The Cost of Hedging - How Actively Should Wind Farm Portfolios Be Hedged?

2021 ◽  
Author(s):  
Benedikt Hoechner
Keyword(s):  
Wind Farm ◽  
Cost Of Hedging ◽  
The Cost

scholarly journals Improved Formulation for the Optimization of Wind Turbine Placement in a Wind Farm

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Zong Woo Geem ◽  
Junhee Hong
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Fossil Fuels ◽  
Optimal Placement ◽  
Branch And Bound Method ◽  
Optimal Layout ◽  
Objective Functions ◽  
Placement Problem ◽  
Optimization Formulation ◽  
The Cost

As an alternative to fossil fuels, wind can be considered because it is a renewable and greenhouse gas-free natural resource. When wind power is generated by wind turbines in a wind farm, the optimal placement of turbines is critical because different layouts produce different efficiencies. The objective of the wind turbine placement problem is to maximize the generated power while minimizing the cost in installing the turbines. This study proposes an efficient optimization formulation for the optimal layout of wind turbine placements under the resources (e.g., number of turbines) or budget limit by introducing corresponding constraints. The proposed formulation gave users more conveniences in considering resources and budget bounds. After performing the optimization, results were compared using two different methods (branch and bound method and genetic algorithm) and two different objective functions.

A Levelized Cost of Energy (LCOE) Model for Wind Farms That Includes Power Purchase Agreement (PPA) Energy Delivery Limits

2016 ◽  
Author(s):  
Maira Bruck ◽  
Navid Goudarzi ◽  
Peter Sandborn
Keyword(s):  
Wind Farm ◽  
Cost Model ◽  
Wind Farms ◽  
Cost Models ◽  
Renewable Energy Resources ◽  
Supply Source ◽  
Levelized Cost ◽  
Cost Of Energy ◽  
Expected Performance ◽  
The Cost

The cost of energy is an increasingly important issue in the world as renewable energy resources are growing in demand. Performance-based energy contracts are designed to keep the price of energy as low as possible while controlling the risk for both parties (i.e., the Buyer and the Seller). Price and risk are often balanced using complex Power Purchase Agreements (PPAs). Since wind is not a constant supply source, to keep risk low, wind PPAs contain clauses that require the purchase and sale of energy to fall within reasonable limits. However, the existence of those limits also creates pressure on prices causing increases in the Levelized Cost of Energy (LCOE). Depending on the variation in capacity factor (CF), the power generator (the Seller) may find that the limitations on power purchasing given by the utility (the Buyer) are not favorable and will result in higher costs of energy than predicted. Existing cost models do not take into account energy purchase limitations or variations in energy production when calculating an LCOE. A new cost model is developed to evaluate the price of electricity from wind energy under a PPA contract. This study develops a method that an energy Seller can use to negotiate delivery penalties within their PPA. This model has been tested on a controlled wind farm and with real wind farm data. The results show that LCOE depends on the limitations on energy purchase within a PPA contract as well as the expected performance characteristics associated with wind farms.

scholarly journals Towards the North Sea wind power revolution

2018 ◽  
Author(s):  
Jens N. Sørensen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Power ◽  
North Sea ◽  
Wind Turbines ◽  
Wind Farm ◽  
Cost Model ◽  
Offshore Wind ◽  
Meteorological Model ◽  
The North ◽  
The North Sea ◽  
The Cost

Abstract. The present work assesses the potential of a massive exploitation of offshore wind power in the North Sea by combining a meteorological model with a cost model that includes a bathymetric analysis of the water depth of the North Sea. The overall objective is to assess if the wind power in the North Sea can deliver the total consumption of electricity in Europe and to what prize as compared to conventional onshore wind energy. The meteorological model is based on the assumption that the exploited area is so large, that the wind field between the turbines is in equilibrium with the atmospheric boundary layer. This makes it possible to use momentum analysis to determine the mutual influence between the atmospheric boundary layer and the wind farm, with the wind farm represented by an average horizontal force component corresponding to the thrust. The cost model includes expressions for the most essential wind farm cost elements, such as costs of wind turbines, support structures, cables and electrical substations, as well as operation and maintenance as function of rotor size, interspatial distance between the turbines, and water depth. The numbers used in the cost model are based on previous experience from offshore wind farms, and is therefore somewhat conservative. The analysis shows that the lowest energy cost is obtained for a configuration of large wind turbines erected with an interspatial distance of about eight rotor diameters. A part of the analysis is devoted to assessing the relative costs of the various elements of the cost model in order to determine the components with the largest potential for reducing the cost price. As an overall finding, it is shown that the power demand of Europe, which is 0.4 TW or about 3500 TWh/year, can be fulfilled by exploiting an area of 190.000 km2, corresponding to about 1/3 of the North Sea, with 100.000 wind turbines of generator size 13 MW on water depths up to 45 m at a cost price of about 7.5 €cents/kWh.

scholarly journals An Ocean of Promise

2017 ◽  
Vol 139 (04) ◽  
pp. 30-35
Author(s):  
Dan Ferber
Keyword(s):  
Supply Chain ◽  
Financial Risk ◽  
Wind Farm ◽  
The United States ◽  
Offshore Wind ◽  
Levelized Cost Of Electricity ◽  
Offshore Wind Power ◽  
Financing Costs ◽  
Technical Innovations ◽  
The Cost

This article reviews the growth of the wind industry and the need for engineering expertise and technical innovations for it. Establishing an offshore wind supply chain would spur the development of better ways to manufacture turbine parts, ship them to sea, assemble them, and maintain them. This could create jobs for engineers of all stripes, including civil, electrical, and mechanical engineers. As the offshore wind power industry grows, costs continue to fall, in part because engineers in the industry are developing better and cheaper technologies. The article also highlights that by guaranteeing large and sustained markets for offshore wind, policies can entice large turbine vendors, blade manufacturers, and other major offshore wind vendors to bid on more US projects. After investigating conditions in the industry in Europe and the United States, a research team reported in early 2015 that put-in-place policies to reduce the cost and financial risk of building an offshore wind farm could slash project financing costs and ultimately cut the levelized cost of electricity by 50%. Experience and better logistics are making the European offshore wind supply chain more efficient.

The inflation hedging capacity of Islamic and conventional equities

2020 ◽  
Vol 47 (6) ◽  
pp. 1377-1399 ◽  
Author(s):  
Zaghum Umar ◽  
Dimitrios Kenourgios ◽  
Muhammad Naeem ◽  
Khadija Abdulrahman ◽  
Salma Al Hazaa
Keyword(s):  
Comparative Performance ◽  
Content Type ◽  
Vector Autoregressive ◽  
The Real ◽  
Cost Of Hedging ◽  
The Cost ◽  
Real Returns ◽  
Equity Indices ◽  
Inflation Hedging

PurposeThis study analyzes the inflation hedging of Islamic and conventional equities by employing 26 indices for the period ranging from January 1996 till August 2018. The authors investigate the decoupling hypothesis for Islamic versus conventional equities across various investment horizons.Design/methodology/approachThe authors employ a vector autoregressive framework coupled with bootstrapping procedure to compute inflation hedging measures. The hedging measures employed account for the inflation hedging capacity in terms of hedging effectiveness as well as the cost of hedging (efficiency). The authors account for various investment horizons ranging from one month to ten years.FindingsAlthough, the authors do not find consistent evidence for the decoupling hypothesis of Islamic and conventional equities in terms of their inflation hedging capacity. However, the authors document that certain Islamic equity indices can be employed to effectively hedge against the risk of inflation.Originality/valueThe main contribution of this study is that the existing literature on the comparative performance of Islamic versus conventional equities against inflation risk is sparse. The purpose of this study is to analyze the inflation hedging attributes of Islamic versus conventional equities, that is, whether Islamic equities render better real returns than their conventional counterparts. It will contribute to the growing literature on the comparison between Islamic and conventional equities by documenting the real return attributes of these two, apparently different, assets. A further contribution is that in order to account for the different investment horizons for different types of investors, this study will quantify the real return attributes of Islamic and conventional equities for short-, medium- and long-term investors.

scholarly journals Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2904 ◽  
Author(s):  
Wenhao Zhuo ◽  
Andrey V. Savkin
Keyword(s):  
Dynamic Programming ◽  
Wind Farm ◽  
Optimization Problem ◽  
Cost Model ◽  
Dynamic Programming Algorithm ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Renewable Generation ◽  
The Cost ◽  
Charge Discharge

In this paper, an optimal control strategy is presented for grid-connected microgrids with renewable generation and battery energy storage systems (BESSs). In order to optimize the energy cost, the proposed approach utilizes predicted data on renewable power, electricity price, and load demand within a future period, and determines the appropriate actions of BESSs to control the actual power dispatched to the utility grid. We formulate the optimization problem as a Markov decision process and solve it with a dynamic programming algorithm under the receding horizon approach. The main contribution in this paper is a novel cost model of batteries derived from their life cycle model, which correlates the charge/discharge actions of batteries with the cost of battery life loss. Most cost models of batteries are constructed based on identifying charge–discharge cycles of batteries on different operating conditions, and the cycle counting methods used are analytical, so cannot be expressed mathematically and used in an optimization problem. As a result, the cost model proposed in this paper is a recursive and additive function over control steps that will be compatible with dynamic programming and can be included in the objective function. We test the proposed approach with actual data from a wind farm and an energy market operator.

scholarly journals Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine

2022 ◽  
Vol 7 (1) ◽  
pp. 1-17
Author(s):  
Alessandro Croce ◽  
Stefano Cacciola ◽  
Luca Sartori
Keyword(s):  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Control Techniques ◽  
Control Optimization ◽  
Cost Of Energy ◽  
The Individual ◽  
The Cost ◽  
Wind Statistics ◽  
The Impact

Abstract. Wind farm control is one of the solutions recently proposed to increase the overall energy production of a wind power plant. A generic wind farm control is typically synthesized so as to optimize the energy production of the entire wind farm by reducing the detrimental effects due to wake–turbine interactions. As a matter of fact, the performance of a farm control is typically measured by looking at the increase in the power production, properly weighted through the wind statistics. Sometimes, fatigue loads are also considered in the control optimization problem. However, an aspect which is rather overlooked in the literature on this subject is the evaluation of the impact that a farm control law has on the individual wind turbine in terms of maximum loads and dynamic response under extreme conditions. In this work, two promising wind farm controls, based on wake redirection (WR) and dynamic induction control (DIC) strategy, are evaluated at the level of a single front-row wind turbine. To do so, a two-pronged analysis is performed. Firstly, the control techniques are evaluated in terms of the related impact on some specific key performance indicators, with special emphasis on ultimate loads and maximum blade deflection. Secondarily, an optimal blade redesign process is performed with the goal of quantifying the modification in the structure of the blade entailed by a possible increase in ultimate values due to the presence of wind farm control. Such an analysis provides for an important piece of information for assessing the impact of the farm control on the cost-of-energy model.

Analysis of Wind Turbine Capacity Factor Improvement by Correcting Yaw Error Using LIDAR

2017 ◽  
Author(s):  
Roozbeh Bakhshi ◽  
Peter Sandborn
Keyword(s):  
Energy Production ◽  
Wind Farm ◽  
Production Capacity ◽  
Wind Farms ◽  
Power Production ◽  
Capacity Factor ◽  
Offshore Wind ◽  
The Common ◽  
Turbine Capacity ◽  
The Cost

Yaw error is the angle between a turbine’s rotor central axis and the wind flow. The presence of yaw error results in lower power production from turbines. Yaw error also puts extra loads on turbine components, which in turn, lowers their reliability. In this study we develop a stochastic model to calculate the average capacity factor of a 50 turbine offshore wind farm and investigate the effects of minimizing the yaw error on the capacity factor. In this paper, we define the capacity factor in terms of energy production, which is consistent with the common practice of wind farms (rather than the power production capacity factor definition that is used in textbooks and research articles). The benefit of using the energy production is that it incorporates both the power production improvements and downtime decreases. For minimizing the yaw error, a nacelle mounted LIDAR is used. While the LIDAR is on a turbine, it collects wind speed and direction data for a period of time, which is used to calculate a correction bias for the yaw controller of the turbine, then it will be moved to another turbine in the farm to perform the same task. The results of our investigation shows that although the improvements of the capacity factor are less than the theoretical values, the extra income from the efficiency improvements is larger than the cost of the LIDAR.

Scheduling and Routing Optimization of Maintenance Fleet for Offshore Wind Farms Using Duo-ACO

2014 ◽  
Vol 1039 ◽  
pp. 294-301 ◽  
Author(s):  
Zhen You Zhang
Keyword(s):  
Wind Farm ◽  
Optimal Solution ◽  
Wind Farms ◽  
Daily Basis ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Routing Optimization ◽  
Renewable Power ◽  
Operation And Maintenance ◽  
The Cost

Wind energy is one of the fast growing sources of renewable power production currently and there is a great demand to reduce the cost of operation and maintenance to achieve competitive energy price in the market especially for offshore wind farms. An offshore wind farm usually comprises a large number of turbines and thus needs a number of service vessels for maintenance. It is already a complicated task to plan the schedule and route for each of the vessels on a daily basis, dealing with several constraints, such as weather window and maintenance demand, at the same time. Even more challenging is to find an optimal solution. This paper propose a method, i.e. Duo Ant Colony Optimization (Duo-ACO), to improve the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet and thus reduce the operation and maintenance (O&M) cost. The proposed metaheuristic method can help operator to avoid a time-consuming process of manually planning the scheduling and routing.

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