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

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.

Reservoirs play a significant role in water resources management and water resource allocation. Traditional flood limited water level (FLWL) of reservoirs is set as a fixed value which over-considers the reservoir flood control and limits the benefits of reservoirs to a certain extent. However, the dynamic control of the reservoir FLWL is an effective solution. It is a method to temporarily increase the water level of the reservoir during the flood season by using forecast information and discharge capacity, and it can both consider flood control and power generation during the flood season. Therefore, this paper focuses on multi-objective optimal scheduling of dynamic control of FLWL for cascade reservoirs based on multi-objective evolutionary algorithm to get the trade-off between flood control and power generation. A multi-objective optimal scheduling model of dynamic control of FLWL for cascade reservoirs which contains a new dynamic control method is developed, and the proposed model consists of an initialization module, a dynamic control programming module and an optimal scheduling module. In order to verify the effectiveness of the model, a cascade reservoir consisting of seven reservoirs in the Hanjiang Basin of China were selected as a case study. Twenty-four-hour runoff data series for three typical hydrological years were used in this model. At the same time, two extreme schemes were chosen for comparison from optimized scheduling schemes. The comparison result showed that the power generation can be increased by 9.17 × 108 kW·h (6.39%) at most, compared to the original design scheduling scheme, while the extreme risk rate also increased from 0.1% to 0.268%. In summary, experimental results show that the multi-objective optimal scheduling model established in this study can provide decision makers with a set of alternative feasible optimized scheduling schemes by considering the two objectives of maximizing power generation and minimizing extreme risk rate.

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

To achieve the optimal solution between construction costs and carbon emissions in the multi-target optimization scheduling, this paper proposes a multi-objective optimization scheduling design for wind–solar energy storage microgrids based on an improved oppositional gradient grey wolf optimization (OGGWO) algorithm. First, two new features were added to the traditional grey wolf optimization (GWO) algorithm to solve the multi-target optimization scheduling of grid-connected microgrids, aiming to improve solution quality and convergence speed. Furthermore, Gaussian walk and Lévy flight are introduced to enhance the search capability of the proposed OGGWO algorithm. This method expands the search range while sacrificing only a small amount of search speed, contributing to obtaining the global optimal solution. Finally, the gradient direction is considered in the feature search process, allowing for a comprehensive understanding of the search space, which facilitates achieving the global optimum. Experimental results indicate that, compared to traditional methods, the proposed improved OGGWO algorithm can achieve standard deviations of 4.88 and 4.46 in two different scenarios, demonstrating significant effectiveness in reducing costs and pollution.

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.

In order to cope with the increasingly complex challenges in power system scheduling, especially in the context of large-scale integration of renewable energy and frequent fluctuations in load demand, this study aims to solve multi-objective optimization problems in power system scheduling, including economy, reliability, and environmental friendliness. First, by introducing a state space model (SSM) to accurately model and predict the dynamic evolution process of the power system in order to grasp the real-time state of the system. On this basis, a multi-objective optimization scheduling model was constructed, which combines various constraints of the power system, adopts an adaptive weight particle swarm optimization (PSO) algorithm for search, and further optimizes the solution using the Cauchy–Lorentz (CL) distribution, aiming to achieve the best balance between economy, environmental protection, and efficiency in power system scheduling and obtain the Pareto optimal solution set. The experimental results indicate that the strategy proposed in this study has significant advantages in improving the operational efficiency of power systems, enhancing system adaptability, and promoting green and low-carbon development. Not only does it provide new theoretical guidance and technical means for the field of power system scheduling, but it also provides strong support for promoting the optimization and transformation of energy structure and achieving sustainable development goals.

Currently the research on the grid workflow scheduling algorithms commonly suffer by one or several of the following drawbacks: grid workflow model of directed acyclic graph (DAG), not enough considerations of multi-dimensional Quality of Service (QoS) parameters and single objective optimal scheduling. Based on abstract grid workflow language (AGWL) model, this paper presents an algorithm for multi-objective optimal grid workflow scheduling with QoS constraints, which make uses of an improved NSGA-II algorithm. Theoretical analysis and experimental results indicate the feasibility and efficiency of the algorithm.KeywordsQuality of service (QoS)grid workflow schedulingNSGA-II

With the upcoming 2022 Winter Olympic Games in Beijing. Power system in Chongli has developed significantly because of the increasing share of renewable energy sources (RESs). Despite the RESs, hydrogen stations and energy storage systems (ESSs) are also installed to providing clean energy which forms an integrated network. In order to achieve the goal of low-carbon Olympics and promote RESs consumption, this paper proposed a multi-objective optimal scheduling for electricity hydrogen ESSs integrated network in Chongli Winter Olympics zone. The objective function of the integrated energy network was developed based on operating costs which contains power grid, hydrogen network and ESSs, in addition, carbon emission was taken as the environmental cost. The restrict conditions include hydrogen storage equipment, electric energy storage equipment, RESs, power grid flow, hydrogen network pressure and power flow, and key equipment operation conditions in integrated energy network. The simulation results indicate that the proposed multi-objective optimal scheduling promote RESs consumption in electric-hydrogen-storage integrated energy network. The innovation is to consider the optimization under different hydrogen prices and the optimization under different PV and Wind output, and to consider the influence of the error. The main contribution of this paper is proposing a multi-objective optimal scheduling strategy to adjust the contradiction between supply and demand in electric-hydrogen-storage integrated energy network by arranging various energy systems reasonably under the premise of security constraints. To provide an academic reference to reduce carbon emissions and promote RESs.KeywordsIntegrated energy networkCarbon emissionsOptimal schedulingWinter olympic gamesHydrogen energy

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.

With the development of cloud computing technology, cloud services put forward higher requirements for Internet bandwidth, traffic, execution time, cost, etc. At the same time, more and more fierce market competition has led to uncontrollable online business, which has resulted in more and more network fluctuations and business fluctuations. These fluctuation risks make cloud services unstable and beyond expectations. Traditional cloud service scheduling algorithms are more based on time, resource balance, execution cost, etc., which are more suitable for stable environment. When there are fluctuations, these algorithms can not take the impact of fluctuations into account, resulting in unreasonable scheduling strategy. In this study, we propose a multi-objective optimization scheduling strategy for cloud service based on fluctuation cost. We use the fluctuation cost to evaluate the potential impact of the fluctuation, construct the resource sequence based on the fluctuation factor, and obtain the fluctuation cost value through the fluctuation cost algorithm. Combined with execution time optimization, task priority and other objectives, particle swarm optimization scheduling algorithm is improved to meet the multi-objective strategy requirements. Finally, the experiment proves that this strategy can better handle the resource scheduling problem when fluctuations occur. By setting different weight values, it can provide more solutions for user decision-making in the actual situation.

Optimal components capacity based multi-objective optimization and optimal scheduling based MPC-optimization algorithm in smart apartment buildings

With the rapid growth of electric vehicle (EV) ownership, the problem of increased peak loads on distribution networks due to large-scale EV integration needs to be addressed. This study proposes an active distribution network multi-objective optimization scheduling method. It takes into account the charging demands of large-scale EVs and aims to minimize distribution network operating costs, reduce net load variance, and maximize the photovoltaic (PV) consumption rate. First, the Monte Carlo sampling method is used to analyze the charging load demands of a large number of EVs. Next, we construct a multi-objective optimization scheduling model for the active distribution network. This model integrates the charging demands of EVs with the operating constraints of the distribution network. To tackle the multi-objective optimization problem, we propose the NSGAII-NDAX algorithm. Additionally, we employ a fuzzy theory-based method to select the Pareto optimal solution set, addressing the challenge of decision-making complexity posed by the large size and information-rich content of the optimal solution set. Finally, the effectiveness of the proposed method in the comparative analysis of multiple scenarios is verified by an improved IEEE 33-node example. The experimental results show that the proposed model and method can effectively utilize EV charging load optimization to reduce the peak-to-valley difference in the system load while ensuring the system’s economic operation and the maximum PV consumption rate. Compared with the other algorithm, the NSGAII-NDAX algorithm has stronger optimization ability and can better handle multi-objective optimization problems.

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.

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.