Smart Grid and demand response technology

The global upsurge in research of smart grid and demand response (DR) is opening up new opportunities and challenges to China's power industry in recent years. The smart grid emphasize the interaction with user particularly, including the information interaction and power interaction, which are achieved through the deployment of various types of DR programs. Meanwhile, the development of smart grid also depends on demand response mechanism in the open market environment. DR coincides with the goal of smart grid which is to achieve a high-security, economical, reliable and efficient power system. So the collaboration between smart grid and demand response is talked in this paper. First,we analyze the relationship between smart grid and demand response systematically.Second,the development conditions of DR project in China and abroad are expounded. Furthermore, the necessity and significance of pushing DR project into smart grid is discussed and summarized, finally, methods and suggestions for achieving the collaboration are presented. (4 pages)

The idea behind `Smart Grid Vision for India' is to transform the Indian power sector into a secure, adaptive, sustainable, and digitally enabled ecosystem that provides reliable and quality energy for all with active participation of stakeholders. Realizing the growing importance of Smart Grid technologies in the Indian power sector, very recently Ministry of Power, Government of India on the recommendation of India Smart Grid Task Force (ISGTF) has shortlisted fourteen (14 Nos.) Smart Grid pilot projects that are planned to be executed in power distribution sector in India. As per the `Smart Grid Roadmap for India', these pilot projects are expected to help technology section guides, develop business cases, policy and regulatory recommendations for larger projects in the next phase, while showcasing the relevance of Smart Grid on different aspects such as, Advanced Metering Infrastructure (AMI), Outage Management System (OMS), Peak Load Management System (PLMS), Renewable Energy (RE) Integration etc. The average estimated cost of each pilot project would be US$ 10 million (approx.) out of which 50% is grant will be provided by Government of India through (budgeted INR 2 Lakh Crores for Smart Grid projects across India under 13th Five-Year Plan) Restructured Accelerated Power Development and Reforms Program (R-APDRP) and rest to be borne either fully by the utility or, shared between the utility and the technology providers, and targeted to be completed by the end of 2014. The main objective of this technical paper is to underline the present scenario of those selected Smart Grid pilots in India, including proposed state-of-the-art Technology Integration, Consumer Coverage (Base), and Key Performance Indicators (KPIs). This study will also capture the Carnegie Mellon's Software Engineering Institute (SEI) Smart Grid Maturity Model (SGMM), developed by the Global Intelligent Utility Network Coalition (GIUNC) as a management tool, towards the Smart Grid transformation to assess the performance (including, current status) of those utilities responsible for pilot demonstrations and provide necessary recommendations to use this framework for establishing future strategies and work plans as pertain to Smart Grid implementations in the country. The purpose of this research will further emphasize on the progress of applicable Smart Grid interoperability and standards relevant to the Indian context, development of indigenous low-cost smart solutions, gap analysis and appropriate changes conducive to the deployment of Smart Grid pilot projects, and discussion on National Smart Grid Mission (NSGM) in India.

Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.

Demand Response (DR) technology makes it possible to have two-way communication between service providers and home appliances inside the customer premise. This state-of-art technology equipped with smart meters, load control devices (smart devices), smart thermostats, smart switches and home energy consoles. DR technology can help us to modernize our electricity system and provide customers with new information and options for managing their electricity use. Customers can reduce or shift their power usage during peak demand periods in response to timebased rates or other forms of financial incentives. Demand response solutions play a key role in several areas: pricing, emergency response, grid reliability, infrastructure planning and design, operations, and deferral. In previous studies Demand Load for smart power grid is a mostly centralized approach. The worth of this study is we are proposing DR as a fully distributed and hybrid approach. We are investigating performance of DR in context of fully centralize, Fully Distributed and Hybrid fashion.

There are many smart grid test beds in the world, and the combination of smart grid technologies in each test bed is somewhat different according to the needs of each country. Among several test beds, it seems that Korean case is the special reference because it was covered with full scale from transmission to customer, and all kinds of smart grid technologies were installed and tested in its test bed. In this paragraph, the readers may understand the draft of smart grid technology in Korea, the directions of smart grid filed application, and the results of smart power grid project.

A new methodology and web calculator, known as the emissions quantification tool (EQT), are designed to estimate the emissions impacts of a growing range of specific smart grid technologies and project types. Significant CO2, NOx, and SO2 reductions are possible with these technologies, but results are highly context dependent. This transparent, modular, reproducible, and publically accessible screening tool draws on well-vetted methodologies from trusted sources and allows comparison of possible and completed smart grid project impacts by diverse stakeholders. The live web calculator and full documentation are available at http://eqt.pnnl.gov.

The necessity to seamlessly integrate emerging smart grid (SG) technologies to the electric distribution system has encouraged the development of demand-side management (DSM) and Demand Response (DR) programs. In particular, DR aggregators will play an important role in the new operational paradigm of SG. To propose a new three-phase optimal dispatch model for multiple DR aggregators. The model can consider several DR service providers by means of the explicit inclusion of their characteristics, in the form of cost functions and capacity, in an optimal dispatch model for DR support at the distribution system level. Hence, this proposal helps to eliminate any possible uncertainty about the provision of DR services by improving the traditional hierarchical scheme adopted based on prices issued by the independent system operator. By means of the unbalanced distribution system modeling, DR involvement of single- and three-phase consumers is considered avoiding aggravating the asymmetric balancing between phases, as opposed to traditional positive-sequence DR approaches. The model is implemented and solved combining the two state-of-the-art computational tools, Matlab® and OpenDSS. The former is used to solve the optimization problem, whereas the latter is used to perform numerical simulations of three-phase unbalanced power flow; both tools allow a straightforward model implementation resulting in a tool easily modified and updated. The effectiveness of the proposed approach is numerically demonstrated using the IEEE 13- and 123-node test feeders, in which undesirable operating scenarios are corrected by the implementation of the optimal dispatch of DR resources in very short computational times. Based on the results, it is shown how the ISO is capable to request DR considering several aggregators competing at the distribution system level. Finally, the loss reduction has been included in the objective function, showing that the proposal optimally dispatches the DR aggregators to conveniently minimize the ISO’s operational costs.

Smart demand response in China: Challenges and drivers

Demand Response (DR) is an important ingredient of the emerging Smart Grid paradigm. Although much of the DR emerging under Smart Grid is targeted at the distribution level, DR programs are regarded as important elements for reliable and economic operation of the transmission system and the wholesale markets. In the context of Energy and Ancillary Service markets facilitated by the ISOs/RTOs, DR programs may be broadly classified as market-based or reliability-based depending on the conditions under which DR is deployed: The former provide for DR vis-a-vis economic signals, while the latter are generally triggered under emergency conditions. Depending on the ISO/RTO market design and applicable operational standards, the market products DR can offer may include Energy, Reserve Ancillary Services (i.e., Supplemental/Non-spinning Reserve, Spinning/Responsive Reserve, or Regulating Reserve), and Capacity. In this paper we first explore different facets of Demand Response under the Smart Grid paradigm. We then briefly summarize the existing and evolving Demand Response programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under Smart Grid and Market paradigms.

In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed.

The goal of this research is to develop a decision model that can be used to identify the technology transfer potential of a research proposal. An organization can use the model to select the proposals whose research outcomes are more likely to move into application. The model begins to close the chasm between research and application – otherwise known as the “valley of death.” A hierarchical decision model, along with desirability curves, was used to understand the complexities of the researcher and recipient relationship, specific to technology transfer. In this research, the evaluation criteria of several research organizations were assessed to understand the extent to which the success attributes that were identified in literature were considered when reviewing research proposals. The quantified model was validated using a case study involving demand response (DR) technology proposals in the Pacific Northwest.

Power systems have been undergoing radical changes in recent years, and their planning and operation will be surely undertaken according to the Smart Grid (SG) vision in the near future. The SG initiatives aim at introducing new technologies and services in power systems, to make the electrical networks more reliable, efficient, secure and environmentally-friendly. In particular, it is expected that communication technologies, computational intelligence and distributed energy sources will be widely used for the whole power system in an integrated fashion. In particular, nowadays, unprecedented challenges like as stringent regulations, environmental concerns, growing demand for high quality, reliable electricity and rising customer expectations are forcing utilities to rethink about electricity generation and delivery from the bottom up. Moreover, the availability of low cost computing and telecommunications technologies, new generation options, and scalable, modular automation systems push utilities to be dynamic, innovative and ambitious enough to take advantage of them. Driven by the dynamics of the new energy environment, leading utilities, technology vendors and government organizations have created a vision of the next generation of energy delivery systems: the Smart Grid. Operational changes of the grid, caused by restructuring of the electric utility industry and electricity storage technology advancements, have created an opportunity for storage systems to provide unique services to the evolving grid. Especially Battery Energy Storage Systems (BESSs), thanks to the large number and variety of services they can provide, are powerful tools for the solution of some challenges that future grids will face. This consideration makes BESSs critical components of the future grids. The BESS can be applied for different services into the different levels of power system chain to satisfy technical challenges and provide financial benefits. In the context of the application of BESSs in SGs, there are two main problems that need to be addressed in a way that exploits the BESS potential, that are linked to their operation and sizing. This thesis focuses on both these aspects, proposing new strategies that allow optimizing the BESS adoption. When dealing with BESSs, sizing and operation are strictly linked. The correct sizing of a BESS, in fact, needs to take into account its operation which in turn will be effected with the aim of optimizing the whole system where it is included. In the first part of this research study, advanced optimal operating strategies were proposed for BESSs by considering both the distribution system operator perspective and the end user. Thus, the proposed operating strategies were performed with the aim of (i) leveling the active power requested by the loads connected to a distribution system (distribution system operator service), (ii) reducing the electricity costs sustained by an end-use costumer that provides demand response (DR) (end user service) and (iii) scheduling a microgrid (µG) with DR resources such as Plug-in Electric Vehicles (PEVs) and Data Centers (DCs) (both the two section service). The proposed strategies also satisfied technical constraints of BESSs and other components of the µG. The second part of the thesis presented the optimal sizing of BESSs aimed at maximizing the benefits related to their use. In the thesis, the sizing, which is performed by considering the end user point of view with reference to both the industrial and residential customers, is effected by adopting both deterministic and probabilistic approaches. With reference to the deterministic approach, a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications was proposed. In case of probabilistic approach, the case of a BESS installed in an industrial facility was considered and the sizing was performed based on the decision theory. Technical improvements and economic benefits of optimal operation and optimal sizing of BESSs in SG are demonstrated by the obtained results which are reported in the numerical applications. More specifically, it was clearly determined that BESSs can offer technical supports into the distribution operator section of the grid in terms of load management and security challenges. Moreover optimal integration of BESSs into the grid was also appealing for end users thanks to valuable amounts of electricity bill cost reduction. Regarding the original contribution of the thesis, the following considerations can be done. With reference to the load leveling service, an innovative two-step procedure (day-ahead scheduling and very short time predictive control) was proposed which optimally controls a BESS connected to a distribution substation in order to perform load leveling. In case of DR, a proper control of the BESS was proposed in order to perform DR under different price schemes, such as Real Time Pricing (RTP) and Time of Use (TOU) without modifying the daily work cycle of the industrial loads. The control procedure allows achieving contemporaneously two important goals that are the reduction of the bill costs and the prolonging the battery's lifetime so further reducing the costs sustained by the customer. With reference to the scheduling of microgrids, the original contribution of the thesis is focused on the proposal of optimization strategies aimed at managing and coordinating, simultaneously, batteries on board of vehicles or equipping data centers' Uninterruptable Power Supply (UPS) and Distributed Generation (DG) units. Also comparisons among different single-objective based strategies are made in order to highlight the most convenient. With reference to the sizing based on deterministic approach, unlike the other relating literature, the innovative contribution is that the closed form procedure takes into account both the technical constraints of the battery and contractual agreements between the customer and the utility. Moreover, in the economical analysis performed for the sizing, which is applied with reference to both residential and small industrial customers and is based on actual TOU tariffs, a wide sensitivity analysis to consider different perspectives in terms of life span and future costs was performed. Some aspects that affect the profitability of the battery, such as technological limitations (e.g. the battery and converter efficiency), economic barriers (e.g. capital cost and the rate of change of the cost) and variation of the load profile along the years were deeply analyzed. In case of sizing based on probabilistic approach, the original contributions of the thesis are mainly referred to the proposal of a new method that uses a decision theory-based process to obtain the best sizing alternative considering the various uncertainties affecting the sizing procedure. The thesis is organized in three chapters which are dealing with integration of BESSs in SGs. The first chapter reports basic concepts and characteristics of BESSs, fundamental components and features of SGs and different services that BESSs can provide. The optimal operation strategies of BESS are considered in second chapter which includes their problem formulation, solving procedures and results. The third chapter deals with the optimal sizing problem of BESSs for which the problem formulation, solving procedures and results are reported. Finally, the conclusions are presented in the last part of thesis.

There is a growing interest in 'smart grid' technologies in both industry and academic circles. Few attempts have been made to develop a written specification consummated with standards agreed upon by members of both coteries, due to lack of government support. Utilities in the state of California are obligated, by state legislature, to create a more: efficient, reliable, and intelligent electric power system. This initiative along with Florida Power & Lighting's 'Smart Grid' pilot program has created a sense of exigency within the industry regarding smart grid technologies and standardizations. Their accomplishments are beginning to shape the policies and standards with marginal input from academic societies, ushering in a very lopsided, and business acclimatized set of standards. We will present and analyze, a SCE 'Smart Grid' use case, in which the utilities back office applications interact with the customer's meter, and provide technical recommendations for system security improvements.

Energy efficiency and power crises are the critical issues that the globe is facing nowadays. Generation diversification, Demand Response, renewable energy accommodation, and reduction of Carbon dioxide emission are some of the issues that cannot be covered within the limitation of the current grid system. Smart Grid has revolutionized the generation, Distribution, and Consumption of electricity by integrating intelligent control, sensing and communication in daily operation of the electric grid. The goal of the Smart Grid is to be environmentally sustainable, for the deployment of this trending technology is a must. This paper addresses the issues that would be faced in the implementation of the smart Grid technology, policy consideration to maximize the benefit of smart grid and enhance the benefit over cost ratio. It explains the three sectors where the smart grid applications can be seen with challenges in bottom-up design, and overview of Smart grid for utilities. A top-down scheme is presented which can help in eliminating the challenges and barriers in the smart grid implementation. Finally cost vs. benefit analysis is made to prove the effectiveness of this trending technology in the power sector.

Approaching to the Smartest technological use the whole world is being aware of using the smartest technology of generating and distributing power throughout the country in a simple but effective system. Smart Grid technology is providing with this unique feature. A lot of features make it possible to differentiate the Smart Grid from the existing traditional grid in various manners. This paper basically focused on the Smart Grid (SG) technology. A technology that has let us dreamt about solution of maximum power problems including generation to distribution ends. In this time of technological advancement where all the governments are facing challenges of meeting the power demand of the consumers, smart grid can bring harmony in demand supply ratio. In this paper a brief detail about Smart Grid Technology, its application has been discussed.