Optimal spinning reserve under load and intermittent generation uncertainty using Monte Carlo simulation

Petcharaks, N.

doi:10.1109/isgt-asia.2015.7387056

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Using up to ten centroids, the computational time increased near linear time to the number of centroids used. Thus, the SUC is a feasible approach compared to using a probability density function and iterating through thousands of DUC problems based on Monte Carlo simulation [25]. Furthermore, simulations using Monte Carlo did not have the advantage of SUC, that is having only single unit commitment schedule.…”

Section: Computation Time Of Unit Commitmentmentioning

confidence: 99%

Stochastic Unit Commitment in Various System Sizes under High Uncertainty Photovoltaic Forecast

Yasirroni

Putranto²,

Sarjiya³

et al. 2023

JNTETI

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This paper proposes a stochastic unit commitment (SUC) approach to solve a day-ahead unit commitment (UC) problem in a system with high uncertainty net load which is caused by photovoltaic (PV) power plants. In contrast with robust unit commitment (RUC) which only considers the worst-case scenario, SUC considers every possible scenario with its probability. Multiple possible PV curves were obtained using k-means clustering on historical data. The proportion of cluster members was used as a weight factor representing the occurrence probability of PV curves. The test was separated into two-step tests, namely day-ahead and real-time markets, using IEEE 10 generating unit system and solved using CPLEX. The results showed that in a day-ahead UC, SUC ($539,896) had lower cost than RUC ($548,005). However, when the total energy generated was considered, the SUC (20.78 $/MWh) cost higher compared to RUC (20.75 $/MWh). It is because the solution proposed by SUC is as robust as the RUC, but the generation cost formulation also considers over-commitment. Thus, SUC produced a fairer price for the independent power producer and electric utility in the day-ahead calculation. The results also showed that in the test environment of the real-time market, SUC was able to produce a robust solution without going into over-commitment. It is clearly shown in a 30 units system test with 10 centroids, in which SUC had a cheaper solution (20.7253 $/MWh) compared to RUC (20.7285 $/MWh), without violating power balance or going to load shedding.

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Section: Computation Time Of Unit Commitmentmentioning

confidence: 99%

Stochastic Unit Commitment in Various System Sizes under High Uncertainty Photovoltaic Forecast

Yasirroni

Putranto²,

Sarjiya³

et al. 2023

JNTETI

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“…The low load demand that happens during the entire day is defined as base load. Appropriate generation scheduling ( GS) Hobbs et al (2001), Nirukkanaporn & Petcharaks (2019a, 2019b, Ongsakul & Dieu (2013), Ongsakul & Petcharaks (2004), Petcharaks (2006Petcharaks ( , 2008Petcharaks ( , 2014Petcharaks ( , 2015, Petcharaks & Ongsakul (2007), Sheble & Fah (1994), Thongheet, Chimhat & Petcharaks (2010), Tseng et al (1999), Wood & Wollenberg (2013), is needed to provide sufficient electricity supply and sufficient spinning reserve. Generation units will be determined to be turned on/ off appropriately to achieve the objective of minimizing the total production cost while satisfying all important operational constraints such as power balance constraint, spinning reserve constraint, minimum up and down time constraints, maximum and minimum limit, etc.…”

Section: Introductionmentioning

confidence: 99%

“…If ESS owners emerge in this structure to offer power in some hours and to consume power in other hours, it would benefit to both stakeholders if they could reduce electric demand and production cost consequently. The generation scheduling problem ( GSP) has been a continuous area of interest in electrical power system continuously since 1966 in order to obtain the cost saving and better environment and better solution for decision making in generating unit operation (Hobbs et al, 2001;Nirukkanaporn & Petcharaks, 2019a, 2019bOngsakul & Dieu, 2013;Ongsakul & Petcharaks, 2004;Petcharaks, 2006Petcharaks, , 2008Petcharaks, , 2014Petcharaks, , 2015Petcharaks & Ongsakul, 2007;Sheble & Fahd, 1994;Thongheet et al, 2010;Tseng et al, 1999;Wood & Wollenberg, 1996). Whereas smart grid is now the target of utilities around the world to supply their growing electricity demand with sustainable supply and advance technology and to preserve environment.…”

Section: Introductionmentioning

confidence: 99%

Energy Storage System Owner as a New Player in an Electricity Structure

Petcharaks,

Chayakulkeeree,

Nirukkanaporn

et al. 2023

JCST

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Daily load demand causes different marginal cost at each hour due to different operation generation units. During peak load period, expensive generation units are determined to be turned on to provide sufficient electricity supply and sufficient spinning reserve. During light load period, generation units could not be unloaded due to their minimum up/down time, startup time and startup cost, causing uneconomic operation of the generating capacities. This excess capacity during light load period can be stored in the energy storage system (ESS) and the power can be released to supply the peak load demand hours to avoid turning on the next expensive unit which resulted in higher marginal cost. Performing as virtual power plant (VPP), ESS owners can seek for market opportunities to enter electric supply industry. This paper aims for proposing an optimal operation of VPP with charging/discharging ESS plan when marginal cost identifies real-time pricing (RTP) at each hour to be used as buying/selling price to VPP. Under strategically charging/discharging scheme, both parties, i.e., utility, and VPP, can achieve benefits in terms of better economic operation, lower system generation cost, increase operational income, while environmental impact is considered. The proposed method is tested on a ten-unit system under a centralized power market structure. Numerical results show that appropriate charging/discharging strategy could provide lower total production cost and offer opportunities for ESS owners as VPPs to obtain arbitrage marginal cost.

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Tie-Line Constrained Multi-Area Generation Scheduling Using Mixed Integer Programming Part I: Problem Formulation

Petcharaks

Nantiwattana

Chayakulkheeree

et al. 2020

2020 International Conference on Power, Energy and Innovations (ICPEI)

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Optimal spinning reserve under load and intermittent generation uncertainty using Monte Carlo simulation

Cited by 4 publications

References 7 publications

Stochastic Unit Commitment in Various System Sizes under High Uncertainty Photovoltaic Forecast

Stochastic Unit Commitment in Various System Sizes under High Uncertainty Photovoltaic Forecast

Energy Storage System Owner as a New Player in an Electricity Structure

Tie-Line Constrained Multi-Area Generation Scheduling Using Mixed Integer Programming Part I: Problem Formulation

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