Programming-based Optimization Research Articles

Optimal control of electric vehicle by Short-term Costate Estimation method of Pontryagin’s minimum principle

Pontryagin’s minimum principle (PMP) is a foundational theory to obtain optimal control of dynamic systems by the change of their state and cost function over time. Compared to programming-based optimization methods, it offers the advantage of providing rapid computation and an algebraic comprehension of the optimal solution. However, particularly for large-scale systems, constructing their costate dynamics and ensuring numerical stability is quite a difficult task. This study introduces a novel method called as the Short-term Costate Estimation method based on PMP (SCEMP). It aims to efficiently obtain optimal control for large-scale systems by employing an iterative algorithm cored by the PMP. The objective is to minimize energy consumption while a car is running, achieved by regulating the distribution ratio of traction torque between the front and rear wheels. The key concept of SCEMP lies in obtaining a discrete optimal control through the sequential process of forward integration of the state equation and backward integration of the costate equation within a pre-defined near-future time frame. Costate dynamics is formulated with a cost function evaluated within a surrogate model, which not only offers a simplified representation of the plant dynamics but also serves as a reference model for controlling the plant model. Verification is conducted within a simulation environment. The SCEMP achieved results that approached the global optimum obtained from Dynamic Programming (DP) within a margin of up to 1%, while requiring approximately one-tenth of the computation time needed by DP.

Multi-Section Magnetic Soft Robot with Multirobot Navigation System for Vasculature Intervention.

Magnetic soft robots have recently become a promising technology that has been applied to minimally invasive cardiovascular surgery. This paper presents the analytical modeling of a novel multi-section magnetic soft robot (MS-MSR) with multi-curvature bending, which is maneuvered by an associated collaborative multirobot navigation system (CMNS) with magnetic actuation and ultrasound guidance targeted for intravascular intervention. The kinematic and dynamic analysis of the MS-MSR's telescopic motion is performed using the optimized Cosserat rod model by considering the effect of an external heterogeneous magnetic field, which is generated by a mobile magnetic actuation manipulator to adapt to complex steering scenarios. Meanwhile, an extracorporeal mobile ultrasound navigation manipulator is exploited to track the magnetic soft robot's distal tip motion to realize a closed-loop control. We also conduct a quadratic programming-based optimization scheme to synchronize the multi-objective task-space motion of CMNS with null-space projection. It allows the formulation of a comprehensive controller with motion priority for multirobot collaboration. Experimental results demonstrate that the proposed magnetic soft robot can be successfully navigated within the multi-bifurcation intravascular environment with a shape modeling error and a tip error of under the actuation of a CMNS through invitro ultrasound-guided vasculature interventional tests.

Active solar eclipse avoidance on the distant retrograde orbit of the Earth-Moon system

Lighting condition is a significant concern for spacecraft because it is directly related to their power and thermal subsystems. For spacecraft moving on a Distant Retrograde Orbit (DRO) of the Earth-Moon System, the lighting condition is severe because the DRO faces frequent long-duration solar eclipses. This study proposes a solar eclipse avoidance strategy for DRO missions. First, the lighting condition on a stable nominal DRO is analyzed, which reveals that long-duration solar eclipses are inevitable and active avoidance maneuvers are essential. Then, a close-loop three-impulse maneuver strategy is developed to deviate the trajectory from the nominal DRO and avoid a single shadow region of the Earth (or the Moon). The analytical solution of the first velocity impulse required for orbit correction at an arbitrary position is derived. The optimal position of imposing the velocity impulse is obtained using a nonlinear programming-based optimization algorithm. Finally, a coordinated shadow avoidance algorithm is proposed to simultaneously avoid the shadow of the Earth and the Moon. Simulations show that the proposed strategy successfully keeps the flying trajectory outside the shadow regions in a 4-year mission time at an average annual velocity impulse cost of 46.936 m/s.

The Indirect Carbon Cost of E-Mobility for Select Countries Based on Grid Energy Mix Using Real-World Data

Electric vehicles are considered by many as an emission-free or low-emission solution to meet the challenge of sustainable transportation. However, the operational input, electrical energy, has an associated cost, greenhouse gasses, which results in indirect emissions. Given this knowledge, we pose the following question: “Are zero-emission transportation targets achievable given our current energy mix?” The objective of this article is to assess the impact of a grid’s energy mix on the indirect emissions of an electric vehicle. The study considers real-world data, vehicle usage data from an electric vehicle, and carbon intensity data for India, the USA, France, the Netherlands, Brazil, Germany, and Poland. Linear programming-based optimization is used to compute the best charging scenario for each of the given grids and, consequently, the indirect emissions are compared to those of a high-efficiency 1.5 L diesel internal combustion engine for the vehicle: a 2019 Renault Clio dCi 85. The results indicate that for grids with low renewable energy penetration, such as those of Poland and India (Maharashtra), an electric vehicle, even when optimally charged, can be classified as neither a low- nor zero-emission alternative to normal thermal vehicles. Also, for grids with elevated levels of variation in their carbon intensity, there is significant potential to reduce the carbon footprint related to charging an electric vehicle. This article provides a real-world perspective of how an electric vehicle performs in the face of different energy mixes and serves as a precursor to the development of robust indicators for determining the carbon reductions related to the e-mobility transition.

Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality

The role of carbon capture technology using monoethanolamine (MEA) is critical for achieving the carbon-neutrality goal. However, maintaining the efficient operation of the post-combustion carbon capture is challenging considering the hyperdimensional design space and nonlinear characteristics of the process. In this work, CO2 capture level from the flue gas in the absorption column is investigated for the post-combustion carbon capture process using MEA. Artificial neural network (ANN) and support vector machine (SVM) models are constructed to model CO2 capture level under extensive hyperparameters tuning. The comparative performance analysis based on external validation test confirmed the superior modelling and generalization ability of ANN for the carbon capture process. Later, partial derivative-based sensitivity analysis is carried out and it is the found that absorbent-based input variables like lean solvent temperature and lean solvent flow rate are the two most significant input variables on CO2 capture level in the absorption column. The optimization problem with the ANN model embedded in the nonlinear programming-based optimization environment is solved under different operating scenarios to determine the optimum operating ranges for the input variables corresponding to the maximum CO2 capture level. This research presents the optimum operating conditions for CO2 removal from the flue gas for the post-combustion carbon capture process using MEA that contributes to achieving the carbon neutrality goal.

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector.

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector.

A General Stochastic Optimization Framework for Convergence Bidding

Convergence (virtual) bidding is an important part of two-settlement electric power markets as it can effectively reduce discrepancies between the day-ahead and real-time markets. Consequently, there is extensive research into the bidding strategies of virtual participants aiming to obtain optimal bids to submit to the day-ahead market. In this paper, we introduce a price-based general stochastic optimization framework to obtain optimal convergence bid curves. Within this framework, we develop a computationally tractable linear programming-based optimization model, which produces bid prices and volumes simultaneously. We also show that different approximations and simplifications in the general model lead naturally to state-of-the-art convergence bidding approaches, such as self-scheduling and opportunistic approaches. Our general framework also provides a straightforward way to compare the performance of these models, which is demonstrated by numerical experiments on the California (CAISO) market.

A multi-objective network design for recycling healthcare waste from large-scale immunization

This paper presents a Goal programming-based optimization model for managing the recycling operations of healthcare waste generated from large-scale vaccination drives. The model proposes an efficient system by integrating the decisions of locating recycling units and the routing of generated waste to them, considering the risks to the environment and the associated population. The objectives include the minimization of setup and transportation costs, risks to the population, and the number of installed units. A set of randomly selected test instances is used to test the effectiveness of the model. The results reveal that a compromised solution offers cost advantages and population risk mitigation. The approach effectively supports the strategic choices in recycling healthcare waste generated from immunization.

Integer linear programming-based optimization methodology for reliability and energy-aware high-level synthesis

Continuous decrease in the transistor technology sizes has enabled much denser packaging of electronic components on chips, which has resulted in integrated circuits with lower costs and area. However, it has also given rise to new issues and challenges in the process of integrated circuit design, including higher vulnerability to soft errors. Modular hardware redundancy is a popular method for improving the reliability of a system against errors at a cost of increasing area and energy consumption. Voltage scaling methods can be employed to tackle high energy costs; however, this also negatively affects the circuit’s reliability and performance. Therefore, designing circuits with all these conflicting parameters is a very challenging task. In this study, we propose integer linear programming (ILP)-based high-level synthesis (HLS) methods to optimize both reliability and/or energy under the area and latency constraints. Our models employ full and partial duplication to improve the system reliability as long as the area constraint permits. They also utilize voltage islands as the energy reduction method of choice. What makes this problem even more interesting and complex is the fact that we use different versions of the same resources that have different area, latency, reliability, and energy values. Although this affects the execution time of the proposed methods, it also gives us more design options. We compared and showed the effectiveness of our methods against a genetic algorithm-based one on several HLS benchmarks. The ILP-based methods return the optimum results most of the time under the given time limits.

Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem

A chance-constrained programming-based optimization model for the dynamic economic emission dispatch problem (DEED), consisting of both thermal units and wind turbines, is developed. In the proposed model, the probability of scheduled wind power (WP) is included in the set of problem-decision variables and it is determined based on the system spinning reserve and the system load at each hour of the horizon time. This new strategy avoids, on the one hand, the risk of insufficient WP at high system load demand and low spinning reserve and, on the other hand, the failure of the opportunity to properly exploit the WP at low power demand and high spinning reserve. The objective functions of the problem, which are the total production cost and emissions, are minimized using a new hybrid chaotic maps-based artificial bee colony (HCABC) under several operational constraints, such as generation capacity, system loss, ramp rate limits, and spinning reserve constraints. The effectiveness and feasibility of the suggested framework are validated on the 10-unit and 40-unit systems. Moreover, to test the robustness of the suggested HCABC algorithm, a comparative study is performed with various existing techniques.

Data-driven mixed-Integer linear programming-based optimisation for efficient failure detection in large-scale distributed systems

Failure detectors (FDs) are fundamental building blocks for distributed systems. An FD detects whether a process has crashed or not based on the reception of heartbeats’ messages sent by this process over a communication channel. A key challenge of FDs is to tune their parameters to achieve optimal performance which satisfies the desired system requirements. This is challenging due to the complexities of large-scale networks. Existing FDs ignore such optimisation and adopt ad-hoc parameters. In this paper, we propose a new Mixed Integer Linear Programming (MILP) optimisation-based FD algorithm. We obtain the MILP formulation via piecewise linearisation relaxations. The MILP involves obtaining optimal FD parameters that meet the optimal trade-off between its performance metrics requirements, network conditions and system parameters. The MILP maximises our FD’s accuracy under bounded failure detection time while considering network and system conditions as constraints. The MILP’s solution represents optimised FD parameters that maximise FD’s expected performance. To adapt to real-time network changes, our proposed MILP-based FD fits the probability distribution of heartbeats’ inter-arrivals. To address our FD scalability challenge in large-scale systems where the MILP model needs to compute approximate optimal solutions quickly, we also propose a heuristic algorithm. To test our proposed approach, we adopt Amazon Cloud as a realistic testing environment and develop a simulator for robustness tests. Our results show consistent improvement of overall FD performance and scalability. To the best of our knowledge, this is the first attempt to combine the MILP-based optimisation modelling with FD to achieve performance guarantees.

Stochastic parallel optimization of an islanded microgrid under multiple source–load uncertainties

This paper proposes a stochastic parallel optimization approach for an islanded microgrid. The proposed approach solves two of the problems faced by such microgrids, namely, how to mitigate the adverse effects of multiple source–load uncertainties and how to accelerate the computation speed, by decomposing an entire microgrid into several entities, i.e., the utility and the end users. A chance constrained programming-based optimization model is established to address the former problem. By discretizing the continuous variables, the chance constraints of multiple uncertainties can be transformed into deterministic constraints. The second problem is solved by incorporating a new linear penalty function into the analytical target cascading method. With this proposed penalty function, the algorithm converges rapidly and the scheduling results for both the utility and the end users are obtained with low computational cost. A simulation is conducted to illustrate the effectiveness of the proposed approach.

A MILP optimization model for siting isolation devices in distribution systems with DERs to enhance reliability

The deployment of distributed energy resources (DER) theoretically improves the reliability of a radial distribution system by taking advantage of the ability to island DERs and customers. However, an isolation device has to be placed in order to create this “island.” On the other hand, a radial distribution system typically lacks isolation devices that are able to detect and isolate a fault, especially on the main distribution feeder, which is nowadays often protected by the substation breaker. This paper proposes a methodology to identify the optimal locations to install isolation devices in a radial distribution network with DER integrations, aiming at reliability improvements of distribution networks. We exploit utility historical load and fault data for generating multiple fault scenarios and develop a stochastic programming-based optimization model for unserved energy minimization. Two case studies on an IEEE 4-bus and 123-bus systems are performed, including some sensitivity analysis.

An interval chance-constrained programming-based optimization model for carbon capture, utilization, and storage system planning

Carbon capture, utilization, and storage (CCUS) are widely regarded as a crucial technological option for industrial large-scale carbon dioxide (CO2) emissions reduction. However, high-cost and uncertainties hinder the widespread application of CCUS technology. In this study, an interval-chance-constrained programming-based optimization model was proposed to address random probability distributions, interval values, complex interactions, and the dynamics of capacity expansion issues.The model was applied to a CCUS project in China. A set of violation probability levels (0.01, 0.05, 0.1, and 0.2) were designed to reflect system costs and risk levels. And then the solutions for system costs, capacity expansion, and operating schemes under four violation probability levels (pi’) could be generated. The results revealed that the model could ensure the highest reliability and largest CO2 storage under pi’ = 0.01. At this probability level, the amount of CO2 storage would range from 4972.05–5429.75 kilotons per annum (ktpa), the CCUS system cost would be highest at $166.57 million, and the net system benefits would be slightly less at $105.91 million. If policymakers strive to achieve the net system benefits of the project, the highest net system benefits would be achieved under pi’ = 0.05. At this probability level, the net system benefits would increase to $135.45 million, the system cost would reduce to $138.62 million, but the total amount of CO2 storage would decrease to between 4090.01 and 4653.24 ktpa, which would entail a high risk of system violation. These findings enable policymakers to determine the trade-offs among system reliability, CO2 reduction, and the benefits of the project. The modeling approach can also address interactions among CCUS activities and the dynamics of facility expansion issues as well as help policymakers develop adaptive operational strategies. This study enriches CCUS research through an interval chance-constrained optimization modeling approach for CCUS system management under multiple uncertainties.

Energy-efficient, EDFA lifetime-aware network planning along with virtualized elastic regenerator placement for IP-over-EON

In this paper, we focus on energy-efficient network planning (including traffic provisioning) along with optimal placement of virtualized elastic regenerators (VERs) for IP-over-elastic optical networks based on a static traffic profile, using a mixed integer linear programming-based optimization model. The proposed model judiciously exploits flexibility of IP core routers, sliceable bandwidth variable transponders (SBVTs) and VERs to accommodate the traffic demands with the minimum power consumption (PC). Optical layer traffic grooming allows to simultaneously originate/terminate multiple lightpaths of different capacities, data slots and maximum transparent reach by a single SBVT. The proposed model also allows to use all functionalities of VER, such as simultaneous regeneration, distance-adaptive transmission option selection, frequency slot merging to be used concurrently for the given static traffic profile. In addition, the proposed model includes lifetime awareness of Erbium-doped fiber amplifier (EDFA) to reduce EDFA failure and associated repairing cost in long run. Using the proposed model, we enhance the average EDFA lifetime by restricting average EDFA occupancy, represented by the ratio of the (average) number of lightpaths being amplified in an EDFA and the maximum possible number of lightpaths that can be amplified in it, even though in the process, the overall PC in network may increase, with reference to the scenario with no EDFA occupancy restriction. The variation in PC and average EDFA lifetime for different permissible (user-defined) average EDFA occupancy are studied. We exhaustively study performance of the model under different network conditions.

A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks

The increased integration of light-duty electric vehicles (EVs) into low-voltage (LV) residential networks imposes capacity issues for the grid operators. For example, the uncoordinated and clustered charging of residential EVs can often overload grid assets, jeopardize network reliability, and violate local voltage constraints. This article proposes a coordinated management system for EVs in LV residential networks with power grid support functionalities to address grid overloading and local voltage constraints violation. The charging and discharging of EV batteries in the network are coordinated via a local EV aggregator. The coordination is realized using multiagent system architecture that provides the EV owners with full decision-making authority and preserves their privacy. The EV coordination and vehicle-to-grid (V2G) resource optimization of the EV aggregator is formulated as a mixed-integer programming-based optimization model to minimize the electricity cost for the EV owners based on a real-time tariff while complying with local grid constraints. The proposed methodology is evaluated via simulation of an LV residential network in Sydney, Australia with actual load demand data. The simulation results indicate the efficacy of the proposed strategy for the electricity cost reduction of the EV owners while mitigating grid overloading and maintaining desired bus voltages.

Application of dynamic programming to optimal energy management of grid-independent hybrid railcars

In this paper, a dynamic programming-based optimization analysis is performed to quantitatively assess the energy-saving potential associated to the battery hybridization of railcars, which is nowadays regarded as a high-potential solution to reduce the greenhouse-gas impact of regional trains. A computationally effective longitudinal model is first developed, starting from an available energetic macroscopic representation modeling tool. The resulting simplified railcar model was first integrated with a battery state of charge simulator and then embedded within a fast constrained optimization algorithm. The optimization outcomes, reachable within reasonable timeframe and considering cost-effective hybridization scenarios, are highly informative for train manufacturers and operators interested in substantial hybridization of railcars. Particularly, fuel savings as high as 18% can be achieved with respect to the current diesel-powered trains. As a consequence, useful design and control guidelines are made available, considering both retrofitting or replacing the existing railway systems (particularly focusing on regional trains), while coping with capital and operating costs, safety, and traffic constraints.

Multi-mode operation of a Liquid Air Energy Storage (LAES) plant providing energy arbitrage and reserve services – Analysis of optimal scheduling and sizing through MILP modelling with integrated thermodynamic performance

Energy storage competitiveness is ubiquitously associated with both its technical and economic performance. This work investigates such complex techno-economic interplay in the case of Liquid Air Energy Storage (LAES), with the aim to address the following key aspects: (i) LAES optimal scheduling and how this is affected by LAES thermodynamic performance (ii) the effect of LAES sizing on its profitability and performance (iii) overall techno-economic assessment of LAES multi-mode operation when providing energy and reserve services. To address these aspects, a Mixed Integer Linear Programming-based optimisation tool has been developed to simulate LAES operation throughout a year while including detailed thermodynamic constraints, thus allowing an accurate performance estimation. The results demonstrate that considering LAES thermodynamic performance in the optimisation ensures a feasible dispatch profile thus avoiding loss of revenues, especially for the multi-mode cases. However, while operation with arbitrage and a portfolio of reserve services is financially promising, it also deteriorates LAES roundtrip efficiency; therefore, a techno-economic balance should be sought. In terms of design, the possibility of independently sizing LAES charge and discharge power is key for tailoring the plant to the specific operating mode. Furthermore, storage energy capacities greater than 2–3 h do not significantly increase LAES profitability under the market conditions considered.

Optimal energy management and shift scheduling control of a parallel plug-in hybrid electric vehicle

This paper deals with design of a control strategy for a parallel powertrain configuration of a plug-in hybrid electric vehicle (PHEV). The control strategy is aimed at minimising fuel consumption and number of gear shift events for a wide range of driving cycles, while keeping the battery state-of-charge within allowable range. A control-oriented backward-looking model of PHEV powertrain is used as a design basis. The control strategy combines a rule-based controller with an equivalent consumption minimisation strategy (ECMS). The ECMS uses both transmission gear ratio and engine torque as control variables, thus eliminating a need for designing a separate gear shift scheduling strategy and exploiting a full potential of powertrain efficiency improvement. The overall control strategy is designed for different characteristic operating regimes including charge depleting, charge sustaining, and blended regimes. The strategy is verified by computer simulations against globally optimal benchmark obtained by using the dynamic programming-based optimisation, whose results are also used for fine tuning of controller parameters.

Emission Constrained Economic Dispatch with PV Energy Penetration

Power system planners are forced to consider the alarming rate of environmental pollution and rapiddepletion of fossil fuels andutilize renewable energy resources to mitigate the environmental effects of thermal power stations. Combined Economic Emission Dispatch(CEED)offers an effectivesolution to reducefossil fuel emissions as well ascost.Since 1985, CEED is considered to be a common optimization strategy. Literature contains lot of optimization methods for the strategy.In the recent times, using PV energy has proved to be a feasible and dependable alternative for electricity generation systems based on fossil fuels. In the developing countries, the dependency on fossil fuels has been seen as inevitable. At present,the use of renewable energy sources is rapidly increasing in inconventional power generation systems. The present paper puts forwardan approach of combining PVenergy-based grid integrated PV system with fossil fuel based thermal power plant using evolutionary programmingbased optimization technique.Among the various optimization techniques, the Particle Swarm Optimization (PSO) is considered to be the most suitable technique for the problem is explained in detailed manner.The proposed method is to combine CEED with the PV energy and thereby reduces the use of conventional energy resources.It also permits an effective utilizationof abundantlyavailable PV energy.It is tested on standard IEEE 30 bus system with the real time ratings of proposed PV plant situated in Tamilnadu.