Optimal Scheduling Strategy Research Articles

Day-ahead Optimal Scheduling Strategy of Biomass-based Integrated Energy System

Under the background of carbon neutrality, biomass-based integrated energy systems are gradually becoming a hot spot for research. This paper uses a multi-energy flow biomass-based integrated energy system of electricity, heat, gas, and fertilizer as the research object. Firstly, the characteristics of the main equipment of the system are analyzed, and the corresponding mathematical model is established. Secondly, with the integrated goal of maximizing net operating profit and CO2 emission reduction, a day-ahead operation optimization model of a biomass-centered integrated energy system is established. Finally, the advantages of the proposed system and the characteristics of each equipment output in the day-ahead dispatch are analyzed based on the calculation examples.

Achieving Cooperative Mobile-Edge Computing Using Helper Scheduling

This paper investigates computing task offloading from an Internet-of-Thing (IoT) device with limited transmit power to a mobile-edge computing (MEC) server located beyond the communication range of the IoT device. We propose an opportunistic cooperative offloading (OCO) strategy that recruits the IoT device’s nearby spatially random idle-state users as helpers and opportunistically schedules one of them to partially execute the latency-critical task, and forwards the rest portion of the task to the MEC server. For the OCO strategy, we investigate the helper scheduling under three cases of system information availability, i.e., the global, partial, and distance information cases, and develop an offloading-outage optimal scheduling scheme for each case. For each scheduling scheme, an approximate expression is derived for the offloading-outage probability, with which the achieved diversity order is also theoretically evaluated. Simulation results verify our performance analysis for the OCO strategy using helper scheduling and show its achieved offloading-outage/energy consumption reduction over multi-helper cooperative offloading that fully uses all helpers for cooperation. In addition, the advantages of the proposed helper scheduling schemes over existing scheduling schemes are also demonstrated by simulations.

Demand response scheduling based on blockchain considering the priority of high load energy enterprises

In order to improve the consumption capacity of wind power plants and deal with the problems of high cost, high risk and low efficiency in traditional demand response, this paper proposes a new optimal scheduling strategy of demand response based on blockchain and the priority of high-capacity enterprises. Based on the analysis of different high-energy load types, the load response dispatching priority and hierarchical electricity price strategy are first put forward. Then the transaction framework of load aggregator, wind power, high-energy enterprise based on blockchain is constructed. Furthermore, the optimization model of load aggregator participating in dispatching is established, which is optimized by multi-objective particle swarm optimization (MOPSO) algorithm. Finally, through the simulation of a certain day in a certain area, the income of aggregators and the cost of wind power plants participating in this dispatching transaction before and after adopting this demand response dispatching scheme are analyzed and compared. It is verified that the demand response scheduling scheme proposed in this paper can effectively guarantee the revenue of load aggregators and improve the wind power consumption level.

Optimal scheduling strategy and benefit allocation of multiple virtual power plants based on general nash bargaining theory

The benefits of small-scale renewable energy resources are restricted by a feed-in-tariff strategy that works with distribution grids only. Virtual Power Plant (VPP) aggregates diverse distributed energy resources on the demand side for efficient control, which can improve their revenue and encourage the growth of renewable energy. Based on the general Nash bargaining theory, this paper proposes a multi-VPP energy-sharing mechanism. This approach enables VPPs to increase their revenue by engaging in peer-to-peer energy transactions. First, a general Nash bargaining-based model is proposed for multi-VPPs energy sharing considering peer-to-peer energy transactions. The model is then decomposed into two subproblems: minimizing the total cost of the VPP energy-sharing alliance, and the cost allocation of peer-to-peer energy transactions. Considering the privacy protection of VPPs, the first subproblem is solved by a distributed algorithm based on the alternating direction multiplier method. In the second subproblem, due to the introduction of a bargaining factor that is positively related to each VPP’s contribution to energy sharing alliance, the cost allocation better reflects their contribution. The numerical test demonstrates the proposed energy-sharing mechanism, with little computational complexity, delivers a cost distribution result that is comparable to the classical Shapley value while promoting the development of renewable energy resources.

Optimal Scheduling of Electric Vehicles Charging and Discharging Strategy Based on Real‐Time Price Considering Multi‐party Interest

In order to mitigate the negative impact brought by the large‐scale grid connection of EVs, it is highly significant to study the optimal charging scheduling strategy of EVs. In this paper, a multiobjective optimization model of ordered charging and discharging with the participation of distribution network operator (DNO), charging station operator (CSO) and EV users is established, which maximize the interest of three parties. At the same time, the real‐time price (RTP) based on the change in load rate is designed and compared with the time‐of‐use (TOU) price model to analyze the impact of both on the interests of all parties. At last, the sparrow search algorithm (SSA) is introduced and three improvements are made. The results demonstrate that different charging modes of EVs and charging price models will affect the balance of interests of the three parties, while the improved SSA performs better under different users' response rates. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Day-ahead optimal scheduling strategy for electrolytic water to hydrogen production in zero-carbon parks type microgrid for optimal utilization of electrolyzer

Under the goal of achieving “carbon peaking and carbon neutrality”, microgrids with hydrogen storage have emerged as a prominent area of development. However, large-scale operation of alkaline electrolytic water hydrogen production units suffers from low efficiency. To address this issue, this paper proposes an optimal scheduling strategy for electrolytic water to hydrogen production in a zero-carbon park type microgrid for optimal utilization of the electrolyzer. Taking into account the economic cost, the interaction rate of the microgrid and the hydrogen demand of the park, a zero-carbon park type microgrid electrolytic water to hydrogen optimization model for the optimal utilization of electrolyzes is constructed. To overcome the challenges of slow convergence speed and local optimality of the traditional algorithm, the Beluga Whale Optimization (BWO) is improved to solve the model. Compared with the PSO algorithm, the BWO algorithm solves the model 10 % faster and has a better optimization finding capability. Finally, the simulation results demonstrate that the daily revenue value of the proposed dispatching strategy is 12.5 % higher than that of the traditional dispatching strategy for microgrids. The impact of the hydrogen selling price and the new energy feed-in tariff on the proposed strategy is also analysed.

Bi-Level Optimal Scheduling Strategy of Integrated Energy System Considering Adiabatic Compressed Air Energy Storage and Integrated Demand Response

Bi-Level Optimal Scheduling Strategy of Integrated Energy System Considering Adiabatic Compressed Air Energy Storage and Integrated Demand Response

Study on orderly charging strategy of EV with load forecasting

The development and popularization of electric vehicles (EVs) is of great significance to environmental protection, energy saving and emission reduction. With the wide popularization of EV, the EV's disorderly charging brings the security hidden trouble to the grid. Firstly, according to the safe operation of power grid and the charging requirements of EVs, an optimal scheduling model based on grid loss is established, then, the optimal scheduling model is transformed by second-order cone relaxation technology. Secondly, because the orderly charging schedule of EV is based on accurate charging load forecasting, this paper based on LSTM-XGBoost dynamic combination forecasting, the dynamic combination model of LSTM and XGBoost is optimized by using Bayesian optimization method, and more accurate charging load forecasting results are obtained. Finally, the accuracy of the prediction method and the effectiveness of the optimal scheduling strategy are verified by the charging data of the EV in the actual area.

Intelligent optimal scheduling strategy of IES with considering the multiple flexible loads

As the energy system continues to evolve and advance, a relatively single energy system gradually turns to the comprehensive energy system. With the advancement of technology related to electric vehicle networks progresses, the flexible load in the power system has been significantly improved, and there will be more and more schedulable resources in the power system. On this basis, the paper mainly studies the integrated energy system including the power station of electric driven vehicles and a variety of flexible loads. First, an intelligent energy system model is built, including wind power, ES, cogeneration units, gas boilers, etc. Secondly, according to the flexible load response of electricity, heat and gas, the dynamic response of flexible loads such as electricity, heat and natural gas is incorporated into the optimal scheduling of the integrated energy system, and a model has been created for optimizing the integrated energy system with the goal of minimizing the system operational expenses. Finally, YALMIP is utilized to formulate the problem during modeling, while CPLEX is employed as the solver to find a solution. Through the example calculation, the system operation cost is reduced by 13.04%, the wind power consumption rate is increased by 8.65%, and the peak valley difference is reduced by 24.58%. This approach has been demonstrated to lower the overall operational expenses of the system, as well as assist in smoothing out peak and off-peak electricity demand, and increase the utilization of wind power.

Day-Ahead Scheduling Strategy Optimization of Electric–Thermal Integrated Energy System to Improve the Proportion of New Energy

The coordinated use of electricity and a heat energy system can effectively improve the energy structure during winter heating in the northern part of China and improve the environmental pollution problem. In this paper, an economic scheduling model of an electric–thermal integrated energy system, including a wind turbine, regenerative electric boiler, solar heat collection system, biomass boiler, ground source heat pump and battery is proposed, and a biomass boiler was selected as the auxiliary heat source of the solar heat collection system. A mixed integer linear programming model was established to take the operating cost of the whole system as the target. A day-ahead optimization scheduling strategy considering the demand side response and improving new energy consumption is proposed. In order to verify the influence of the coordinated utilization of the flexible load and energy storage equipment on the optimal scheduling in the model built, three scenarios were set up. Scenario 3 contains energy storage and a flexible load. Compared with scenario 1, the total cost of scenario 3 was reduced by 51.5%, and the abandonment cost of wind energy was reduced by 43.3%. The use of a flexible load and energy storage can effectively reduce the cost and improve new energy consumption. By increasing the capacity of the energy-storage device, the wind power is completely absorbed, but the operation and maintenance cost is increased, so the capacity of energy storage equipment is allocated reasonably according to the actual situation.

An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG

The popularization of combined cooling, heating and power microgrid containing renewable energy sources (RES-CCHP MG) can effectively alleviate the energy crisis and reduce the emission of air pollutants. However, uncertainties in renewable energy generation and load negatively affect the operation of microgrid. To solve this problem, a multi-time scale optimal dispatch strategy combining robust optimization and rolling optimization is proposed. First, the price-based demand response is introduced to establish the RES-CCHP MG model that is more in line with practical application requirements. In the day-ahead dispatching stage, robust adjustment coefficients are introduced to improve the defects of traditional robust optimization which is conservative, and a two-layer robust optimization model is established to improve the ability to resist risks. In the intra-day dispatching stage, in order to reduce the impact of day-ahead prediction error and real-time power fluctuation, the hierarchical rolling optimization model based on model prediction control is established to face the situation that different loads have different response speeds. The simulation results show that compared with the traditional optimal dispatching strategy, the operating cost and the peak-to-valley difference of electric load are reduced by 5.4% and 14.7%, respectively, and the utilization of renewable energy is increased by 4.8%.

Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs

Aiming at the problem of the scheduling scheme of urban low-altitude logistics unmanned aerial vehicles (UAVs), this paper establishes a demand-based UAV scheduling scheme model using an improved simulated annealing algorithm, taking minimizing the cost of distribution as the objective function and considering restrictions such as UAV performance constraints, airspace constraints, and distribution constraints, among others. For verification, actual express data and airspace constraints in Shanghai are taken as examples. Two urban air traffic networks are constructed using road and building data. The analysis results show that the planning scheme of this model is superior to other forecasting models in terms of delivery cost and delivery time. In addition, this model can flexibly calculate the optimal scheduling scheme under the constraints of multiple parameters, according to the requirements of delivery volume, delivery distance, UAV performance, etc.

Risk aware decomposition of online scheduling for large flexible consumers considering the age of information

The stability and security of the power system is significantly influenced by large electricity consumers, especially those with flexible demand, whose electricity purchase scheduling has been extensively studied. However, the high penetration of renewable energy and various widespread distributed storage systems have increased uncertainty of the power system, making it difficult to plan effective day-ahead electricity purchase for major electrical consumers. Online scheduling is therefore required so that the surplus can be sold to and the insufficient can be purchased from the real-time market at the lowest electricity cost. To this end, we formulate a multi-stage online scheduling model for large power consumers with flexible demand. To improve the efficiency of online scheduling, we decompose the problem into multiple single-stage sub-problems. Aimed at each single-stage scheduling, an online algorithm is designed to ensure the performance of optimal scheduling strategies maintains at a level comparable to the offline fixed strategies obtained from an oracle who has complete knowledge of the problem’s settings. We prove that our proposed online energy schedule algorithm is able to achieve O(T) regret bound for the single-stage online scheduling problem. To further highlight the significance of data and improve the multi-stage performance of our online scheduling algorithm, we also involve the notion of age of information combined with regret bound to help determine the optimal interval length.

Optimal scheduling for electricity–gas integrated energy system under the carbon-neutrality goal

In light of China’s carbon-neutral policy and the Energy Internet, it is crucial to reduce the carbon emissions resulting from the operation of the electricity–gas integrated energy system (EGIES). To address this issue, this paper proposes an optimal scheduling strategy for EGIES in line with the carbon-neutrality goal. Firstly, the oxygen-enriched combustion power plant, equipped with a power to gas unit, is modeled to maximize its carbon capture ability. Next, an optimal scheduling model for EGIES is proposed, which considers the bonus-penalty ladder carbon price and the oxygen-enriched combustion power plant, with the aim of minimizing the total operation costs and carbon emissions of the EGIES. Finally, the proposed model is applied to an EGIES, consisting of a 33-bus electric distribution network and a 20-bus natural gas network. The simulation results demonstrate that the proposed method can reduce the operation costs of the EGIES by up to 22.5% and the carbon emissions by up to 75.1t when compared to existing models.

Distributed robust operation of integrated energy system considering gas inertia and biogas–wind renewables

Multiple uncertainties caused by renewable energy and loads bring challenges to the secure operation of integrated energy system (IES). Concurrently, the centralized control methods based on global information of IES cannot preserve the data privacy of each individual subsystem. To deal with these issues, this paper proposes a distributed two-stage robust optimization (TRO) scheduling strategy for IES considering gas inertia and biogas–wind renewables. To address the uncertainties of wind turbines output and electricity loads in IES, a TRO model is first established, and the column and constraint generation (C&CG) algorithm is employed to handle the min–max–min problem of TRO model. On this basis, a distributed cooperative scheduling framework based on the consensus-based alternating direction method of multipliers (C-ADMM) for IES is developed, which requires no overall system information but only local information. Additionally, natural gas pipelines that consider gas inertia are used as gas storage facilities, which provides IES with additional scheduling resources and flexibility. Furthermore, an adaptive step-size strategy is proposed to adjust the step-size of ADMM in real time, which reduces the dependence of ADMM performance on the initial parameters selection and improves its convergence performance. The simulation results in a three-subsystem joint IES are presented to demonstrate the effectiveness of the proposed distributed TRO scheduling strategy.

Optimal production scheduling with multi-round information interaction for demander-dominated decentralized scheduling problem

Demander-dominated market scenarios are becoming increasingly common owing to the emergence of alternative service providers and competitive market environment. However, these scenarios have not been considered in previous studies pertaining to decentralized scheduling problems. Thus, service providers cannot formulate optimal production scheduling schemes for such scenarios. In this study, we investigate a demander-dominated decentralized scheduling problem in which the demander can adopt the private-strategic behavior of transferring partial orders towards its alternative service providers. The aim of this study is to provide guidance to service providers for developing high-quality production scheduling solutions under asymmetric information. First, we design a multi-round information interaction mechanism with a learning strategy to realize information interaction. Subsequently, a metaheuristic algorithm termed MAM is developed based on the multi-round information interaction mechanism to solve the proposed problem. A problem-dependent initialization method and a solution generation method integrating the learning strategy are developed to improve the search efficiency. Experimental results indicate the usefulness of the initialization method and learning strategy. Based on a comparison with two well-established adapted algorithms, the effectiveness of the proposed algorithm is confirmed, particularly for instances with loose due dates. Furthermore, we analyze the effect of MAM on both the service provider and the demander by comparing it with traditional centralized approaches. Statistical results show that the proposed algorithm yields high-quality solutions for the service provider and that maintaining the confidentiality of private information is conducive to the demander, particularly when the due dates are tight.

Optimal Scheduling of Multi-energy System Considering Economy and Energy Conservation

In recent years, the optimal scheduling of multienergy has become the focus of the research. Against this background, this paper builds a model of a multienergy flow system of cooling, heating, and power, and advanced adiabatic compressed air energy storage (AA-CAES) is introduced to smooth wind power generation (WPG) and supply heating/cooling energy. Simulated annealing algorithm (SAA) is employed to energy-saving scheduling of the system with “exergy assessment” method. The energy-saving index and the exergy efficiency are compared in different cases. SAA is compared with the particle swarm optimization (PSO) algorithm in solving the optimal scheduling strategy. The cooling, heating, and power demands of an industrial park and WPG in typical days are employed to the simulation. The scheduling results of exergy input of SAA are far less than those of PSO in typical days of different seasons. The multienergy system without AA-CAES is also modeled, and energy-saving economic scheduling is carried out. The exergy efficiency of the system with AA-CAES is between 38% and 58% while the exergy efficiency of the system without AA-CAES is merely between 27% and 48%.

Multi-agent reinforcement learning enabled link scheduling for next generation Internet of Things

In next generation Internet of Things (NG-IoT) networks, numerous pieces of information are aggregated from the user devices and sensor nodes to the local computing units for further computing to support high-level applications. Those multitudinous transmission demands have raised new challenges for current link scheduling protocols. The centralized link scheduling protocols are inappropriate in some large-scale NG-IoT scenarios. The previously distributed link scheduling uses the randomized transmission scheme to avoid interference, making it hard to utilize the bandwidth resources fully. The multi-agent machine learning (MAML) technique is a potential approach to finding the most optimal link scheduling strategy. At the same time, the over-large state space will take a long time for them to approach the optimal solution, which reduces the practicality of the MAML. To fully utilize the bandwidth resource and improve the efficiency of link scheduling, this paper studies a multi-agent reinforcement learning enabled link scheduling problem. Different from the conventional MAML techniques that randomly select a state to do their exploration, in our multi-agent reinforcement learning algorithm, a good state is firstly obtained within polynomial time steps by executing a distributed and randomized sub-algorithm. We say a state is good if it is not far from the optimal state. Then, our multi-agent reinforcement learning scheme starts from the good state and does its exploration with an ɛ greedy scheme, which significantly reduces the time steps to get close to the optimal link scheduling strategy. Extensive simulations are conducted to investigate the performance of our work.

Hybrid JAYA algorithm for workflow scheduling in cloud

Workflow scheduling and resource provisioning are two of the most critical issues in cloud computing. Developing an optimal workflow scheduling strategy in the heterogeneous cloud environment is extremely difficult due to its NP-complete nature. Various optimization algorithms have been used to schedule the workflow so that users can receive Quality of Service (QoS) from cloud service providers as well as service providers can achieve maximum gain but there is no such model that can simultaneously minimize execution time and cost while balancing the load among virtual machines in a heterogeneous environment using JAYA approach. In this article, we employed the hybrid JAYA algorithm to minimize the computation cost and completion time during workflow scheduling. We considered the heterogeneous cloud computing environment and made an effort to evenly distribute the load among the virtual machines. To achieve our goals, we used the Task Duplication Heterogeneous Earliest Finish Time (HEFT-TD) and Predict Earliest Finish Time (PEFT). The makespan is greatly shortened by HEFT-TD which is based on the Optimistic Cost Table. We used a greedy technique to distribute the workload among Virtual Machines (VMs) in a heterogeneous environment. Greedy approach assigns the upcoming task to a VM which have lowest load. In addition, we also considered performance variation, termination delay, and booting time of virtual machines to achieve our objectives in our proposed model. We used Montage, LIGO, Cybershake, and Epigenomics datasets to experimentally analyze the suggested model in order to validate the concept. Our meticulous experiments show that our hybrid approach outperforms other recent algorithms in minimizing the execution cost and makespan, such as the Cost Effective Genetic Algorithm (CEGA), Cost-effective Load-balanced Genetic Algorithm (CLGA), Cost effective Hybrid Genetic Algorithm (CHGA), and Artificial Bee Colony Algorithm (ABC).

Day-ahead scheduling of the electrothermal integrated system considering multiple thermal inertia

For the problem of optimal scheduling of integrated energy systems involving wind power consumption, by mining multiple thermal inertia, a stochastic optimal scheduling strategy with consideration of mixed uncertainties is proposed. Firstly, the network thermal inertia is explored by considering the dynamic characteristics and thermal storage of heat network. Then the heat load flexibility constraint is introduced to model the thermal inertia of buildings. Finally heat pumps and heat storage are used as time-shiftable loads or sources, which are jointly modelled with CHP units to solve the peak-valley mismatch of electric and heat loads, and on this basis, the model is established. In this paper, the Barry Island’s case which is composed of 33-node thermal network coupled with 9-node grid system is used as an example for analysis, and results show that by exploring the multiple thermal inertia, both the complementarity and the flexible dispatch between multiple heterogeneous energy sources in integrated energy system can be realized.