A market-based transmission planning for HVDC grid—case study of the North Sea

There is significant interest in building HVDC transmission to carry out transnational power exchange and deliver cheaper electricity from renewable energy sources which are located far from the load centers. This paper presents a market-based approach to solve a long-term TEP for meshed VSC-HVDC grids that connect regional markets. This is in general a nonlinear, non-convex large-scale optimization problem with high computational burden, partly due to the many combinations of wind and load that become possible. We developed a two-step iterative algorithm that first selects a subset of operating hours using a clustering technique, and then seeks to maximize the social welfare of all regions and minimize the investment capital of transmission infrastructure subject to technical and economic constraints. The outcome of the optimization is an optimal grid design with a topology and transmission capacities that results in congestion revenue paying off investment by the end the project's economic lifetime. Approximations are made to allow an analytical solution to the problem and demonstrate that an HVDC pricing mechanism can be consistent with an AC counterpart. The model is used to investigate development of the offshore grid in the North Sea. Simulation results are interpreted in economic terms and show the effectiveness of our proposed two-step approach

Analysis of North Sea offshore wind power variability

This paper evaluates, for a 2030 scenario, the impact on onshore power systems in terms of the variability of the power generated by 81 GW of offshore wind farms installed in the North Sea. Meso-scale reanalysis data are used as input for computing the hourly power production for offshore wind farms, and this total production is analyzed to identify the largest aggregated hourly power variations. Based on publicly available information, a simplified representation of the coastal power grid is built for the countries bordering the North Sea. Wind farms less than 60 km from shore are connected radially to the mainland, while the rest are connected to a hypothetical offshore HVDC (High-Voltage Direct Current) power grid, designed such that wind curtailment does not exceed 1% of production. Loads and conventional power plants by technology and associated cost curves are computed for the various national power systems, based on 2030 projections. Using the MATLAB-based MATPOWER toolbox, the hourly optimal power flow for this regional hybrid AC/DC grid is computed for high, low and medium years from the meso-scale database. The largest net load variations are evaluated per market area and related to the extra load-following reserves that may be needed from conventional generators.Parts of this work were funded by Agentschap.NL, the Netherlands, now RVO.nl (Rijksdienst voor Ondernemend Nederland [25], under the project North Sea Transnational Grid (NSTG). The NSTG project is a cooperation between Delft University of Technology and the Energy Research Center of the Netherlands

Valuation of measurement data for low voltage network expansion planning

The introduction of electric vehicles and photovoltaics is changing the residential electricity consumption. Distribution network operators (DNO) are investing in an advanced metering infrastructure (AMI) to enable cost reduction through smart grid applications. The DNO also benefits from the additional measurement data the AMI gives for the network planning process. The availability of AMI data can be limited by the cost of communication and by privacy concerns. To determine the social welfare of an AMI, the economic gains should be estimated. For the planning of the low voltage (LV) network, a method for determining the value of an AMI still needs to be developed. Therefore, a planning methodology which allows various levels of measurement data availability has been developed. By applying this approach the value of different levels of AMI from an LV-network planning perspective can be determined. To illustrated the application of this approach a case study for the LV-network of a Dutch DNO is performed. The results show that an increase in measurement data can lead to €49-254 lower LV-network reinforcement costs. A detailed analysis of the results shows that already 50% of the possible cost reduction can be achieved if only 65% of the households have AMI data available.</p

Valuation of measurement data for low voltage network expansion planning

The introduction of electric vehicles and photovoltaics is changing the residential electricity consumption. Distribution network operators (DNO) are investing in an advanced metering infrastructure (AMI) to enable cost reduction through smart grid applications. The DNO also benefits from the additional measurement data the AMI gives for the network planning process. The availability of AMI data can be limited by the cost of communication and by privacy concerns. To determine the social welfare of an AMI, the economic gains should be estimated. For the planning of the low voltage (LV) network, a method for determining the value of an AMI still needs to be developed. Therefore, a planning methodology which allows various levels of measurement data availability has been developed. By applying this approach the value of different levels of AMI from an LV-network planning perspective can be determined. To illustrated the application of this approach a case study for the LV-network of a Dutch DNO is performed. The results show that an increase in measurement data can lead to €49-254 lower LV-network reinforcement costs. A detailed analysis of the results shows that already 50% of the possible cost reduction can be achieved if only 65% of the households have AMI data available.</p

Developing a transnational electricity infrastructure offshore: interdependence between technical and regulatory solutions

This paper aims at identifying the options for designing an offshore electricity grid and the legal instruments to create such a grid. It will make a first attempt at presenting the technical and legal considerations which coastal states, EU and national legislators and policy makers should take into account in the coming years when planning and weighing their grid design options. By contrast to the onshore system where the current grid is the result of many decades of local, regional, national and international developments, the situation offshore is different in the sense that currently there is more or less no grid. Moreover, the legal basis for developing such a grid is different offshore than onshore. Therefore designing a system which looks beyond national interests and concepts represents a major challenge. We will discuss whether such a new development as the construction of an offshore electricity grid should be a matter of national policy or should a multilateral or international approach be preferred

Deep learning methods for on-line flexibility prediction and optimal resource allocation in smart buildings

Unprecedented high volume of data is available with the upward growth of the advanced metering infrastructure. Because the built environment is the largest user of electricity, a deeper look at building energy consumption holds promise for helping to achieve overall optimization of the energy system. Yet, a knowledge transfer from the fusion of extensive data is under development. To overcome this limitation, in the big data era, more and more machine learning methods appear to be suitable to automatically extract, predict and optimized building electrical patterns by performing successive transformation of the data. More recently, there has been a revival of interest in deep learning methods as the most advance on-line solutions for large-scale and real databases. Enabling real-time applications from the high level of aggregation in the smart grid will put end-users in position to change their consumption patterns, offering useful benefits for the system as a whole.<br/

Deep learning methods for on-line flexibility prediction and optimal resource allocation in smart buildings

Unprecedented high volume of data is available with the upward growth of the advanced metering infrastructure. Because the built environment is the largest user of electricity, a deeper look at building energy consumption holds promise for helping to achieve overall optimization of the energy system. Yet, a knowledge transfer from the fusion of extensive data is under development. To overcome this limitation, in the big data era, more and more machine learning methods appear to be suitable to automatically extract, predict and optimized building electrical patterns by performing successive transformation of the data. More recently, there has been a revival of interest in deep learning methods as the most advance on-line solutions for large-scale and real databases. Enabling real-time applications from the high level of aggregation in the smart grid will put end-users in position to change their consumption patterns, offering useful benefits for the system as a whole.<br/

Big IoT data mining for real-time energy disaggregation in buildings (extended abstract)

In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question. In this paper, we propose a hybrid approach to address this problem. It combines sparse smart meters with deep learning methods, e.g. Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs), to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values. The proposed approach was validated on a real database, namely the Reference Energy Disaggregation Dataset

Unlocking the hidden potentioal of electricity distribution grids

For the grid, electric vehicles can be seen as flexible loads since they stand still for at least 90% of the time. The coupling of these flexible loads to the grid can bring advantages for the power system. However, to connect electric cars enough capacity is needed. Already existing capacity might be made available for this extra load by using the flexibility of the electric vehicles and controlling them well. This paper describes an analysis of the existing capacity of the distribution grid of Enexis B.V. which might become available for flexible loads if they are coupled to the grid in an intelligent way. The available capacity of a part of the distribution grid owned and operated by Enexis B.V. is estimated, based on measured data. Further, it is described how this capacity can be used by flexible loads. It is also briefly discussed how this can be facilitated by the introduction of the so-called ‘Mobile Smart Grid’, which includes secondary systems to control the load