Optimal Residential Demand Response in Distribution Networks

Shi, Wenbo; Li, Na; Xie, Xiaorong; Chu, Chi-Cheng; Gadh, Rajit

doi:10.1109/jsac.2014.2332131

Cited by 178 publications

(86 citation statements)

References 15 publications

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“…As stated in the introduction, the present work builds on conclusion of the analysis in [3]. Most closely related work to our proposal is the scheme presented in [11] which is very similar to [5] if prices are interpreted as control signals. The authors in [11] propose to solve a similar problem but their approach requires convergence of the algorithm to produce an allocation that does not violate total capacity constraint.…”

Section: Related Workmentioning

confidence: 91%

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Advanced demand response solutions for capacity markets

Kaddah¹,

Kofman²,

Mathieu³

et al. 2015

2015 11th International Conference on Innovations in Information Technology (IIT)

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Abstract-The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. Indeed, it enables visibility and control on the various appliances that may consume, store or generate energy within a home. It has been shown that a centralized control on the appliances of a set of households leads to efficient DR mechanisms; unfortunately, such solutions raise privacy and scalability issues. In this paper we propose an IoT-based DR approach that deals with these issues. Specifically, we propose and analyze a scalable two levels control system where a centralized controller allocates power to each house on one side and, each household implements an IoTbased DR local solution on the other side. A limited feedback to the centralized controller allows to enhance the performance with little impact on privacy. The solution is proposed for the general framework of capacity markets.

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Section: Related Workmentioning

confidence: 91%

“…Recent work has proposed schemes that are capable of taking into account flexibility of any generic appliance ( [11], [12]). Authors in [12] propose a customer reward scheme that incentivizes users to accept direct control of loads.…”

Section: Related Workmentioning

confidence: 99%

Advanced demand response solutions for capacity markets

Kaddah¹,

Kofman²,

Mathieu³

et al. 2015

2015 11th International Conference on Innovations in Information Technology (IIT)

View full text Add to dashboard Cite

show abstract

“…Different optimization decisions are made for different applications (e.g., demand response and energy/power management) that are typically formulated as non-linear optimization problems with different objectives. Extensive algorithms have been proposed for them [7,8]. Given EV owners' charging profiles and real-time power price, Mal et al developed a V2G scheduling algorithm working at a large scale EV charging structure [14].…”

Section: Optimization: Making a Control Decision For Optimal Operationsmentioning

confidence: 99%

“…These properties complicate the microgrid management, and the microgrid EMS must be able to handle them in an appropriate manner. Next, a microgrid operation involves running a list of energy applications including demand response and coordinated EV charging as well as running innovative control algorithms [7,8] that are not necessarily implemented in a single system. Therefore, the microgrid EMS must be able to interface with them seamlessly.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Microgrid Energy Management System

et al. 2016

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A microgrid is characterized by the integration of distributed energy resources and controllable loads in a power distribution network. Such integration introduces new, unique challenges to microgrid management that have never been exposed to traditional power systems. To accommodate these challenges, it is necessary to redesign a conventional Energy Management System (EMS) so that it can cope with intrinsic characteristics of microgrids. While many projects have shown excellent research outcomes, they have either tackled portions of the characteristics or validated their EMSs only via simulations. This paper proposes a Microgrid Platform (MP), an advanced EMS for efficient microgrid operations. We design the MP by taking into consideration (i) all the functional requirements of a microgrid EMS (i.e., optimization, forecast, human-machine interface, and data analysis) and (ii) engineering challenges (i.e., interoperability, extensibility, and flexibility). Moreover, a prototype system is developed and deployed in two smart grid testbeds: UCLA Smart Grid Energy Research Center and Korea Institute of Energy Research. We then conduct experiments to verify the feasibility of the MP design in real-world settings. Our testbeds and experiments demonstrate that the MP is able to communicate with various energy devices and to perform an energy management task efficiently.

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“…In [3], the authors have presented a model for designing consumers' optimal set of consumption under DR programs and considering the operation constraints of distribution networks. This model has been presented as an integrated optimization problem in which maximization of the consumers' social welfare and reducing the distribution network losses are considered as the objective function.…”

mentioning

confidence: 99%

Mitigation of active and reactive demand response mismatches through reactive power control considering static load modeling in distribution grids

Bajool

Shafie‐khah

Gazafroudi

et al. 2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

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Abstract-Demand response is known as one of the basic components of smart grids that plays an important role in shaping load curves. In most of the prior reports on applying demand response programs, reactive power and load dependency to voltage magnitude have been ignored in distribution grids. In this paper, firstly, we show that the ignorance of the mentioned phenomena can cause a mismatch between the expected value of demand response and the experimental value. This mismatch is known as the demand response mismatch (DRM), which is dependent on some parameters such as load type, load reduction percentage, and network power factor. To overcome this problem, this paper presents a reactive power control model. In addition, a mixed integer nonlinear program is proposed to find the optimal size and location of STATCOMs and the optimal transformer tap settings that minimize the DRM. In this paper, the 16-bus U.K. generic distribution system (UKGDS) is employed to prove the capability of the presented method in DRM reduction.

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Optimal Residential Demand Response in Distribution Networks

Cited by 178 publications

References 15 publications

Advanced demand response solutions for capacity markets

Advanced demand response solutions for capacity markets

Design and Implementation of a Microgrid Energy Management System

Mitigation of active and reactive demand response mismatches through reactive power control considering static load modeling in distribution grids

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