Search Results (736)

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

The reconstruction of nodal prices and generation patterns in electricity markets with network constraints constitutes a challenging inverse analysis problem due to congestion-induced non-uniqueness and limited observability. This study introduces node symmetry analysis as a novel early indicator of locational marginal price (LMP) growth in power systems with high renewable energy penetration. Symmetric nodes, defined as nodes with identical generation cost structures and comparable network topology, exhibit near-identical price signals under uncongested conditions. In this study, the term “price” refers to the LMP obtained from the DC-OPF market-clearing model under scenarios with high renewable energy penetration. Deviations from this symmetry, quantified through price differences between symmetric node pairs (ΔLMP), serve as sensitive indicators of emerging network stress and congestion, providing early warning of peak-price events. Using DC power flow sensitivities and congestion indicators, LMPs are reconstructed in a simplified five-node test system under three scenarios: baseline operation, severe transmission congestion, and high renewable generation variability. Results show strong correlations between symmetry violations and system-wide price increases. In congested scenarios, ΔLMP exceeding €2/MWh consistently precedes peak prices by 1–2 h, demonstrating the metric’s predictive capability. Integration of storage further highlights the operational value of symmetry-based analysis, showing reductions in curtailed renewable generation and peak prices. The proposed framework offers a computationally efficient and interpretable tool for congestion diagnosis, price trend forecasting, and inverse market analysis, with potential scalability to larger AC networks and stochastic scenarios. These findings provide actionable insights for system operators, market participants, and regulators seeking to enhance flexibility, reliability, and economic efficiency in high-renewable electricity markets. Full article

As modern power markets integrate more renewable generation, day-ahead energy clearing remains the central procurement step, while flexibility products are procured to ensure that the cleared energy schedule can be operated securely. This paper proposes a closed-loop framework linking the Power Exchange (PX) and the Independent System Operator (ISO) to bridge energy-market settlement and network-feasible operation. The PX performs staged day-ahead clearing with energy settled first, followed by aAutomatic generation control (AGC) and spinning reserve (SR) procured from the residual headroom of committed (energy-awarded) units. The ISO then validates the cleared schedule using an equivalent current injection (ECI)-based screening. This paper uses a single-period (single-hour) IEEE 30-bus case setting; multi-period scheduling and intertemporal constraints are not modeled. When congestion is detected, power-flow tracing identifies the main contributors and guides a minimal-change redispatch. The ISO-feasible dispatch is then sent back to the PX for re-clearing, aligning prices and welfare with an executable operating point. The resulting nonconvex clearing problems with valve-point effects and prohibited operating zones are solved by Artificial Protozoa Optimizer with Social Learning (APO–SL) and evaluated against representative metaheuristic baselines. IEEE 30-bus studies show that off-peak and average-load cases pass ISO screening directly, whereas the peak case tightens reserve headroom (SR capped at 39.08 MW) and triggers congestion. After ISO feedback and energy re-clearing, line loadings return within limits. The ISO-feasible dispatch changes the marginal accepted offer and lifts the MCP (3.73 → 4.38 $/MWh). The welfare value reported here follows the paper’s settlement-based definition (purchase total minus accepted offer cost), and it increases accordingly (113.77 → 190.17 $/h). Full article

With the widespread integration of smart buildings equipped with distributed photovoltaics (PV) and electric vehicles (EVs), distribution networks face significant challenges arising from source-load fluctuations. Conventional centralized dispatch approaches are constrained by communication bottlenecks and data privacy requirements. These limitations make it difficult to achieve global coordination while preserving the autonomy of individual entities. This paper proposes a hierarchical coordination framework for the coordinated operation of distribution networks and smart buildings. The distribution management system (DMS) and building energy management systems (BEMSs) perform independent optimization within their respective domains. Only aggregated boundary power information is exchanged to protect data privacy, enabling cross-entity coordination under information boundary constraints. Building-side models incorporating thermal dynamics, EV charging and discharging, and PV generation are developed, along with a distribution network power flow model. To solve the coordinated optimization problem, an Anderson-accelerated alternating direction method of multipliers (AA-ADMM) is introduced. A safeguarding mechanism based on combined residuals is incorporated to enhance convergence efficiency and stability. Case studies on the IEEE 33-bus test system demonstrate that compared with the uncoordinated baseline, the proposed method reduces network loss by 12.1% and lowers PV curtailment from 9.20% to 0.52%, while improving voltage profiles without significantly compromising occupant comfort or EV travel requirements. In addition, AA-ADMM achieves convergence with up to 66% fewer iterations than standard ADMM. Full article

An improved optimizer based on projection-iterative methods (IPIMO) is proposed to address the optimal configuration problem of multi-type energy storage systems (MT-ESS), with the objective of achieving synergistic minimization of comprehensive costs, including both investment and operational expenditures. A comprehensive energy system model is established, integrating photovoltaic power, wind power, and six typical energy storage technologies—lithium-ion battery, flywheel energy storage, supercapacitors, valve-regulated lead-acid battery, compressed air energy storage, and redox flow battery. Four typical operational scenarios are designed to validate the adaptability and robustness of the algorithm. A systematic evaluation of IPIMO’s comprehensive performance is conducted by comparing it with the weighted average method (WA), the single-energy storage optimization method (SEO), the projection-iterative-methods-based optimizer algorithm (PIMO), and the genetic algorithm (GA). Simulation results demonstrate that IPIMO exhibits superior convergence performance, achieving stable convergence rapidly and significantly outperforming PIMO and GA. Moreover, IPIMO achieves the lowest total cost across all four scenarios, with an average of $46,837, representing reductions of 6.54% compared to the benchmark weighted average method and 11.8% compared to the SEO. Additionally, IPIMO adaptively adjusts the allocation ratios of energy storage types based on scenario characteristics, prioritizing energy-type storage in stable scenarios while increasing the proportion of fast-response storage to 49.1% in fluctuating scenarios, thereby demonstrating its strong scenario adaptability. Full article

With the rapid evolution of a new power system characterized by a high proportion of renewable energy, system operations have become increasingly random, variable, and uncertain. The system model exhibits features such as high dimensionality, multiple time scales, stochastic behavior, and nonlinearity. This paper proposes a large-scale computational power system model architecture based on cloud-edge-terminal collaboration. By defining functional roles within the cloud-edge-terminal structure and implementing a global model coordination mechanism, the approach enables an organic integration of global awareness, local adaptation, dynamic training, and online optimization for power system problem models. At the cloud level, various object models and the power grid topology are constructed. The edge generates typical problem models for the power system, while the terminal devices produce lightweight models adapted to local grids. This architecture supports collaborative modeling for key business scenarios such as power flow analysis, stability assessment, and reactive power optimization. The study focuses on the training methods of distilled parameters within the terminal models to enhance their adaptability for real-world deployment in power systems. Simulation results demonstrate that the cloud-edge-terminal model offers excellent scalability, adaptability, and real-time performance for computations in new power systems, effectively supporting localized, intelligent operations and decision-making within the system. Full article

The present study deals with the problem of irregular temperature distribution, simultaneous under-firing and over-firing, and their resultant efficiency and quality problems in a mechanical lime vertical kiln powered by domestic waste flue gas. The numerical simulation and structure optimization were carried out based on a 150 kg/h pilot-scale kiln. This combined model was built on the ANSYS Fluent 2022 R1 platform with UDF and UDS, incorporating limestone decomposition kinetics to enable the solution of gas and solid energy equations separately, and simulation of complex transfer and reaction processes. To correct the separation of flows at one inlet, a symmetric four-direction (00, 900, 1800, 2700) air intake plan was suggested. The findings show that this design essentially transforms the internal flow field into uniform and symmetrical temperature and concentration distributions. The calcination region contained both gas and solid temperatures in the optimum range to produce active lime. Specifically, the optimized kiln achieved a temperature range of 1190–1450 K in the calcination zone, a decomposition rate of approximately 82.7% (compared to 5.3% in the original model), and an increase in effective CaO content from 81.7% to 87.7%, with validation errors below 15%. It was demonstrated that the model is reliable, since the outlet simulated values correlated well with the measured ones. The preheating, calcining, and cooling zones’ heights of the optimized kiln adhered to the design requirements. This research is innovative in its application of a multi-physics coupling model with a varying heat source in a kiln and, in turn, identifies the synergism improvement process in the flow, temperature, concentration, and reaction fields. Full article

With the high penetration of distributed energy resources (DERs), which are characterized by stochasticity and intermittency, traditional centralized optimization methods face challenges such as communication packet loss, low reliability, and poor scalability in large-scale DC microgrids. Therefore, distributed optimization methods have attracted attention due to their robustness and scalability. This paper extends our previous conference work by proposing a convex-relaxation-based distributed control strategy for DC microgrids with constant power loads (CPLs) and maximum power point tracking (MPPT)-controlled distributed generations (MPPT-DGs). Furthermore, a control strategy based on distributed observers is designed to achieve global optimal control under sparse communication networks. First, an exact convex relaxation method is applied to transform the original non-convex optimal power flow (OPF) problem into a convex problem, with theoretical guarantees of exactness. Then, the Karush–Kuhn–Tucker (KKT) conditions are equivalently transformed into a consensus-based optimality condition and integrated into the distributed control framework. Next, small-signal stability analysis is performed to verify the system’s robustness. To reduce communication costs, a distributed observer-based control strategy is proposed, which can achieve optimal control under sparse communication networks. The impact of communication delays on system stability is also investigated. Finally, the simulation results verify the accuracy of convex relaxation, the effectiveness of the proposed control strategy, and its performance under communication delay. Full article

Hybrid renewable energy systems (HRESs), mixing conventional and renewable power sources and occasionally storage units, have become the norm regarding electricity generation. Robust long-term planning of such systems requires stakeholders to test different layouts and system configurations, while their operational management relies on forecasting surpluses and deficits to achieve optimal decision making. However, both tasks, which in fact constitute a flow allocation problem across power networks, are subject to multiple peculiarities, arising from the nonlinear dynamics of the underlying processes, subject to numerous technical and operational constraints. Interestingly, a mutual problem emerges in water resource systems, also comprising network-type storage, abstraction and conveyance components. In this vein, triggered from well-established simulation approaches from the water domain, we introduce a generic (i.e., topology-free) and time-agnostic framework, the key methodological elements of which are: (a) the graph-based representation of the power fluxes; (b) the effective handling of energy uses and constraints through virtual nodes and edges; (c) the implementation of priorities via proper assignment of virtual costs across all graph components; and (d) the configuration of the overall problem as a network linear programming context, which allows the use of exceptionally fast solvers. Specific adjustments are required to address highly complex issues within HRESs, particularly the representation of conventional thermal and pumped-storage hydropower units, as well as the power losses across transmission lines. The modeling approach is stress-tested by means of configuring a hypothetical HRES in a non-interconnected Aegean island, i.e., Sifnos, Greece. Full article

Optimal Power Flow Operation (OPFO) is a large-scale, nonlinear, and highly constrained optimization problem that plays a central role in achieving economical, reliable, and environmentally sustainable power system operation. Despite the widespread use of metaheuristic algorithms for OPFO, many methods primarily depend on global-best updates or complex hybrid operators, leading to issues like premature convergence and diminished population diversity. Furthermore, recent literature tends to focus on numerical improvements without sufficiently addressing the underlying interaction structures that ensure stability in convergence. To address these limitations, this paper proposes an Improved Starfish Optimization (ISFO) algorithm incorporating a hybrid fitness-aware population-based search mechanism for solving OPFO problems involving the simultaneous regulation of synchronous generator outputs, on-load tap-changing transformer ratios, and reactive power compensation devices. The proposed method introduces an adaptive Fitness-Aware Collective (FAC) interaction strategy that systematically models pairwise fitness relationships to guide attraction toward superior solutions and repulsion from inferior ones, thereby strengthening exploitation while preserving diversity through controlled stochastic peer-based perturbations. A dual-mode search framework further balances global exploration and local intensification without introducing additional control parameters, enhancing robustness and scalability. The OPFO problem is formulated as a constrained nonlinear optimization model, where equality constraints enforce power flow balance equations and inequality constraints represent operational limits of generators, transformers, voltages, and transmission lines. The proposed ISFO is validated on the IEEE 57-bus power system under three operating scenarios: fuel cost minimization, transmission loss minimization, and emission minimization. Comparative results demonstrate consistent superiority over the standard Starfish Optimization Algorithm (SFOA). In cost minimization, ISFO reduces the total generation cost from 41,697.85 $/h to 41,669.34 $/h while simultaneously decreasing real power losses by 5.22%. Under loss minimization, ISFO achieves a minimum transmission loss of 10.77 MW, corresponding to a 9.23% reduction relative to SFOA, with improved convergence stability. For emission minimization, ISFO attains the lowest emission level of 1.474 ton/h, representing a 6.65% reduction compared to SFOA, alongside an additional 5.67% reduction in system losses. Statistical evaluations based on 30 independent runs further confirm the robustness and reliability of the proposed approach, demonstrating reduced variance, narrower confidence intervals, and statistically significant improvements across all investigated objectives. Full article

The large-scale integration of wind turbine generators (WTGs) and photovoltaic (PV) generation increases operational uncertainty and can exacerbate stability limitations in weak transmission networks, motivating the use of green hydrogen energy storage systems (HESS). This paper presents a probabilistic planning framework for the joint siting and sizing of HESS to support hybrid WTG–PV integration under stochastic wind, solar irradiance, and load conditions. The proposed framework explicitly couples Monte Carlo-based probabilistic power flow with weak-grid security constraints by enforcing FVSI-based voltage-stability limits and an SSI-based system-strength requirement within the optimization loop, rather than treating these indices as post-analysis checks. The planning problem is formulated using a weighted-sum scalarization to minimize life-cycle carbon footprint and active power losses, subject to security constraints based on the Fast Voltage Stability Index (FVSI) and a system-strength constraint expressed through a System Strength Index (SSI). To solve the resulting constrained, nonlinear optimization problem, a sequential hybrid metaheuristic that couples Whale Optimization (exploration) with Osprey Optimization (exploitation) is developed. The framework is implemented in MATLAB using MATPOWER and evaluated on a modified IEEE 39-bus system. Simulation results report an annual carbon footprint of 22.16 Mt CO₂eq/yr, an improvement of 9.2% and 5.3% relative to PSO and GA/PSO baselines, respectively, while increasing the weakest-bus SSI to 4.68 (bus 7). The resulting HESS design comprises a 296.9 MW electrolyzer, a 262.7 MW fuel cell, and 28,012 kg of hydrogen storage. Full article

The transition towards high-renewable power systems introduces high-dimensional nonlinearity and uncertainty, rendering traditional offline look-up table schemes prone to control mismatch against “unseen” contingencies. Meanwhile, existing response-driven approaches face a dilemma between the computational latency of physics-based optimization and the safety risks of end-to-end AI. To bridge this gap, this paper proposes a Response-Driven Optimal Emergency Control Framework that ensures both millisecond-level speed and rigorous physical constraints. First, a deep learning-based predictor is employed to extract spatiotemporal features from real-time PMU data, enabling high-fidelity prediction of stability margins. Crucially, instead of direct black-box control, the data-driven model is utilized to derive linear control sensitivities via a batch-processing perturbation mechanism. This transforms the intractable Transient Stability Constrained Optimal Power Flow (TSC-OPF) problem into a real-time solvable Linear Programming model. Case studies on a regional AC/DC hybrid grid demonstrate that the proposed framework achieves high prediction accuracy and effectively restores stability in mismatch scenarios where traditional schemes fail. Furthermore, the decision speed of the proposed method is significantly improved compared to traditional time-domain simulations, thus strictly satisfying the real-time requirements of the second line of defense. Full article

The increasing use of integrated renewable energy sources (RESs) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and assets optimization. This paper aims to provide a comprehensive review of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the high penetration of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. Five key areas of the energy sector are identified where AI tools are applied: forecasting electricity generation from RES; forecasting demand and price fluctuations on the electricity spot market; the real-time management of energy flows and assets in active microgrids; and data processing and analyzing, and general industrial direction. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. This digital transformation is a gradual process with consecutive steps. To improve understanding and clarity, the authors present a three-phase roadmap of AI adoption. To develop an adequate AI integration strategy, it is necessary to understand the technologies, algorithms, hierarchical structure, and connections within this structure. Accordingly, the article presents a taxonomy of the hierarchical structure of AI. The subsequent step involves the sequential construction of a digitalization model. Here, the authors consider it necessary to present a 4-layer structure model of AI energy democracy. Finally, through a comparative analysis of different types of intelligent applications for energy problem solving, guidelines are provided for successful decision making in compliance with the specified harmonized standards and protocols. Full article

The proliferation of distributed photovoltaic (PV) generation introduces significant operational challenges for distribution networks, including voltage instability and elevated technical losses. While modern PV inverters capable of static synchronous compensator (STATCOM) functionality—forming PV-STATCOM systems—offer a promising solution, their optimal integration remains a complex mixed-integer non-linear programming (MINLP) problem. This paper addresses this gap by proposing a novel hybrid evaluator–optimizer framework for the optimal daily placement and sizing of PV-STATCOM devices. The framework synergistically integrates the metaheuristic crow search algorithm (CSA) for global exploration of discrete device locations with a high-fidelity, multi-period optimal power flow (OPF) model—implemented efficiently in Julia with the Ipopt solver—for continuous operational evaluation and constraint validation. The methodology incorporates realistic 24 h load and solar irradiance profiles. Extensive validation on standard IEEE 33- and 69-bus test systems demonstrates the efficacy of the proposed approach. The results indicate substantial reductions in daily energy losses—by up to 70.4% and 72.9% for the 33- and 69-bus systems, respectively—and corresponding operational costs, outperforming recent state-of-the-art metaheuristic and convex optimization methods reported in the literature. The CSA also exhibits robust convergence and repeatability across multiple independent runs. This work contributes a computationally efficient, open-source planning tool that leverages modern optimization solvers, providing a scalable and effective strategy for enhancing the power quality and economic performance of PV-rich distribution networks. Full article

This paper proposes a quasi-dynamic Volt–Var control strategy for radial distribution networks based on the optimal sizing of a distribution static synchronous compensator (D-STATCOM) using a genetic algorithm (GA). The objective is to enhance voltage regulation and reduce technical energy losses under variable loading conditions while preserving nonlinear AC power flow fidelity. The IEEE 33-bus test system was modeled in DIgSILENT PowerFactory (v2021), and the D-STATCOM installation bus was selected based on a rigorous literature-supported placement criterion derived from optimization-based studies. Three representative demand scenarios—minimum, average, and maximum loading—were defined to approximate quasi-dynamic operation over a daily cycle. The GA was implemented in MATLAB (R2023b) to solve a normalized nonlinear multi-objective optimization problem that simultaneously minimizes total active power losses and the aggregate voltage deviation index. The optimized reactive power capacities obtained were 0.49 Mvar, 1.1933 Mvar, and 2.30 Mvar for the minimum, average, and maximum demand scenarios, respectively. These configurations achieved active power loss reductions of 27.5%, 24.602%, and 23.44% under the corresponding loading levels while improving voltage regulation at the critical bus (bus 18) and maintaining system voltages within the admissible 0.95–1.05 p.u. range. Through quasi-dynamic interpolation of operating points, the daily performance assessment showed a 24.11% reduction in total energy losses and a 38.28% decrease in the average voltage deviation. A statistical robustness analysis confirmed stable convergence behavior across independent executions. The results demonstrate that the proposed framework provides a computationally efficient, planning-oriented approach for reactive power compensation in distribution systems subject to demand variability. Full article