The Transition to Clean Energy: Are People Living in Island Communities Ready for Smart Grids and Demand Response?

Technology cannot develop without up gradation and this concept is deeply accepted in the field of power grid which has given birth to the concept of an interactive power network known as the smart grid system. The new realm of a smart grid signifies renovation of the existing power grid to a new version which will incorporate distributed renewable energy sources (DER) and at the same optimize the uses of power at the consumer end during the high price of electricity which is called demand side management. The renewable generation is weather dependent and is incapable of supplying the peak loads which is expected to increase, due to the escalation of demand of both heat and power in residential load as well as industrial sector. This temporal variability of renewable energy sources may degrade the reliability of electricity to its consumers which throws a severe challenge to power system engineering. In order to extricate this unavoidable constraint, smart grid with the deployment of high speed communication network can introduce demand response as a optimization resource since consumer is not using all the loads for all the times. Demand response optimization programs (DR) are thus dedicated to schedule the energy consumption behavior of end-users in a smart power grid in an optimal fashion. It has been demonstrated in various research works on this problem that the simultaneous use of smart grid communication technologies with DR programs helps utilities to save electricity economically by peak shaving of demand. This paper presents an comprehensive review of various modern techniques of demand response adopted in power industry employing smart power grid and also present possible benefits that demand response can introduce in economic supply of electricity with quality and high standards of reliability in future power grids.

The research and practice of smart grid around the world, as well as the collaboration between smart grid and demand response (DR), are summarized. Furthermore, the issue on the collaboration between smart grid and DR is elaborated systematically, including the DR-related smart grid technologies, the benefits of the collaboration. Smart grid technologies for DR involve multiple technologies in many aspects and several typical ones are: remote connect/disconnect, smart meter, consumer portal, home-area network, enhanced customer service, etc. The collaboration between DR and smart grid can bring multi-facet benefits and is conducive to facilitate a win-win situation. Furthermore, the collaboration benefits of DR and smart grid initiatives are demonstrated. Finally, suggestions and assumptions for achieving the collaboration between smart grid and DR are presented.

Introduction: Smart Grids and Sustainable Energy Transformation.- PART I History, Evolution and Latest Development of Smart Grids.- Smart grids: overview of concepts, applications and latest developments.- PART II Technical Characteristics of Smart Grids.- Getting smart: the smart grid systems and the state-of-the-art technologies.- Towards a European renewable-based energy system enabled by smart grid: status and prospects.- Demand response, dynamic pricing and smart metering: enabling energy-saving and smart houses.- Microgrids, virtual power plants and our distributed energy future.-.Transmission and distribution systems of smart grids: current status and future trends.- Communication and Network Security Requirements for Smart Grid.- PART III Stakeholders in Perspective: Interests, Power, and Conflict.- Smart regulation for smart grids.- Smart grids from the consumers' perspective.- A business case for smart grid technologies: a systemic perspective.- PART IV National Case studies.- A smart transmission grid for Europe: challenges in developing grid enabling technologies.- Smart-grid technologies and progress in the USA.- Towards sustainable energy systems through collaboration between government and business: the Japanese case.- Smart grids and socio-technical systems transition: a government-led approach in Korea.- Super smart grid and low carbon economy in China.- Intelligent grid in Australia: a value proposition for distributed energy.- PART V Conclusion.- Conclusions.

Today's electric grid is undergoing drastic changes to evolve into a smart grid. Deregulation of the integrated and monopolistic power system into genco, transco and disco has led to tremendous competition among these players. These entities are in the process of developing innovative smart grid strategies that can improve their reliability and profit. In this thesis work, some of the smart grid initiatives by discos have been explored.;This thesis work is driven by two major objectives. The primary objective is to explore Demand Response (DR), develop its comprehensive model and to analyze various effects and implications of DR on distribution networks. The second major objective of the thesis is to integrate the developed demand response model into a microgrid market optimization. A microgrid network is a real world demonstration of smart grid that integrates and coordinates various demand side resources into its operation. For this reason, a microgrid has been chosen in this work so that it offers a broader scope where in addition to DR models, Battery Energy Storage System (BESS) and Distributed Energy Resources (DER) or Distributed Generation (DG) can also be modeled and integrated.;This thesis develops a model for DR by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These Price Elasticity Matrices (PEMs) are utilized to calculate the level of demand response for a given consumer. DR thus obtained is applied to a real world distribution network considering a day-ahead real time pricing scenario to study the effects of demand reduction and redistribution on system voltage and losses. Results show considerable boost in system voltage that paves way for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).;Following this, the thesis develops a market optimization model for an islanded microgrid that includes Smart Grid elements namely DR, DERs and BESS. Comprehensive models for DR and BESS have been developed and integrated into the optimization program. Demand Side Bidding (DSB) by DR Aggregators is introduced into the proposed double sided microgrid energy market by utilizing the DR models developed. The optimization program uses Linear Programming (LP) technique to determine the dispatch schedule of DERs, BESS and the level of DR to minimize the operating cost of the microgrid market. A time series simulation of a large microgrid test system is performed to show

Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in the transmission of electricity through the traditional grid, the concepts of smart grids and demand response have been developed. In such systems, a large amount of data is generated daily from various sources such as power generation (e.g., wind turbines), transmission and distribution (microgrids and fault detectors), load management (smart meters and smart electric appliances). Thanks to recent advancements in big data and computing technologies, Deep Learning (DL) can be leveraged to learn the patterns from the generated data and predict the demand for electricity and peak hours. Motivated by the advantages of deep learning in smart grids, this paper sets to provide a comprehensive survey on the application of DL for intelligent smart grids and demand response. Firstly, we present the fundamental of DL, smart grids, demand response, and the motivation behind the use of DL. Secondly, we review the state-of-the-art applications of DL in smart grids and demand response, including electric load forecasting, state estimation, energy theft detection, energy sharing and trading. Furthermore, we illustrate the practicality of DL via various use cases and projects. Finally, we highlight the challenges presented in existing research works and highlight important issues and potential directions in the use of DL for smart grids and demand response.

Smart Grid, the future of the present electric grid has become one of the burning issues in energy and power industries and research. Being a developing country, Bangladesh has not yet taken any remarkable initiative regarding smart grid. Whereas, there is a strong possibility that a careful adaption of modern electric grid could significantly improve the present dissatisfaction among the consumers due to interrupted supply of electricity. Load shedding is the traditional approach of balancing supply and demand through scheduled power cut off. But in Bangladesh, customizing the load shedding through smart power grid, user satisfaction could be greatly improved. In this paper, an approach to smart grid and demand side load curtailment (DSLC) is proposed to reduce peak load for customizing load shedding and thus optimizing consumer satisfaction. Here, demand side load curtailment will be a key component of future smart grid that can help reduce peak load, reshape the load profile and adapt the increasing demand to generated power with the massive deployment of smart meters. DSLC empowers the load curtailment rather than full cutting off the power line. The proposed technique categorizes the consumers into three categories namely High, medium and low consumption users based on their consumption history and load profile. Moreover, the home appliances of the consumers of different categories also divided into three more categories namely high load, medium load and low load. Smart meter that every consumer owns is responsible for communicating with the different appliances connected and clustering them into appropriate category based on prior load threshold information provided. This paper describes a way to reduce the pick load selecting the victim consumer list and curtailing load in real time environment providing the consumer a minimum support rather than complete power cut off and thus optimizing the user satisfaction. To observe the various aspects of the model an experiment has been demonstrated in the paper.

Smart and super grid visions have attracted electrical engineers worldwide. Smart grid offers innumerable advantages over conservative electric grid due to widespread integration of information and communication technologies (ICT). Information technology (IT) stimulates conscious in dumb electric grid hardware to think at its own how to operate economically in varying load conditions conducting real-time state estimation. Smart grid promises to offer bidirectional power flow capability and self-healing ability against faults. Advance metering infrastructures (AMI) help utilities track down power thefts supplying consumer home automation facilities. Smart grid helps stiff utilities to reduce operational losses and generation deficient utilities to implement live load shedding instead of revolving blackouts to help customers keeping their lights on during virtual load shedding. Smart grid promises package is subject to its complete implementation by the end of long road ahead. Communication capacity and speed limitations, cyber security risks, faults and traffic congestions have to be considered prior to rollout. This paper describes a live load shedding scheme for existing electric grids on 11kV feeders using smart grid option and evaluates technical, economic, societal and bio-effects challenges that smart grid vision might face ahead.

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 major goal of the power industry is to serve the demand of electricity consumers as reliable as possible but at an affordable cost. Toward the goal, however, the industry faces substantial barriers such as aging infrastructures, growing demand, and limited budgets for reinforcements. A great portion of infrastructures which were built decades ago need to be retired. The growing demand needs system reinforcement and expansion. These, in turn, require considerable amounts of investment which is in contradiction with highly limited budget of the power industry. This critical situation forces the industry to utilize the existing system more efficiently and wisely. To this end, the concept of smart grid has been recently proposed by the area researchers to enhance the performance of power systems. Smart grid refers to an electricity grid which is equipped with advanced technologies dedicated to managing the system in a sustainable, reliable, and economic manner. Smart grids have several aspects which have to be thoroughly investigated before their implementation in the real world. Demand response is one of the key integral parts of a smart grid. It refers to any voluntary change in electricity usage in response to signals from the grid operator. Demand response provides system operators with an opportunity to modify the normal consumption pattern when electricity procurement prices are higher, or service reliability is jeopardized. The focus of this chapter is on potential impacts of demand response on the operation of power systems. Although demand response may have significant impacts on generation and transmission levels, its impacts on the operation of distribution networks are studied here. This is due to the fact that distribution networks have captured the least attention and experienced the minimum advancements during the past decades, and thus, they are the appropriate place to be improved when efficiency enhancement is the objective. The first and foremost goal of this chapter is to quantify potential benefits of demand response to distribution network operation. To do so, brief definition of demand response is followed by explanations about different demand response programs. Then, demand response benefits are counted. Thereafter, a distribution network hosting several residential customers is utilized to quantify the benefits of demand response in the operation of distribution networks. Disaggregated load profiles associated with residential customers and their flexibility are employed to modify the total load profile. Then, by applying the modified load profile to the network, impacts of demand response on the network losses, voltage profiles, and loading levels are studied. It is demonstrated that even activating demand response potential of a portion of customers can lead to significant improvements in the parameters. Finally, demand response barriers and associated solutions are described.

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.

Assessing capacity credit of demand response in smart distribution grids with behavior-driven modeling framework

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.

To combat global climate change, the reduction of carbon emissions in different industries, particularly the power industry, has been gradually moving towards a low-carbon profile to alleviate any irreversible damage to the planet and our future generations. Traditional fossil-fuel-based generation is slowly replaced by more renewable energy generation while it can be harnessed. However, renewables such as solar and wind are stochastic in nature and difficult to predict accurately. With the increasing content of renewables, there is also an increasing challenge to the planning and operation of the grid.
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\nWith the rapid deployment of smart meters and advanced metering infrastructure (AMI), an emerging approach is to schedule controllable end-use devices to improve energy efficiency. Real-time pricing signals combined with this approach can potentially deliver more economic and environmental advantages compared with the existing common flat tariffs. Motivated by this, the thesis presents an automatic and optimal load scheduling framework to help balance intermittent renewables via the demand side.
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\nA bi-level consumer-utility optimization model is proposed to take marginal price signals and wind power into account. The impact of wind uncertainty is formulated in three different ways, namely deterministic value, scenario analysis, and cumulative distributions function, to provide a comprehensive modeling of unpredictable wind energy. To solve the problem in off-the-shelf optimization software, the proposed non-linear bi-level model is converted into an equivalent single-level mixed integer linear programming problem using the Karush-Kuhn-Tucker optimality conditions and linearization techniques. Numerical examples show that the proposed model is able to achieve the dual goals of minimizing the consumer payment as well as improving system conditions. The ultimate goal of this work is to provide a tool for utilities to consider the demand response model into their market-clearing procedure.
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\nAs high penetration of distributed renewable energy resources are most likely applied to remote or stand-alone systems, planning such systems with uncertainties in both generation and demand sides is needed. As such, a three-level probabilistic sizing methodology is developed to obtain a practical sizing result for a stand-alone photovoltaic (PV) system. The first-level consists of three modules: 1) load demand, 2) renewable resources, and 3) system components, which comprise the fundamental elements of sizing the system. The second-level consists of various models, such as a Markov chain solar radiation model and a stochastic load simulator. The third-level combines reliability indices with an annualized cost of system to form a new objective function, which can simultaneously consider both system cost and reliability based on a chronological Monte Carlo simulation and particle swamp optimization approach. The simulation results are then tested and verified in a smart grid laboratory at the University of Hong Kong to demonstrate the feasibility of the proposed model.
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\nIn summary, this thesis has developed a comprehensive framework of demand response on variable end-use consumptions with stochastic generation from renewables while optimizing both reliability and cost. Smart grid technologies, such as renewables, microgrid, storage, load signature, and demand response, have been extensively studied and interactively modeled to provide more intelligent planning and management for the smart grid.

The significantly high energy consumption of data centers constitutes a major load on the smart power grid. Data center demand response is a promising solution to incentivize the cloud providers to adapt their consumption to the power grid conditions. These policies not only mitigate the operational stability issues of the smart grid but also potentially decrease the electricity bills of cloud providers. Cloud providers can improve their contribution and reduce their energy cost by collaboratively managing their workload. Through cooperation in the form of cloud federations, providers can spatially migrate their workload to better utilize the benefits provided by demand response schemes over multiple locations. To this end, this work considers an interaction system between the independent cloud providers and the corresponding smart grid utilities in the context of a demand response program. Leveraging the cooperative game theory, this paper presents a federation formation among the cloud providers in the presence of a location-dependent demand response program. A distributed algorithm that is coupled with an optimal workload allocation problem is applied. The effect of the federation's formation on the clouds' profits and on the smart grid performance is analyzed through simulation. Simulation results show that cooperation increases the clouds' profits as well as the smart grid performance compared to the noncooperative case.