Uncertainty Of Wind Generation Research Articles

Optimal energy and reserve management of a smart microgrid incorporating parking lot of electric vehicles/renewable sources/responsive-loads considering uncertain parameters

Due to the increasing presence of electric vehicles (EVs) in urban electricity distribution networks, distribution network operators face the challenge of energy management. A smart parking lot (SPL), renewable energy sources (RESs) such as photovoltaic systems (PV) and wind turbines (WT), and local dispatchable generators (LDG) such as microturbines (MT) and fuel cells (FC) are integrated as a microgrid (MG) while an energy management system is presented in this study which considers the uncertainties of wind speed, solar irradiation, and load consumption. The optimal operation of the SPL, which serves as a load and energy generation source for the distribution network, is done in this article with the goal of lowering costs. To cut costs, demand response program (DRP) based on a time of use (TOU) tariff is utilized, which moves a part of load from on-peak to off-peak time intervals, flattening the load curve. The goal is to reduce the operational expenses of the upstream grid (UG), LDGs, and SPL while taking into account the technical and physical limits of the units. Furthermore, for dealing with the uncertainties of load consumption and wind generation, this research employs a new uncertainty modeling method based on Hong's two-point estimate method. The suggested model is investigated by applying the General Algebraic Modeling System (GAMS) software and is formulated as mixed linear programming (MIP). The suggested model includes both a spinning reserve of LDG and a SPL, and the simulation results verified that the DRP has a good impact on lowering operation costs.

Economic dispatch for electricity merchant with energy storage and wind plant: State of charge based decision making considering market impact and uncertainties

This paper investigates how the market impact of electricity merchants and uncertainty of wind generation affect their co-optimized scheduling policy, specifically for merchants who have both energy storage and wind plants. In the existing literature, merchants' trading actions are usually assumed not to affect market prices; however, a large-scale energy storage merchant's actions can affect market prices. To this end, we approximate the electricity price by a linear function of the quantity of power traded by the merchant in the reward function to achieve decision-making incorporating the market impact. This paper utilizes the dynamic programming approach to analyze merchants' optimal multi-period decision-making incorporating market impact, uncertain wind generation, and energy storage constraints. First, our results demonstrate that for a merchant with co-located energy storage facilities and wind power plants, the energy storage's feasible state of charge (SOC) range can be segmented into four possible sub-ranges by three analytically developed SOC reference points. The unique optimal trading decision can be achieved by comparing the current energy inventory and the SOC references of the next period. Second, our results show that market impact and uncertainties substantially change the optimal storage scheduling policy by impacting the values of the reference points. To mitigate the negative effect of the merchant's market impact on buying and selling actions, the merchant may reduce the amount of generating or pumping electricity each period to maximize profit. Moreover, we identify and investigate the trade-off between market price and transaction quantity. Our findings provide co-optimized scheduling guidance for electricity merchants with co-located energy storage and renewable power plants systems.

Multi-Objective Optimal Power Flow including Wind and Solar Generation Uncertainty Using New Hybrid Evolutionary Algorithm with Efficient Constraint Handling Method

A new hybrid decomposition-based multiobjective evolutionary algorithm is proposed for optimal power flow (OPF) including wind and solar generation uncertainty. This study recommends a novel constraint-handling method, which adaptively adds the penalty function and eliminates the parameter dependency on penalty function evaluation. The summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of multiobjective optimization algorithms. The OPF problem is modeled as a multiobjective optimization problem with four objectives such as minimizing (i) total fuel cost (TC) including the cost of renewable energy source (RES), (ii) total emission (TE), (iii) active power loss (APL), and (iv) voltage magnitude deviation (VMD). The impact of RESs such as wind and solar energy sources on integration is considered in optimal power flow cost analysis. The costs of RESs are considered in the OPF problem to minimize the overall cost so that the impact of intermittence and uncertainty of renewable sources is studied in terms of cost and operation wise. The uncertainty of wind and solar energy sources is described using probability distribution functions (PDFs) such as Weibull and lognormal distributions. The efficiency of the algorithm is tested on IEEE 30-, IEEE 57-, and IEEE 118-bus systems for all possible conditions of renewable sources using Monte Carlo simulations.

Risk-Averse Stochastic Midterm Scheduling of Thermal-Hydro-Wind System: A Network-Constrained Clustered Unit Commitment Approach

With the increasing penetration of renewable energy, designing the efficient midterm (i.e., annual) schedule for multi-source power systems is valuable in providing substantial cost reductions and flexibilities while ensuring system security against uncertainties of renewables. This paper presents a risk-averse two-stage stochastic midterm scheduling model for thermal-hydro-wind systems against short-term uncertainties of renewable generation. A network-constrained clustered unit commitment (NC-CUC) model is proposed to effectively incorporate short-term operation constraints into the midterm scheduling model, for improving the solution accuracy while reducing computational burden. Specifically, as the traditional CUC model cannot ensure consistent unit commitment status of generators across all scenarios, a power flow-based unit clustering method is proposed to improve the accuracy of short-term operation results. In addition, the conditional value-at-risk (CVaR) is used to quantify the system operational risk caused by uncertainties of wind generation and natural water inflows. Case studies show that the risk-averse two-stage stochastic midterm scheduling model can ensure operation dynamics and solution efficiency, and the proposed clustering method can achieve a tradeoff between solution accuracy and computational burden for the stochastic network-constrained CUC model.

Techno-environmental assessment of gas-based power plant and renewable unit in the presence of power to gas system and gas storages for emission reduction using information gap decision theory

Carbon dioxide is the most common greenhouse gas and a major contributor to global warming. Utilization of power to gas systems (P2G) in conventional power plants to reduce the CO2 emissions is one of the promising approaches. In this paper, a P2G system combined from electrolysis and methanation units is proposed for recycling CO2 emission. This P2G system is considered to operate environmentally and economically integrated with a gas-based power plant (GBPP) and renewable energy resources like wind generation unit. In addition, wind generation uncertainty parameter is modeled through three various strategies as robustness, opportunity, and basic functions by using the information gap decision theory (IGDT) optimization method considering the wind generation uncertainty parameter, which has a major impact on the optimal operation of the proposed GBPP-P2G system. Furthermore, CH4 and CO2 gas storage units are utilized in the P2G system to enhance the reduction of CO2 emission. Results reveal the effectiveness of the proposed GBPP-P2G system in sharply decreasing the CO2 emission and wind curtailment.

A two-point estimate approach for energy management of multi-carrier energy systems incorporating demand response programs

Gas-based power plants have attracted more attention in providing electrical energy worldwide because of their lower costs and air pollution. In addition, the use of multi-carrier energy systems has several advantages, such as sustainability benefits and improving performance in supplying the energy demand. This study aims to optimize the total operation cost of multi-carrier energy systems considering the uncertain parameters. The storage technology and consumption side assist the operator in achieving lower costs based on conceptions of demand response programs. Therefore, this study presents a comprehensive mathematical model for the coordinated operation of integrated multi-carrier energy systems while the operational constraints of both gas and power networks are considered. Furthermore, this paper utilizes a new uncertainty modeling method based on Hong's two-point estimate method for addressing the uncertainties of load consumption and wind generation. The proposed model is applied to a gas and power multi-carrier energy system through four case studies. The results affirm the high performance of the presented method and investigate the influence of demand response programs in both sides of energy carriers.

Reliability Assessment of Renewable Power Systems Considering Thermally-Induced Incidents of Large-Scale Battery Energy Storage

The battery energy storage system (BESS) has been envisaged as an effective solution for renewable energy accommodation in power systems. However, the residual capacity and maximum power of large-scale BESS are highly affected by thermally-induced incidents such as battery degradation and Thermal Runaway (TR) propagation. In the prior-art studies, the impacts of thermally-induced incidents on the BESS service performance have not been well modeled, resulting in relatively over-optimistic reliability estimation of power systems. In this paper, the reliability of large-scale grid-connected BESSs as well as its impacts on the overall reliability of power systems are investigated considering the battery degradation and TR propagation. To quantify the time-varying performance of the BESS, a multi-state model is constructed. The proposed model describes the aging process of batteries inside the BESS, incorporating the combined effects of sequential TR and the performance degradation of the surrounding batteries due to heat absorption. Based on the Monte Carlo method, scenarios that reflect the uncertainties of the intermittent wind generation and fluctuating loads are simulated. An optimal scheduling model is deployed, and a solution algorithm is proposed to calculate the scheduling results of the BESS in the real-time performance range subject to its thermal conditions. Case studies are conducted to validate the effectiveness of the proposed model and technique.

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.

Frequency response support assessment from uncertain wind generation

Large share of uncertain generation from Wind Power Plants (WPPs) in the power systems has a detrimental impact on system frequency stability, as WPPs inherently do not provide inertial response support. Currently, extensive research is being carried out to envisage Synthetic Inertia (SI) & Primary Frequency Response (PFR) support from WPPs. Modelling of inertial response support from WPPs and its consideration in stochastic scheduling requires investigation. System inertia condition varies over a day and hence, accurate estimation of PFR requirement is a challenging task. This paper develops a novel approach to model SI & PFR support from WPPs and simultaneously estimate the system inertia condition & PFR requirement in a day-ahead stochastic scheduling framework. Wind generation uncertainty is characterized by probabilistic scenarios. IEEE Reliability Test System (RTS) is used to carry out the case studies at different wind penetration levels. Numerical results demonstrate that the SI and PFR support from WPPs reduce the overall cost by 15% and wind curtailment reduction up to 35%. SI support from WPP adds around 12% to overall system inertia. The proposed model could be enhanced for the optimal tuning of inertial, droop constants, and modelling of multiple uncertainties.

Economic-Emission Dispatch Problem in Power Systems With Carbon Capture Power Plants

Despite the increasing level of renewable power generation in power grids, fossil fuel power plants still have a significant role in producing carbon emissions. The integration of carbon capturing and storing systems to the conventional power plants can significantly reduce the spread of carbon emissions. In this article, the economic-emission dispatch of combined renewable and coal power plants equipped with carbon capture systems is addressed in a multiobjective optimization framework. The power system's flexibility is enhanced by hydropower plants, pumped hydro storage, and demand response program. The wind generation and load consumption uncertainties are modeled using stochastic programming. The dc power flow model is implemented on a modified IEEE 24-bus test system. Solving the problem resulted in an optimal Pareto frontier, while the fuzzy decision-making method found the best solution. The sensitivity of the objective functions concerning the generation-side is also investigated.

Decentralized robust dispatch for multi‐area AC/DC system considering wind power uncertainty

This paper proposes a decentralized robust dispatch method for multi-area AC/DC system considering wind power uncertainty. The accommodated wind power interval model is adopted to handle the inherent uncertainty and volatility of wind generation, which is modelled based on the affinely adjustable robust optimization method. The accommodated wind power interval and the automatic generation control participation factors are optimized simultaneously to accommodate more wind energy and ensure operational reliability of the entire multi-area system. The flexible adjustment model of HVDC converter station is also presented to accommodate more wind power. Then, the resulting economic dispatch model for multi-area system is solved in a fully decentralized and parallel manner using accelerated alternating direction method of multipliers algorithm to preserve the information privacy and dispatch independence of each region. Case studies illustrate the effectiveness of the proposed model.

Hybrid stochastic risk-based approach for a microgrid participating in coupled active and reactive power market

In recent years, simultaneous participation in energy and ancillary services (AS) markets has been very profitable for microgrids (MG). High penetration of renewable energy sources (RES) in energy supply, due to the uncertainties of these products, increases the need for AS. Also, active and reactive powers are completely related, so in this paper the microgrid simultaneous participation in the active and reactive power and ancillary services (regulation up and regulation down, spinning reserve and non-spinning reserve) markets is modeled considering uncertainty of wind and solar generations. The relation between active and reactive power generation of each generator is calculated based on capability diagrams and mathematical equations. Conditional value at risk (CVaR) is used for risk management, and probability of calling ancillary services is calculated. Uncertainties of wind and solar generations are modeled using their probability distribution functions (PDFs). The ERCOT market simulation is discussed to calculate the participation of each unit in all the mentioned markets based on real-world data.

Insecurity Early Warning for Large Scale Hybrid AC/DC Grids Based on Decision Tree and Semi-Supervised Deep Learning

Fast insecurity early warning is the key technique to resist the dynamic insecurity risk, which becomes intractable due to the strong nonlinearity of hybrid AC/DC grids and the high uncertainty of wind generation. Considering dynamic security constraints and wind power uncertainty, this paper presents an insecurity early warning method based on decision tree (DT) and semi-supervised deep learning. First, semi-supervised deep learning is deployed to estimate the dynamic security limit of the critical interface of hybrid AC/DC grids. The system security is assessed by comparing the actual power transfer of the critical interface with the security limit. Then, operating conditions (OCs) are ranked into different insecure levels according to the type of preventive control actions that is needed to ensure the system security. Finally, oblique DT is utilized to identify insecurity classification boundaries in the wind power injection space. Insecure OC sets are constructed based on these classification boundaries. Simulation results of the real-life Jiangsu-Shanghai interconnected grid in east China demonstrate that the proposed method can fast construct the insecure OC sets corresponding to different insecure levels.

Optimal coordination of thyristor controlled series compensation and transmission expansion planning: Distributionally robust optimization approach

This paper proposes a distributionally robust optimization method for assessing economic benefits of transmission expansions with thyristor controlled series compensation devices. To accommodate the uncertainty of wind generations and electrical vehicle loads, a distributionally robust optimization method is proposed to use historical data instead of predefined distribution probabilities. In the proposed framework, firstly, a confidence set for unknown distributions are constructed based on the historical data. Then, based on worst-case distributions within a constructed confidence set, the optimal results of transmission expansion planning and allocation of thyristor controlled series compensation device are obtained. In order to take advantage of the flexible alternative current transmission devices (FACTS) in both economic and technical considerations, the optimal allocation of thyristor controlled series compensation is integrated into the transmission expansion planning. This requires AC-based transmission expansion planning model. AC power flow suffers from high computational complexity. To tackle this issue, the linearized AC power flow model is utilized. Furthermore, to solve the optimization problem, the combined technique including Bender's decomposition and cut-and-column are utilized. The numerical results on the Garver 6-bus and IEEE 118-bus systems demonstrate the effectiveness of the proposed method in comparison with scenario-based stochastic and robust optimization methods. Besides, the impacts of the thyristor controlled series compensation on total cost reduction has been investigated.

Price and subsidy under uncertainty: Real-option approach to optimal investment decisions on energy storage with solar PV

Renewable generation sources still have not achieved economic validity in many countries including Korea, and require subsidies to support the transition to a low-carbon economy. An initial Feed-In Tariff (FIT) was adopted to support the deployment of renewable energy in Korea until 2011 and then was switched to the Renewable Portfolio Standard (RPS) to implement more market-oriented mechanisms. However, high volatilities in electricity prices and subsidies under the RPS scheme have weakened investment incentives. In this study we estimate how the multiple price volatilities under the RPS scheme affect the optimal investment decisions of energy storage projects, whose importance is increasing rapidly because they can mitigate the variability and uncertainty of solar and wind generation in the power system. We applied mathematical analysis based on real-option methods to estimate the optimal trigger price for investment in energy-storage projects with and without multiple price volatilities. We found that the optimal trigger price of subsidy called the Renewable Energy Certificate (REC) under multiple price volatilities is 10.5% higher than that under no price volatilities. If the volatility of the REC price gets doubled, the project requires a 26.6% higher optimal investment price to justify the investment against the increased risk. In the end, we propose an auction scheme that has the advantage of both RPS and FIT in order to minimize the financial burden of the subsidy program by eliminating subsidy volatility and find the minimum willingness-to-accept price for investors.

Adjustable robust optimization approach for two-stage operation of energy hub-based microgrids

Growing demand for energy carriers has led to an increased interest in developing and managing multiple energy carrier microgrids. Furthermore, the volatile nature of renewable resources as well as the uncertain electrical and thermal demands imposes significant challenges for the operation of microgrids. Motivated by this, the paper leverages a min max min robust framework for short-term operation of microgrids with natural gas network to capture the uncertainty of wind generation and electrical/thermal loads. The proposed model is linearized and solved using the column-and-constraint generation (C&CG) procedure that decomposes the framework into a master problem and a sub-problem. The master problem minimizes the unit commitment cost, while the sub-problem determines the dispatch cost associated with the worst realization of uncertainties via a max min objective function. Also, polyhedral uncertainty sets are defined with budget of uncertainty parameter that adjusts the trade-off between the operation cost and the degree of robustness. The effectiveness of the framework is assessed and discussed via a 21-node energy hub-based microgrid. It can be seen that the solution immunizes against all realizations of uncertainties, whereby increasing the budget of uncertainty and the forecast error, the system robustness is improved. Moreover, the dual variables of the sub-problem are converted to the primary variables in order to evaluate the unit commitment and energy dispatch results.

Optimal Collaborative Expansion Planning of Integrated Cooling and Power System for Low-Latitude Distribution Networks

In low latitudes, ice storage air conditioners (ISACs) have been widely used to cool while locally responding to the distribution network demand. However, due to the lack of the direct cold energy exchange between ISACs, the cooling load could only be shifted on the time scale instead of the space scale, resulting in an unsatisfactory regulation result. To address the above problem, this article proposes a novel collaborative expansion planning scheme for integrated cooling and power system. Firstly, to increase the regulation flexibility, a novel cold energy supply system is designed, where ice making stations and trucks are used to produce and deliver ices to multiple terminal ISACs. On this basis, taking the capacity of the wind turbines (WTs), large ice makers (LIMs), and trucks as configuration decisions, an optimal expansion planning model is established considering wind generation uncertainties. This model is converted into a classic mixed-integer second-order cone programming (MISOCP) problem using linear techniques, and efficiently solved by the Benders decomposition method. Finally, Shapley value method in economics is used to fairly distribute the revenues between the grid operator (GO) and ISAC owners. Simulation studies on IEEE 14-node distribution network indicate the proposed expansion model is effective and beneficial.

Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

The integration of renewable generation adds complexity to the operation of the power system due to its unpredictable characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal kriging and analog approaches are used to forecast wind power generation and used as the input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal kriging captures the spatial and temporal information available in the database to improve wind forecasts. We evaluate the performance of using the spatio-temporal kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results demonstrate that the use of kriging based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

Frequency-constrained multi-source power system scheduling against N-1 contingency and renewable uncertainty

Security continues to be the most critical concern in power system operation, which is exceptionally important under low inertia and high uncertainty situations induced by an increasing penetration of renewable sources. This paper presents a transient frequency-constrained two-stage stochastic scheduling model to study the day-ahead operation of thermal-hydro-wind-demand response systems. Specifically, (i) limits on rate-of-change-of-frequency and maximum frequency derivation are introduced to provide sufficient governor reserve from multiple sources for guaranteeing dynamic frequency performance against N-1 contingency; (ii) The de-loaded mode of variable speed wind turbines is proposed for leveraging economics of system operation and security of dynamic frequency response; (iii) Virtual inertial constant of power systems with variable speed wind turbines is calculated to simulate performance of the virtual inertial control; (iv) Demand response is considered in the stochastic framework to mitigate the time delay of delivering virtual inertia. Multiple scenarios are generated via Monte Carlo method to simulate wind generation uncertainties. The proposed two-stage stochastic mixed-integer nonlinear programming model with binaries in both stages is solved via an improved generalized Benders decomposition, which takes about 40% less time than the standard generalized Benders decomposition. Several enhanced strategies are discussed to improve computational performance of the generalized Benders decomposition. Numerical simulations illustrate effectiveness of the proposed approach in coordinating multi-resource scheduling of power systems against N-1 contingencies and uncertainties with guaranteed frequency performance. Specifically, numerical results on the 6-bus system illustrate that: (i) The optimal wind generation penetration level of 40% could well balance the wind curtailments and the benefits of variable speed wind turbines for regulation provision; (ii) With 50% penetration of wind generation, doubling the capacity of demand response can reduce the amount of wind curtailment 50.42%, and decrease the transient frequency deviation to 0.25Hz.

Deterministic and probabilistic multi-objective placement and sizing of wind renewable energy sources using improved spotted hyena optimizer

In this paper, deterministic and probabilistic optimal placement and sizing of wind turbines (WTs) in distribution networks are investigated with two objectives: reducing loss and improving voltage profile and stability index. A new improved meta-heuristic method, named β-chaotic sequence spotted hyena optimizer, is proposed to determine the optimal size and location of wind turbines. The proposed method is implemented on IEEE 33-bus and 69-bus radial distribution networks. The performance of the method is verified with finding the optimal location and size of WTs, which exhibited lower power loss and better minimum voltage and voltage stability index with more convergence speed in comparison with the conventional spotted hyena optimizer and particle swarm optimization, and previous studies. Additionally, the probabilistic placement and sizing of WTs are implemented considering the uncertainty of wind generation and the network demand based on Monte Carlo simulation. The results showed that losses increased, the voltage profile weakened, and the voltage stability index was reduced, compared with the deterministic method. For 33 bus network, the loss, minimum voltage, and voltage stability index in two WTs application are recorded as 28.79 kW, 0.9811, and 31.12 p. u, respectively, using the deterministic method, while the values of 34.56 kW, 0.9789, and 30.72 p. u are recorded using the probabilistic method. On the other hand, for 69 bus network, these values are recorded as 18.60 kW, 0.9833, and 67.13 p. u using the deterministic method, and 29.36 kW, 0.9794, and 65.62 p. u using the probabilistic method. Therefore, the resultsclearly show that the probabilistic method is more realistic and accurate than the deterministic method due to consideration of load and wind generation intrinsic changes with all possible probabilities, based on which the network operators can have a more accurate view of the impact of renewable resources on the network characteristics, hence making better decisions to improve them.