Turbulent dispersion in cloud-topped boundary layers

Compared to dry boundary layers, dispersion in cloud-topped boundary layers has received less attention. In this LES based numerical study we investigate the dispersion of a passive tracer in the form of Lagrangian particles for four kinds of atmospheric boundary layers: 1) a dry convective boundary layer (for reference), 2) a "smoke" cloud boundary layer in which the turbulence is driven by radiative cooling, 3) a stratocumulus topped boundary layer and 4) a shallow cumulus topped boundary layer. We show that the dispersion characteristics of the smoke cloud boundary layer as well as the stratocumulus situation can be well understood by borrowing concepts from previous studies of dispersion in the dry convective boundary layer. A general result is that the presence of clouds enhances mixing and dispersion ¿ a notion that is not always reflected well in traditional parameterization models, in which clouds usually suppress dispersion by diminishing solar irradiance. The dispersion characteristics of a cumulus cloud layer turn out to be markedly different from the other three cases and the results can not be explained by only considering the well-known top-hat velocity distribution. To understand the surprising characteristics in the shallow cumulus layer, this case has been examined in more detail by 1) determining the velocity distribution conditioned on the distance to the nearest cloud and 2) accounting for the wavelike behaviour associated with the stratified dry environmen

Atmospheric flows in large wind farms

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.Energy & Industr

The Power of Electric Vehicles - Exploring the Value of Flexible Electricity Demand in a Multi-actor Context

Electric vehicles (EVs) have the potential to play a crucial role in clean and intelligent power systems. The key to this potential lies in the flexibility that EVs provide by the ability to shift their electricity demand in time. This flexibility can be used to facilitate the integration of renewable energy sources by adjusting EV demand to the variable production of wind or solar energy. On the other hand, the same flexibility can be employed to reduce peaks in network load that could result from a massive adoption of EVs. This PhD thesis aims to improve the understanding of the value of flexible EV demand in the context of multi-actor power systems with a high share of renewable energy sources. We first explore flexible EV demand from a distribution network point of view, and then in the light of renewable energy integration. Moreover, we also bring these perspectives together and investigate mechanisms to align the different objectives related to the distribution networks and renewable energy integration. This thesis thus demonstrates the value of demand response in the sustainable power systems of the future.Energy & IndustryTechnology, Policy and Managemen

How Renewable Energy is Reshaping Europe’s Electricity Market Design

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.Energy & Industr

Incorporating indirect costs into energy system optimization models: Application to the Dutch national program Regional Energy Strategies

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

Reduction of price volatility using thermostatically controlled loads in local electricity markets

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.Energy & Industr

Deep Reinforcement Learning for Active Wake Control

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.</p

Routekaart Energieopslag 2030: Systeemintegratie en de rol van energieopslag

Energy & Industr

Network impacts and cost savings of controlled EV charging

This paper investigates the distribution system impacts of electric vehicle (EV) charging. The analysis is based on a large number of operational distribution networks in The Netherlands. Future load profiles have been constructed by adding different EV charging profiles to household loads and solving the power flows to assess the network impacts on various network levels. The results indicate that controlled charging of EVs leads to significant reduction of overloaded network components that have to be replaced, but the impact varies per network level. Overall, in the uncontrolled charging scenarios roughly two times more replacements are needed compared to the controlled charging scenario. Furthermore, it was shown that for the controlled charging scenario the overall reduction in net present value due to energy losses and the replacement of overloaded network components is approximately 20% in comparison with the uncontrolled charging scenario. The results suggest that the deployment of a flexible and intelligent distribution network is a cost-beneficial way to accommodate large penetrations of EV

A spatially explicit planning approach for power systems with a high share of renewable energy sources

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.Energy & IndustryEnergy Technolog