Security constraints for wind power operation

ABSTRACTThis paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.

Analysis of a multi turbine offshore wind farm connected to a single large power converter operated with variable frequency

Maximum generation power evaluation of variable frequency offshore wind farms when connected to a single power converter

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

A thermal unit commitment (UC) computer program has been used at the Riyadh Energy Control Centre of Saudi Consolidated Electric Company, Central Region, (SCECO Central) since 1982. Since then, system growth, plant aging, turbine degradation, ambient temperature effect on unit output, transmission constraints (line loading), MVAr/voltage control problem at light loads, and stringent operating requirements (spinning reserve, etc.) have imposed many constraints on the program, making it nearly impossible to produce a generation allocation schedule that is acceptable to system operators. A study undertaken to decide what should be done with the UC program and how the operator could be supplied with a daily generation schedule that is sufficiently accurate and practically feasible is described. Because it was not feasible to provide a new unit commitment program or to modify the existing one, it was decided to tailor the unit commitment input dataset such that the constraints are appropriately represented. In addition, it was decided to write a suite of computer programs (UCPROG) in FORTRAN that would process necessary input data and results of the UC program to achieve a more realistic, detailed, but precise unit allocation schedule.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

To determine the association between estimated muscle fiber typology and the start and turn phases of elite swimmers during competition. International and national competition racing performance was analyzed from 21 female (FINA points = 894 ± 39: 104.5 ± 1.8% world record ratio [WRR]) and 25 male (FINA points = 885 ± 54: 104.8 ± 2.1% WRR) elite swimmers. The start, turn, and turn out times were determined from each of the swimmers' career best performance times (FINA points = 889 ± 48: 104.7 ± 2.0% WRR). Muscle carnosine concentration was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and was expressed as a carnosine aggregate z score relative to an age- and gender-matched nonathlete control group to estimate muscle fiber typology. Linear mixed models were employed to determine the association between muscle fiber typology and the start and turn times. While there was no significant influence of carnosine aggregate z score on the start and turn times when all strokes and distance events were entered into the model, the swimmers with a higher carnosine aggregate z score (ie,faster muscle typology) had a significantly faster start time in 100-m events compared with the swimmers with a lower carnosine aggregate z score (P = .02, F = 5.825). The start and turn times were significantly faster in the male compared with the female swimmers in the 100-m events compared with other distances, and between the 4 different swimming strokes (P < .001). This study suggests that start times in sprint events are partly determined (and limited) by muscle fiber typology, which is highly relevant when ∼12% of the overall performance time is determined from the start time.

Wind speed scenario generation based on dependency structure analysis

As an attempt to reduce reliance on fossil fuels to satisfy electricity demand, the penetration of renewable sources like wind or solar power has experienced rapid growth in recent years. Due to their intermittent nature, the exact contribution of renewable sources to electricity supply is at least partly unknown. In order to be able to accommodate this uncertainty in real-time, sufficient capacities in the form of thermal and hydro power plants must be available. On that account, power system operators solve the so-called unit commitment problem after bidding in the day-ahead market closes to derive schedules for their power plants which can ensure system reliability at low costs. In literature, two approaches have mainly been studied in this context: Robust and stochastic unit commitment. Being a worst-case formulation, robust unit commitment puts its focus on reliability. A common drawback of this approach is that it tends to deliver over-conservative schedules. Stochastic unit commitment on the other hand creates more cost-effective schedules by preparing for the expected case, but either fails to guarantee system reliability or puts a high workload on the CPU. The limitations of known formulations have sparked interest in so-called hybrid approaches, which aim at combining the ideas of robust and stochastic unit commitment in a favorable way. This paper reports a novel hybrid approach to solve the unit commitment under uncertainty, which yields both robust and cost-efficient schedules. The new method respects the continuous nature of uncertainties and is thus in particular favorable for applications in power systems with high penetration of volatile renewable sources. By merging the ideas of robust and stochastic unit commitment, the proposed hybrid formulation minimizes the expected worst-case dispatch costs. Our method relies on partitioning the continuous range of the uncertainties into subsets. By means of the number of partitions, the solution can be adjusted between the conservative robust and the cost-efficient stochastic unit commitment in a user-friendly manner. A Benders decomposition algorithm is derived to solve the hybrid unit commitment efficiently. Finally, a case study confirms the superior performance of the proposed method.

Since many countries worldwide are going toward the liberalisation of electric energy markets, the need of forecast tools for unit commitment is growing. These tools allow us determining a reliable forecast for the hourly production and price and use of each generator. These are crucial pieces of information for all the players in the market, which enable to compute lower bounds for expected prices on real markets. Although unit commitment is a classical problem, and many professional or research software products are available for its solution, it is not easy to find a tool that is able to manage systems with a large number of generators, taking into account the dynamic constraints between different hours on a long time horizon, and the transmission constraint between several areas. The proposed methodology for optimal unit commitment has been developed with the purpose of giving a reliable solution for systems with hundreds of generators, radially connected areas. This solution performs commitment and dispatch hour by hour, searching for overall optimal solutions, taking into account the above-mentioned constraints, on a time horizon on 1 year. The algorithm has been tested on a real system, namely the Italian one and final results are produced within a reasonable computation time.

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

This paper presents centralized control for a wind farm using model predictive control (MPC) method. In order to solve intra-day unit commitment (UC) problem, the UC problem is solved by using the MPC method with short-term wind power forecasting. We introduce a new dynamics model for the UC with some constraints to utilize the benefits of the MPC method. The objective function considering the operation and maintenance costs is formulated by adding a new variable in order to average the operating time of each wind turbine (WT) within the whole time. The proposed method could solve the UC problems on-line using the prediction time horizon that could be selected flexibly considering time horizon based on wind power forecasting errors. From the simulation study using 10 WTs, we observed that the proposed dynamics model of UC effectively provided the optimal solution to each scenario. Numerical study will validate that the proposed method can be applied to solving the UC problem of a large scale wind farm by aggregating WTs.

This chapter describes and formulates three important problems for the economic management of power systems, namely, the Unit Commitment (UC), the Economic Dispatch (ED), and the Network Constrained Unit Commitment (NCUC) problems. The UC problem determines the commitment of generating units with the aim of minimizing operating costs while supplying the demand and meeting technical and security constraints. Then, once the commitment of generating units is decided, the ED problem determines the actual power output of each generating unit that is needed to supply all demands at minimum cost, while complying with the technical constraints of the transmission network. Finally, if both the commitment of generating units and transmission constraints are considered simultaneously, the resulting problem is known as the NCUC problem. These three problems are mostly considered in electricity markets throughout the US.

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

New metaheuristic methodology for solving security constrained hydrothermal unit commitment based on adaptive genetic algorithm

In wind farm (WF) systems, the set-points of wind turbine generators (WTGs) is determined by the wind farm operator. A change in the value of set-point in two consecutive intervals leads to power fluctuations in the WF system, i.e. power deviation. A large amount of power deviation may adversely affect the operation of the WF system. Therefore, this paper proposes a strategy for operation of a WF system to minimize the power deviation of WTGs during operation time. By using the proposed strategy, the change in set-points of WTGs is minimized and the output power of WTGs is therefore smoother and avoids unnecessary fluctuations. Several grid-code constraints are also considered for operation of the WF system. This ensures the WF system to operate in compliance with the requirements from transmission system operator. Besides, an additional strategy is also proposed to monitor all events in the WF system. Whenever an event occurs in the system, the set-points of WTGs are rescheduled considering the event. Therefore, the proposed operation strategy is also capable of handling events in the WF system. Finally, the simulation results with and without the proposed method are compared to show the effectiveness of the proposed method.