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  • 1.
    Crosara, Alessandro
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems. Pöyry.
    Tomasson, Egill
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Generation Adequacy in the Nordic and Baltic Region: Case Studies from 2020 to 2050: Flex4RES Project Report2019Report (Other academic)
    Abstract [en]

    Generation adequacy is a concern in today's electricity market where intermittent renewable energy sources are rapidly becoming a greater share of the generation mix. This study focuses on the Nordic and Baltic power system that is comprised of the system areas of the Nord Pool spot market. Sequential Monte Carlo Simulation is applied to assess the generation adequacy of this multi-area system for several future case studies, based on scenarios defined within the Nordic Flex4RES project. The report gives insights into the characteristics of these adequacy problems that the system could face in a more sustainable future, quantifies their magnitude and presents their characteristics. Finally, a solution based on the demand flexibility of residential electric heating is discussed, as a way to counter capacity deficit problems.

  • 2.
    Tomasson, Egill
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Generation Adequacy Analysis of Multi-Area Power Systems With a High Share of Wind Power2018In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 33, no 4, p. 3854-3862Article in journal (Refereed)
    Abstract [en]

    There is growing concern regarding generation adequacy within the power system industry. The ever-increasing injection of intermittent renewable resources makes it harder than before to estimate the reliability of modern power systems using traditional approaches. This paper develops a framework for estimating the reliability of modern power systems that have considerable levels of wind power generation. Monte Carlo simulation is applied using a very efficient importance sampling technique based on the cross-entropy method as well as the Copula theory. Tailor-made importance sampling functions for conventional generation, load, and wind power generation drastically reduce the number of samples required to estimate reliability parameters of interest. The methodology enables simulation of multi-area power systems with considerable amount of correlated wind power generation in each of the different areas. Simulation results confirm the efficiency as well as the accuracy of the proposed method and show that it is orders of magnitude faster than crude Monte Carlo simulation.

  • 3.
    Tomasson, Egill
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Improved Importance Sampling for Reliability Evaluation of Composite Power Systems2017In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 32, no 3, p. 2426-2434Article in journal (Refereed)
    Abstract [en]

    This paper presents an improved way of applying Monte Carlo simulation using the crossentropy method to calculate the risk of capacity deficit of a composite power system. By applying importance sampling for load states in addition to the generation and transmission states in a systematic manner, the proposed method is many orders of magnitude more efficient than the crude Monte Carlo simulation and considerably more efficient than other crossentropy-based algorithms that apply other ways of estimating the importance sampling distributions. An effective performance metric of system states is applied in order to find optimal importance sampling distributions during presimulation that significantly reduces the required computational effort. Simulations, using well-known IEEE reliability test systems, show that even problems that are nearly intractable using crude Monte Carlo simulation become very manageable using the proposed method.

  • 4.
    Tomasson, Egill
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Multi-area generation adequacy and capacity credit in power system analysis2018In: 2017 IEEE Innovative Smart Grid Technologies - Asia: Smart Grid for Smart Community, ISGT-Asia 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1-6Conference paper (Refereed)
    Abstract [en]

    A generator's contribution to the generation adequacy of a power system is more accurately captured by its capacity credit than by its installed capacity. The capacity credit takes into account factors such as forced outages and limited energy supply and the latter is especially important for volatile renewable sources that behave quite differently from dispatchable sources. Their installed capacity gives very limited information about their contribution to the generation system adequacy. Traditional approaches for calculating the capacity credit treat the power system as a single area and calculate an aggregate value for the whole system. In this paper, a multi-area approach is introduced which is able to quantify how the capacity credit is distributed between different power system areas. A combination of an iterative multi-variate Newton approach and a Monte Carlo simulation with an efficient sensitivity analysis allows this to be achieved in a computationally economical way.

  • 5.
    Tomasson, Egill
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Multi-area power system reliability evaluation by application of copula theory2016In: IEEE Power and Energy Society General Meeting, IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    Evaluating the risk of capacity deficit in large interconnected power systems is an important task in planning studies in order to supply the demand in the system at a certain risk level. It makes it possible to identify in which parts of the system reinforcements are needed in terms of generating capacity and/or interconnections. In modern power systems, with tie lines interconnecting countries and continents as well as an increasing amount of intermittent renewable resources, areas become dependent on each other for generating capacity not only under rare hazardous operating conditions but also in what can be considered as normal operation. This paper presents a novel way of taking inter-area load correlation into account when calculating the risk of capacity deficit by applying Copula sampling. The Monte Carlo simulation method is applied in addition to a Cross-Entropy based importance sampling technique to reduce computation time. The resulting procedure is a computationally effective general method of evaluating the risk of capacity deficit in a large scale multi-area interconnected power system.

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