Search Results (699)

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

In the last decades, the introduction of intermittent renewable energy sources has transformed the operation of power systems. In this framework, ancillary service markets (ASMs) play an important role, due to their contribution in supporting system operators to balance demand and supply and managing real-time contingencies. Usually, ASMs require that energy is committed before actual participation, hence scheduling systems of plants and microgrids are required to compute the dispatching program and bidding strategy before needs of the market are revealed. Since possible ASM requirements are given as input to scheduling systems, the chance of accessing accurate estimates may be helpful to define reliable dispatching programs and effective bidding strategies. Within this context, this paper proposes a methodology to estimate the revenue range of energy exchange proposals in the ASM. To this end, the possible revenues are discretized into ranges and a classification pattern recognition algorithm is implemented. Modeling is performed using extreme gradient boosting. Input data to be fed to the algorithm are selected because of relationships with the production unit making the proposal, with the location and temporal indication, with the grid power dispatch and with the market regulations. Different tests are set up using historical data referred to the Italian ASM. Results show that the model can appropriately estimate rejection and the revenue range of awarded bids and offers, respectively, in more than 82% and 70% of cases. Full article

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems. Full article

With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system. Full article

With the increasing penetration of wind and photovoltaic (PV) power in modern power systems, accurate power forecasting has become crucial for ensuring grid stability and optimizing dispatch strategies. This study focuses on multiple wind farms and PV plants, where three deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and a hybrid Transformer–BiLSTM model—are constructed and systematically compared to enhance forecasting accuracy and dynamic responsiveness. First, the predictive performance of each model across different power stations is analyzed. The results reveal that the LSTM model suffers from systematic bias and lag effects in extreme value ranges, while Bi-LSTM demonstrates advantages in mitigating time-lag issues and improving dynamic fitting, achieving on average a 24% improvement in accuracy for wind farms and a 20% improvement for PV plants compared with LSTM. Moreover, the Transformer–BiLSTM model significantly strengthens the ability to capture complex temporal dependencies and extreme power fluctuations. Experimental results indicate that the Transformer–BiLSTM consistently delivers higher forecasting accuracy and stability across all test sites, effectively reducing extreme-value errors and prediction delays. Compared with Bi-LSTM, its average accuracy improves by 19% in wind farms and 35% in PV plants. Finally, this paper discusses the limitations of the current models in terms of multi-source data fusion, outlier handling, and computational efficiency, and outlines directions for future research. The findings provide strong technical support for renewable energy power forecasting, thereby facilitating efficient scheduling and risk management in smart grids. Full article

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper, we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources, including remote-controlled switches (RCSs), mobile hydrogen emergency resources (MHERs), and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling, enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages, including prevention, fault isolation, and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events. Full article

With the large-scale integration of renewable energy into the power system, meteorological uncertainty poses challenges to the safe and stable operation of the system. Traditional uncertainty optimization methods struggle to balance robustness and economy. This paper proposes a Wasserstein distance-based distributionally robust optimization strategy that considers covariate factors for a renewable energy power grid considering meteorological uncertainty. By introducing covariate factors to construct the Wasserstein ambiguity set, the intrinsic connection between weather uncertainty and the output of new energy is effectively depicted. The optimization problem is transformed into a solvable form of mixed integer linear programming by using linear decision rules and duality theorems, and the distributionally robust optimization scheduling problem is solved based on the improved cross optimization algorithm. Simulation results based on the IEEE 33 system show that under the same worst-case expected energy shortage of 20 kWh, the proposed method achieves an expected total dispatch cost of approximately CNY 0.534 million, reducing cost by about 0.4%, 0.9%, and 1.8% compared with conventional Wasserstein DRO, KL-divergence DRO, and Moment Information DRO; when the radius of the Wasserstein ball is 1, using the CSO algorithm reduces the runtime by 59.4% compared with the solver. It effectively reduces operating costs and solution speed while ensuring system security, offering a new approach for the optimal dispatch of power systems with a high penetration of renewable energy. Full article

With the application and continuous development of green energy within the park integrated energy systems (PIESs), environmental pollution and resource depletion caused by traditional energy sources have been effectively mitigated. However, the existing research primarily focuses on fixed operating conditions, leading to significant wastage of renewable energy. To enhance the integration of renewable energy and improve overall energy efficiency, in this paper, a seasonal park integrated energy system (SPIES) based on an adjustable cooling–heating–power ratio (SPIESchpr) strategy is proposed to maximize the energy utilization efficiency and system operational economy. In SPIESchpr, to achieve additional carbon emission reductions, a novel seasonal laddered carbon trading mechanism (SLCTM) is proposed. Compared to traditional carbon trading methods, the SLCTM significantly improves the low-carbon performance of PIES. Finally, the effectiveness of the proposed SPIESchpr is validated through three scenario analyses and a detailed case study of typical daily operations. The experimental results demonstrate that, compared to fixed heat-to-cool ratios and conventional carbon trading mechanisms, the proposed SPIESchpr significantly reduces both total operational costs and carbon emissions during both heating and cooling seasons. Consequently, the proposed SPIESchpr not only enhances the energy efficiency, economic benefits, and carbon reduction potential of PIES but also provides a valuable reference for year-round operational dispatching strategies. Full article

Myanmar’s electricity supply relies mainly on hydropower and gas-fired generation, yet rural electrification remains limited, with national access at approximately 60%. The National Electrification Plan (NEP) aims for universal access via nationwide grid expansion, but progress in remote areas is constrained by financial limits and suspended external funding. This study evaluates the techno-economic feasibility of decentralized microgrids as an alternative to conventional grid extension under current budgetary conditions. We integrate a terrain-adjusted MV line-cost model with (i) PLEXOS capacity expansion and chronological dispatch for centralized supply and (ii) HOMER Pro optimization for PV–diesel–battery microgrids. Key indicators include LCOE, NPC, CAPEX, OPEX, reliability (ASAI/max shortage), renewable fraction, and unserved energy. Sensitivity analyses cover diesel, PV, and battery prices, as well as discount rate variations. The results show microgrids are more cost-effective in terrain-constrained regions such as Chin State, particularly when accounting for transmission and delayed generation costs, whereas grid extension remains preferable in flat, accessible regions like Nay Pyi Taw. Diesel price is the dominant cost driver across both regions, while battery cost and discount rate affect Chin State more, and PV cost is critical in Nay Pyi Taw’s solar-rich context. These findings provide evidence-based guidance for rural electrification strategies in Myanmar and other developing countries facing similar financial and infrastructural challenges. Full article

The increasing penetration of renewable energy into the grid has given rise to an emerging challenge of maintaining the supply–demand balance. Conventional supply-side regulation is now insufficient to maintain this balance, necessitating flexible resources from the demand side to address this challenge. Buildings, as important energy end-use consumers, possess abundant flexible resources and can play a significant role in responding to grid dispatch via demand response. Therefore, grid-interactive buildings (GIBs) have garnered widespread attention. This technology coordinates the scheduling of distributed renewable energies, energy storage, and adjustable loads via advanced control methodologies, leading to the reshaping of building load profiles to enhance grid flexibility. However, the realization of energy flexibility in GIBs has not yet been comprehensively identified in the literature. To narrow the knowledge gap, this review compared GIBs with other technologies of building energy management to highlight the distinct features of GIBs. Additionally, the flexible energy strategies of GIBs were explored, combined with flexible resources within buildings, and the feasible pathways for these strategies were also addressed. Based on the scheduling scenarios in GIBs, the performance characteristics of various control methodologies were compared and analyzed. Finally, an evaluation framework for GIBs was established. This review will facilitate the shift of buildings from traditional energy consumers to flexible resources that actively respond to the grid and provide critical support for the grid stability and reliability. Full article

Optimization dispatch with flexible load participation in new power systems significantly enhances renewable energy accommodation, though the potential of flexible loads remains underexploited. To improve renewable utilization efficiency, promote wind/PV consumption and reduce carbon emissions, this paper establishes a low-carbon economic optimization dispatch model for active distribution networks incorporating flexible loads and tiered carbon trading. First, a hybrid SSA (Sparrow Search Algorithm)–CNN-LSTM model is adopted for accurate renewable generation forecasting. Meanwhile, multi-type flexible loads are categorized into shiftable, transferable and reducible loads based on response characteristics, with tiered carbon trading mechanism introduced to achieve low-carbon operation through price incentives that guide load-side participation while avoiding privacy leakage from direct control. Considering the non-convex nonlinear characteristics of the dispatch model, an improved Beluga Whale Optimization (BWO) algorithm is developed. To address the diminished solution diversity and precision in conventional BWO evolution, Tent chaotic mapping is introduced to resolve initial parameter sensitivity. Finally, modified IEEE-33 bus system simulations demonstrate the method’s validity and feasibility. Full article

This work focuses on optimizing energy dispatch in a hydroelectric power plant (HPP) with a large number of generating units (GUs) and uncertainties caused by sediment accumulation in the water intakes. The study was realized at Jirau HPP, and integrates Markov Decision Processes (MDPs) and Particle Swarm Optimization (PSO) to minimize losses and enhance the performance of the plant’s GUs. Given the complexity of managing the huge number of units (50) and mitigating load losses from sediment accumulation, this approach enables real-time decision-making and optimizes energy dispatch. The methodology involves modeling the operational characteristics of the GUs, developing an objective function to minimize water consumption and maximize energy efficiency, and utilizing MDPs and PSO to find globally optimal solutions. Our results show that this methodology improves efficiency, reducing the turbinated flow by $0.9\%$ while increasing energy generation by $0.34\%$ and overall yield by $0.33\%$ compared to the HPP traditional method of dispatch over the analyzed period. This strategy could be adapted to varying operational conditions, and could provide a reliable framework for hydropower dispatch optimization. Full article

Achieving net-zero emissions in arid and high-solar-yield regions demands innovative, cost-effective, and scalable energy technologies. This study conducts a comprehensive techno-economic analysis and assessment of a novel hybrid photovoltaic–thermal solar collector (U.S. Patent No. 11,431,289) that integrates a reverse flat plate collector and mini-concentrating solar thermal elements. The system was tested in Qatar and Germany and simulated via a System Advising Model tool with typical meteorological year data. The system demonstrated a combined efficiency exceeding 90%, delivering both electricity and thermal energy at temperatures up to 170 °C and pressures up to 10 bars. Compared to conventional photovoltaic–thermal systems capped below 80 °C, the system achieves a heat-to-power ratio of 6:1, offering an exceptional exergy performance and broader industrial applications. A comparative financial analysis of 120 MW utility-scale configurations shows that the PVT + ORC option yields a Levelized Cost of Energy of $44/MWh, significantly outperforming PV + CSP ($82.8/MWh) and PV + BESS ($132.3/MWh). In addition, the capital expenditure is reduced by over 50%, and the system requires 40–60% less land, offering a transformative solution for off-grid data centers, water desalination (producing up to 300,000 m³/day using MED), district cooling, and industrial process heat. The energy payback time is shortened to less than 4.5 years, with lifecycle CO₂ savings of up to 1.8 tons/MWh. Additionally, the integration with Organic Rankine Cycle (ORC) systems ensures 24/7 dispatchable power without reliance on batteries or molten salt. Positioned as a next-generation solar platform, the Hassabou system presents a climate-resilient, modular, and economical alternative to current hybrid solar technologies. This work advances the deployment readiness of integrated solar-thermal technologies aligned with national decarbonization strategies across MENA and Sub-Saharan Africa, addressing urgent needs for energy security, water access, and industrial decarbonization. Full article

Elevator Group Control Systems (EGCSs) play a key role in managing the passenger flow and consumption of energy in modern buildings. However, existing EGCS algorithms are typically only applied to real-time passenger calls, which does not take the long-term statistics of passenger requirement into account. To address this gap, we propose a standby strategy that proactively repositioning idle elevators even if there is no passenger call. It calculates a combined score that balances the expected waiting time and the energy consumption to determine the optimal standby floors for idle elevators. We implement this strategy on a simple baseline dispatcher using the closest car algorithm and introduce tunable parameters to adjust the standby behavior. Experiments on mid-rise and high-rise building scenarios show that the standby strategy significantly reduces the average waiting time for passengers by more than 24% in both cases. Moreover, because this strategy operates independently of the core dispatcher, it can be combined with existing EGCS algorithms to further improve waiting time without compromising core energy optimizations. These findings demonstrate that proactive standby repositioning is an effective complementary approach for next-generation elevator control systems and offers a practical way to reduce waiting times under realistic office building traffic conditions. Full article

Ultra-short-term photovoltaic (PV) cluster power prediction (PCPP) is crucial for intra-day energy dispatch. However, it faces significant challenges due to the chaotic nature of atmospheric systems and errors in meteorological forecasting. To address this, we propose a novel ultra-short-term PCPP strategy that introduces a dynamic smoothing mechanism for PV clusters. This strategy introduces a smoothing convergence function to quantify sequence fluctuations and employs dynamic clustering based on this function to identify PV stations with complementary smoothing effects. We model the similarities in fluctuation amplitude, trend correlation, and degree correlation among sub-cluster nodes using a spatiotemporal heterogeneous dynamic graph convolutional neural network (STHDGCN). Three dynamic heterogeneous graphs are constructed to represent these spatiotemporal evolutionary relationships. Furthermore, a bidirectional temporal convolutional neural network (BITCN) is integrated to capture the temporal dependencies within each sub-cluster, ultimately predicting the output of each node. Experimental results using real-world data demonstrate that the proposed method reduces the normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) by an average of 6.90% and 4.15%, respectively, while improving the coefficient of determination (R²) by 34.36%, compared to conventional cluster prediction approaches. Full article