7- https://doi.org/10.3990/1.9789036541978
Effective and efficient coordination of flexibility in smart grids
This thesis addresses the challenge of coordinating demand side management in smart grids with increasing renewable energy integration by developing scalable optimization methods, such as enhanced planning and dynamic dispatch, achieving near-optimal results within 1-10% of lower bounds, and implementing a flexible energy interface to facilitate practical deployment.
Translate Article 
Renewable energy is starting to play a serious role in the electricity world, gradually displacing the reliable (though polluting and resource-finite) conventional electricity generation technology that has served us over the last century. However, renewables offer much less control over the production of electricity, and thereby ask for new sources of flexibility. Storage is expected to become one of the key ingredients for the further development of the energy transition, as it can bridge the gap between supply and demand in time. As a lot of renewable generation is added at the lower tiers of the grid, storage can also help to keep energy local, and thereby reduce costly grid investments and transport losses, bridging the gap between supply and demand in space. Although physical energy storage (e.g. in batteries) is generally expensive, demand side management (DSM) promises to provide a different form of "storage" at (almost) no additional cost by exploiting the intrinsic flexibility within electricity consuming and producing devices. The energy transition introduces many new devices that have some flexibility in their electricity consumption or production, such as electric vehicles (EVs), heat pumps or combined heat and power (CHP) systems. What remains is to control this sea of flexibility and let the devices play their part in the smart grid. However, the control of devices in DSM turns out to be a hard problem, because the flexibility in devices is restricted, scattered, and there are costs associated with the use of the flexibility. To decide which devices are used (turned on or off) to reach some given goal, coordination is used to exploit the diversity of devices (in space). Furthermore, the control decisions impact the situation in the near future. To account for this, planning approaches may be used to exploit the flexibility of the devices over time. Together, this leads to a problem that is coupled in space and time, which is in general too large to be optimized directly, and should therefore be addressed in practice with heuristics or approximate methods. In this thesis, we address this DSM coordination/optimization problem. In this context, earlier work at the University of Twente led to TRIANA as a scalable optimization and control approach for DSM in smart grids. TRIANA partitions the optimization problem according to the hierarchical structure of the electricity grid, and splits up the DSM control problem in three phases: forecasting, planning, and real-time control. Although the approach is scalable and conceptually elegant, it simplifies the problem to such an extent that the solutions are sometimes far from being optimal. Therefore, the phases of TRIANA should be considered as dependent problems: for example, the result of real-time control depends on the forecasting and planning phases, and the planning phase should already account for this. We introduce more sophisticated planning methods (column generation and profile steering) to improve the planning results, and place these methods in a general model. To evaluate the methods, we took part in the development of an extensive simulation scenario called Flex Street. For this scenario we determine a lower bound on the cost to manage this scenario. Both of the developed planning methods bring the plan closer to the optimum than the original planning method from TRIANA (within 1-2% of the lower bound of the Flex Street scenario in a deterministic setting). A key strategy to keep the developed approaches scalable is a local optimization that already takes the needs of the nodes higher up in the hierarchical structure into account. Flexible devices are in general a major source of uncertainty themselves, since their operation depends on human behaviour, which makes the forecasting of available flexibility for specific devices difficult. Dynamic dispatch approaches address this uncertainty by exploiting the interchangeability of devices, meaning that we decide just-in-time which specific devices are going to be used, e.g. with a flexibility auction. Although this dynamic dispatching makes the approach more robust against disturbances of individual devices, it also makes the reasoning about the behaviour of the system more difficult for the planning. We propose a method to plan such a system based on the simulation of the dispatch process, where the planning result determines the configuration of a controller. We evaluate the method with a subset of Flex Street, and find that the method achieves results within 2-10% of the lower bound, depending on the considered configuration. This approach gives robust results even with large forecast errors and a small number of devices. To bring DSM methodologies to practice, there are still some barriers at a household level. One of these barriers is a limited standardization of the interface to flexible devices, leading to high software development and maintenance costs. A challenge in this standardization is that control methods differ in their perspective on flexibility. The energy flexibility interface (EFI) reacts on this challenge by proposing to communicate the structure of energy flexibility instead of a specific perspective on flexibility. We develop a comprehensive TRIANA energy application prototype that implements the EFI. The prototype supports the decentralized planning and control of real devices on low cost embedded hardware, and demonstrates that the concepts developed in this thesis are applicable in an externally given framework. It also shows that EFI maps to multiple perspectives on energy flexibility in addition to only just-in-time auction based methods. Concluding, this work lays a foundation for the further development of a flexible, effective and efficient coordination approach for flexibility in smart grids, bringing the dream of DSM - and thereby the cost effective implementation of the energy transition - a bit closer to reality.
- Conference Article
- 11
- 10.1109/isgteurope.2013.6695406
- Oct 1, 2013
A new framework was created to estimate technical flexibility in electricity consumption of commercial and industrial customers. By classifying flexibility, building blocks could be created for several sources of flexibility, which can be inserted into a framework, allowing the construction of a power profile, including flexibility potential. The proposed framework gives insight in maximal potential of Demand Side Management (DSM) and is applicable for Smart (micro) Grid project developers.
- Conference Article
- 32
- 10.1109/sgc.2013.6733807
- Dec 1, 2013
Demand Side Management (DSM) will be one of the most important parts of future smart power grid. The DSM algorithms help consumers to be more active contributors in the power system in order to achieve system objectives by scheduling their shiftable load. In this paper, we review the challenges in this area of research by categorizing the DSM problems into four important categories. The load scheduling can be done using a proper two-way communication network which has its own challenges in the smart grid. The important issues of security and privacy are considerable in every communication network and consequently in DSM network system. On the other hand, successful DSM programs need consumers' contribution in the system which can be achieved in a fair system. Recently, there are some works on the fairness of the DSM algorithms with different definition for the fair system.
- Book Chapter
- 3
- 10.1002/9781119422099.ch7
- Jul 31, 2017
Demand-side management (DSM) has a key role in the Smart Grid (SG) concept to control the demand-side consumption and reduce peak loads. The ability to shift peak loads and provide the energy efficiency through better demand-side management is currently one of the most promising approaches to solve problems related to peak demand. DSM has some different terms such as demand-side energy management (DSEM), load energy management (LEM), demand response (DR), and automated load management (ALM) and all terms refer to the balancing of energy generation and consumption. DSM includes all the process in demand energy systems such as utilities renovation operations, metering, energy pricing, monitoring, customer comfort, home energy management systems, and so on. In addition, DSM has superior advantage that it is less expensive to intelligently influence a load than to build a new power plant or install some electric storage device. All these DSM strategies are used for optimum and efficient energy consumption. In this chapter, the general perspective is given for DSM under SG concept and describes the DSM architecture and its benefits of the customer side and utility side. Also, it explains the DR techniques and classified DR programs and give some information about dynamic pricing and smart metering. Then the impact of DR programs is discussed in residential energy management perspectives. It also gives some details about home energy management (HEM) concept. Finally, it discusses about DSM standards. In conclusion, the existing DSM applications and what could be done in the future works are discussed.
- Research Article
- 234
- 10.1016/j.energy.2020.119598
- Dec 15, 2020
- Energy
Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective
- Research Article
- 33
- 10.1016/j.procs.2020.03.319
- Jan 1, 2020
- Procedia Computer Science
Ensuring the reduction in peak load demands based on load shifting DSM strategy for smart grid applications
- Research Article
- 862
- 10.1109/msp.2012.2186410
- Sep 1, 2012
- IEEE Signal Processing Magazine
The future smart grid is envisioned as a large scale cyberphysical system encompassing advanced power, communications, control, and computing technologies. To accommodate these technologies, it will have to build on solid mathematical tools that can ensure an efficient and robust operation of such heterogeneous and large-scale cyberphysical systems. In this context, this article is an overview on the potential of applying game theory for addressing relevant and timely open problems in three emerging areas that pertain to the smart grid: microgrid systems, demand-side management, and communications. In each area, the state-of-the-art contributions are gathered and a systematic treatment, using game theory, of some of the most relevant problems for future power systems is provided. Future opportunities for adopting game-theoretic methodologies in the transition from legacy systems toward smart and intelligent grids are also discussed. In a nutshell, this article provides a comprehensive account of the application of game theory in smart grid systems tailored to the interdisciplinary characteristics of these systems that integrate components from power systems, networking, communications, and control.
- Research Article
- 44
- 10.1016/j.erss.2019.101277
- Sep 3, 2019
- Energy Research & Social Science
Smoothing peaks and troughs: Intermediary practices to promote demand side response in smart grids
- Research Article
- 3
- 10.1049/iet-stg.2018.0050
- Apr 1, 2018
- IET Smart Grid
Inaugural Editorial
- Research Article
- 55
- 10.3390/en11102801
- Oct 17, 2018
- Energies
The curtailing of consumers’ peak hours demands and filling the gap caused by the mismatch between generation and utilization in power systems is a challenging task and also a very hot topic in the current research era. Researchers of the conventional power grid in the traditional power setup are confronting difficulties to figure out the above problem. Smart grid technology can handle these issues efficiently. In the smart grid, consumer demand can be efficiently managed and handled by employing demand-side management (DSM) algorithms. In general, DSM is an important element of smart grid technology. It can shape the consumers’ electricity demand curve according to the given load curve provided by the utilities/supplier. In this survey, we focused on DSM and potential applications of DSM in the smart grid. The review in this paper focuses on the research done over the last decade, to discuss the key concepts of DSM schemes employed for consumers’ demand management. We review DSM schemes under various categories, i.e., direct load reduction, load scheduling, DSM based on various pricing schemes, DSM based on optimization types, DSM based on various solution approaches, and home energy management based DSM. A comprehensive review of DSM performance metrics, optimization objectives, and solution methodologies is’ also provided in this survey. The role of distributed renewable energy resources (DERs) in achieving the optimization objectives and performance metrics is also revealed. The unpredictable nature of DERs and their impact on DSM are also exposed. The motivation of this paper is to contribute by providing a better understanding of DSM and the usage of DERs that can satisfy consumers’ electricity demand with efficient scheduling to achieve the performance metrics and optimization objectives.
- Research Article
- 5
- 10.26180/5db7fd080a237
- Oct 29, 2019
- Monash University Law Review
This article assesses the contribution which the Smart Grid can make to climate change mitigation and adaptation. The Smart Grid amalgamates information and communications technology (‘ICT’) and electrical capabilities to improve flexibility, security, reliability, efficiency, and the safety of the electricity system. Demand side management (‘DSM’) is increased as consumers gain better control over their electricity use and respond to prices. At the same time, a smart grid includes diverse and distributed energy resources, including energy storage, and accommodates electric vehicle charging. Although much of the literature to date assesses the interface between the Smart Grid and climate change mitigation, there is barely any mention of the adaptation benefits emanating from Smart Grid technology. If the Smart Grid improves efficiency and DSM and encourages distributed energy sources its mitigation benefits are clear. Yet the fragility of electricity networks to climate change impacts suggests that the Smart Grid might also assist utilities to respond to blackouts, and other climate change induced crises, more effectively than is currently possible. The article also assesses the regulatory consequences which are attendant upon the adoption of a Smart Grid in Australia.
- Book Chapter
- 10.1007/978-3-030-18488-9_16
- Aug 31, 2019
Applying decentralized renewable energy in the built environment is a good approach to reduce the CO2 emissions. However this is not without restrictions towards the stability of the energy grid. Using the flexibility within energy generation, distribution infrastructure, renewable energy sources and the built environment is the ultimate sustainable strategy within the built environment. However, at the moment this flexibility on building level is still to be defined. The IEA Annex 67 defines this specific flexibility. Our research is aimed at developing, implementing and evaluating process new control strategies for improving the energy interaction within the building, its environment and the energy infrastructure by effectively incorporating the occupants’ needs for health (ventilation) and comfort (heating/cooling). A bottom-up approach, starting from the user up to the smart grid, offers new possibilities for using buildings’ energy flexibility to stabilize the electrical grid. New intelligent process control concepts are necessary which make use of the dynamic possibilities offered by multi-agent systems in combination with building energy management systems. Increasing demand for electrical energy use in buildings and the corresponding carbon emissions has further emphasized the need for the implementation of strategies that improve the energy performance of buildings. Demand-side management (DSM) strategies, which aim to actively manage user behaviour and how appliances consume energy, is a rapidly growing concept with the potential to contribute worthwhile improvement in building energy performance. A coordinated distributed demand-side management strategy framework for cooling in combination with a battery electrical storage system is presented and implemented in an office building in order to test the concept. The results showed that DSM strategies can be applied while maintaining thermal comfort.
- Conference Article
- 10.5339/qfarc.2016.eesp1497
- Jan 1, 2016
I. Introduction The trend towards high penetration of renewable energy sources (RES) in the energy mix and particularly grid-connected photovoltaic (PV) systems in the low voltage (LV) network, offers the benefits of green decentralized generation, at the cost of the development of energy management tools to alleviate potential problems. More specifically, the fact that for most consumption profiles the PV energy production does not coincide with the electricity demand, forces the grid to act as a sink and a source thus requiring re-adaptation of the grid operation [1]. To this extent, an advanced demand side management (DSM) scheme can be introduced to mitigate RES operational issues and contribute to managing effectively congestion problems. In this work, a price-based DSM tool has been developed in order to arrive at an effective Time of Use (ToU) tariff with improved DSM results. In this scope, smart meters (SMs) have been deployed at three hundred households with grid-connected PV systems installed a...
- Research Article
- 123
- 10.1016/j.micpro.2019.102886
- Aug 29, 2019
- Microprocessors and Microsystems
Demand side management of small scale loads in a smart grid using glow-worm swarm optimization technique
- Research Article
- 135
- 10.1109/tgcn.2018.2797533
- Jun 1, 2018
- IEEE Transactions on Green Communications and Networking
Demand side management (DSM) is an essential property of smart grid systems. Along with increasing expectations related to power quality from customers, and as new types of loads emerge, such as electric vehicles, local (renewable) energy generation, and stationary and mobile energy storage, it is critical to develop new methods for DSM. In this paper, we first construct a more efficient and reliable communication infrastructure in smart grid based on cognitive radio technology, which is an essential component for enabling DSM. Then, we propose a distributed energy storage planning approach based on game algorithm in DSM, which helps users select the appropriate size of storage units for balancing the cost in the planning period and during its use. Since planning problems may lead to consumer discomfort, we propose a cost function consisting of the billing, generation costs, and discomfort costs to balance users’ preferences with the payment. Furthermore, a game theory-based distributed energy management scheme is developed in DSM without leaking user privacy, which is used as inner optimization in our proposed distributed energy storage planning approach. In this energy management scheme, Nash equilibrium is obtained with minimum information exchange using proximal decomposition algorithm. Simulation results show superior performance of our proposed DSM mechanism in reducing the peak-to-average ratio, total cost, user’s daily payment, and energy consumption in smart grid communication networks.
- Research Article
- 64
- 10.1016/j.scs.2022.104260
- Oct 20, 2022
- Sustainable Cities and Society
Machine learning based demand response scheme for IoT enabled PV integrated smart building
Save Important notes in documents
Highlight text to save as a note, or write notes directly
You can also access these Documents in Paperpal, our AI writing tool
Powered by our AI Writing Assistant
