The collaboration between demand response and smart grid

This paper demonstrates the latest Smart Grid and Demand Response pilot projects results and achievements in UAE. It also identifies the challenges that require the introduction of Smart Grid, the investigation of the life cycle cost/benefits, the analysis of Smart Grid deployments and finally, to define the road map of Smart Grid and the pillars to achieve the required smartness. The deliverable of this study addresses the future technology of Smart Grid in terms of road map, action plan, and recommendations to achieve Smart Grid objectives. Initial results show that by applying demand response initiatives it could reduce the annual peak demand by 8.5% or more.

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 promise of smart grids is very attractive. However, it is not yet clear what the future smart grid will look like. Although most researchers acknowledge that users will play a more prominent role in smart grids, there is a lot of uncertainty on this issue. To counter the strong technological bias in smart grid research and literature, we propose that research should focus more on the social and business dimension of smart grid developments. The main elements of such a research agenda are: Developing more socially embedded visions on smart grids and the services it will provide A shift in the focus on developing smart grids components and systems towards the services it will deliver Development and testing of innovative user-centered business models and ecosystems. More general, on the role of users in smart grids, the main lesson is that user roles should be taken more seriously in relation to smart grids: experts should no longer regard users exclusively and/or simply as potential barriers to smart grid innovation but also as important stakeholders and potential participants in the innovation process.

Smart grid research in New Zealand – A review from the GREEN Grid research programme

How to peak carbon emissions in China's power sector: A regional perspective

With the rapid development of smart grids, Demand Response Management (DRM) is expected to become the most effective and reliable solution to reduce peak load in electricity grids for adaptation of fluctuating electricity supply. It aims to shape the users' electricity loads by exchanging information in real time between the power utility and demand sides. Thus, a real-time communication mechanism needs to be designed for information transmission in demand response. In this paper, we propose a task-oriented communication mechanism to balance the electricity load and supply as quickly as possible in real-time demand response. The cumulative mean square error (MSE) between the electricity load and supply is defined as the performance metric of the designed communication mechanism. On this basis, both centralized direct control and distributed control through electricity price have been considered in demand response. Under these control mechanisms, task-oriented incremental source coding is introduced for the minimization of cumulative MSE between the electricity load and supply. Finally, numerical results are presented to show the potential of our proposed incremental source coding mechanism for real-time demand response.

Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions during the execution of DR. Firstly, considering the bounded rationality of residential users, a population dynamic model is proposed to describe the decision behavior on whether to participate in the DR project, and then the evolutionary process of consumers participating in DR is analyzed. Secondly, for the DR participants, they have to compete dispatching amounts for maximal profit in a day-ahead bidding market, hence, a non-cooperative game model is proposed to describe the competition behavior, and the uniqueness of the Nash equilibrium is analyzed with mathematical proof. Then, the distributed algorithm is designed to search the evolutionary result and the Nash equilibrium. Finally, a case study is performed to show the effectiveness of the formulated models.

High level challenges that motivate the evolution towards smart grids include (i) the anticipated electrification of transportation, including electrical vehicles (EVs), and (ii) the increasing penetration of distributed renewable energy sources (DRES). This chapter will discuss how the extra grid load stemming from the EVs can be handled, including the context of reduced control over power generation in light of DRES adoption (especially solar and wind power). After a basic introduction to common EV charging technology, we give two illustrative examples of controlling EV charging: avoiding peaks, and balancing against renewable generation. We then qualitatively present possible demand response (DR) strategies to realize such control. Finally, we highlight the need for, and underlying principles of, (smart grid) simulation tools, e.g., to study the effectiveness of such DR mechanisms.

As China's wind power has been expanding, the volatility, discontinuity and uncertainty of wind power have brought great difficulties to consumption of the wind power. With the development of smart grid, it becomes possible to improve the capacity of wind power consumption by introducing real-time price and demand response theory into the market. Firstly, theories of smart meters, real-time price, and demand side response were introduced. Then taking transfer cost into consideration, a demand response model based on real-time price is proposed to optimize the load curve. At last, the wind power consumption models were built respectively according to the load curves before and after the demand response. Results show that these models can effectively reduce the abandon of wind power and increase the amount of wind power consumption.

A cooperative game analysis for the allocation of carbon emissions reduction responsibility in China's power industry

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.

This paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.

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.

Smart demand response in China: Challenges and drivers

Faced with the problem of fossil energy depletion and the power industry’s low-carbon requirements, low-carbon technologies in collaboration with market mechanisms, supplemented by flexible resources, are critical to achieving the low-carbon operation of integrated energy systems (IES). This paper establishes an IES considering multiple types of low-carbon factors and demand response. Firstly, the IES is deemed to participate in the carbon trading market and introduce a ladder-type carbon trading mechanism at the low-carbon policy level. Then, at the low-carbon technology level, carbon capture power plants and power-to-gas equipment are introduced to refine the modeling of the power-to-gas process. Secondly, the integrated energy system includes pluralistic energy storage technology and demand response to increase the IES’s flexibility. Based on multiple types of low carbon factors and demand response mechanisms, the scheduling model for energy efficiency improvement is constructed with the lowest sum of the cost of wind abandonment penalty, the cost of purchasing energy, the cost of equipment operation and maintenance, and the cost of carbon trading as the optimization objective, and solved by a two-stage optimization method. Five energy efficiency indicators are presented to efficiently evaluate dispatching results: wind power consumption rate, carbon trading cost, actual carbon emissions, total cost, and load fluctuation. Finally, according to an arithmetic test system based on various operation scenarios, the proposed model may increase the IES’s comprehensive energy efficiency under the coupling effect of multiple types of low-carbon factors and demand response.