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Demand Response Optimization for Smart Energy Systems

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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 (30 April 2020) | Viewed by 22928

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Special Issue Editor

Prof. Dr. João P. S. Catalão

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Guest Editor

Faculty of Engineering of the University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: power system operations and planning; hydro and thermal scheduling; wind and price forecasting; distributed renewable generation; demand response and smart grids

Special Issue Information

Dear Colleagues,

With the evolution of decentralized energy systems, proper energy management and scheduling has become of paramount importance. IoT-enabling of Smart Grids is paving the way for more dynamic, albeit complex, energy markets. With this paradigm shift, novel Demand Response (DR) schemes are emerging to adapt to the constantly evolving structure of power systems and markets, especially with the increasing proliferation of small-scale prosumers.

A key enabler of modern DR schemes is the employment of energy scheduling and management tools, which are primarily based on optimization methods. Whether the objective is to maximize grid flexibility, energy efficiency, consumer comfort levels, or economic benefits of market participants, optimization tools are of vital importance to determine the best approach for real-time operation and management of emerging smart energy systems.

This special issue aims to attract innovative and disruptive work on the application of optimization tools on smart energy systems and/or the use thereof to enable DR schemes. With the current paradigm shift in energy systems, novel and disruptive ideas are encouraged, whether in terms of the optimization tools, DR schemes, or the interaction thereof. Potential benefits, impacts, and/or drawbacks of the proposed methods must be properly demonstrated through the use of realistic case studies with well-elaborated modeling and simulation and/or comprehensive experimental tests. Economic viability analyses (e.g. cost–benefit and SWOT) are also important.

We look forward to receiving your submissions on the topic of “Demand Response Optimization for Smart Energy Systems”. Thank you.

Prof. João P. S. Catalão
Guest Editor

Manuscript Submission Information

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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

Demand Response
Energy Management
Optimization Tools
Energy Scheduling
Energy Markets

Published Papers (7 papers)

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Research

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23 pages, 2282 KiB

Open AccessArticle

Demand Response Optimization Model to Energy and Power Expenses Analysis and Contract Revision

by Filipe Marangoni, Leandro Magatão and Lúcia Valéria Ramos de Arruda

Energies 2020, 13(11), 2803; https://doi.org/10.3390/en13112803 - 1 Jun 2020

Cited by 4 | Viewed by 1973

Abstract

This paper proposes a mathematical model based on mixed integer linear programming (MILP). This model aids the decision-making process in local generation use and demand response application to power demand contract adequacy by Brazilian consumers/prosumers. Electric energy billing in Brazil has some specificities which make it difficult to consider the choice of the tariff modality, the determination of the optimal contracted demand value, and demand response actions. In order to bridge this gap, the model considers local generation connected to the grid (distributed generation) and establishes an optimized solution indicating power energy contract aspects and the potential reduction in expenses for the next billing period (12 months). Different alternative sources already available or of interest to the consumer can be considered. The proposed mathematical model configures an optimization tool for the feasibility analysis of local generation use and, concomitantly, (i) checking the tariff modality, (ii) revising the demand contract, and (iii) suggesting demand response actions. The presented result shows a significant reduction in the energy and power expenses, which confirms the usefulness of this proposal. In the end, the optimized answers promote benefits for both, the consumer/prosumer and the electric utility. Full article

(This article belongs to the Special Issue Demand Response Optimization for Smart Energy Systems)

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17 pages, 1013 KiB

Open AccessArticle

Consumers’ Willingness to Accept Time-of-Use Tariffs for Shifting Electricity Demand

by Swantje Sundt, Katrin Rehdanz and Jürgen Meyerhoff

Energies 2020, 13(8), 1895; https://doi.org/10.3390/en13081895 - 13 Apr 2020

Cited by 21 | Viewed by 3468

Abstract

Time-of-use (TOU) electricity tariffs represent an instrument for demand side management. By reducing energy demand during peak times, less investments in otherwise necessary, costly, and CO₂ intensive redispatch would be required. We use a choice experiment (CE) to analyze private consumers’ acceptance of TOU tariffs in Germany. In our CE, respondents choose between a fixed rate tariff and two TOU tariffs that differ by peak time scheme and by a control of appliances’ electricity consumption during that time. We use a mixed logit model to account for taste heterogeneity. Moreover, investigating decision strategies, we identify three different strategies that shed light on drivers of unobserved taste heterogeneity: (1) Always choosing the status quo, (2) always choosing the maximum discount, and (3) choosing a TOU tariff but not always going for the maximum discount. Overall, about 70% of our 1398 respondents would choose a TOU tariff and shift their electricity demand, leading to a decline in energy demand during peak times. Rough estimates indicate that this would lead to significant savings in electricity generation, avoiding up to a mid to large-sized fossil-fuel power plant. Full article

(This article belongs to the Special Issue Demand Response Optimization for Smart Energy Systems)

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20 pages, 5210 KiB

Open AccessArticle

Optimal Operation of Critical Peak Pricing for an Energy Retailer Considering Balancing Costs

by Hye Yoon Song, Gyu Sub Lee and Yong Tae Yoon

Energies 2019, 12(24), 4658; https://doi.org/10.3390/en12244658 - 7 Dec 2019

Cited by 10 | Viewed by 2485

Abstract

Recently, there have been frequent fluctuations in the wholesale prices of electricity following the increased penetration of renewable energy sources. Therefore, retailers face price risks caused by differences between wholesale prices and retail rates. As a hedging against price risk, retailers can utilize critical peak pricing (CPP) in a price-based program. This study proposes a novel multi-stage stochastic programming (MSSP) model for a retailer with self-generation photovoltaic facility to optimize both its bidding strategy and the CPP operation, in the face of several uncertainties. Using MSSP, decisions can be determined sequentially with realization of the uncertainties over time. Furthermore, to ensure a global optimum, a mixed integer non-linear programming is transformed into mixed integer linear programming through three linearization steps. In a numerical simulation, the effectiveness of the proposed MSSP model is compared with that of a mean-value deterministic model based on a rolling horizon method. We also investigate the optimal strategy of a retailer by changing various input parameters and perform a sensitivity analysis to assess the impacts of different uncertain parameters on the retailer’s profit. Finally, the effect of the energy storage system on the proposed optimization problem is investigated. Full article

(This article belongs to the Special Issue Demand Response Optimization for Smart Energy Systems)

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18 pages, 5094 KiB

Open AccessArticle

Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy

by Haesum Ali, Akhtar Hussain, Van-Hai Bui, Jinhong Jeon and Hak-Man Kim

Energies 2019, 12(19), 3701; https://doi.org/10.3390/en12193701 - 27 Sep 2019

Cited by 12 | Viewed by 2843

Abstract

Integration of demand response programs in microgrids can be beneficial for both the microgrid owners and the consumers. The demand response programs are generally triggered by market price signals to reduce the peak load demand. However, during islanded mode, due to the absence of connection with the utility grid, the market price signals are not available. Therefore, in this study, we have proposed a distributed demand response program for an islanded multi-microgrid network, which is not triggered by market price signals. The proposed distributed demand response program is based on welfare maximization of the network. Based on the welfare function of individual microgrids, the optimal power is allocated to the microgrids of the network in two steps. In the first step, the total surplus power and shortage power of the network is determined in a distributed way by using the local surplus/shortage information of each microgrid, which is computed after local optimization. In the second step, the total surplus of the network is allocated to the microgrids having shortage power based on their welfare functions. Finally, the allocated power amount and the initial shortage amount in the microgrid is used to determine the amount of load to be curtailed. Diffusion strategy is used in both the first and the second steps and the performance of the proposed method is compared with the widely used consensus method. Simulation results have proved the effectiveness of the proposed method for realizing distributed demand response for islanded microgrid networks. Full article

(This article belongs to the Special Issue Demand Response Optimization for Smart Energy Systems)

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15 pages, 1463 KiB

Open AccessArticle

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

by Zhanle Wang, Raman Paranjape, Zhikun Chen and Kai Zeng

Energies 2019, 12(15), 2867; https://doi.org/10.3390/en12152867 - 25 Jul 2019

Cited by 18 | Viewed by 2631

Abstract

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies. Full article

(This article belongs to the Special Issue Demand Response Optimization for Smart Energy Systems)

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19 pages, 6588 KiB

Open AccessArticle

Coordinated Voltage Regulation by On-Load Tap Changer Operation and Demand Response Based on Voltage Ranking Search Algorithm

by Qiangqiang Xie, Xiangrong Shentu, Xusheng Wu, Yi Ding, Yongzhu Hua and Jiadong Cui

Energies 2019, 12(10), 1902; https://doi.org/10.3390/en12101902 - 18 May 2019

Cited by 14 | Viewed by 2669

Abstract

The growing penetration of photovoltaic (PV) systems may cause an over-voltage problem in power distribution systems. Meanwhile, charging of massive electric vehicles may cause an under-voltage problem. The over- and under-voltage problems make the voltage regulation become more challenging in future power distribution systems. Due to the development of smart grid and demand response, flexible resources such as PV inverters and controllable loads can be utilized for voltage regulation in distribution systems. However, the voltage regulation needs to calculate the nonlinear power flow; as a result, utilizing flexible resources for voltage regulation is a nonlinear scheduling problem requiring heavy computational resources. This study proposes an intelligent search algorithm called voltage ranking search algorithm (VRSA) to solve the optimization of flexible resource scheduling for voltage regulation. The VRSA is built based on the features of radial power distribution systems. A numerical simulation test is carried out on typical power distribution systems. The VRSA is compared with the genetic algorithm and voltage sensitivity method. The results show that the VRSA has the best optimization effect among the three algorithms. By utilizing flexible resources through demand response, the tap operation times of on-load tap changers can be reduced. Full article

(This article belongs to the Special Issue Demand Response Optimization for Smart Energy Systems)

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Review

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32 pages, 11050 KiB

Open AccessReview

Prosumer Flexibility: A Comprehensive State-of-the-Art Review and Scientometric Analysis

by Matthew Gough, Sérgio F. Santos, Mohammed Javadi, Rui Castro and João P. S. Catalão

Energies 2020, 13(11), 2710; https://doi.org/10.3390/en13112710 - 28 May 2020

Cited by 32 | Viewed by 5914

Abstract

There is a growing need for increased flexibility in modern power systems. Traditionally, this flexibility has been provided by supply-side technologies. There has been an increase in the research surrounding flexibility services provided by demand-side actors and technologies, especially flexibility services provided by prosumers (those customers who both produce and consume electricity). This work gathers 1183 peer-reviewed journal articles concerning the topic and uses them to identify the current state of the art. This body of literature was analysed with two leading textual and scientometric analysis tools, SAS© Visual Text Analytics and VOSviewer, in order to provide a detailed understanding of the current state-of-the-art research on prosumer flexibility. Trends, key ideas, opportunities and challenges were identified and discussed. Full article

(This article belongs to the Special Issue Demand Response Optimization for Smart Energy Systems)

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