Wind Uncertainty Research Articles

Distributionally Robust Joint Chance-Constrained Dispatch for Electricity–Gas–Heat Integrated Energy System Considering Wind Uncertainty

With the increasing penetration of wind power, the uncertainty associated with it brings more challenges to the operation of the integrated energy system (IES), especially the power subsystem. However, the typical strategies to deal with wind power uncertainty have poor performance in balancing economy and robustness. Therefore, this paper proposes a distributionally robust joint chance-constrained dispatch (DR-JCCD) model to coordinate the economy and robustness of the IES with uncertain wind power. The optimization dispatch model is formulated as a two-stage problem to minimize both the day-ahead and the real-time operation costs. Moreover, the ambiguity set is generated using Wasserstein distance, and the joint chance constraints are used to ensure that the safety constraints (e.g., ramping limit and transmission limit) can be satisfied jointly under the worst-case probability distribution of wind power. The model is remodeled as a mixed-integer tractable programming issue, which can be solved efficiently by ready-made solvers using linear decision rules and linearization methods. Case studies on an electricity–gas–heat regional integrated system, which includes a modified IEEE 24-bus system, 20 natural gas-nodes, and 6 heat-node system, are investigated for verification. Numerical simulation results demonstrate that the proposed DR-JCCD approach effectively coordinates the economy and robustness of IES and can offer operators a reasonable energy management scheme with an acceptable risk level.

Carbon emission flow oriented multitasking multi‐objective optimization of electricity‐hydrogen integrated energy system

A grid-connected electricity-hydrogen integrated energy system (EH-IES) is proposed here for the cooperative analysis of electricity, heat, cooling and hydrogen energy flows, where carbon emission flow (CEF) is introduced to allocate the carbon emission. Simultaneously, an improvement multitasking multi-objective optimization (MT-MOO) algorithm is applied to optimize the operation of this EH-IES. The proposed EH-IES optimizes the operational cost, carbon dioxide emission and energy loss while considering the uncertainties of future energy demand, wind and photovoltaic (PV) power outputs. The CEF model is introduced here to allocate the carbon emission among the EH-IES, which can calculate the carbon emission in the energy flow. In order to solve the MOO model, a MT-MOO algorithm is proposed to utilize the implicit information of different optimization tasks. However, there are harmful interactions between the different optimization tasks when the relativity between different optimization tasks is low. Therefore, the paper introduces an online learning random mating probability method which calculates the transformation extent of implicit information online between different optimization tasks. Simulation results show that the proposed EH-IES has good feasibility and efficiency, and the proposed algorithm has better convergence performance than comparing intelligence algorithms.

A zonal optimization solution to reliability security constraint unit commitment with wind uncertainty

In restructured power systems, Independent System Operator manages and handles the Reliability-Security Constraint Unit Commitment problems including reliability and security issues. In this paper, a novel two-level optimization approach is proposed to achieve reliable, secure, and optimal generation units’ schedule while wind generation units are presents, in which the power system is divided to a number of zones. Uncertainties and time-dependent sources such as wind generation units are the most significant reasons for reliability challenges. In the proposed approach, load forecast and wind generation units’ uncertainty have been considered. An intelligent search technique is implemented for reliability study. The proposed method is evaluated by simulation of the 6-bus, 118-bus, and 4672-bus test systems. The two-level optimization is compared to robust optimization and centralized methods. The results show the effectiveness and significance of this method.

Enhanced block-sparse adaptive Bayesian algorithm based control strategy of superconducting magnetic energy storage units for wind farms power ripple minimization

This article presents a novel enhanced block-sparse adaptive Bayesian algorithm (EBSABA) to fully control proportional-integral (PI) controllers of superconducting magnetic energy storage (SMES) units. The main goal is to smooth the real power output from grid-tied wind farms (WFs) and enhance its power quality, which represents a significant concern in the industry. In this regard, two WFs are tied to the network and each one is equipped with a SMES unit. The proposed algorithm takes into consideration the effect of actuating error signal and its magnitude to online update the PI controller gains. The proposed control strategy is applied to all power electronic circuit converters. To obtain a realistic work, practical measured data of wind speed that recorded from Hokkaido Island are incorporated into the analyses. Moreover, a three-drive train model represents a turbine model. A practical 10 MW SMES unit is connected at the WFs terminals. The effectiveness of proposed SMES units is verified by comparing its results with those obtained by using the least mean square-PI SMES units and optimal PI SMES units by genetic algorithm under wind speed variability and uncertainty. The real power can be smoothened by more than 10% using the proposed system at some intervals. The validity of the study is tested by the simulation results that are carried out by PSCAD environment. The controlled SMES units can further improve the power quality of WFs.

Effective Budget of Uncertainty for Classes of Robust Optimization

Robust optimization (RO) tackles data uncertainty by optimizing for the worst-case scenario of an uncertain parameter and, in its basic form, is sometimes criticized for producing overly conservative solutions. To reduce the level of conservatism in RO, one can use the well-known budget-of-uncertainty approach, which limits the amount of uncertainty to be considered in the model. In this paper, we study a class of problems with resource uncertainty and propose a robust optimization methodology that produces solutions that are even less conservative than the conventional budget-of-uncertainty approach. We propose a new tractable two-stage robust optimization approach that identifies the “ineffective” parts of the uncertainty set and optimizes for the “effective” worst-case scenario only. In the first stage, we identify the effective range of the uncertain parameter, and in the second stage, we provide a formulation that eliminates the unnecessary protection for the ineffective parts and, hence, produces less conservative solutions and provides intuitive insights on the trade-off between robustness and solution conservatism. We demonstrate the applicability of the proposed approach using a power dispatch optimization problem with wind uncertainty. We also provide examples of other application areas that would benefit from the proposed approach.

A Grid-Connected Microgrid Model and Optimal Scheduling Strategy Based on Hybrid Energy Storage System and Demand-Side Response

The power gap between supply and demand in the microgrid caused by the uncertainty of wind and solar output and users’ electricity consumption needs to be absorbed by the hybrid energy storage devices and the demand-side electricity price response. To maximize the service life of the lithium battery pack, this paper optimizes a reasonable ratio of the supercapacitor pack’s daily charge and discharge times to the daily cycle times of the lithium battery pack. The model construction includes two parts: power prediction and multi-objective optimization modeling. In the case study, a microgrid district under the Guizhou Power Grid is analyzed and discussed. Based on the predicted wind output, solar output, and load demand on a certain day, the optimal scheduling results have been obtained. On the one hand, a reasonable ratio regarding the daily charge and discharge times of hybrid energy storage devices has been obtained under the optimized parameter k in the model. Correspondingly, the daily operation and maintenance of the lithium battery pack is minimum. On the other hand, when the hybrid energy storage devices and demand-side electricity price response are included and not, the changes on the supply and demand sides (a) and of three evaluation indicators (b) are compared, respectively. Thus, the effectiveness of the model in this paper is verified.

Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning

Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting models. This paper introduces a new approach using variational auto-encoding and hybrid transfer learning to forecast wind power for large-scale regional windfarms. Transfer learning is applied to windfarm data collections to boost model training. However, multiregional windfarms consist of different wind and weather conditions, which makes it difficult to apply transfer learning. Therefore, we propose a hybrid transfer learning method consisting of two feature spaces; the first was obtained from an already trained model, while the second, small feature set was obtained from a current windfarm for retraining. Finally, the hybrid transferred neural networks were fine-tuned for different windfarms to achieve precise power forecasting. A comparison with other state-of-the-art approaches revealed that the proposed method outperforms previous techniques, achieving a lower mean absolute error (MAE), i.e., between 0.010 to 0.044, and a lowest root mean square error (RMSE), i.e., between 0.085 to 0.159. The normalized MAE and RMSE was 0.020, and the accuracy losses were less than 5%. The overall performance showed that the proposed hybrid model offers maximum wind power forecasting accuracy with minimal error.

A data-enhanced distributionally robust optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty

With growing penetrations of wind power in electricity systems, the coordinated dispatch of integrated electricity and natural gas systems is becoming a popular research topic. Distributionally robust optimization can cope with the wind uncertainty of integrated electricity and natural gas systems by providing optimal solutions for the worst-case probability distribution. However, limited historical wind data hinder the estimation of worst-case probability distribution. As a breakthrough in artificial intelligence, generative adversarial networks can be established to approximate a complex uncertain probability distribution from raw data and generate realistic data subject to the identical distribution. This paper proposes a data-driven optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty, whose probability distribution is free. Based on limited historical data, the data-driven generative adversarial network generates artificial wind power data, which helps to improve the estimation of worst-case probability distribution in distributionally robust optimization. Moreover, the robustness of optimization solutions can be adjusted cost-effectively by controlling the auxiliary data number. In a case study, optimization solutions of the proposed method are shown to achieve a lower probability of chance constraint violation at a nearly negligible cost increase compared with those from four typical optimization methods.

Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery

Optimizing day-ahead generation schedules of hydro–wind–photovoltaic (PV) complementary systems (HWPCSs) can help to promote the accommodation of wind and solar energies. However, it is challenging to formulate appropriate generation schedules for the large HWPCS that contains cascade hydropower plants, in particular, a steady requirement of power delivery is considered in the optimization model. To further improve complementary performance of the large HWPCS, we propose a risk-averse day-ahead generation scheduling approach that considers the steady requirement of power delivery. First, a representative scenario set is used to characterize forecast uncertainties of the wind and PV power. Then, a multi-objective optimal generation scheduling model with consideration of the operational risks of electricity curtailment and power shortage is proposed. Finally, a two-layer nested optimization framework is designed to derive the system’s generation schedule. The clean energy base in the upper Yellow River basin, China was selected as a case study. The results show that: (1) forecast uncertainties of wind and PV power are more likely to induce power shortage risk in summer and autumn, but to induce electricity curtailment risk in spring and winter; (2) without using extra constraint handling strategies, the proposed approach could directly yield a stair-shaped power delivery curve, which is good for long-distance power transmission applications; and (3) compared with a traditional method without considering the operational risks, the proposed generation scheduling approach could significantly reduce the comprehensive risk rate by 65% on average, while the cascade hydropower production and peak shaving performance are satisfactory. Therefore, the proposed approach is effective in guiding the day-ahead generation scheduling of the HWPCSs that contain cascade hydropower plants.

Optimal FOPID Controllers for LFC Including Renewables by Bald Eagle Optimizer

In this study, a bald eagle optimizer (BEO) is used to get optimal parameters of the fractional-order proportional–integral–derivative (FOPID) controller for load frequency control (LFC). Since BEO takes only a very short time in finding the optimal solution, it is selected for designing the FOPID controller that improves the system stability and maintains the frequency within a satisfactory range at different loads. Simulations and demonstrations are carried out using MATLAB-R2020b. The performance of the BEO-FOPID controller is evaluated using a two-zone interlinked power system at different loads and under uncertainty of wind and solar energies. The robustness of the BEO-FOPID controller is examined by testing its performance under varying system time constants. The results obtained by the BEO-FOPID controller are compared with those obtained by BEO-PID and PID controllers based on recent metaheuristics optimization algorithms, namely the sine–cosine approach, Jaya approach, grey wolf optimizer, genetic algorithm, bacteria foraging optimizer, and equilibrium optimization algorithm. The results confirm that the BEO-FOPID controller obtains the finest result, with the lowest frequency deviation. The results also confirm that the BEO-FOPID controller is stable and robust at different loads, under varying system time constants, and under uncertainty of wind and solar energies.

Integrated Electricity and Natural Gas Demand Response in Flexibility-Based Generation Maintenance Scheduling

Increasing emission concerns about greenhouse gases have led to an increasing tendency to use renewable energy sources (RERs) in the power system. Nevertheless, the probabilistic nature of RERs has led to an enhanced require to flexibility provision. Hence, it is necessary to implement a flexibility-based generation maintenance scheduling. For this purpose, it has used the flexibility index of the system in order to evaluate the flexibility of the power system. In flexibility studies, modeling and predicting the variability of renewable resources is important. In this paper, the uncertainties of wind are considered through forecasting by deep learning method in Python. Gas-fired power plants are one of the most important suppliers of flexibility in the supply-side. Therefore, the reliable operation of power system depends on the of natural gas availability. Furthermore, gas demand is subject to various uncertainties, especially in cold seasons, which will have significant effects on power system. in this paper, power-to-gas (P2G) technology as energy storages is modeled to mitigate the impact of wind output and gas demand uncertainty. Meanwhile, integrated natural gas and electricity demand response such as event-based and time-based model has been applied as a flexibility provision from demand-side point of view. In this paper, the objectives of reducing emission and costs, leveling the reserve margin and increasing flexibility are considered as the objectives of optimizing generation maintenance scheduling. In order to solve the multi-objective problem, the augmented Epsilon constraint method has been used. The proposed model has been implemented on a modified IEEE RTS 24 bus.

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.

Stochastic AC Transmission Expansion Planning: A Chance Constrained Distributed Slack Bus Approach With Wind Uncertainty

Integrating more renewable energy sources into the grid leads to a more vulnerable power system and presents challenges for power system planners. This paper proposes a probabilistic overload constraint based AC transmission expansion planning model. In terms of economic dispatch, generation adequacy and network constraints are assessed using a probabilistic participation factor power flow. Generation participation factors, which are handled in the AC power flow equations, are used to manage mismatch power that comes from uncertainties as well as transmission loss of the system in a distributed slack bus concept. Combinations of load, wind and N-1 contingency uncertainties are evaluated with Monte Carlo Simulation and the loading limits for the existing and candidate lines and violations on bus voltages are enforced as probabilistic constraints using chance constraint programming. The investment decisions taken under various operating conditions are re-evaluated under a new set of wind power uncertainty that differs from those utilized in the optimization process, and the resulting TEP decisions are analyzed from a risk perspective. The proposed method allows for the uncertain operating conditions to be easily and accurately adapted to the ACTEP optimization. The optimization results show that the participation factor power flow is a promising tool for evaluating probabilistic operating conditions, which can be used to explore the possibility of violations under different uncertainty conditions.

Decentralized Trajectory Optimization for Multiple UASs under Uncertainty of Intent and Wind

To guarantee the operational safety of unmanned aircraft systems (UASs) in short time scales, it is important to adopt the systems for planning conflict-free trajectories which are robust against the uncertainty of the tactical flight intent of neighboring UASs and the wind conditions. Thus, the present paper proposes a new decentralized trajectory optimization method that enables to track the strategic four-dimensional trajectories as much as possible while maintaining the necessary separation from the neighboring UASs under the above-stated uncertainty. The proposed method adopts a fully decentralized formulation for separation constraints which is enabled by the worst-case estimate of the uncertainty of the neighboring UAS's decision and wind. The effectiveness of the method is demonstrated through numerical simulations.

Analysis of transmission line tower subjected to wind loading

Heavy electrical transmission lines are carried by transmission line towers at a suitable and safe height above the ground. These towers must bear its own weight and distress created by transmission line wires. Despite that it should also survive against all-natural factors such as fierce winds, earthquakes, and snow loads. For a safe and cost-effective design, transmission line towers must fulfil both all structural and electrical standards. This manuscript discusses the numerous types of transmission towers and their configurations as per the Indian Standard IS 802:2015. Further this paper contains the details of the design and simulation of 500 kV double circuit conductors, which is one of the commonly adopted transmission towers. For the said purpose STAAD Pro. V8i Select Series, computation tool has been adopted which is based on the finite element computations. During computational analysis wind load, structural dead load, and seismic load according to IS:1893:2002 (part 1) has been considered. The clearance from the ground (h1), the lowermost conductor wire's maximum sag (h2), conductor wires' vertical spacing (h3), and the distance between the earth wire and the highest conductor wire in terms of vertical distance (h3), all are added up to get the final height of transmission tower which is 43.5 m. The earth wire is also known as the ground wire, is always sited at the very topmost of the steel tower. It features an 8.32 m square base with a width of 8.32 m. The three major phases of the fragility analysis procedure for transmission towers under wind loading are as follows: Nonlinear dynamic analysis, regression analysis, and creation of Wind loadings and uncertainty models. After that, the random variables and probability distributions corresponding to them are examined. Finally, based on the commonly adopted transmission towers a hypothetical case study has been conducted for a real-world functioning tower which is subjected to wind loading along with other critical loadings. The obtained result shows that if we are considering a larger number of uncertain characteristics, then it causes the fragility curve to become more disperse, and further it is observed that the angle of attack for the wind load has a substantial effect on the fragility curve.

Stochastic Planning of Integrated Energy System via Frank-Copula Function and Scenario Reduction

Uncertainty introduces both significant complexity and the high risk of suboptimal investment decisions into power system planning. Considering the multiple uncertainties of wind and solar power output, load demands, and energy prices as well as pollutant emission factors during the planning period, a multi-scenario stochastic programming model of an integrated energy system (IES) is constructed in this paper. Scenarios of wind and solar power output are generated based on non-parametric kernel density estimation and the Frank-Copula function; scenarios of load demands are generated through DeST software, and energy prices and pollutant emission factors are generated corresponding to a uniform distribution. Then the generated scenario results of wind and solar power output and load demands are reduced by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -means clustering; the generated scenarios of energy prices and pollutant emission factors are reduced by discrete approximation of continuous distribution based on Gaussian quadrature. An illustrative example with 8 cases is performed to analyze the influences of each uncertain parameter on the optimal configuration and economy of the IES.

Optimal Dispatch of WT/PV/ES Combined Generation System Based on Cyber-Physical-Social Integration

With the energy transition, the cyber-physical system (CPS) is expanded to the cyber-physical-social system (CPSS), and connected by the communication network, which is introduced in the form of a highly reliable cellular star network in this paper. Due to the uncertainties of wind turbine/photovoltaic power outputs, an improved rolling ultra-short-term forecast method with high reliability is proposed, which is based on long short-term memory networks, and the superiority is proved by comparing with multi-layer perception neural networks, linear regression, support vector regression and random forest. Then, two stochastic optimization models are constructed, one is based on CPS, the other is based on CPSS. As for social factors, electric vehicle aggregator is stimulated by cost to actively participate in the dispatch operation. Moreover, in order to ensure the stable operation of the smart energy system, the frequency deviation of automatic generation control system is studied as one security constraint. The optimal results display that social factors can not only make the system more economic and flexible, but also further increase the consumption of RE. Moreover, accurate forecast results can improve the performance of optimization dispatch operations.

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Using wind-availability forecasts in day-ahead unit commitment can require expensive real-time operational adjustments. We examine the benefit of conducting interim recommitment between day-ahead unit commitment and real-time dispatch. Using a simple stylized example and a case study that is based on ISO New England, we compare system-operation costs with and without interim recommitment. We find an important tradeoff-later recommitment provides better wind-availability forecasts, but the system has less flexibility due to operating constraints. Of the time windows that we examine., hour-20 recommitment provides the greatest operational-cost reduction.

Incorporating Spatial and Temporal Correlations to Improve Aggregation of Decentralized Day-Ahead Wind Power Forecasts

In some electricity markets, individual wind farms are obliged to provide point forecasts to the power purchaser or system operator. These decentralized forecasts are usually based on on-site meteorological forecasts and measurements, and thus optimized for local conditions. Simply adding decentralized forecasts may not capture some of the spatial and temporal correlations of wind power, thereby lowering the potential accuracy of the aggregated forecast. This paper proposes the explanatory variables that are used to train the kernel density estimator and conditional kernel density estimator models to derive day-ahead aggregated point and probabilistic wind power forecasts from decentralized point forecasts of geographically distributed wind farms. The proposed explanatory variables include (a) decentralized point forecasts clustered using the clustering large applications algorithm to reduce the high-dimensional matrices, (b) hour of day and month of year to account for diurnal and seasonal cycles, respectively, and (c) atmospheric states derived from self-organizing maps to represent large-scale synoptic circulation climatology for a study area. The proposed methodology is tested using the day-ahead point forecast data obtained from 29 wind farms in South Africa. The results from the proposed methodology show a significant improvement as compared to simply adding the decentralized point forecasts. The derived predictive densities are shown to be non-Gaussian and time-varying, as expected given the time-varying nature of wind uncertainty. The proposed methodology provides system operators with a method of not only producing more accurate aggregated forecasts from decentralized forecasts, but also improving operational decisions such as dynamic operating reserve allocation and stochastic unit commitment.

Stochastic short-term scheduling of a wind-solar-hydro complementary system considering both the day-ahead market bidding and bilateral contracts decomposition

Complementary operation of hydropower, wind and photovoltaic (PV) power is one of the most cost-efficient solutions for integration of intermittent wind and PV power. In a deregulated market, synergetic optimization of day-ahead (DA) market bids and the decomposition of daily physical bilateral contracts (BCs) can increase profits for a wind-solar-hydro complementary system (WSHCS) by coordinating the spot market and the forward market. However, this short-term scheduling problem is challenging because of complex market mechanisms and uncertainties of multiple energy resources and market prices. A stochastic scheduling model is established to address this issue. First, uncertainties of wind, PV power and DA market prices are characterized by stochastic scenarios simulation with the adoption of the simultaneous backward reduction algorithm. Then, a two-layer nested optimization framework is conducted to optimize both the DA market bidding strategy and the decomposition of daily BCs. A WSHCS in China’s Yalong River Basin is selected as a case study. Results show that: the proposed model can decompose the total energy of the daily BC reasonably by leaving flexible bidding spaces in the DA market according to price variation, thus larger volume of bidding energy can be sold when DA market prices are higher than the contract price. Besides, the optimal decomposition outperforms the average decomposition of daily BCs with larger benefits, less power curtailment, and higher water resources utilization efficiency. These findings verify the effectiveness and applicability of the proposed model for synergetic optimization of both the decomposition of daily BCs and DA market bids for a WSHCS.