Wind Power Uncertainty Research Articles

Two-stage optimal dispatching of multi-energy virtual power plants based on chance constraints and data-driven distributionally robust optimization considering carbon trading.

Multi-energy virtual power plant (MEVPP) has attracted more and more attention due to its advantages in renewable energy consumption and carbon emission reduction. However, the characteristics of multi-energy coupling and the access of renewable energy may lead to some challenges in the operation of MEVPP. In this paper, a data-driven distributionally robust chance constraints optimization model (DD-DRCCO) is proposed for the dispatching of MEVPP. Firstly, the uncertainties of wind power and photovoltaic output forecasting errors are modeled as an ambiguity set based on the Wasserstein metric. Secondly, combined with the chance constraint, the expected probability of the inequality constraint with uncertain variables is limited to the lowest allowable confidence level to improve the reliability of the model. Thirdly, the forecast errors of wind power and photovoltaic are considered in the constraint conditions, so that the system can effectively resist the interference of uncertain output. Besides, based on the strong duality theory, the DD-DRCCO model is equivalent to a MILP problem which is easy to solve. Finally, simulations implemented on a typical MEVPP are delivered to show that our proposed model: 1) The model is data-driven, and the conservativeness is kept at a low level, and the solution time is about 7s~8s; 2) The MEVPP system can achieve a balance between economy and low-carbon, making the total operation cost reduced by 0.89% compared with no increase of electric boiler; 3) The CO2 emission during the operation of the MEVPP system was significantly reduced by about 87.33 kg.

An efficient analytical approach for operational reliability evaluation of integrated electricity‐heat energy systems with variable wind power

AbstractThe increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method.

A Novel Adaptive Manta-Ray Foraging Optimization for Stochastic ORPD Considering Uncertainties of Wind Power and Load Demand

The optimal control of reactive powers in electrical systems can improve a system’s performance and security; this can be provided by the optimal reactive power dispatch (ORPD). Under the high penetration of renewable energy resources (RERs) such as wind turbines (WTs), the ORPD problem solution has become a challenging and complex task due to the fluctuations and uncertainties of generated power from WTs. In this regard, this paper solved the conventional ORPD and the stochastic ORPD (SORPD) at uncertainties of the generated power from WTs and the load demand. An Adaptive Manta-Ray Foraging Optimization (AMRFO) was presented based on three modifications, including the fitness distance balance selection (FDB), Quasi Oppositional based learning (QOBL), and an adaptive Levy Flight (ALF). The ORPD and SORPD were solved to reduce the power loss (PLoss) and the total expected PLoss (TEPL), the voltage deviations (VD) and the total expected VD (TEVD). The normal and Weibull probability density functions (PDFs), along with the scenario reduction method and the Monte Carlo simulation (MCS), were utilized for uncertainty representations. The performance and validity of the suggested AMRFO were compared to other optimizers, including SCSO, WOA, DO, AHA, and the conventional MRFO on the IEEE 30-bus system and standard benchmark functions. These simulation results confirm the supremacy of the suggested AMRFO for the ORPD and SORPD solution compared to the other reported techniques.

A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power

In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.

Expected value model of microgrid economic dispatching considering wind power uncertainty

New energy microgrid is one of the development trends of the future power grid. As there are wind turbines and other new energy equipment in the microgrid, its output is easily affected by unstable weather factors. Moreover, the complexity of dispatching caused by the storage battery in the microgrid and the non-linear factors of the diesel generator have brought great challenges to the economic dispatching of the microgrid. To tackle these problems, this paper establishes the expected value model of microgrid economic dispatching considering the uncertainty of wind power generation and the factors such as battery and diesel generator and proves that this expected value model is convex. On this basis, the piecewise linearization method is proposed to find the global optimal solution of the economic dispatch. Finally, a representative microgrid is optimized to verify the effectiveness of expected value model.

Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model

Uncertainty in wind power is often unacceptably large and can easily affect the proper operation, quality of generation, and economics of the power system. In order to mitigate the potential negative impact of wind power uncertainty on the power system, accurate wind power forecasting is an essential technical tool of great value to ensure safe, stable, and efficient power generation. Therefore, in this paper, a hybrid intelligent model based on isolated forest, wavelet transform, categorical boosting, and quantile regression is proposed for deterministic and probabilistic wind power prediction. First, isolated forest is used to pre-process the original wind power data and detect anomalous data points in the power sequence. Then, the pre-processed original power sequence is decomposed into sub-frequency signals with better profiles by wavelet transform, and the nonlinear features of each sub-frequency are extracted by categorical boosting. Finally, a quantile-regression-based wind power probabilistic predictor is developed to evaluate uncertainty with different confidence levels. Moreover, the proposed hybrid intelligent model is extensively validated on real wind power data. Numerical results show that the proposed model achieves competitive performance compared to benchmark methods.

A chance-constrained programming approach to optimal planning of low-carbon transition of a regional energy system

Low-carbon transition of energy systems is an inevitable trend to address climate change challenges. For developing regions, proper planning is essential for reducing transition costs during low-carbon transition of their energy systems, featuring a higher proportion of intermittent renewable power connected to power grids. Impact of uncertainty must be considered for more feasible planning of peak-shaving and energy storage units. In this study, a chance-constrained programming approach to optimal planning of low-carbon transition of a regional energy system is presented. This approach considers uncertainties of wind power, photovolatic (PV) power and load to ensure power supply reliability. A developing region in central China is taken as a case study. Results show that considering uncertainty, an additional 4.79% of power generation capacity needs to be installed per year on average, with a 3.02% increase in the transition cost. Finally, sensitivity analysis results are provided, showing that a rapid increase in transition costs occurs when the confidence level exceeds 99%. The results in this study provide references for decision-makers to plan the low-carbon transition of energy systems as well as weighing transition costs against energy supply stability.

A multi-objective optimization model for the coordinated operation of hydropower and renewable energy

The rapid growth of wind and solar energy sources in recent years has brought challenges to power systems. One challenge is surging wind and solar electric generation, understanding how to consume such generation is important. Achieving the complementarity of hydropower and renewable energies such as wind and solar power by utilizing the flexible regulation performance of hydropower is helpful to provide firm power to help renewable energy consumption. However, the multi-energy complementary operation mode will change the traditional hydropower operation mode, causing challenges to the comprehensive utilization of hydropower. In this paper, a multi-objective optimal scheduling model is built by considering coordinated hydro-wind-solar system peak shaving and downstream navigation. First, the Gaussian mixture model is adopted to quantify the uncertainty of wind and solar power. Then, a hydro-wind-solar coordinated model was built to obtain the standard deviation of the residual load and the standard deviation of the downstream water level. Finally, the ε-constraint method is used to solve for the Pareto optimality. The results demonstrate the following: 1) The proposed model can effectively determine hydropower output schemes that can coordinate wind and solar power output to reconcile peak shaving and navigation; 2) The downstream hydropower stations’ reverse regulation of the upstream hydropower station is a positive factor in reconciling conflicts; and 3) Reasonable planning of wind power and solar power is helpful for hydro-wind solar power complement operation.

The coalitional aggregation of wind energy: A stochastic duality approach

The inherent uncertainty of wind power poses a major challenge to modern electricity grid. This paper mainly investigates a scenario where a crowd of wind power producers form a coalition by cooperatively offering their aggregated power output, aiming to mitigate these risks created by the inherent uncertainty of wind power. The main results are as follows. Firstly, a stochastic two-stage programming problem is proposed to formulate the capacity management with respect to a wind power producers’ coalitional game. Secondly, we give a constructive proof to illustrate that a wind power producers’ coalitional game has a nonempty core using the theory of cooperative game. Meanwhile, we also show that the form of a coalition always reduces their expected costs. Thirdly, we propose a stochastic duality approach to design a fair cost allocation scheme for players of a wind power producers’ coalition. Finally, we consider a concave function structure, indicating that there exists economies of scale, and extend this proposed duality theory to the wind power producers’ coalitional game under a concave capacity investment cost structure. Furthermore, we find that a wind power producers’ coalition is balanced under the concave capacity investment cost function. Our results provide an incentive for collaboration strategies in wind power setting, and promote sustainable development of wind power.

Stochastic Wind Farm Expansion Planning With Decision-Dependent Uncertainty Under Spatial Smoothing Effect

The global experience on wind farm development reveals that the wind power uncertainty is affected by large-scale wind farm sizes due to the spatial smoothing effect. The dependence on wind farm sizes features the wind power uncertainty as decision-dependent uncertainty (DDU) during the wind farm expansion process. This paper proposes a stochastic expansion planning model for large-scale wind farms considering the DDU and addresses the coupling relationship between expansion decisions and DDU. Specifically, to explore how the structural features of the expansion model change with DDU in different forms, the proposed expansion model with DDU is established based on Point Estimate Method (PEM). Via the explicit expression between decisions and decision-dependent statistical moments of uncertain parameters in the PEM-based model, an in-depth discussion of model features is implemented. An iterative solution method with convergence analysis is proposed based on the explored model features to tackle the DDU. Besides, a modified Benders decomposition method is adopted in each iteration for the PEM-based two-stage optimization model potentially containing both min-min and min-max terms. The effects of DDU on wind farm expansion schemes are analyzed. Case studies verify the proposed expansion model and the solution method.

Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading

As a promising solution to realize the energetic complementarity and energy cascade utilization, integrated energy system with a large-scale integration of renewable energy garners considerable attention recently. However, the renewable energy curtailment and uncertainty become two prominent challenges. To the end, we establish a hydrogen-based integrated energy system (HIES) operation framework comprising a refined power to gas (P2G) and carbon capture system (CCS) under carbon trading. Subsequently, a Wasserstein metric based distributional robust optimization method (WDRO) is developed to address wind power uncertainty. Finally, numerical analysis is performed. Simulations results show that: (1) the proposed approach can realize a low-carbon electric energy supply and consumption mode and satisfies the hydrogen demand through internal equipment of the system; (2) compared with the benchmark, the proposed model reveals outstanding economic, environmental and energy performance by obtaining 31.81% decline in operation cost, a 20.19% mitigation in CO2 emission, 7.89% reduction of wind power curtailment and a higher energy efficiency; (3) the validation of WDRO model is demonstrated by the data-driven and robustness test. Moreover, the model exhibits advantages due to its trade-off between conservativeness and computational complexity. The optimization results provide a good reference for the HIES investment strategy and government carbon pricing decision.

Demand response model by locational marginal electricity–carbon price considering wind power uncertainty and energy storage systems

With the increasing prominence of global carbon emissions, how to achieve carbon emission reduction in power systems is an urgent issue. Early research introduced carbon cost in the objective functions or constraints in an optimization model. However, the carbon cost cannot account for the time-varying carbon emissions caused by load electricity consumption. To address this challenge, this paper proposes a demand response (DR) program based on the locational marginal prices on both electricity consumption and carbon emission to reshape the load consumption pattern, which guide the load from both “electricity perspective” and “carbon perspective”. This model can reduce the carbon emissions of the power system while considering the economics of power purchase and the traditional DR to peak load shifting. Meanwhile, historical data fitting, Monte Carlo sampling and scenario reduction techniques are used to take full account of wind power uncertainties. The energy storage (ES) systems are introduced to simultaneously arbitrage from both electricity and carbon markets by appropriate charging strategies. The simulation results using PJM 5-bus system show that the locational marginal electricity–carbon price proposed in this paper can reduce the carbon emissions of the power system while ensuring an economic operation. By adding the carbon emission factor to the price, the load demand not only follows the fluctuation of electricity price, but also shows the trend of following the change of carbon emission.

A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power

Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The model enables the planner to make a trade-off between the market power indices and the investment cost. Using comparable results of the IEEE 118-bus system, we show that the proposed TEP outperforms the existing models in terms of market power indices and facilitates open access to the transmission network for all market participants.

Deep Reinforcement Learning Based Unit Commitment Scheduling under Load and Wind Power Uncertainty

The intermittent nature of renewable energy sources and fluctuating electricity demand induce significant uncertainty that needs to be tackled with computationally efficient solution techniques to provide reliable and cost-effective generation schedules of power systems. In this work, we present a deep reinforcement learning (DRL) based approach for the day-ahead scheduling of generation resources under demand and wind power uncertainties. The proposed approach yields a causal policy relying only on historical uncertainty realizations and forecast data that is trained with an actor-critic-based reinforcement learning algorithm. Through safe exploration, the DRL-based approach guarantees a feasible commitment schedule without any operational constraint violations. We conduct computational experiments on the IEEE 39-bus and 118-bus test cases to demonstrate the effectiveness of the proposed solution strategy and improvement over existing approaches, including a deterministic approach with point forecasts and the stochastic dual dynamic integer programming method. The results show that the proposed approach enjoys superior performance in terms of computational efficiency and incurred operational costs, with significant reduction in penalty costs caused by insufficient net load supply than the deterministic approach.

Symbiotic organisms search-based multi-objective optimal placement of distributed generators considering source and load uncertainty

Integration of Distributed Generation (DG) in the Distribution network will reduce the network expansion costs and increase the network reliability in terms of reduction in voltage magnitude deviations.This paper presents Symbiotic Organisms Search (SOS) based technique for finding out the best size and position of DG in Radial Distribution networks to improve the voltage profile and voltage security state of the Distribution network. The primary objectives of the paper are minimization of bus voltage variation and maximization of voltage stability index of the network as a multi objective optimization problem in presence of uncertainty of sources and loads. In this paper the uncertainty of solar power, wind power and load are modelled using 2m Point Estimate Method along with SOS algorithm.To show the effect of DG placement on voltage security state of Distribution system, the system is classified into three states based on the values of voltage stability index (VSI). Simulation results as obtained from two standard (IEEE) radial distribution networks prove the efficiency and accuracy of the proposed SOS method. The results of SOS based method are compared with some other techniques as found in recent literature, which shows that SOS algorithm outperforms other standard optimization techniques.

Day-ahead dispatch of novel battery charging and swapping station based on distributionally robust optimization

Battery swapping station (BSS) is a promising way to support the proliferation of electric vehicles (EVs). This paper upgrades BSS to a novel battery charging and swapping station (NBCSS) with wind power, photovoltaic power, energy storage and gas turbine integrated, which is equivalent to a microgrid with flexibility further enhanced. An integrated model of batteries based on the state of charge (SOC) interval is put forward to release the complexity of separate modeling of each battery, where the charging and discharging priorities are embedded. Then, a distributionally robust optimization (DRO) model for the day-ahead dispatch of NBCSS is presented considering the uncertainties of wind power, photovoltaic power and load. This model minimizes the worst-case expected total cost over a family of distributions characterized by an ambiguity set. By employing the affine decision rules, the primitive two-stage DRO model can be eventually reformulated as a tractable mixed-integer linear program. Finally, case studies are conducted to demonstrate the effectiveness of the proposed method. The results show that the charging and discharging freedom of batteries enhances the operational flexibility of NBCSS and reduces the 22.9 % of the total cost. And the proposed method has the superiority of decision-making over the deterministic and adaptive robust optimization ones.

Chance‐constrained co‐expansion planning for power systems under decision‐dependent wind power uncertainty

AbstractVariability and uncertainty in wind resources pose significant challenges to the expansion planning of wind farms and associated flexible resources. In addition, the spatial smoothing effect, indicating the impact of wind farm scale on aggregated wind power prediction errors, further aggravates the challenge. This paper proposes a chance‐constrained co‐expansion planning method considering the spatial smoothing effect, where the expansion of wind farm capacity, batter energy storage capacity, and power transmission lines are co‐optimized. Specifically, a decision‐dependent uncertainty (DDU) model is established capturing the dependency of wind power uncertainties on wind farm expansion decisions under the spatial smoothing effect. Unlike traditional optimization diagram where decisions are made under only decision‐independent uncertainty (DIU) with fixe properties, properties of decision‐dependent uncertain parameters would be inversely altered by decisions. To effectively tackle the coupling relation between decisions and DDU, DDU‐based chance constraints are formulated in an analytical manner, where the decisions and decision‐dependent uncertain parameters are expressed in a closed form. Eventually, with piecewise linearization of the DDU model and the polynomial approximation of cumulative distribution function of uncertain parameters, the proposed chance‐constrained optimization model with DDU is converted into a mixed‐integer second‐order cone program (MISOCP). Case studies verify the effectiveness of the proposed method.

An efficient framework for exploiting operational flexibility of load energy hubs in risk management of integrated electricity-gas systems

Faced with increasing operational uncertainties, e.g. wind power variation, the risk management of integrated electricity-gas systems (IEGSs) has become extremely vital, which puts forward a higher requirement for flexible resources. On the demand side of IEGSs, local energy hubs (LEHs) composed of different energy devices can provide flexible resources for system risk management through multi-energy substitution. However, it can be difficult to exploit the operational flexibility of massive LEHs in IEGS operation owing to two major issues, namely 1) privacy concerns of individuals; 2) computation efficiency requirements. To address this, an efficient framework based on flexible regions is innovatively proposed for exploiting LEH flexibility in the risk management of IEGSs. The flexible region is estimated to characterize the adjustable range of energy inputs to LEHs considering the operational requirements of internal energy devices. On this basis, essential flexibility-related information of LEHs is directly utilized for system risk management, which can preserve individual privacy and avoid time-consuming iteration. Firstly, a generalized method based on Minkowski Summation is proposed to efficiently determine the flexible region of LEH, which contains time-independent and temporal-related parts. The two regions are formulated separately considering the multi-energy substitution of LEH and the charging/discharging process of electric storage. The flexible region is then mathematically formulated as a set of linearized equations, which can be directly incorporated into the system scheduling model. On this basis, a risk-based two-stage optimization model is developed for the coordinate dispatch of IEGSs and LEHs considering wind power uncertainties. Case studies demonstrate that the exploitation of LEH flexibility can effectively mitigate operational risk levels of IEGSs and reduce system costs. Besides, the proposed model has the advantage of high computation efficiency compared to the existing iteration-based method.

Modeling for wind-thermal combined bidding considering bilateral tail information

The stochastic output of wind power will lead to the penalty of bidding deviation in the spot market and bring bidding risk, restricting the participation of wind power in market competition. The combined bidding strategy for multiple power generation stakeholders can help alleviate the output uncertainty of wind power, reducing the bidding deviation penalty. This paper aims at the modeling method for wind-thermal combined bidding in the spot power market. Conditional value at risk (CVaR) is used to describe the bidding risk of the combined bidding. To better reflect the bidding risk, the bilateral tail information of the upper and lower deviations is modeled according to the revenue function. The uncertainties of wind power output and the clearing price in the day-ahead market are considered, and the risk decision-making model for wind-thermal combined bidding is established. The model is solved by the chaotic particle swarm optimization algorithm with constraint handling (EDFC-PSO). The results show that the proposed modeling method can describe the risk indicators in detail, providing a better reference for market bidding. Also, the combination of the wind farm and the thermal power unit can reduce the risk of wind power in bidding and improve its profitability in the day-ahead market. Besides, the risk speculation of the wind farm can be found when facing the inevitable increased risks. It means the behaviors of the market participants can be better explained using the proposed method in this paper.

Evolving long short-term memory neural network for wind speed forecasting

Wind speed forecasting plays a crucial role in reducing the risk of wind power uncertainty, which is vital for power system planning, scheduling, control, and operation. However, it is challenging to obtain accurate wind speed forecasting results since wind speed series contain complex fluctuations. In this paper, a novel wind speed forecasting model is proposed by using a genetic algorithm (GA) and long short-term memory neural network (LSTM), where GA is used for evolving the architectures and hyper-parameters of the LSTM, called EvLSTM, because there is no clear knowledge to determine these crucial parameters. In the proposed EvLSTM forecasting model, a flexible gene encoding strategy, crossover operation, and mutation operation are proposed to describe the different architectures and hyper-parameters of the LSTM during the evolutionary process. In addition, to overcome the weakness of the single method for wind speed forecasting, the ensemble EvLSTM (EnEvLSTM) is proposed by using the no negative constraint theory ensemble learning strategy, whose weight coefficients are determined by the differential evolution algorithm. The proposed EvLSTM and EnEvLSTM forecasting models are evaluated on two real-world wind farms located in Inner Mongolia, China and Sotavento Galicia, Spain. Experimental results on two forecasting horizons demonstrate the superiority of EvLSTM and EnEvLSTM in terms of three performance indices and three statistical tests.