Energy management optimization of a hybrid power production unit based renewable energies

Hybrid power production units seem to be an interesting alternative for supplying isolated sites. This study proposes a new supervision strategy in order to ensure an optimized energy management of the hybrid system. The considered hybrid unit includes a wind generator (WG), a fuel cell (FC), an electrolyzer (EL) and a supercapacitor (SC). An overall power supervision approach was designed to guarantee the power flow management between the energy sources and the storage elements. The aim of the control system is to provide a permanent supply to the isolated site by adapting production to consumption according to the storage level. A mathematical analysis of the hybrid system using models implemented in Matlab/Simulink software was developed. Simulation results illustrate the performance of the control strategy for an optimal management of the hybrid power production unit under different scenarios of power generation and load demand.     

Bidding strategy of wind-thermal energy producers

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.     

Wind farm efficiency by adaptive neuro-fuzzy strategy

A wind power plant which consists of a group of wind turbines at a specific location is also known as wind farm. The engineering planning of a wind farm generally includes critical decision-making, regarding the layout of the turbines in the wind farm, the number of wind turbines to be installed and the types of wind turbines to be installed. Two primary objectives of optimal wind farm planning are to minimize the cost of energy and to maximize the net energy production or to maximize wind farm efficiency. In the design process of a wind farm the aerodynamic interactions between the single turbines have become a field of major interest. The upwind turbines in a wind farm will affect the energy potential and inflow conditions for the downwind turbines. The flow field behind the first row turbines is characterized by a significant deficit in wind velocity and increased levels of turbulence intensity. Consequently, the downstream turbines in a wind farm cannot extract as much power from the wind as the first row turbines. Therefore modeling wind farm power production, cost, cost per power unit and efficiency is necessary to find optimal layout of the turbines in the wind farm. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate wind farm efficiency according to turbines number in wind farm. This soft computing methodology is implemented using MATLAB/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.     

Tidal turbine array optimisation using the adjoint approach

Oceanic tides have the potential to yield a vast amount of renewable energy. Tidal stream generators are one of the key technologies for extracting and harnessing this potential. In order to extract an economically useful amount of power, hundreds of tidal turbines must typically be deployed in an array. This naturally leads to the question of how these turbines should be configured to extract the maximum possible power: the positioning and the individual tuning of the turbines could significantly influence the extracted power, and hence is of major economic interest. However, manual optimisation is difficult due to legal site constraints, nonlinear interactions of the turbine wakes, and the cubic dependence of the power on the flow speed. The novel contribution of this paper is the formulation of this problem as an optimisation problem constrained by a physical model, which is then solved using an efficient gradient-based optimisation algorithm. In each optimisation iteration, a two-dimensional finite element shallow water model predicts the flow and the performance of the current array configuration. The gradient of the power extracted with respect to the turbine positions and their tuning parameters is then computed in a fraction of the time taken for a flow solution by solving the associated adjoint equations. These equations propagate causality backwards through the computation, from the power extracted back to the turbine positions and the tuning parameters. This yields the gradient at a cost almost independent of the number of turbines, which is crucial for any practical application. The utility of the approach is demonstrated by optimising turbine arrays in four idealised scenarios and a more realistic case with up to 256 turbines in the Inner Sound of the Pentland Firth, Scotland.     

Validation of a three-dimensional viscous–inviscid interactive solver for wind turbine rotors

MIRAS is a newly developed computational model that predicts the aerodynamic behavior of wind turbine blades and wakes subject to unsteady motions and viscous effects. The model is based on a three-dimensional panel method using a surface distribution of quadrilateral singularities with a Neumann no penetration condition. Viscous effects inside the boundary layer are taken into account through the coupling with the quasi-3D integral boundary layer solver Q³UIC. A free-wake model is employed to simulate the vorticity released by the blades in the wake. In this paper the new code is validated against measurements and/or CFD simulations for five wind turbine rotors, including three experimental model rotors [20–22], the 2.5 MW NM80 machine [23] and the NREL 5 MW virtual rotor [24]. Such a broad set of operational conditions and rotor sizes constitutes a very challenging validation matrix, with Reynolds numbers ranging from 5.0⋅10⁴ to 1.2⋅10⁷.     

Open loop wind turbine emulator

A new wind turbine emulator (WTE) is presented, which is able to simulate the turbine power curves without using a closed loop control system. The state of the art emulators use DC or AC motors, closed loop controlled by processors with the turbine power curves recorded. The presented emulator has a DC voltage source, a power resistor and a DC motor with independent excitation. The motor power curve has a shape similar to a wind turbine power curve for a given wind speed; the wind speed variations can be emulated by the variations of the DC voltage source. The open loop emulator is completely different and new, because it works in open loop and does not require the presence of a processor.The following elements are included: the theoretical foundations of the emulator, the emulator power curves adjustment procedure to simulate a commercial wind turbine and the experimental tests.     

Online multi-step prediction for wind speeds and solar irradiation: Evaluation of prediction errors

We propose a general method for predicting multiple steps ahead of our target system and estimating simultaneously the prediction errors in a real time. The requirement of the proposed method is that we have a time series of the target system. We demonstrate the method by artificial data, real wind speed data, and real solar irradiation data.     

Structural design of spars for 100-m biplane wind turbine blades

Large wind turbine blades are being developed at lengths of 75–100 m, in order to improve energy capture and reduce the cost of wind energy. Bending loads in the inboard region of the blade make large blade development challenging. The “biplane blade” design was proposed to use a biplane inboard region to improve the design of the inboard region and improve overall performance of large blades. This paper focuses on the design of the internal “biplane spar” structure for 100-m biplane blades. Several spars were designed to approximate the Sandia SNL100-00 blade (“monoplane spar”) and the biplane blade (“biplane spar”). Analytical and computational models are developed to analyze these spars. The analytical model used the method of minimum total potential energy; the computational model used beam finite elements with cross-sectional analysis. Simple load cases were applied to each spar and their deflections, bending moments, axial forces, and stresses were compared. Similar performance trends are identified with both the analytical and computational models. An approximate buckling analysis shows that compressive loads in the inboard biplane region do not exceed buckling loads. A parametric analysis shows biplane spar configurations have 25–35% smaller tip deflections and 75% smaller maximum root bending moments than monoplane spars of the same length and mass per unit span. Root bending moments in the biplane spar are largely relieved by axial forces in the biplane region, which are not significant in the monoplane spar. The benefits for the inboard region could lead to weight reductions in wind turbine blades. Innovations that create lighter blades can make large blades a reality, suggesting that the biplane blade may be an attractive design for large (100-m) blades.     

Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm

Deregulation and restructuring in power systems, the ever-increasing demand for electricity, and concerns about the environment are the major driving forces for using Renewable Energy Sources (RES). Recently, Wind Farms (WFs) and Fuel Cell Power Plants (FCPPs) have gained great interest by Distribution Companies (DisCos) as the most common RES. In fact, the connection of enormous RES to existing distribution networks has changed the operation of distribution systems. It also affects the Volt/Var control problem, which is one of the most important schemes in distribution networks. Due to the intermittent characteristics of WFs, distribution systems should be analyzed using probabilistic approaches rather than deterministic ones. Therefore, this paper presents a new algorithm for the multi-objective probabilistic Volt/Var control problem in distribution systems including RES. In this regard, a probabilistic load flow based on Point Estimate Method (PEM) is used to consider the effect of uncertainty in electrical power production of WFs as well as load demands. The objective functions, which are investigated here, are the total cost of power generated by WFs, FCPPs and the grid; the total electrical energy losses and the total emission produced by WFs, FCPPs and DisCos. Moreover, a new optimization algorithm based on Improved Shuffled Frog Leaping Algorithm (ISFLA) is proposed to determine the best operating point for the active and reactive power generated by WFs and FCPPs, reactive power values of capacitors, and transformers’ tap positions for the next day. Using the fuzzy optimization method and max-min operator, DisCos can find solutions for different objective functions, which are optimal from economical, operational and environmental perspectives. Finally, a practical 85-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method.     

Probabilistic load flow calculation with quasi-Monte Carlo and multiple linear regression

In this paper, quasi-Monte Carlo combined with multiple linear regression (QMC-MLR) is proposed to solve probabilistic load flow (PLF) calculation. A distinguishing feature of the paper is that PLF is approached by a low-dimensional problem with the concept of the effective dimension, and thus QMC based on low-discrepancy sequences is used to improve the sampling efficiency of the Monte Carlo simulation (MCS). Moreover, according to the relationship between linear correlation and linear regression, the MLR-based correlation control technique is developed to arrange the orders of samples in order to introduce prescribed dependences between variables. The proposed method is tested with the IEEE 118-bus system. Simulation results indicate that the MLR-based technique is robust and efficient in handling correlated non-normal variables and the proposed method shows better performances in PLF calculation compared with other MCS techniques, including simple random sampling (SRS), Latin hypercube sampling (LHS) and Latin supercube sampling (LSS).