Load Forecast Errors Research Articles

Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming

Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean energy source with great potential. However, the difference in the uncertainty characteristics of RES and electric–thermal loads poses a significant challenge to the optimal schedule of an integrated energy system (IES). Therefore, for the different characteristics of the multiple uncertainties of IES, this paper proposes a type-II fuzzy interval chance-constrained programming (T2FICCP)-based optimization model to solve the above problem. In this model, type-II fuzzy sets are used to describe the uncertainty of RES in an IES, and interval numbers are used to describe the load uncertainty, thus constructing a T2FICCP-based IES day-ahead economic scheduling model. The model was resolved with a hybrid algorithm based on interval linear programming and T2FICCP. The simulations are conducted for a total of 20 randomly selected days to obtain the advance operation plan of each unit and the operation cost of the system. The research results show that the T2FICCP optimization model has less dependence on RES output power and load forecasting error, so can effectively improve the economy of IES, while ensuring the safe and stable operation of the system.

Day-ahead and intraday multi-time scale microgrid scheduling based on light robustness and MPC

An optimized microgrid scheduling model is established considering demand responses, forecast errors, and the effects of uncertainties in different scheduling stages. A day-ahead, intraday, multi-time scale economic scheduling method based on light robust optimization and model predictive control (MPC) is also proposed. In the day-ahead, long-time-scale scheduling stage, light robustness optimization is used to cope with low-frequency components in prediction errors and uncertainties, and mitigates the deviations between the day-ahead scheduling plan and the actual economic scheduling outcomes under source and load forecast errors and uncertainties. At the intraday, short-time-scale stages, the MPC tracks the day-ahead light robustness economic scheduling plan, considering the high frequency components of prediction errors and uncertainties, so as to achieve robust open-loop control, better tracking results, and better economy. Analytical results demonstrate the effectiveness of the method.

Normal-transformation-based probabilistic load flow with correlated wind and load forecast errors

With aggressive government policies driving the electric power systems around the globe towards high renewable energy penetration, methodologies for considering forecast uncertainties in power system analysis and planning are gaining relevance. Probabilistic load flow (PLF) is a useful tool to carry out risk-based power system analysis and planning. This article discusses the need and philosophy of Normal-transformations, elaborating on Nataf transformation and polynomial normal transformation (PNT), to solve PLF and presents a unified approach for representation. Through detailed statistical modeling, the Gaussian mixture (GM) model is found to fit the historical wind and load power forecast error data better compared to widely used parametric models. Sampling of correlated marginal distributions, for scenario generation in PLF, requires the quantile function of the GM distribution, which is not available in a closed expression. A strategy, using quantile functions of Gaussian component sampling and interpolation, to approximate the GM’s quantile function is presented. The presented approximation enables the use of GM for solving PLF with correlated marginals, which is not addressed in the literature. This work also contributes to prevailing research questions by two new performance comparisons: between various higher-order PNT embedded to point estimate methods (PEMs) for PLF, and between PEM and Quasi-Monte Carlo (QMC) methods for PLF analysis. When PEMs are employed for PLF, there is no improvement observed in going for higher than third order PNT (3PNT). Results show that estimating the first four moments of the output variables through QMC-PLF is more accurate compared to PEM-PLF. Formulae useful for implementation of various higher-order PNT, for QMC and PEM, are derived and presented for direct use. Finally, the impact of correlated non-normal modeling of inputs on PLF output variables’ distribution is demonstrated.

Operation-adversarial scenario generation

This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e.g., seasons), and, at the same time, “stressful” to the system operations and dispatch decisions. The measure of stress used in this paper is based on the operating cost increases due to net load changes. The proposed operation-adversarial cGAN (OA-cGAN) internalizes a DC optimal power flow model and seeks to maximize the operating cost and achieve a worst-case data generation. The training and testing stages employed in the proposed OA-cGAN use historical day-ahead net load forecast errors and has been implemented for the realistic NYISO 11-zone system. Our numerical experiments demonstrate that the generated operation-adversarial forecast errors lead to more cost-effective and reliable dispatch decisions.

A scenario-based two-stage stochastic optimization approach for multi-energy microgrids

With the increase in renewable energy penetration, the impact of uncertain factors on the efficient operation of multi-energy microgrids (MEMGs) is becoming more and more prominent. Considering the source-load uncertainties of MEMGs, a two-stage stochastic optimization approach based on scenario analysis is proposed in this paper. First, mixed distribution and conditional distribution were used to fit the forecast errors of wind power and multiple loads respectively, so as to provide basic data for scenario generation. Then, an improved K-means clustering algorithm based on relative entropy was used for scenario reduction. This algorithm ensured the scenario reduction speed while maintaining the probability distribution characteristics of the generated scenarios. Taking the day-ahead forecast and scenario analysis results of the sources and loads as inputs, a two-stage stochastic optimization model of MEMG based on random fluctuation stabilization was constructed. In the first stage, equipment outputs are formulated by deterministic optimization based on day-ahead forecasts. In the second stage, the forecast errors are regarded as fluctuations and combined with scenarized source and load variables, energy storage equipment are given priority to stabilize scenario fluctuations. At the same time, based on the conditional value at risk (CVaR), the outputs of energy supply and storage equipment can be flexibly adjusted under different risk preferences to realize the efficient operation of MEMG. The example simulation showed that the proposed stochastic optimization approach make better use of energy storage equipment, make scheduling plans according to different risk preferences to deal with uncertainty flexibly.

A Reliable Short‐Term Power Load Forecasting Method Based on VMD‐IWOA‐LSTM Algorithm

To reduce the short‐term load forecasting (STLF) error of off‐line forecasting model, a VMD‐IWOA‐LSTM (VIL) method for STLF is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the historical power load signals. Then, the decomposed signals are reconstructed according to the similarity of Pearson correlation coefficient (PCC), and meteorological data are chosen for each reconstructed component based on the set PCC threshold. The long short‐term memory (LSTM) models are used to predict the corresponding components, and improved whale optimization algorithm (IWOA) is used to optimize the parameters in LSTM. Finally, the forecast results of each component are added together to get the final forecast result. The experimental results of power load data in a certain area show that the proposed method has the advantages of strong anti‐interference performance and high prediction accuracy compared with other methods, and has strong practicability. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Robust Scheduling of Networked Microgrids for Economics and Resilience Improvement

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate the advantages of networked microgrids over independent microgrids in terms of reducing total operating cost and improving the resilience of power supply.

Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

The operation of the integrated energy system will be affected by uncertainties, leading to sub-optimal design decisions. Accurate and effective modeling of these uncertainties is essential to ensure the optimal integration of renewable energy in the integrated energy system. This article first gives the basic optimal operation model of the integrated energy system, and analyzes various uncertain factors in the optimal operation of the integrated energy system. The source side mainly considers the intermittent and volatility of renewable energy output. The load side mainly considers the uncertainty of load forecast errors. The network side mainly considers the characteristics of equipment under variable conditions, equipment failures, and the uncertainty of the coupling of multi-energy flow systems. The energy storage side mainly considers the uncertainty of mobile energy storage grid connection and virtual energy storage participation in demand response. The multi-energy trading market mainly considers the uncertainty of market trading methods and market operation mechanisms. Then, various integrated energy system uncertainty modeling techniques and solving methods are summarized. Finally, it looks forward to the future research directions and challenges faced by the optimal operation of the integrated energy system under the influence of uncertainty.

Robust Microgrid Scheduling Considering Unintentional Islanding Conditions

This work presents a novel microgrid scheduling model considering the stochastic unintentional islanding conditions as well as forecast errors of both renewable generation and loads. By optimizing the dispatch of distributed energy resources (DERs), utility grid, and demand, the proposed model is targeted to minimize total operating cost of the microgrid, including start-up and shut-down cost of distributed generators (DGs), operation and maintenance (O&M) cost of DGs, cost of buying/selling power from/to utility grid, degradation cost of energy storage systems (ESSs) and cost associated with load shedding. To capture the stochastic unintentional islanding conditions and conventional forecast errors of renewable generation and loads, a two-stage adaptive robust optimization is proposed to optimize the objective function in the worst case scenario of the modeled uncertainties. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The result obtained ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. The proposed model is validated with results of case studies on a microgrid consisting of various DGs and ESSs.

Ship power load forecasting based on PSO-SVM.

Compared with the land power grid, power capacity of ship power system is small, its power load has randomness. Ship power load forecasting is of great significance for the stability and safety of ship power system. Support vector machine (SVM) load forecasting algorithm is a common method of ship power load forecasting. In this paper, water flow velocity, wind speed and ship speed are used as the features of SVM to train the load forecasting algorithm, which strengthens the correlation between features and predicted values. At the same time, regularization parameter C and standardization parameter σ of SVM has a great influence on the prediction accuracy. Therefore, the improved particle swarm optimization algorithm is used to optimize these two parameters in real time to form a new improved particle swarm optimization support vector machine algorithm (IPSO-SVM), which reduces the load forecasting error, improves the prediction accuracy of ship power load, and improves the performance of ship energy management system.

Optimization Strategy of Configuration and Scheduling for User-Side Energy Storage

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage.

Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting

Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that simulates sparrow foraging predatory behavior. It is used to optimize the parameters (such as weight, bias, etc.) of the ILSTM-NN. The results of the actual examples are used to prove the accuracy of load forecasting. It can improve (decrease) the MAPE by about 20% to 50% and RMSE by about 44.1% to 52.1%. Its ability to improve load forecasting error values is tremendous, so it is very suitable for promoting a domestic power system.

Accuracy of load and generation forecasts for the operational planning of power distribution systems

Operational planning in future power distribution systems will depend on load and generation forecasts as decisions and analyses will be performed in an increasingly coordinated manner at lead times. Forecasts on distribution level are significantly less accurate than on transmission level. The evaluation of forecast accuracy is therefore necessary to ensure system security. In this paper, forecast errors of wind power, photovoltaic power and load are evaluated for a medium-voltage grid region. Various influencing variables on forecast accuracy are examined. The evaluations show that forecasts in particular become worse for low aggregation levels and higher forecast horizons. In addition, the forecast error depends on characteristics such as the time of day or the magnitude of the forecast. For a secure system operation, forecast errors as well as their dependencies should be taken into account. Operational decisions should be made within the shortest possible lead time.

Active and reactive power coordinated optimal dispatch in active distribution network based on model predictive control

With the proliferation of the distributed energy resources (DERs), the scheduling and control of the distribution network have become more complicated. To cope with the uncertainty nature of distributed generation, a multi-timescale optimal dispatch method in active distribution network (ADN) based on the model predictive control (MPC) is proposed in this paper. First, based on MPC, a hierarchical scheduling framework for ADN is established, including long-timescale stage, and short-timescale stage. Then, via coordinated control of various resources in the ADN, i.e., distributed generators, energy storage, capacitor banks and OLTC transformer, the impact of intermittent renewable energy and load forecast errors can be reduced. Finally, considering the coupling characteristics of active and reactive power in the ADN, a joint active and reactive power optimization model is proposed to further reduce the network loss. Numerical simulation on a modified IEEE-33 distribution network system verifies the correctness and superiority of the proposed scheduling approach.

Medium-long term load forecasting method considering industry correlation for power management

Medium-long term load forecasting is recognized as the basis of economic dispatch in power systems, and high-precision load forecasting results can provide effective support for power management. Therefore, a medium-long term load forecasting method based on the deep learning algorithm is proposed with industry correlation considered. First, the missing monthly load data is supplemented based on the iterative interpolation method, and min–max normalization method is used for data preprocessing. On this basis, the Pearson correlation coefficient is adopted for quantifying the industry correlation and selecting the strongly related industries for load forecasting. Then, a machine learning model long-short term memory (LSTM) is utilized to forecast the medium-long term industry load with the historical data of related industries. Finally, the feasibility and accuracy of the proposed method are illustrated by the case studies on 2 major industries and 30 related industries of Jiangsu Province, China. The load forecasting error shown in the simulation results of the method proposed is kept within 5%, which is much better than other traditional methods.

Load forecasting of electric vehicle charging station based on grey theory and neural network

The rapid development of electric vehicles (EVs) makes the load of electric vehicle charging stations (EVCSs) affect the power grid. Aiming at the low accuracy of charging station load forecasting caused by the number of EVs, temperature and electricity price, and other factors, this paper proposes a load forecasting method of EVCSs based on a combination of multivariable residual correction grey model (EMGM) and long short-term memory (LSTM) network. Firstly, load influencing factors are analysed, and the grey theory is introduced into the load forecast of EVCSs. The role of EMGM in taking into account the effects of multiple factors and eliminating cumulative errors is analysed. Then, the EMGM and LSTM networks are combined to establish a mapping from the influencing factor data to the forecast, reducing the load forecast error of EVCs. Simulation and experimental results show that the accuracy of EVCSs’ load forecasting can be improved by this method.

A hybrid charging management strategy for solving the under-voltage problem caused by large-scale EV fast charging

A hybrid charging management system (HCMS) is proposed to solve the nodal under-voltage problem in medium-voltage radial distribution networks caused by the concentrated high-power charging of electric vehicles (EVs), which contains a pre-centralized charging management platform (PCMP) and local charging management controllers (LCMCs) located at fast charging stations (FCSs). The proposed PCMP can formulate half-hourly charging power threshold plans for LCMCs based on a pre-centralized voltage regulation algorithm (PVRA) to prevent concerned nodes from under-voltage. Two sub-strategies, i.e., charging management sub-strategy (CMS) and cooperative control sub-strategy (CCS), are integrated in the LCMS, which can aggregate EVs charging at different FCSs to provide the voltage support service for the concerned node, so that the under-voltage problem of these concerned nodes can be alleviated. The load forecasting error, load uncertainty, and the need of the plug-and-play are considered as well. Finally, an IEEE 69-bus test system is used for simulation verification. Simulation results verify that the HCMS can be used to solve the nodal under-voltage problem caused by centralized high-power charging of EVs.

Real-time coordination optimization method of active distribution network considering different stakeholders

In order to reduce the impact of renewable energy output and load forecasting error on the network operation optimization and realize the mutual benefit and win-win situation of multiple stakeholders, a real-time coordination optimization method of active distribution network considering different stakeholders is proposed in this paper. Firstly, the daily real-time coordination and optimization framework of active distribution network is proposed. Then, the intraday real-time rolling two-level optimal scheduling model was solved by nonlinear particle swarm optimization and primal dual interior point method. The results of the calculation examples show that the proposed scheduling model and method can maximize the common interests of different subjects of the distribution network and flexible resources, so that the network can guarantee lower operating costs on the basis of safe operation.

Investigating the Effect of Uncertainty Characteristics of Renewable Energy Resources on Power System Flexibility

This study investigates the effect of uncertainty characteristics of renewable energy resources on the flexibility of a power system. The more renewable energy resources introduced, the greater the imbalance between load and generation. Securing the flexibility of the system is becoming important to manage this situation. The degree of flexibility cannot be independent of the uncertainty of the power system. However, most existing studies on flexibility have not explicitly considered the effects of uncertainty characteristics. Therefore, this study proposes a method to quantitatively analyze the effect of uncertainty characteristics on power system flexibility. Here, the uncertainties of the power system indicate the net load forecast error, which can be represented as a probability distribution. Of the characteristics of the net load forecast error, skewness and kurtosis were considered. The net load forecast error was modeled with a Pearson distribution, which has been widely used to generate the probability density function with skewness and kurtosis. Scenarios for the forecast net load, skewness, and kurtosis were generated, and their effects on flexibility were evaluated. The simulation results for the scenarios based on a modified IEEE-RTS-96 revealed that skewness is more effective than kurtosis. The proposed method can help system operators to efficiently respond to changes in the uncertainty characteristics of renewable energy resources.

Multiple time‐scale economic dispatching strategy for commercial building with virtual energy storage under demand response mechanism

In recent years, commercial building technology integrating renewable energy power generation has developed rapidly. However, the stochastic, intermittent renewable energy power generation and the error of load forecasting of a commercial building (CB) are easy to cause frequent fluctuation of tie-line power, which brings challenges to the economic and safe operation of CB. In order to reduce the impact of renewable energy and load uncertainty on CB, and improve the power stabilization ability of CB, this paper proposes a multiple time-scale (MTS) economic dispatch strategy for CB with building virtual energy storage system (VESS) and vehicle to building (V2B) under demand response mechanism. Firstly, taking CB as terminal load carriers, a new heat balance model based on the R-C network is proposed, and the charging and discharging process of building VESS is analyzed. Secondly, by analyzing the travel trip law and charging and discharging behavior of EVs, an interactive control strategy between EVs and CB is proposed, that is, the control strategy of V2B. Then, Day-ahead optimal economic dispatch mode with 1 hour as the time interval is established with the objective of minimizing the daily operation cost and considering the user temperature comfort factor of CB. Based on the Day-ahead dispatching plan, the coordinated scheduling of VESS and V2B on the long time scale (15 minutes) and short time scale (1 minute) is used to establish a MTS rolling optimization model in Intra-hour adjustment stage. This model is used to modify the Day-ahead plan and stabilize the tie-line power fluctuation. Finally, an example shows that the proposed Day-ahead dispatching model can reduce the daily operating cost, and the Intra-hour adjustment model can stabilize the tie-line power fluctuation on MTS.