Uncertainty Of Wind Generation Research Articles

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.

Assessment of the Impact of the Forecast Uncertainty of Wind and Photovoltaic Generation on Large-scale Power Transmission

In this paper, we proposed a method to assess the impact of the uncertainty of forecast error of wind and photovoltaic generation on power transmission. First, we proposed a probabilistic model to characterize the uncertainty of the forecast error and established an index system to assess the performance of the transmission system in various aspects. Second, we adopted the Latin hypercube sampling technique to obtain the expectation and the variance of the assessment indices. Third, we introduced a sensitivity method to measure the variation of the probabilistic characteristics of the assessment indices propagated from the variation of the forecast error. Finally, we used a stochastic multi-attribute decision-making method to make the ranking of all renewable power plants by the comprehensive influence of the uncertainty of forecast error on them. With this ranking, it is able to judge which renewable power plant’s forecast accuracy should be improved in order to most effectively control the error of the power transmission. The effectiveness of the proposed models and methods are briefly verified by a case.

Integration of wind generation uncertainties into frequency dynamic constrained unit commitment considering reserve and plug in electric vehicles

Increased penetration of renewable resources in power systems and consequently cut back in the inertia of the whole power system is a significant concern for Unit Commitment (UC) in terms of energy scheduling and frequency dynamics management. The new modeling of the UC problem presented in this paper provides optimal scheduling of the energy and reserve considering the frequency dynamics of the power system. In this work, impact of Wind Turbines (WT) as the clean generation and Plug-in Electric Vehicles (PEV) as the energy storage system are investigated considering a less conservative probabilistic modeling uncertainty of the wind. The paper targets to protect the security of frequency dynamics taking into account the Demand Response (DR) program and contribution of PEVs succeeding a generation loss. The Frequency Dynamics-constrained Unit Commitment (FDUC) considering PEVs was formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem taking into consideration DR and WTs uncertainties. The proposed MINLP problem was then reformulated by the Reformulation-Linearization Technique (RLT) to derive a Mixed-Integer Linear Programming (MILP) problem. An IEEE 6-bus power system was served as a test system to evaluate the proposed approach and simulation results of four different Case study scenarios were obtained. Results revealed that the proposed approach can ensure frequency security and reduce the operational costs.

MPC and robustness optimisation‐based EMS for microgrids with high penetration of intermittent renewable energy

This study develops a three-stage energy management system (EMS) for renewable energy microgrid operation. The core of this framework is based on a unit commitment problem integrated with model predictive control (MPC) to address the problem of uncertainty in renewable sources. Meanwhile, it is shown that an MPC approach may be insufficient to fully address the hurdles for optimal and safe operation of wind power-integrated energy systems due to the severity of wind speed fluctuations within even short time intervals. Spinning reserve resources can have a positive impact to ensure a reliable operation, yet their availability is highly dependent on the existence and capacity of dispatchable energy sources, such as diesel generators, in energy systems. Consequently, a supplementary Constrained Information Gap Decision Theory approach is utilised in this study to optimise the system's robustness against severe uncertainty of wind generations. In order to evaluate the presented framework, a descriptive index is first introduced, and then the model is applied to an isolated microgrid. The results indicate that by deploying these three stages, the renewable energy support index increases, ensuring an optimal, reliable, and safe operation.

Probabilistic decomposition‐based security constrained transmission expansion planning incorporating distributed series reactor

This study presents a probabilistic transmission expansion planning model incorporating distributed series reactors, which are aimed at improving network flexibility. Although the whole problem is a mixed-integer non-linear programming problem, this study proposes an approximation method to linearise it in the structure of the Benders decomposition (BD) algorithm. In the first stage of the BD algorithm, optimal number of new transmission lines and distributed series reactors are determined. In the second stage, the developed optimal power flow problem, as a linear sub-problem, is performed for different scenarios of uncertainties and a set of probable contingencies. The Benders cuts are iteratively added to the first stage problem to decrease the optimality gap below a given threshold. The proposed model utilises the Monte Carlo simulation method to take into account uncertainty of wind generations and demands. Several case studies on three test systems are presented to validate the efficacy of the proposed approach.

Risk-Aware Bilevel Optimal Offering Strategy of a Joint Wind/Storage Unit Based on Information Gap Decision Theory

This article presents a robust approach for the optimal offering of joint wind/storage units in day-ahead nodal energy markets. The problem is formulated as a bilevel optimization problem wherein the upper level, the joint unit profit is maximized and at the lower level, the market is cleared by the system operator. The bilevel optimization problem is effectively formulated as a mathematical program with equilibrium constraints and converted to a trackable mixed-integer linear program. Information gap decision theory (IGDT) is implemented to manage the uncertainties caused by wind generation and price uncertainty of the system. Two different strategies are considered for optimal offering by IGDT including risk-averse and opportunity seeker strategies. While in the risk-averse approach conservative offers are submitted to assure a minimum profit, in the opportunity seeker strategy there is an optimistic view to uncertainties which can be exploited to increase the gained profit.

Optimal stochastic bilevel scheduling of pumped hydro storage systems in a pay-as-bid energy market environment

• The energy storage arbitrage problem is investigated for pay-as-bid energy market. • The problem is modeled as a bi-level programming resulting price-quantity pairs. • The bi-level problem is linearized and solved efficiently using Mccormick envelops. • The complementarity constraints modeled exactly using SOS1 variables. • The results are compared to show the different scheduling of storage system. This paper proposes a stochastic bilevel optimization approach to owners of pumped hydro storage systems (PHSSs) to participate in pay-as-bid power market and provide optimal bids and offers. The price offering of other generation units is modeled by stochastic programming. The upper-level of the proposed bilevel programming seeks maximization of the profit of the PHSS arbitrage, where the lower-level assures the optimal system dispatching (and market-clearing), and keeps the network security. The bilevel optimization is then transferred into a single-level equivalent via Karush-Kuhn-Tucker (KKT) complementarity conditions. The equations of the KKT conditions are linearly modeled using special ordered sets of type 1 (SOS1) variables. Furthermore, the bilinear objective function of the upper-level is approximated using McCormick envelopes relaxation method, in order to obtain the solutions as fast as possible and making the problem as mixed-integer linear programming. The proposed method is verified on the IEEE 24-bus reliability test system (RTS) considering different cases. The operation of a single PHSS is assessed in the network's normal and congested conditions. The results show that the PHSS achieve more revenue in a limited network as the offered prices go up to price cap in some periods. In the studied cases, the profit of energy arbitrage by the PHSS increases from $ 3302 in a normal network, to $ 8170 in a limited network. Moreover, the effect of wind generation uncertainty on the arbitrage problem is investigated using five sub-scenarios dedicated to wind generation. It is shown that the arbitrage profit is more sensitive on generation cost uncertainty rather than wind generation uncertainty. Furthermore, it is revealed that the expected profit in the presence of wind turbines is slightly lower than that without wind turbines. This is due to the fact that the wind generation power is always accepted and dispatched in the market and lowers the load demand and deteriorates the arbitrage opportunity. In this case the expected profit of the PHSS is decreased from $ 3302 to $ 3288. Furthermore, the effects of three PHSS units in the mentioned network is investigated. For a particular PHSS in the studied system, it is found that the increase of PHSS units in the network decreased the expected profit from $ 3302 to about $ 3176. Also, the location of PHSS units is deduced as an influential factor on profitability of merchant storage facilities.

Composite generation and transmission expansion planning toward high renewable energy penetration in Iran power grid

This study proposes a composite generation and transmission expansion planning (CGTEP) with high renewable penetration in Iran power grid. The goal is to minimise the generation and transmission investment and operation costs, start-up/shut-down costs, and the cost of energy not served during a 10-year planning horizon. The problem is formulated in a mixed-integer linear format, considering power flow limits, ramping rate limits, minimum up/down times of the generation units, uncertainty of wind and solar generations, and deterministic and probabilistic reliability constrains including reserve margins for generation capacities and expected energy not supplied boundary. In addition, according to the specific geographic characteristics of Iran, import and export of the electricity, as well as the limitation of water resources for power generation, is modelled in the CGTEP problem. The proposed CGTEP method is implemented in business as usual and renewable target cases without considering the uncertainty, and RT case with uncertainty considerations. In RT cases, 30% renewable penetration is targeted to reduce the CO2 emission by 10%. In addition, the capability of Iran power grid in providing green energy for the neighbouring countries is investigated in this study.

Temporal Decomposition-Based Stochastic Economic Dispatch for Smart Grid Energy Management

This paper presents a temporal decomposition strategy to decompose security-constrained economic dispatch (SCED) over the scheduling horizon with the goal of reducing its computational burden and enhancing its scalability. A set of subproblems, each with respect to demand response, normal constraints, and $N-1$ contingency corrective actions at a subhorizon, is formulated. The proposed decomposition deals with computational complexities originated from intertemporal interdependencies of system equipment, i.e., generators’ ramp constraints and state of charge of storage devices. The concept of overlapping intervals is introduced to make SCED subproblems solvable in parallel. Intertemporal connectivity related to energy storage is also modeled in the context of temporal decomposition. Besides, reserve up and down requirements are formulated as data-driven nonparametric chance constraints to account for wind generation uncertainties. The concept of $\phi -$ divergence is used to convert nonparametric chance constraints to more conservative parametric constraints. A reduced risk level is calculated with respect to wind generation prediction errors to ensure the satisfaction of system constraints with a confidence level after the true realization of uncertainty. Auxiliary problem principle is applied to coordinate SCED subproblems in parallel. Numerical results on three test systems show the effectiveness of the proposed algorithm.

Data-Driven Wind Generation Admissibility Assessment of Integrated Electric-Heat Systems: A Dynamic Convex Hull-Based Approach

This paper proposes a data-driven approach to assess the wind generation accommodation capability of the integrated electric-heat system. The overall assessment model is constructed based on the two-stage robust decision-making framework, where the uncertainty of wind generation is described by a convex hull formed by historical data. To take a balance between modeling accuracy and computation tractability, the convex hull is firstly approximated by the intersection of a set of low-dimensional convex hulls, named the approximated convex hull, and then the approximated convex hull based uncertainty set is further simplified by the vertex coefficient discretization treatment. A uniform-compression-expansion scheme for the identified worst-case data samples is derived to realize the evolution of the dynamic convex hull as well as to bridge the gap between the first- and the second-stage problems. A modified column-and-constraint generation algorithm is devised to solve the overall model. Simulation results on two test systems verify the effectiveness and the scalability of the proposed method.

Optimal Capacity Allocation of Energy Storage System considering Uncertainty of Load and Wind Generation

Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs). However, the uncertainty of load demands and wind generations (WGs) may have a significant impact on the capacity allocation of ESSs. To solve the problem, a novel optimal ESS capacity allocation scheme for ESSs is proposed to reduce the influence of uncertainty of both WG and load demands. First, an optimal capacity allocation model is established to minimize the ESS investment costs and the network power loss under constraints of DN and ESS operating points and power balance. Then, the proposed method reduces the uncertainty of load through a comprehensive demand response system based on time-of-use (TOU) and incentives. To predict the output of WGs, we combined particle swarm optimization (PSO) and backpropagation neural network to create a prediction model of the wind power. An improved simulated annealing PSO algorithm (ISAPSO) is used to solve the optimization problem. Numerical studies are carried out in a modified IEEE 33-node distribution system. Simulation results demonstrate that the proposed model can provide the optimal capacity allocation and investment cost of ESSs with minimal power losses.

A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing

In deregulated environment, the wind power producers (WPPs) will face the challenge of how to increase their revenues under uncertainties of wind generation and electricity price. This paper proposes a method based on deep reinforcement learning (DRL) to address this issue. A data-driven controller that directly maps the input observations, i.e., the forecasted wind generation and electricity price, to the control actions of the wind farm, i.e., the charge/discharge schedule of the relevant energy storage system (ESS) and the reserve purchase schedule, is trained according to the method. By the well-trained controller, the influence of the uncertainties of wind power and electricity price on the revenue can be automatically involved and an expected optimal decision can be obtained. Furthermore, a targeted DRL algorithm, i.e., the Rainbow algorithm, is implemented to improve the effectiveness of the controller. Especially, the algorithm can overcome the limitation of the conventional reinforcement learning algorithms that the input states must be discrete, and thus the validity of the control strategy can be significantly improved. Simulation results illustrate that the proposed method can effectively cope with the uncertainties and bring high revenues to the WPPs.

Probabilistic optimal reactive power planning with onshore and offshore wind generation, EV and PV uncertainties

This article proposes a probabilistic optimal reactive power planning keeping in view the uncertainties in renewable generations (wind and photovoltaic systems), loads, and electric vehicle charging. High voltage direct current based on voltage-sourced converters is an important part in the connection between offshore wind farms and grid. In this article, a probabilistic ac–dc load flow method is used, which includes the connection between the offshore wind farms and grid. For the reactive power planning, capacitor and static Var compensator (SVC) banks have been used. A strategy has been adopted to find the rating of SVCs and capacitor banks. The extensive simulation studies have been performed on a modified IEEE-118 bus system and the results obviates the aptitude of proposed method.

Marginal Bottleneck Identification in Power System Considering Correlated Wind Power Prediction Errors

This letter investigates how to identify the marginal bottleneck, which is defined as the constraint most likely to be violated with the increasing wind generation uncertainty of power system in real-time dispatch. The presented method takes the correlation of wind power prediction error (WPPE) into account, leading to an ellipsoidal formulation of wind power generation region (WGR). Based on constructed WGR, the identification procedure is formulated as a max-max-min problem, which is solved by the algorithm based on iteration linear program with the proposed method to select appropriate initial points of WPPE. Finally, two cases are tested, demonstrating the efficacy and efficiency of the procedure to identify marginal bottleneck.

Withholding strategies for a conventional and wind generation portfolio in a joint energy and reserve pool market: A gaming-based approach

This work considers a strategic producer whose generation portfolio consists of conventional and wind power production. Based on the single leader-follower game, a bi-level complementarity model is constructed to derive optimal capacity withholding strategies for this portfolio in a pool-based market. The upper level of the model represents the maximization of the strategic producer’s expected profits while the lower level represents the security-constrained economic dispatch conducted by the independent system operator. The market clearing scheme refers to energy-only markets optimizing jointly scheduled energy and reserves through a two-stage stochastic programming. The first stage illustrates the day-ahead market clearing and the second stage illustrates the balancing market clearing taking into consideration the wind generation uncertainty. With the use of the Karush-Kuhn-Tacker optimality conditions the initial bi-level model is recast into a mathematical programming with equilibrium constraints model which is then reduced into an equivalent mixed integer linear programming using the strong duality theorem and disjunctive constraints. The proposed algorithm derives optimal scheduled thermal and wind energy as well as reserve deployments. It also provides optimal offers based on the endogenous formation of local marginal prices under network constraints and different wind energy penetration levels.

A Chance-Constrained Multistage Planning Method for Active Distribution Networks

This paper introduces a multistage planning method for active distribution networks (ADNs) considering multiple alternatives. The uncertainties of load, wind and solar generation are taken into account and a chance constrained programming (CCP) model is developed to handle these uncertainties in the planning procedure. A method based on a k-means clustering technique is employed for the modelling of renewable generation and load demand. The proposed solution methodology, which is based on a genetic algorithm, considers multiple planning alternatives, such as the reinforcement of substations and distribution lines, the addition of new lines, and the placement of capacitors and it aims at minimizing the net present value of the total operation cost plus the total investment cost of the reinforcement and expansion plan. The active network management is incorporated into planning method in order to exploit the control capabilities of the output power of the distributed generation units. To validate its effectiveness and performance, the proposed method is applied to a 24-bus distribution system.

Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems

With the rapid development of electric power industry, the problem of dispatching renewable energy resources attracts worldwide attention. This paper presents a stochastic scheduling model to study the day-ahead coordination of a multi-source power system. The proposed stochastic scheduling model aims to find a base-case solution with relatively stable operation cost in the presence of uncertain renewable generation. Wind and photovoltaic generation uncertainties are modeled as scenarios using the Monte Carlo simulation method. Considering that wind and photovoltaic generations present complementary characteristics, scenarios are generated with correlations between wind and photovoltaic generations by Copula theory. To better reflect the characteristics of historical wind data, the fluctuation of wind power is also considered when scenarios are generated. The fast scenario reduction method is applied as a tradeoff between accuracy and computational speed. Numerical simulations indicate the effectiveness of the proposed approach in the coordinated scheduling of Thermal-Hydro-Wind- Photovoltaic systems.

Distributed Energy Management Strategy for Reaching Cost-Driven Optimal Operation Integrated With Wind Forecasting in Multimicrogrids System

This paper considers the cost-driven optimal energy management strategy under a complex environment, multimicrogrids system. To provide the flexibility of load in depth, the heating, ventilating, and air conditioning (HVAC) load is investigated and explicitly formulated incorporated with indoor temperature dynamic function. The operation cost of wind generation includes the basic cost and additional cost to deal with the uncertainty of wind generation, which provides the tradeoff between optimality and possibility. Furthermore, the energy management problem of multimicrogrid is presented according to different characteristics of generation devices, storage devices, and load. A distributed neurodynamic algorithm is presented to solve the nonsmooth optimization of energy management of multimicrogrids system. By this method, the only information exchanged among microgrids is the intermediate variable when computing. The simulation results validate the effectiveness of the proposed cost-driven energy management strategy.

Flexibility Provisions From a Fast Charging Facility Equipped With DERs for Wind Integrated Grids

This paper presents a new mathematical model to design a fast charging facility (FCF) with distributed energy resources (DERs) to provide flexibility services in wind integrated power grids. Two different ownership structures of the FCF and wind generation facility (WGF) are considered. The DER options considered for FCF design are solar photovoltaic (PV) units and battery energy storage systems. The effects of wind power uncertainty on power system operations are mitigated through the designed FCF with DERs via the upward and downward flexibility provisions. Monte Carlo simulations are used to simulate the uncertainties in PV and wind generation, and market price. Studies considering an 18 MW WGF and the National Household Travel Survey 2009 data are presented to demonstrate the effectiveness of the flexibility provisions from the proposed design of FCF with DERs.

Optimal allocation of wind capacity considering the impact of generation uncertainty based on zonotope limited security regions

The increasing wind capacity integration brings new challenges to power system planning and operation, due to the intermittency and uncertainty of its power generations. This paper determines the optimal allocation of wind capacity with considerations of the impact of uncertainty in wind generation using the zonotope-based method, for the sack of reducing the possible violations of operation limits caused by wind power fluctuations. The uncertain variations of wind power are bounded by a zonotope and then propagated through a linearised power flow to get another zonotope, which captures all possible values of the system state variables. The state-bounding zonotope is then added to the optimal model to quantify and assess the impact of generation uncertainty on system static performance. The proposed method is applied to IEEE 33-bus system, the results prove the validity of the method and show that the zonotope based set-theoretic method is more efficient than probabilistic methods when focusing on the operation limits with considerations of the uncertainty of multiple renewable energy sources.