Uncertainty Of Wind Generation Research Articles

Active Distribution System Reinforcement Planning With EV Charging Stations–Part II: Numerical Results

The second part of this two-paper series discusses two case-studies to confirm the effectiveness of the scenario-based stochastic model developed in the first part for the multistage joint expansion planning of distribution systems and electric vehicle charging stations. Numerical results obtained in the 18-bus distribution system confirm the effectiveness of proposed approach through the minimization of total investment and operational costs, and the adequate characterization of uncertainties. The proposed solution determines the optimal construction/reinforcement of substations, electric vehicle charging stations and feeders, and the placement of distributed generators and capacitor banks along the three-stage planning horizon. The comparative analysis of proposed approach, against the deterministic and robust approaches; confirms its superiority in dealing with the uncertainties of wind and photovoltaic generation, and conventional loads and electric vehicle demands. Finally, the scalability of proposed approach is confirmed using the IEEE 123-bus distribution system.

Introducing Uncertainty Components in Locational Marginal Prices for Pricing Wind Power and Load Uncertainties

With substantially increasing penetration levels of wind power, electric power system flexibility is needed to address the variability and uncertainty of wind power output. Thus, it has become an urgent issue to obtain an optimal tradeoff between economics and reliability, and to price system uncertainties. This paper proposes a new electricity market-clearing mechanism based on locational marginal prices (LMPs) for pricing uncertain generation and load. The uncertainty contained locational marginal price (U-LMP) is derived from a distributionally robust chance-constrained optimal power flow model in which only the first-order and second-order moments of the uncertain sources’ probability distribution are needed. Compared with traditional LMPs, the proposed U-LMP formulation includes two new uncertainty components: transmission line overload uncertainty price and generation violation uncertainty price. These LMP uncertainty components are the price signals reflecting the system costs as a result of wind generation and demand uncertainty at different locations. Finally, using parametric case studies, the relationship among uncertainty levels, system generation cost, and LMP uncertainty components are established. Case studies performed on the PJM 5-bus and IEEE 118-bus systems verify the proposed U-LMP method.

Unit commitment under imperfect foresight – The impact of stochastic photovoltaic generation

This paper investigates the impact of uncertain photovoltaic generation on unit commitment decisions for the German rolling planning procedure employing a large-scale stochastic unit commitment electricity market model (stELMOD). A novel approach to simulate a time-adaptive intra-day photovoltaic forecast, solely based on an exponential smoothing of deviations between realized and forecast values, is presented. Generation uncertainty is then incorporated by numerous multi-stage scenario trees that account for a decreasing forecast error over time. Results show that total system costs significantly increase when uncertainty of both wind and photovoltaic generation is included by a single forecast, with more frequent starting processes of flexible plants and rather inflexible power plants mainly deployed at part-load. Including the improvement of both wind and photovoltaic forecasts by a scenario tree of possible manifestations, the scheduling costs could be significantly reduced in representative weeks for spring and summer. In general, stochastic representations increase the need for congestion management as well as more frequent use of storage in the model, leading to a more realistic depiction of the markets.

Optimal joint energy and reserve scheduling considering frequency dynamics, compressed air energy storage, and wind turbines in an electrical power system

Abstract Frequency dynamics management along with energy and reserve scheduling is a challenging issue in Unit Commitment (UC) of modern power systems due to the replacement of synchronous generators with renewable systems and consequently, the decay in system inertia. This study proposes a new Interval Unit Commitment (IUC) model for optimal energy and reserve scheduling in which frequency dynamics of the power system are taken into account. The Wind Turbines (WTs) and Compressed Air Energy Storage (CAES) are considered for this modeling. The aim of paper is to retain the frequency dynamic security by considering Demand Response (DR) program and injections of fast response CAESs following a generation loss. Also, the uncertainty of wind generation is taken into account using a probabilistic model. The Frequency Dynamics-constrained Unit Commitment (FDUC) considering CAESs as named FDUCC is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem considering uncertainties of DR and WT. The proposed method is examined on a 6-bus test system and simulation results are derived in four different case studies. The obtained results indicate that the proposed model can guarantee frequency security while it reduces operation costs.

Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties

Flexible distributed energy resources, such as energy storage systems (ESSs), are increasingly considered as means for mitigating challenges introduced by the integration of stochastic, variable distributed generation (DG). The optimal operation of a distribution system with ESS can be formulated as a multi-period optimal power flow (MPOPF) problem which involves scheduling of the charging/discharging of the ESS over an extended planning horizon, e.g., for day-ahead operational planning. Although such problems have been the subject of many works in recent years, these works very rarely consider uncertainties in DG, and almost never explicitly consider uncertainties beyond the current operational planning horizon. This article presents a framework of methods and models for accounting for uncertainties due to distributed wind and solar photovoltaic power generation beyond the planning horizon in an AC MPOPF model for distribution systems with ESS. The expected future value of energy stored at the end of the planning horizon is determined as a function of the stochastic DG resource variables and is explicitly included in the objective function. Results for a case study based on a real distribution system in Norway demonstrate the effectiveness of an operational strategy for ESS scheduling accounting for DG uncertainties. The case study compares the application of the framework to wind and solar power generation. Thus, this work also gives insight into how different approaches are appropriate for modeling DG uncertainty for these two forms of variable DG, due to their inherent differences in terms of variability and stochasticity.

A new perspective on wind integrated optimal power flow considering turbine characteristics, wind correlation and generator reactive limits

The operation of power system is facing newer challenges due to inherent uncertainty in wind generation. The current study fills the gap in the available literature by combining the nature of uncertainty of wind power in terms of varying wind speed correlation, turbine characteristics and generator reactive power limits. Wind speed correlation is modelled using Weibull distribution and Gaussian copula; the turbine characteristics considered are rated power and turbine performance index (TPI); the generators considered are doubly fed induction generators (DFIG) whose reactive power limits are varied based on converter current limits and grid voltage conditions. The above variations are combined to simulate a large number of dependent wind power scenarios which are then incorporated as two wind farms replacing the conventional generators at bus 11 and bus 13 in a modified IEEE 30 bus 6 generator system. Optimal power flow solution is obtained for each of the scenarios and the impact on generation cost, emission, losses, capacity factor and voltage profile is investigated. Another novelty in this work is computation of voltage violation index (VVI) for varying correlation and turbine characteristics when the system is subjected to contingency states of load increase, line outage and their combination.

Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation

For the integration of distributed generations such as large-scale wind and photovoltaic power generation, the characteristics of the distribution network are fundamentally changed. The intermittence, variability, and uncertainty of wind and photovoltaic power generation make the adjustment of the network peak load and the smooth control of power become the key issues of the distribution network to accept various types of distributed power. This paper uses data-driven thinking to describe the uncertainty of scenery output, and introduces it into the power flow calculation of distribution network with multi-class DG, improving the processing ability of data, so as to better predict DG output. For the problem of network stability and operational control complexity caused by DG access, using KELM algorithm to simplify the complexity of the model and improve the speed and accuracy. By training and testing the KELM model, various DG configuration schemes that satisfy the minimum network loss and constraints are given, and the voltage stability evaluation index is introduced to evaluate the results. The general recommendation for DG configuration is obtained. That is, DG is more suitable for accessing the lower point of the network voltage or the end of the network. By configuring the appropriate capacity, it can reduce the network loss, improve the network voltage stability, and the quality of the power supply. Finally, the IEEE33&69-bus radial distribution system is used to simulate, and the results are compared with the existing particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM). The feasibility and effectiveness of the proposed model and method are verified.

Risk-based Reserve Coordinative Unit Commitment for a Large-scale Wind-storage System

The increasingly high integration of wind power into power systems will further increase the power uncertainty and make it difficult to calculate the requirement of the reserve. In this article, a risk-based reserve optimization approach is proposed to quantitatively evaluate the reserve requirement of a large-scale wind-storage system. Conditional value-at risk (CVaR) is adapted to calculate the risk reserve managing the uncertainty of wind generation; this risk reserve achieves the optimal risk without sacrificing system reliability. An energy storage system (ESS) is employed to undertake the role of reserve transfer (RT) by cooperating with wind generation and conventional thermal units in the decision-making of unit commitment (UC); their cooperation and coordination can be achieved by building a bilevel optimization model with a day-ahead risk-constrained unit commitment model and a real-time risk reserve adjustment model. By using the duality principle and the big-M method, the formulations are converted into a mixed integer linear programing problem (MILP) that is solved using a column and constraint generation algorithm (C&CG). The model is tested on the six-bus system and the IEEE 118-bus system. The simulation results show that the ESS can transfer the reserve capacity of the thermal generator and improve the ability to accommodate the uncertainty of wind power generation, thereby demonstrating the effectiveness of the proposed methodology.

MPC-based approach for online demand side and storage system management in market based wind integrated power systems

Ever-increasing rate of wind power generation as an uncertain and variable source of energy, face new challenges in power system operation. For this reason, optimal real-time operation of wind integrated power systems in profit based markets considering the effects of probabilistic future variations of wind speed is one of the main concerns of system operators. For this purpose, some solutions such as demand response (DR) programs and energy storage systems (ESS) are widely being used to cover and manage wind generation uncertainty. But some of them such as DR programs can add some extra sources of uncertainty due to unpredictable customer’s behavior. To this end, this paper proposes an online model-based predictive control approach for optimal real-time operation of wind integrated power systems including DR and ESS facilities. Discrete-time manner, re-optimization characteristic, and adaptability are the main features of the proposed MPC method which make it well-suited to address high uncertainties regarding wind power generation and customer’s behavior. Besides, MPC considers all interactive effects of the control facilities in accordance with the expected wind farm output power in the future prediction horizon to maximize wind power utilization and so enhance social welfare. In addition, the uncertain nature of wind power is modeled using Markov chain Monte Carlo method. For efficiency evaluation of the proposed approach, simulation is implemented in MATLAB software using YALMIP optimization toolbox for the 8-bus test system. Results confirm the acceptable performance of the proposed approach in reducing operation cost through optimal uncertainties management.

Electrical LeaderRank method for node importance evaluation of power grids considering uncertainties of renewable energy

Node importance evaluation of power grids plays an important role in the safe operation and planning of power systems. This paper proposes an electrical LeaderRank (ELR) method to identify the important nodes in complex power grids, considering the renewable energy uncertainties, system topological structure, transmission power flow and the loss of loads caused by cascading failures. The proposed method uses an ELR value function to represent the importance of a system node, which can be derived iteratively from the weighted distribution strategy of its in-linked nodes. Furthermore, the uncertainties of wind and solar energy generation are modelled as interval numbers, and the interval power flow calculation is employed to obtain the interval power in transmission lines, direct adjacent matrix, and finally the interval ELR values. The performance of ELR method has been fully tested and benchmarked on the IEEE 118-bus power system and the Western Liaoning Power Grid of China. Comparative results on four performance criteria have not only demonstrated the validity and superiority of the proposed method, but also confirmed its capability to cope with the node importance evaluation of practical power grids.

Bundled Generation and Transmission Planning Under Demand and Wind Generation Uncertainty Based on a Combination of Robust and Stochastic Optimization

Bundled generation and transmission expansion planning (BGTEP) aims to solve problems related to ascendant demand of power systems. A BGTEP model is considered in this paper and the optimal planning for a long-term period is obtained such that the cost of installation and operation would be minimized. Also, due to the recent orientation toward renewable energy sources, the influence of wind farms is involved in the methodology. An important aspect of load and wind power is their uncertain nature and the characteristic of being unforeseen. This matter is under consideration by a bounded and symmetric uncertainty optimization approach. In fact, the combination of two uncertainty methods, i.e., robust and stochastic optimization approaches are utilized and formulated in this paper. Besides, to cope with this uncertainty, Weibull distribution (WD) is considered as wind distribution, while load distribution is counted by a normal distribution (ND). A unique approximation approach for WD to be considered as ND is presented. In addition, a linear formulation is obtained by alternative constraints in order to drastically reduce the level of complexity of the formulation. Accordingly, a mixed-integer linear programming formulation is proposed to solve the BGTEP problem. The modified 6-bus and IEEE 24-bus RTS test systems are used to prove the applicability of the proposed method.

A stochastic MPC based approach to integrated energy management in microgrids

In this paper, a stochastic model predictive control (SMPC) approach to integrated energy (load and generation) management is proposed for a microgrid with the penetration of renewable energy sources (RES). The considered microgrid consists of RES, controllable generators (CGs), energy storages and various loads (e.g., curtailable loads, shiftable loads). Firstly, the forecasting uncertainties of load demand, wind and photovoltaic generation in the microgrid as well as the electricity prices are represented by typical scenarios reduced from a large number of primary scenarios via a two-stage scenario reduction technique. Secondly, a finite horizon stochastic mixed integer quadratic programming model is developed to minimize the microgrid operation cost and to reduce the spinning reserve based on the selected typical scenarios. Finally, A SMPC based control framework is proposed to take into account newly updated information to reduce the negative impacts introduced by forecast uncertainties. Through a comprehensive comparison study, simulation results show that our proposed SMPC method outperforms other state of the art approaches that it could achieve the lowest operation cost.

Stochastic SCUC considering compressed air energy storage and wind power generation: A techno-economic approach with static voltage stability analysis

As the high penetration of wind power increases intermittent generation in power systems, large-scale energy storage systems (ESSs) are necessary to mitigate this variable generation and increase operational flexibility. The variable power generation and the ESSs scheduling may affect voltage security issue of the power systems, making it important to include and assess static voltage stability (SVS) as an important aspect of security in the power system operation studies. Under this perspective, this paper proposes a stochastic security-constrained unit commitment (SCUC) model integrating the bulk ESSs in the power system with the high penetration of wind power. The modeled bulk ESSs are compressed air energy storages (CAESs). A practical criterion of the SVS is considered as an operational constraint to ensure that the stochastic SCUC decision is economically and technically optimal. The wind power uncertainty is handled using an adaptive point estimate method (APEM) due to its decreased computation time and acceptable accuracy, compared to other methods. Also, to reach more practical results, flexible reactive power capability of DFIG-based wind farm is modeled probabilistically. In order to effectively adjust the SVS margin, a two-stage approach is developed in the proposed stochastic SCUC model which includes voltage stability relaxed stochastic SCUC (VSR-SSCUC) as the first stage and voltage stability constrained stochastic SCUC (VSC-SSCUC) as the second stage. The proposed stochastic SCUC model is solved using a mixed-integer non-linear programming (MINLP) method and is applied to IEEE 30-bus and IEEE 57-bus test systems. Simulation results show that the SVS margin can be effectively adjusted using the proposed two-stage approach. In the stage of VSC-SSCUC, expected operation cost is higher than the other stage, whereas a considerable SVS margin is obtained. Proper scheduling of the CAESs can reduce the impact of wind generation uncertainty on supply side variability and improve the SVS of the power system, as well as decrease the expected operation cost. Moreover, the proposed APEM is compared with three conventional methods, namely, Monte Carlo simulation (MCS), scenario reduction method (SRM), and deterministic approach, in numerical studies that show the APEM is more efficient than the conventional methods for this analysis.

Optimal energy management of a renewable-based isolated microgrid with pumped-storage unit and demand response

Abstract This paper proposes a new stochastic optimization framework for day-ahead (DA) energy management of a renewable-based isolated rural microgrid. Non-dispatchable distributed generations (DGs) including wind and photovoltaic (PV) power plants have been operated by the microgrid in order to meet consumers' demand. The main challenge in this condition is to keep balance between generation and demand at all times. In order to cope with this challenge, a pumped-storage unit and an incentive-based demand response program have been used in this work. Moreover, the accurate model of pumped-storage unit, responsive demand and distribution network and also the inherent uncertainty of wind and PV power generation have been considered in this paper. Finally, the effectiveness of the proposed optimization framework has been evaluated through extensive numerical studies. The results show that the pumped-storage unit optimal scheduling and demand response implementation, significantly improve economic and technical performance indexes.

Risk–Based Admissibility Analysis of Wind Power Integration into Power System With Energy Storage System

The increasing high penetration of wind power will further increase the uncertainty in power systems, and three key issues should be addressed: 1) determining the maximum accommodation level of wind power without sacrificing system reliability; 2) quantifying the potential risk when the wind generation realization is beyond the prescribed uncertainty sets; and 3) how to reduce the risk loss. Motivated by these, a risk-based two-stage robust unit commitment (RUC) model is proposed to analyze the admissibility of wind power. In this model, the electricity storage system (ESS) is utilized for managing the wind power uncertainty to reduce the risk loss. Different from a determined uncertainty set in the previous RUC, the proposed method can flexibly adjust the uncertainty set by optimizing the operational risks including the wind spillage risk and load shedding risk. Conditional Value-at Risk (CVaR) is adopted to describe the risk loss when the real wind power output is beyond the predefined uncertainty set. Meanwhile, the low-probability, high-influence events are taken into the account based on CVaR to determine the optimal acceptable wind generation considering the tradeoff between reliability and economics. The proposed model is solved effectively by the modified column and constraint generation method. Case studies on two benchmark systems illustrate that the ESS can reduce the risk loss of power system and improve the ability to accommodate the uncertainty of wind generation.

Stochastic dynamic programming approach to managing power system uncertainty with distributed storage

Wind integration in power grids is challenging because of the uncertain nature of wind speed. Forecasting errors may have costly consequences. Indeed, power might be purchased at highest prices to meet the load, and in case of surplus, power may be wasted. Energy storage may provide some recourse against the uncertainty of wind generation. Because of their sequential nature, in theory, power scheduling problems may be solved via stochastic dynamic programming. However, this scheme is limited to small networks by the so-called curse of dimensionality. This paper analyzes the management of a network composed of conventional power units and wind turbines through approximate dynamic programming, more precisely stochastic dual dynamic programming. A general power network model with ramping constraints on the conventional generators is considered. The approximate method is tested on several networks of different sizes. The numerical experiments also include comparisons with classical dynamic programming on a small network. The results show that the combination of approximation techniques enables to solve the problem in reasonable time.

Dynamic wavelet decomposition based multi-objective operation model for HESS enabling wind power output smoothing

The uncertainty of wind generation brings risk to power grid due to the variability and intermittency of wind. One promising technique to address challenge is deploying hybrid energy storage system (HESS) in wind energy conversion system to shape the wind power output by coordinating the operation of multi-type storage devices. In this paper, a multi-objective operation model of HESS based on dynamic wavelet decomposition (DWD) is presented. The model is characterized by two novelties: i) Life-cycle cost of storage devices and smoothness performance of wind output are simultaneously considered in the objective; ii) Parameters of wavelet analytics need to be optimally determined in a predictive control scheme. An improved NSGA-II is designed to provide efficient solution by analyzing Pareto frontier. A realistic case is studied to validate the presented method.

Design and robust optimization of a novel industrial continuous heat treatment furnace

This paper designs and robustly schedules a novel AAHP (air to air heat pump) based ICHTF (industrial continuous heat treatment furnace) which is driven by a solar Stirling engine and a wind turbine. The proposed flame-making process equipped with two inside and outside air fans, an expansion valve, compressor, condenser, and an evaporator to supply the heating demand with the high-temperature more than 1000 K.The heating energy obtained from the AAHP is not produced during the combustion reactions but is transferred from the ambient air to the furnace chamber. Therefore, use of solar, wind and AAHP in an industrial furnace with no need to fossil fuels not only reduces the stack pollutant emissions and ozone-depleting substances, but also increases the economic savings in the consumptions of petroleum products and electricity, significantly. In this study, an energy-exergy based mathematical framework is comprehensively presented to model the proposed ICHTF in the presence of renewable energy sources such as air, solar, and wind. Moreover, an interval robust economic optimization strategy is introduced to minimize total energy procurement cost using the forecasted wind speed as inputs and taking into account the upper and lower limits of the wind power for modeling its uncertainties. In the robust model, the range of wind power is divided into several consecutive nested subintervals. The proposed ICHTF is simulated on a benchmark small-scale industrial sector which is located in a tropical region in order to minimize total electricity cost considering the operational constraints of Stirling cycle and AAHP considering the wind generation uncertainties. Simulation results reveal the feasibility and strength of the proposed ICHTF by solving a RNLP (robust nonlinear programming) problem using the CONOPT solver under GAMS (general algebraic mathematical system) environment.

Stochastic Networked Microgrid Energy Management With Correlated Wind Generators

Power distribution systems are transitioning from a traditional single distribution system operator (DSO) to a more integrated flexible network that incorporates multiple microgrids (MGs). This paper presents a novel stochastic framework to investigate the impact of correlated wind generators on energy management of DSO and MGs. In order to decouple the problem formulation in DSO and MGs, a linear approximation is proposed which enables parallel processing of the optimization problems. Further, a stochastic framework based on improved point estimate method is proposed to model the uncertainties in load and wind generation, and account for the statistical correlations among wind generators. The proposed method is applied to a modified IEEE 69 bus test feeder and the results are validated in terms of efficiency and accuracy.

Probabilistic voltage stability assessment of distribution networks with wind generation using combined cumulants and maximum entropy method

From the voltage stability viewpoint, a proper probabilistic analysis of the active distribution networks is essential for distribution system operator to identify and rank the weak buses of system. This paper introduces a probabilistic voltage stability index (VSI) to study the local stability of radial distribution networks including wind generation uncertainty. This index can identify the most sensitive bus to the voltage collapse. The proposed model combines the cumulants with the maximum entropy technique based on a backward/forward sweep technique. The suggested method evaluates not only the voltage magnitude, but also the voltage stability condition of each node in a probabilistic manner. Furthermore, the uncertainties of distributed generation and load demand are taken into account by this index. The proposed methodology is applied to IEEE 33-bus and 69-bus distribution test systems, and the results are interpreted. Four scenarios are studied to assess the impacts of substation voltage changes and different probability density functions of load on the distribution functions of voltage and VSI. Moreover, the effects of voltage dependent load model on the probabilistic VSI are studied. The results of different case studies have demonstrated that the proposed approach is fast and accurate.