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  • 1.
    Duvnjak Zarkovic, Sanja
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Stankovic, Stefan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Shayesteh, Ebrahim
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Reliability improvement of distribution system through distribution system planning: MILP vs. GA2019In: 2019 IEEE Milan PowerTech, 2019Conference paper (Refereed)
    Abstract [en]

    Distribution system planning (DSP) is very important because it can result in reliability enhancement and large cost savings for both utilities and consumers. DSP is a complex nonlinear problem, which can be solved with different optimization methods. This paper compares two such optimization methods, conventional (mixed-integer linear programming - MILP) and meta-heuristic (genetic algorithm - GA), applied to the DSP problem: construction of feeders in distribution power system from scratch. The main objective of DSP is to minimize the total cost, where both the investment and operational outage costs are considered, while the reliability of the whole system is maximized. DSP problem is applied to an actual distribution system. Solution methods are outlined, and computational results show that even though GA gives reasonably good results in faster computation time, MILP provides a better optimal solution with simpler implementation.

  • 2.
    Fazlagic, Berina
    et al.
    KTH.
    Wallnerström, C. J.
    Shayesteh, Ebrahim
    KTH.
    Hilber, Patrik
    KTH.
    Development of the utilisation and smart grid incentive scheme within the Swedish revenue cap regulation2017In: CIRED - Open Access Proceedings Journal, Institution of Engineering and Technology, 2017, Vol. 2017, no 1, p. 2696-2699, article id 1Conference paper (Refereed)
    Abstract [en]

    This study provides a summary on how Swedish distribution system operators (DSO) are regulated after a revenue cap model, and describes a potential development on the current utilization incentive scheme within this regulation. The analyses are based on data from a Swedish DSO, which have been elaborated with the use of demand response program. The outcome of the demand response simulation has in a later step been applied to calculate the incentive in the revenue cap regulation. Two different calculation approaches are used and compared in order to calculate the load factor in the revenue cap regulation. The results of the case study show that by applying a weighted daily load factor, the DSO in the case study can receive ~3% additional economic income compared to applying an average daily load factor in the incentive calculation. The motivation behind applying weighted load factor is to prioritize days with high energy consumption since those days have more impact on the costs. Most important, the analysis display that replacing the average load factor with a weighted load factor have a non-negligible impact on the incentive calculation and hence if the change fulfill its purpose enough.

  • 3.
    Mazidi, Peyman
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical University, Spain.
    Bobi, Miguel A. Sanz
    Shayesteh, Ebrahim
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Impact of health indicators on maintenance management and operation of power systems2017In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078, Vol. 231, no 6, p. 716-731Article in journal (Refereed)
    Abstract [en]

    This article proposes a maintenance management and risk reduction approach. The approach introduces two reliability-based indexes called condition indicator and risk indicator. Condition indicator is a unit-less parameter that comes directly from monitored condition of a component and converts the categorical condition into a numerical value. Risk indicator in megawatt represents the risk imposed by the health of a component onto the system. To demonstrate application of the indicators, they are implemented through an hourly network constraint unit commitment problem and applied in a test system where the analysis of impact of condition of the generators to the operation is the new contribution. The results demonstrate how addition of such indicators will impact the operation of the grid and maintenance scheduling. The results show the benefit for the system operator as the overall failure risk in the system is taken into account, and the benefit for the asset owner as the direct impact of the maintenance to be carried out can be investigated. Two of the main outcomes of the maintenance management and risk reduction approach are as follows: asset owners can analyze their maintenance strategies and evaluate their impacts in the maintenance scheduling, and system operators can operate the grid with higher security and lower risk of failure.

  • 4.
    Pham, Cong-Toan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Månsson, Daniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Shayesteh, Ebrahim
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Reliability consideration in the energy storagesystem design process2018In: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Energy storage systems are an important asset inthe future power grid, ensuring the safety and reliability inface of growing power consumption and intermittent supply.Occupying a vital role, the reliability of the storage system itselfhas to be guaranteed and taken into account for, especially inthe design procedure. This study utilizes reliability methods toanalyze the storage’s system reliability and optimize the systemsize to an appropriate level. A residential building with a 3.3kWphotovoltaic system serves as a case study to analyze differentenergy storage types and their resulting optimum systemstructure. The paper also includes an economic evaluation tomeasure the storage’s suitability for the particular case with thephotovoltaic system.

  • 5.
    Shayesteh, E.
    et al.
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Gayme, D. F.
    Amelin, Mikael
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    System reduction techniques for storage allocation in large power systems2018In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 95, p. 108-117Article in journal (Refereed)
    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.

  • 6.
    Shayesteh, Ebrahim
    et al.
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Amelin, Mikael
    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-Station Equivalents for Short-Term Hydropower Scheduling2016In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 31, no 6, p. 4616-4625Article in journal (Refereed)
    Abstract [en]

    Hydropower scheduling in day-ahead electricity markets is complex due to uncertainty in the electricity price. Internal cascade dependency of hydro power plants can also increase this complexity. One way to overcome this complexity is to replace the original hydropower system by an equivalent system, which provides simulation results sufficiently close to the ones of the original system. This paper presents a method to obtain multi-station equivalent models using a bilevel optimization problem, where the objective is to minimize the difference in outcomes between the original and the equivalent models. This bilevel problem is then transformed into a single-level optimization problem that can be solved using standard optimization techniques. Finally, the errors between the simulation results of the original and equivalent hydropower models are computed and analyzed for a Swedish system to show the accuracy of different multi-station equivalents.

  • 7.
    Shayesteh, Ebrahim
    et al.
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Reliability-Centered Asset Management Using Component Reliability Importance2016In: 2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    Asset management is an important topic in all fields especially in power system which has very high investment costs and very expensive elements. Reliability Centered Asset Management (RCAM) is an effective technique to perform the power system asset management with quantitative methods such that, on the one hand, the total cost is minimized and, on the other hand, the reliability of the system is maximized Nevertheless, the need for an appropriate optimization-based algorithm for RCAM implementation in power system is still sensed. This paper proposes an algorithm to fulfil such needs including the following steps. First, the component reliability importance index is calculated for all components of the system. Then, a set of all potential maintenance strategies of each component are defined and together with the component reliability importance indices are used as inputs in the third step. In the third step, an optimization problem is proposed to select the optimum maintenance strategy for each component in the system. The proposed three-step algorithm is tested on a Swedish distribution system. The results highlight the advantages of the proposed method for well-organizing the maintenance strategies for all components of the system.

  • 8.
    Shayesteh, Ebrahim
    et al.
    KTH, School of Electrical Engineering (EES).
    Yu, J.
    KTH, School of Electrical Engineering (EES).
    Hilber, Patrik
    KTH, School of Electrical Engineering (EES).
    Maintenance optimization of power systems with renewable energy sources integrated2018In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 149, p. 577-586Article in journal (Refereed)
    Abstract [en]

    This paper proposes a quantitative maintenance optimization problem for developing reliability centred maintenance for a power system with renewable energy sources. Reliability and cost are two important interlinked aspects considered by system operators in many deregulated power systems. Reliability centred maintenance is an effective method to consider both of these aspects when performing the maintenance optimization. Nevertheless, this method has not adequately studied for a power system with renewable energy sources included. According to the maintenance optimization problem proposed in this paper, first, the most critical components of the system are selected. Then, a set of maintenance strategies are proposed for all critical components. After that, the total cost of each maintenance strategy for all critical components are calculated as the summation of operation, maintenance, environmental, and interruption costs. Finally, the best maintenance strategy for each critical component is selected by identifying the lowest total cost of different maintenance strategies. The proposed method is tested on IEEE 14-bus system. The results show that the proposed maintenance optimization method provides a useful way for deciding the most proper maintenance strategies for the studied system.

  • 9.
    Song, Meng
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Amelin, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
    Shayesteh, Ebrahim
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Impacts of flexible demand on the reliability of power systems2018Conference 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.

1 - 9 of 9
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