Generation Scheduling Research Articles

Comparative Study on Forecasting of Schedule Generation in Delhi Region for the Resilient Power Grid Using Machine Learning

Comparative Study on Forecasting of Schedule Generation in Delhi Region for the Resilient Power Grid Using Machine Learning

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

This paper addresses a novel multi-skill resource-constrained project scheduling problem with flexible resource profiles (F-MSRCPSP), in which the resource allocation of each activity consists of a certain number of discrete resources and is allowed to be adjusted over its duration. The F-MSRCPSP aims, therefore, to determine the flexible resource profile of each activity to minimize the make-span and total cost simultaneously. Then, a hybrid multi-objective fruit fly optimization algorithm is proposed to handle the concerned problem. In the proposed algorithm, two flexible parallel and serial schedule generation schemes are introduced, aiming to schedule activities and adjust allocated resource combinations. Additionally, two heuristic strategies are proposed to effectively select suitable resource combinations for activities. Moreover, a series of operators has been developed, including the rejoining operator, empirical re-arrangement operator, and empirical re-selection operator. These operators aim to accelerate the convergence speed and enhance the exploration of the proposed algorithm. Finally, the orthogonal test is used to select the optimal parameter combination, and comparative experiments based on tests with different scales are conducted, along with a t-test. The experimental results demonstrate that MOFOA-HS is effective in solving the F-MSRCPSP.

A combined interval prediction system based on fuzzy strategy and neural network for wind speed

Wind energy exhibits strong fluctuations and intermittencies. The accurate prediction of wind speed is of considerable significance for the operation and maintenance of wind farms and the safety of the power grid. However, previous studies have often ignored the impact of data noise on trend prediction, and lacked effective data pre-processing methods and adaptive interval prediction schemes, resulting in poor prediction results. To improve the accuracy of prediction, this paper proposes a wind-speed combination interval prediction system based on a fuzzy strategy and neural network. This study used a fuzzy strategy to pre-process the data and proposed a joint optimization algorithm that uses multiple multi-objective metaheuristic algorithms to optimize the neural network jointly. An improved prediction hybrid algorithm was used to reconstruct the predicted results. Finally, based on the fuzzy theory and neural network, interval prediction schemes that can adapt to different situations were proposed. Wind-speed data from the Penglai Wind Farm in China were used for verification. The mean absolute percentage error of the wind-speed point prediction was 3.75%, and the prediction interval coverage probability of the wind-speed interval prediction was 97.92%. The experimental results showed that the proposed model not only outperforms other comparable models but also improves the prediction interval coverage probability by 176.4% compared with the baseline model. This proves that the integrated hybrid model can improve the accuracy and effectiveness of wind-speed prediction. The accurate interval prediction of wind speed can result in the complementary scheduling of renewable energy generation and the sustainable development of the energy field, and support intelligent upgrades.

Construct watchlist security constraints for monthly unit commitment considering wind power uncertainty

AbstractMonthly unit commitment (UC) is a decision‐making process that determines the optimal generation scheduling of mid and long‐term power system, often operating as a subproblem within monthly electricity market operations and long‐term maintenance scheduling optimization in China. Dealing with transmission constraints in monthly security‐constrained unit commitment (SCUC) is considered to be challenging, but its simplification can be achieved through eliminating inactive security constraints. Additionally, the uncertainty associated with wind power needs to be given due consideration in monthly scheduling. Here, a three‐step watchlist construction strategy is proposed to facilitate the rapid screening of overloaded power flow constraints in monthly unit commitment, including three lists (i.e. list of risk, list of concern, and list of interest) under different conservatism. Importantly, the shift factor method is utilized to screen the potential overloaded constraints arising from the redispatch process for hedging the effects of large wind uncertainties. Results obtained from the testing of IEEE‐118 system indicates that more than 99% of active power flow constraints could be successfully screened out when wind power fluctuation is at 20%. As such, the proposed scheme provides an effective method to consider uncertainty associated with large‐scale wind power generation integrated into the power grid.

Energy Storage Requirements to match Wind Generation and Demand applied to the UK network

Increasing the contribution from Renewable Energy Systems to the electricity network places increasing demands on both, power quality assurance as well as energy storage or back-up generation availability. Based on a set of forty typical UK years, generated by matching 10 years of hourly wind speed observations at a typical site in Scotland to four years of hourly UK demand data from the National Grid, the statistics of the hour-by hour matching of wind generation to the demand and the cumulative effects of electricity surplus or deficit is analysed to obtain measures for the energy requirements and their associated time scales to complement the wind power through energy storage technologies or complementary scheduled generation. The findings for the UK case suggest that the overwhelming majority of energy balancing occurs over a time scale of less than a week, where the energy storage capacity is around 10 MWh per MW of installed wind capacity.

Localized management of distributed flexible energy resources

The integration of distributed energy resources continues to be a challenge for the traditional electricity grid. Aside from additional variability in the net power demand, there is often a temporal discrepancy between generation and consumption schedules which can exacerbate demand peaks and troughs, leading to system overloading, voltage violations, and a low system load factor. This study proposes a control system approach to unify the management of the different types of flexible energy resources through Localized Demand Control. In this system, all flexible energy resources are coordinated using a power line carrier frequency signal to follow a preferred demand curve at the distribution transformer level. The system’s functionality has been confirmed in an actual 5-house microgrid without adverse effects on end-user comfort. Meanwhile, scaled-up simulation studies have shown improvements in the system load factor by as much as 189%, and increased hosting capacity by 40% for electric vehicles and 16% for solar photovoltaic panels.

An ensemble model for short-term wind power prediction based on EEMD-GRU-MC

As a kind of clean and renewable energy, wind power is of great significance for alleviating energy crisis and environmental pollution. However, the strong randomness and large volatility of wind power bring great challenges to the dispatching and safe operation of the power grid. Hence, accurate and reliable short-term prediction of wind power is crucial for the power grid dispatching department arranging reasonable day-ahead generation schedules. Targeting the problem of low model prediction accuracy caused by the strong intermittency and large volatility of wind power, this paper develops a novel ensemble model for short-term wind power prediction which integrates the ensemble empirical mode decomposition (EEMD) algorithm, the gated recurrent unit (GRU) model and the Markov chain (MC) technique. Firstly, the EEMD algorithm is used to decompose the historical wind power sequence into a group of relatively stationary subsequences to reduce the influence of random fluctuation components and noise. Then, the GRU model is employed to predict each subsequence, and the predicted values of each subsequence are aggregated to get the preliminary prediction results. Finally, to further enhance the prediction accuracy, the MC is used to modified the prediction results. A large number of numerical examples indicates that the proposed EEMD-GRU-MC model outperforms the six benchmark models (i.e., LSTM, GRU, EMD-LSTM, EMD-GRU, EEMD-LSTM and EEMD-GRU) in terms of multiple evaluation indicators. Taking the spring dataset of the ZMS wind farm, for example, the MAE, RMSE and MAPE of the EEMD-GRU-MC model is 1.37 MW, 1.97 MW, and from 1.76%, respectively. Moreover, the mean prediction error of the developed model in all scenarios is less than or close to 2%. After 30 iterations, the proposed model uses an average of about 35 min to accurately predict the wind power of the next day, proving its high computation efficiency. It can be concluded that the ensemble model based on EEMD-GRU-MC is a promising prospect for short-term wind power prediction.

An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a balancing composite motion optimization (BCMO) approach

The efficient management and optimal scheduling of generation resources in multi-supply microgrid (MG) are essential for ensuring cost-effective and reliable power supply. Renewable energy resources such as wind turbines (WTs), photovoltaic (PV) systems, micro turbine (MT) batteries, and fuel cells (FCs) are utilized. This paper presents Balancing Composite Motion Optimization (BCMO) for day-ahead cost-based generation scheduling in multi-supply MG. The BCMO approach combines the advantages of composite motion optimization techniques to minimize the overall generation cost. The proposed method considers demand forecasts, renewable energy availability, and real-time pricing to optimize generation schedule. The microgrid is modeled as mathematical optimization problem, considering multiple generation sources, including renewable energy, conventional generators, and energy storage systems, that are used to optimize the above factors. The objective function is formulated to minimize the total generation cost while satisfying demand requirements and operational constraints. By considering the cost of generation from different sources and availability of renewable energy, the proposed approach enables the microgrid to operate economically efficiently. The objective function is formulated to minimize the total generation cost while satisfying the demand requirements and operational constraints. By considering the cost of generation from different sources and the availability of renewable energy, the proposed approach enables the microgrid to operate in an economically efficient manner. The performance of the proposed system is executed on the MATLAB working platform and compared with various approaches. The best BCMO solution in cold sunny, warm sunny, cold cloudy days and warm cloudy are 290.8821, 358.7908, 261.324, and 2265.055., respectively.

Short-term prediction of the power generation of wind turbines.

Since the erection of the first wind turbine in Hungary in 2001, the proportion of power generated from wind has increased considerably, furthermore an even faster growth is expected in the next years. However, the more widespread use of wind energy plays a role in the fact that deviations from the international exchange schedule are becoming more frequent. In this paper we summarize the present situation concerning the usage of wind power in Hungary, detail the results and the limitations of our investigations carried out on the public data of the meteorological forecasts that are the primary sources of power generation schedules of wind power plants and suggest possible improvements on the reliability of the schedules based on the forecasts.

Hourly load prediction based feature selection scheme and hybrid CNN‐LSTM method for building's smart solar microgrid

AbstractThe short‐term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the rooftop with the battery management system (BMS) to enhance energy efficiency management. However, the traditional methodologies only processed the weather parameters and power demand information for short‐term load prediction, ignoring the collected data from SSM and BMS by the advanced metering infrastructures (AMI), which probably improved prediction accuracy. In this research, many accumulated data of building and SSM are collected before methodology implementation. Considering the diversities of accumulated parameters from SSM and BMS, an adaptive convolution neural network long short‐term memory (CNN‐LSTM) is proposed for hourly electrical load prediction. The CNN could extract the critical large‐scale input feature, while the LSTM could achieve better accurate forecasts. The Pearson correlation matrix is calculated for the feature selection scheme from different data units. The hyperparameter tuning is utilized for obtaining the optimized hybrid CNN‐LSTM algorithm. The K‐fold cross‐validation is employed for deep learning algorithm verification, which includes LSTM, GRU, CNN, and Bi‐LSTM methodologies. The results prove that the hybrid CNN‐LSTM achieved outperformed improvements, which are 20.57%, 29.63%, 19.06% in MSE, MAE, MAPE, and 21.24%, 22.02%, 3.82% in validating MSE, MAE, MAPE, respectively. The hybrid CNN and LSTM combined with the feature selection scheme achieve superior predicting accuracies, proving the adaptability ability for integrating into the energy management system (EMS) of the building's SSM.

Optimal Energy Management in Autonomous Power Systems With Probabilistic Security Constraints and Adaptive Frequency Control

The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly varying operating conditions and uncertainty of renewable sources, the conflicting objectives across different timescales, the limited amount of reliable power sources and energy storage. The state of charge management when energy storage is used for multiple services, such as optimal scheduling and frequency support, is one of the most notorious problems in this context. To address this issue, an optimal energy management system is proposed in this paper. It co-optimizes the primary frequency control layer and the dispatch schedule of conventional generators and energy storage by taking advantage of an algorithm that provides adaptive active power demand uncertainty quantification, theoretical guarantees for frequency stability, and bounds for the reserves for frequency support assigned to the energy storage system. A pattern-based reformulation of the frequency stability constraints is derived enabling the efficient solution of the involved optimization problem, being a test case of an isolated offshore oil and gas platform presented for validation.

A swarm intelligence and deep learning strategy for wind power and energy storage scheduling in smart grid

In today's world, rising energy demands are a significant challenge, and the smart grid emerges as a solution for sustainable energy management. An essential view of advancing the Smart Grid (SG) capabilities is the collaborative scheduling of Wind Power Generation (WPG) and energy storage. It plays a significant role in elevating SG efficiency, reliability, and environmental sustainability. This kind of strategic planning is essential to increase coordination between WPG and flexible deployment of Energy Storage Systems (ESS). Efficient SG functions will be maintained, and energy sources can be regulated with demand variations. Putting an emphasis on assumptions and empirical data is vital in conventional techniques. When it comes to the continuously shifting environment of SG and RE resources, traditional approaches aren't highly reliable or adaptable. The present article uses a hybrid model that integrates Deep Reinforcement Learning (DRL) and Particle Swarm Optimization (PSO) to address those drawbacks. The primary purpose of it is to help with the joint scheduling of WP and ESS. This technique is what permits DRL to reach selections rapidly in convoluted, ever-changing environments. The proposed approach, when combined with PSO's effectiveness for variable optimization, will result in improved scheduling findings. The framework additionally exploits the finest use of ESS, but it also effectively addresses the challenging task of integrating dynamic WP with the SG. Reliable and cost-effective supply is ensured by the system's design. The accuracy, stability, and versatility of the suggested approach to the dynamic features of Wind Energy (WE) and storage management are incomparable to traditional approaches. The findings indicate the method's actual validity and its significance for improving SG functions. Applying state-of-the-art statistical techniques for holistic optimization of RE resources and storage systems is emphasized by the framework. Owing to minimizing Energy Consumption (EC) and lowering greenhouse gas emissions, this study provides a significant step towards achieving the goal of effective and eco-friendly SG functions.

Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems

Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.

Hybrid model for microgrid short term load forecasting based on machine learning

Short-term load forecasting (STLF) is crucial for microgrid (MG) operators to optimize energy generation and storage schedules based on anticipated load variations. Accurately predicting peak demand periods enables operators to ensure sufficient power supply while minimizing reliance on expensive backup sources, resulting in cost savings, and improved overall system efficiency. Residential MG power demand is highly dynamic due to external factors like residents’ lifestyles, behaviors, and weather responses, leading to significant irregularity and management challenges. To deal with these challenges, we propose a hybrid STLF model that combines Artificial Neural Networks (ANN) and Fuzzy Logic (FL), referred to as the Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was trained and tested using real power consumption data. We evaluated the performance of the ANFIS model by comparing it with another ANN model using three evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The trained ANFIS model achieved an MAPE of 8.7528% and an RMSE of 0.4752kw, while the ANN model achieved an MAPE of 8.8123% and an RMSE of 0.4816kw. These results confirm the accuracy of the hybrid ANFIS model compared to ANN. The ANFIS model demonstrated its ability to capture complex and nonlinear relationships between various factors affecting load demand, making it suitable for handling the dynamic nature of MG load.

Joint Optimization of Power Generation and Voyage Scheduling in Ship Power System Based on Operating Scene Clustering and Multi-Task Deep Reinforcement Learning

Joint Optimization of Power Generation and Voyage Scheduling in Ship Power System Based on Operating Scene Clustering and Multi-Task Deep Reinforcement Learning

Joint Generation and Voyage Scheduling of All-Electric Ships Considering Power Supply Capacity Constraints

Joint Generation and Voyage Scheduling of All-Electric Ships Considering Power Supply Capacity Constraints

Hybrid Technologies and Genetic Algorithms Applied to School Lecture Timetable Generation

Hybrid Technologies and Genetic Algorithms Applied to School Lecture Timetable Generation

Renewable Energy Sources constrained Generation Scheduling of Thermal Units in a Regulated Power Industry

Renewable energy production technologies use resources directly from the environment to generate electric power. These energy sources such as sunshine, wind, tides and biomass are some of the more popular gifts from the nature. The renewable energy resources emit nothing into atmosphere or never produce green house gas or pollute the environment at all. Even if the renewable energy sources are safe to the atmosphere, these resources meet out smaller percent of power requirement internationally This paper a population based optimization algorithm of Corona Virus Herd Immunity Optimizer (CVHIO) is proposed to solve the renewable energy constrained generation scheduling problem. The objective function of the problem is to minimize the total operating cost of hybrid system with satisfying standard operating constrains of thermal, wind and solar power plants. Initially the variables of the problem are effectively optimized using CVHIO algorithm. . Numerical example with IEE 39 bus test system (ten thermal twenty four hour) with an equivalent Solar and Wind generators are considered to prove the performance of CVHIO approach.

Mitigating the Impact of Photovoltaic Power Ramps on Intraday Economic Dispatch Using Reinforcement Forecasting

The rapid growth of large-scale photovoltaic (PV) power generation has received considerable attention, where a major challenge is that PV power ramp events can adversely affect the operations of integrated power systems. Therefore, effective forecast and dispatch methodologies are critical. However, it is relatively difficult to predict an accurate PV power ramp rate. Another issue is that, since forecast and dispatch are typically considered in research as two separate problems, forecast modeling does not involve the knowledge of dispatch. In this case, unbiased PV forecast is unpractical for PV ramp events, increasing their negative effects. Besides, such a unified optimization with both forecast and dispatch cannot be solved easily since the problem is strongly nonlinear. Motivated by these challenges, a reinforcement forecasting framework is proposed for intraday economic dispatch in this study. First, the PV power ramp rate forecasting is categorized into two tasks of multi-classification and class expectation prediction, where the class continuity is considered in order to improve the forecast distribution. Next, a reinforcement learning agent is established for connecting PV forecast and system dispatch, which adjusts PV power generation schedules based on real-time system feedbacks. Comparative studies are conducted on an intraday system dispatch environment with energy storage. The results of the studies indicate that the proposed framework can provide higher economic benefits than those under benchmark schemes.

The impact of project network topology and resource restrictions on the performance of schedule generation schemes: a comparative study

The impact of project network topology and resource restrictions on the performance of schedule generation schemes: a comparative study