Proposed Scheduling Scheme Research Articles

Research on optimal scheduling strategy of source-grid-load-storage in rural low-voltage distribution network

The increasing demand for centralised power consumption in rural agriculture may result in low voltage and three-phase imbalance issues at the end of the distribution network. Furthermore, the extensive implementation of distributed photovoltaic (PV) systems may cause an imbalance between agricultural power load and PV output, exacerbating the issue of PV losses. In this paper, an optimal scheduling strategy for an integrated source-networkload- storage system is proposed to solve the above problems. The proposed strategy involves a dispatch model that includes PV power generation and energy storage, with the objective of maximising PV consumption and minimising operating costs. The PPO algorithm is finally employed to solve the cooperative optimisation model for source-grid-load-storage. The proposed scheduling strategy has been validated through examples, and the results demonstrate its effectiveness in ensuring the economic and safe operation of rural distribution networks.

A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electricity Price

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting

Integrating distributed generations (DGs) into distribution networks poses a challenge for active distribution networks (ADNs) when managing distributed resources for optimal scheduling. To address this issue, this paper proposes a day-ahead and intra-day scheduling approach based on a multi-microgrid system. It starts with a CNN-LSTM-based generation and load forecasting model to address the impact of generation and load uncertainties on the power grid scheduling. Then, an optimal day-ahead and intra-day scheduling framework for ADN and microgrids is introduced using predicted generation and load information. The day-ahead scheduling is responsible for optimizing the power interactions between ADN and the connected microgrids, while intra-day scheduling focuses on minimizing the operational costs of microgrids. The effectiveness of the proposed scheduling strategy is verified via case studies performed on a modified IEEE 33-node ADN. The results show that the network loss of ADN and the operation costs of microgrids are reduced by 17.31% and 32.81% after the microgrid is integrated into the ADN. The peak-valley difference in microgrids decreased by 13.12%. The simulation shows a significant reduction in operational costs and load fluctuations after implementing the proposed day-ahead and intra-day scheduling strategy. The seamless coordination between the day-ahead scheduling and intra-day scheduling allows for the precise adjustment of transfer power, alleviating peak load demand and minimizing network losses in the ADN system.

Safe reinforcement learning based optimal low-carbon scheduling strategy for multi-energy system

Multi-energy system with distributed energy resources has become the inevitable trend in recent years due to their potential for creating the efficient and sustainable energy infrastructure, with a strong ability on carbon emission reduction. To accommodate the uncertainties of renewable energy generation and energy demand, model-free deep reinforcement learning methods are emerging for energy management in multi-energy system. However, traditional reinforcement learning methods still have operation safety issue of violating the physical constraints of multi-energy system. To address the challenges, a low-carbon scheduling strategy based on safe soft actor-critic algorithm is proposed in this paper. Firstly, an electricity-thermal-carbon joint scheduling framework is constructed, where carbon trading mechanism is incorporated to further motivate carbon emission reductions. Secondly, the energy cost and carbon trading cost are simultaneously integrated in the objective function, and the dynamic optimization problem of multi-energy system is modeled as a constrained Markov decision process by taking into account the diverse uncertainties. Then, a novel safe soft actor-critic method is proposed to achieve the benefits of economic and carbon emissions, where the security networks and Lagrangian relaxation are introduced to deal with operation constraints. The case study validates that the proposed scheduling strategy can reduce the energy cost and carbon trading cost by up to 26.24% and 33.73% within constraints, compared with existing methods.

Locational marginal price based scheduling strategy for effective utilization of battery energy storage in PV integrated distribution system

Integration of renewable energy resources (RERs) and complexities associated with varying load patterns urge the demand for energy storage systems (ESS) to ensure real power balancing of distribution system. Among various ESS, battery energy storage system (BESS) guarantees the reliable operation of microgrids in terms of economic and technical functionalities. The scheduling of microgrids becomes essential for the economic and optimal performance of both generating sources and loads. In this work, the load-shifting capability of BESS is utilized for the optimal scheduling of grid-connected microgrids. BESS has a significant role in the effective utilization of power from RERs during grid balancing. This paper discusses the scheduling of grid-connected microgrid with two scheduling strategies, which differ in the charging behaviour of BESS. Scheduling decisions are based on the value of locational marginal price (LMP) of distribution system. In scheduling strategy-I, BESS charges at off-peak hours, either from the PV system or from the grid. Meanwhile, in scheduling strategy- II, BESS charges from the PV system at peak or off-peak hours. These two scheduling methods are also compared with the scheduling of the distribution system without integrating the microgrid. The cost of power generation, power from the main grid, and active power losses of the distribution system are calculated by performing optimal power flow (OPF). The effectiveness of the proposed scheduling strategies are validated on the IEEE-69 bus system for 24 h time duration by using MATPOWER simulation tool. The results obtained after OPF establishes the requirement of effective utilization of BESS in PV integrated distribution system.

An adaptable scheduling model for mixed flexible flow line with production continuity and processing capacity

In industrial production, in order to maximize demand fulfillment with limited processing capacity, manufacturers introduce a mixed-model flexible flow line (MFFL), which allows concurrent processing of multiple SKUs and produces more products in both quantity and category. In this study, we develop an adaptive mixed-integer linear programming (MILP) scheduling model for the MFFL problem. The model captures two key constraints in industrial production: production continuity and process capability. For the MFFL problem, we also propose a five-level scheduling strategy that integrates on-time and in-full (OTIF), early production, late delivery, using safety stock, and finally cutting demand. The scheduling strategy and model are demonstrated by real cases with high efficiency. Interestingly, the proposed scheduling strategy and model also achieve machine load balancing in production. Furthermore, we explore the impact of production continuity and introduce alternative objective functions to reduce overproduction with acceptable increases in solving time. This study is helpful for manufacturers to satisfy the demand as much as possible under the constraint of processing capacity and the requirement of production continuity.

Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning

In this paper, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

A day-ahead coordinated scheduling strategy for source storage and load considering demand response and lines loss

Day-ahead scheduling strategy is an effective way to improve the renewable energy accommodation. To increase the renewable energy accommodation in the regional power grids, reduce the total costs of the power system, and improve the supply reliability of the power system, this research suggests a multi-time-scale “source-storage-load” coordinated dispatching strategy that considers the distribution and characteristics of pumped energy storage and loss of the network. Taking the wind curtailment penalty costs, the system operating costs, and the load loss penalty costs as the objective functions, a day-ahead coordinated scheduling strategy for source storage and load considering demand response and lines loss is established. Finally, the commercial software package CPLEX is called through the MATLAB platform to complete the optimization of mixed integer programming. Simulation results shows that the proposed scheduling strategy could build the power generation plant, effectively adjust the output power of pumped storage, and regulate the assumption of translationable load and transferable load.

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.

Sliding mode control for Markovian jump systems under a switched scheduling protocol

SummaryThis article is concerned with the sliding mode control for Markovian jump systems under transmission constraints. To deal with node congestions, a switched channel scheduling scheme integrating round‐robin (RR) protocol and weight try‐once‐discard (WTOD) protocol is proposed to orchestrate the priority of multiple sensor nodes. At each transmission instant, a system performance‐based detector is adopted to select one scheduling protocol between RR and WTOD in a certain event‐triggering manner, by which only one sensor node has the priority to access the network. Compared with conventional protocols, the proposed scheduling scheme establishes a dynamic coupling mechanism between channel resource and scheduling rules, aiming to strike a balance between improving system performance and reducing communication resource requirements. Then, a token‐dependent sliding mode controller is constructed, and a genetic algorithm‐based optimization problem is given to achieve smaller convergent domain. Finally, numerical examples are shown to verify the present control strategy.

Optimal scheduling of electric-gas-heat system considering dual virtual energy storage of gas network management and heat network storage

Aiming at the problem of wind curtailment caused by the lack of system flexibility, an optimal scheduling strategy for improving the flexibility of the electricity-gas-heat interconnection system by the coordinated operation of the gas network management and storage characteristics and the heat storage characteristics of the heat network is proposed. Firstly, the influence of gas network storage and heat network storage on improving flexibility is analyzed respectively. Then, an electricity-gas-heat interconnected system scheduling model considering the dynamic characteristics of gas network management and t the delay characteristics of heat storage in heat network, which greatly improves the imbalance between supply and demand of flexibility in space and time brought by the anti-peak regulation characteristics of wind power to the system. Finally, the IEEE-24 nodes power system, Belgium’s 20-node natural gas network and 6-node thermal network are used, for example, analysis. The results show that the proposed scheduling scheme improves the flexibility of the system while its operating cost and wind curtailment cost are the lowest, which promotes the consumption of wind power.

Research on Regional Energy System Planning Considering Flexible Access of Electric Vehicles

The high proportion of renewable energy sources and intermittent loads pose challenges to the flexible operation of regional energy systems. To improve the flexible operation level of regional energy system, this paper considers the uncertainty of energy storage charging and discharging, demand-side management, and V2G of electric vehicles, and establishes a flexible resource supply capacity and cost model. With the goal of minimum flexibility demand and economic optimization, the implementation of flexible supply and demand balance constraints is added. The advantages and disadvantages of the traditional resource scheduling scheme and the unified flexible resource scheduling method are compared from the net load volatility, the maximum operating volatility and the whole time scale flexible supply and demand balance index. The algorithm analysis proves the advantages of the proposed scheduling scheme in improving the flexibility of the regional energy system, which can describe the flexibility of the regional energy system well and can be quantitatively characterized.

Cost-efficiency based residential power scheduling considering distributed generation and energy storage

The permeation of renewable energy into smart house is a key characteristic of the future power system that brings a significant challenge to the peak load management in the power sector. In this paper, we propose cost-efficiency based residential power scheduling scheme considering distributed generation and energy storage. In which, a cost-efficiency model designed by the ratio of consumption benefit to its cost is introduced to capture the residential users' electricity consumption behaviors and reflect their economic efficiency. A discomfort cost is constructed to represent the losses associated with power scheduling when facing the incentives given by the smart grid. Specially, not only residential load consumption utility, but also that of the renewable energy generation and energy storage devices are applied to compose the consumption benefit，and an energy storage model to be established to plan the charging and discharging of energy storage batteries. Day-Ahead Bidding Price and Real-time consumption management models are introduced to test the performance of the proposed scheduling scheme. Simulation results demonstrate that the proposed method increases the residential consumer's daily cost efficiency by 1.5834, and reduces its daily electricity bill by 38.43 % and that about 36.86 % of energy is saved in the smart grid while the proposed scheduling strategy for energy consumption can effectively smooth the load curve in smart grid.

An efficient task scheduling in fog computing using improved artificial hummingbird algorithm

IoT edge devices have become more popular due to the rapid growth in IoT applications in recent years. Task scheduling reduces latency and application computation times, while improving quality of service. In this paper, we introduce a new version of Artificial Hummingbird Algorithm (AHA) with Opposition-Based Learning (OBL), chaos mechanism, and Differential Evolution (DE) algorithm, called CODA. AHA is improved by using DE algorithms to determine the optimal configuration of chaotic maps and OBL for determining the optimal initial population. As a result of CODA's high ability, local optima can be avoided and exploration of a region of interest can be improved. CODA is then utilized to schedule tasks in fog computing systems. Analytic Hierarchy Process (AHP) is used to determine the priority of tasks. Task scheduling is primarily intended to reduce energy consumption, duration, and costs. To compare CODA's performance with that of other well-known meta-heuristics, 50 basic functions were used as benchmarks. Additionally, the proposed scheduling scheme is evaluated through different simulations. Energy consumption, makespan, and cost are better as a result of the implemented algorithm. When compared to the existing algorithms that include Artificial Hummingbird Algorithm (AHA), Gravitational Search Algorithm (GSA), Moth-Flame Optimization (MFO), Seagull Optimization Algorithm (SOA), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA), Particle swarm optimization (PSO), Multi-Verse Optimizer (MVO), and Differential evolution (DE), the proposed CODA shows better output in satisfying the task scheduling process. On average, the CODA-based task scheduling model outperforms other research studies in terms of makespan by 46%, cost by 8%, and energy consumption by 41%.

Benefit of wind networking for aircraft arrival scheduling in terminal manoeuvring area

The effectiveness of air traffic management depends on the amount of obtained information and the accuracy of prediction, leading to a reduction in uncertainty disturbances, and an improvement in the robustness of decision-making results. Among all controlled areas, the Terminal Manoeuvring Area (TMA) is a complex and sophisticated airspace surrounding a coordinated airport in which there exists a high density of traffic, especially with arrivals converging from different directions. With the purpose of improving safety levels and operational efficiency, this paper addresses the aircraft arrival scheduling problem at the microscopic level, integrating the meteorological uncertainty based on wind networking into the optimization model. Wind uncertainty affects the flight speed, which in turn leads to uncertainty in the flight arrival time at one waypoint. Conflict detection and resolution are performed at waypoints and links in a predefined network, based on the predicted time information from the designated route. In order to optimize the time and speed for each aircraft entering TMA, this paper proposes a mathematical model that seeks to minimize the number of potential conflicts, flight delays and the number of flights deviating from the original plan under two assumptions. The first one considers regular weather forecasts for predicting the future positions of aircraft for conflict detection. The second one investigates the potential benefit for aircraft to share their local wind measure thanks to the ADSB-IN and ADSB-OUT channels. A resolution approach is proposed combining a simulated annealing algorithm and a simulation framework. Using data from a case study with 437 flights overflying the Paris Charles de Gaulle (CDG) airport TMA, we demonstrate the validity of the proposed scheduling strategy. A performance analysis is then conducted between standard forecast and wind networking.

Freshwater minimization in multistage multiproduct batch processes through intentional waiting

The growing demand for industrial water has motivated techniques for water reuse within the ambit of production scheduling. In batch processes, these seek to minimize freshwater usage together with an economic objective, such as makespan. It may, however, be possible to increase reuse potential by compromising on the makespan objective and intentionally delaying the start of stages so as to enable better time synchronization. This work seeks to develop a scheduling methodology for batch processes that reduce freshwater requirements by intentionally waiting between stages. A genetic algorithm-based formulation is proposed to develop such a production schedule where delays up to a maximum allowed waiting duration are introduced if it enables water reuse. The proposed scheduling strategy is demonstrated using a textile industry case study. Our results show that, on average, a 20% decrease in freshwater usage can be obtained for a single-batch campaign while for a multi-batch campaign, the savings in freshwater is nearly 59% for a 19% increase in makespan.

Integration of compressed air energy storage into combined heat and power plants: A solution to flexibility and economy

To achieve carbon neutrality, conventional coal-fired combined heat and power (CHP) plants require higher operation flexibility to improve the grid's accommodation for renewable energy. Based on the promising converging interests between compressed air energy storage (CAES) and CHP, a novel CHP-CAES system with higher operation flexibility, energy efficiency, and round-trip efficiency (RTE) is proposed in this paper. The novelty of this system is that the released compression heat of CAES during the charge process can be recovered by the CHP, and the heat consumed during the discharge of CAES comes from the primary district heating network (DHN) instead of CHP. Firstly, the theoretical feasibility of the proposed CHP-CAES system is verified. Then, the operation flexibility, energy efficiency, and RTE of the CHP-CAES system are investigated. Results show that when the output heat of the CHP-CAES system is the design value of 150 MW, the power overload capacity can be increased by 3.04 %, and the feasible minimum load is decreased by 2.00 % of the design power. The maximum energy efficiency of the CHP-CAES system is increased from 68.38 % to 69.57 %, compared with the separate CHP system. Besides, the RTE of the CAES component is increased from 64.35 % to 77.05 %. Further, optimized scheduling strategies of this CHP-CAES system are proposed for the renewables accommodation scenario. Results show that the imbalance between power provision and demand can be notably reduced from 416.68 MWh to 1.01 MWh after the integration of the CAES system. Moreover, the profitability of the CHP-CAES system also would be improved by 14,449CHY by adopting the proposed scheduling strategies on the reference day, benefiting from the time-of-use (TOU) electricity price.

Multi-Timescale Optimal Operation Strategy for Renewable Energy Power Systems Based on Inertia Evaluation

To enhance the operational dependability of renewable energy power systems with high proportions, this study proposes a multi-timescale optimization strategy based on the inertia evaluation model. Firstly, the inertia evaluation model is established based on the factors influencing the inertia demand of the power system, and the concept of the inertia margin coefficient is introduced. Secondly, to address the uncertainties associated with sustainable energy output and the cost of carbon emissions, a multi-timescale optimization operation model is formulated for day-ahead, intraday, and real-time operations, aimed at economic optimization. The output status of each unit is obtained and adjusted in a timely manner in the next stage, while meeting the system’s inertia demand, to derive the final scheduling strategy. Lastly, a sensitivity analysis of the inertia margin coefficient is conducted through simulations to validate the effectiveness and cost-efficiency of the proposed scheduling strategy.

Scheduling strategy for networked control system based on variable sampling period and deadband feedback

SummaryA scheduling strategy of variable sampling period combined with deadband feedback for networked control system is proposed. Variable sampling period algorithm can allocate a reasonable sampling period to each controlled loop according to the network utilization and packet transmission time. Deadband feedback algorithm can alleviate network congestion by appropriately adjusting the packets in the network when the networked control system cannot be scheduled. According to the actual overload and utilization of the network, the designed scheduling strategy dynamically adjusts the sampling period and priority, and improves the performance of the system combined with deadband feedback. Based on the TrueTime platform, the proposed scheduling strategy is verified on a three controlled loops networked control system with interference nodes and limited network resources. The simulation results demonstrate that the designed scheduling strategy can overcome the uncertainty of the upper bound of network resources, improve output control performance, reduce integral absolute error value of the controlled loop, and shorten the packet transmission time. The overall control performance of the system is improved. The designed scheduling strategy is effective.

Research on Data-Driven Optimal Scheduling of Power System

The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback–Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example shows that in a power system with a high proportion of renewable energy generating units connected to the grid, the proposed scheduling strategy can meet the load demand under different load trends. In the dispatch cycle with different renewable energy generation rates, renewable energy can be absorbed to the maximum extent to ensure the safe and economic operation of the grid.