Generation Scheduling Research Articles

Study on plant generator scheduling based on mixed integer model

Due to the outbreak of the new crown epidemic, the production plans of most factories have been affected to a certain extent, and there may be some changes in the production machinery and manpower arrangements of the factories. In this paper, we study how to arrange the dispatch strategy of generators under the condition of power demand in a specified time. Based on the minimum cost, a mixed integer model was established, which provided a solution for some factories to control the scheduling of machines on time during the outbreak of the epidemic. According to the established model, a factory is programmed to solve the problem, and the minimum power generation cost in a week is given 1589130 CNY. In special cases, the working generator set must set aside 20% of the power generation capacity margin to cope with the unexpected situation. At this time, the generator’s daily production cost is as small as 239870 CNY while giving the corresponding generator scheduling scheme. Finally, this paper does a sensitivity analysis of the total daily power generation cost when the power generation margin changes, and finds that the total daily cost of the generator is sensitive to the power generation margin.

COVID-19 impacts on Indian power system planning and operation

Governments around the world are implementing different measures like lockdown and stay-at-home orders to stop the spread of coronavirus amongst the common masses. It has caused electricity consumption to decrease and inverting the gap between demand and generation. Load-generation balance is maintained by shutting down or operating the fossil fuel generations at a low plant load factor due to must-run obligations over renewable generation. During lockdown periods, variable renewables’ proportion in total energy consumption has reached record highs. The power system’s inertia stability has decreased with displacement of conventional resources from generation schedule. It created new challenges for system operators to meet increased variability. New forecasting methodologies are adopted for modified peaks and shapes of the load curves. Power sector utilities are combating the issues of decreasing demand and liquidity caused by revenue shortfall. Cross-border restrictions on fuel imports are also affecting the portfolio of generation resources in long term. The ongoing pandemic is causing a paradigm shift in financial, political and technical aspects of power sector. This requires active engagement from government to help the sector thrive in difficult times and continue to work smoothly. In this context, this paper presents a comprehensive review of pandemic impacts experienced on power sector in the area of planning and operations. Also, inputs are provided from policy and operation perspectives to improve grid reliability and security during and post-event conditions. The fast implementation of demand side management programs is recommended to improve grid stability. Tariff restructuring, enhancing flexibility and incorporating consumer behaviour in load forecasting have been suggested to have a more diversified and resilient power sector in the post-COVID era.

Economic-environmental scheduling of microgrid considering V2G-enabled electric vehicles integration

As an important part of the Energy Internet, microgrid (MG) scheduling problem has always attracted great attention, especially under the background that large-scale penetration of electric vehicles (EVs) into buildings poses both opportunity and challenge to the MG energy management. This research presents a two-stage optimization strategy, for improving the economic and environment-friendly operation of MG considering EVs integration. In Stage 1, model of EVs under coordinate charging/discharging stimulated by time-of-use incentive mechanism is established, and the peak–valley hours are dynamically divided to obtain a load curve with minimized peak-to-valley difference (PVD). In Stage 2, aiming for the best tradeoff between the generation cost and pollutant emission, daily optimal scheduling of the MG generators is efficiently calculated according to the varying power demand. For enhancing the convergence, an advanced genetic algorithm with elite preservation strategy is employed. Two cases from a residential MG system is exploited to validate the proposed method, and the results show that firstly the power supply pressure could be obviously relieved owing to the load shifting effect of the coordinated vehicle-to-grid (V2G) service reflected by the decreased PVD (Case 1: from 76.16 to 54.35 kW, Case 2: from 76.25 to 46.19 kW); simultaneously, via applicable power planning of the MG components, cost saving and emission reduction can be both achieved (Case 1: ¥190.12 under V2G management compared to ¥281.66 under basic load, Case 2: ¥177.28 compared to ¥350.24), ensuring the feasibility of the control strategy, which promotes the economical-environmental and reliable operation of the MG system.

Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and R2. Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and R2 of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households.

Optimum economic operation of coordinated power system based on turbulent water flow optimization

ABSTRACT Owing to increase in power demand, generation costs and environmental anxieties, renewable energy needs to be utilized to conserve energy and to reduce atmospheric pollutants. This study aims to present a new physics inspired metaheuristic algorithm, turbulent water flow optimization (TWFO) for scheduling the power generation of hydrothermal power systems incorporating with pumped storage units (PSU). TWFO is inspired by a natural phenomenon of action of whirlpools in water bodies. Different characteristics of power plants including valve point effect, transmission losses and multiple fuel sources are considered. A heuristic constraint handling mechanism is embedded in the proposed method to satisfy all the equality and inequality constraints. To exemplify the effectiveness of the TWFO, the proposed method has been validated on three coordinated power generation scheduling problems with different characteristics, and compared with particle swarm optimization, sine cosine algorithm, Harris hawks optimization, and other state-of-the-art techniques in the literature. Numerical results clearly signify that the TWFO is competent to obtain superior solutions than the other compared approaches, both in the quality of the solutions and the rate of convergence. Moreover, the fuel cost attained by the TWFO addressing PSU is lesser than that without addressing PSU. The proposed work ensures the economic operation of coordinated power system and increases the renewable energy utilization.

Multiple equipment scheduling and AGV trajectory generation in U-shaped sea-rail intermodal automated container terminal

As a key development direction of future ports, automated container terminals will play a crucial part in the development of multimodal transportation. Based on the newly constructed U-shaped yard layout in sea-rail intermodal container terminal, this paper focuses on the integrated scheduling of horizontal transport equipment and handling equipment, as well as path planning for AGVs. A bi-level programming model is established to obtain the yard crane scheduling scheme and determine the specific trajectories of AGVs with the goal of minimizing the makespan and total waiting time of AGVs and trucks. We design a spatio-temporal greedy strategy based bi-level genetic algorithm to schedule tasks and resolve conflicts in light of the multi-objective and hierarchical characteristics of the problem. Results indicate that the proposed algorithm could resolve the path conflicts effectively and obtain the integrated scheme that hardly affect the operation process of sea-rail intermodal containers.

Automated University Lecture Schedule Generator based on Evolutionary Algorithm

university is a complicated work so in the implementation it have violation of the constraints and it also takes a lot of time since it is created manually. In this paper evolutionary algorithm (EA) is used to create an effective and feasible schedules based on the real data input that is obtained from each department. The objective functions in EA contribute in gaining the fitness function to solve the constraints problem in the schedule by applying weighting for each hard constraints. The objective function is gained from the total of infringement in each soft constraints addition by score weighting. The genetic operator used in EA is stochastic variation Operator. As far as the reproduction operator is concerned, the tournament selection was used with size 3. Crossover operator is conducted after selection process with crossover probability equal to 0.05 and mutation rate is 0.1. The size of population was set to 9 and stopping criteria algorithm was left run for fitness value = 1. The simulation result shows that EA can create lecture schedules efficiently and feasibly. Moreover, it is also faster with the execution time of the proposed EA is less than 30 and easier than creating manually.

Benders‐Decomposition‐Based Voltage‐Stability‐Constrained Unit‐Commitment Taking Reactive Power Limit into Account

To determine economic‐efficient and secure generation schedules, unit commitment (UC) with network and stability constraints will play an important role in the electricity market. We define a voltage‐stability‐constrained unit commitment problem taking reactive power limit into account (VSCUC‐QLIM) since the decreasing of synchronous generators may threat the voltage stability of electricity systems. This VSCUC‐QLIM problem is modeled as a mixed‐integer nonlinear programming with equilibrium constraints, which is too complex to be solved directly. Therefore, we apply Benders decomposition to solve this complex problem: the master problem consists of the UC problem to determine the commitment states for generators, and the subproblem consists of the voltage‐stability‐constrained optimal power flow with complementarity constraints problem. However, the subproblem calculation may not converge depending on the commitment states due to the following reasons: (i) any power‐flow solution cannot be obtained or (ii) the voltage stability requirement cannot be satisfied on the commitment state given by the master problem. To deal with these difficulties, two partial problems of the subproblem are added as a pre‐subproblem and post‐subproblem in the proposed method. The combination of these subproblems generate different types of Benders cut depending on the condition. The proposed method was implemented on MATPOWER, and evaluated for daily scheduling on an IEEE six‐bus test system and IEEJ standard WEST10 system. The evaluation results validated the effectiveness of the proposed method from the perspective of economic efficiency and voltage stability. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Elite collaborative search algorithm and its application in power generation scheduling optimization of cascade reservoirs

The joint optimization scheduling model of cascade reservoirs has the characteristics of high dimensionality, dynamic, nonlinear, multivariate, and strong constraint. Around its solution problem, this paper carries out the research of the efficient model solution method based on the group intelligent optimization algorithm. Firstly, given the defect of the standard collaborative search algorithm that it is easy to falls into local optimization and weakening of global search ability in the later stage, three efficient improvement strategies are proposed through parameter randomization, elite reinforcement learning, and elite help learning. And based on this, an elite collaborative search algorithm (ECSA) coupled with the three improvement strategies is established, which enhances the ability of individuals to search globally and jump out of local optimality, and it has strong robustness and optimization ability. Secondly, taking six cascade power stations in the Yalong River Basin as an example, the power generation dispatching model of cascade reservoirs considering various constraints such as reservoir discharge flow and water level restriction are constructed, and six typical runoff scenarios are selected, and the joint power generation scheduling test is carried out by using five algorithms, i.e., ECSA, CSA, PSO, GWO, and WOA. Finally, through the comparison of the scheduling results, it is found that compared with the other algorithms, the solution obtained by the proposed ECSA in this paper can effectively improve the overall power generation of the basin and the efficiency of water resource utilization in river basins. Taking the normal year as an example, compared with the CSA、PSO、GWO，and WOA method, the average annual increase of power generation by the ECSA method is 32, 4778, 4153, and 2288 million kWh respectively.

Optimal Operation of Regional Microgrids With Renewable and Energy Storage: Solution Robustness and Nonanticipativity Against Uncertainties

Due to prevailing uncertainties of renewable energy and time coupling constraints of energy storage (ES), robustness and nonanticipativity of scheduling results directly influence the operational feasibility of regional microgrids. To this end, this paper proposes a new multistage generation scheduling method for regional microgrids with renewables and ES, which can simultaneously guarantee the robustness and nonanticipativity of scheduling solutions. First, a feasibility proposition is established based on the backward derivation thoughts, which addresses the feasibility requirements of microgrids against uncertainties of renewables and loads. Second, based on this feasibility proposition, a scenario-based multistage robust scheduling model with robust and nonanticipative constraints is presented, in which the uncertainties of transaction prices, renewables, and loads are simulated via representative scenarios to guarantee the economic performance of scheduling results. Third, with on/off decisions of thermal units as well as safe ranges of ES energy levels and thermal outputs derived from the scheduling model, a real-time rolling economic dispatch model is established to determine active and reactive power as well as nodal phase voltage values. Numerical tests implemented on a real regional microgrid illustrate efficacy of the proposed method.

Fundamental Studies of Smart Distributed Energy Resources along with Energy Blockchain

This article studies the broad methodology and major application of smart distributed energy resources (DER) in terms of energy generation, consumption, transaction, and power scheduling. This article simplifies a general DER system into a generic type of integrated DER model. This model is used to investigate a smart DER system that transforms three input parameters, (3I parameters) into three critical output functions(3O functions); hence, the model is also called the 3I3O model. The power at a common connection joint can be enabled by a computer that makes computerized decisions to utilize smart DER. Therefore, the computer algorithm collects various data fed into a computer for deep learning and artificial intelligence (AI) decision making. The authors demonstrate important results and the best solutions to meet power demand, offer an economic advantage and have a low carbon footprint for consumers. Moreover, several network blockchain options are discussed. EBC and DER represent an ideal combination with advantages in managing exergy through so-called intelligent power technology. This technology is discussed in detail and includes special hardware, software, and a broad set of computerized intelligence. Finally, the exergy that can possibly be achieved for smart DER systems is discussed.

Aggregated Net-load Forecasting using Markov-Chain Monte-Carlo Regression and C-vine copula

Net-load is the difference between total load and renewable generation and acts as an effective system load to which dispatchable generators are scheduled and system flexibility requirements are estimated. This necessitates accurate net-load forecasts for optimum scheduling and flexibility requirement estimations. Net-Load Forecasting (NLF) got only little attention in existing literature even though it is essential for optimal generation scheduling and power system flexibility requirement estimations. Time series and machine learning models have been used for NLF in recent years, however, there is vast scope existing for improving accuracy. Probabilistic forecasting models are widely used for various forecasting problems such as load, wind, and solar generation forecasting as those models show improved forecasting accuracy. In this context, this paper proposes a novel probabilistic aggregated very short-term NLF model based on Markov Chain Monte Carlo (MCMC) Regression. MCMC uses data augmentation, where unobserved variables are simulated from their posterior distribution and this makes MCMC approach suitable for regression. Further, MCMC forecasts are improved by incorporating C-vine copula-based Joint Probability Distribution (JPD) of expected load, wind, and solar generation forecasting errors. The C-vine copula is suitable for such multi-variable JPD estimation due to its enormous flexibility in stochastic multivariate dependence modelling compared to standard elliptical and Archimedean copulas. Results show that the proposed NLF model outperforms reference models and can produce accurate net-load forecasts.

Innovative Unit Commitment Modeling Approach Considering Multi-infeed VSC-HVDC Links

This paper introduces an improved optimization model for the unit commitment (UC) problem of AC power systems considering multi-infeed VSC-HVDC links. The proposed formulation is based on the linear modeling of the AC network and point-to-point HVDC links, where power losses are properly considered by piecewise linearization of AC lines, DC links and VSC units. This UC model is featured by a mixed integer linear programming (MILP) framework, thus enabling effective calculations of the optimal hourly generation scheduling for multi-infeed VSC-HVDC power grids. These sophisticated power grid studies are associated with day-ahead electricity markets carried out by power system engineers at control centers. To demonstrate the applicability of the proposed MILP-based UC model, the IEEE RTS 24-bus test system is used to carry out two case studies. The first incorporates three point-to-point HVDC links where an in-depth analysis is carried by comparing its outcomes, with and without HVDC links. The second case study features two interconnected areas having three-infeed HVDC links and several tens of generation units. Both systems are analyzed for a 24-hr planning horizon. It is confirmed that the UC model described in this paper permits to study practical power grids with multi-infeed VSC-HVDC links.

Real-time adjustment way of reservoir schedule forecasting projects based on improved variable oblivion factor least square arithmetic coupling Kalman filters

Flood forecasting method is an important non-engineering power generation scheduling method. However, due to various sources of uncertainty and the influence of observation and other factors, there are always errors in the results of flood forecasting, and these errors will reduce the application effect of the forecast results in reservoir optimization. Therefore, in order to improve the accuracy, we should analyze the sources of uncertainty, estimate the probability of the error range, and thus reduce the forecast error. In this paper, three improved real-time adjustment methods of variable forgetting factor least squares arithmetic coupled Kalman filter are proposed: adaptive Kalman method, ZK method and KZ method. It effectively avoids the shortcomings of the traditional Kalman method. We adjusted the Feng Shu Ba Reservoir flood forecast in real time for example, and then applied it and compared it with other methods. The results show that they have better simulation effect.

A model coupling current non-adjustable, coming adjustable and remaining stages for daily generation scheduling of a wind-solar-hydro complementary system

To formulate the daily generation scheduling of a wind-solar-hydro complementary system (WSHCS), the hourly forecasts of the reservoir inflow, wind speed, and sunlight intensity within a day and their longer prediction can be used to assess the benefit of the day-ahead and remaining stages, respectively. Thus, the current two-stage model coupling these forecasts can efficiently maximize the total benefits of day-ahead and remaining stages. However, the two-stage model does not consider the significant difference between non-adjustable and adjustable periods for hydropower. Namely, the hydro unit commitment should be decided in the current stage (non-adjustable period), and it can be adjusted in the coming stage (adjustable period). To address this issue, a three-stage model, including the day-ahead non-adjustable stage, the day-ahead adjustable stage, and the remaining period stage, is proposed to formulate a daily generation scheduling of the WSHCS. The Guandi wind-solar-hydro hybrid power plant on China's Yalong River is selected as a case study. Results show that compared with the two-stage model, the proposed three-stage operation model can increase the average energy production of the WSHCS by 2.23 GWh for typical days, and the average power curtailment rate reduces by 0.26%. A three-layer nested approach can simplify from 576 to 26 decision variables for the high-dimensional three-stage model, and improve the computational efficiency. Thus, the proposed method could guide the daily generation scheduling of a WSHCS.

An Improved Optimization Algorithm for Aeronautical Maintenance and Repair Task Scheduling Problem

The maintenance of carrier-based aircraft is a critical factor restricting the availability of aircraft fleets and their capacity to sortie and operate. In this study, an aeronautical maintenance and repair task scheduling problem for carrier-based aircraft fleets in hangar bays is investigated to improve the maintenance efficiency of aircraft carrier hangar bays. First, the operational process of scheduling aeronautical maintenance tasks is systematically analyzed. Based on maintenance resource constraints and actual maintenance task requirements, a wave availability index and load balance index for the maintenance personnel are proposed for optimization. An aeronautical maintenance task scheduling model is formulated for carrier-based aircraft fleets. Second, model abstraction is performed to simulate a multi-skill resource-constrained project scheduling problem, and an improved teaching-learning-based optimization algorithm is proposed. The algorithm utilizes a serial scheduling generation scheme based on resource constraint advancement. Finally, the feasibility and effectiveness of the modeling and algorithm are verified by using simulation cases and algorithm comparisons. The improved teaching-learning-based optimization algorithm exhibits improved solution stability and optimization performance. This method provides theoretical support for deterministic aeronautical maintenance scheduling planning and reduces the burden associated with manual scheduling and planning.

Accelerated dual dynamic integer programming applied to short-term power generation scheduling

The short-term generation scheduling (STGS) problem defines which units must operate and how much power they must deliver to satisfy the system demand over a planning horizon of up to two weeks. The problem is typically formulated as a large-scale mixed-integer linear programming problem, where off-the-shelf commercial solvers generally struggle to efficiently solve realistic instances of the STGS, mainly due to the large-scale of these models. Thus, decomposition approaches that break the model into smaller instances that are more easily handled are attractive alternatives to directly employing these solvers. This paper proposes a dual dynamic integer programming (DDiP) framework for solving the STGS problem efficiently. As in the standard DDiP approach, we use a nested Benders decomposition over the time horizon but introduce multiperiod stages and overlap strategies to accelerate the method. Simulations performed on the IEEE-118 system show that the proposed approach is significantly faster than standard DDiP and can deliver near-optimal solutions.

Economic Emission Generation Scheduling of Interconnected Energy System Using Hybrid Optimization Technique

Economic Emission Generation Scheduling of Interconnected Energy System Using Hybrid Optimization Technique

Boosting the power grid resilience under typhoon disasters by coordinated scheduling of wind energy and conventional generators

Typhoon poses a significant challenge to the reliable and safe operation of power grids. A novel priority-load based preventive scheduling method that considers conventional generators and wind power is proposed to fully exploit the potential of wind power for resilience enhancement. First, the wind speed variation rule in different positions of the typhoon wind speed field is studied according to the characteristics of typhoon. Second, considering the influence of strong wind on the failure rates of transmission lines and tie-lines connecting the power sources and the main grid, the vulnerability curve is used to establish the fault model of lines during typhoon disasters. Third, the utility function of typhoon disaster is represented by the S-type curve, and satisfaction for typhoon resistance (SFTR) index is defined as the objective of coordinated scheduling model. The results show that SFTR can be increased by a factor of 27.9% by exploiting the wind power potential. Compared with the load shedding based method, the priority-load based method proposed in this paper can improve SFTR by 47.6% at most from the perspective of improving the capacity of satisfying important loads to enhance system resilience.

Solution for short-term generation scheduling of cascaded hydrothermal system with turbulent water flow optimization

For economic operation of hybrid power systems, short-term hydrothermal planning is a significant operation which is focused to reduce the fuel costs of thermal power plants while fulfilling the different constraints of electrical and hydraulic networks. This paper addresses a newly developed algorithm, turbulent water flow optimization (TWFO) to solve the hydrothermal scheduling problem. TWFO is based on the whirlpool phenomenon which is formed through turbulent flow of water in seas or oceans. To enrich the solution quality of TWFO and to avoid trapping into local optimum, a chaotic logistic map is employed in the algorithm. The proposed TWFO is used to offer an optimal hourly generation schedule of hydrothermal power system considering the spillage effects of hydro plants, and the valve point loading effects, transmission losses and multi-fuel sources of thermal power plants. Besides, a repair mechanism is applied to fulfil all the equality and inequality constraints and to govern the solutions in the direction of feasible search space. The superiority of the proposed TWFO is examined on three hybrid power systems and the results of TWFO are compared with the performance of recent heuristic approaches including monarch butterfly optimization, Harris hawks optimization, red fox optimizer, remora optimization algorithm, student psychology-based optimization, and other erstwhile approaches reported in the literature. Numerical results demonstrate that the TWFO shows a superior performance in comparison with other approaches.