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Pierluigi Mancarella

Bio: Pierluigi Mancarella is an academic researcher from University of Melbourne. The author has contributed to research in topics: Demand response & Distributed generation. The author has an hindex of 51, co-authored 303 publications receiving 10667 citations. Previous affiliations of Pierluigi Mancarella include University of Manchester & Polytechnic University of Turin.


Papers
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Journal ArticleDOI
01 Mar 2009-Energy
TL;DR: In this article, the authors present a comprehensive input-output matrix approach aimed at modelling small-scale trigeneration equipment taking into account the interactions among plant components and external energy networks.

197 citations

Journal ArticleDOI
01 Mar 2008-Energy
TL;DR: In this article, a novel approach based upon an original indicator called trigeneration CO 2 emission reduction (TCO 2 ER ) was proposed to assess the emission reduction of CO 2 and other GHGs from CHP and CCHP systems with respect to the separate production.

188 citations

Journal ArticleDOI
TL;DR: The key point is that energy shifting can be deployed inside the local DMG system to respond to given DR signals without reducing the users' energy demand and thus without affecting their comfort level.
Abstract: In this paper, a comprehensive dedicated framework is set up to analyze distributed multi-generation (DMG) systems for the purpose of identifying and quantifying their potential to participate in real-time demand response (DR) programmes. At first, flexibility of DMG systems with multiple interconnected plant components is exploited to identify the optimal operational strategy in the presence of half-hourly pricing. Then, the costs and benefits of providing further real-time DR are assessed by taking into account different energy shifting strategies. The novel concept of electricity shifting potential is introduced to establish the upper limit to the possible reduction of the electricity flowing from the electrical grid to the DMG system. The maximum profitable energy shifting that can be activated in the presence of given DR incentives is established on the basis of a DR profitability map. The key point is that energy shifting can be deployed inside the local DMG system to respond to given DR signals without reducing the users' energy demand and thus without affecting their comfort level. Examples of real-time DR for a trigeneration system referring to half-hourly periods during selected Summer and Winter days are illustrated and discussed.

170 citations

Journal ArticleDOI
TL;DR: A review of the main design features of existing microgrids is undertaken in light of the experience gained during the realization of the Prince Lab microgrid at Polytechnic University of Bari, Italy, and the main control functions required to guarantee an economic, reliable and secure operation of a microgrid are reviewed.

170 citations

Journal ArticleDOI
TL;DR: A two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material and the utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is demonstrated.
Abstract: This paper presents a two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material. Aggregated residential electro-thermal technologies (ETTs), such as electric heat pumps and (micro-) combined heat and power, are modeled in a unified nontechnology specific way. Day-ahead optimization is carried out considering uncertainty in outdoor temperature, electricity and hot water consumption, dwelling occupancy, and imbalance prices. Building flexibility is exploited through specification of a deadband around the set temperature or a price of thermal discomfort applied to deviations from the set temperature. A new expected thermal discomfort (ETD) metric is defined to quantify user discomfort. The efficacy of exploiting the flexibility of various residential ETT following the two approaches is analyzed. The utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is also demonstrated. Such quantification may be useful in the determination of DR contracts set up by energy service companies. Case studies for a U.K. residential users’ aggregation exemplify the model proposed and quantify possible cost reductions that are achievable under different flexibility scenarios.

162 citations


Cited by
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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal Article
TL;DR: This research examines the interaction between demand and socioeconomic attributes through Mixed Logit models and the state of art in the field of automatic transport systems in the CityMobil project.
Abstract: 2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

4,784 citations