A stochastic MILP energy planning model incorporating power market dynamics

Koltsaklis, Nikolaos E.; Nazos, Konstantinos

doi:10.1016/j.apenergy.2017.08.040

Cited by 31 publications

(9 citation statements)

References 54 publications

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“…A considerable advantage of those approaches, is the provision of more robust scheduling, enhancing power system reliability and stability. The unit commitment problem is important, not only for short-term dispatching of power units, but also for the medium-term [3,4] and long-term [5] planning of power systems, as well as for risk management for market participants [6,7].…”

Section: Introductionmentioning

confidence: 99%

Policy Implications of Power Exchanges on Operational Scheduling: Evaluating EUPHEMIA’s Market Products in Case of Greece

Koltsaklis

Dagoumas

2018

Energies

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A vital component for the development of a functioning internal electricity market is the adoption by each European member state of the Pan-European Hybrid Electricity Market Integration (EUPHEMIA) for the day-ahead market solution. The consideration of the national power market’s characteristics enables more realistic market design towards the implementation of the so-called “Target Model”. This work considers a series of factors, including the EUPHEMIA order types, their use by market participants, the relative competitiveness of power generators, the impact of interconnected markets, the existence of market players with dominant positions, and the existence of specific regulations such as the minimum average variable cost restriction on offers by producers, as well as the strategy adopted by market participants. The main goal of this paper is to provide a comprehensive analysis on the adoption of EUPHEMIA’s algorithm in case of the Greek wholesale market, based on a relevant research project funded by the Joint Research Centre of the European Commission to support the Hellenic Regulatory Authority of Energy on its decision-making. The paper contributes to the relevant literature on the quantification of the impacts of the EUPHEMIA algorithm in the case of the Greek wholesale market, providing insights on the crucial aspects affecting realistic, market-based decision-making.

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Section: Introductionmentioning

confidence: 99%

Policy Implications of Power Exchanges on Operational Scheduling: Evaluating EUPHEMIA’s Market Products in Case of Greece

Koltsaklis

Dagoumas

2018

Energies

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“…This method transforms continuous distribution into discrete scenarios where optimization at each realization of stochastic parameter is weighted with the corresponding discrete probability (Betancourt-Torcat and Almansoori, 2015). Koltsaklis and Nazos (2017), Vithayasrichareon andMacGill (2012), Tekiner et al (2010) and Min and Chung (2013) apply the Monte Carlo simulation to address the uncertain parameters. This approach generates random scenarios based on continuous distributions that can be based on historical data.…”

Section: Literature Reviewmentioning

confidence: 99%

A stochastic programming model for an energy planning problem: formulation, solution method and application

et al. 2021

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The paper investigates national/regional power generation expansion planning for medium/longterm analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants.Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the -constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments show that the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future. Keywords Energy planning • Stochastic programming • Multi-objective optimization 1 IntroductionGlobal electricity production has grown continuously year by year since 1974. According to International Energy Agency (www.iea.org), world gross electricity production has increased from 6,299 to 26,730 TWh between 1974 and 2018 with an average annual growth rate of 3.3 %. Electricity generation can be divided into three types, namely fossil fuel-based, nuclear, and renewable energy power generation. The fossil fuelbased plants include coal, petroleum, and natural gas which produce large quantity of cheap energy with greenhouse gas (GHG) emissions. The renewable energy consists of hydro, solar, wind, geothermal and biomass which generate clean energy (without GHG emissions or potentially carbon neutral in the case of biomass). However, the cost and feasibility of renewable energy are highly dependent on the potential of regions and its weather conditions (Thangavelu et al, 2015). The strategic energy planning includes the medium-to long-term expansion of the electricity generating capacity. Cost effective and low emission energy

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“…Uncertainty related to some of these random variables can be handled using, for example, Monte-Carlo methods, stochastic programming, or robust optimization. These methods have been applied to longterm energy system models (Kanudia & Loulou, 1998;Messner, Golodnikov, & Gritsevskii, 1996), capacity expansion planning (Dehghan, Amjady, & Kazemi, 2014;Jin et al, 2014), and operational optimization (Koltsaklis & Nazos, 2017).…”

Section: Long-term Uncertaintymentioning

confidence: 99%

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

Helistö

Kiviluoma

Holttinen

et al. 2019

WIREs Energy & Environment

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In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four-level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft-linked investment and operational models, (3a) bidirectionally soft-linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co-optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics. generation expansion planning, integration of renewable energy sources, operational constraints, power system planning, temporal representation | INTRODUCTIONPower systems can play a crucial role in decarbonizing energy systems as they can relatively easily integrate large amounts of renewable generation. However, the increasing amount of wind and solar power, as well as other forms of variable generation (VG), has a strong impact on the operation of power systems through the variability and uncertainty of wind speed (Wu et al.,

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A stochastic MILP energy planning model incorporating power market dynamics

Cited by 31 publications

References 54 publications

Policy Implications of Power Exchanges on Operational Scheduling: Evaluating EUPHEMIA’s Market Products in Case of Greece

Policy Implications of Power Exchanges on Operational Scheduling: Evaluating EUPHEMIA’s Market Products in Case of Greece

A stochastic programming model for an energy planning problem: formulation, solution method and application

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

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