Operation Scheduling Method Research Articles

Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows

This article aims to solve the problems of high cost, low efficiency, and task delay caused by the lack of scientific scheduling strategies during the agricultural harvest season. Considering the constraints of operation time windows, a multimachine multiobjective cross-regional collaborative operation scheduling model is constructed with the goal of minimizing the transfer distance of agricultural machinery and nonoperational scheduling costs. A multiobjective genetic algorithm (HTSMOGA) based on a hybrid time window priority rule and a tabu search strategy is designed. The initial population is generated by utilizing time window priority rules, genes with earlier operation start times are preferentially retained, and a mixed search strategy is introduced to avoid local optimal solutions. Experiments were conducted among 24 farms in a region of Hebei Province, and the results showed that, compared with other algorithms, the HTSMOGA achieved an average reduction of 17.18 % in the transfer distance of agricultural machine operations and 19.36 % in the nonoperational waiting time. Therefore, this study provides a rational and feasible solution for the cross-regional operation scheduling of agricultural machinery cooperatives and provides theoretical support for ensuring the timely completion of harvesting tasks and the cost savings and efficiency of agricultural machinery operations.

Operation scheduling method in an off-grid considering frequency violation probability caused by short-term variation

The use of renewable energy (RE) resources in off-grid applications is a promising solution from both the economic and environmental perspectives. However, the employment of RE generation should consider the uncertainty in their generation output; in particular, short-term variations in generation output result in serious frequency violations. The development of the off-grid planning, operation, and scheduling considering frequency variation is significant. This study proposes a probabilistic model for the frequency variation caused by load variation, which includes the dynamics of rotating generators and frequency-supporting smart inverters. The proposed method calculates the maximum frequency deviation by transforming the block, considering the output limit of the governor and smart inverters. This calculation is fast because it does not require time-sequential simulation. The effectiveness of the proposed method for off-grid operational scheduling problems was demonstrated through numerical studies. The results show that the proposed method can evaluate the same results as time series simulations 1500 times faster. Additionally, frequency deviations caused by short-term variations can be avoided, and battery capacity for zero frequency deviation is estimated with a 5% error. These results have the potential to contribute significantly to the operation of the grid with a large portion of RE resources.

Robust optimal operation scheduling method for integrated energy system considering flexible energy storage and mixed hydrogen natural gas

ABSTRACT At present, the number of electric vehicles is increasing day by day. The full integration of electric vehicles and multi-energy coupled integrated energy systems is the future development trend. This paper introduces a novel form of flexible energy storage in the integrated energy system ;(IES): electric vehicle (EV). And It proposes that mixed natural gas hydrogen be used as the primary energy source for IES, which replaces traditional natural gas. Combined with the above, the IES is scheduled to meet the system’s multi-load for heating, cooling, and electricity demands. Based on the integrated energy system’s structure and energy conversion principles, a robust two-stage optimization model for the IES is proposed to minimize the daily operation scheduling costs. The proposed model considers the uncertainty of distributed renewable generation and flexible energy storage. Uncertain adjustment parameters are introduced into the model, which can flexibly adjust the conservatism of the scheme. Using the column and constraint generation algorithm (C&CG) and the strong duality theory, the model presented in this paper is solved and the optimal scheduling scheme is obtained. The final simulation results validate the effectiveness of the model and algorithm. The addition of flexible energy storage and mixed natural gas hydrogen effectively improves the economy and flexibility of the system while reducing carbon emissions costs. Consideration of uncertainty improves system robustness and safety. In addition, sensitivity analyses of the influence of the proportion of hydrogen mixed with natural gas, the setting of uncertain adjustment parameters, electricity prices, weather, and load fluctuations on system operation. The results show that compared to existing research, electric vehicles as a flexible energy storage device and mixed natural gas hydrogen supply, increase the economic benefits of IES by 19.8% and 23%, respectively.

Coordinated Optimization Scheduling of Data Center and Electricity Retailer Based on Cooperative Game Theory

As the number of internet data centers (IDC) increases, the energy consumption of modern data centers is growing rapidly, which takes an increasing part of the global electricity consumption. In implementation, to cover the electricity consumption and reduce the electricity bill, data centers are often deployed with renewable energy, energy storage system (ESS) and conventional generators, which has brought the characteristics of both flexible electricity load and micro grid to the data centers. Therefore, the data center micro grids can adjust its operations according to the real time electricity price and cut down the operational cost. However, the rising of electricity will force the data center to start up the conventional generators, which leads to the incensement of operational cost and carbon tax. In view of this, we propose a coordinated operation scheduling method of data centers and electricity retailers based on cooperative game theory. In this method, a cooperative game union comprised of the data center and the electricity retailer is established to minimize the total cost. And the extra income of the alliance is distributed using the Shapley value method. A simulation is launched on Gurobi software, and the results have illustrated that the proposed method can reduce the data center operational cost by 5%.

Robust Bi-Level Planning Method for Multi-Source Systems Integrated With Offshore Wind Farms Considering Prediction Errors

Considering the economy, reliability, and output characteristics of multiple power sources (MPS) and energy storage (ES) comprehensively, a multi-source system integrated with offshore wind farms (OWFs) and its construction cost, and operating and maintenance cost model are established. The system is mainly composed of OWFs, thermal power plants, gas turbine power plants, and pumped hydro storage plants. Given the economy of the power system and offshore wind power accommodation, a bi-level optimal capacity configuration and operation scheduling method is proposed for the multi-source system integrated with OWF clusters with the objective function of optimal total cost. Then, a robust bi-level planning method for the multi-source system integrated with OWFs considering the dual uncertainty of load and offshore wind power prediction is proposed, in which the upper and lower models are solved by an improved particle swarm optimization (PSO) algorithm and CPLEX solver, respectively. Based on the method, the cost-optimal capacity configuration and operation scheduling scheme of an MPS and ES can be obtained. Finally, an OWF group in Shandong Province is taken as an example to check the validity and feasibility of the proposed method.

Revisiting Network Telemetry in COIN: A Case for Runtime Programmability

Applications based on the compute-in-the-network (COIN) paradigm require flexible network telemetry data to drive effective allocation decisions. Telemetry systems collect such data based on queries specifying the precise traffic metrics or features required. Recent advances in programmable switch hardware have led to highly efficient methods to compute query results using in-network resources. However, current approaches fail to meet the simultaneous requirements of dynamic traffic loads, diverse query types, and query dynamics. In this work, we argue that telemetry systems should be cast as active runtime schedulers rather than static data sources. COIN-based applications can then dynamically submit telemetry queries on the fly and react to their results in a closed-loop fashion – enabling a new generation of telemetry-driven reactive applications. As a first step toward this vision, we present a single-switch telemetry operation scheduling method. Our empirical evaluation demonstrates that such a method can reduce overhead by an order of magnitude compared to worst case allocations. This initial exploration opens the door to a multi-tier scheduling framework combining switch hardware with software-based compute platforms to meet the telemetry demands of future COIN-based applications.

An optimal probabilistic spinning reserve quantification scheme considering frequency dynamic response in smart power environment

These frequency responses in tandem with spinning reserve are a prominent problem in the future smart grid environment due to the increase of non-dispatchable energy sources. However, this new environment allows incorporating new technologies, that is, distributed energy resource (DER), energy storage systems (ESSs), and shiftable loads, to prevent emergency situations and blackouts. This paper proposes a novel, energy reserve operational scheduling method for future smart grid frequency enhancement considering wind generation, ESSs, thermal generating units, shiftable loads, and power system frequency response characteristics. The proposed method divides the reserve into two different parts, that is, active power reserve, which is responsible for smoothing the frequency fluctuations due to load variations and the second part is essential to control the frequency after large disturbances. The spinning reserve required for safe operation of the power system is determined based on a reliability criterion, that is, total expected energy not supplied (TEENS). The proposed method aims to maintain the frequency response in a stable mode by incorporating active power reserve from both generation and demand-sides. IEEE 30-bus and RTS-96 systems are utilized to verify the proposed reserve scheduling methods using GAMS programming environment. The obtained results verify the superiority and usefulness of the proposed method over others.

Intraday multi-objective hierarchical coordinated operation of a multi-energy system

An intraday, multi-objective, hierarchical and coordinated operation scheduling method for a multi-energy system (MES), which uses 15-min and 5-min scheduling intervals for different energy subsystems, is proposed. According to the characteristics of MES and the response time of energy conversion equipment, energy subsystems are dispatched on different dispatch intervals instead of unified dispatch intervals to dispatch energy subsystems. In a case study, the prediction error rate for a load is 5% and 2% when the dispatch interval is 15 min and 5 min, respectively; the prediction error rates for wind and solar energy output are 10% and 5%, respectively. Dispatching different subsystems with different intervals according to their characteristics reduces the impact of source and load uncertainties, which improves energy management. Multi-energy power dispatch considers exergy efficiency and the operation cost to improve the utilization energy efficiency. The Tchebycheff method, which considers the fuzzy entropy weight, is employed to balance the objectives between highest exergy efficiency and lowest operation cost. Numerical studies demonstrate that the operation cost of a multi-objective system is ¥500.8877 higher than that of a system with the objective of lowest operation cost. The exergy efficiency is increased by 3.94%, and the operation cost, which is reduced by ¥1706.9606, is 2.13% lower than that for the objective of highest exergy efficiency. Thus, the proposed method can balance the objectives between highest exergy efficiency and lowest operation cost. These findings provide a multi-dimensional dispatch scheme for dispatchers and highlight the development potential of an MES.

Robustly coordinated operational scheduling of a grid‐connected seaport microgrid under uncertainties

The modern seaport system is becoming more electrified by integrating energy storage system, offshore renewable energy sources (RESs), and shore-to-ship power supply system. The energy management and operational scheduling aim to maximise the profit gain by transacting energy with the main grid, reduce the operation cost while minimising the emission at seaport territory. This work proposes a robustly coordinated operational scheduling method of a seaport microgrid as a grid-integrated energy hub under uncertain renewable energy sources power output and load demand. In the day-ahead operation planning, generators, unit commitment and shore-to-ship load schedules are optimised. To address the uncertainty in the renewable energy sources, the day-ahead operation planning is modelled as a two-stage robust optimisation model with conflicting objectives and solved with the column and constraints generation algorithm. These results are used as input parameters for the hour-ahead generation scheduling in the following day. With uncertainty realisation, a shorter time horizon with higher time resolution deterministic optimisation is carried out for the generation dispatch. The proposed method is verified with the IEEE-33 bus distribution network with different uncertainties sets. The simulation results have verified the robustness and effectiveness of the method in dealing with uncertainties. Coordination between day-ahead and hour-ahead not only improves the accuracy and performance of the final dispatch result but also meets the distribution network constraints at generation and load buses.

Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm

The accommodation of high-penetration renewable power poses a considerable challenge to power grids. Coal-fired combined heat and power (CHP) stations are forced to enhance their operational flexibility by applying heat-power decoupling technologies. Power-to-heat devices, including electric boilers and heat pumps, are capable to enhance the operational flexibility of coal-fired CHP stations. The problem regarding the operation scheduling of a CHP station with multiple CHP units and power-to-heat devices is addressed in this study. Operation optimization models integrated with detail CHP unit models are developed, and the particle swarm optimization algorithm is utilized as the optimization algorithm. Then, a case study are carried out. Results show that the unequal distribution of heating and power loads among coal-fired CHP units can decrease the total irreversibility caused by heating steam pressure regulation. The operation scheduling method provided in this study can decrease the total coal consumption by 14.14 and 14.70 t/day for the CHP station integrated with an electric boiler and a heat pump, respectively. As a result, 1204.7 and 1252.44 ton CO2, and an additional ∼182 and ∼190 kUSD/year can be saved for the reference CHP station integrated with an electric boiler and a heat pump, respectively.

Multi-Objective Optimal Scheduling of a Hybrid Ferry with Shore-to-Ship Power Supply Considering Energy Storage Degradation

To improve the operation efficiency and reduce the emission of a solar power integrated hybrid ferry with shore-to-ship (S2S) power supply, a two-stage multi-objective optimal operation scheduling method is proposed. It aims to optimize the two conflicting objectives, operation cost (fuel cost of diesel generators (DGs), carbon dioxide (CO2) emission tax and S2S power exchange) and energy storage (ES/ESS) degradation cost, based on the preference of the vessel operator and solar photovoltaic (PV) power output. For the day-ahead optimization, interval forecast data of the PV is used to map the solution space of the objectives with different sets of weight assignment. The solution space from the day-ahead optimization is used as a guide to determine the operating point of the hour-ahead optimization. As for the hour-ahead scheduling, more accurate short-lead time forecast data is used for the optimal operation scheduling. A detailed case study is carried out and the result indicates the operation flexibility improvement of the hybrid vessel. The case study also provides more in-depth information on the dispatching scheme and it is especially important if there are conflicting objectives in the optimization model.

Multiagent-System-Based Operation Scheduling of Large Ports’ Power Systems With Emissions Limitation

Ports and its surrounding areas are major energy consumers and polluters. Optimal operation scheduling of port power demand may provide effective solutions for port efficiency improvement and the limitation of the emissions from ships at berth. Port power demand can be effectively regulated as a variety of flexible loads like shore-to-ship electric power supplies, refrigerated containers, plugged-in electric vehicles, etc., exist in large ports. The main goal of this paper is to propose an innovative multiobjective operation scheduling method for large ports that will limit in-port emissions, minimize operation cost, and support the electric network of the port area. A major operation constraint of a port demand management system is the large number of the controlled and supervised port loads. Smart grids exploiting multiagent systems have been proved very effective in such complex systems. The proposed method is applied to a realistic case study of a large port power system and evaluated by detailed simulations.

Modeling the Operation Mechanism of Combined P2G and Gas-Fired Plant With CO2 Recycling

The rapid development of power-to-gas (P2G) technology is promoting the integration of renewables and the interaction between multiple energy systems. In this paper, the concept of combined P2G and gas-fired power plant (GFPP) system is proposed as a potential solution for the globally recognized imperatives to reduce CO2 emission and utilize renewable energy. The proposed system is featured by the on-site conversion of CO2 back to the fuels of GFPP and thus recycles CO2 without long-distance transport. This paper further establishes the mathematical model of the proposed system. Detailed gas, electricity and heat flow inside the system are explained and the input/output relationship is derived as well. This paper also studies the operational range and optimal scheduling method of the proposed system to quantify its ability on the reduction of CO2 emission and utilization of renewables. According to the estimation, the GFPP-P2G system is able to utilize approximately 30% of the surplus renewables for electricity generation and 20% for heat generation. The combined GFPP-P2G system is also compared with the standalone GFPP through several cases. The numerical study shows great effect of the proposed system on the utilization of renewables and reduction of CO2 emission.

Improved NSGA model for multi objective operation scheduling and its evaluation

Reasonable operation can increase the income of the hospital and improve the patient’s satisfactory level. In this paper, by using multi object operation scheduling method with improved NSGA algorithm, it shortens the operation time, reduces the operation costand lowers the operation risk, the multi-objective optimization model is established for flexible operation scheduling, through the MATLAB simulation method, the Pareto solution is obtained, the standardization of data processing. The optimal scheduling scheme is selected by using entropy weight -Topsis combination method. The results show that the algorithm is feasible to solve the multi-objective operation scheduling problem, and provide a reference for hospital operation scheduling.

Research on Hierarchical Scheduling of Operational Operation of Polar Meteorological Satellite Ground System

Meteorological satellite ground application system business operation scheduling is the core of the entire business operation, directly related to the entire system operating efficiency. This paper designs a hierarchical satellite terrestrial system business operation scheduling method. First of all, the satellite service is classified into first-level and second-level scheduling. Then, based on the hierarchical scheduling, task scheduling and scheduled scheduling are implemented. Through hierarchical scheduling and planning and scheduling to achieve the entire system job scheduling, thereby enhancing the efficiency of task scheduling and improve the overall system operating efficiency.

Optimal Weekly Operation Scheduling of Pumped Storage Hydropower Plant Considering Optimal Hot Reserve Capacity in a Power System with a Large Penetration of Photovoltaic Generation

SUMMARYIn recent years, a large amount of photovoltaic (PV) generation capacity has been installed in power systems. However, the power output from the PVs is random and intermittent in nature. Therefore, PV generation poses many challenges to power system operation. To estimate the impact of these issues on power system operation, we propose an optimal weekly demand and supply simulation method considering large penetration of PV generation. In this paper, an operation scheduling method considering optimal hot reserve capacity is studied. The effectiveness of the proposed method is shown by using Monte Carlo simulation.

Coordinated Volt/Var Control in Distribution Systems with Distributed Generations Based on Joint Active and Reactive Powers Dispatch

One of the most significant control schemes in optimal operation of distribution networks is Volt/Var control (VVC). Owing to the radial structure of distribution systems and distribution lines with a small X/R ratio, the active power scheduling affects the VVC issue. A Distribution System Operator (DSO) procures its active and reactive power requirements from Distributed Generations (DGs) along with the wholesale electricity market. This paper proposes a new operational scheduling method based on a joint day-ahead active/reactive power market at the distribution level. To this end, based on the capability curve, a generic reactive power cost model for DGs is developed. The joint active/reactive power dispatch model presented in this paper motivates DGs to actively participate not only in the energy markets, but also in the VVC scheme through a competitive market. The proposed method which will be performed in an offline manner aims to optimally determine (i) the scheduled active and reactive power values of generation units; (ii) reactive power values of switched capacitor banks; and (iii) tap positions of transformers for the next day. The joint active/reactive power dispatch model for daily VVC is modeled in GAMS and solved with the DICOPT solver. Finally, the plausibility of the proposed scheduling framework is examined on a typical 22-bus distribution test network over a 24-h period.

水道送水システムにおける電力デマンドレスポンスのためのポンプ運転スケジューリング方式

Environmental concern has recently led to saving of electric power for water transmission and distribution systems in water utilities. To cope with the problem, effective water supply operation and appropriate management of water pressure have been researched. Moreover, after a large earthquake in eastern Japan, the problem of electricity shortage has been evident. To address the problem, the water saving which results in the electricity saving has been encouraged and the driving of waterworks facilities has been shifted from a peak demand time zone to a low demand time zone in order to achieve power peak cut. We propose a transmission pump operation scheduling method that minimizes the maximum value of power consumption in a time zone of demand response request. The optimal operational plan of pumps is made by mathematical programming so that the power consumption is minimized under constraints that the each reservoir keeps water level to enable 12-hour consecutive water supply and the water level does not go under lower limit during and after a time period of demand response. Simulation tests showed that the proposed method reduced power consumption by more than 65% compared with the current pump operation.

Economic-environmental active and reactive power scheduling of modern distribution systems in presence of wind generations: A distribution market-based approach

Distribution System Operator (DSO) is responsible for active and reactive power scheduling in a distribution system. DSO purchases its active and reactive power requirements from Distributed Energy Resources (DERs) as well as the wholesale electricity market. In this paper, a new economical/environmental operational scheduling method based on sequential day-ahead active and reactive power markets at distribution level is proposed to dispatch active and reactive powers in distribution systems with high penetration of DERs. In the proposed model, after day-ahead active power market was cleared the participants submit their reactive power bids and then the reactive power market will be settled. At distribution level, developing a Var market, in which DERs like synchronous machine-based Distributed Generation (DG) units and Wind Turbines (WTs) could offer their reactive power prices, DERs are motivated to actively participate in the Volt/VAr Control (VVC) problem. To achieve this purpose, based on the capability curves of considered DERs, innovative multi-component reactive power bidding structures for DERs are introduced. Moreover, the effect of reactive power market clearing on the active power scheduling is explicitly considered into the proposed model by rescheduling of active power by usage of energy-balance service bids. On the other hand, environmental concerns that arise from the operation of fossil fuel fired electric generators are included in the proposed model by employing CO2 emission penalty cost. The suggested reactive power market is cleared through a mixed-integer nonlinear optimization program. The effectiveness of the proposed scheduling model is investigated through a typical 22-bus distribution test system over a 24-h period.

太陽光発電大量導入時における時間断面毎の最適な運転予備力を考慮した揚水発電所最適週間運用計画

In recent years, a large amount of photovoltaic (PV) generations have been installed in power systems. However, the power output from the PVs is random and intermittent in nature. Therefore, the PV generations pose many challenges to the power system operation. To estimate the impact of these issues on the power system operation, we propose optimal weekly demand and supply simulation method considering a large penetration of photovoltaic generations. In this paper, a study for operation scheduling method considering optimal hot reserve capacity is presented. The effectiveness of the proposed method is shown by using Monte Carlo simulation.