Near-Optimal Method for Siting and Sizing of Distributed Storage in a Transmission Network

Pandžić, Hrvoje; Wang, Yishen; Qiu, Ting; Dvorkin, Yury; Kirschen, Daniel S.

doi:10.1109/tpwrs.2014.2364257

“…Constraints (6) - (8) represent capacity bounds on the (dis)charge power and storage capacity, with binary variable b h ensuring that the storage plant is operated with a strict separation of the electricity consumption and generation phase, i.e., p c h · p d h = 0 ∀ h. If however simultaneous charging and discharging is technically feasible (e.g., in certain pumped-hydro storage (PHS) plants), it is profitable to ignore the nonsimultaneity constraint during negative price periods. 7 The nonnegativity of c cyc and (9) -(10) represent the convex relaxation 8 of the max operator illustrated in Fig. 4, with C fix,e the investment cost of the energy-component of the storage plant.…”

Section: Day-ahead Electricity Storage Arbitragementioning

confidence: 99%

Price-based unit commitment electricity storage arbitrage with piecewise linear price-effects

Brijs

¹

,

Geth

²

,

Siddiqui

³

et al. 2016

Journal of Energy Storage

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Electricity storage plants can be used for many applications, with one of the most studied applications being arbitrage in the day-ahead market. Terms of use: Documents inAlthough the arbitrage value is related to the presence of price spreads, it also depends on the effect of (dis)charge actions on prices, as arbitrage generally reduces price spreads by increasing off-peak prices when charging and decreasing peak prices when discharging. As such, there are two important assumptions in price-based unit commitment arbitrage models: first, whether the storage operator is assumed to have perfect knowledge of future prices, and second, whether they recognize that their (dis)charge actions may affect those prices, i.e., the price-taking or pricemaking assumption. This article proposes a comprehensive formulation of the arbitrage problem including detailed operating constraints, and focuses on relaxing the price-taking assumption by considering real-world price-effect data, published in the form of hourly piecewise linear relationships between quantity and price based on submitted bids, which are referred to as "market resilience functions". These can be used to (1) evaluate the price-taking and price-making assumptions based on simplified price-effects, and to (2) provide an upper limit to the arbitrage value under the assumption that prices and price-effects are known at the decision stage. In addition, a stepwise approximation to the piecewise linear functions is developed to reduce computation time, i.e., from mixed-integer nonconvex quadratic programming to mixed-integer linear programming, while providing lower-and upper bound approximations to the arbitrage value. The developed models are applied to the Belgian day-ahead market for 2014, and show that the price-effect has a strong impact on the operation and arbitrage value of large-scale storage.

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“…In [9] the storage deployment problem is combined with transmission expansion planning problem without considering wind power generation and uncertainties. A three stage allocation of energy storage devices is proposed in [10] based on analyzing system operational costs for one year with probabilistic wind generation data. A multi-objective framework is presented in [11] with analyzing system operational costs and voltage improvements under limited number of scenarios for wind generation.…”

Section: Sets and Indices Gmentioning

confidence: 99%

Robust computational framework for mid‐term techno‐economical assessment of energy storage

Maghouli¹,

Soroudi

²

,

Keane

³

2016

IET Generation, Transmission & Distribution

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Abstract-Rapid expansion and integration of wind energy is restrained due to transmission capacity constraints and conventional generation technologies limited operational flexibility in todays power systems. Energy storage is an attractive option to integrate and utilize more renewable energy without major and timely upgrade of existing transmission infrastructure. Also it can be considered as a mean for differing the reinforcement plans. The evaluation of energy storage deployment projects is a challenging task due to severe uncertainty of wind power generation. In this paper a robust techno-economic framework is proposed for energy storage evaluation based on Information Gap Decision Theory for handling wind generation uncertainty. The total social cost of the system including conventional generators' fuel and pollution cost and wind power curtailment cost is optimized considering generators operational constraints and transmission system capacity limitations based on the DC model of the power grid. The effect of storage devices on system performance is evaluated taking into account wind power uncertainty. The proposed method is conducted on the modified IEEE Reliability Test System (IEEE-RTS) and the modified IEEE 118 bus test system to assess its applicability and performance in midterm robust evaluation of energy storage implementation plans.Index Terms-DC optimal power flow, Storage devices, Transmission capacity constraints, Wind power generation, Information Gap Decision Theory. NOMENCLATURE Sets & Indices Capacity of Wind turbine installed in busCharging/discharging efficiency of the energy storage unitCritical/opportunistic increase/decrease of base cost in RA/RS approach ($) ∆t Duration of time period t ag/bg/cg Fuel cost coefficient of thermal unit g P I. INTRODUCTION A. Background and motivationsMany countries have long term plans to increase the share of renewable energy resources in their generation mix. Among these renewable resources there has been increasing interest to invest and integrate more wind power in to existing power systems. However, increasing the wind penetration introduces profound challenges to traditional operating and planning practices. These new challenges arise mainly because of the variable nature of wind energy which results in none optimal operation of electric power systems. Traditionally it is accepted that electricity supply and consumption must remain in instantaneous balance the opportunity to store electrical energy in storage devices may now change this paradigm. Wind power intermittency and variability requires other fast ramping power sources to balance the supply-demand equilibrium in power systems [1]. Because of limiting capacity of fast ramping generation technologies such as hydro and pumped storage power plants, significant integration of wind power to existing power infrastructure requires major system upgrades or implementing energy storage facilities [2]. Besides, using energy storage facilities will result in more utilization of existing assets and ...

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“…Market participants are only incentivized to integrate new flexible resources when the investment is profitable. Although electricity storage plants can be used for many applications (e.g., arbitrage, portfolio optimization, frequency control, voltage support, black-start service [4,5]) and maximizing the value of storage requires the aggregation of different applications, one of the most studied and well-known applications is arbitraging day-ahead (DA) market electricity prices [6,7]. This article focuses on the arbitrage application as the sole revenue source.…”

Section: Motivationmentioning

confidence: 99%

“…7 The nonnegativity of c cyc and (9) - (10) represent the convex relaxation 8 of the max operator illustrated in Fig. 4, with C fix,e the investment cost of the energy-component of the storage plant.…”

mentioning

confidence: 99%

“…, index h-1 indicates h = 24 of the previous daily optimization period, except for the 7 During negative price periods the storage operator is paid to consume electric energy, and will therefore attempt to fill the storage buffer as quickly as possible. When the upper limit of the storage buffer is reached during these periods, it is profitable to charge and discharge simultaneously, thereby being remunerated for the incurred efficiency losses.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects

Brijs

¹

,

Geth

²

,

Siddiqui

³

et al. 2016

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Electricity storage plants can be used for many applications, with one of the most studied applications being arbitrage in the day-ahead market. Terms of use: Documents inAlthough the arbitrage value is related to the presence of price spreads, it also depends on the effect of (dis)charge actions on prices, as arbitrage generally reduces price spreads by increasing off-peak prices when charging and decreasing peak prices when discharging. As such, there are two important assumptions in price-based unit commitment arbitrage models: first, whether the storage operator is assumed to have perfect knowledge of future prices, and second, whether they recognize that their (dis)charge actions may affect those prices, i.e., the price-taking or pricemaking assumption. This article proposes a comprehensive formulation of the arbitrage problem including detailed operating constraints, and focuses on relaxing the price-taking assumption by considering real-world price-effect data, published in the form of hourly piecewise linear relationships between quantity and price based on submitted bids, which are referred to as "market resilience functions". These can be used to (1) evaluate the price-taking and price-making assumptions based on simplified price-effects, and to (2) provide an upper limit to the arbitrage value under the assumption that prices and price-effects are known at the decision stage. In addition, a stepwise approximation to the piecewise linear functions is developed to reduce computation time, i.e., from mixed-integer nonconvex quadratic programming to mixed-integer linear programming, while providing lower-and upper bound approximations to the arbitrage value. The developed models are applied to the Belgian day-ahead market for 2014, and show that the price-effect has a strong impact on the operation and arbitrage value of large-scale storage.

show abstract

Near-Optimal Method for Siting and Sizing of Distributed Storage in a Transmission Network

Cited by 265 publications

References 21 publications

Price-based unit commitment electricity storage arbitrage with piecewise linear price-effects

Price-based unit commitment electricity storage arbitrage with piecewise linear price-effects

Robust computational framework for mid‐term techno‐economical assessment of energy storage

Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects

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