Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid

Cecati, Carlo; Citro, Costantino; Siano, Pierluigi

doi:10.1109/tste.2011.2161624

Cited by 304 publications

(120 citation statements)

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“…However, some of them may be discrete in reality (e.g., on/off). A two-stage approach [35] can be used to solve this issue. In the first stage, a solution is obtained assuming that all the control variables are continuous.…”

Section: Demand Responsementioning

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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Section: Demand Responsementioning

confidence: 99%

Design and Implementation of a Microgrid Energy Management System

et al. 2016

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“…The method can be used by DNOs to better allocate PVs at more advantageous locations in terms of consumers' benefits and cost reduction, network constraints and reliability. Conventional planning of distribution networks involving renewable energy sources integration have not considered the combination of ANM schemes and DR on the operation of distribution network [12][13].…”

Section: Contributionsmentioning

confidence: 99%

Active distribution networks planning with integration of demand response

Mokryani

2015

Solar Energy

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-This paper proposes a probabilistic method for active distribution networks planning with integration of demand response.Uncertainties related to solar irradiance, load demand and future load growth are modelled by probability density functions. The method simultaneously minimizes the total operational cost and total energy losses of the lines from the point of view of distribution network operators with integration of demand response over the planning horizon considering active management schemes including coordinated voltage control and adaptive power factor control. Monte Carlo simulation method is employed to use the generated probability density functions and the weighting factor method is used to solve the multi-objective optimization problem. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. Index Terms -

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“…23 The overheating hours (h over ) are defined as the number of working hours when the indoor 24 temperature is higher than 26°C, while the underheating hours (h under ) when the indoor 25 temperature is lower than 19°C. Both the average building temperature and the temperatures 1 in the 12 different zones considered were evaluated.…”

Section: The Simulation Model 20mentioning

confidence: 99%

“…In the latter 13 case, generally the utility sends different signals to turn on/off those end users devices 14 suitable for such operating conditions [23]. For the case study a switch off time of 15 15 minutes per hour, randomly positioned, was assumed for the TABS.…”

Section: Dsm Mechanismsmentioning

confidence: 99%