Strategic Sizing of Energy Storage Facilities in Electricity Markets

Nasrolahpour, Ehsan; Kazempour, Jalal; Zareipour, Hamidreza; Rosehart, William

doi:10.1109/tste.2016.2555289

Cited by 131 publications

(73 citation statements)

References 32 publications

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“…Thus, current development of energy storage will mostly depend on independent investors which have profit maximizing objectives and other constraints on expected profit. A profit maximizing bilevel approach for investment planning of energy storage systems which will ensure that the owner of the energy storage will maximize its benefits has been proposed in [23,29,30]. However, neither of the proposed models include other sources of flexibility such as hydro and flexible demand which are currently the main competitors of emerging energy storage systems.…”

Section: Knowledge Gapmentioning

confidence: 99%

Optimal Investment Planning of Bulk Energy Storage Systems

2018

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Many countries have the ambition to increase the share of renewable sources in electricity generation. However, continuously varying renewable sources, such as wind power or solar energy, require that the power system can manage the variability and uncertainty of the power generation. One solution to increase flexibility of the system is to use various forms of energy storage, which can provide flexibility to the system at different time ranges and smooth the effect of variability of the renewable generation. In this paper, we investigate three questions connected to investment planning of energy storage systems. First, how the existing flexibility in the system will affect the need for energy storage investments. Second, how presence of energy storage will affect renewable generation expansion and affect electricity prices. Third, who should be responsible for energy storage investments planning. This paper proposes to assess these questions through two different mathematical models. The first model is designed for centralized investment planning and the second model deals with a decentralized investment approach where a single independent profit maximizing utility is responsible for energy storage investments. The models have been applied in various case studies with different generation mixes and flexibility levels. The results show that energy storage system is beneficial for power system operation. However, additional regulation should be considered to achieve optimal investment and allocation of energy storage.

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Section: Knowledge Gapmentioning

confidence: 99%

Optimal Investment Planning of Bulk Energy Storage Systems

2018

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“…Over the past few years, a number of works studied the pool strategy of a merchant storage system in energy-only markets [4], [5], [6], [7], the joint energy and reserve markets as day-ahead settlement [2], [3], and the joint energy and reserve markets (day-ahead settlement) along with balancing settlement in real-time [8], [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…In [4] and [5], [6], [7], the strategic decisions of a price-taker and a price-maker storage system are derived, respectively, all considering an energy-only market. Price-taker (price-maker) refers to an entity, whose operation decisions cannot (can) affect the market-clearing outcomes, i.e., clearing price and quantities.…”

Section: Introductionmentioning

confidence: 99%

“…Price-taker (price-maker) refers to an entity, whose operation decisions cannot (can) affect the market-clearing outcomes, i.e., clearing price and quantities. References [5], [6], [7] address a price-maker storage system that competes in an imperfectly competitive market.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to [2], [3], [4], [8], [9], [10], [11], we model a price-maker storage system capable of capturing opportunities made by the bidirection impacts of the storage system's operation and market conditions. On the other hand, in studies which model storage system as a price-maker facility [5], [6], [7], storage system's decisions are made in an energy-only market. Considering high integration of wind power, an important observation is that the large-scale storage systems tend to offer strategically in the ancillary services markets, in particular reserve.…”

Section: Introductionmentioning

confidence: 99%

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A Bilevel Model for Participation of a Storage System in Energy and Reserve Markets

Nasrolahpour

Kazempour

Zareipour

et al. 2018

IEEE Trans. Sustain. Energy

Self Cite

146

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Abstract-We develop a decision-making tool based on a bilevel complementarity model for a merchant price-maker energy storage system to determine the most beneficial trading actions in pool-based markets, including day-ahead (as joint energy and reserve markets) and balancing settlements. The uncertainty of net load deviation in real-time is incorporated into the model using a set of scenarios generated from the available forecast in the day-ahead. The objective of this energy storage system is to maximize its expected profit. The day-ahead products of energy storage system include energy as well as reserve commitment (as one of the ancillary services), whereas its balancing product is the energy deployed from the committed reserve. The proposed model captures the interactions of different markets and their impacts on the functioning of the storage system. It also provides an insight for storage system into clearing process of multiple markets and enables such a facility to possibly affect the outcomes of those markets to its own benefit through strategic price and quantity offers. The validity of the proposed approach is evaluated using a numerical study. Reserve requirement of the market at time t, (MW) Q t,k Net load deviation at time t under scenario k, (MW) D. Upper-level variables e s,t Stored energy of storage system s at time t, (MWh) u s,t Binary decision variable indicating the operation mode of storage system s at time t o s,t Price bid/offer by storage system s at time t, ($/MWh) p s,t Energy quantity bid/offer by storage system s at time t, (MW) r s,t Reserve capacity bid/offer by storage system s at time t, (MW) E. Lower-level variables (day-ahead market clearing) p g,t Scheduled production of generator g in day-ahead market at time t, (MW) r g,t Committed reserve from generator g in day-ahead market at time t, (MW) p d,t Consumption of demand d at time t, (MW) r d,t Committed reserve from generator demand d in dayahead market at time t, (MW) p s,t Scheduled energy production/consumption of storage system s in day-ahead market at time t, (MW) r s,t Committed reserve from storage system s in dayahead market at time t, (MW) λ t Day-ahead market-clearing price (as a dual variable) at time t, ($/MWh). F. Lower-level variables (balancing market clearing) q g,t,k Energy deployed from reserve of generator g in balancing market at time t under scenario k, (MW) q d,t,k Energy deployed from reserve of demand d in balancing market at time t under scenario k, (MW) l d,t,k Involuntarily curtailed load of demand d in balancing market at time t under scenario k, (MW) q s,t,k Energy deployed from reserve of storage system s in balancing market at time t under scenario k, (MW) λ t,k Balancing market-clearing price (as a dual variable) at time t under scenario k, ($/MWh).2 µ, ρ Dual variables corresponding to inequality day-ahead and balancing lower-level constraints. See Sections (II-B) and (II-C) for details.

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Influence of the Intraday Electricity Market Structure on the Degradation of Li‐Ion Batteries Used to Firm Photovoltaic Production

et al. 2022

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This article considers the introduction of Li‐ion batteries in photovoltaic power plants to firm their energy production and analyzes the dependence of their degradation on the structure of the electricity market where the power production is traded. The operation of the batteries is decided as a result of successive optimization problems that benefit from the use of deep‐learning‐based irradiance forecasting tools with low prediction error, which allows the batteries to keep a small size. In addition, state‐of‐the‐art battery aging models are handled to derive a realistic lifetime prognosis. The simulation results obtained by using real data from three European locations, which have different irradiance patterns and market structures, show how the proposed control strategy makes it possible to decouple the saturation rate of the batteries from the climatic conditions of the plant location. Furthermore, regarding the market structure, the results show that the shorter the energy block and the closer the lead time, the lower is the degradation of the batteries.

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Strategic Sizing of Energy Storage Facilities in Electricity Markets

Cited by 131 publications

References 32 publications

Optimal Investment Planning of Bulk Energy Storage Systems

Optimal Investment Planning of Bulk Energy Storage Systems

A Bilevel Model for Participation of a Storage System in Energy and Reserve Markets

Influence of the Intraday Electricity Market Structure on the Degradation of Li‐Ion Batteries Used to Firm Photovoltaic Production

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