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Energies
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Demand Side Management of Distributed and Uncertain Flexibilities
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Demand Side Management of Distributed and Uncertain Flexibilities

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 12048

Special Issue Editors

Dr. Klaus Rheinberger

E-Mail Website
Guest Editor
Research Center Energy, Vorarlberg University of Applied Sciences, Dornbirn 6850, Austria
Interests: demand side management; electro-mobility; power systems; economic systems; optimization and simulation
Dr. Peter Kepplinger

E-Mail Website
Guest Editor
Research Center Energy, Vorarlberg University of Applied Sciences, 6850 Dornbirn, Austria
Interests: demand side management; integration of renewables; system dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Energies on the subject "Demand Side Management of Distributed and Uncertain Flexibilities." The concurrent growth of energy consumption and of the share of renewable energies has increased the need for optimization and control techniques of flexibilities on the demand side of electric power and energy systems. Various approaches have been proposed differing in objectives, model complexity, communication effort and computational load.

This Special Issue will deal with novel contributions that investigate the optimization and control of distributed and uncertain flexibilities. In order to maintain the integrity, transparency and reproducibility of contributions, authors will

  • use the following publicly available data set that includs households' demand, PV production, EVs’ utilization, total renewable energy generation, and day-ahead prices: https://github.com/klaus-rheinberger/DSM-data
  • release their computer code either by deposition in a recognized, public repository or upload as supplementary information to the publication.

Authors are allowed to use additional data sets if these are also freely accessible and are referenced by the authors.

Topics of interest for publication include, but are not limited to:

  • Distributed Demand Side Management
  • Incentive Based Demand Response
  • Uni- and Bidirectional Communication Infrastructures
  • Real Time Pricing
  • Bilevel optimization
  • Power Matching
  • Model Predictive Control
  • Cost Minimization
  • Welfare Maximization
  • Peak Shaving
  • Minimization of Customer Discomfort
  • Grid Security Maximization
  • Maximization of Renewable Energy Integration
  • Power Loss Minimization

Dr. Klaus Rheinberger
Dr. Peter Kepplinger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart Grid
  • Demand Side Management
  • Electric Vehicles
  • Energy Storage Systems
  • Optimization
  • Control
  • Scheduling

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Published Papers (4 papers)

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Research

25 pages, 579 KiB  
Article
Aggregation of Demand-Side Flexibilities: A Comparative Study of Approximation Algorithms
by Emrah Öztürk, Klaus Rheinberger, Timm Faulwasser, Karl Worthmann and Markus Preißinger
Energies 2022, 15(7), 2501; https://doi.org/10.3390/en15072501 - 29 Mar 2022
Cited by 11 | Viewed by 2796
Abstract
Traditional power grids are mainly based on centralized power generation and subsequent distribution. The increasing penetration of distributed renewable energy sources and the growing number of electrical loads is creating difficulties in balancing supply and demand and threatens the secure and efficient operation [...] Read more.
Traditional power grids are mainly based on centralized power generation and subsequent distribution. The increasing penetration of distributed renewable energy sources and the growing number of electrical loads is creating difficulties in balancing supply and demand and threatens the secure and efficient operation of power grids. At the same time, households hold an increasing amount of flexibility, which can be exploited by demand-side management to decrease customer cost and support grid operation. Compared to the collection of individual flexibilities, aggregation reduces optimization complexity, protects households’ privacy, and lowers the communication effort. In mathematical terms, each flexibility is modeled by a set of power profiles, and the aggregated flexibility is modeled by the Minkowski sum of individual flexibilities. As the exact Minkowski sum calculation is generally computationally prohibitive, various approximations can be found in the literature. The main contribution of this paper is a comparative evaluation of several approximation algorithms in terms of novel quality criteria, computational complexity, and communication effort using realistic data. Furthermore, we investigate the dependence of selected comparison criteria on the time horizon length and on the number of households. Our results indicate that none of the algorithms perform satisfactorily in all categories. Hence, we provide guidelines on the application-dependent algorithm choice. Moreover, we demonstrate a major drawback of some inner approximations, namely that they may lead to situations in which not using the flexibility is impossible, which may be suboptimal in certain situations. Full article
(This article belongs to the Special Issue Demand Side Management of Distributed and Uncertain Flexibilities)
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29 pages, 12730 KiB  
Article
Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets
by Daud Mustafa Minhas, Josef Meiers and Georg Frey
Energies 2022, 15(5), 1619; https://doi.org/10.3390/en15051619 - 22 Feb 2022
Cited by 8 | Viewed by 2553
Abstract
To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home [...] Read more.
To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%. Full article
(This article belongs to the Special Issue Demand Side Management of Distributed and Uncertain Flexibilities)
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12 pages, 537 KiB  
Article
Which Strategy Saves the Most Energy for Stratified Water Heaters?
by Michael J. Ritchie, Jacobus A. A. Engelbrecht and M. J. (Thinus) Booysen
Energies 2021, 14(16), 4859; https://doi.org/10.3390/en14164859 - 9 Aug 2021
Cited by 3 | Viewed by 2147
Abstract
The operation of water heating uses a substantial amount of energy and is responsible for 30% of a household’s overall electricity consumption. Determining methods of reducing energy demand is crucial for countries such as South Africa, where energy supply is almost exclusively electrical, [...] Read more.
The operation of water heating uses a substantial amount of energy and is responsible for 30% of a household’s overall electricity consumption. Determining methods of reducing energy demand is crucial for countries such as South Africa, where energy supply is almost exclusively electrical, 88% of it is generated by coal, and energy deficits cause frequent blackouts. Decreasing the energy consumption of tanked water heaters can be achieved by reducing the standing losses and thermal energy of the hot water used. In this paper, we evaluate various energy-saving strategies that have commonly been used and determine which strategy is best. These strategies include optimising the heating schedule, lowering the set-point temperature, reducing the volume of hot water used, and installing additional thermal insulation. The results show that the best strategy was providing optimal control of the heating element, and savings of 16.3% were achieved. This study also determined that the magnitude of energy savings is heavily dependent on a household’s water usage intensity and seasonality. Full article
(This article belongs to the Special Issue Demand Side Management of Distributed and Uncertain Flexibilities)
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23 pages, 1354 KiB  
Article
Practically-Achievable Energy Savings with the Optimal Control of Stratified Water Heaters with Predicted Usage
by Michael J. Ritchie, Jacobus A.A. Engelbrecht and Marthinus J. Booysen
Energies 2021, 14(7), 1963; https://doi.org/10.3390/en14071963 - 1 Apr 2021
Cited by 13 | Viewed by 2957
Abstract
Residential water heaters use a substantial amount of electrical energy and contribute to 25% of the energy usage in the residential sector. This raises concern for users in countries with flat rate electricity fees and where fossil fuels are used for electricity generation. [...] Read more.
Residential water heaters use a substantial amount of electrical energy and contribute to 25% of the energy usage in the residential sector. This raises concern for users in countries with flat rate electricity fees and where fossil fuels are used for electricity generation. Demand side management of tanked water heaters is well suited for energy-focused load reduction strategies. We propose a strategy for providing an electric water heater (EWH) with the optimal temperature planning to reduce the overall electrical energy usage while satisfying the comfort of the user. A probabilistic hot water usage model is used to predict the hot water usage behaviour for the A*-based optimisation algorithm, which accounts for water stratification in the tank. A temperature feedback controller with novel temperature and energy-correcting capabilities provides robustness to prediction errors. Three optimal control strategies are presented and compared to a baseline strategy with the thermostat always on: The first ensures temperature-matched water usages, the second ensures energy-matched water usages, and the third is a variation of the second that provides Legionella prevention. Results were obtained for 77 water heaters, each one simulated for four weeks. The median energy savings for predicted usage were 2.2% for the temperature-matched strategy, and 9.6% for both of the energy-matched strategies. We also compare the practical energy savings to the ideal scenario where the optimal scheduling has perfect foreknowledge of hot water usages, and the temperature and energy-matched strategies had a 4.1 and 11.0 percentage point decrease from the ideal energy savings. Full article
(This article belongs to the Special Issue Demand Side Management of Distributed and Uncertain Flexibilities)
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