC MPPTs, AWPI, and Fuzzy SMC MPPTs, DSM-PI to validate the optimal system.

Abstract The present research motivates the enhancement of power quality in a PV fed DSTATCOM employed in a local distribution network. As per the power quality demand from the consumer, regulation of power has become major focus in presence of renewable systems in the distribution network. The distribution network majorly shows resistance towards the severe uncertainties of the grid and load variations. Therefore, the present research proposes hybrid DSTATCOM for the distribution system with various uncertain load conditions. To enhance the performance of proposed system, a robust SRF controller has been implemented for the generation of reference signal for VSI of the DSTATCOM along with DC offset error compensation (DOEC) based phase-locked loops. In the Hybrid DSTATCOM, regulation of dc-link voltage also plays significant position for the performance of the system. Therefore, in this research paper, bat optimization based MPPT has been implemented for the regulation of dc link voltage at the desired level. The system has been developed in the Matlab/Simulink and results have been analyzed. The system has been also validated under experimental conditions in the laboratory, which shows the systems efficiency.

The green transition of energy production systems is one of the most critical tasks for society nowadays. Concentrated solar power (CSP) plants require direct normal irradiance (DNI) to produce electricity. Nevertheless, the highest DNI values are usually found in regions with limited water availability, which can be a sustainability issue when using cooling technologies. Furthermore, deploying new infrastructure has significant socio-economic implications, requiring careful evaluation of CSP facility locations. In this work, a multiobjective mixed-integer linear programming model is developed, considering several variables related to production (such as DNI, temperature, and available land), environmental factors (such as water consumption by cooling systems), and social concerns associated with the deployment of the facilities. The model evaluates each region of Spain to choose the optimal location for five scenarios of technology substitution. The results show that decisions prioritize DNI values and seasonal energy demand followed by investment cost and social impact, while we avoid wet-cooling technology to increase job creation. Furthermore, a fully renewable electricity system using 9 CSP plants requires an estimated investment of 785 B€2025, presents a competitive LCOE of 0.086-0.093 €/kWh, and has the potential to contribute to a reduction in national unemployment by approximately 2.5 to 21%.

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes.