Domestic energy management methodology for optimizing efficiency in Smart Grids

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and Virtual Power Plant (VPP) to adapting to fluctuating generation of wind turbines. In this paper a generic management methology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Smart grids for rural conditions and e-mobility - Applying power routers, batteries and virtual power plants

Significant reductions of greenhouse gas emission by use of renewable energy sources belong to the common targets of the European Union. Smart grids address intelligent use and integration of conventional and renewable generation in combination with controllable loads and storages. Two special aspects have also to be considered for smart grids in future: rural conditions and electric vehicles. Both, the increasing share of renewable energy sources and a rising demand for charging power by electrical vehicles lead to new challenges of network stability (congestion, voltage deviation), especially in rural distribution grids. This paper describes two lighthouse projects in Europe (“Well2Wheel” and “Smart Rural Grid”) dealing with these topics. The link between these projects is the implementation of the same virtual power plant technology and the approach of cellular grid cells. Starting with an approach for the average energy balance in 15 minutes intervals in several grid cells in the first project, the second project even allows the islanded operation of such cells as a microgrid. The integration of renewable energy sources into distribution grids primary takes place in rural areas. The lighthouse project “Smart Rural Grid”, which is founded by the European Union, demonstrates possibilities to use the existing distribution system operator infrastructure more effectively by applying an optimised and scheduled operation of the assets and using intelligent distribution power routers, called IDPR. IDPR are active power electronic devices operating at low voltage in distribution grids aiming to reduce losses due to unbalanced loads and enabling active voltage and reactive power control. This allows a higher penetration of renewable energy sources in existing grids without investing in new lines and transformers. Integrated in a virtual power plant and combined with batteries, the IDPR also allows a temporary islanded mode of grid cells. Both projects show the potential of avoiding or postponing investments in new primary infrastructure like cables, transformers and lines by using a forward-looking operation which controls generators, loads and batteries (mobile and stationary) by using new grid assets like power routers. While primary driven by physical restrictions as voltage-band violations and energy balance, these cells also define and allow local smart markets. In consequence the distribution system operators could avoid direct control access by giving an incentive to the asset owners by local price signals according to the grid situation and forecasted congestions.Peer ReviewedPostprint (published version

Improving controllability of complex networks by rewiring links regularly

Network science have constantly been in the focus of research for the last decade, with considerable advances in the controllability of their structural. However, much less effort has been devoted to study that how to improve the controllability of complex networks. In this paper, a new algorithm is proposed to improve the controllability of complex networks by rewiring links regularly which transforms the network structure. Then it is demonstrated that our algorithm is very effective after numerical simulation experiment on typical network models (Erd\"os-R\'enyi and scale-free network). We find that our algorithm is mainly determined by the average degree and positive correlation of in-degree and out-degree of network and it has nothing to do with the network size. Furthermore, we analyze and discuss the correlation between controllability of complex networks and degree distribution index: power-law exponent and heterogeneit

Demand-Response Based Energy Advisor for Household Energy Management

Home energy management systems (HEMS) are set to play a key role in the future smart grid (SG). HEMS concept enables residential customers to actively participate in demand response programs (DR) to control their energy usage, reduce peak demand and therefore contribute to improve the performance and reliability of the grid. The aim of this paper is to propose an energy management strategy for residential end-consumers. In this framework, a demand response strategy is developed to reduce home energy consumption. The proposed algorithm seeks to minimise peak demand by scheduling household appliances operation and shifting controllable loads during peak hours, when electricity prices are high, to off-peak periods, when electricity prices are lower without affecting the customer’s preferences. The overall system is simulated using MATLAB/Simulink and the results demonstrate the effectiveness of the proposed control strategy in managing the daily household energy consumption.Peer reviewe

Rethinking Electricity Restructuring

Electric utility restructuring was initiated in the 1990s to remedy the problem of relatively high electricity costs in the Northeast and California. While politicians hoped that reform would allow low-cost electricity to flow to highcost states and that competition would reduce prices, economists wanted reform to eliminate regulatory incentives to overbuild generating capacity and spur the introduction of real-time prices for electricity. Unfortunately, high-cost states have seen little price relief, and competition has had a negligible impact on prices. Meanwhile, the California crisis of 2000-2001 has led many states to adopt policies that would once again encourage excess capacity. Finally, real-time pricing, although the subject of experiments, has yet to emerge. Most arresting, however, is the fact that restructuring contributed to the severity of the 2000-2001 California electricity crisis and (some scholars also argue) the August 2003 blackout in the Northeast, without delivering many efficiency gains. The poor track record of restructuring stems from systemic problems inherent in the reforms themselves. We recommend total abandonment of restructuring and a more thoroughgoing embrace of markets than contemplated in current restructuring initiatives. But we recognize that such reforms are politically difficult to achieve. A second-best alternative would be for those states that have already embraced restructuring to return to an updated version of the old, vertically integrated, regulated status quo. It's likely that such an arrangement would not be that different from the arrangements that would have developed under laissez faire

A Three-Step Methodology to Improve Domestic Energy Efficiency

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.\u