Competition in smart distribution grids

Smart grid has become the hot topic to domestic and foreign about the development trend of power grid in recent years, and communication technology is foundation of the development of smart grid; the paper forecasts the smart grid communications automation and development trend.Further from intelligent dispatching center, smart grid, smart distribution grid and users, in view of the existing and emerging intelligent and automatic application were communication needs analysis, and provide a reliable basis for the smart grid communications planning.

In real-time large-scale optimization problems, such as in smart grids, centralized algorithms may face difficulties in handling fast-varying system conditions, such as high variability of renewable-based distributed generators (DGs) and controllable loads (CLs). Further, centralized algorithms may encounter computation and communication bottlenecks while handling a large number of variables. To tackle these issues, consensus-based distributed strategies have been proposed recently. However, distributed computational intelligence (CI)-based techniques can provide a much better near-optimal solution within fewer iterations of the algorithm, which is a critical requirement in smart grids. Therefore, in this paper, a consensus-based dimension-distributed CI technique is proposed for real-time optimal control in smart distribution grids in which a large number of DGs and CLs are present. The proposed approach considers each DG or CL as a separate private entity, which is more relevant from the perspective of smart grid optimization. In the proposed consensus-based framework, each DG or CL is associated with an agent, and each agent is allowed to communicate only with its neighboring agents. The effectiveness of the proposed approach in terms of convergence, adaptability, and optimality with respect to a centralized algorithm and a benchmark algorithm is shown through simulations on 30-node and 119-node distribution test systems.

Locational signals to reduce network investments in smart distribution grids: What works and what not?

Smart grid is an essential part of the world's changing energy landscape. The uptake of smart grid by utilities is however hampered by the perceived complexity and large capital investment required to implement resilient Information Communication Technology (ICT) and networks that can support self-healing smart grids. Furthermore, failure to meet stringent smart grid communication reliability requirements can have disastrous consequences for grid owners and users. Many of the challenges utilities face with their smart grid ICT network development plans can be attributed to a reliance on conventional designs centred on expanding network hardware infrastructure in vast grid networks. What is needed to overcome these challenges is alternative ways of designing smart grid communication networks, using software defined approaches. This paper proposes a framework that supports software defined designs by combining the principles of the Internet of Things (IoT) paradigm and Machine-to-Machine (M2M) communication. Using the frameworks of software defined systems and virtualisation, our proposed architecture aims to provide a more flexible and affordable approach to developing and maintaining grid communication networks while offering a high level of network reliability, required for various smart grid applications. An evaluation of the implementation of this architecture in a design, based on a real city distribution grid, shows that a software defined communication network is easy to implement when using virtual machines running on existing computer infrastructure and that it is capable of supporting the network functions required for improving communication reliability. We conclude that this approach can potentially promote the adoption of smart grid by utilities while offering new innovative ways for smart grid developers to improve their grid designs.

This paper introduces the concept of intelligent distribution network, development of smart grid technology with meaning and background as well as domestic and foreign research and development status of Smart Distribution Grid. It focuses on the advanced smart distribution grid distribution automation, self-healing distribution network control technology, AMI technology, the impact of distributed generation and micro-network after the access to the smart distribution grid, and construction of smart distribution network problems to be solved.

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

With the development of economic and technology, global resources crisis and environment issues have become increasingly prominent, all of these lead new challenges to the electric power grid. In the development of future power grid, Smart Grid (SG) represents a new direction of future development. Smart Distribution Grid (SDG) is an important breakthrough in SG as the last part of the power system to the user. Firstly, this paper gives the definition of SDG. Then it introduces the key technologies according to the requirement of SDG. Finally, it is concluded that SDG is the inevitable result of the development.

The growth of wind farms in Brazil brings new challenges to the Brazilian electrical system, such as the adequacy of regulation for system operation, the supply-demand balance, the quality of the supplied power, and the system stability. New technologies associated with smart grids will enable an increase in intermittent renewable sources connected to the power distribution system. D-STATCOM (Distribution Static Synchronous Compensator) provides the necessary resources to prevent technical problems in systems with high penetration of wind power plants. It makes possible the wind farms to meet the requirements of network connection as power factor, voltage output and fault ride through requirements, as well as increases the transmission capacity and the network stability. This paper presents the dynamic behavior obtained by computer simulation of a distribution grid at 34.5 kV with D-STATCOM and wind farms. The use of D-STATCOM contributed to increasing the stability margin of the system, allowing the permanence of the three wind farms during fault and post-fault conditions.

The vision of smart grids lie in large scale, distributed electrical power systems, equipped with communication and information technologies for automated and sustainable electricity production and distribution. Smart grids require communication with individual energy consumers for enabling energy providers to create a control-feedback loop using time-dependent pricing. Emerging broadband wireless networks hold the key in providing this efficient communication between energy consumers and smart power grids. In this paper we first propose the use of LTE multicast between the aggregator and energy consumers for efficient demand-response management in smart grids. We formulate the multicast scheduling and resource allocation problem for optimal SG-DR programs and provide a greedy solution. Performance analysis and OMNET++ simulation results, using data from Korean power corporation, demonstrate that our proposed LTE multicast and MBSFN solutions are capable of providing low latency (≤ 40ms) and packet drop (≤ 6%), thus resulting in almost 25% shift in peak energy load and up to 30% reduction in energy production cost.

Smart grid technologies are expected to increase system efficiency and reliability using advancement in automation, communication, computation, and optimization. Several control algorithms have been developed or enhanced for operation and control of smart distribution grid. In recent years, volt/var control (VVC) algorithms have been proposed for energy saving in distribution systems due to advancement provided by smart grid automation and control. VVC helps to lower distribution feeder voltage to decrease energy consumption. However, quantification of this impact is hard due to several external factors such as weather and end users' behavior. On the other hand, existing evaluation methods of VVC algorithms are either not suitable for long-term study or intrusive. This paper addresses these issues by proposing a new technique to estimate “distribution system demand without smart grid implementation” utilizing “data measured from system with smart grid implementation.” Interdependence of voltage and load is considered based on estimated load characteristic. Our key contribution is developing a method for quantifying the real-time energy saving of smart grid automation and control focused on VVC and implementing in an electric utility in eastern Washington, USA. The algorithm is developed using both commercial and open-source software.

The social smart grid: Dealing with constrained energy resources through social coordination

In the smart distribution grid, deep integration of power system and information system is the development direction. In order to effectively excavate the potential value of massive data generated in planning and operation phases, the storage of big data is one of the key technologies. This paper proposes a big data storage method for smart distribution grid based on neo4j graph database. Use the common information model (CIM) of power system for reference to establish topology model of smart distribution grid. The storage method of multivariate heterogeneous data is presented integrated the advantages of graph database and relational database. Simulation results on the IEEE standard test systems show that the proposed big data storage method is able to effectively improve data storage and analysis efficiency of smart distribution grid, and a practical citizen power grid is adopted to verified the effectiveness.

An efficient dependable smart power grid relies on the secure real-time data collection and transmission service provided by a monitoring system. In such a system, the measuring units, such as phasor measurement units (PMUs) and smart meters (SMs), are critical. These measuring equipments function as sensors in the smart grid. Data exchanges between these sensors and the central controller are protected by various security protocols. These protocols usually contain computationally intensive cryptographic algorithms that cause heavy energy overhead to the sensor nodes. Since PMUs and SMs are mostly energy-constrained, the problem of how to ensure the secure communication with minimum energy cost becomes a critical issue for the functionality of the whole smart grid. In this article, we focus on the low power secure communication of the PMUs and SMs. We take two wireless sensor platforms as examples to experimentally investigate the approaches and principles of reconciling the two conflicting system requirements-communication security and low energy consumptions. The proposed methods are general ones and applicable to other energy-constrained yet security sensitive systems.

IT security management in distributed Smart Grids has been discussed in a number of recent works, mainly from the viewpoint of how to prevent unauthorized access, either organizationally or cryptographically. In this paper, we aim to extend this viewpoint by introducing the concept of emergent security in cyber-physical energy systems. The main contribution of this paper is the description of four architectural layers, providing a structured approach for security assessment and evaluation. The proposed emergent security layer shifts the perspective of IT security towards incorporating real-time measurements, decoupling feedback of components, increasing topology-awareness, and implementing distributed counter-measures and participant reputation systems. We propose an iterative process for designing resilient distributed control algorithms for Smart Grids. This process acknowledges the new challenges for security management in critical infrastructures due to the recent advances in inter connectivity of systems. The proposed approach is validated using an implementation of Smart Grid control and a co-simulation of a cyber-physical energy system, indicating the importance of the introduced emergent security layer.

There are many kinds of characteristics of smart distribution grid, such as the complex structure, the complexity of the users, and the factors that affect the power grid loss, which not only related to the spatial distribution of the load, but also the load on the time scale of the power changes. Aiming at the relationship between the load and the network loss in the smart distribution grid, the relationship between the load and the network loss is taken into consideration, and the deep intrinsic relationship between the load and the loss is extracted and the corresponding temporal and spatial characteristic parameters are presented. Based on the spatial parameters and time parameters of the relationship between the equivalent load and the network loss, a time-space characteristic analysis model of equivalent load is established. Based on the data of a regional distribution network in Jiangsu Province, the correlation between the equivalent load and the network loss is analyzed, and its feasibility is verified. And to analyze its correlation to explore the deep link between load and loss.