Time-varying Cost Research Articles

Time-varying cost modeling and maintenance strategy optimization of plateau wind turbines considering degradation states

Plateau wind power has great potential in reducing carbon emissions; however, compared with other renewable energy, its economics still need to be improved. As an effective approach to enhance its economic feasibility, maintenance strategy optimization aims to reduce maintenance costs per kilowatt-hour and extend equipment lifespan. This paper proposes a multi-objective optimization model for the maintenance decision-making of plateau wind turbines that considers the degradation state. It incorporates: i) modeling the maintenance process of plateau wind turbines by combining time-based and state-based methods; ii) considering the time-varying maintenance costs in complex environments; and iii) employing a multi-objective optimization method to find the optimal strategy that meets maintenance requirements. The complexity considered in the model mainly includes the randomness of the operating duration for each equipment state, the temporal variability of equipment distribution and installation costs, and the uncertainty in maintenance effectiveness. The proposed optimization method is applied to a wind farm in the Yunnan-Guizhou Plateau, China.The results indicate that traditional maintenance strategies underestimate maintenance costs and equipment lifespan losses. Compared with conventional maintenance strategies, this method can reduce equipment maintenance costs by 24.07 % and extend its operating life by 11.58 %. Additionally, this paper has conducted a series of parametric analyses to enhance the generalization performance of the model. The proposed method effectively addresses the economic issues of plateau wind turbine maintenance and provides a valuable decision-making tool for guiding the long-term maintenance of wind turbines in complex environments.

Load-aware continuous-time optimization for multi-agent systems: toward dynamic resource allocation and real-time adaptability

In the realm of next-generation mobile communication networks, characterized by dynamic and evolving workloads, the efficient resource allocation becomes paramount for achieving optimal performance. This paper addresses the intricate challenges of multiagent resource management through the integration of stochastic learning automata and continuous-time optimization techniques, presenting an innovative solution for real-time adaptability and dynamic load balancing. The central aim is to achieve optimal values for local time-varying cost functions while taking into account resource constraints and the feasibility of local allocation. In the context of multi-agent resource allocation systems, where conditions vary over time, the algorithm exhibits a dynamic adaptability, continuously adjusting to the evolving optimal solutions tailored for each agent. Utilizing stochastic learning automata, the algorithm effectively addresses the time-varying load-balancing function in response to dynamic user demands. Furthermore, we introduced an scalable solution for mobility robustness optimization based on deep reinforcement learning (DRL-MRO). This method dynamically identifies network-wide optimal parameter values across the entire network to accommodate diverse mobility patterns, ensuring the effectiveness of load balancing parameters that seamlessly adapt to the dynamic configuration of the network. The overarching goal is to maintain a consistent quality of service level for each agent, thereby enhancing the overall system performance. Simulation outcomes unequivocally affirm the superior performance of the proposed load balancing approach, rooted in continuous-time optimization and stochastic learning automata, surpassing existing schemes across diverse system configurations.

Continuous Distributed Robust Optimization of Multiagent Systems With Time-Varying Cost

Continuous Distributed Robust Optimization of Multiagent Systems With Time-Varying Cost

Distributed Fixed-Time Optimization With Time-Varying Cost: Zero-Gradient-Sum Scheme

Distributed Fixed-Time Optimization With Time-Varying Cost: Zero-Gradient-Sum Scheme

Prescribed Time Consensus Control of Multiagent Systems With Minimized Time-Varying Cost Function

Prescribed Time Consensus Control of Multiagent Systems With Minimized Time-Varying Cost Function

Finite-time/fixed-time distributed optimization for nonlinear multi-agent systems with time-varying cost function

Distributed optimization, combining cooperative control and optimization objectives, is vital for practical applications. While current research predominantly focuses on linear multi-agent systems, the real-world demands often involve nonlinear systems with finite or fixed time constraints. This paper addresses the finite-time/fixed-time distributed optimization for nonlinear multi-agent systems with time-varying cost function. Under appropriate assumptions, we propose a finite-time distributed protocol and a fixed-time distributed protocol for the nonlinear multi-agent systems to ensure all agents achieve consensus and minimize the time-varying cost function, relaxing the conditions for strong convexity and bounded gradient difference of the cost function. Simulation experiments validate the effectiveness of these distributed protocols.

Distributed Online Constrained Optimization With Feedback Delays.

We investigate multiagent distributed online constrained convex optimization problems with feedback delays, where agents make sequential decisions before being aware of the cost and constraint functions. The main purpose of the distributed online constrained convex optimization problem is to cooperatively minimize the sum of time-varying local cost functions subject to time-varying coupled inequality constraints. The feedback information of the distributed online optimization problem is revealed to agents with time delays, which is common in practice. Every node in the system can interact with neighbors through a time-varying sequence of directed communication topologies, which is uniformly strongly connected. The distributed online primal-dual bandit push-sum algorithm that generates primal and dual variables with delayed feedback is used for the presented problem. Expected regret and expected constraint violation are proposed for measuring the performance of the algorithm, and both of them are shown to be sublinear with respect to the total iteration span T in this article. In the end, the optimization problem for the power grid is simulated to justify the proposed theoretical results.

Online Inverse Optimal Control for Time-Varying Cost Weights.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations.

Integration of sales, inventory, and transportation resource planning by dynamic-demand joint replenishment problem with time-varying costs

This paper addresses the joint replenishment problem in the context of sales and operations planning. Unlike traditional ones that assume static demands, unit costs, and transportation capacity, we consider dynamic-deterministic demands from sales plans and time-varying unit costs and transportation capacity to deal with the recent logistics conditions. We then introduce the capacitated dynamic-demand joint replenishment problem with time-varying costs. To efficiently solve this problem, a three-phase approach is proposed: (1) simplifying the problem and determining ideal inventory quantities using mixed integer linear programming, (2) estimating policy variables for each item using the covariance matrix adaptation evolution strategy, and (3) updating the ideal inventory quantities based on evaluated shortages until all demands are satisfied. Our method outperforms conventional approaches with at least 12 times faster solution runtimes in tests with up to 100 items. We also obtain the insight that the cost-efficient replenishment plan changes according to the increase rate of the unit transportation cost.

Distributed Finite-Time Optimization of Multi-Agent Systems With Time-Varying Cost Functions Under Digraphs

Distributed Finite-Time Optimization of Multi-Agent Systems With Time-Varying Cost Functions Under Digraphs

Fully Distributed Prescribed-Time Optimization with Time-Varying Cost Function

Fully Distributed Prescribed-Time Optimization with Time-Varying Cost Function

Analysis of the correlating or competing nature of cost-driven and emissions-driven demand response

Multi-objective optimization is an important tool for sustainable decision making. In process operations, the nature of tradeoffs between objectives may change over time, due to, for example, changes in power cost and emissions signals. This work seeks to understand the correlating vs. competing nature of demand response objectives, and their variation in time, through a time-varying ammonia production case study. Results show that correlation between cost and emissions is near perfect when the time-varying cost and emissions factor profiles are in phase, and low when they are anti-phase. Furthermore, analysis of real historical data from different locations around the world suggests that for the majority of the time, cost and emissions objectives are strongly correlated. Trends observed are robust over different times of the year and different locations in the same ISO; however, considering ISOs in different parts of the world can provide different results due to variation in generator mix. An approach for determining compromise operating strategies when cost and emissions are competing is also presented.

Distributed online optimization for heterogeneous linear multi-agent systems with coupled constraints

This paper studies a class of distributed online convex optimization problems for heterogeneous linear multi-agent systems. Agents in a network, knowing only their own outputs, need to minimize the time-varying costs through neighboring interaction subject to time-varying coupled inequality constraints. Based on the saddle-point technique, we design a continuous-time distributed controller which is shown to achieve constant regret bound and sublinear fit bound, matching those of the standard centralized online method. We further extend the control law to the event-triggered communication mechanism and show that the constant regret bound and sublinear fit bound are still achieved while reducing the communication frequency. Additionally, we study the situation of communication noise, i.e., the agent’s measurement of the relative states of its neighbors is disturbed by a noise. It is shown that, if the noise is not excessive, the regret and fit bounds are unaffected, which indicates the controller’s noise-tolerance capability to some extent. Finally, a numerical simulation is provided to support the theoretical conclusions.

Online Partial Service Hosting at the Edge

We consider the problem of service hosting where an application provider can dynamically rent edge computing resources and serve user requests from the edge to deliver a better quality of service. A key novelty of this work is that we allow the service to be hosted partially at the edge that enables a fraction of the user query to be served by the edge. We model the total cost for (partially) hosting a service at the edge as a combination of the latency in serving requests, the bandwidth consumption, and the time-varying cost for renting edge resources. We propose an online policy called α-RetroRenting (α-RR) that dynamically determines the fraction of the service to be hosted at the edge in any time-slot, based on the history of the request arrivals and the rent cost sequence. As our main result, we derive an upper bound on α-RR’s competitive ratio with respect to the offline optimal policy that knows the entire request arrival and rent cost sequence in advance. In addition, we provide performance guarantees for our policy in the setting where the request arrival process is stochastic. We conduct extensive numerical evaluations to compare the performance of α-RR with various benchmarks for synthetic and trace-based request arrival and rent cost processes and find several parameter regimes where α-RR’s ability to store the service partially greatly improves cost-efficiency.

MARGANVAC: metal artifact reduction method based on generative adversarial network with variable constraints

Objective. Metal artifact reduction (MAR) has been a key issue in CT imaging. Recently, MAR methods based on deep learning have achieved promising results. However, when deploying deep learning-based MAR in real-world clinical scenarios, two prominent challenges arise. One limitation is the lack of paired training data in real applications, which limits the practicality of supervised methods. Another limitation is that image-domain methods suitable for more application scenarios are inadequate in performance while end-to-end approaches with better performance are only applicable to fan-beam CT due to large memory consumption. Approach. We propose a novel image-domain MAR method based on the generative adversarial network with variable constraints (MARGANVAC) to improve MAR performance. The proposed variable constraint is a kind of time-varying cost function that can relax the fidelity constraint at the beginning and gradually strengthen the fidelity constraint as the training progresses. To better deploy our image-domain supervised method into practical scenarios, we develop a transfer method to mimic the real metal artifacts by first extracting the real metal traces and then adding them to artifact-free images to generate paired training data. Main results. The effectiveness of the proposed method is validated in simulated fan-beam experiments and real cone-beam experiments. All quantitative and qualitative results demonstrate that the proposed method achieves superior performance compared with the competing methods. Significance. The MARGANVAC model proposed in this paper is an image-domain model that can be conveniently applied to various scenarios such as fan beam and cone beam CT. At the same time, its performance is on par with the cutting-edge dual-domain MAR approaches. In addition, the metal artifact transfer method proposed in this paper can easily generate paired data with real artifact features, which can be better used for model training in real scenarios.

On the relation between dynamic regret and closed-loop stability

In this work, we study the relations between bounded dynamic regret and the classical notion of asymptotic stability for the case of a priori unknown and time-varying cost functions. In particular, we show that bounded dynamic regret implies asymptotic stability of the optimal steady state for a constant cost function. For the case of an asymptotically stable closed loop, we first derive a necessary condition for achieving bounded dynamic regret. Then, given some additional assumptions on the system and the cost functions, we also provide a sufficient condition ensuring bounded dynamic regret. Our results are illustrated by examples.

A deep learning-based approach to a newsvendor problem considering uncertainty and time-varying costs

A deep learning-based approach to a newsvendor problem considering uncertainty and time-varying costs

Capturing spatial, time-wise and technological detail in hydrogen supply chains: A bi-level multi-objective optimization approach

This study presents a methodological framework for designing a hydrogen supply chain (HSC) utilizing a superstructure that incorporates various alternatives for feedstock, production, storage, transportation, and distribution. The framework features a mixed-integer non-linear mathematical model that includes innovative factors such as power efficiencies, continuous sizing capacities, and time-varying costs of different energy feedstocks related to the learning rate. Furthermore, emerging production technologies such as alkaline electrolysis and proton exchange membrane water electrolysis, along with steam methane reforming with carbon capture, utilization, and storage, are considered. To address the resulting bi-objective optimization problem that aims to minimize both total daily costs and greenhouse gas emissions, a specialized solution strategy based on bi-level decomposition and a matheuristic algorithm is developed. This methodology for designing HSCs is applied to a case study in southern France, demonstrating the effectiveness of the solution technique in approximating the Pareto frontier. By analyzing in detail selected relevant solutions along the Pareto front, insights into the spatial, temporal, and technological deployment of the HSC are gained, enabling decision-makers to make informed choices based on economic and environmental criteria.

Distributed Continuous-Time Algorithm for Time-Varying Optimization With Affine Formation Constraints

In this note, we investigate a continuous-time distributed optimization problem with time-varying cost functions and affine formation constraints, which are described by the stress matrices rather than the standard Laplacians. The objective is to minimize the sum of local time-varying cost functions, each of which is known by only one individual agent. The optimal solution is a time-varying affine transformation of a nominal configuration rather than some constants. To tackle the difficulty caused by the dynamic aspect of the local cost functions and handle affine formation constraints, the fixed-time distributed estimator and distributed gradient tracking technique are developed respectively to compensate the time variation of solution trajectory and calculate the weighted sum of local gradients to eliminate the tracking error. The time-varying optimal solution trajectory is thus accurately tracked with the proposed estimator-based gradient tracking algorithm. Using appropriately chosen coefficients, the tracking error is guaranteed to vanish at an exponential rate. The proposed estimator-based gradient tracking algorithm is further validated through numerical simulations.

Online Distributed Seeking for First-Order Nash Equilibria of Nonconvex Noncooperative Games With Multiple Clusters

In this brief, we study the problem of online distributed seeking for first-order Nash equilibria of nonconvex noncooperative games with multiple clusters. In this game, each cluster is composed of multiple players, whose goals are to cooperatively minimize the sum of time-varying cost functions in their own cluster. Each player can only have access to its own cost function and action set, and can only communicate with its immediate neighbors in the same cluster through a time-varying graph. Moreover, cost functions cannot be known by players in advance. Unlike existing studies on noncooperative games, the cost function we consider is nonconvex. To address this challenge, an online distributed dual averaging algorithm is proposed. Interestingly enough, the performance of the algorithm is measured by regrets involving the first-order Nash equilibrium condition, and the sublinearity of the regrets is achieved under the proposed algorithm. The validity of the results is illustrated by a numerical example.