Flexible Constraints Research Articles

Flexibility-Constrained Energy Storage System Placement for Flexible Interconnected Distribution Networks

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large.

Multi-depot and product vehicle routing problem with precedence constraint and selective backhauling in animal feed manufacturing industry

ABSTRACT Multi-depot vehicle routing with precedence constraints and selective backhauling, along with practical challenges such as outsourcing, demand splitting, and flexible pickup and delivery formats, is a common issue in the animal feed industry. However, few studies address demand splitting and flexible pickup constraints. The industry struggles with inadequate management, leading to difficulties in efficiently utilizing limited vehicles for both raw material pickup and product delivery. This inefficiency results in higher transportation costs, including subcontractor vehicle expenses. This study proposes the Extended Adaptive Grey Wolf Optimizer to minimize total transportation costs by incorporating essential factors such as multi-depots, outsourcing services, optimization of split delivery and pickup operations, precedence constraints, and selective backhauling. The results show a remarkable 97.6% improvement toward optimality, representing a significant enhancement of 38.1% improvement over current practice. The findings emphasize the benefits to the industry of adopting proper route sequencing and utilizing subcontractor vehicles.

Adaptive horizon size moving horizon estimation with unknown noise statistical properties

Abstract Moving horizon estimation (MHE) is an effective technique for state estimation. It formulates state estimation as an optimization problem over a finite time interval and is characterized by inherent robustness, flexibility, and explicit constraint handling capabilities. The horizon size is a crucial parameter influencing the estimation performance of MHE. However, the selection of the horizon size remains an open research question in the field of MHE. In this paper, we propose a novel adaptive horizon size MHE strategy that dynamically adjusts the horizon size based on the value of the objective function. This approach aims to improve the state estimation performance of MHE in real-time applications. Unlike conventional MHE methods that rely on a fixed horizon size, our adaptive strategy enhances robustness against unknown noise statistics by adjusting the horizon size. We analyze the convergence property of the estimation error and provide guidelines for parameter design to ensure optimal performance. The effectiveness and superiority of the proposed method are demonstrated through simulations involving an oscillatory system and a target tracking application under non-stationary noise conditions.

Nonpremixed Approaches for Fuel Flexible, Low NOx Combustors in High-Efficiency Gas Turbines

Abstract Lean, premixed combustor designs have almost completely replaced non-premixed combustors for industrial gas turbine applications where NOx emissions are regulated. Nonetheless, these designs have also introduced turndown and fuel flexibility constraints and made combustion instabilities and flashback more problematic. Future gas turbine applications will require combustors to accommodate a range of alternative fuels, provide operational flexibility, and compete for services with a host of new technologies, including energy storage and fuel cells. The purpose of this paper is to propose nonpremixed, multi-stage designs for the next generation of high turndown, high fuel flexibility, low NOx combustion designs – referred to here as a Nonpremixed, Rich, Relaxation, Lean (NRRL) combustor. The key concept we explore is non-premixed combustion, followed by additional fuel mixing to locally fuel-rich conditions, a relaxation stage, and then a lean stage. This non-premixed approach can handle essentially any fuel composition, including pure hydrogen, liquid fuels, pure methane, pure ammonia, and any combination in between while breaking the NOx-CO tradeoff and reducing combustion instability risk. This paper provides chemical reactor network modeling calculations to identify key kinetic processes and time scales required for such a concept. This concept has completely inverted sensitivities from lean, premixed systems which prefer short residence times, low pressures, and low temperatures to minimize NO formation. This concept prefers long residence times, high pressures, and high temperatures, indicating a very different set of design trades for part load and off-design performance.

Guiding vector field and self-structuring learning network-based formation control of underactuated surface vessels in static obstacle environments with flexible performances

To address the problem of underactuated surface vessel (USV) formation control in static obstacle environments with model uncertainties and time-varying external disturbances, a model-free formation control strategy is proposed in this paper. First, based on the guiding vector field (GVF), a composite GVF is developed to guide USV formation to the desired position and to avoid multiple static obstacles. Second, a flexible constraint strategy is introduced, and the constraint boundary conditions are appropriately relaxed to avoid singularities in the obstacle environment. Then, based on the Mexican hat wavelet function, the self-structuring fuzzy Mexican hat wavelet cerebellar model articulation controller (SCMAC), and a self-structuring fuzzy Mexican hat wavelet brain emotional learning controller (SBELC), are proposed to achieve model-free control. In addition, the self-structuring algorithm is embedded into SCMAC and SBELC to achieve autonomous optimization of the controller structure and to reduce the computational effort of the control system. The salient features in the proposed control strategy are as follows. First, the proposed model-free formation control strategy does not have to rely on accurate model information. Second, collisions are effectively avoided, and good control performance is guaranteed even under the influence of disturbances and static obstacles. Third, the proposed self-structuring algorithm achieves automatic construction of the controller structure. Finally, the signals in the control system are proven to be bounded, and the simulation results verify the feasibility and superiority of the proposed model-free control strategy.

Parameter-Insensitive Min Cut Clustering With Flexible Size Constrains.

Clustering is a fundamental topic in machine learning and various methods are proposed, in which K-Means (KM) and min cut clustering are typical ones. However, they may produce empty or skewed clustering results, which are not as expected. In KM, the constrained clustering methods have been fully studied while in min cut clustering, it still needs to be developed. In this paper, we propose a parameter-insensitive min cut clustering with flexible size constraints. Specifically, we add lower limitations on the number of samples for each cluster, which can perfectly avoid the trivial solution in min cut clustering. As far as we are concerned, this is the first attempt of directly incorporating size constraints into min cut. However, it is a NP-hard problem and difficult to solve. Thus, the upper limits is also added in but it is still difficult to solve. Therefore, an additional variable that is equivalent to label matrix is introduced in and the augmented Lagrangian multiplier (ALM) is used to decouple the constraints. In the experiments, we find that the our algorithm is less sensitive to lower bound and is practical in image segmentation. A large number of experiments demonstrate the effectiveness of our proposed algorithm.

PAAD: Panelization algorithm for architectural designs.

Due to the competitive nature of the construction industry, the efficiency of requirement analysis is important in enhancing client satisfaction and a company's reputation. For example, determining the optimal configuration of panels (generally called panelization) that form the structure of a building is one aspect of cost estimation. However, existing methods typically rely on rule-based approaches that may lead to suboptimal material usage, particularly in complex designs featuring angled walls and openings. Such inefficiency can increase costs and environmental impact due to unnecessary material waste. To address these challenges, this research proposes a Panelization Algorithm for Architectural Designs, referred to as PAAD, which utilizes a genetic evolutionary strategy built on the 2D bin packing problem. This method is designed to balance between strict adherence to manufacturing constraints and the objective of optimizing material usage. PAAD starts with multiple potential solutions within the predefined problem space, facilitating dynamic exploration of panel configurations. It approaches structural rules as flexible constraints, making necessary corrections in post-processing, and through iterative developments, the algorithm refines panel sets to minimize material use. The methodology is validated through an analysis against an industry implementation and expert-derived solutions, highlighting PAAD's ability to surpass existing results and reduce the need for manual corrections. Additionally, to motivate future research, a synthetic data generator, the architectural drawing encodings used, and a preliminary interface are also introduced. This not only highlights the algorithm's practical applicability but also encourages its use in real-world scenarios.

The zero-debt puzzle in BRICS countries: Disentangling the financial flexibility and financial constraints hypotheses

This study analyzes the zero-debt decisions of BRICS firms using a bivariate probit model. The leading hypotheses are financial flexibility and financial constraints. On the demand-side, our findings reveal that managerial debt aversion, early lifecycle stage, growth opportunities, solvency, and concentrated ownership contribute to the lack of debt. Similarly, a country's institutional quality correlates with firms' debt-free status. On the supply-side, creditors fund companies with poor financial records in countries with robust markets and economic freedom. Financial flexibility and restrictions leading to zero debt are linked to firm and institutional characteristics in emerging countries.

Dynamic flexible scheduling with transportation constraints by multi-agent reinforcement learning

Reinforcement learning-based methods have addressed production scheduling problems with flexible processing constraints. However, delayed rewards arise due to the dynamic arrival of jobs and transportation constraints between two successive operations. The flow time of operations can only be determined after processing due to the possibility that the solution for job sequencing may change if new operations are inserted in dynamic environments. Job sequencing is often overlooked in single-agent-based scheduling methods. The lack of information sharing between multiple agents necessitates that researchers manually design reward functions to fit the relationship between optimization objectives and rewards, thereby reducing the accuracy of the learned policies. Thus, this paper proposes a multi-agent-based scheduling optimization framework that facilitates collaboration between the agents of both machines and jobs to address dynamic flexible job-shop scheduling problems (DFJSP) with transportation time constraints. Then, this paper formulates the Partial Observation Markov Decision Process and constructs a reward-sharing mechanism to tackle the delayed reward issue and facilitate policy learning. Finally, we develop an improved multi-agent dueling double deep Q network algorithm to optimize scheduling policy during long-term training. The results show that, compared with the state-of-the-art methods, the proposed method efficiently shortens the weighted flow time under the trained and unseen scenarios. Additionally, the case study results demonstrate its efficiency and responsiveness. It indicates that the proposed method efficiently addresses production scheduling problems with complex constraints, including the insertion of jobs, transportation time constraints, and flexible processing routes.

Multi-objective placement and sizing of energy hubs in energy networks considering generation and consumption uncertainties

This paper presents the placement and sizing of energy hubs (EHs) in electricity, gas, and heating networks. EH is a coordinator framework for various power sources, storage devices, and responsive loads. For simultaneous modeling of economic, operation, reliability, and flexibility indices, the proposed scheme is expressed as a three-objective optimization in the form of Pareto optimization based on the sum of weighted functions. The objective functions of this problem respectively minimize the planning cost of EHs (equal to the total cost of construction of hubs and their expected operating cost), the expected energy loss of the mentioned networks, and the expected energy not-supplied (EENS) of these networks in the case of an N − 1 event. The problem is constrained by power flow equations and operation and reliability constraints of these network together with the EH planning and operation model, and flexibility constraints of the EHs. Then, to achieve unique optimal solution in the shortest possible time, a linear approximation model is extracted for the proposed scheme. Moreover, scenario-based stochastic programming (SBSP) is employed to model uncertainties of load, energy cost, renewable power, and accessibility of the mentioned network equipment. Finally, the obtained numerical results indicate the capability of the proposed scheme in enhancing the economic and flexibility situation of EHs and improving the reliability and operation status of energy networks along with achieving optimal planning and operation for EHs.

Investigating the role of functional mutations in leucine binding to Sestrin2 in aging and age-associated degenerative pathologies using structural and molecular simulation approaches

Leucine is the native known ligand of Sestrin2 (Sesn2) and its interaction with Sesn2 is particularly important, as it influences the activity of mTOR in aging and its associated pathologies. It is important to find out how leucine interacts with Sesn2 and how mutations in the binding pocket of leucine affect the binding of leucine. Therefore, this study was committed to investigating the impact of non-synonymous mutations by incorporating a broad spectrum of simulation techniques, from molecular dynamics to free energy calculations. Our study was designed to model the atomic-scale interactions between leucine and mutant forms of Sesn2. Our results demonstrated that the interaction paradigm for the mutants has been altered thus showing a significant decline in the hydrogen bonding network. Moreover, these mutations compromised the dynamic stability by altering the conformational flexibility, sampling time, and leucine-induced structural constraints that consequently caused variation in the binding and structural stability. Molecular dynamics-based flexibility analysis revealed that the regions 217–339 and 371–380 demonstrated a higher fluctuation. Noteworthy, these regions correspond to a linker (217–339) and a loop (371–380) that cover the leucine binding cavity that is critical for the ‘latch’ mechanism in the N-terminal, which is essential for leucine binding. Further validation of reduced binding and modified internal motions caused by the mutants was obtained through binding free energy calculations, principal components analysis (PCA), and free energy landscape (FEL) analysis. By unraveling the molecular intricacies of Sesn2-leucine interactions and their mutations, we hope to pave the way for innovative strategies to combat the inevitable tide of aging and its associated diseases. Communicated by Ramaswamy H. Sarma

Design of planar comb-driven MEMS torsional mirror

The MEMS torsional mirror is an important MEMS optical device, and its related applications are very wide, including LiDAR, projection equipment, optical communication, and so on. At present, the electrostatically driven MEMS torsional mirror mainly adopts a vertical comb driver to achieve torsional motion, which has some disadvantages such as a complicated manufacturing process, low yield, and high driving voltage. In this paper, a novel MEMS torsional mirror structure is proposed, which provides flexible elastic constraint points for the torsional mirror with the bottom support rod structure as the center, and relies on the combination of planar comb and support rod to form a tie rod structure to achieve torsion, and achieves a torsion Angle of ±15.55° under the driving voltage of 65 V. The use of a planar comb instead of a vertical comb greatly reduces the difficulty of manufacturing. Finally, it is verified that the system stability is good, the correlation drift does not affect the results within the range, the fifth-order modes are within the reasonable range, and the maximum stress is 671 MPa, which is controlled within the range of fracture stress of silicon material.

Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning

Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which computes a reward maximizing policy from a given start to a far away goal state while satisfying cost constraints) with a low-level goal conditioned RL agent (which estimates cost and reward values to move between nearby states). A major advantage of CoSHRL is that it can handle constraints on the cost value distribution (e.g., on Conditional Value at Risk, CVaR) and can adjust to flexible constraint thresholds without retraining. We perform extensive experiments with different types of safety constraints to demonstrate the utility of our approach over leading approaches in constrained and hierarchical RL.

Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm’s performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern.

Energy systems capacity planning under high renewable penetration considering concentrating solar power

With the rapid development of renewable generation and the promotion of “Clean Heating”, the uncertainty of renewables as well as rising energy demand has posed significant problems to the energy system’s economical and flexible operation. As a renewable energy generation technology, concentrating solar power (CSP) with thermal energy storage (TES) offers a promising approach by providing operational flexibility and thermal energy supply to the energy system. We propose a long-term high-resolution planning model considering the operational characteristics of CSP and improved flexibility constraints of energy systems under high renewable penetration to meet the electricity and heat demand simultaneously in this paper. By applying the proposed model to a typical “Clean Heating” area in China, it demonstrates that the optimal deployment by integrating CSP can reduce the peak-valley depth of coal units, minimize the renewable curtailment and carbon emissions by 37.64 % and 19.05 % respectively with more than 5.98 % cost reduction at 85 % renewable penetration compared with the base scenario, which provides an economic and stable zero-carbon pathway for multi-energy systems.

The inferiors imitate the superiors: the government’s low-carbon concerns and the renewable energy technology of firms

AbstractExisting research has focused mainly on the technological effects of rigid constraints such as low-carbon policies, but there has been little exploration of flexible constraints such as the government’s low-carbon concerns. To explore the role of flexible low-carbon constraints and effective paths for achieving low-carbon development, this paper theoretically and empirically investigates the influence of the government’s low-carbon concerns on local firms’ innovation in renewable energy technology and the underlying mechanism. Benchmark analysis shows that the government’s low-carbon concerns can improve renewable energy technology among firms in an area. Mechanism analysis reveals that low-carbon concerns promote firms’ renewable energy technology adoption by increasing the amount of research and development (R&D) investment and government subsidies and energy use costs of firms in the area. Heterogeneity analysis reveals that heterogeneity exists across technologies, firms, industries and locations in terms of the incentive impact of the government’s low-carbon concerns on the level of renewable energy technology innovation. Extensive analysis indicates that the government’s low-carbon concerns have a long-term incentive impact on the renewable energy technology of local firms and a positive demonstration impact on the renewable energy technology of neighboring firms. This study is helpful in that it explores the major driving factors behind the creation of a low-carbon economy and the achievement of climate targets.

How are middle‐ and older‐age women employees perceived and treated at work? A review and analysis

AbstractBuilding on the rich array of literatures that explore women's ageing and employment, we conduct a comprehensive review of research on middle‐ and older‐age women and work (including menopause and post‐menopause). In reviewing these studies, we blend our interdisciplinary discussions across several domains. We reflect on the question: How are employed, middle‐ and older‐age women treated in organizations, and what are employers’ perceptions of these workers? Through our analysis, we identify and critique two predominant, conflicting yet inter‐related themes. These are, namely, notions of constraint (and women's supposed/perceived reduced competencies as they age) versus ideas of flexibility (foregrounding assumptions that employed women experience fewer limitations as they age). As a theoretical lens for theorizing the constraints theme, we draw upon the concept of abjection, highlighting how employer perceptions of women's health as supposedly diminishing can lead to discriminatory treatment at work. In analysing theoretical assumptions regarding apparently enhanced flexibility among middle‐ and older‐age women, we note how women's differing circumstances and requirements might go unrecognized. We argue the need to deepen theoretical understandings about ideas of flexibility during women's middle and older age. Reflecting on the limitations of both themes, we consider the implications for future research agendas.

Exploiting Data Locality to Improve Performance of Heterogeneous Server Clusters

We consider load balancing in large-scale heterogeneous server systems in the presence of data locality that imposes constraints on which tasks can be assigned to which servers. The constraints are naturally captured by a bipartite graph between the servers and the dispatchers handling assignments of various arrival flows. When a task arrives, the corresponding dispatcher assigns it to a server with the shortest queue among [Formula: see text] randomly selected servers obeying these constraints. Server processing speeds are heterogeneous, and they depend on the server type. For a broad class of bipartite graphs, we characterize the limit of the appropriately scaled occupancy process, both on the process level and in steady state, as the system size becomes large. Using such a characterization, we show that imposing data locality constraints can significantly improve the performance of heterogeneous systems. This is in stark contrast to either heterogeneous servers in a full flexible system or data locality constraints in systems with homogeneous servers, both of which have been observed to degrade the system performance. Extensive numerical experiments corroborate the theoretical results. Funding: This work was partially supported by the National Science Foundation [CCF. 07/2021–06/2024].

Pore parameter selection for fused deposition modeling of gas diffusion layers in proton exchange membrane fuel cells

AbstractBecause of the global energy crisis, many researchers have focused their efforts on fuel cells. The gas diffusion layer (GDL) is a fundamental component of proton exchange membrane (PEM) fuel cells. The manufacturing procedures for traditional carbon‐based GDLs are sophisticated and energy‐intensive, and have design flexibility constraints, making it difficult to build complicated forms and structures. Additive manufacturing is widely used in a variety of fields, with the fused deposition modeling (FDM) technique being the most popular method. Prior to conducting systematic research on the printed GDLs, it is necessary to investigate the pore parameters with the support of FDM technique. In this research, the initial step involves material selection, FDM printer selection, and modeling. Subsequently, printing analysis and sample characterization are conducted to select an acceptable pore shape. A water drainage test determines the appropriate pore size, followed by additional tests for acceptable porosity range. The square pore shape is determined to be more suitable. A pore side length of 1.0 mm is identified. Furthermore, a suitable pore range of 10.9%–30.3% is identified. Next step, the printed GDLs will undergo further testing to investigate their suitability to be used in PEM fuel cells.

Engineering the growth and electrochemical assessments of phosphorous-doped nitrogen-based carbon nanofibers with 3D-interconnected weaving network structure for high-energy symmetric supercapacitors

The growing demand for sustainable energy sources has led to a change in attention towards developing cost-effective, high-performance energy storage devices. The construction of porous carbon network nanostructures with high surface area is complex for current-generation supercapacitors, mainly due to molecular flexibility and carbon production constraints. This work successfully produced a porous carbon nanostructure by doping phosphorous into nitrogen-based carbon nanofibers (P-doped NCNFs) utilizing a simple and controllable approach. This process entailed electrospinning diammonium hydrogen phosphate and polyacrylonitrile, subsequent high-temperature carbonization, and substantial segmented hydrogen peroxide activation processes. The P-doped NCNFs had a notable surface area of 100.69 m2 g−1, characterized by a distinct 3D-interconnected weaving network morphology. The 1 % P-doped NCNFs exhibited an exceptionally high capacitance of 265 ± 2 F g−1 when tested in a three-electrode setup at a current density of 0.5 A g−1. In addition, the constructed symmetrical supercapacitors with two identical P-doped NCNFs using a neutral Na2SO4 electrolyte exhibited remarkable electrochemical characteristics, which include a substantial capacitance of 225 ± 2 F g−1 at a current density of 0.5 A g−1, a high energy density of 30.9 Wh kg−1, an excellent Coulombic efficiency of 98.8 % over 6000 cycles, an impressive power density of 250 W kg−1, and significant capacitance retention of 85.6 %. These findings suggest that P-doped NCNFs could be excellent options for next-generation high-performance supercapacitors.