A variety of wind farm control strategies exist in order to reduce unfavorable wake effects in large wind farms. While strategies like wake steering already reached a high maturity level, it is interesting to compare them to more recently proposed strategies. Such a comparison can form the basis for the development of a symbiotic wind farm control toolbox, from which a control strategy is chosen and activated depending on the operating conditions. The present study compares wake steering with helix control across a wide range of turbine spacings and wind directions using large-eddy simulation (LES). The size of the search space is made computationally tractable for LES by adopting a setup based on one physical upstream turbine and a distribution of virtual downstream turbines which do not exert any thrust force. It is found that helix control is beneficial for full wake overlap and turbine spacing of less than six rotor diameters whereas wake steering proves to be optimal further downstream and for partial wake overlap. Furthermore, the results show that the helix control setpoint in the proximity of full wake overlap scenarios is less susceptible to wind direction variations. This finding indicates that the combination of wake steering and helix control has potential for the design of a wind farm controller which is more robust in full wake overlap scenarios and can reduce the need for large yaw offset adjustments.

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The performance of wind farms can substantially increase when their individual turbines deviate from their own greedy control strategy and instead also take into account downstream turbines operating in the wake. The helix approach is a recently introduced dynamic wind farm control strategy that tackles this issue by leveraging individual pitch control to accelerate wake recovery. Its effective implementation requires detailed knowledge about the scaling between control input and the resulting power gain and turbine loading across the farm. In the present work this scaling is explored by means of large-eddy simulation of a two-turbine farm in the conventionally neutral atmospheric boundary layer. A parameter sweep for the amplitude of the helix is performed showing monotonous increase of the farm's power output with increasing pitch amplitude within the considered range of zero to six degrees. The scaling of the power gain suggests that a threshold amplitude should be exceeded for effective speed-up of the wake recovery, whereas the damage equivalent loads computed for the turbines indicate an upper limit for the amplitude despite increasing power gains.

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Energy system optimization models are widely used to aid long-term investment decision-making for energy systems. From a socio-technical system viewpoint, existing models focus on the cost modeling of the technical subsystem, while the indirect costs of the social subsystem are not often modeled. This paper incorporates indirect costs into such a model, including those associated with generation capacity, energy production, and bilateral trades, respectively. As a proof-of-concept, the model has been applied to a case study for the Dutch power system, reflecting the Dutch national program Regional Energy Strategies, where regions collectively plan wind and solar energy capacities. We conclude that incorporating indirect costs significantly changed the optimal investment capacities and the associated costs for the regions compared to benchmark results from the conventional models. Furthermore, in this case study, a potential free-rider problem with regard to the national climate target occurs. Our model is used as a negotiation simulator to inform the regions about the hypothetical free-riding behaviors and thus helps to achieve a socially acceptable investment plan. The proposed energy system optimization model with indirect costs goes beyond the prevalent cost-minimization paradigm, and can be used to study transaction costs, trading barriers, and willingness to pay.@en

As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyzes annual energy production and wake losses for future, multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Even for turbine types with similar rated power but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability and boundary layer height has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated to between 2 and 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 m s−1. Using a high-fidelity modeling technique, the present work provides insights into the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.@en

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large-eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross-verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large-scale meteorological models as boundary conditions.

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Wind farms suffer from so-called wake effects: when turbines are located in the wind shadows of other turbines, their power output is substantially reduced. These losses can be partially mitigated via actively changing the yaw from the individually optimal direction. Most existing wake control techniques have two major limitations: they use simplified wake models to optimize the control strategy, and they assume that the atmospheric conditions remain stable. In this paper, we address these limitations by applying reinforcement learning (RL). RL forgoes the wake model entirely and learns an optimal control strategy based on the observed atmospheric conditions and a reward signal, in this case the power output of the farm. It also accounts for random transitions in the observations, such as turbulent fluctuations in the wind. To evaluate RL for active wake control, we provide a simulator based on the state-of-the-art FLORIS model in the OpenAI gym format. Next, we propose three different state-action representations of the active wake control problem and investigate their effect on the performance of RL-based wake control. Finally, we compare RL to a state-of-the-art wake control strategy based on FLORIS and show that RL is less sensitive to changes in unobservable data.

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The growing capacity of intermittent energy sources causes more frequent system imbalances as well as congestion. Demand flexibility is a valuable resource that can be used to resolve these. Unfortunately, flexibility can also contribute to congestion, particularly when used to balance the grid. Using flexibility to solve grid problems without creating new ones requires well-designed financial incentives. Congestion management mechanisms (CMMs) are a primary example of such incentives. The question is which of these is most effective in preventing congestion with minimal impact on trading on the imbalance market. This question is answered by comparing traditional CMMs such as grid tariffs to a local flexibility market on their impact on the load in the grid and the lost value of flexibility on the imbalance market. This analysis shows that energy tariffs are not suited for preventing congestion. Capacity tariffs are able to prevent congestion but they impose limitations on the consumer which significantly reduce the value of flexibility on the imbalance market. The flexibility market, an example of a local market, is effective if aggregators do not have a position day ahead or if the distribution system operator limits the buying of flexibility a day before delivery.

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As we are transitioning to an energy system based on renewable sources, the atmosphere is becoming one of our primary energy sources. Understanding atmospheric flows through wind farms has become an issue of large economic and societal concern.@en

Recently, given the increased integration of renewables and growing uncertainty in demand, the wholesale market price has become highly volatile. Energy communities connected to the main electricity grid may be exposed to this increasing price volatility. Additionally, they may also be exposed to local network congestions, resulting in price spikes. Motivated by this problem, in this paper, we present a coordination mechanism between entities at the distribution grid to reduce price volatility. The mechanism relies on the concept of duality theory in mathematical programming through which explicit constraints can be imposed on the local electricity price. Constraining the dual variable related to price enables the quantification of the demand-side flexibility required to guarantee a certain price limit. We illustrate our approach with a case study of a congested distribution grid and an energy storage system as the source of the required demand-side flexibility. Through detailed simulations, we determine the optimal size and operation of the storage system required to constrain prices. An economic evaluation of the case study shows that the business case for providing the contracted flexibility with the storage system depends strongly on the chosen price limit.

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Variable Renewable Energy Sources (VRES) are characterized by intensive land-use and variable production. In existing optimization models that minimize the total cost of the energy system, location-specific VRES production profiles are often used to estimate VRES potential, but land-use and land cover aspects have been largely ignored. In this study, we therefore connect the literature in land cover assessment, VRES potential estimation and energy system optimization modelling by proposing a spatially explicit planning approach. This approach was applied to a case of the Netherlands to showcase its applicability and strength and to give results towards various RES targets. A baseline land-use scenario, a scenario with stricter constraints on land-use that reflects social resistance and spatial policy on wind energy and, thirdly, a scenario assuming unlimited land availability were analyzed. The baseline scenario results show the optimal geographical distribution of the generation capacities over the Netherlands. Wind energy dominates the generation mix and storage is only present at the 100% RES target. Under the strict constraints on land-use, 92% of the suitable land in the country will be deployed to place wind turbines in order to reach 100% RES share compared to 37% in the baseline case. However, the cost of electricity only increases by no more than 5 €/MWh. The unlimited land scenario highlights that the regional optimized capacities are infeasible. Apart from the useful results from the case study, the proposed approach is a first-of-a-kind contribution to the literature and provides a data-driven way to operationalize the location-specific land-use of VRES such that the role of the constraints on the land-use of VRES can be revealed and that policy-relevant results can be obtained.@en

Price volatility in electricity markets could significantly increase as a result of the increase in demand due to the electrification of heating and transport and intermittent power generation from large scale integration of renewable energy sources. In some parts of the grid, price volatility may be even more extreme due to congestion. Energy storage and price responsive demand provide a potential source of flexibility to reduce excessive variations in price. In this paper, we investigate the potential of one such type of price responsive demand, namely thermostatically controlled loads, to mitigate against this adverse economic effect through a coordination mechanism that gives explicit constraints on the local electricity price. In a simulation based study that focuses on an energy community situated in a congested part of the distribution grid, we investigate to what extent thermostatically controlled loads can provide load reduction in order to cap prices at a specified limit. Results show that congestion and the resulting price spikes can effectively be mitigated by exploiting the thermal inertia of the households.

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This paper reviews the literature on the modeling approaches on decentralized energy investment and operation in the prosumer era. The study has several contributions. Firstly, it adds investment models into the review which have not been previously reviewed for decentralized energy modeling. Secondly, a modeling framework consisting of four building blocks is proposed that covers model conceptualization and model operationalization. Thirdly, the relationship between trading mechanisms and model methods is revealed using four evaluation criteria. Furthermore, by reviewing the papers, several trends in the literature are found. Operational models and local markets have been extensively studied, while wholesale market integration and investment models lack scientific attention. Among different trading mechanisms, the usage of bilateral contracts is most commonly seen. Lastly, optimization models significantly outnumber other model methods, and then it follows that their pitfalls such as the scalability of the model and the existence of stable outcomes need to be further addressed in future research.@en

The large-scale integration of renewables to the electrical grid is resulting in the increase of price volatility in electricity markets. This increase is undesirable from both electricity producer and consumer perspectives. In this paper, we present a framework that allows consumers to hedge against the price volatility. Using optimization duality theory, we quantify the amount of demand-side flexibility that an Energy Storage System (ESS) is required to provide for constraining marginal prices to a consumer's maximum willingness to pay for electricity. The ESS is operated using Model Predictive Control (MPC) and depends on renewable generation forecasts. Forecast uncertainties are accounted through probabilistic constraints that are applied on the ESS operation. Probabilistic constraints enable the Energy Storage Operator to set a priori robustness guarantees on the solution which are cheaper than robust approaches. Through simulations it is demonstrated that the formulation is able to successfully hedge against price volatility considering uncertainty.

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Infinite-horizon non-stationary Markov decision processes provide a general framework to model many real-life decision-making problems, e.g., planning equipment maintenance. Unfortunately, these problems are notoriously difficult to solve, due to their infinite dimensionality. Often, only the optimality of the initial action is of importance to the decision-maker: once it has been identified, the procedure can be repeated to generate a plan of arbitrary length. The optimal initial action can be identified by finding a time horizon so long that data beyond it has no effect on the initial decision. This horizon is known as a solution horizon and can be discovered by considering a series of truncations of the problem until a stopping rule guaranteeing initial decision optimality is satisfied. We present such a stopping rule for problems with unbounded rewards. Given a candidate policy, the rule uses a mathematical program that searches for other possibly optimal initial actions within the space of feasible truncations. If no better action can be found, the candidate action is deemed optimal. Our rule runs faster than comparable rules and discovers shorter, more efficient solution horizons.@en

Accurate short-term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high-resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower-resolution models. Recent computational advances have enabled the use of large-eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence-resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof-of-concept study on the prospect of leveraging these ultra high-resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high-frequency information is lost. Therefore, a statistical post-processing approach is explored on the basis of smoothing and feature engineering from the high-frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.

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Recently, the volatility associated with marginal prices has increased due to large scale integration of renewable generation. Price volatility is undesirable from a consumer perspective. To address this issue, we present a framework for hedging that uses duality theory for quantifying the amount of demand-side flexibility required for constraining marginal prices to the consumers maximum willingness to pay for electricity. Using our formulation, we investigate the ability of an Energy Storage System (ESS), as a demand-side flexibility source, to hedge against electricity price volatility across a multi-time period horizon while accounting for its intertemporal constraints. Additionally, we analyze the economical benefit that operating the ESS under information forecasts brings to the consumers.

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Locational Marginal Price (LMP) is a dual variable associated with supply-demand matching and represents the cost of delivering power to a particular location if the load at that location increases. In recent times it become more volatile due to increased integration of renewables that are intermittent. The issue of price volatility is further heightened during periods of grid congestion. Motivated by these problems, we propose a market design where, by constraining dual variables, we determine the amount of demand-side flexibility required to limit the rise of LMP. Through our proposed approach a price requesting load can specify its maximum willingness to pay for electricity and through demand-side flexibility hedge against price volatility. For achieving this, an organizational structure for flexibility management is proposed that exhibits the coordination required between the Distribution System Operator (DSO), an aggregator and the price requesting load. To demonstrate the viability of our proposed formulation, we run an illustrative simulation under infinite and finite line capacities.

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To ensure security of supply and incentivize reliable investment in generation capacity, capacity markets (CMs) have been implemented or are being considered. However, demand response (DR) and electrical energy storage (EES) also contribute to system adequacy. In this paper, we analyse the change in the need for a CM if DR and EES are available, in the presence of a growing portfolio share of intermittent renewable energy sources electricity (RES-E). We present a novel hybrid electricity market model of the transition to a low-carbon electricity system which uses optimization for short-term market operations and agent-based simulation of long-term decisions. DR and EES may significantly reduce the risk of shortages in an energy-only market, even if investment decisions are myopic, like in our model, as compared to an energy-only market without flexibility options. We also present a novel mechanism for contribution of EES to the CM. This reduces the cost of the CM and improves the business case for EES. In our model, DR and EES achieve almost the same improvement of security of supply as a CM, but they do so at a lower cost. Therefore, the case for a centralized CM is weakened in a system with even a limited share of DR and medium-term EES, as presented in our model. These results depend on the duration of scarcity events and the cost of EES and DR. Refinement of the model representation will be required to extrapolate these conclusions to real markets with other types of DR, EES and CMs.

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We present a systematic review of the challenges to the regulation of electricity markets that are posed by the integration of variable renewable energy sources. System integration is the key to developing the required flexibility, because flexibility options exist at all system levels and within the competitive as well as in the regulated (network) domains. The fluctuating nature of variable renewable energy changes the dynamics of investment decisions. We develop a framework for analysing relations between aspects of the regulation of the power sector that need to be coordinated in order to achieve (or at least improve) economic efficiency. We base the framework on the technical functionalities of the electricity infrastructure, which we group along three dimensions: system level (from retail/distribution to transmission/wholesale), geographic scope (the connection between electricity systems) and time scales (from real-time operations and balancing markets to the investment time scale). The framework helps identify regulatory challenges—potential inefficiencies due to a lack of coordination—and to place them into context. The picture that emerges from this approach is that the institutional fragmentation of the European electricity sector will become increasingly burdensome as the development variable renewable energy requires ever closer coordination between countries, between the different levels of the electricity system and between markets that serve different time scales. Interactions between elements of market design and regulation such as congestion management, renewable energy policy and system adequacy policy affect each other and are an additional reason for a system integration approach to regulation.@en