Abstract

Decarbonization is one of the biggest challenges of our time. The European Union, as other organizations have already designed the strategies to achieve a more sustainable future. The integration of renewable energy sources is not a technical challenge anymore. However, due to the variability of these type of technologies some actions should be implemented so the conventional ones are not needed any more. One crucial solution is the implementation of demand side management actions which focus on controlling the demand rather than the generation. This thesis identifes the diferent actions on diferent demand side management actions and their performance in Spain. Also, an analysis regarding implemented policies in other European countries is performed to identify successful actions that could be later implemented in Spain. Ingeniería de la Energí

Abstract

In the recent years various Demand Side Management (DSM) projects were successfully completed on Department of Water Affairs (DWA) pump schemes. Each pump scheme consists of a number of pump stations which transfer water to power stations in the Mpumalanga Highveld area and municipal off-takes. The pump stations in a scheme are interconnected via rivers and pipe lines in order to transfer water from national key-point dams to different water users. Therefore, to manage the Highveld's water balance, all the interconnecting schemes work together to keep the important dams at a maximum operating level. Introducing load management on these sites, local- and regional strategies were developed and rolled out. Local strategies involve upgrades to existing pump stations, while regional strategies involve the interactions of the different pump schemes. With the completion of the local strategies, regional strategies for combined water balance and energy optimisation can be developed. By building a system that is more `visible', data is automatically collected. This will then be used to develop an advanced management strategy for the DWA. Therefore, in this article an overview is given on completed load management projects, planned future projects and the strategy to develop a combined electricity and water supply strategy for the DWA http://www.ieee.org/conferences_events/conferences/xplore_conference_proceedings.html

Abstract

Demand-side management and demand response are proposed as a means to solve different objectives in smart grids, such as, e.g., maximizing self-consumption of a house or peak shaving. Crucial components in these approaches are load shiftable/steerable devices, so-called smart appliances. Although several studies already use these devices and determine how to use their flexibility, their impact on — or value for — the overall system is not studied. This paper provides a methodology to calculate the value of flexibility of smart devices. The methodology makes it possible to quantitatively compare the impact of these devices for different kinds of objectives. The developed methodology is applied in a case study to compare the flexibility of white goods, home batteries and electric vehicles. The results indicate among others that smart white goods may not always be that important for smart grids.

Abstract

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Electric vehicles (EVs) could become a controllable grid load by using demand side management techniques, but this requires an information and communications technology (ICT) infrastructure and aggregator agents, to coordinate the EV charging process. To determine the opportunity cost of aggregators, it is necessary to analyze the extra charges in the electricity market, due to the EV charging demand. The EV energy consumption is modeled following agent-based techniques, and the data used corresponds to the Iberian day-ahead market and Spanish mobility needs in 2012. The simulation results show that EVs would significantly influence the electricity price on the day-ahead market, depending on the EV charging behavior.

Abstract

Residential distribution networks in Europe are undergoing rapid changes. As high-power flexible loads, such as electric vehicle (EV) chargers, become more prevalent, the risk of network congestion increases. This is exacerbated by tariff structures which do not give incentives to limit simultaneous power consumption. Network charges in this case may not reflect the true costs of usage, as network costs are driven mainly by simultaneous load peaks.We present a systematic assessment of a new class of tariffs that is currently gaining attention in the Netherlands: capacity subscription models. We argue that this tariff structure is more cost-reflective and fair than the current fixed network fee and show how it helps to prevent transformer overloading in a simple simulation model of a neighborhood of 100 households constrained by a LV transformer, where a varying number of EVs are added.

Abstract

The number of (hybrid) electric vehicles is growing, leading to a higher demand for electricity in distribution grids. To investigate the effects of the expected peak demand on distribution grids, a stress test with 15 electric vehicles in a single street is conducted and described in this paper. The test is conducted in a neighbourhood where both transformers and households are equipped with measurement devices. A significant maximum power consumption increase (more than double) is observed at one transformer when both the electric vehicles and domestic loads stress the network. The observed voltage drop in the network is 17V. Analysis further shows that the hosting capacity is around 15%–20% for the investigated networks and that under voltage is unlikely to occur. The measurements are compared to a simulation and the results show that the simulations predict the actual measurement accurately.

Abstract

Demand Side Management (DSM) is a popular approach for grid-aware peak-shaving. The most commonly used DSM methods either have no look ahead feature and risk deploying flexibility too early, or they plan ahead using predictions, which are in general not very reliable. To counter this, a DSM approach is presented that does not rely on detailed power predictions, but only uses a few easy to predict characteristics. By using these characteristics alone, near optimal results can be achieved for electric vehicle (EV) charging, and a bound on the maximal relative deviation is given. This result is extended to an algorithm that controls a group of EVs such that a transformer peak is avoided, while simultaneously keeping the individual house profiles as flat as possible to avoid cable overloading and for improved power quality. This approach is evaluated using different data sets to compare the results with the state-of-the-art research. The evaluation shows that the presented approach is capable of peak-shaving at the transformer level, while keeping the voltages well within legal bounds, keeping the cable load low and obtaining low losses. Further advantages of the methodology are a low communication overhead, low computational requirements and ease of implementation. Document type: Article

Abstract

Electric vehicles (EV) can be considered as energy storage with availability, energy and capacity constraints that can provide flexibility to the power system in the form of balancing products when aggregated. In this paper, we develop a two-stage stochastic optimization problem that maximizes the profit of a risk-averse EV aggregator for bids on the day ahead in both energy and Frequency Containment Reserve (FCR) markets. Unidirectional charging is examined, while we take into account uncertainty from prices and vehicle availability. Case studies are carried out in different Nordic bidding areas based on historical EV charging data. We identify a strong temporal alignment of EV availability and high FCR-N prices. Results show that consumption is shifted largely towards early hours of the morning. When compared to a reference cost of charging case, up to 50% of the cost of charging can be recovered in Norway, and 100% in Sweden. "p"QC 20190710. QC 20191203

Abstract

The Nordic power system is facing the challenge of the ongoing decrease of synchronous generation along with increased penetration of inverter based renewable generation leading to reduced system inertia. Meanwhile, the electrification of the transport sector will result in a significant amount of additional electrical loads. However, the electrification of private transport is a technology of growing interest that can provide flexibility to the power system if adequately utilized. Electric vehicles (EV) can be considered as temporary energy storage with availability, energy and capacity constraints. In this paper, we use first hand data of a real EV fleet of Tesla vehicles and their historical driving patterns to develop a two-stage stochastic optimization problem. This model maximizes the profit of a risk-averse EV aggregator that aims to place optimal bids on the day ahead in both energy and Frequency Containment Reserve (FCR) markets. Only uni-directional charging is examined, while we take into account uncertainty from prices and vehicle utilization. Case studies are carried out modelling individual vehicle driving behavior in different Nordic price areas in both winter and summer. We identify a strong alignment of EV availability and periods of high FCR prices. Results show that consumption is shifted largely towards early hours of the morning. When compared to a  reference ”cost of charging case”, up to 50% of the cost of charging can be covered in Norway, while the entire cost is met in Sweden. "p"QC 20181017