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Global climate change has caused governments worldwide to take actions to improve their energy efficiency. This paper investigates how China's Top-1000 program, a command-and-control type of energy-saving mandate, has affected the operational choices of firms, and in turn, their profitability. We apply the propensity score matching method to find "identical twins" for the participants in the Top- 1000 program, then conduct a difference-in-differences analysis on the matched sample. Our findings suggest that the profitability of the enterprises targeted for energy savings decreased by one-third, mainly due to increased production costs. The targeted enterprises tended to increase their fixed assets per capita, which was associated with improvements in energy efficiency. Furthermore, compared to similar untargeted enterprises, there was a significant slowdown in the production growths of the targeted enterprises, raising concerns about carbon leakage due to increased production by less efficient producers. [ABSTRACT FROM AUTHOR]

• The established deep learning model is capable of considering the impacts of 3D heterogeneity of oil shale on production and energy consumption. • The optimization procedure simultaneously optimized well constraints and well locations. • The evolution patterns of optimal parameter values across varying levels of energy constraints are identified. The in-situ conversion process (ICP) involves complicated thermal-reactive-compositional flow processes where the production parameters are closely interdependent. Therefore, manual optimization of the production parameters of ICP through numerical simulation is arduous and time-consuming. This paper introduces a computational framework that integrates deep learning techniques and particle swarm optimization (PSO) to automatically optimize the values of production parameters of ICP, and thus locating the highest energy efficiency and the optimum energy usage. This approach utilizes a 3D CNN model to predict key metrics. The prediction process considers the detailed 3D heterogeneity of oil shale, resulting in remarkably high prediction accuracy, as evidenced by determination coefficients (R2) above 0.99. Subsequently, the trained CNN model is integrated to the PSO algorithm to automatically fine-tune the production parameters to optimize the energy efficiency of ICP. The optimization results yield three significant findings. First, as the energy consumption limit increases, the optimal number of heaters, well spacing, and heating temperature exhibit an upward trend, but stabilized beyond the threshold of 7 × 105 kWh. Secondly, the optimal input energy (7 × 105 kWh) is found for the given ICP model. Lastly, the analysis reveals that variations in initial reservoir pressure or bottomhole pressure have limited impact on cumulative oil production and energy usage. [ABSTRACT FROM AUTHOR]

For data collection systems, it is important to rationally allocate link capacity to each source node so that the base station can receive fresh data. In recent years, a metric called age of information (AoI) has appeared to measure the freshness of the received information. In this paper, we build a model in which multiple heterogeneous source nodes with energy constraints transmit samples to a base station via a shared capacity-constrained channel. Then, with the objective of minimizing average weighted AoI in data collection systems, we propose a strategy where each source node has two buffers to store its latest partially transmitted sample and its complete latest collected sample, respectively. We establish the AoI model, sample transmission model and energy consumption model for the two buffers, and design a scheduling algorithm two-buffers strategy algorithm in this strategy. Finally, the proposed algorithm has been compared with the other four scheduling algorithms by simulation. The results show that the proposed algorithm performs better than them in terms of the average weighted AoI. [ABSTRACT FROM AUTHOR]

In this paper, the input-to-state stability (ISS) control problem is studied for cyber-physical systems (CPSs) in the presence of asynchronous denial-of-service (DoS) attacks. To enhance the exponential ISS, an intelligent packet-based control method with buffering is proposed by introducing the acknowledgement signal (ACK), in which the trial transmission attempts of the control packet are intelligently determined by the situation (successful or failed) of its last sampling transmission instant after identifying the unnecessary transmission points and the necessary transmission points. Then, inspired by the packet-based control technology with buffering, the sufficient condition for attempting transmission of the control packet is given, in which the exponential ISS with maximum robustness index can be preserved. Compared with the existing method with a fixed transmission rate for the control packet, it is shown that the considered framework improves the exponential ISS performance with equal or less communication resource costs. [ABSTRACT FROM AUTHOR]

In this paper, we formulate and analyze the problem of secure control in the context of networked linear parameter varying (LPV) systems. We consider an energy-constrained, pulse-width modulated (PWM) jammer, which corrupts the control communication channel by performing a denial-of-service (DoS) attack. In particular, the malicious attacker is able to erase the data sent to one or more actuators. In order to achieve secure control, we propose a virtual actuator technique under the assumption that the behavior of the attacker has been identified. The main advantage brought by this technique is that the existing components in the control system can be maintained without need of retuning them, since the virtual actuator will perform a reconfiguration of the plant, hiding the attack from the controller point of view. Using Lyapunov-based results that take into account the possible behavior of the attacker, design conditions for calculating the virtual actuators gains are obtained. A numerical example is used to illustrate the proposed secure control strategy. [ABSTRACT FROM AUTHOR]

In this paper, we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature. [ABSTRACT FROM AUTHOR]

This paper is concerned with the leader-follower consensus of multiagent systems with wireless communications with the main objective of reducing the power consumption. First, by assuming that the sampling period jumps from one to another only from a given set, a new stochastic sampling approach is introduced to reduce the sampling frequency of each agent. Then, only 1-D of the sampled data is selected, and transmitted to its neighboring agents. Finally, each agent is scheduled to communicate with others intermittently. A unified Markovian system model is proposed to capture the above stochastic sampling, measurement selection scheme and intermittent transmission process, and such a novel protocol can significantly reduce the power consumption. Based on the Lyapunov stability theory and the Markovian jump system approach, the distributed consensus-based controller gain is obtained by solving an optimization problem. The advantage of the proposed consensus protocol is verified by two simulation examples. The simulation results explicitly show our result is more energy-efficient than that of existing one. [ABSTRACT FROM PUBLISHER]

It has been shown that the heating, ventilation, and air conditioning (HVAC) systems of commercial buildings can offer ancillary services to power systems without loss of comfort. In this paper, we propose a new control framework for reliable scheduling and provision of frequency reserves by aggregations of commercial buildings. The framework incorporates energy-constrained frequency signals, which are adopted by several transmission system operators for loads and storage devices. We use a hierarchical approach with three levels: 1) reserve capacities are allocated among buildings (e.g., on a daily basis) using techniques from robust optimization; 2) a robust model predictive controller optimizes the HVAC system consumption typically every 30 minutes; and 3) a feedback controller adjusts the consumption to provide reserves in real time. We demonstrate how the framework can be used to estimate the reserve capacities in simulations with typical Swiss office buildings and different reserve product characteristics. Our results show that an aggregation of approximately 100 buildings suffices to meet the 5-MW minimum bid size of the Swiss reserve market. [ABSTRACT FROM PUBLISHER]

This paper addresses a scheduling problem with a continuously divisible, cumulative and renewable resource with limited capacity. During its processing, each task consumes a part of this resource, which lies between a minimum and a maximum requirement. A task is finished when a certain amount of energy is received by it within its time window. This energy is received via the resource and an amount of resource is converted into an amount of energy with a non-decreasing and continuous function. The goal is to find a feasible schedule, which is already NP-complete, and then to minimize the resource consumption. For the case where all functions are linear, we present two new mixed-integer linear programs (MILP), as well as improvements of an existing formulation. We also present a detailed version of the adaptation of the well-known 'left-shift/right-shift' satisfiability test for the cumulative constraint and the associated time-window adjustments to our problem. For this test, three ways of computing relevant intervals are described. Finally, a hybrid branch-and-bound using both the satisfiability test and the MILP is presented with a new heuristic for choosing the variable on which the branching is done. Computational experiments on randomly generated instances are reported in order to compare all of these solution methods. [ABSTRACT FROM AUTHOR]

An efficient method to solve the long-term security constrained unit commitment (SCUC) is a requisite for power system operators in many countries, particularly those with many coal-fired generating units, which require a long time to start up and shut down. However, the complexity that is introduced by the energy or fuel constraints in the long-term SCUC problem often results in exorbitant computation time. In this paper, a new type of approach, which is termed the inducing-objective-function (IOF)-based method, is proposed. This method significantly improves the computational efficiency. In the proposed method, the inducing factors are introduced into the objective function of the SCUC relaxation problem to induce more UC state variables to satisfy the integrality requirement after several iterations. According to the relaxation solution, the objective function of the original SCUC model is modified. Thus, the process of solving the modified model using the branch-and-cut algorithm can be more efficient. Because the solution of this modified model can be near-optimal for the original model, the gap between the near-optimal and the optimal solution is estimated to decide whether the near-optimal solution is acceptable for practical application. If the near-optimal solution is not acceptable, it will be used as the initial solution to solve the original model. Because the initial solution is of high quality, it can significantly speed up the branch-and-cut process. Two case studies with different scales demonstrate the robustness, the efficiency and the prospect of the proposed method for practical applications. [ABSTRACT FROM AUTHOR]

This paper is concerned with the weighted observation fusion Kalman filtering problem for a class of multi-sensor fusion systems with random measurement delays and energy constraints. To reduce the energy consumptions, each sensor intermittently sends information to the fusion center in a random way. By using the full rank decomposition approach, the observation fusion equation is derived. Without resorting to the augmentation technique, optimal weighted observation fusion Kalman filter (WOFKF) is given, and it is proved that the performance of the WOFKF is equivalent to that of the centralized fusion Kalman filter. Simulations show the effectiveness of the proposed fusion methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we study the problem of stabilizing a linear system with a single long time-varying delay in the input. Under the assumption that the open-loop system is stabilizable and not exponentially unstable, a finite dimensional static time-varying linear state feedback controller is obtained by truncating the predictor based controller and by adopting the parametric Lyapunov equation based controller design approach. As long as the time-varying delay is exactly known and bounded, an explicit condition is provided to guarantee the stability of the closed-loop system. It is also shown that the proposed controller achieves semi-global stabilization of the system if its actuator is subject to either magnitude saturation or energy constraints. Numerical examples show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles. [ABSTRACT FROM AUTHOR]