Impact of wind power on the unit commitment, operating reserves, and market design

Kiviluoma, Juha; O’Malley, Mark; Tuohy, Aidan; Meibom, Peter; Milligan, Michael; Lange, Bernhard; Holttinen, Hannele; Gibescu, Madeleine

doi:10.1109/pes.2011.6039621

Cited by 29 publications

(15 citation statements)

References 25 publications

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“…However, a lot of new problems came up for power system operation and planning because of intermittent and uncertain nature of wind power. As wind power generation depends on the chaotic weather system [1], the forecast error of wind power is quite large comparing to that of power load. Thus, the risks of wind farm bidding in power market are obvious.…”

Section: P Pmentioning

confidence: 99%

Optimal intra-day coordination of wind farm and pumped-hydro-storage plant

Ding

Song

2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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Intra-day markets have been set up in some power markets with large scale integration of wind power generation. Inspired by Spanish market, this paper proposes a modified intraday bidding mechanism to better accommodate stochastic wind energy. Then the coordinated operation of wind farm (WF) and pumped-hydro-storage plant (PHSP) is studied. Firstly, wind power forecast errors are classified according to forecast wind speed. The corresponding error distribution is represented by a versatile probability function, which is explicitly reversible. Secondly, a chance-constrained optimization formulation is proposed to obtain the optimal bidding strategy of WF and PHSP. Case studies and sensitivity analyses are carried out. Simulation results demonstrate that the intra-day bidding mechanism can effectively utilize the updated wind power forecast data, and the proposed optimization method can increase profit for the union of WF and PHSP. Index Terms-intra-day bidding, optimal coordination, wind farm, pumped-hydro-storage plant, chance-constrained optimization, versatile probability function, wind power forecast error I. NOMENCLATURE Sets: I={1,2…s}Set of day-ahead wind power forecast scenarios. K={1,2…24}Set of 10-min periods in the future 4-8 hours. J={1,2…20}Set of 1-hour periods in the future 8-28 hours. Functions: ( ) Pr ⋅ Probability of given event ( ) E ⋅ The expectation of stochastic variable. ( ) K t S ⋅ The profit in interval t of set K. ( ) J t S ⋅ The profit in interval t of set J. ( ) t ξ ⋅ Random wind power output at the given forecast wind speed. Decision Variables: , w t i p Power generation of wind farm at interval t, scenarios i. .cn). , , , d c t i t i

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Section: P Pmentioning

confidence: 99%

Optimal intra-day coordination of wind farm and pumped-hydro-storage plant

Ding

Song

2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

View full text Add to dashboard Cite

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“…However, a very important application is for wind energy integration studies in which utilities must balance uncertain loads and supplies and must plan for the dispatch and transmission of electricity [9,10]. Balancing authorities must test how increasing the amount of wind in their portfolio will impact their system, how to best allocate spinning reserves and how extreme events may be handled [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

2015

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Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements.

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“…The impact of large-scale wind penetration on a power system has been subject for a wide spectrum of researches in the literature; on the need for operating reserves [12], unit commitment [13], balancing power [14], and transmission system planning [15]. This study aims to investigate this impact on the entire energy system in an integrated method by considering the potential of both heat and power sectors in absorbing fluctuating RES intersected with the nuclear power.…”

Section: Introductionmentioning

confidence: 99%

Wind Integration into Energy Systems with a High Share of Nuclear Power—What Are the Compromises?

2015

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Towards low-carbon energy systems, there are countries with ongoing plans for expanding their nuclear power capacity, and simultaneously advancing the role of variable renewable energy sources (RES), namely wind and solar energy. This crossroads of capital-intensive, baseload power production and uncontrollable, intermittent RES may entail new challenges in the optimal and economic operation of power systems. This study examines this case by hourly analysis of a national-level energy system with the EnergyPLAN modeling tool, coupled with wind integration simulations (including uncertainty) implemented using MATLAB. We evaluate the maximum feasible wind integration under different scenarios for nuclear power plants, energy demand, and the flexibility of energy infrastructure for a real case study (Finland). We propose wind-nuclear compromise charts to envision the impact of any mix of these two technologies on four parameters: total costs, power exchange, carbon emissions, and renewable energy integration. The results suggest that nuclear power constrains the room for maximum uptake of wind energy by a descending parabolic relationship. If nuclear power production exceeds 50% of the total power demand, wind will be unlikely to penetrate in shares over 15% of the respective demand. Moreover, we investigate the role of four flexibility options: demand side management, electrical energy storage, smart electric heating, and large-scale heat pumps (backed with thermal energy storage). Heat pumps (which are in connection with combined heat and power (CHP) and district heating systems) offer the highest efficiency in balancing excess power from variable RES. However, power-to-heat options offer a limited OPEN ACCESSEnergies 2015, 8 2494 capability for absorbing high-level excess power, as oversupply arises mainly in the periods with relatively low demand for heat. This calls for longer-term energy storage and/or other flexibility options to achieve the planned targets in wind-nuclear scenarios.

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Impact of wind power on the unit commitment, operating reserves, and market design

Cited by 29 publications

References 25 publications

Optimal intra-day coordination of wind farm and pumped-hydro-storage plant

Optimal intra-day coordination of wind farm and pumped-hydro-storage plant

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

Wind Integration into Energy Systems with a High Share of Nuclear Power—What Are the Compromises?

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