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Battery energy storage systems (BESSs) have the potential to reduce end users’ electricity bills by shifting their grid demand in response to price incentives. Some electricity retailers charge end users for their peak demand in the billing cycle regardless of the time of occurrence. Yet, using BESSs to reduce demand charges is challenging, since the net demand of end users can be highly uncertain due to low aggregation and on-site renewable generation. In this article, an approximate dynamic programming (ADP) methodology is developed to control a BESS to minimize an end user’s electricity bill, which includes both an energy charge and monthly peak demand charge. To address time-varying uncertainty, the net demand is modeled using a periodic autoregressive (PAR) model, which is then used to formulate a Markov Decision Process whose objective is to minimize the sum of energy, peak demand, and battery usage costs. A backward ADP strategy is developed which is enabled by new closed-form expressions for the probability distribution of the expected stage and tail costs when a radial basis function (RBF) value function approximation (VFA) is employed. The approach is applied to real net demand data from a small Australian residential community and compared to two benchmark policies: a lookup table VFA and a model predictive control (MPC) approach. The results show that the proposed approach reduces the average monthly peak demand by about 25% (yielding an 8% reduction in the electricity bill), whereas the best-performing benchmark policy reduced the peak demand by about 17%. [ABSTRACT FROM AUTHOR]

Availability of the big data from Bluetooth MAC Scanners (BMS) over the network provides opportunities to trace the movement of the individual Bluetooth-equipped vehicles on the network. However, BMS might not perfectly detect all the devices within its detection zone. For dense urban networks, the scanner scanning zones can significantly overlap, which complicates the detailed reconstruction of the vehicle trajectory. Addressing the need, this paper proposes a Slit based Trajectory Reconstruction (STATER) algorithm where for each BMS, a slit is defined that considers the overlap and connectivity with other BMS, and thereafter the trajectory is reconstructed considering the shortest path over the observed sequence of slits. A numerical simulation framework is proposed to thoroughly test the proposed STATER algorithm at various levels of ambiguity and randomness in the input dataset. The testing results indicate that the reconstructed trajectories could capture more than 90% (true positive) of the actual path and an average error (false positive) of 11.3% at different randomness levels considered in the experiments. As proof of concept, STATER is applied on one-day data from the entire Brisbane network with 0.56m trips, the computational performance for which supports its practical applicability. [ABSTRACT FROM AUTHOR]

For concentrating Solar Tower (ST) power plants, heliostats must be cleaned to maintain high productivity, but this comes at the cost of cleaning expenditures. Striking the correct balance remains challenging, due in part to the fact that soiling losses are location-dependent, stochastic, seasonal, and spatially inhomogeneous across the field. In this paper, novel reflectance-based cleaning policies are developed that trigger and prioritize cleaning of different solar field sectors based on reflectance measurements. In contrast to existing approaches, these policies have the potential to mitigate the effect of stochastic soiling losses and allocate finite cleaning resources by considering the spatial inhomogeneity of soiling. The optimization of the policy is conducted using the approximate Markov Decision Process (MDP) paradigm that utilizes a simulation model based on a recently developed physical soiling model. The proposed approach is applied to a case study on a hypothetical ST plant located in South Australia. The proposed policies are benchmarked with other traditional time-based cleaning policies and a previously developed reflectance-based policy. The results indicate a considerable benefit of sectorial reflectance-based cleaning strategies to other benchmarked policies (i.e. ∼ 2 % savings on total cleaning costs). Moreover, in case where the per-cleaning costs (e.g. water, fuel) are significant compared to the fixed costs (e.g. truck depreciation), the savings of proposed sectorial cleaning policies are greater (∼ 10% savings). • A reflectance-based sectorial cleaning policy for solar collectors is proposed. • Reflectance loss is modelled using a physical soiling model and weather parameters. • Cleaning policy is optimized using an Approximate Markov Decision Process paradigm. • The proposed policy is applied to a hypothetical plant in Woomera, South Australia. • Policies are benchmarked against other time-based and simplified cleaning policies. [ABSTRACT FROM AUTHOR]

In order to improve the performance of a surrogate model-based optimization method for building optimization problems, a new active sampling strategy employing a committee of surrogate models is developed. This strategy selects new samples that are in the regions of the parameter space where the surrogate model predictions are highly uncertain and have low energy use. Results show that the new sampling strategy improves the performance of surrogate model-based optimization method. A comparison between the surrogate model-based optimization methods and two simulation-based optimization methods shows better performance of surrogate model-based optimization methods than a simulation-based optimization method using the PSO algorithm. However, the simulation-based optimization using Ant Colony Optimization found better results in terms of optimality in later stages of the optimization. However, the proposed method showed a better performance at the early optimization stages, yielding solutions within 1% of the best solution found in the fewest number of simulations. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the economic benefit of exploiting partial opportunities for maintenance, which arise from the reduction of costs of doing partial maintenance utilizing the opportunity given. A typical example is an externally induced production stop with a duration that may not be sufficient to a complete maintenance activity. This paper combines partial opportunities and condition‐based maintenance (CBM) strategies and proposes an innovative maintenance optimization method considering time‐varying economic conditions. This scenario naturally fits a broad range of assets with a finite design life (including long‐life machinery and infrastructure), operating under variable economic conditions and usage‐intensities. The maintenance optimization problem is formulated in this study as a finite‐horizon Markov decision process, where the randomly occurring opportunities are accounted for by augmenting the time‐varying, decision‐dependent transition probabilities. A dynamic programming approach is subsequently used to obtain the optimal CBM policy, consisting of time‐varying thresholds on equipment condition and the cost of conducting maintenance during the arrived opportunity. A case study of such a maintenance policy for an induced draft fan of a sugar production system located in Queensland, Australia, is undertaken to evaluate the benefits of the proposed methodology against more traditional approaches that neglect opportunities or consider only replete opportunity durations. [ABSTRACT FROM AUTHOR]

This paper proposes a vehicle trajectory planning method for automated on-ramp merging. Trajectory planning tasks of an on-ramp merging vehicle and a mainline facilitating vehicle are formulated as two related optimal control problems. Rather than specifying the merge point via external computational procedures, the location and time that the on-ramp vehicle merges into the mainline are determined endogenously by the optimal control problem of the facilitating vehicle. Bounds on vehicle acceleration are explicitly considered. The Pontryagin Maximum Principle is applied to find the solutions of the optimal control problems. In order to accommodate the constantly changing external environment, the proposed optimal control method is subsequently implemented in a recursive planning framework. Because of the nature of the problem, the length of the planning horizon is time-varying, unlike the conventional model predictive control applications where the planning horizon is a fixed length. Numerical experiments are conducted to study the performances of the proposed methodology under the influence of different leading vehicle trajectories and with different lengths of the planning updating interval. In particular, an experiment involving a real-world leading vehicle trajectory and considering different traffic demand levels are presented. The proposed methodology performs well in these experiments and has demonstrated good potential in real-time applications. [ABSTRACT FROM AUTHOR]

Highlights • Multi-vibration data collected from slurry pump are analyzed with a novel method. • RUL are estimated from the suggested method with the help of Kalman Filter. • The results show that the suggested method is accurate in the two different cases. Abstract Slurry pumps handle abrasive and corrosive working fluids and their degradation rate can vary significantly depending on the composition of the slurry, making maintenance scheduling challenging. The paradigm of condition-based maintenance with accurately predicted remaining useful life (RUL) has the potential to significantly save on the total cost of maintenance. In this paper, a new methodology is presented for the RUL estimation for slurry pumps based on the fusion of data emitted from multiple vibration sensors, which enables the construction of a more reliable degradation index. Subsequently, the trend of the new degradation index is predicted using a Kalman Filter to estimate the parameters of a degradation trend line. Finally, an interval estimation of the RUL is obtained by analytically extrapolating the state space model to a pre-defined threshold. The proposed method is deployed to estimate the RUL for a slurry pump in a real production environment with multiple maintenance events, in contrast to previous studies which use limited, single run-to-failure data sets. The results show that the suggested method is capable to predict the RUL of the available datasets, even in the case where one channel is malfunctioning. [ABSTRACT FROM AUTHOR]

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system. [ABSTRACT FROM AUTHOR]

In this article, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification, and monitoring methods are detailed that allow one to identify an HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment. [ABSTRACT FROM AUTHOR]