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Demand Response in Electricity Markets

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 October 2018) | Viewed by 43647

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Special Issue Editors

Prof. Dr. Henrik Madsen

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Guest Editor

Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Lyngby, Denmark
Interests: demand response; electricity market; smart grid; power system operation; ancillary services; grey-box modelling; forecasting; control theory; bidding strategies

Dr. Ali Pourmousavi Kani

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Guest Editor

School of Information Technology and Electrical Engineering, Faculty of Engineering, Architecture and Information Technology, Global Change Institute (GCI), University of Queensland, St Lucia, QLD 4072, Australia
Interests: Bulk power system operation and electricity market; Battery characterization, optimal sizing and operation in different applications; Aggregation of small storage and demand response resources; Demand response at the device level

Special Issue Information

Dear Colleagues,

Future power system will host significant amount of renewable generation inevitably. These energy resources are naturally undispatchable and unpredictable, and do not necessarily follow the load demand. Therefore, safe and secure operation of the future power system will require extra flexibility in real-time operation to compensate the varying generation. This will not be possible by large synchronous rotating machines, as they are slow, less economically efficient and polluting. In this regard, Demand Response Programs (DRP) are attracting a lot of attention. Preliminary studies on Demand Response (DR) resources in integrated energy systems have already projected incredible potential to act as flexibility resources for power systems operations. Nevertheless, there are still many questions and concerns related to DR resources involvement into the electricity and energy markets, which have to be properly addressed. This Special Issue is an attempt to encourage researchers from different discipline to offer solutions and algorithms to effectively incorporate DR resources in electricity and energy markets. These include the conventional day-ahead and real-time wholesale markets as well as P2P electricity trading considering stochasticity, unpredictability, and non-linearity of the phenomenon. In this framework, physical and virtual energy storages and electric vehicles are also considered as DR resources. A special focus will be on how to model, forecast and control flexible resources in intelligent and integrated energy systems.

Prof. Dr. Henrik Madsen
Dr. Seyyed Ali Pourmousavi Kani
Guest Editors

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Keywords

demand response aggregation
electric vehicle
energy storages
market bidding mechanism
demand response in P2P trading
ancillary services
forecasting and control of flexibility
stochastic demand response
market operation with demand response
dynamic flexibility modelling and control
integrated energy systems
ICT solutions for demand response

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Published Papers (10 papers)

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Research

29 pages, 10040 KiB

Open AccessArticle

Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods

by Orhan Altuğ Karabiber and George Xydis

Energies 2019, 12(5), 928; https://doi.org/10.3390/en12050928 - 10 Mar 2019

Cited by 71 | Viewed by 7632

Abstract

In this paper day-ahead electricity price forecasting for the Denmark-West region is realized with a 24 h forecasting range. The forecasting is done for 212 days from the beginning of 2017 and past data from 2016 is used. For forecasting, Autoregressive Integrated Moving Average (ARIMA), Trigonometric Seasonal Box-Cox Transformation with ARMA residuals Trend and Seasonal Components (TBATS) and Artificial Neural Networks (ANN) methods are used and seasonal naïve forecast is utilized as a benchmark. Mean absolute error (MAE) and root mean squared error (RMSE) are used as accuracy criterions. ARIMA and ANN are utilized with external variables and variable analysis is realized in order to improve forecasting results. As a result of variable analysis, it was observed that excluding temperature from external variables helped improve forecasting results. In terms of mean error ARIMA yielded the best results while ANN had the lowest minimum error and standard deviation. TBATS performed better than ANN in terms of mean error. To further improve forecasting accuracy, the three forecasts were combined using simple averaging and ANN methods and they were both found to be beneficial, with simple averaging having better accuracy. Overall, this paper demonstrates a solid forecasting methodology, while showing actual forecasting results and improvements for different forecasting methods. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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17 pages, 10553 KiB

Open AccessArticle

A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

by Tianli Song, Yang Li, Xiao-Ping Zhang, Jianing Li, Cong Wu, Qike Wu and Beibei Wang

Energies 2019, 12(1), 64; https://doi.org/10.3390/en12010064 - 26 Dec 2018

Cited by 9 | Viewed by 4298

Abstract

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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17 pages, 2326 KiB

Open AccessArticle

A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response

by Eunjung Lee, Dongsik Jang and Jinho Kim

Energies 2018, 11(12), 3417; https://doi.org/10.3390/en11123417 - 6 Dec 2018

Cited by 7 | Viewed by 2723

Abstract

Recent demand response (DR) research efforts have focused on reducing the peak demand, and thereby electricity prices. Load reductions from DR programs can be viewed as equivalent electricity generation by conventional means. Thus, utility companies must pay incentives to customers who reduce their demand accordingly. However, many key variables intrinsic to residential customers are significantly more complicated compared to those of commercial and industrial customers. Thus, residential DR programs are economically difficult to operate, especially because excess incentive settlements can result in free riders, who get incentives without reducing their loads. Improving baseline estimation accuracy is insufficient to solve this problem. To alleviate the free rider problem, we proposed an improved two-step method—estimating the baseline load using regression and implementing a minimum-threshold payment rule. We applied the proposed method to data from residential customers participating in a peak-time rebate program in Korea. It initially suffered from numerous free riders caused by inaccurate baseline estimation. The proposed method mitigated the issue by reducing the number of free riders. The results indicate the possibility of lowering the existing incentive payment. The findings indicate that it is possible to run more stable residential DR programs by mitigating the uncertainty associated with customer electricity consumption. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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21 pages, 848 KiB

Open AccessArticle

Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets

by Venkat Durvasulu and Timothy M. Hansen

Energies 2018, 11(12), 3361; https://doi.org/10.3390/en11123361 - 1 Dec 2018

Cited by 21 | Viewed by 3542

Abstract

In most U.S. market sponsored demand response (DR) programs, revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. This paper presents an aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX). Using the DRX structure, DR providers can participate in energy markets as a service to benefit bulk-power market entities. The benefits and challenges to each market entity using DR-as-a-service are presented in an extended review. The DRX model in this study is a market entity that operates with the day-ahead market to select DR offers that minimize electric utility payments. A case study was performed using the proposed DRX model on the IEEE 24-bus system, augmented to represent actual bulk-power market prices to study factors that influence utility payments under the DRX-market paradigm. Two high-price days of the PJM market were simulated, and it was shown for a single day on the augmented test case that spending $69,955 for DR-as-a-service results in a reduction of utility payments of $864,199. The day-ahead generator supply curve, network congestion, and DR curtailment were found to be the most influencing factors that impact the benefit of using DR-as-a-service. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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23 pages, 4103 KiB

Open AccessArticle

An Integration Mechanism between Demand and Supply Side Management of Electricity Markets

by Zixu Liu, Xiaojun Zeng and Fanlin Meng

Energies 2018, 11(12), 3314; https://doi.org/10.3390/en11123314 - 27 Nov 2018

Cited by 5 | Viewed by 3018

Abstract

One of the main challenges in the emerging smart grid is to jointly consider the demand and supply, which is also reflected in the wholesale market (supply side) and the retail market (demand side). When integrating the demand and supply side into one framework, the mechanism for determining the market clearing price has been changed. This is due to the demand variations in the demand side in response to the market clearing price and the change of generation costs in the supply side from the demand variation. In order to find the best balance between the supply and demand under the demand response management scheme, this paper proposes a new integrated supply and demand coordination mechanism for the electricity market and smart pricing methods for generator and retailers. Another important contribution of this paper is to develop an efficient algorithm to find the match equilibrium between the demand and supply sides in the new proposed mechanism. Experimental results demonstrate that the new mechanism can effectively handle unpredictable demand under dynamic retail pricing and support the ISO to dispatch the generation economically. It can also help in achieving the goals of dynamic pricing such as maximizing the profits for retailers. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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22 pages, 5537 KiB

Open AccessArticle

The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand

by Nur Mohammad and Yateendra Mishra

Energies 2018, 11(12), 3296; https://doi.org/10.3390/en11123296 - 26 Nov 2018

Cited by 25 | Viewed by 3854

Abstract

This paper presents an interactive trading decision between an electricity market operator, generation companies (GenCos), and the aggregators having demand response (DR) capable loads. Decisions are made hierarchically. At the upper-level, an electricity market operator (EMO) aims to minimise generation supply cost considering a DR transaction cost, which is essentially the cost of load curtailment. A DR exchange operator aims to minimise this transaction cost upon receiving the DR offer from the multiple aggregators at the lower level. The solution at this level determines the optimal DR amount and the load curtailment price. The DR considers the end-user’s willingness to reduce demand. Lagrangian duality theory is used to solve the bi-level optimisation. The usefulness of the proposed market model is demonstrated on interconnection of the Pennsylvania-New Jersey-Maryland (PJM) 5-Bus benchmark power system model under several plausible cases. It is found that the peak electricity price and grid-wise operation expenses under this DR trading scheme are reduced. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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13 pages, 1721 KiB

Open AccessArticle

The Impact of Substituting Production Technologies on the Economic Demand Response Potential in Industrial Processes

by Michael Schoepf, Martin Weibelzahl and Lisa Nowka

Energies 2018, 11(9), 2217; https://doi.org/10.3390/en11092217 - 24 Aug 2018

Cited by 19 | Viewed by 4684

Abstract

Given the low carbon transformation of our energy systems, demand response has the potential to increase the adaptability of electricity demand to a volatile electricity supply. In this article, we investigate the demand response potential for the case where substituting technologies are available in an energy-intensive industrial production process. The available production technologies may not only differ in their technical characteristics, but also vary by the necessary input materials. We present a generic linear optimization model for such a production process and apply it to a real-world example in the paper industry. The results show that the question of which substituting technologies are used in an optimal production schedule to which degree, is highly influenced by the combination of current input parameters such as prices. In direct consequence, the corresponding demand response potential is not a fixed number. From an operational perspective, this input dependency implies that the price relation of raw input materials used in substituting technologies can be a crucial driving force for the ability and willingness of industrial enterprises to provide demand response. In addition, from a strategic perspective, long-run investments in demand response potentials may rely on expected price development of major input factors. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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22 pages, 1928 KiB

Open AccessArticle

The Effects of Dynamic Pricing of Electric Power on Consumer Behavior: A Propensity Score Analysis for Empirical Study on Nushima Island, Japan

by Thanh Tam Ho, Sarana Shinkuma and Koji Shimada

Energies 2018, 11(8), 2175; https://doi.org/10.3390/en11082175 - 20 Aug 2018

Cited by 5 | Viewed by 4131

Abstract

This study aimed to investigate the change of consumer behavior in electric power consumption after the application of dynamic pricing via real-time feedback. Afield experiment of dynamic pricing was carried out on Nushima Island, which is located in Hyogo Prefecture in central Japan. The panel data of hourly electric power consumption among 50 households (including 22 control households and 28 treated households) were collected from a baseline survey (14 days before the dynamic pricing experiment was conducted) and during the 14-day experimental period. Propensity score analysis with local linear matching was employed to analyze the average treatment effects of dynamic pricing on consumer behavior. The results report that dynamic pricing plays a crucial role in reducing consumers’ electric power consumption—by 9.6% compared to the pre-experimental period. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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19 pages, 4036 KiB

Open AccessArticle

Analysis and Design of a Compound-Structure Permanent-Magnet Motor for Hybrid Electric Vehicles

by Qiwei Xu, Jing Sun, Dewen Tian, Wenjuan Wang, Jianshu Huang and Shumei Cui

Energies 2018, 11(8), 2156; https://doi.org/10.3390/en11082156 - 17 Aug 2018

Cited by 1 | Viewed by 3601

Abstract

On the basis of the excellent driving force demand of hybrid electric vehicles (HEVs), this paper studies the torque property of the compound-structure permanent-magnet motor (CSPM motor) used for HEVs, which is influenced by magnetic field oversaturation and variable nonlinear parameters. Firstly, the system configuration of HEVs based on CSPM motor and its working mode are introduced. Next, the state equation of CSPM motor in three-phase stationary coordinate system is proposed in order to investigate its torque performance; then, the factors affecting the output torque are gained. Finite element method (FEM)-based electromagnetic parameters analysis and design is carried out, to raise the output torque and reduce the torque ripple of CSPM motor. Besides, optimized design parameters are used to establish the FEM model, and the simulation results of electromagnetic performances for the CSPM motor before and after optimization are given to verify the rationality of optimization. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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17 pages, 5288 KiB

Open AccessArticle

Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty

by Kyu-Hyung Jo and Mun-Kyeom Kim

Energies 2018, 11(4), 740; https://doi.org/10.3390/en11040740 - 24 Mar 2018

Cited by 14 | Viewed by 4571

Abstract

With the increasing penetration of renewable energy, it is difficult to schedule unit commitment (UC) in a power system because of the uncertainty associated with various factors. In this paper, a new solution procedure based on a multi-scenario tree method (MSTM) is presented and applied to the proposed stochastic UC problem. In this process, the initial input data of load and wind power are modeled as different levels using the mean absolute percentage error (MAPE). The load and wind scenarios are generated using Monte Carlo simulation (MCS) that considers forecasting errors. These multiple scenarios are applied in the MSTM for solving the stochastic UC problem, including not only the load and wind power uncertainties, but also sudden outages of the thermal unit. When the UC problem has been formulated, the simulation is conducted for 24-h period by using the short-term UC model, and the operating costs and additional reserve requirements are thus obtained. The effectiveness of the proposed solution approach is demonstrated through a case study based on a modified IEEE-118 bus test system. Full article

(This article belongs to the Special Issue Demand Response in Electricity Markets)

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