Proposed Scheduling Model Research Articles

ERTH scheduler: enhanced red-tailed hawk algorithm for multi-cost optimization in cloud task scheduling

Effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. Therefore, we propose an enhanced Red-tailed Hawk algorithm (named ERTH) based on multiple elite policies and chaotic mapping, while applying this approach in conjunction with the proposed scheduling model to optimize the efficiency of task scheduling in cloud computing environments. We apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original RTH and other conventional algorithms. The proposed ERTH algorithm has better convergence speed and stability in most cases of small and large-scale tasks and performs better in minimizing the task completion time and system load cost. Specifically, our experiments show that the ERTH algorithm reduces the total system cost by 34.8% and 36.4% relative to the traditional algorithm for tasks of different sizes. Further, evaluations in the IEEE Congress on Evolutionary Computation (CEC) benchmark test sets show that the ERTH algorithm outperforms the traditional or emerging algorithms in several performance metrics such as mean, standard deviation, etc. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing.

An individualized adaptive distributed approach for fast energy-carbon coordination in transactive multi-community integrated energy systems considering power transformer loading capacity

The optimization of multi-community integrated energy systems (MCIES), as a proficient means of integrating distributed energy resources and diverse loads, poses a compelling challenge. This challenge revolves around strategically harnessing the energy-carbon synergy advantages within these systems, while factoring in the heterogeneity among different communities and the constraints imposed by electrical connectivity devices. Facing the challenge, this paper proposes an individualized adaptive distributed approach for energy-carbon coordination in MCIES with power transformers (PT-MCIES). Firstly, a feasible region is constructed based on current and temperature limits to assess the loading capacity of power transformers. Based on this, a transactive energy (TE) based co-optimization model for PT-MCIES considering energy sharing and carbon trading is developed, where rolling horizon optimization is adopted to cope with the randomness from renewable energy and loads. For the TE problem, the alternating direction method of multipliers (ADMM) algorithm is employed to achieve distributed optimization. Uncertain renewable energy and loads, along with variable rolling horizons, make it difficult for the traditional ADMM to ensure convergence speed in the co-optimization of PT-MCIES. Accordingly, an individualized adaptive ADMM (IAADMM) embedded a spectral penalty parameter selection rule is adopted. Simulation results show that the proposed scheduling model can effectively guarantee the safe operation of power transformers with the suggestion of a feasible region, and achieve higher economic and environmental benefits than the reference one that ignores energy-carbon coordination. Furthermore, the proposed IAADMM exhibits superior performance in terms of convergence speed and robustness to the initial penalty parameter when compared to ADMM and its various variants.

Collaborative scheduling and benefit allocation for waste-to-energy, hydrogen storage, and power-to-gas under uncertainties with temporal relevance

Waste-to-energy (WTE) is a technology that converts inexhaustible waste into electricity, thus significantly alleviating energy crisis. Integrating hydrogen storage into WTE provides an effective way to store and utilize surplus electricity from WTE on-site (by producing hydrogen). Additionally, integrating power-to-gas (P2G) into WTE provides an effective way to reduce CO2 emission from WTE on-site (by producing natural gas). To this end, a novel waste-driven renewable energy system (WRES), which is consisted of WTE, hydrogen storage, and P2G, has been proposed. However, the uncertain waste supply and electrical load markedly interfere with WRES operation. Meanwhile, when WTE, hydrogen storage, and P2G belong to different agents, the collaborative benefit from WRES operation should be rationally allocated among agents for system sustainability. This paper endeavors to achieve WRES scheduling and collaborative benefit allocation for WTE, hydrogen storage, and P2G under uncertainties. Firstly, a temporal relevance based distributionally robust optimization model is proposed for WRES scheduling under uncertainties, in which the possible range of the joint distribution for uncertainties is depicted by data covariance matrices involved ambiguity set. Secondly, collaborative benefit is allocated according to WTE, hydrogen storage, and P2G contributions, in which Gauss-Legendre quadrature formula is integrated with Aumann-Shapley value method to reduce calculation complexity. Finally, simulation results show that 1) the proposed scheduling model guarantees the economic and stable operation of WRES under uncertainties. 2) after considering the temporal relevance, WRES operation benefit is 8.35 % higher, which indicates that the proposed scheduling model has superiority in decision conservatism by introducing temporal relevance to remove impractical distributions in ambiguity set. 3) the proposed allocation model rationally distributes the collaborative benefit according to contributions, and presents lower calculation complexity, e.g., the calculation time is 95.52 % lower than the Shapely value method. This paper provides policy insights to promote widespread application and sustainable operation of WRES.

Research on Continuous Casting–Hot Rolling Scheduling Model Involving Reheating Furnace Conversion Mode and Improved Bat Optimization Solution Algorithm

As the main energy-consuming equipment, the reheating furnace plays an important role in the scheduling of continuous casting and hot rolling for a steel mill. This paper studies the energy consumption of cold and hot slab conversion mode in a reheating furnace, forming four conversion relationships, with the goal of minimizing the coupling function between the maximum completion time of the hot rolling and the energy waste of the reheating furnace, and modelling the scheduling of a continuous casting–hot rolling process. An improved ordered pair bat algorithm is used to solve for special process conditions of the proposed scheduling model. The effectiveness of the proposed model and algorithm is verified by solving the different slab charging sequence and conversion mode. Moreover, a comparative study of different types of batches is also conducted, and it is found that the model can save more than 5000 GJ per month for the steel mill, which can achieve the goal of effective energy saving and increasing enterprise profit.

A novel demand response-based distributed multi-energy system optimal operation framework for data centers

To address the increasing energy demands in data centers, distributed multi-energy system (DMES) can provide an efficient solution. This study proposes a DMES coupled by photovoltaics, wind turbines, ground source heat pump, internal combustion engines, electric chillers, absorption chillers, and energy storage device. The optimal scheduling of DMES with source-load interaction plays a crucial role in improving the performance of data centers. To achieve this, an integrated demand response (IDR) strategy is established, considering time-of-use pricing and renewable energy peak subsidies. Then, a clean and economical optimal operation model for DMES based on proposed IDR strategy and carbon trading mechanisms is established. Furthermore, to enhance the robustness of scheduling, this study analyzes the impacts of uncertainties in renewable energy output and demand response on DMES operation using the Monte Carlo method and interval method. Case study results demonstrate that the proposed IDR strategy increases electricity and cooling loads while reducing heat loads during the peak period of renewable energy generation, compared to the traditional IDR strategy. The proposed scheduling model, which incorporates the proposed IDR strategy and carbon trading mechanism, improves data center performance by reducing total costs by 1.85%, carbon emissions by 0.56%, electricity purchases by 2.93%, while increasing renewable energy utilization by 0.77%. The results also highlight the critical role of energy storage devices, with an optimal initial charge state is 0.5. Additionally, optimization considering multiple uncertainties, results in higher total costs and broader interval widths compared to single-factor uncertainty optimization, but reduces system operational risk.

Two-stage stochastic robust optimization scheduling of electric–thermal microgrid with solid electric thermal storage

During the periods of high heat load (HL) demand in winter, the increased heat output of combined heat and power units (CHPs) can significantly compress the electric power regulation range, thereby posing a threat to the safety of the power system. The solid electric thermal storage (SETS) can be employed as the regulating resource for both electric and thermal systems, expanding the dispatch space of microgrids to promote renewable energy consumption. In this paper, a two-stage stochastic robust optimization scheduling model of an electric–thermal microgrid with SETS is proposed, and the electric–thermal bi-directional regulation characteristics of SETS are considered. First, a SETS operation model based on the heat transfer characteristics of the homogeneous material-type thermal storage unit is established. Second, a regional thermal inertia model under a constant-flow variable-temperature system is established, which integrates HL at different locations to the heat source, avoiding the calculation of variation in water temperature, thus reducing the calculation difficulty. Finally, the two-stage stochastic robust optimization scheduling model of an electric–thermal microgrid with SETS is established. The model decouples the power and heat generation of CHP through the bi-directional regulation function of SETS. Case studies demonstrate the validity and effectiveness of the proposed scheduling model.

Hybrid traffic scheduling in time‐sensitive networking for the support of automotive applications

AbstractTime‐sensitive networking (TSN) is considered one of the most promising solutions to address real‐time scheduling in in‐vehicle network due to its capabilities for providing deterministic service. The TSN working group proposed various traffic shaping mechanisms, while deterministic scheduling of hybrid traffic is still not effectively solved since the traffic requirements are difficult to satisfy by standalone or combined mechanisms with fixed time slot divisions. This article presents a time‐aware multi‐cyclicqueuing and forwarding scheduling model, that integrates the no‐wait enabled time‐aware shaper and multi‐cyclic queuing and forwarding shaping models. Then, a scheduling solution, dubbed “TSN scheduling optimizer” (TSO) is proposed that combines optimization methods and incremental techniques. TSO aims to balance the load to maximize flow schedulability while guaranteeing the service requirements of hybrid traffic. Simulation evaluations through OMNeT++ provide a performance assessment of this proposed scheduling model, which can satisfy multiple types of traffic transmission requirements. Furthermore, TSO is compared with other baseline scheduling solutions, and TSO shows efficacy regarding execution time and schedulability.

Research on optimal scheduling method of active distribution network considering multi-type flexible resources

With the increase in the utilization rate of renewable energy, the volatility and uncertainty of active distribution networks have become more significant. Comprehensive consideration of the coordination and optimization of multiple resources can improve the flexibility of the system and promote energy consumption. Effective substitution Quantitative modeling is carried out considering a large number of flexible resources based on the flexibility of nodes and networks, combined with the physical characteristics of node flexible resources. Through the analysis of the implementation principle, control mode, and operation characteristics of the soft open point of network flexibility resource, the operation model of the soft open point with combined energy storage is established. On this basis, there are two elasticity evaluation indicators: elasticity adequacy and transmission adequacy. And we incorporate flexible benefits into optimization goals. Multiple optimization scheduling models have been created, including economy node flexibility, and network flexibility. Finally, multiple target particle swarm optimization algorithms will use the proposed scheduling model, combined with trend calculation and validation, using an improved IEEE33 node system.

Automated rail-water intermodal transport container terminal handling equipment cooperative scheduling based on bidirectional hybrid flow-shop scheduling problem

The importance of rail-water intermodal transport is becoming more and more prominent in recent years. As a key node of rail-water intermodal transportation, the operation efficiency of automated container terminal directly affects the overall transportation efficiency. However, there are few studies on the collaborative scheduling of automated container terminal equipment in rail-water intermodal transportation. Therefore, this paper focuses on the ‘Automated Quay Crane (AQC)-Automated Straddle Carrier (ASC)-Automated Rail Mounted Gantry Crane (ARMG)’ operation system and addresses the operational efficiency requirements of the Automated Rail-Water Intermodal Transport Container Terminal (ARWITCT), Based on the bidirectional Hybrid Flow-shop Scheduling Problem (HFSP), a scheduling optimization model for the ARWITCT is established with the objective of minimizing the maximum completion time. To solve this complex scheduling problem, Particle Swarm Optimization based on Adaptive Tent Chaos Mapping (PSO-ATCM) is designed. In this algorithm, a parameter adaptive adjustment strategy and a Tent Chaos Mapping Optimization Strategy are introduced to help PSO jump out of local optimum. Problem-feature-based encoding and decoding methods are designed for the problem. An ‘insertion-translation’ mechanism is used to judge and resolve the conflicts between import and export containers during the decoding process. The effect of proposed scheduling model and the performance of the proposed algorithm are validated via comprehensive comparison experiments. To validate the effectiveness of proposed scheduling model, we compare the unidirectional HFSP model with the bidirectional HFSP model. The proposed PSO-ATCM is compared with standard Genetic Algorithm, Particle Swarm Optimization algorithm and Adaptive Genetic Algorithm. Computational results demonstrate that the proposed model is effective and PSO-ATCM performs better than other three compared algorithms.

Optimization of battery swapping stations with heterogeneity, charging degradation and PV-option

Greenhouse gas emissions-related issues have been extensively discussed in the past years, with over 70 countries already committed to a carbon-neutral economy by 2050. The electrification of transportation modals has increased following these goals, where Electric Vehicles (EVs) are starting to take Internal Combustion Engine Vehicles (ICEV) market share all over the globe. Besides the particular complexity in comparing EVs and ICEVs, challenges involving the nature of EVs and their integration with cities, such as the lack of public locals for charging, are also critical and interfere with their development. In this context, this work aims at studying the problem of a Battery Swapping Station (BSS), a structure where the EVs users swap their depleted batteries for fully or partially charged ones. In order to simulate the BSS daily operations and battery charging schedule, a novel Mixed Integer Linear Programming (MILP) model is proposed, taking into account battery heterogeneity, the use of local photovoltaic (PV) production, and battery degradation based on charging profile. A collection of BSS operation metrics are designed to evaluate the solution quality of the proposed scheduling model. A numerical experiment comprising four case studies based on real data from the US power and transportation systems is presented, with insights and analyses on the PV and grid power use, as well as a BSS financial comparison against close-related benchmark scheduling approaches, together with sensitivity analyses on BSS sizing plan and costumers service. Results highlight the importance of battery degradation in the optimization model, since its consideration as an operational cost brought a reduction of 16%.

Stochastic optimal scheduling strategy of cross-regional carbon emissions trading and green certificate trading market based on Stackelberg game

Under the urgent goal of "carbon peaking and carbon neutralization" in China and based on the distribution characteristics of renewable energy, it is essential to promote the large-scale consumption of renewable energy and increase the proportion of large-scale renewable energy in market transactions. Therefore, a stochastic optimal scheduling model that combines the Stackelberg game, cross-regional carbon emissions trading, and tradable green certificate transaction to consider the uncertainty of renewable energy power generation is proposed. To encourage more market participants to participate in the tradable green certificate trading, the model uses Stackelberg game theory to analyze the complex interest relationship between different market participants and obtain a scheduling scheme that balances the interests of different participants. To give full play to the role of the trading mechanism on the cross-regional system, the tradable green certificate trading mechanism and the carbon emission trading mechanism are combined to optimize the overall allocation of green certificates and carbon emission rights, to stimulate renewable energy generation, limit the carbon emission of traditional thermal power units and promote energy conversion. Finally, the modified IEEE 39-bus system and Hami power grid (in Western China) are used as examples to illustrate the feasibility and effectiveness of the proposed scheduling model. The results show that the proposed strategy improves the cross-regional system economy and reduces emissions, fully reflects the monetary value of the external characteristics of renewable energy, guides renewable energy investment and power grid planning, and promotes the consumption of renewable energy.

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.

Negative carbon optimal scheduling of integrated energy system using a non-dominant sorting genetic algorithm

An integrated energy system, as a system where multiple energy sources coexist, is important for achieving energy conservation and emission reduction and upgrading the energy structure. In this study, a regional integrated energy system capable of absorbing CO2 emissions is proposed. The system can absorb 38.24% of the CO2 generated in the system by using the alkaline solution produced by electrolysis of water for hydrogen production. At the same time, the system can recover the waste heat generated by electrolysis of water and fuel cells, which can meet 42.42% of the heat demand. In order to better achieve efficient utilization between energy sources of the integrated energy system, a nonlinear mixed integer dynamic scheduling optimization model is constructed with multiple objectives of minimizing economic operating costs, CO2 emissions, and energy consumption. The model is solved using a non-dominated ranking genetic algorithm, and the optimal scheduling scheme is determined by combining compromise schemes. Meanwhile, the system can spontaneously select the most appropriate scheduling strategy based on the proposed scheduling model. The results show that it saves 7.23% of operation cost, 33.04% of CO2 emission, and 9.94% of energy consumption compared with the conventional cooling, heating, and power triple-supply system. The system is not only a stable source of energy such as electricity and hydrogen, but also achieves energy saving and emission reduction.

Bi-layer economic scheduling for integrated energy system based on source-load coordinated carbon reduction

Under the background of “dual carbon” target, integrated energy system (IES) is an effective way to achieve a high proportion of renewable energy consumption and carbon emission reduction. In order to deeply explore the energy-saving and emission-reduction potential of the source-load side of the IES, the coordinated adjustment is made to the source-side energy production and the load-side energy consumption. And by adding the electric vehicle charging station (EVCS) to the IES, electric vehicles are added to the scheduling process as controllable loads and mobile energy storage to realize energy interaction among multiple stakeholders. On this basis, this study establishes an IES-EVCS bi-layer economic scheduling model based on source-load coordinated carbon reduction. The model introduces the ladder-type carbon trading mechanism, combined with the demand response characteristics of flexible loads, to jointly guide the economic low-carbon operation of the IES. In order to closely connect the upper and lower layers, the multi-source dynamic pricing mechanism is proposed to guide electric vehicles to adjust the charging and discharging plan. Case studies verify that the proposed scheduling model can effectively optimize the load curve, improve the level of renewable energy consumption, and reduce carbon emissions.

A novel day-ahead scheduling approach for multi-power system considering dynamic frequency security constraint

The ever-growing proportion of renewable energy results in the decrease of frequency regulation capability of the multi-power system. Hence, to ensure the stability of the multi-power system with a high proportion of renewable energy, a novel day-ahead scheduling approach is proposed considering the frequency security constraint. Firstly, the dynamic changing process of frequency due to the power disturbance is analyzed. Secondly, a frequency security constraint considering the dynamic process is modeled and incorporated into the scheduling model of the multi-power system. The proposed scheduling model ensures that the maximal frequency deviation of the dynamic process is kept within the permitted range. Next, to ensure the stable operation of the system and make full use of renewable energy, the proposed scheduling model aims at minimizing the net load fluctuation and intermittent new energy curtailment. Finally, simulation results show that the proposed optimal scheduling model considering dynamic frequency security constraint can maintain the stability and frequency security of the multi-power system under power disturbance.

Economic Optimization Scheduling Based on Load Demand in Microgrids Considering Source Network Load Storage

As a large number of flexible elements such as distributed power and flexible load are connected to microgrids, the economic improvement of microgrids has become an important topic through rational energy distribution. In this paper, an economic scheduling model considering the load demand for a microgrid system under the mechanism of a peak–valley tariff is proposed. A mathematical model of the microgrid components is proposed to determine the exchange power between the microgrid and main network. Meanwhile, an improved War Strategy Optimization (WSO) algorithm is proposed to investigate three scenarios: (i) without batteries, (ii) with batteries and (iii) with batteries and demand response. Additionally, demand response optimization is carried out with the Particle Swarm Optimization (PSO) algorithm and the improved WSO is compared with four other algorithms in scenario (iii). The comparison shows that the improved WSO algorithm has a better optimization performance in solving the proposed scheduling model.

A fine-grained task scheduling mechanism for digital economy services based on intelligent edge and cloud computing

Digital economy is regarded countries as an inevitable choice to promote economic growth and provides new opportunities and new paths for the high-quality development of economy. For the Chinese market, the strong base behind cloud computing is the unstoppable development trend of the digital economy. In digital economy, the cloud as infrastructure becomes the base of the pyramid to build the digital economy. To relieve the pressure on the servers of the digital economy and develop a reasonable scheduling scheme, this paper proposes a fine-grained task scheduling method for cloud and edge computing based on a hybrid ant colony optimization algorithm. The edge computing task scheduling problem is described, and assumptions are set to simplify the difficulty of a scheduling solution. The multi-objective function is solved by using a hybrid ant colony optimization algorithm which solves computational problems by finding the optimal solution with the help of graphs. Ant colony optimization algorithm is easy to use and effective in scheduling problems. The proposed scheduling model includes an end-device layer and an edge layer. A terminal device layer consists of devices used by the clients that may generate computationally intensive tasks and are sometime uncapable to complete the tasks. The proposed scheduling policy migrates these tasks to a suitable place where they can be completed while meeting the latency requirements. The CPUs of the idle users on the end-device layer are used for other CPU-overloaded terminals. The simulation results, in terms of energy consumptions, and task scheduling delays, show that the task scheduling performance is better under the application of this method and the obtained scheduling scheme is more reasonable.

Two-stage robust unit commitment with the cascade hydropower stations retrofitted with pump stations

Cascade hydropower stations are excellent flexible resources to regulate the drastic fluctuations of wind and photovoltaic power generation in the hybrid energy system. By constructing pump stations between two adjacent upstream and downstream reservoirs, the conventional cascade hydropower stations can be transformed into a cascade pumped hydro energy storage (CPHES) system, which further can promote the integration of clean energy resources. In this paper, a two-stage robust unit commitment model for the cascade hydropower stations retrofitted with pump stations is established to address the renewable energy uncertainties. A short-term scheduling framework is proposed for the hybrid energy system including CPHES, which coordinates the operating cost and clean energy curtailment. Besides, a modified nested column-and-constraint algorithm is employed to solve the two-stage robust optimization problem with integer resources. Numerical tests performed on a modified IEEE 24-bus system and a large-scale practical power system verify the effectiveness of the proposed scheduling model and algorithm.

Decentralized and coordinated scheduling model of interconnected multi-microgrid based on virtual energy storage

The joint coordinated optimization scheduling of multi-microgrid can reduce the risk of accommodation of renewable energy and the operation costs. In this paper, a decentralized and coordinated scheduling method of interconnected multi-microgrid based on virtual energy storage is proposed. Firstly, the microgrid is modelled as a virtual energy storage model, and the demand and supply are packaged into the charge and discharge states of the virtual energy storage model for information privacy of microgrid. Secondly, the probability characteristics of renewable energy prediction error and system flexible resource are used to quantify the operation risk of microgrid and the available charge and discharge power. The decentralized and coordinated scheduling method contains two-levels optimization model to optimize the power of tie-line and virtual energy storage. The upper is the central coordinate controller scheduling model, and the lower is the optimization scheduling model of sub-microgrid. The analytical target cascading is used to decouple the established upper and lower level models for independent optimization and solution. The proposed scheduling model ensures independent operation of each microgrid, and realizes decentralized autonomy and centralized coordinated management of multi-microgrid. Case studies demonstrate the correctness and a well economy of the proposed scheduling method.

DP based multi-stage ARO for coordinated scheduling of CSP and wind energy with tractable storage scheme: Tight formulation and solution technique

The concentrating solar power plants (CSP) have well potential in coordinating with the ever-increasing wind energy during power scheduling. However, the existing studies individually design the day-ahead or intra-day optimization of coordinated scheduling between CSP and wind power, which makes the scheduling decisions not optimal in terms of economic and environmental benefits. Additionally, the non-anticipativity of scheduling decisions are not considered in most of them. This paper proposes a novel dynamic programming (DP) formulated multi-stage robust reserve scheduling (DPMRS) model, which is the first attempt to realize the day-ahead and intra-day joint optimization for coordinated scheduling of CSP and wind power. Under the framework of multi-stage adaptive robust optimization (ARO), DPMRS model enforces the non-anticipativity of scheduling. Besides, a convex modelling technique for thermal energy storage (TES) is presented to ensure the tractability of DPMRS model, whose effectiveness is proved mathematically. Moreover, to efficient solve the DPMRS model, a robust dual dynamic programming with accelerated upper approximation (RDDP-AU) solution methodology is developed, and the mathematical proof for its convergence is provided. Numerical studies on the modified IEEE RTS-79 system and a real-world system in Northwest China validate the effectiveness of the proposed scheduling model and solution methodology. The simulation results demonstrate the DPMRS model brings a 17.22% reduction in scheduling cost, and reduces 57.39% curtailment of renewable energy. Compared with the conventional algorithm, the RDDP-AU significantly reduces the computational consumption by 87.56%, and with the error less than 0.074%.