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  • 1.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    CHP operation scheduling under uncertainty: Estimating the value of thermal energy storage2014In: Proceedings from the 14th International Symposium on District Heating and Cooling / [ed] Anna Land, 2014Conference paper (Refereed)
  • 2.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Constructing Bidding Curves for a CHP Producer in Day-ahead Electricity Markets2014In: ENERGYCON 2014 - IEEE International Energy Conference, IEEE Computer Society, 2014, p. 487-494Conference paper (Refereed)
    Abstract [en]

    The operation of Combined Heat and Power (CHP) systems in liberalized electricity markets depends both on uncertain electricity prices and uncertain heat demand. In the future, uncertainty is going to increase due to the increased intermittent power induced by renewable energy sources. Therefore, the need for improved planning and bidding tools is highly important for CHP producers. This paper applies an optimal bidding model under the uncertainties of day-ahead market prices and the heat demand. The problem is formulated in a stochastic programming framework where future scenarios of the random variables are considered in order to handle the uncertainties. A case study is performed and conclusions are derived about the CHP operation and the need for heat storage.

  • 3.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Monte carlo simulation of district heating system short-term operation in electricity markets2017In: Energetika, ISSN 0235-7208, Vol. 63, no 3, p. 93-104Article in journal (Refereed)
    Abstract [en]

    Energy generation in district heating (DH) systems is usually done in combined heat and power (CHP) units which can efficiently produce both useful heat and electric power. There can also exist heat only boilers, electric heaters, heat pumps and heat storage tanks. The coupling of heat and power generation in the CHP units and the possibility to store heat for later use makes the short-term operation scheduling of such systems quite challenging. Furthermore, big DH systems produce power that is sold in the electricity markets. This makes the operation scheduling problem even more complex as the uncertainty of the electricity prices in the markets should be considered. To make optimal decisions under uncertainty, various mathematical optimization tools were developed, such as stochastic programming and robust optimization. In this paper, an approach based on a Monte Carlo simulation is followed. Initially, a model of DH system short-term operation and power trading is mathematically formulated. Then, this model is used to run a Monte Carlo simulation for a case study system where the values of stochastic parameters are simulated using autoregressive models. Results demonstrate that simulation is fast, taking 300-400 runs to converge. A comparison of two system configurations shows that the use of heat storage increases the daily expected profit by 11%. Finally, the electricity price volatility in this case study is such that mainly two CHP units are operating for most of the time.

  • 4.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Probabilistic day-ahead CHP operation scheduling2015In: 2015 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, IEEE conference proceedings, 2015Conference paper (Refereed)
    Abstract [en]

    The production scheduling of combined heat and power plants is a challenging task. The need for simultaneous production of heat and power in combination with the technical constraints results in a problem with high complexity. Furthermore, the operation in the electricity markets environment means that every decision is made with unknown electricity prices for the produced electric energy. In order to compensate the increased risk of operating under such uncertain conditions, tools like stochastic programming have been developed. In this paper, the short-term operation scheduling model of a CHP system in the day-ahead electricity market is mathematically described and solved. The problem is formulated in a stochastic programming framework where the uncertain parameters of day-ahead electricity prices and the heat demand are incorporated into the problem in the form of scenarios. A case study is also performed with a CHP system operating in a district heating network and the value of heat storage capacity is estimated.

  • 5.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Hesamzadeh, Mohammad Reza
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Forecasting Balancing Market Prices Using Hidden Markov Models2016In: 2016 13TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM), IEEE conference proceedings, 2016Conference paper (Refereed)
    Abstract [en]

    This paper presents a Hidden Markov Model (HMM) based method to predict the prices and trading volumes in the electricity balancing markets. The HMM are quite powerful in modelling stochastic processes where the underlying dynamics are not apparent. The proposed method provides both one hour and 12-36 hour ahead forecasts. The first is mostly useful to wind/solar producers in order to compensate their production imbalances while the second is important when submitting the offers to the day ahead markets. The results are compared to the ones from Markov-autoregressive model.

  • 6.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Levihn, Fabian
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    District heating system operation in power systems with high share of wind power2017In: 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)
    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.

  • 7.
    Dimoulkas, Ilias
    et al.
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Khastieva, Dina
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric power and energy systems.
    Constructing Offering Curves for a CSP Producer in Day-ahead Electricity Markets2016In: 6th Solar Integration Workshop: International workshop on integration of solar power into power systems / [ed] Uta Betancourt / Thomas Ackermann, 2016, p. 424-431Conference paper (Refereed)
    Abstract [en]

    In many countries, the installation and operation of concentrated solar power plants has been promoted with high feed-in tariffs and other incentives. However, as this technology is becoming more mature and the installation costs are being reduced, the incentives are minimized or totally abolished. Under these new economic conditions, there is an increased need for operation planning and power trading tools that will help the operators of such systems to make optimal decisions under the various uncertainties they face. This paper provides a model that can be used to derive the offering curves of a CSP producer in the day-ahead (spot) market. The model can also be used for the hourly short-term operation planning of the system. In order to tackle with the uncertainties of electricity prices and solar irradiance, the stochastic programming framework is used and a risk measure is incorporated into the model. A case study is conducted to show the applicability of the model.

  • 8.
    Gonzalez, Jose Luis
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Dimoulkas, Ilias
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Operation Planning of a CSP Plant in the Spanish Day-ahead Electricity Market2014Conference paper (Refereed)
    Abstract [en]

    This paper addresses the short-term operation planning of a concentrated solar power (CSP) plant equipped with a backup fuel boiler and operating in the Spanish day-ahead electricity market. The problem is formulated as a mixed-integer linear programming model. Forecasted values of electricity prices and direct sun irradiation are considered. The main concern in the problem is to set an optimal use of the backup system in order to increase power generation and maximize the profits. Interaction between the solar and fuel heated systems is considered through heat balance constraints while parameters referring to the boiler are independent from the rest of the system allowing various types of boilers to be tested. A realistic case study provides results of a CSP plant operating a) without boiler, b) with a natural gas boiler and c) with a biomass boiler. Results demonstrate the advantages of the proposed model.

  • 9.
    Moiseeva, Ekaterina
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Hesamzadeh, Mohammad Reza
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Dimoulkas, Ilias
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Tacit Collusion with Imperfect Information: Ex-Ante Detection2014Conference paper (Refereed)
    Abstract [en]

    The liberalization of electricity markets had a significant impact on the whole process of optimal dispatch. The positive effect of competition is partly canceled out by profit-maximizing behavior of market participants. Strategic generators can exercise market power or collude tacitly with other participants to ensure higher electricity price and therefore higher profits. In this paper we study the generators' possibility to come to a tacit collusion by distributed computations. Due to the strategic nature of market interactions, the information corresponding to the power production by generating companies is often fully confidential outside the generating unit. Such information includes the data about planned and emergency outages, capacity constraints, ramping rates specific to the units, probability of failures, etc. Under certain assumptions the constraint set of a generating unit can be represented as a convex set. Using a novel multi-agent distributed constrained optimization algorithm we show how market participants with confidential constraint sets can reach the collusion on the production share, while maximizing their own profit. We compare this outcome to the outcome of Nash equilibrium over specified time horizon and conclude, if both generators find it profitable to collude. This modeling is important for early detection and prevention of tacit collusion.

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