Jan Pierik

A Novel Distributed Direct-Voltage Control Strategy for Grid Integration of Offshore Wind Energy Systems Through MTDC Network

Although HVDC transmission systems have been available since mid-1950s, almost all installations worldwide are point-to-point systems. In the past, the lower reliability and higher costs of power electronic converters, together with complex controls and need for fast telecommunication links, may have prevented the construction of multiterminal DC (MTDC) networks. The introduction of voltage-source converters for transmission purposes has renewed the interest in the development of supergrids for integration of remote renewable sources, such as offshore wind. The main focus of the present work is on the control and operation of MTDC networks for integration of offshore wind energy systems. After a brief introduction, this paper proposes a classification of MTDC networks. The most utilized control structures for VSC-HVDC are presented, since it is currently recognized as the best candidate for the development of supergrids, followed by a discussion of the merits and shortcomings of available DC voltage control methods. Subsequently, a novel control strategy-with distributed slack nodes-is proposed by means of a DC optimal power flow. The distributed voltage control (DVC) strategy is numerically illustrated by loss minimization in an MTDC network. Finally, dynamic simulations are performed to demonstrate the benefits of the DVC strategy.

Optimal Power Flow Control of VSC-Based Multiterminal DC Network for Offshore Wind Integration in the North Sea

Europe is rapidly expanding its wind energy capacity, especially offshore. Hence, the construction of a multiterminal dc (MTdc) network could bring several advantages to accommodate the generated electrical energy, but will also bring many challenges. This paper focuses on one of these challenges, namely the operation and control of an MTdc network. Moreover, a study is carried on how to optimally operate and control an offshore MTdc network for integration of wind energy in the North Sea. An evolutionary strategy called covariance matrix adaptation (CMA) is employed to obtain optimal power flows inside the offshore network. The MTdc grid is composed of 19 nodes, interconnecting nine offshore wind farms to five European countries. The optimal power flow results obtained from the CMA algorithm are tested in a dynamic simulation model to check the control strategy performance in different case studies.

Optimization of social welfare and transmission losses in offshore MTDC networks through multi-objective genetic algorithm

A multi-objective approach, for an envisioned future DC independent system operator (ISO), on how to optimally operate an offshore multi-terminal DC network is presented in this paper. A pool market is used, in which the ISO receives the bids, of both producers and consumers connected to the offshore network, and determines the electricity spot price. A trade-off between maximization of the social welfare and the minimization of transmission losses is analyzed. The offshore multi-terminal DC (MTDC) network here implemented is based on recent studies [1]. The multi-objective optimization algorithm (MOOA) determines an optimal power flow (OPF), which guarantees the network constrains - e.g. DC voltages boundaries, maximum DC cable current and power produced at the offshore wind farms - as defined by the ISO are all respected. Even on DC Networks the system losses, capitalized over a year, can be in the range of tenths of millions of euros [2]. Consequently, a fair power losses allocation among loads and generators has an important impact on their benefits. Therefore, a losses allocation technique is implemented in the algorithm. System security is also taken into consideration. In order to enhance the DC system stability with regard to predictable changes - demand and generation evolution - and unpredictable events - e.g. an outage at of one of the DC voltage controlling stations - the results of the OPF are also tested to make sure that the offshore network always remains at least N-1 secure [3].

Description and Comparison of DC Voltage Control Strategies for Offshore MTDC Networks: Steady-State and Fault Analysis

Abstract Estimates are that circa 40 GW of offshore wind power capacity is going to be installed throughout Europe by the end of this decade. In this scenario, a pan-European offshore grid network is needed in order to efficiently integrate large amounts of offshore wind into the different European countries’ transmission networks. In this paper, the dynamic model of a multi-terminal HVDC (MTDC) transmission system composed of voltage-source converters is presented. Afterwards, the dynamic models are used to compare four different methods for controlling the DC voltage inside MTDC networks, viz.: droop control, ratio control, priority control and voltage margin method. Lastly, a case study is performed in a four-node MTDC network and the different control strategies are compared during steady-state and an onshore three-phase fnult.

Optimal power flow of VSC-based multi-terminal DC networks using genetic algorithm optimization

Europe is rapidly expanding its offshore wind energy capacity. Hence, the construction of a multi-terminal dc (MTDC) infrastructure to accommodate the generated electrical energy brings several advantages, but also comes with many challenges. Operation and control of a MTDC network is one of these challenges. This paper explains the operation and control of MTDC networks. Moreover, a study is carried on how to optimally operate and control an offshore VSC-based MTDC network. It focus on the development plans for an offshore transnational grid in the North Sea. A genetic algorithm (GA) will be employed to obtain an optimal power flow inside the offshore network. The MTDC grid is composed of 19 nodes, interconnecting 9 OWFs to 5 European countries. The optimal power flow results obtained from the genetic algorithm are tested in a simulation model for three case studies.

Modular approach for the optimal wind turbine micro siting problem through CMA-ES algorithm

Although, only in recent years, northern European countries started to install large offshore wind farms, it is expected that by 2020, several dozens of far and large offshore wind farms (FLOWFs) will be built in the Baltic, Irish and North seas. These FLOWFs will be constituted of a considerable amount of wind turbines (WTs) packed together, leading to an energy density increase. However, due to shadowing effects between WTs, power production is reduced, resulting in a revenues decrease. Therefore, when FLOWFs are considered, wake losses reduction is an important optimization goal. This work presents a modular approach to optimize the energy yield of FLOWFs through an evolutionary algorithm. In order to do so the algorithm is set to find an optimal WF layout. The method consists of a modular strategy where the site wind rose information is used in different steps, which accelerates the calculation speed of the wake losses. The results presented demonstrate the method effectiveness. A computational time decrease is observed when compared to the standard optimization strategy, without jeopardizing the quality of the optimal layouts achieved.

A clustering approach for the wind turbine micro siting problem through genetic algorithm

Offshore wind farms with high installed capacities and located further from the shore are starting to be built by northern European countries. Furthermore, it is expected that by 2020, several dozens of large offshore wind farms (LOWFs) will be built in the Baltic, Irish and North seas. These LOWFs will be constituted of a considerable amount of wind turbines (WTs) packed together. Due to shadowing effects between turbines, the power production is reduced, resulting in a decreased wind farm efficiency. Hence, when LOWFs are considered, wake losses reduction is an important optimization goal that needs to be considered. This work presents a clustering approach to optimize the energy production of LOWFs through a genetic algorithm (GA). The method consists of a turbine clustering strategy where the optimal wind farm layout is obtained in different steps. The number of turbines used in each step is increased until all turbine locations have been optimized. The results demonstrate the method effectiveness. A computational time decrease and a reduction of the problem search space are observed when compared to the standard optimization strategy, without jeopardizing the quality of the optimal layouts achieved.

Steady State Electrical Design, Power Performance and Economic Modeling of Offshore Wind Farms

Abstract A load flow model has been developed for the evaluation of thirteen different electrical architectures for large offshore wind farms. In a case study, these architectures have been evaluated for two wind farm sizes (100 and 500 MW) and two distances to shore (20 and 60 km). The case study has shown that systems C1 (string layout) and C2 (star layout), have the lowest contribution of the electrical system to the price per kWh (Partial Levelized Production Cost PLPC). C1 and C2 system prices are 19.7 and 24.9 MEuro (100 MW, 20 km), 36.9 and 42.1 MEuro (100 MW, 60 km), 91.7 and 109.5 MEuro (500 MW, 20 km) and 132.9, 150.7 MEuro (500 MW, 60 km). For comparison another case study with prolonged life-time and predicted price decrease of power electronic components is shown too.

Comparison of offshore power transmission technologies: A multi-objective optimization approach

This paper presents an economic comparison between HVAC and HVDC transmission systems for an offshore wind farm with 500 MW installed power. Both transmission systems will be compared in terms of energy losses and total investment costs. The main objective is to obtain optimal tradeoffs between these criteria when different distances to shore are considered.

A Smart Grid simulation centre at the Institute for Energy and Transport — Integration of large amounts of offshore wind energy

In this paper the Smart Grid simulation centre facilities of the Institute for Energy and Transport (IET), Joint Research Centre (JRC) of the European Commissions (EC) are presented, providing a specific application of our work. The Smart Grid Simulation Centre is intended to combine electrical power components and communication/control equipment with system simulation tools. In this way the Centre can test grid elements and evaluate different operation scenarios under various conditions. As a specific activity the cooperation in accessing multiterminal grids is described in this paper.