Short-term Operation Scheduling Research Articles

On-Orbit Servicing Optimization Framework with High- and Low-Thrust Propulsion Tradeoff

This paper proposes an on-orbit servicing logistics optimization framework capable of performing the short-term operational scheduling and long-term strategic planning of sustainable servicing infrastructures that involve high-thrust, low-thrust, and/or multimodal servicers supported by orbital depots. The proposed framework generalizes the state-of-the-art on-orbit servicing logistics optimization method by incorporating user-defined trajectory models and optimizing the logistics operations with the propulsion technology and trajectory tradeoff in consideration. Mixed-integer linear programming is leveraged to find the optimal operations of the servicers over a given period, whereas the rolling horizon approach is used to consider a long time horizon accounting for the uncertainties in service demand. Several analyses are carried out to demonstrate the value of the proposed framework in automatically trading off the high- and low-thrust propulsion systems for both short-term operational scheduling and long-term strategic planning of on-orbit servicing infrastructures.

Framework for Modeling and Optimization of On-Orbit Servicing Operations Under Demand Uncertainties

This paper develops a framework that models and optimizes the operations of complex on-orbit servicing infrastructures involving one or more servicers and orbital depots to provide multiple types of services to a fleet of geostationary satellites. The proposed method extends the state-of-the-art space logistics technique by addressing the unique challenges in on-orbit servicing applications and integrates it with the Rolling Horizon decision-making approach. The space logistics technique enables modeling of the on-orbit servicing logistical operations as a Mixed-Integer Linear Program whose optimal solutions can efficiently be found. The Rolling Horizon approach enables the assessment of the long-term value of an on-orbit servicing infrastructure by accounting for the uncertain service needs that arise over time among the geostationary satellites. Two case studies successfully demonstrate the effectiveness of the framework for 1) short-term operational scheduling and 2) long-term strategic decision making for on-orbit servicing architectures under diverse market conditions.

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO). Originally, each MFO moth is restricted to fly toward a predetermined flame (solution), causing it to be trapped in the local optimum of that flame and reducing its performance. To address these drawbacks, BAMFO represents four alternative characteristics for moths to discover better flames all over the search space. The exploration and exploitation performances of BAMFO via these characteristics are balanced by a random parameter competing with the criteria of each characteristic. A repair strategy working with the priority list is created to adjust any unit status from the BAMFO simulation to satisfy all UC constraints. Then, Lambda-iteration method is adopted to solve economic dispatch problems. The proficiency of BAMFO is verified in various scales of unit systems for short-term operational scheduling. The results show that BAMFO yields better solutions than do other methods. The Wilcoxon signed rank test was conducted to prove the predominance of BAMFO.

A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

Short-Term Operation Scheduling of a Microgrid under Variability Contracts to Preserve Grid Flexibility

The conventional microgrid (MG) price-based operation scheme with respect to the hourly market price considers only profit maximization from energy transactions and disregards variability. This causes flexibility burdens on the main grid system operator (SO), which must then utilize its ramping capability to cover the net load variability. As the proportion of renewable energy sources (RESs) involving intermittency in MGs continues to increase owing to global energy policies, net load variability within shorter time intervals has also increased, making proper management guidelines necessary. Thus, this paper proposes an MG-SO variability contract on intra-hour and inter-hour time intervals for regulating variability such that the SO can support and distribute its relevant costs between the MG and the SO. To prove the effectiveness of the proposed contract, an MG variability contract-based scheduling model is also proposed, and the results were compared with those of the price-based model. A case study demonstrates that the introduction of RESs increases the variability in shorter intervals and that the suggested contract is effective in terms of decreasing the variability with increased MG operating costs. A sensitivity analysis between the reduced variability and additional operating costs was also conducted in the case study.

Integrated activity planning: a proven approach to optimising operations outcomes

Integrated activity planning (IAP) is a proven approach for dealing with complexity and uncertainty when operating complex production systems. IAP is driven by the need to deliver on production forecasts, optimise the decision making for the planning, scheduling and execution of activities impacting production, mitigate the risk associated with major shutdowns or bringing capital projects online and optimise interfaces to allow engineering, supply, maintenance and operations to work together seamlessly. Planning is typically spread across several time horizons, including long-term strategic business plans, medium-term program plans and short-term maintenance plans and operational activity scheduling. This paper provides a definition of IAP, a description of its value, a detailed case study from one of the world’s leading IAP implementations and a series of lessons learned.

Monte Carlo simulation of district heating system short-term operation in electricity markets

Energy generation in district heating (DH) systems is usually done in combined heat and power (CHP) units which can efficiently produce both useful heat and electric power. There can also exist heat only boilers, electric heaters, heat pumps and heat storage tanks. The coupling of heat and power generation in the CHP units and the possibility to store heat for later use makes the short-term operation scheduling of such systems quite challenging. Furthermore, big DH systems produce power that is sold in the electricity markets. This makes the operation scheduling problem even more complex as the uncertainty of the electricity prices in the markets should be considered. To make optimal decisions under uncertainty, various mathematical optimization tools were developed, such as stochastic programming and robust optimization. In this paper, an approach based on a Monte Carlo simulation is followed. Initially, a model of DH system short-term operation and power trading is mathematically formulated. Then, this model is used to run a Monte Carlo simulation for a case study system where the values of stochastic parameters are simulated using autoregressive models. Results demonstrate that simulation is fast, taking 300–400 runs to converge. A comparison of two system configurations shows that the use of heat storage increases the daily expected profit by 11%. Finally, the electricity price volatility in this case study is such that mainly two CHP units are operating for most of the time.

Linear Formulation for Short-Term Operational Scheduling of Energy Storage Systems in Power Grids

This paper presents linear programming (LP) formulations for short-term energy time-shift operational scheduling with energy storage systems (ESSs) in power grids. In particular, it is shown that the conventional nonlinear formulations for electric bill minimization, peak shaving, and load leveling can be formulated in the LP framework. New variables for the peak and off-peak values are introduced in peak shaving and load leveling model, and the historical peak value for demand charge are considered in the electric bill minimization model. The LP formulations simplify computation while maintaining the accuracy for including linear technical constraints of ESSs, such as the state-of-charge, charging/discharging efficiency, output power range, and energy limit considering the life cycle of ESS. Proposed LP formulations have been implemented and verified in practical power systems and a large-scale industrial customer using historical data.

Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units

Renewable resources, especially wind power, are widely integrated into the power systems nowadays. Managing uncertainty of the large scale wind power is often known as one of the most challenging issues in the power system operation scheduling. Additionally, energy storage systems (ESSs) have been widely investigated in the power systems owing to their valuable applications, especially renewable energy smoothing and time shift. In this paper, a stochastic unit commitment (UC) model is proposed to assess the impact of the wind uncertainty impact on ESSs and thermal units schedule in UC problem. Wind uncertainty is modeled based on the two measures. First, the wind penetration level is changed with respect to the basic level. Second, the wind forecasting error is modeled through a normal probability distribution function with different variances. The ESSs are modeled based on several technical characteristics and optimally scheduled considering different levels of the wind penetration and forecasting accuracies. The proposed formulation is a stochastic mixed integer linear programming (SMILP) and solved using GAMS software. Simulation results demonstrate that the wind uncertainty have a considerable impact on operation cost and ESSs schedule while proposed optimum storage scheduling through the stochastic programming will reduce the daily operational cost considerably.

Short-Term Operation Scheduling in Renewable-Powered Microgrids: A Duality-Based Approach

This paper considers the operation scheduling problem in renewable-powered microgrids, which is used to determine the least-cost unit commitment (UC) and the associated dispatch, while meeting load, environmental, and system operating requirements. The intermittency nature of the renewable energy sources, as well as microgrid's capacity to operate either in parallel with, or autonomously of, the traditional power grid, pose new challenges to this classic optimization task. A probability-based concept, probability of self-sufficiency (PSS), is introduced to indicate the probability that the microgrid is capable of meeting local demand in a self-sufficient manner. Furthermore, to the best of our knowledge, we make the first attempt in approaching the mixed-integer UC problem from a convex optimization perspective, which leads to an analytical closed-form characterization of the optimal commitment and dispatch solutions. The simulation results show that 1) the proposed method achieves an efficient performance that incurs no loss of optimality with lower complexity than existing algorithms; 2) an energy storage system (ESS) with suitable capacity contributes to the self-sufficiency target of a microgrid, and the stored energy varies less remarkably as the microgrid tends to operate more independently; 3) the proposed method provides guidelines in deciding the ESS size to achieve a desired PSS.

一种输电线路雷击概率预测方法及其在输电系统短期可靠性评估中的应用

输电线路长期暴露在大气环境之中，雷电天气下输电线路易发生雷击跳闸事件，严重影响输电系统可靠性，有必要针对雷电天气下的输电网可靠性进行评估。本文研究了输电线路在雷电天气下的故障概率模型，并用于在预报天气状况下的输电网可靠性预测(评估)。首先根据历史雷电记录建立雷击故障概率模型，然后根据此概率模型对未来雷电天气下的输电线路雷击故障概率进行计算，并进而计算输电网可靠性指标。通过IEEE RTS-79测试系统验证了所提方法的有效性，算例结果表明，雷电天气对输电线路和系统可靠性有较大的影响。该模型可以指导雷电天气下电网短期运行和在线调度决策。 The transmission lines are exposed to the atmospheric environment and will be affected by adverse weather such as lightning storm, so that it will affect the reliability of transmission system. This paper studies the fault probability model of transmission line during the lightning storm, and evaluates the short-term reliability of transmission system in the forecasting weather condition. Firstly, we build the lightning strike fault probability model of the transmission line based on historical lightning record information, then calculate the lightning strike probability under the forecasting weather conditions, and furthermore evaluate the reliability index of transmission system. We utilize IEEE RTS-79 system to verify the validity of the proposed model and the results show that lightning has great negative influence on the transmission lines and the reliability of transmission system. The reliability evaluation model proposed in this paper can guide the short-term operation and online scheduling for transmission system operators.

Short-Term Reliability Evaluation of Transmission System Using Lightning Strike Probability Prediction

The transmission lines are exposed to the atmosphere nature and will be affected by adverse weather such as lightning storm, so that it will affect the reliability of transmission system. This paper studies the fault probability model of transmission line during the lightning storm, and evaluates the short-term reliability of transmission system in the forecasting weather condition. Firstly, build the lightning strike fault probability model of the transmission line based on historical lightning record information, then calculate the lightning strike probability under the forecasting weather conditions, furthermore evaluate the reliability index of transmission system. Utilizing IEEE RTS-79 system to verify the validity of the proposed model and the results show that lightning has great negative influence on the transmission lines and the reliability of transmission system. The reliability evaluation model proposed in this paper can guide the short-term operation and online scheduling for transmission system operators.

Short-term operation scheduling of a hydropower plant in the day-ahead electricity market

This paper presents a novel approach to solve the short-term operation scheduling problem of a hydropower plant that sells energy in a deregulated electricity market with the objective of maximizing its revenue. This paper proposes a nonlinear programming based scheduling model that determines both the optimal unit commitment (start-ups and shut-downs scheduling) and the generation dispatch of the committed units (hourly power output). The power generated by each hydro unit is considered as a nonlinear function of the water discharge and the volume of the associated reservoir. The dependence of the units’ operating limits (maximum and minimum water flows) on the actual gross head has been also taken into account in this model. The results from a case study are also presented to illustrate the application of the proposed approach in a real hydro plant.

Economic Impact of Electricity Market Price Forecasting Errors: A Demand-Side Analysis

Several techniques have been proposed in the literature to forecast electricity market prices and improve forecast accuracy. However, no studies have been reported examining the economic impact of price forecast inaccuracies on forecast users. Therefore, in this paper, the application of electricity market price forecasts to short-term operation scheduling of two typical and inherently different industrial loads is examined and the associated economic impact is analyzed. Using electricity market price forecasts as the expected next-day electricity prices, optimal operating schedules and the associated costs are determined for each load. These costs are compared with those of a ?perfect? price forecast scenario in which actual prices are used to determine the operating schedules. Numerical results and discussions are provided based on price forecasts with different error characteristics.

Scheduling semiconductor wafer fabrication by using ordinal optimization-based simulation

Computational efficiency is one of the major challenges of applying simulation to short-term operation scheduling of semiconductor wafer fabrication factories (fabs), which are characterized by re-entrant process flows, stringent production control requirements and fast changing technology and business environments. The paper explores the application of the ordinal optimization (OO)-based simulation technique to efficiently selecting good rules for scheduling wafer fabrications. An efficient simulation tool, which makes use of OO and optimal computing budget allocation techniques, is developed. Experiments with the OO-based simulation tool are conducted for static selection of good rules under different factors such as initial state, performance index and time horizon. Results indicate that one to two orders of computation time reduction over traditional simulations can be achieved and that what rules are good varies with factors of initial state, performance index and time horizon. These results motivate our further investigation about applications to dynamic selection of dispatching rules upon the occurrence of two significant uncertain events: holding of significant amount of wafers-in-process due to engineering causes and major machine failures. Results demonstrate the value of dynamic rule selection for uncertainty handling, the insightful selection of good rules and the needs for future research.

Summary of the panel session "Coordination Between Short-Term Operation Scheduling and Annual Resource Allocations"

This paper summarizes the discussions of the panel session entitled "Coordination Between Short-Term Operation Scheduling and Annual Resource Allocations" which was held during the 1994 IEEE PES Winter Meeting at New York. The panel discussions focused on the need of coordinations, the conceptual framework to achieve effective coordinations, and electric utilities' experiences in coordinating resource (e.g. fuel, emission, hydro) allocation decisions and short-term operation scheduling decisions.