Demand response and energy storage systems: An industrial application for reducing electricity costs. Part II: Numerical application

Carpinelli, G.; Khormali, Shahab; Mottola, Fabio; Proto, Daniela

doi:10.1109/speedam.2014.6872040

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…This technology is widely used in industries that need hot water for their products. These dispatchable and low-cost systems can continuously generate energy, and so, participating in various DR programs does not need to stop the product line [195].…”

Section: Bmentioning

confidence: 99%

An Overview of Demand Response: From its Origins to the Smart Energy Community

Honarmand¹,

Hosseinnezhad

Hayes

et al. 2021

IEEE Access

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The need to increase network efficiency, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants' economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select a proper plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.

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Section: Bmentioning

confidence: 99%

An Overview of Demand Response: From its Origins to the Smart Energy Community

Honarmand¹,

Hosseinnezhad

Hayes

et al. 2021

IEEE Access

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“…Previous DSR studies have resulted in the following: static Stackelberg game theory for voluntary load curtailment programs [1]; numerical calculation method for DSR when a battery energy storage system (BESS) is utilized [2]; solutions for DSR by means of automatic lighting [3]; DSR with micro-CHP systems [4]; an overview for DSR methods in high consumption industries and examples of market tools that support DSR [5]. In [6] an automated complex system for LS in industry is described, which takes into account stock restrictions, maintenance schedules, and crew management.…”

Section: Introductionmentioning

confidence: 99%

Value Stream Mapping for Evaluation of Load Scheduling Possibilities in a District Heating Plant

Melsas

Rosin

Drovtar

2016

TEEE

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The aim of this paper is to provide a solution for load scheduling by implementing value stream mapping, which is a straightforward enough for production management. Decision makers in the industry should have a clear understanding about positive effect from load scheduling and its effect to production outcome and process availability. Value stream mapping is a well-known process optimization tool from lean production philosophy. The aim of value stream mapping is to shorten the lead time of industrial processes and to reduce the intermediate stock amounts. By complementing value stream map with process energy intensity and energy stored in intermediate stocks, we can promote load scheduling possibilities. Our methodology provides a tool that is understandable and traceable for industry-minded decision makers. Finally, we present a real life test example for the new methodology, which is based on the production process of a district heating plant.

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“…It is outlined that the forecast of the peak value of the power that the facility must request from the grid can be performed easily in the dayahead scheduling and, then, also this quantity can be assumed to be assigned in the second step of the procedure. More detailed discussion on the advantages obtained by applying the two-step procedure is reported in the companion paper "Part II: numerical applications" [16] where either the day ahead scheduling and the very short time predictive control are applied to an actual industrial facility application.…”

Section: Introductionmentioning

confidence: 99%

Demand response and energy storage systems: An industrial application for reducing electricity costs. Part I: Theoretical aspects

Carpinelli

Khormali

Mottola

et al. 2014

2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion

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In this paper, the benefits that can be derived from the installation of an energy storage system in an industrial facility are analyzed, and an optimal control strategy of the storage system is proposed. The proposed control strategy is based on a two-step procedure and aims at (i) reducing the electricity costs sustained by an industrial customer that provides demand response and (ii) satisfying technical constraints for the maximization of the efficiency and lifecycle of the storage system. The procedure was performed based on two different time periods, i.e., day ahead and very short time. The day-ahead scheduling evaluates the peak value of the power that the facility must request from the grid and the periods in which the battery is allowed to charge and discharge; the very short-time control evaluates the battery's charging/discharging power to minimize costs. This paper reports the theoretical aspects of the optimization models for the two-step procedure, whereas the companion paper "Part II: numerical applications" proposes the results of numerical applications of the proposed approach on an actual industrial facility.

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Demand response and energy storage systems: An industrial application for reducing electricity costs. Part II: Numerical application

Cited by 6 publications

References 5 publications

An Overview of Demand Response: From its Origins to the Smart Energy Community

An Overview of Demand Response: From its Origins to the Smart Energy Community

Value Stream Mapping for Evaluation of Load Scheduling Possibilities in a District Heating Plant

Demand response and energy storage systems: An industrial application for reducing electricity costs. Part I: Theoretical aspects

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