Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling

Using hydrogen (H) and ammonia (NH) for renewable energy storage has the potential to enable economical power and heat supply with high renewable penetrations, especially in remote locations which are characterized by high energy costs. In this work we assess the economic competitiveness of renewable combined heat and power (CHP) systems in Mahaka HI, Nantucket MA, and Northwest Arctic Borough (NWAB) AK by optimally designing these systems for scenarios in which power and heat can be purchased over a range of historical energy prices as well as when 100% renewable supply is required. We use a combined optimal design and scheduling model which minimizes annualized net present cost by determining optimal technology selection and size simultaneously with optimal schedules for each period of a system operating horizon aggregated from full year hourly resolution data via a consecutive temporal clustering algorithm. We find that renewable generation meets at least 85% of power demands and 75% of heat demands under the lowest energy prices investigated. Higher conventional energy prices lead to increased renewable penetration which is facilitated by renewable NH as a seasonal energy storage medium, as are 100% renewable CHP systems. NH is used for power generation with heat cogeneration in all three locations, as well as directly for heating in NWAB. On an annual cost basis, NH-enabled 100% renewable CHP is only 3% more expensive in Mahaka and NWAB than systems which can purchase energy at the lowest prices, while it is 15% more expensive in Nantucket.     

Optimal hybrid control of interarea oscillation in power system using energy storage systems

Unmanaged renewables integration into the power system will raise the possibility of small-signal instability due to higher load deviations in the transmission lines. In power grids with a higher penetration level of the renewables, the load deviation can cause interarea oscillation in the grid. Meanwhile, large-scale battery energy storage systems are promising solutions to enhance power system stability by smoothening the load profile and their fast response. To damp the interarea oscillations without compromising the voltage stability, we need to consider the battery's dynamic model in axis. The battery's dynamic model in axis allows us to integrate the battery into the power system and simulate both the battery's active and reactive power injection/absorption. In this paper, we model the battery energy storage on axis. Then, the battery model is augmented into the two-area four-machine power system. An optimum hybrid controller using linear quadratic regulator techniques is designed to damp generators' frequency deviations. The results show that the interarea oscillations are damped without losing the voltage stability of the system.     

Direct synthesis of fixed-order multi-objective controllers

This paper introduces a new methodology for the design of fixed-order multi-objective output feedback controllers. The problem comprises a set of linear matrix inequalities and an additional rank constraint. The primary idea is to classify convex subsets of the set of rank constrained matrices in such formulations, based on which two noniterative and relatively fast methods are developed. The proposed methods require solving a convex optimization problem at each step and can be applied with any weighted summation of design objectives such as performance, performance, passivity, and regional pole assignment. Several benchmark systems with performance criteria including H_∞, mixed H₂/H_∞, and pole placement are used to demonstrate a marked improvement in comparison to the existing methods.     

Error estimates for the FEM approximation of optimal sparse control of elliptic equations with pointwise state constraints and finite-dimensional control space

In this work, we derive an a priori error estimate of order for the finite element approximation of a sparse optimal control problem governed by an elliptic equation, which is controlled in a finite dimensional space. Furthermore, box-constrains on the control are considered and finitely many pointwise state-constrains are imposed on specific points in the domain. With this choice for the control space, the achieved order of approximation for the optimal control is optimal, in the sense that the order of the error for the optimal control is of the same order of the approximation for the state equation.     

A priori and a posteriori error estimates of finite‐element approximations for elliptic optimal control problem with measure data

We analyze both a priori and a posteriori error analysis of finite‐element method for elliptic optimal control problems with measure data in a bounded convex domain in (d = 2or3). The solution of the state equation of such type of problems exhibits low regularity due to the presence of measure data, which introduces some difficulties for both theory and numerics of the finite‐element method. We first prove the existence, uniqueness, and regularity of the solution to the optimal control problem. To discretize the control problem, we use continuous piecewise linear elements for the approximations of the state and co‐state variables, whereas piecewise constant functions are used for the control variable. We derive a priori error estimates of order for the state, co‐state, and control variables in the L²‐norm. Further, global a posteriori upper bounds for the state, co‐state, and control variables in the L²‐norm are established. Moreover, local lower bounds for the errors in the state and co‐state variables and a global lower bound for the error in the control variable are obtained in the case of two space dimensions (d = 2). Numerical experiments are provided, which support our theoretical results.     

Sliding mode control of time‐varying delay Markov jump with quantized output

This paper investigates the quantized sliding mode control of Markov jump systems with time‐varying delay. A dynamical adjustment law is explored to quantize the system output. By constructing an observer‐based integral sliding surface, a sliding mode controller is designed to take over the dynamical motion of state estimation and ensure the reachability of sliding surface. A new scaling manner is developed to build the bound between the system output and quantized error. With the help of separation strategies for controller synthesis and general transition probabilities and a lower bound theorem for nonlinear integral terms, a new synthesis method to ensure the required stability and meet the required performance is proposed in the form of linear matrix inequalities. The validity of the proposed control method is illustrated by a numerical example.     

An optimal control model for COVID-19, zika,dengue, and chikungunya co-dynamics with reinfection

The co-circulation of different emerging viral diseases is a big challenge from an epidemiological point of view. The similarity of symptoms, cases of virus co-infection, and cross-reaction can mislead in the diagnosis of the disease. In this article, a new mathematical model for COVID-19, zika, chikungunya, and dengue co-dynamics is developed and studied to assess the impact of COVID-19 on zika, dengue, and chikungunya dynamics and vice-versa. The local and global stability analyses are carried out. The model is shown to undergo a backward bifurcation under a certain condition. Global sensitivity analysis is also performed on the parameters of the model to determine the most dominant parameters. If the zika-related reproduction number ${ℛ}_{\text{0Z}}$ is used as the response function, then important parameters are: the effective contact rate for vector-to-human transmission of zika (${\beta }_2^h$, which is positively correlated), the human natural death rate (${\vartheta }^h$, positively correlated), and the vector recruitment rate (${\mathrm{\Psi }}^v$, also positively correlated). In addition, using the class of individuals co-infected with COVID-19 and zika (${ℐ}_{\text{CZ}}^h$) as response function, the most dominant parameters are: the effective contact rate for COVID-19 transmission (${\beta }_1$, positively correlated), the effective contact rate for vector-to-human transmission of zika (${\beta }_2^h$, positively correlated). To control the co-circulation of all the diseases adequately under an endemic setting, time dependent controls in the form of COVID-19, zika, dengue, and chikungunya preventions are incorporated into the model and analyzed using the Pontryagin's principle. The model is fitted to real COVID-19, zika, dengue, and chikungunya datasets for Espirito Santo (a city with the co-circulation of all the diseases), in Brazil and projections made for the cumulative cases of each of the diseases. Through simulations, it is shown that COVID-19 prevention could greatly reduce the burden of co-infections with zika, dengue, and chikungunya. The negative impact of the COVID-19 pandemic on the control of the arbovirus diseases is also highlighted. Furthermore, it is observed that prevention controls for zika, dengue, and chikungunya can significantly reduce the burden of co-infections with COVID-19.     

Cholera‐schistosomiasis coinfection dynamics

The present work investigates the coinfection dynamics of the cholera and schistosomiasis diseases. The steady states of the model are examined. We obtain results for the model in detail and present the stability results whenever the basic reproduction number is less than unity ( ). For each submodel, the existence of backward bifurcation is presented and for the coinfection model. Furthermore, we formulate an optimal control problem with an appropriate set of control variables. The optimal control problem and the associated results are derived and discussed. The optimal control problem and the suggested controls are utilized to obtain optimal control characterizations. Numerical results are presented by choosing various optimal control strategies for the early elimination of both infections from the population. It is suggested that appropriate uses and application to the population could significantly reduce the infection. Therefore, based on our findings, we suggest to the public health department that the only possible cost‐effective strategy for the elimination of schistosomiasis and cholera coinfection is the combination of both diseases' preventive measures and the treatment of schistosomiasis.     

In Vivo Kinematics of Functional Ankle Instability Patients and Lateral Ankle Sprain Copers During Stair Descent

Patients with mechanic ankle instability experience increased tibiotalar and subtalar joint laxity. However, in vivo joint kinematics in functional ankle instability (FAI) patients and lateral ankle sprain (LAS) copers, especially during dynamic activities, are poorly understood. Ten FAI patients, 10 LAS copers, and 10 healthy controls were included in this study. A dual fluoroscopic imaging system was used to analyze the tibiotalar and subtalar joint kinematics during stair descent. Five key poses of stair descent were analyzed. Kinematic data from six degrees of freedom were calculated utilizing a solid modeling software. The range of motion and joint positions in each degree of freedom were compared among the three groups. The tibiotalar joints of FAI patients and LAS copers were significantly more inverted than those of healthy controls during the foot strike (p = 0.016, = 0.264). The subtalar joints of FAI patients were significantly more anteriorly translated (pose 2, p = 0.003, = 0.352; pose 3, p < 0.001, = 0.454; pose 4, p = 0.004, = 0.334), inverted (pose 4, p = 0.027, = 0.234; pose 5,p = 0.034, = 0.221), and externally rotated (pose 4, p = 0.037, = 0.217; pose 5; p = 0.004, = 0.331) than those of healthy controls during the mid‐stance and the heel off. The FAI patients showed excessive tibiotalar inversion and subtalar joint hypermobility during stair descent. Meanwhile, the LAS copers maintained subtalar joint stability, and only showed excessive tibiotalar inversion in foot strike. These data provide insight into the mechanisms behind the development of FAI after initial LAS. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1860–1867, 2019     

Hemodialysis using a low bicarbonate dialysis bath: Implications for acid-base homeostasis

The low bath bicarbonate concentration ([]) used by a nephrology group in Japan (25.5 mEq/L), coupled with a bath [acetate] of 8 mEq/L, provided an opportunity to study the acid-base events occurring during hemodialysis when flux is from the patient to the bath. We used an analytic tool that allows calculation of delivery during hemodialysis and the physiological response to it in 17 Japanese outpatients with an average pre-dialysis blood [] of 25 mEq/L. Our analysis demonstrates that addition is markedly reduced and that all of it comes from acetate metabolism. The added to the extracellular fluid during treatment (19.5 mEq) was completely consumed by H+ mobilization from body buffers. In contrast to patients dialyzing with higher bath [] values in the US and Europe, organic acid production was suppressed rather than stimulated. Dietary analysis indicates that these patients are in acid balance due to the alkaline nature of their diet. In a larger group of patients using the same bath solution, pre-dialysis blood [] was lower, 22.2 mEq/L, but still in an acceptable range. Our studies indicate that a low bath [] is well tolerated and can prevent stimulation of organic acid production.     

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

In this article, a novel intermittent projected subgradient algorithm is presented to solve the randomized optimal consensus problem for heterogeneous multiagent systems with time-varying communication topologies. The multiagent systems achieve the consensus meanwhile minimizing the global objective function via the proposed algorithm, where f_i(x) is the convex objective function of agent i itself. Due to the common Bernoulli distribution adopted in the existing random optimization algorithm without considering the different computing capability of each agent. An individual projection probability is assigned for each agent based on computing capabilities so that either making projection or taking average is chosen according to the above probability which can effectively avoid overload for some agents with lower computing capabilities and improve the reliability of the overall systems. A new sufficient step-size condition is given to ensure all agents converge to the optimal solution with probability one. Finally, a numerical example is also given to validate the proposed method.     

PRESAS: Block-structured preconditioning of iterative solvers within a primal active-set method for fast model predictive control

Model predictive control (MPC) for linear dynamical systems requires solving an optimal control structured quadratic program (QP) at each sampling instant. This article proposes a primal active-set strategy, called PRESAS , for the efficient solution of such block-sparse QPs, based on a preconditioned iterative solver to compute the search direction in each iteration. Rank-one factorization updates of the preconditioner result in a per-iteration computational complexity of , where m denotes the number of state and control variables and N the number of control intervals. Three different block-structured preconditioning techniques are presented and their numerical properties are studied further. In addition, an augmented Lagrangian based implementation is proposed to avoid a costly initialization procedure to find a primal feasible starting point. Based on a standalone C code implementation, we illustrate the computational performance of PRESAS against current state of the art QP solvers for multiple linear and nonlinear MPC case studies. We also show that the solver is real-time feasible on a dSPACE MicroAutoBox-II rapid prototyping unit for vehicle control applications, and numerical reliability is illustrated based on experimental results from a testbench of small-scale autonomous vehicles.     

Nonlinear multiobjective and dynamic real-time predictive optimization for optimal operation of baseload power plants under variable renewable energy

Considering the increase of disruptive variable renewable energy penetration into the power grid, this article focuses on the investigation of a multiobjective and dynamic real-time optimization framework to address the cycling of large-scale power plants under renewable penetration. In this framework, a parallelized particle swarm optimization step is first performed to generate feasible initial points. Then, a multiobjective and dynamic real-time optimization formulation generates optimal trajectories. The benefit of predictive capability is investigated for the dynamic component, which introduces the novel nonlinear multiobjective and dynamic real-time predictive optimization approach. Two multiobjective formulations to obtain Pareto front optimal in real time are explored: the modified Tchebycheff-based weighted metric and $\epsilon$-constraint methods. Economic and environmental objectives are considered in this study. A novel topical discussion on the intersection of dynamic real-time optimization with model predictive control is also presented. The developed framework is successfully applied to a baseload coal-fired power plant with postcombustion CO₂ capture. Results indicate that the approach can be deployed for a large-scale system if automatic differentiation, model reduction, and parallelization are adopted to improve computational tractability, with computational improvement up to 120-folds after performing these steps. Finally, market and carbon policies showed an impact on the optimal compromise between the objectives with an additional 63 ton of CO₂ captured under favorable market conditions.     

Urologic complications after transplantation of 225 en bloc kidneys from small pediatric donors ≤20 kg: Incidence,management, and impact on graft survival

Pediatric en bloc kidney transplants (EBKs) from small deceased pediatric donors are associated with increased early graft loss and morbidity. Yet, urologic complications post‐EBK and their potential impact on graft survival have not been systematically studied. We retrospectively studied urological complications requiring intervention for 225 EBKs performed at our center January 2005 to September 2017 from donors ≤20 kg into recipients ≥18 years. Overall ureteral complication incidence after EBK was 9.8% (n = 22) (12% vs 2% for EBK donors 10 vs 10 kg, respectively [P = .031]). The most common post‐EBK urologic complication was a stricture (55%), followed by urine leak (41%). In all, 95% of all urologic complications occurred early within 5 months posttransplant (median, 138 days). Urologic complications could be successfully managed nonoperatively in 50% of all cases and had no impact on graft or patient survival. In summary, urologic complications after EBK were common, associated with lower donor weights, occurred early posttransplant, and were often amenable to nonoperative treatment, without adversely affecting survival. We conclude that the higher urologic complication rate after EBK (1) should not prevent increased utilization of small pediatric donor en bloc kidneys for properly selected recipients, and (2) warrants specific discussion with EBK recipients during the preoperative consent process.     

Linear‐quadratic control problems with L1‐control cost

We analyze a class of linear‐quadratic optimal control problems with an additional L¹‐control cost depending on a parameter β. To deal with this nonsmooth problem, we use an augmentation approach known from linear programming in which the number of control variables is doubled. It is shown that if the optimal control for a given is bang‐zero‐bang and the switching function has a stable structure, the solutions are Lipschitz continuous functions of the parameter β. We also show that in this case the optimal controls for β ^* and a with | β ? β ^* | sufficiently small coincide except on a set of measure . Finally, we use the augmentation approach to derive error estimates for Euler discretizations. Copyright © 2014 John Wiley & Sons, Ltd.     

New delay‐dependent exponential stability for neutral Markovian jump systems with mixed delays and nonlinear perturbations

This paper deals with the problem of delay‐dependent exponential stability for neutral Markovian jump systems with mixed delays and nonlinear perturbations. Based on Lyapunov stability theory and linear matrix inequality method, some new exponential stability criteria are presented. The difference between this paper and other existing results is that the lower bounds of the neutral delay, the upper bounds of the neutral delay and discrete delay are considered, which will obtain some less conservative stability analysis results. Numerical examples are given to show that the proposed criteria improve the existing results.     

Self‐triggered linear quadratic networked control

This paper deals with networked control systems design, under the constraint of limited bandwidth on the communication channel. A linear quadratic problem for a fixed sampling period is solved, and this result is used for the development of and performance indexes, yielding to the statement and solution of and optimal control problems. Finally, a self‐triggered controller is designed with a switched system approach in order to improve performance. Several examples are presented in order to illustrate the validity of the developed theory. Copyright © 2013 John Wiley & Sons, Ltd.