Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 73) Show all publications
Shinde, P. & Amelin, M. (2019). Agent-Based Models in Electricity Markets:A Literature Review. In: : . Paper presented at IEEE ISGT Asia 2019. IEEE conference proceedings
Open this publication in new window or tab >>Agent-Based Models in Electricity Markets:A Literature Review
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The complexity of modeling electricity markets is increasing, due to for example increased penetration of renewable energy sources, electric vehicles and more active participation from the demand side. An analysis of a system with multiple market participants displaying certain behavior, could be difficult through an optimization problem. Due to several advantages, agent based approach can be followed to model the behavior ofdifferent market participants in the electricity markets. Lately, considerable amount of research work has been carried out on developing Agent-Based Models (ABMs) for electricity markets.This paper is aimed at providing a review of the literature on different applications of ABM in electricity markets. It is shown that ABMs have been applied to a wide range of studies of different parts of electricity trading. Some specific electricity marketsmodeled with ABMs have also been mentioned. According tothe literature survey, the research gap is highlighted and future scope of work in this area is discussed.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2019. p. 6
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-262740 (URN)
Conference
IEEE ISGT Asia 2019
Note

QC 20191024

Available from: 2019-10-20 Created: 2019-10-20 Last updated: 2019-10-24Bibliographically approved
Song, M., Amelin, M., Wang, X. & Saleem, A. (2019). Planning and operation models for an EV sharing community in spot and balancing market. IEEE Transactions on Smart Grid
Open this publication in new window or tab >>Planning and operation models for an EV sharing community in spot and balancing market
2019 (English)In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061Article in journal, News item (Refereed) Published
Abstract [en]

An EV sharing scheme is emerging in residential and business sector. It enables community members to share EVs and make reservations when they plan to use a car. The paper proposes a framework for planning and operation of a shared EV fleet. The framework coordinates charging and reservation assignment, and supports the community to participate in wholesale electricity market. It includes two stochastic programming models for day-ahead planning and real-time operation management. Bids on spot market are determined in the day-ahead planning. A preliminary plan for the bids on balancing market, the charging schedule of each EV and the assignment of each reservation are also determined in this phase. The preliminary plan is further refined during the real-time operation management. A numerical study based on Swedish driving behaviours and Nordic electricity market rules is performed to demonstrate an application of the proposed framework. Examples of the bidding curves on spot and balancing market are illustrated. Results also show a dynamic assignment for reservations during the planning horizon, while the eventual assignment is determined during the real-time operation management. Comparison with a base case shows that the proposed models enable the community to benefit from participating in the wholesale electricity market.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-248293 (URN)10.1109/TSG.2019.2900085 (DOI)
Note

QC 20190520

Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-10-09Bibliographically approved
Khastieva, D., Hesamzadeh, M. R., Vogelsang, I., Rosellón, J. & Amelin, M. (2019). Value of energy storage for transmission investments. Energy Strategy Reviews, 24, 94-110
Open this publication in new window or tab >>Value of energy storage for transmission investments
Show others...
2019 (English)In: Energy Strategy Reviews, ISSN 2211-467X, E-ISSN 2211-4688, Vol. 24, p. 94-110Article in journal (Refereed) Published
Abstract [en]

This paper investigates joint investment planning of transmission lines and energy storage. Energy storage can be seen as a complement to transmission infrastructure and can be used for transmission deferral. On the other hand, under certain conditions, when the expected profit of both sectors depends on congestion in the system, transmission and energy storage can be seen as competitors. The transmission sector is in this study assumed to be a natural monopoly and operation and planning of transmission lines is performed by an independent company whereas the energy storage owner company operates and invests under competitive market rules. Three main questions are addressed in this paper. First of all, will additional energy storage capacity contribute to the growth of social welfare? Second, how will incentive regulation of the transmission network affect the need for energy storage? Third, how will the choice of incentive regulation affect the value of energy storage. This paper first provides an overview of incentive regulation which can be applied to transmission investments. Then case studies based on a 6-node power system network and the IEEE 118-node system are proposed in order to answer the aforementioned questions. The results of the case studies show that energy storage investments complement transmission expansion and contribute to higher social welfare values. The benefits from energy storage investments are significantly higher under two investigated incentive regulations as compared to the case without incentive regulation. Thus, the transmission investment planning process should consider energy storage options.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Energy storage, Incentive regulation, Stochastic programming, Transmission investments, Wind generation
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-246458 (URN)10.1016/j.esr.2019.01.008 (DOI)000466911300008 ()2-s2.0-85061636092 (Scopus ID)
Note

QC 20190329

Available from: 2019-03-29 Created: 2019-03-29 Last updated: 2019-05-29Bibliographically approved
Dimoulkas, I., Herre, L., Khastieva, D., Nycander, E., Amelin, M. & Mazidi, P. (2018). A hybrid model based on symbolic regression and neural networks for electricity load forecasting. In: International Conference on the European Energy Market, EEM: . Paper presented at 15th International Conference on the European Energy Market, EEM 2018; Lodz; Poland; 27 June 2018 through 29 June 2018. IEEE Computer Society, Article ID 8469901.
Open this publication in new window or tab >>A hybrid model based on symbolic regression and neural networks for electricity load forecasting
Show others...
2018 (English)In: International Conference on the European Energy Market, EEM, IEEE Computer Society, 2018, article id 8469901Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a hybrid model for electricity load forecasting. Symbolic regression is initially used to automatically create a regression model of the load. Then the explanatory variables and their transformations that have been selected in the model are used as input in an artificial neural network that is trained to predict the electricity load at the output. Therefore symbolic regression operates as a feature selection-creation method and forecasting is done by the artificial neural network. The proposed hybrid model has been successfully used in an electricity load forecasting competition.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
Forecasting competition, Load forecasting, Neural networks, Symbolic regression, Commerce, Electric power plant loads, Forecasting, Power markets, Regression analysis, Electricity load, Electricity load forecasting, Explanatory variables, Hybrid model, Regression model, Electric load forecasting
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-247176 (URN)10.1109/EEM.2018.8469901 (DOI)000482771100114 ()2-s2.0-85055476841 (Scopus ID)9781538614884 (ISBN)
Conference
15th International Conference on the European Energy Market, EEM 2018; Lodz; Poland; 27 June 2018 through 29 June 2018
Note

QC 20190507

Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-10-28Bibliographically approved
Song, M., Amelin, M., Shayesteh, E. & Hilber, P. (2018). Impacts of flexible demand on the reliability of power systems. In: : . Paper presented at 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).
Open this publication in new window or tab >>Impacts of flexible demand on the reliability of power systems
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Demand response provides flexibility to power systems through adjusting the power consumption. This study investigates the impact of flexible demands on the system reliability with real-time price-based demand response. It assumes that the power demand is sensitive to nodal price and the price is communicated to consumers as soon as it is cleared on market. The uncertainties of nodal price and potential flexibility are considered. Models are proposed for the optimal operation of a power system with and without demand response, respectively. The proposed models are evaluated through application to a 6-bus system using Monte Carlo simulation. The result shows that the reliability indices LOLP and EENS are improved for the system and for each bus when the demand is sensitive to nodal price. Moreover, the nodal prices decrease, reflecting a more efficient operation and a lower electricity price charged on consumers.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-250468 (URN)10.1109/ISGT.2018.8403357 (DOI)2-s2.0-85050701617 (Scopus ID)
Conference
2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
Note

QC 20190520

Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2019-10-09Bibliographically approved
Song, M., Amelin, M., Wang, X. & Saleem, A. (2018). Optimized operational management of an EVsharing community integrated with battery energystorage and PV generation. In: International Conference on the European Energy Market, EEM 2018: . Paper presented at 15th International Conference on the European Energy Market, EEM 2018; Lodz; Poland; 27 June 2018 through 29 June 2018. IEEE Computer Society, Article ID 8469821.
Open this publication in new window or tab >>Optimized operational management of an EVsharing community integrated with battery energystorage and PV generation
2018 (English)In: International Conference on the European Energy Market, EEM 2018, IEEE Computer Society, 2018, article id 8469821Conference paper, Published paper (Refereed)
Abstract [en]

Sharing schemes are emerging in residential and business sectors to reduce the purchase and operation cost of individuals. This paper proposes a framework to support the operational management of a shared EV fleet. An optimization algorithm is developed to coordinate the charging and reservation assignment using mixed integer programming. The integration with local PV production and battery storage is taken into account. A booking algorithm is also developed to determine whether a reservation can be accepted or not. Monte Carlo simulation is performed in the case study to demonstrate an application of the proposed framework with the Swedish travel patterns. The result provides an overview about the utilization rate of the fleet with different number of EVs, which can support the investment decision of an EV sharing community. The result also shows that the EVs and battery are effectively coordinated to minimize the total cost, satisfy the reservations and comply with grid limits.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
International Conference on the European Energy Market, EEM, ISSN 2165-4077
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-248294 (URN)10.1109/EEM.2018.8469821 (DOI)2-s2.0-85055572394 (Scopus ID)9781538614884 (ISBN)
Conference
15th International Conference on the European Energy Market, EEM 2018; Lodz; Poland; 27 June 2018 through 29 June 2018
Note

QC 20190520

Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-10-09Bibliographically approved
Song, M. & Amelin, M. (2018). Price-Maker Bidding in Day-Ahead Electricity Market for a Retailer With Flexible Demands. IEEE Transactions on Power Systems, 33(2), 1948-1958
Open this publication in new window or tab >>Price-Maker Bidding in Day-Ahead Electricity Market for a Retailer With Flexible Demands
2018 (English)In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 33, no 2, p. 1948-1958Article in journal (Refereed) Published
Abstract [en]

This study develops a short-term planning model to determine the bidding curves on a day-ahead market for a price-maker retailer with flexible power demand. It concerns the interactions between the spot price and the flexible demand. Both conditional value-at-risk and volume deviation risk are taken into account. A numerical study using the data from the Nordic electricity market is performed to investigate the influence of risk factors on the retailer's profit, risk levels, average spot price, and total consumption. Three types of price elasticity are compared to show that the retailer can benefit from the flexibility in demand side in some cases. The flexibility also leads to lower spot prices so that the customers in real-time price-based demand response can face a lower electricity price for per-unit power consumption.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Bidding curve, demand response, price elasticity, price-maker, retailer, risk
National Category
Economics Energy Systems
Identifiers
urn:nbn:se:kth:diva-224017 (URN)10.1109/TPWRS.2017.2741000 (DOI)000425530300070 ()2-s2.0-85028448673 (Scopus ID)
Note

QC 20180323

Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2019-10-09Bibliographically approved
Shayesteh, E., Gayme, D. F. & Amelin, M. (2018). System reduction techniques for storage allocation in large power systems. International Journal of Electrical Power & Energy Systems, 95, 108-117
Open this publication in new window or tab >>System reduction techniques for storage allocation in large power systems
2018 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 95, p. 108-117Article in journal (Refereed) Published
Abstract [en]

Semi-Definite Relaxation (SDR) techniques for AC optimal power flow (OPF) have recently been proposed as a means of obtaining a provably global optimal solution for many IEEE benchmark power systems. Solving the resulting semi-definite programs (SDP) can, however, be computationally intensive. Therefore new algorithms and techniques that enable more efficient computations are needed to extend the applicability of SDP based AC OPF algorithms to very large power networks. This paper proposes a three-stage algorithm for AC OPF based storage placement in large power systems. The first step involves network reduction whereby a small equivalent system that approximates the original power network is obtained. The AC OPF problem for this equivalent system is then solved by applying an SDR to the non-convex problem. Finally, the results from the reduced system are transferred to the original system using a set of repeating optimizations. The efficacy of the algorithm is tested through case studies using two IEEE benchmark systems and comparing the solutions obtained to those of DC OPF based storage allocation. The simulation results demonstrate that the proposed algorithm produces more accurate results than the DC OPF based algorithm.

Place, publisher, year, edition, pages
Elsevier Ltd, 2018
Keywords
Equivalent power system, Large power system planning, Optimal Power Flow (OPF), Semi-Definite Programming (SDP), Storage allocation, Acoustic generators, Electric load flow, Electric network analysis, Electric power transmission networks, Storage allocation (computer), Ac optimal power flows, Global optimal solutions, Large power systems, Optimal power flows, Semi-definite program (SDP), Semi-definite programming, Semidefinite relaxation, Optimization
National Category
Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-216799 (URN)10.1016/j.ijepes.2017.08.007 (DOI)000416497900009 ()2-s2.0-85027683518 (Scopus ID)
Note

Export Date: 24 October 2017; Article; CODEN: IEPSD; Correspondence Address: Shayesteh, E.; KTH Royal Institute of Technology, School of Electrical EngineeringSweden; email: ebrahim.shayesteh@ee.kth.se; Funding details: OISE-1243482, NSF, Norsk Sykepleierforbund; Funding text: Partial support by the NSF (grant OISE-1243482, the WindInspire project) is gratefully acknowledged. QC 20171114

Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2017-12-18Bibliographically approved
Dimoulkas, I. & Amelin, M. (2018). Use of temperature scenariosl medium and long-term probabilistic heat demand forecasting in district heating. Euroheat and Power (English Edition), 15(4), 10-16
Open this publication in new window or tab >>Use of temperature scenariosl medium and long-term probabilistic heat demand forecasting in district heating
2018 (English)In: Euroheat and Power (English Edition), ISSN 1613-0200, Vol. 15, no 4, p. 10-16Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Verlags und Wirtschaftsges. der Elektrizitatswerke, 2018
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-247456 (URN)2-s2.0-85057611747 (Scopus ID)
Note

QC 20190415

Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-05-22Bibliographically approved
Dimoulkas, I., Amelin, M. & Levihn, F. (2017). District heating system operation in power systems with high share of wind power. Journal of Modern Power Systems and Clean Energy, 5(6), 850-862
Open this publication in new window or tab >>District heating system operation in power systems with high share of wind power
2017 (English)In: Journal of Modern Power Systems and Clean Energy, ISSN 2196-5625, E-ISSN 2196-5420, Vol. 5, no 6, p. 850-862Article in journal (Refereed) Published
Abstract [en]

The integration of continuously varying and not easily predictable wind power generation is affecting the stability of the power system and leads to increasing demand for balancing services. In this study, a short-term operation model of a district heating system is proposed to optimally schedule the production of both heat and power in a system with high wind power penetration. The application of the model in a case study system shows the increased flexibility offered by the coordination of power generation, consumption and heat storage units which are available in district heating systems.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2017
Keywords
Combined heat and power (CHP), District heating systems, Flexible power system operation, Heat storage
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-220217 (URN)10.1007/s40565-017-0344-6 (DOI)000416327000003 ()2-s2.0-85037054066 (Scopus ID)
Note

QC 20171219

Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2017-12-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6000-9363

Search in DiVA

Show all publications