Collaborative optimization scheduling strategy for HVAC with a three-layer optimization architecture

In this paper, we provide expressions for the secrecy outage probability (SOP) for suboptimal and optimal opportunistic scheduling schemes in a reconfigurable intelligent surface (RIS) aided single antenna system with multiple eavesdroppers in approximate closed form. A suboptimal scheduling (SS) scheme is analyzed, which is used when the channel state information (CSI) of the eavesdropping links is unavailable, and the optimal scheduling (OS) scheme is also analyzed, which is used when the global CSI is available. For each scheme, we provide a simplified expression for the SOP in the high signal-to-noise ratio (SNR) regime to demonstrate its behavior as a function of the key system parameters. At high SNR, the SOP saturates to a constant level which decreases exponentially with the number of RIS elements in the SS scheme and with the product of the number of RIS elements and the number of users in the OS scheme. We also show that the derived SOP of the SS scheme can directly provide the SOP for the best antenna-user pair scheduling scheme in a multiple antenna system. We compare the performance of the opportunistic user scheduling schemes with that of a non-orthogonal multiple access (NOMA) based scheduling scheme which chooses a pair of users in each time slot for scheduling and we show that the opportunistic schemes outperform the NOMA-based scheme. We also derive a closed-form expression for the SOP of a decode-and-forward (DF) relay-aided scheduling scheme in order to compare it with that of the RIS-aided system. It is found that the RIS-aided system outperforms the relay-aided system when the number of RIS elements is sufficiently large. An increased number of RIS elements is required to outperform the relay-aided system at higher operating frequencies.

With the popularization of off shore wind power generation, the trend of large-scale integration into the power grid is inevitable, and it is necessary to consider some of its impacts on the power grid. The active distribution network is one of the methods to achieve flexible scheduling of offshore wind power generation and intermittent power sources. Therefore, considering the disturbance effects of offshore wind power generation and distributed power, a collaborative optimization scheduling strategy is proposed in this article. Firstly, the active distribution network “source network” model is established, and flexible regulation of distributed electricity and offshore wind power generation can be realized. Secondly, a three-stage collaborative optimization scheduling strategy is proposed, it can realize controllable output power of distributed power on the source side, reconfigurable network on the grid side, and orderly charging of offshore wind power generation on the load side. Finally, the simulation analysis of the IEEE 33-node distribution network is carried out to verify the effectiveness of the proposed collaborative optimization scheduling strategy. The results indicate that proposed the collaborative optimization strategy can improve the operational status of the active distribution network, and realize flexible regulation of distributed electricity and offshore wind power generation.

Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism

Secrecy outage analysis of multi-user multi-eavesdropper cellular networks in the face of cochannel interference

The huge parameter scale and data access of existing CNN models, as well as the diversity of computing modes of different CNN models or different layers in the same model, lead to the low overall computing efficiency of computing systems that can only support a single or fixed mapping mode and on-chip data cache. Using the strategy of hardware and software coordination design, we combine the adaptive memory access optimization mechanism based on deep reinforcement learning with the dynamic partition method of on-chip elastic cache, and automatically searches the optimal cyclic scheduling strategy for different CNN operation layers according to the relevant parameters of the storage structure in the reconfigurable CNN processor. By designing the on-chip interconnection structure, address mapping logic and dynamic partition method, elastic storage can dynamically partition the address mapping space according to different scheduling strategies during accelerator operation. In the CNN processor architecture based on Eyeriss and TPU architectures, compared with the optimal scheduling strategy, the proposed scheduling strategy and on-chip cache partitioning method can improve the energy efficiency of the two reconfigurable CNN processors by almost 3 times and 4 times respectively. Moreover, compared with the fixed capacity dual cache structure, the elastic storage partitioning method proposed in this paper can reduce the power consumption by 30.28% and 18.43%, respectively, using the same scheduling strategy. Compared with the latest research based on FPGA platform, the reconfigurable CNN processor in this paper can improve the computational efficiency and computational energy efficiency by almost 10 times and 2 times respectively.

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

Global issues such as global warming, rising sea level, melting Arctic and Antarctic glaciers, deterioration of the natural environment, and depletion of fossil energy have attracted increasing attention. As the concept of environmental protection has become increasingly popular, the concept of new energy automobiles has been proposed to keep the heat up. Especially in recent years, with the advantages of energy conservation, environmental protection, and low noise, new energy automobiles have changed from concept to industry and have been generally accepted and recognized by consumers in the consumer market. The increasingly stringent energy crisis and emission regulations have put forward more stringent requirements for modern automobiles. The thermal management of the new generation of intelligent automobiles is not only limited to simply solving the problem of engine heat dissipation but also involves reliability, power, economy, emissions, and comfort. It is an important vehicle development technology with many performances such as performance. The integrated thermal management of new energy vehicles includes engine cooling, oil cooling, air conditioning refrigeration, HVAC heating, supercharged intercooling, low cycle thermal fatigue, and thermal damage. For new energy vehicles such as hybrid and pure electric vehicles, it also includes motor cooling, motor controller cooling, and power battery temperature control. For the purpose of vehicle thermal management optimization design, this paper innovatively proposes IVTM technical solutions, relying on multidimensional numerical calculation coupling and multiobjective collaborative optimization control to integrate system design, scheme evaluation, performance analysis, dynamic control, and collaborative optimization. Through the integrated thermal management collaborative control strategy based on the full operating conditions of the new energy vehicle, the comprehensive improvement of multiple evaluation indicators such as system design performance, thermal management, and control performance and economy is achieved.

In response to the demands of low-carbon economic operation and high-quality energy utilization for park-level integrated energy system clusters (PIES-C), this paper proposes a tri-stage collaborative optimization strategy for PIES-C based on an exergy-carbon spatiotemporal coupling pricing mechanism. In the first stage, a multi-objective collaborative optimization model for park-level PIES-C is established, categorizing energy interaction roles based on differences in energy purchase and sale. The second stage innovatively proposes an exergy-carbon spatiotemporal coupling pricing mechanism led by power-selling parks, which incorporates exergy value and carbon emission constraints to guide power-purchasing parks in optimizing their energy consumption behaviours. The third stage employs an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) to identify high-quality Pareto optimal solutions that balance the energy efficiency, low-carbon, and economic objectives of PIES-C. Within a typical daily operational cycle, this strategy achieves a 13.1% reduction in total system economic costs, a 10.2% decrease in carbon emission intensity, and an 8.7% improvement in energy conversion exergy efficiency. These results validate its engineering applicability in PIES-C and its significant contribution to the realization of energy-saving and low-carbon objectives.

Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling

To address the issue of low energy utilization in microgrid energy storage, this paper proposes an optimal scheduling strategy for DC microgrid groups based on battery spatiotemporal sharing. First, by analyzing the coordination relationship between the truck and battery, their spatiotemporal transfer model is developed. On this basis, an energy sharing model of the battery which considers the special load safety guarantee requirement is established. Moreover, an optimal energy scheduling strategy of the DC microgrid group which considers the power balance constraint is designed to minimize the total system cost. Finally, numerical simulation is carried out. simulation results validate the effectiveness of the proposed strategy.

The success of the smart grid majorly depends on the advanced communication architectures. An advanced smart grid network should satisfy the future demands of the electric systems in terms of reliability and latency. The latest 4th-generation (4G) wireless technology, the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), is a promising choice for smart grid wide area networks (WAN), due to its higher data rates, lower latency and larger coverage. However, LTE is not a dedicated technology invented for smart grid, and it does not provide Quality of Service (QoS) guarantee to the smart grid applications. In this paper, we propose an optimal LTE uplink scheduling scheme to provide scheduling timeguarantee at the LTE base station for different class of traffic, with a minimal number of total resource blocks. A lightweight heuristic algorithm is proposed to obtain the optimal allocation of resource blocks for each class of traffic. In the simulation, we compare the proposed optimal scheduling scheme and two existing scheduling schemes, the Large-Metric-First scheduling scheme and the Guaranteed Bit Rate (GBR) /Non-GBR scheduling scheme. The comparison results demonstrate that the proposed optimal scheduling can use less resource blocks to satisfy the scheduling time requirements than the other two existing scheduling schemes.

In this paper, we study scheduling schemes for a two-user two-way relaying system. Two transmission modes are considered, i.e., point-to-point direct transmission and two-way amplify-and-forward (AF) relaying. An optimal scheduling method that opportunistically selects the best transmission mode for each user with instantaneous channel state information (CSI) is proposed to minimize the sum bit-error-rate (BER). However, for optimal scheduling, the scheduler requires the knowledge of all the channels. To reduce the feedback information of CSI, we also propose a suboptimal scheduling scheme that only the CSI of the two direct links is explored to select the transmission mode. Simulation results show that both the optimal and suboptimal schemes outperform the fixed direct transmission and fixed two-way relayed transmission, and the performance gap between the optimal and suboptimal scheduling scheme is small. These results show that the suboptimal scheduling scheme provides a good trade-off between implementation complexity and system performance.

An optimal grid scheduling method considering coupling degree of nodes and duality of reciprocal inverse mapping

The collaborative energy scheduling of source‐load‐energy storage has great potential to meet the active control requirements of power‐distribution networks. In this study, a federated deep reinforcement learning framework was developed to facilitate collaborative energy scheduling and maximize the total economic benefit in a distribution network. Then, considering the application of Markov decision processes for energy scheduling, a spatial temporal graph convolutional network transformer based power generation packaging model for renewable energy sources was presented, and a collaborative energy scheduling strategy based on a federated duelling deep Q‐network was designed. The simulation results indicate that the developed collaborative scheduling strategy can maximize the economic benefits of a power distribution network while ensuring data privacy.

Aiming at the problems faced by Distributed Sensor Networks (DSNs) in complex environment, such as signal quality degradation, network dynamics and resource limitation, this paper proposes a DSNs collaborative optimization framework for complex environment. The framework adopts a three-layer collaborative architecture of perception-processing-control, constructs a dynamic interference prediction model, and combines variational modal decomposition (VMD) and adaptive filtering to realize lightweight signal processing. Design the topology control and multi-objective resource allocation algorithm based on virtual force field (VFF) to improve the adaptive ability of the network; A cross-layer collaborative optimization model is established, and its effectiveness is verified by distributed consistency algorithm and hardware-in-the-loop simulation platform. The experimental results show that the proposed method is superior to the traditional algorithm in signal reconstruction error, processing delay, energy consumption and network lifetime, transmission delay, energy consumption balance and other key indicators, which significantly improves the robustness, adaptability and energy efficiency of DSNs in complex environment.