Option Value of Demand-Side Response Schemes Under Decision-Dependent Uncertainty

Giannelos, Spyros; Konstantelos, Ioannis; Štrbac, Goran

doi:10.1109/tpwrs.2018.2796076

Cited by 40 publications

(27 citation statements)

References 27 publications

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“…Through the deployment of energy storage, the planner obtains the "real" option to delay/defer or displace/avoid undertaking conventional investments. In this case, when there is at least one source of uncertainty considered in the model, the economic total net benefit accrued from the deployment of this technology is known as Option Value of energy storage [11,12].…”

Section: Energy Storage and Option Value In The India Contextmentioning

confidence: 99%

“…In this context, smart grid technologies, such as energy storage, can constitute an option that can provide the required strategic flexibility, which will enable the network planner to hedge against the risk of stranded conventional assets by allowing the displacement and/or the deferral of large capital commitments on a conditional basis until the realization of a scenario suggests that they would be economically justified. This leads to the definition of the Option Value, which is the value of the total flexibility of energy storage, under the presence of uncertainty, and can be quantified as the net economic benefit accrued from its deployment in the system [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

et al. 2021

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Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system.

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Section: Energy Storage and Option Value In The India Contextmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

et al. 2021

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“…Step 4: Calculate the UGFs of the total response capacity of DRPs, the total output capacity of GUs, and the surplus capacity at moment t k , according to (18), (22) and (29), respectively.…”

Section: Algorithm Of Short-term Reliability Assessment Of Generatmentioning

confidence: 99%

“…Therefore, uncertainty of DR has attracted considerable scholarly attention recently. Some research has been carried out on DR uncertainty [9], [10], while other research has mainly focused on the impact of DR uncertainty on power systems: some academic literature addresses the optimal scheduling and operation of power systems with uncertain DR, such as unit commitment [11], [12], economic dispatch [13], and optimal reserve market clearing [14]; some pays particular attention to the impact of DR uncertainty on renewable energy accommodation [15]- [18]; some discusses the market mechanism design for DR uncertainty [19]- [21]; and a little has been concerned with the planning of power systems considering DR uncertainty [22].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Reliability Assessment of Generating Systems Considering Demand Response Reliability

et al. 2020

IEEE Access

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Demand response (DR), one kind of flexible resources, can decrease the operating costs of power systems and improve their reliability. However, DR is not absolutely reliable due to its inherent uncertainty, so its function of improving system reliability is restricted. In order to estimate the risk of generating systems during the period of DR events, the short-term reliability assessment of generating systems considering DR reliability is studied in this paper. Firstly, a multi-state continuous-time Markov chain (CTMC) model of DR response capacity is established and the state division of response capacity is performed by the method of mean-standard deviation (MSD) classification. Secondly, the transition matrix of DR response capacity is estimated according to the sequence of DR response capacity. Thirdly, the CTMCs of demand response providers' (DRP) response capacity and those of generating units' (GU) output capacity are converted into universal generating functions (UGF) by Lz-transform. Finally, the transient distribution of DR response capacity and GU output capacity are derived, and the short-term reliability assessment method for generating systems considering DR reliability is proposed. A case study on a revised IEEE-RTS79 system shows the application of the presented method. The method proposed in this paper can assist system operators to evaluate the reliability of generating systems during the period of DR events. INDEX TERMS Demand response reliability, Markov chain, mean-standard deviation classification, short-term reliability assessment of generating systems, universal generating function.

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“…In this context, energy storage (ES) can constitute a technology option that can provide the required security of supply as well as a wide range of benefits to the electricity system operation and investment. Such benefits include strategic investment flexibility for the network planner to hedge against exogenous and endogenous uncertainty [1,2], support for the real-time balancing of electricity supply and demand [3] through the provision of ancillary services [4], power quality improvement [5], decentralized coordination of distributed energy resources within microgrids [6] and provision of security of supply through reduction of peak demand [7][8][9] via temporal arbitrage [10]. Such benefits can enable greater penetration of low-carbon generation resources, which can have environmental benefits as well as economic ones [11] given that low-carbon generation is characterized by lower operating costs than conventional generation based on fossil fuels.…”

Section: Introductionmentioning

confidence: 99%

Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology

et al. 2020

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The ongoing electrification of the heat and transport sectors is expected to lead to a substantial increase in peak electricity demand over the coming decades, which may drive significant investment in network reinforcement in order to maintain a secure supply of electricity to consumers. The traditional way of security provision has been based on conventional investments such as the upgrade of the capacity of electricity transmission or distribution lines. However, energy storage can also provide security of supply. In this context, the current paper presents a methodology for the quantification of the security contribution of energy storage, based on the use of mathematical optimization for the calculation of the F-factor metric, which reflects the optimal amount of peak demand reduction that can be achieved as compared to the power capability of the corresponding energy storage asset. In this context, case studies underline that the F-factors decrease with greater storage power capability and increase with greater storage efficiency and energy capacity as well as peakiness of the load profile. Furthermore, it is shown that increased investment in energy storage per system bus does not increase the overall contribution to security of supply.

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Option Value of Demand-Side Response Schemes Under Decision-Dependent Uncertainty

Cited by 40 publications

References 27 publications

Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

Short-Term Reliability Assessment of Generating Systems Considering Demand Response Reliability

Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology

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