Offshore wind farm electrical design using a hybrid of ordinal optimization and mixed‐integer programming

Electrical layout design is, for offshore wind farms (OWF), a complex problem that has a far‐reaching impact on both plant cost and reliability. A full optimization of the layout, as opposed to just selecting the most favorable pre‐established configuration, is required in order to capture all the potential efficiencies. However, classical optimization methods such as mixed‐integer programming (MIP) might not be applicable to large OWFs. This paper describes a novel combination of ordinal optimization (OO) and MIP that is able to deal with large problems in reduced computation times with a statistical optimality guarantee. The algorithm is applied to a real case study taken from Barrow Offshore Wind Farm in the East Irish Sea. Copyright © 2014 John Wiley & Sons, Ltd.     

TOPFARM: Multi‐fidelity optimization of wind farms

A wind farm layout optimization framework based on a multi‐fidelity optimization approach is applied to the offshore test case of Middelgrunden, Denmark as well as to the onshore test case of Stag Holt – Coldham wind farm, UK. While aesthetic considerations have heavily influenced the famous curved design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, and the cost of wake turbulence induced fatigue degradation of different wind turbine components. A multi‐fidelity concept is adapted, which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU‐FP6 TOPFARM project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake‐induced fatigue degradation cost function on the basis of the interpolation in a database of simulations performed for various wind speeds and wake setups with the aero‐elastic code HAWC2 and the dynamic wake meandering model. The third level, not considered in this present paper, includes directly the HAWC2 and the dynamic wake meandering model in the optimization loop in order to estimate both the fatigue costs and the AEP. The novelty of this work is the implementation of the multi‐fidelity approach in the context of wind farm optimization, the inclusion of the fatigue degradation costs in the optimization framework, and its application on the optimal performance as seen through an economical perspective. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimization of cable layout designs for large offshore wind farms

Installation of a wind farm exposes several problems such as site selection, placement of wind turbines in the site, and designing of cable infrastructure within the farm. The latter problem, called cable layout design, is the determination of cable connections among turbines and one or more transmitters such that energies generated by turbines will be sent through the cable routes, and eventually gathered at the transmitter(s). This problem is especially important for offshore wind farms where the featured and expensive cables are used. The main objective of the present study is to address the cable layout design problem of offshore wind farms to reduce cable costs in the design using optimization-based approaches. The problem, firstly, is modelled as a mixed integer linear program (MIP) under a set of real-life constraints such as different cable and transmitter types and non-crossing connections between the turbines. Then, a novel mathematical model, which is a modification of the MIP model by imposing several heuristic rules, is proposed to solve the layout problem of large offshore wind farms. Experiments on a set of small- and moderate-sized test instances reveal that the heuristic model, MIP_H, reduces the computer time nearly 55% compared to that of MIP model while the average cable costs generated by the models are close to each other. MIP_H, besides its efficiency, provides more cost-effective layouts compared to MIP model for large-sized real-life examples. Additionally, a comparative study on MIP_H and existing methods in the literature shows that MIP_H is able to solve all instances of the real-life examples providing less cable costs in average.     

Current facts about offshore wind farms

This paper reviews offshore wind projects with a wide perspective. The current situation of the offshore wind market is presented, pointing out the countries leading the process in terms of installed capacity and in terms of technological leadership. Feasibility studies of alternative offshore wind farms (OWFs) are interesting not only in relation to the business but in relation to the techno-economical analyses that engineering researchers need to do. Details about the average energy yield assessment, the costs and the price for the purchased energy are commented on, as key elements of those feasibility studies. The higher cost of renewable energy sources of electricity (RESE) when compared with conventional sources, demands appropriate policy support. The European regulatory framework and the support schemes established by European Member States are presented, as well as the role that different transmission system operators (TSOs) are playing at the moment. Finally, most of the OWFs currently operating are presented, analysing the technical characteristics of their electric subsystems: the wind energy conversion systems (WECSs) transforming the kinetic energy of the wind into electricity, the collector system (CS) gathering the power output of all the turbines to a central collection point (CCP) and the transmission system (TS) taking that power to the onshore main grid.     

含风电场的电力系统经济——排放多目标调度

从火电机组燃料成本和污染物排放两方面入手,构建了含风电场的电力系统发电调度运营管理多目标优化模型。引入了一种新的概率分布模型——截断多用途分布模型(TVD)来表征风电场,并简化风电的不确定性,同时引入基于TVD的可调节置信区间(ACI)风电场成本函数模型及一种基于列维飞行及解决非凸问题的改进型闪电算法(ILFA),可在随机多目标框架中有效地解决经济—排放调度(EED)问题。最后,通过算例与其他经典分布模型进行对比分析,结果表明所提模型可更准确地反映风电情况,该算法在平衡经济成本和污染物排放方面有效。     

Large-Scale Integration of Wind Generation Including Network Temporal Security Analysis

This paper presents a methodology to assess large-scale wind generation projects that considers their effect on network security. The proposed method is based on contingency analysis, including temporal study. Inputs to the simulation are grid model, forecasted load, conventional generation profiles, and wind variability of proposed projects. A time-step simulation is run for the time horizon to produce benefit indices for every location (bus) in the system. The congested transmission elements that require expansion are identified and ranked as part of the simulation. Each wind project in the proposed portfolio can result in benefits or costs for grid security. Policy makers can then use the method to design policies that ensure preservation of long-term system security. Developers could use the tool to identify security effects and assess their wind portfolios. Measuring network security and determining benefits of large-scale wind projects is a complex planning task that involves several aspects: temporal wind variability, spatial distribution of flows, multiple load and generation profiles, and numerous possible contingencies. All these wind project development aspects must be isolated to identify and correctly assign security costs and benefits     

Best economical hybrid energy solution: Model development and case study of a WDS in Portugal

This work aims at the development of an integrated model for analysis and optimization of operating strategies of Water Distribution Systems (WDS), taking into consideration economical-hydraulic-power performances. For this purpose, a software tool has been developed based on the following procedures: (i) an Artificial Neural Network (ANN) to determine the best economical hybrid energy system; (ii) for the ANN training process an energy Configuration type and Economical base Simulator model (CES) is used; (iii) as well a Hydraulic and Power Simulator model (HPS) to describe the hydraulic system behaviour; (iv) a performance assessment tool based on an optimization module to minimize pumping costs and maximize the hydraulic reliability and energy efficiency is then implemented. The Artificial Neural Network uses scenarios with only grid supply, grid combined with hydro-turbine, or with wind turbine and a mutual solution, with hydro and wind turbine. The results obtained show how the model is useful for decision support solutions in the planning of sustainable hybrid energy solutions that can be applied to water distribution systems or others existent hydro-systems, allowing the improvement of the global energy efficiency. A real case study of a small WDS in Portugal is analyzed.     

计及风电条件风险价值的鲁棒柔性超前调度

提出一种考虑大规模风电并网的超前优化调度方法,引入风电条件风险价值来评估风电消纳风险。建立基于鲁棒优化的柔性超前调度模型,以平衡运行成本与风电条件风险价值。根据该模型,对AGC机组的基点功率、参与因子、柔性容量进行协同优化,还可得到各风电场输出功率的可容许区域。提出一种基于大M法和分解法的求解双线性规划模型的高效算法。所提模型及算法结合鲁棒优化与随机优化的优点,在保证计算效率的同时,可避免鲁棒优化的过度保守。仿真结果验证了所提模型及算法的有效性。     

Economic and environmental implications of different approaches to hedge against wind production uncertainty in two-settlement electricity markets: A PJM case study

This paper examines the economic and environmental outcomes of four two-settlement electricity market clearing designs. The first design corresponds to a Deterministic Market Clearing (DMC) similar to the mechanism currently used in organized wholesale electricity markets in the United States. The other three designs account for the day-ahead (DA) wind power production uncertainty into the DA market mechanisms either implicitly or explicitly. An Augmented Deterministic Market Clearing (ADMC) design introduces DA ramp-capability products. These products ensure adequate and ramp-feasible electricity generation capacity commitments in the DA stage to cope with the real-time realization of wind power generation. A Hybrid Deterministic Market clearing (HDMC) design augments ADMC by explicitly integrating a characterization of wind power production uncertainty into the residual unit commitment (RUC) process, which is run after the DA market is closed, using stochastic programming. The last design, referred to as stochastic market clearing (SMC), uses stochastic optimization to explicitly account for wind power production uncertainty in the DA market clearing mechanisms (i.e. DA unit commitment and economic dispatch).The four market clearing designs are assessed by simulating the electricity market operations of a test system and comparing their results in terms of operating costs, prices, costs and revenues of different types of producers, consumer's payments, integration of wind power, and air emissions. The test system has 12% of the capacity of PJM's fossil-fired power generation fleet, and uses data on coincident demand and wind power production from the Bonneville Power Administration (BPA) system during years 2010–2014. The simulations are performed hourly for a whole year.Results show that SMC is superior as its costs reductions are more than two times the improvements attained by ADMC and HDMC. Also, SMC results in electricity prices that are better aligned with operation costs, cuts the spread between the day-ahead and real-time prices by >40%, reduces out-of-market short-term revenue sufficiency payments by 58%, reduces CO₂ emissions by 3.52%, and decreases power plants' cycling. HDMC is a distant second-best market design. Relative to DMC, it achieves a reduction in total costs that is less than half the reduction achieved by SMC, a reduction in out-of-market payments that is 80% of the reduction attained by SMC, and an increase in wind power integration that is <10% the improvement obtained under SMC.     

Compressed Air Energy Storage in an Electricity System With Significant Wind Power Generation

In this paper, a stochastic electricity market model is applied to estimate the effects of significant wind power generation on system operation and on economic value of investments in compressed air energy storage (CAES). The model's principle is cost minimization by determining the system costs mainly as a function of available generation and transmission capacities, primary energy prices, plant characteristics, and electricity demand. To obtain appropriate estimates, notably reduced efficiencies at part load, start-up costs, and reserve power requirements are taken into account. The latter are endogenously modeled by applying a probabilistic method. The intermittency of wind is covered by a stochastic recombining tree and the system is considered to adapt on increasing wind integration over time by endogenous modeling of investments in selected thermal power plants and CAES. Results for a German case study indicate that CAES can be economic in the case of large-scale wind power deployment     

几何约束条件下海上风电场布局优化方法研究

考虑实际工程需求,开发一种几何约束条件下海上风电场智能布局优化方法。该方法使用Gaussian模型计算风力机尾流区的速度亏损,并以最大化风电场年发电量为目标采用差分进化算法进行优化,可满足海上风电场布局时的各类几何约束。利用该方法分别在3行、4行、7行几何约束下对中国某海上风电场的风力机排布方式进行优化。结果显示,相比于原始布局方案,在考虑海缆铺设成本增加的情况下布局优化方案可提升风电场年发电量2.13%～2.64%。进一步分析表明,布局优化过程中可行解数量的设置需综合考虑智能算法寻优难度的影响。     

Integrated offshore wind farm design: Optimizing micro‐siting and cable layout simultaneously

Electrical layout and turbine placement are key design decisions in offshore wind farm projects. Increased turbine spacing minimizes the energy losses caused by wake interactions between turbines but requires costlier cables with higher rates of failure. Simultaneous micro‐siting and electrical layout optimization are required to realize all possible savings. The problem is complex, because electrical layout optimization is a combinatorial problem and the computational fluid‐dynamics calculations to approximate wake effects are impossible to integrate into classical optimization. This means that state‐of‐the‐art methods do not generally consider simultaneous optimization and resort to approximations instead. We extend an existing model that successfully optimizes cable design to simultaneously consider micro‐siting. We use Jensen's equations to approximate the wake effect in an efficient manner, calibrating it with years of mast data. The wake effects are precalculated and introduced into the optimization problem. We solve simultaneously for turbine spacing and cable layout, exploiting the tradeoffs between these wind farm features. We use the Barrow Offshore Wind Farm as a case study to demonstrate realizable savings up to 6 MEUR over the lifetime of the plant, although it is possible that unforeseen design constraints have implications for whether the savings seen in our model are fully realizable in the real world. In addition, the model provides insights on the effects of turbine spacing that can be used to simplify the design process or to support negotiations for surface concession at the earlier stages of a project.     

The economics of large-scale wind power in the UK A model of an optimally mixed CEGB electricity grid

Previous calculations of the economics of large-scale wind power have been generally limited to the evaluation of the marginal cost of energy, assuming that the addition of a wind farm to an electricity grid does not change the mix of base, intermediate and peak load plant in that grid. Here a simple but powerful numerical generation planning model has been constructed for grids containing wind farms and three classes of thermal power station, but no storage. Electricity demand and available power are specified by empirically based probability distribution functions and the plant mix which minimizes the total annualized costs of the generating system is determined. Capacity credit of wind power is automatically taken into account in the optimization. Using the model, the breakeven costs of wind energy in a model British CEGB grid, containing coal, nuclear, oil and wind driven power plant, are evaluated under various conditions. For a wide range of parameter values, large-scale wind power is likely to be economically competitive in this grid.     

Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms

Wind turbine spacing is an important design parameter for wind farms. Placing turbines too close together reduces their power extraction because of wake effects and increases maintenance costs because of unsteady loading. Conversely, placing them further apart increases land and cabling costs, as well as electrical resistance losses. The asymptotic limit of very large wind farms in which the flow conditions can be considered ‘fully developed’ provides a useful framework for studying general trends in optimal layouts as a function of dimensionless cost parameters. Earlier analytical work by Meyers and Meneveau (Wind Energy 15, 305–317 (2012)) revealed that in the limit of very large wind farms, the optimal turbine spacing accounting for the turbine and land costs is significantly larger than the value found in typical existing wind farms. Here, we generalize the analysis to include effects of cable and maintenance costs upon optimal wind turbine spacing in very large wind farms under various economic criteria. For marginally profitable wind farms, minimum cost and maximum profit turbine spacings coincide. Assuming linear‐based and area‐based costs that are representative of either offshore or onshore sites we obtain for very large wind farms spacings that tend to be appreciably greater than occurring in actual farms confirming earlier results but now including cabling costs. However, we show later that if wind farms are highly profitable then optimization of the profit per unit area leads to tighter optimal spacings than would be implied by cost minimization. In addition, we investigate the influence of the type of wind farm layout. © 2016 The Authors Wind Energy Published by John Wiley & Sons Ltd     

Reliability-based layout optimization in offshore wind energy systems

Existing methods for optimizing wind array layouts typically use power or cost objectives and rarely consider reliability-based objectives. Component and system failure rates, however, are dependent on location-specific wind conditions, are influenced by array layout and wake interactions, and have a direct and significant impact on capital costs, operational costs, and power production. Although wind power plant models exist that calculate wind loads with sufficient resolution to capture component loading dynamics from wind conditions, they are computationally expensive and thus not suitable for research applications requiring many evaluations, particularly optimization. This study describes the development of computationally efficient, reliability-based layout optimization methods, enabling us to explore the relationship between component reliability and layout optimization. These methods include the surrogate modeling of the planet bearing life based on varying wind conditions simulated in FAST.Farm and the formulation of reliability-based objectives based on failure cost and power production models. Through demonstration of this method, we explore how wind conditions, objective functions, and capacity density influence reliability-based layout optimization. Results indicate that considering reliability alongside power production can reduce failure costs associated with replacement costs and downtime whilemaintaining or improving power production. Our conclusions highlight the opportunity for wind power plant developers to integrate reliability and operational expenditures alongside performance and capital expenditure objectives in plant design and development to improve plant performance and costs.     

A framework for the design & operation of a large-scale wind-powered hydrogen electrolyzer hub

Due to the threat of climate change, renewable feedstocks & alternative energy carriers are becoming more necessary than ever. One key vector is hydrogen, which can fulfil these roles and is a renewable resource when split from water using renewable electricity. Electrolyzers are often not designed for variable operation, such as power from sources like wind or solar. This work develops a framework to optimize the design and operation of a large-scale electrolyzer hub under variable power supply. The framework is a two-part optimization, where designs of repeated, modular units are optimized, then the entire system is optimized based on those modular units. The framework is tested using a case study of an electrolyzer hub powered by a Dutch wind farm to minimize the levelized cost of hydrogen. To understand how the optimal design changes, three power profiles are examined, including a steady power supply, a representative wind farm power supply, and the same wind farm power supply compressed in time. The work finds the compressed power profile uses PEM technology which can ramp up and down more quickly. The framework determines for this case study, pressurized alkaline electrolyzers with large stacks are the cheapest modular unit, and while a steady power profile resulted in the cheapest hydrogen, costing 4.73 €/kg, the typical wind power profile only raised the levelized cost by 2%–4.82 €/kg. This framework is useful for designing large-scale electrolysis plants and understanding the impact of specific design choices on the performance of a plant.     

计及风光不确定性的新能源虚拟电厂多时间尺度优化调度

针对中国西北地区新能源消纳问题,该文聚合风力发电、光伏发电、光热电站、电储能装置组成虚拟电厂（VPP）,提出一种基于鲁棒随机优化理论的新能源虚拟电厂多时间尺度优化调度策略。首先对风力发电、光伏发电、光热电站与电储能装置进行数学描述,在此基础上建立VPP多时间尺度优化调度模型。在日前调度层中,以VPP运行效益最大为目标,依据风光日前预测出力建立日前优化调度模型;在时前调度层中,以VPP运行成本最小为目标,根据风光时前预测出力建立时前调度修正模型。同时,为了衡量风电、光伏发电出力不确定性对系统的运行影响,建立VPP随机优化调度模型。仿真结果验证该模型可提高运行效益与新能源消纳能力。     

Joint market clearing in a stochastic framework considering power system security

This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.     

Cost effective second generation AC-modules: Development and testing aspects

In the framework of the European research project PV2GO, a new AC-module inverter was developed, taking into account all relevant aspects from a European market's point of view (standards, market, application, and research and development goals). The project goal was to achieve the overall system costs of 3 Euro per Wp for a modular plug-and-play photovoltaic system. For the photovoltaic-module, a standard 130-Wp Eurosolare module was chosen. The research and development (R&D) goal was to develop an advanced DC-control system consisting of a state-of-the-art programmable digital device and an Application Specific Integrated Circuit (ASIC) for the AC-control of the inverter. According to the topology concept, thermal and magnetic designs were optimized with regard to production technology and packaging for large-scale production. The new AC-modules were tested in a number of field-test sites in various parts of Europe and their reliability was assessed through Highly Accelerated Stress Tests. Efficiency and power quality have been tested in the laboratory. Further in the PV2GO project an optimization study of the manufacturing process of the new generation of AC-modules for high volume output was done. Another task was the pre-certification procedure to assure compliance with the European guidelines and standards.     

Trading wind power through physically settled options and short‐term electricity markets

Wind power producers participating in today's electricity markets face significant variability in revenue streams, with potential high losses mostly due to wind's limited predictability and the intermittent nature of the generated electricity. In order to further expand wind power generation despite such challenges, it is important to maximize its market value and move decisively towards economically sustainable and financially viable asset management. In this paper, we introduce a decision‐making framework based on stochastic optimization that allows wind power producers to hedge their position in the market by trading physically settled options in futures markets in conjunction with their participation in the short‐term electricity markets. The proposed framework relies on a series of two‐stage stochastic optimization models that identify a combined trading strategy for wind power producers actively participating in both financial and day‐ahead electricity markets. The proposed models take into consideration penalties from potential deviations between day‐ahead market offers and real‐time operation and incorporates different preferences of risk aversion, enabling a trade‐off between the expected profit and its variability. Empirical analysis based on data from the Nordic region illustrates high efficiency of the stochastic model and reveals increased revenues for both risk neutral and risk averse wind producers opting for combined strategies.