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Demand Response Optimization Techniques for Smart Power Grids

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 25107

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Special Issue Editors

Dr. Islam Safak Bayram

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Guest Editor

Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: smart grids; electric vehicles; energy storage for grid applications; multivector energy applications; power quality
Special Issues, Collections and Topics in MDPI journals

Dr. Muhammad Ismail

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Guest Editor

Department of Computer Science, Faculty of Engineering, Tennessee Tech University, Cookeville, TN, USA
Interests: smart grids; networking; cyber-physical security; blockchain; resource allocation; machine learning; optimization; stochastic modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As part of the net-zero emission goals, the future of electric power grids is currently shaped by higher penetration levels of renewable energy sources, increasing adoption rates of plug-in electric vehicles (PEVs), and electrification of heating and cooling appliances. This transformation calls for dynamic energy management and scheduling of demand-side activities that can be realized by employing a set of enabling technologies such as wireless networks, smart meters, internet-of-things (IoT)-based sensors, and intelligent load switches. Demand response (DR) schemes have emerged as a way to shape electricity consumption profiles to optimize the operational costs typically defined as a combination of electricity prices, customer comfort, and load flexibility.

While there is a growing body of literature on the optimization of DR in single residential units or microgrids, in this Special Issue, we are particularly interested in multi-dimensional joint optimization problems. For instance, DR can be used as a tool to enhance power quality, adjust the self-consumption levels of photovoltaic (PV) rooftop systems, increase PV handling capacity of distribution grids, and optimally schedule PEV loads to reduce solar “duck curves” or wind energy curtailment. Another key area of interest is the application of data analytics to load aggregation, managing houses at scale, and devising dynamic pricing methodologies. Furthermore, since DR schemes rely on information exchange among utility companies and customers, security and privacy issues of DR systems should receive appropriate consideration.

This Special Issue is an ideal venue to make innovative contributions to novel architectures, optimization, and control of DR. We invite field experiments, simulation-based, and/or analytical studies with well-elaborated realistic case studies and real-world datasets.

Dr. Islam Safak Bayram
Dr. Muhammad Ismail
Guest Editors

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Keywords

Linear and integer programming for demand response
Dynamic and stochastic optimization for demand response
Game-theoretic optimization for demand response
Data management, analytics, and machine learning for demand response
Dynamic pricing for demand response
Electric vehicle load management and smart charging
Electric vehicle charging and discharging coordination for demand response
Charging and discharging of energy storage units for demand response
Renewable energy integration for demand response.

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Published Papers (6 papers)

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Research

13 pages, 2738 KiB

Open AccessArticle

A Mobile Energy Storage Unit Serving Multiple EV Charging Stations

by Mohamed M. Elmeligy, Mostafa F. Shaaban, Ahmed Azab, Maher A. Azzouz and Mohamed Mokhtar

Energies 2021, 14(10), 2969; https://doi.org/10.3390/en14102969 - 20 May 2021

Cited by 4 | Viewed by 3025

Abstract

Due to the rapid increase in electric vehicles (EVs) globally, new technologies have emerged in recent years to meet the excess demand imposed on the power systems by EV charging. Among these technologies, a mobile energy storage system (MESS), which is a transportable storage system that provides various utility services, was used in this study to support several charging stations, in addition to supplying power to the grid during overload and on-peak hours. Thus, this paper proposes a new day-ahead optimal operation of a single MESS unit that serves several charging stations that share the same geographical area. The operational problem is formulated as a mixed-integer non-linear programming (MINLP), where the objective is to minimize the total operating cost of the parking lots (PLs). Two different case studies are simulated to highlight the effectiveness of the proposed system compared to the current approach. Full article

(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids)

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25 pages, 4696 KiB

Open AccessArticle

A Synthetic Approach for Datacenter Power Consumption Regulation towards Specific Targets in Smart Grid Environment

by Mengmeng Zhao and Xiaoying Wang

Energies 2021, 14(9), 2602; https://doi.org/10.3390/en14092602 - 2 May 2021

Cited by 1 | Viewed by 1997

Abstract

With the large-scale grid connection of renewable energy sources, the frequency stability problem of the power system has become increasingly prominent. At the same time, the development of cloud computing and its applications has attracted people’s attention to the high energy consumption characteristics of datacenters. Therefore, it was proposed to use the characteristics of the high power consumption and high flexibility of datacenters to respond to the demand response signal of the smart grid to maintain the stability of the power system. Specifically, this paper establishes a synthetic model that integrates multiple methods to precisely control and regulate the power consumption of the datacenter while minimizing the total adjustment cost. First, according to the overall characteristics of the datacenter, the power consumption models of servers and cooling systems were established. Secondly, by controlling the temperature, different kinds of energy storage devices, load characteristics and server characteristics, the working process of various regulation methods and the corresponding adjustment cost models were obtained. Then, the cost and penalty of each power regulation method were incorporated. Finally, the proposed dynamic synthetic approach was used to achieve the goal of accurately adjusting the power consumption of the datacenter with least adjustment cost. Through comparative analysis of evaluation experiment results, it can be observed that the proposed approach can better regulate the power consumption of the datacenter with lower adjustment cost than other alternative methods. Full article

(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids)

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22 pages, 2466 KiB

Open AccessFeature PaperArticle

Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning

by Oleh Lukianykhin and Tetiana Bogodorova

Energies 2021, 14(8), 2274; https://doi.org/10.3390/en14082274 - 18 Apr 2021

Cited by 4 | Viewed by 2373

Abstract

Ancillary services rely on operating reserves to support an uninterrupted electricity supply that meets demand. One of the hidden reserves of the grid is in thermostatically controlled loads. To efficiently exploit these reserves, a new realization of control of voltage in the allowable range to follow the set power reference is proposed. The proposed approach is based on the deep reinforcement learning (RL) algorithm. Double DQN is utilized because of the proven state-of-the-art level of performance in complex control tasks, native handling of continuous environment state variables, and model-free application of the trained DDQN to the real grid. To evaluate the deep RL control performance, the proposed method was compared with a classic proportional control of the voltage change according to the power reference setup. The solution was validated in setups with a different number of thermostatically controlled loads (TCLs) in a feeder to show its generalization capabilities. In this article, the particularities of deep reinforcement learning application in the power system domain are discussed along with the results achieved by such an RL-powered demand response solution. The tuning of hyperparameters for the RL algorithm was performed to achieve the best performance of the double deep Q-network (DDQN) algorithm. In particular, the influence of a learning rate, a target network update step, network hidden layer size, batch size, and replay buffer size were assessed. The achieved performance is roughly two times better than the competing approach of optimal control selection within the considered time interval of the simulation. The decrease in deviation of the actual power consumption from the reference power profile is demonstrated. The benefit in costs is estimated for the presented voltage control-based ancillary service to show the potential impact. Full article

(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids)

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20 pages, 3601 KiB

Open AccessArticle

Techno-Economic Analysis of On-Site Energy Storage Units to Mitigate Wind Energy Curtailment: A Case Study in Scotland

by Seda Canbulat, Kutlu Balci, Onder Canbulat and I. Safak Bayram

Energies 2021, 14(6), 1691; https://doi.org/10.3390/en14061691 - 18 Mar 2021

Cited by 18 | Viewed by 7036

Abstract

Wind energy plays a major role in decarbonisation of the electricity sector and supports achieving net-zero greenhouse gas emissions. Over the last decade, the wind energy deployments have grown steadily, accounting for more than one fourth of the annual electricity generation in countries like the United Kingdom, Denmark, and Germany. However, as the share of wind energy increases, system operators face challenges in managing excessive wind generation due to its nondispatchable nature. Currently, the most common practice is wind energy curtailment in which wind farm operators receive constraint payments to reduce their renewable energy production. This practice not only leads to wastage of large volumes of renewable energy, but also the associated financial cost is reflected to rate payers in the form of increased electricity bills. On-site energy storage technologies come to the forefront as a technology option to minimise wind energy curtailment and to harness wind energy in a more efficient way. To that end, this paper, first, systematically evaluates different energy storage options for wind energy farms. Second, a depth analysis of curtailment and constraint payments of major wind energy farms in Scotland are presented. Third, using actual wind and market datasets, a techno-economic analysis is conducted to examine the relationship between on-site energy storage size and the amount of curtailment. The results show that, similar to recent deployments, lithium-ion technology is best suited for on-site storage. As case studies, Whitelee and Gordon bush wind farms in Scotland are chosen. The most suitable storage capacities for 20 years payback period is calculated as follows: (i) the storage size for the Gordonbush wind farm is 100 MWh and almost 19% of total curtailment can be avoided and (ii) the storage size for the Whitlee farm is 125 MWh which can reduce the curtailment by 20.2%. The outcomes of this study will shed light into analysing curtailment reduction potential of future wind farms including floating islands, seaports, and other floating systems. Full article

(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids)

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19 pages, 3459 KiB

Open AccessArticle

Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption

by Hedayat Saboori, Shahram Jadid and Mehdi Savaghebi

Energies 2021, 14(3), 736; https://doi.org/10.3390/en14030736 - 31 Jan 2021

Cited by 28 | Viewed by 3314

Abstract

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator. Full article

(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids)

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26 pages, 8658 KiB

Open AccessArticle

A Case Study in Qatar for Optimal Energy Management of an Autonomous Electric Vehicle Fast Charging Station with Multiple Renewable Energy and Storage Systems

by Abdulla Al Wahedi and Yusuf Bicer

Energies 2020, 13(19), 5095; https://doi.org/10.3390/en13195095 - 30 Sep 2020

Cited by 14 | Viewed by 5585

Abstract

E-Mobility deployment has attained increased interest during recent years in various countries all over the world. This interest has focused mainly on reducing the reliance on fossil fuel-based means of transportation and decreasing the harmful emissions produced from this sector. To secure the electricity required to satisfy Electric Vehicles’ (EVs’) charging needs without expanding or overloading the existing electricity infrastructure, stand-alone charging stations powered by renewable sources are considered as a reasonable solution. This paper investigates the simulation of the optimal energy management of a proposed grid-independent, multi-generation, fast-charging station in the State of Qatar, which comprises hybrid wind, solar and biofuel systems along with ammonia, hydrogen and battery storage units. The study aims to assess the optimal sizing of the solar, wind and biofuel units to be incorporated in the design along with the optimal ammonia, hydrogen and battery storage capacities to fulfill the daily EV demand in an uninterruptable manner. The main objective is to fast-charge a minimum of 50 EVs daily, while the constraints are the intermittent and volatile nature of renewable energy sources, the stochastic nature of EV demand, local meteorological conditions and land space limitations. The results show that the selection of a 468 kWp concentrated photovoltaic thermal plant, 250 kW-rated wind turbine, 10 kW biodiesel power generator unit and 595 kWh battery storage system, along with the on-site production of hydrogen and ammonia, to generate 200 kW power via fuel cells can achieve the desired target, with a total halt of on-site hydrogen and ammonia production during October and November and 50% reduction during December. Full article

(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids)

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