Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties

Mashlakov, Aleksei; Pournaras, Evangelos; Nardelli, Pedro H. J.; Honkapuro, Samuli

doi:10.1016/j.apenergy.2021.116706

Cited by 32 publications

(10 citation statements)

References 47 publications

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“…However, a VPP can be more economical for managing uncertainties and optimizing a set of diverse resources with different characteristics. Forecasting the grid frequency involves considerable uncertainty which is compounded by other uncertainties such as photovoltaic generation Mashlakov et al (Mashlakov et al, 2021). As the future grid frequency deviations are not known even in the short term, it is the frequency reserve resource provider's duty to ensure that the reserve resource capacity is available and ready to be activated in the event of a frequency disturbance (Subramanya et al, 2021).…”

Section: Nowcasting For Vpp With Photovoltaic Generationmentioning

confidence: 99%

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

et al. 2021

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The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.

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Section: Nowcasting For Vpp With Photovoltaic Generationmentioning

confidence: 99%

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

et al. 2021

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“…The stationary requirement of FCR (i.e., the European PFR market), is considered in applications for paper mills [28], heat pumps [29], battery storages [30,31], wind power [32], refrigerators [33], and uninterruptible power supply (UPS) systems [34]. Similar work has been completed with battery storages for the UK PFR market (Enhanced Frequency Response) [35].…”

Section: Related Workmentioning

confidence: 99%

“…Validation against these new requirements necessitates empirical validation by measuring power output in response to test frequency signals [36]. Neither of these approaches can be integrated in a straightforward way to the multi-objective optimization approaches used in the previous works [28][29][30][31][32]. However, the control approaches based on logic algorithms [35] and proportional integral-derivative (PID) control [33] could be more applicable to the new validation procedures [36], which could inform the adjustment of the control parameters, until the validation is passed.…”

Section: Related Workmentioning

confidence: 99%

Validating the Real-Time Performance of Distributed Energy Resources Participating on Primary Frequency Reserves

2021

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A significant body of research has emerged for adapting diverse intelligent distributed energy resources to provide primary frequency reserves (PFR). However, such works are usually vague about the technical specifications for PFR. Industrial practitioners designing systems for PFR markets must pre-qualify their PFR resources against the specifications of the market operator, which is usually a transmission system operator (TSO) or independent system operator (ISO). TSO and ISO requirements for PFR have been underspecified with respect to real-time performance, but as fossil-fuel based PFR is being replaced by various distributed energy resources, these requirements are being tightened. The TSOs of Denmark, Finland, Norway, and Sweden have recently released a joint pilot phase specification with novel requirements on the dynamic performance of PFR resources. This paper presents an automated procedure for performing the pre-qualification procedure against this specification. The procedure is generic and has been demonstrated with a testbed of light emitting diode (LED) lights. The implications of low bandwidth Internet of Things communications, as well as the need to avoid abrupt control actions that irritate human users, have been investigated in the automated procedure.

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“…The degradation cost of the BESS during the operation is included by penalizing excessive charge-discharge cycling with a coefficient β as follows: where p b d,t is a decision variable of scheduled battery storage power, C inv is the investment cost of the BESS ( C/kWh), n cyc is the estimated lifetime in equivalent cycles, and DOD max is the maximum allowed depth of discharge (DoD). In addition, other market-related components can be added to the objective function, such as revenue from the provision of a BESS for the frequency regulation service [39]. This BESS application is especially demanding for a lowcarbon power system with an increasing share of renewable generation.…”

Section: Microgrid Energy Storage Management a Optimization Problemmentioning

confidence: 99%

“…The objective functions in Eq. ( 8) are normalized for the uncertain scenario using weighted min-max normalization [46] applied to prosumer flexibility modeling in [39]. For instance, in the case of the cost reduction objective, the minimum value is acquired from the economical scenario, whereas the maximum is derived from the reliable scenario.…”

Section: ) Implementation Detailsmentioning

confidence: 99%

Weather-Driven Predictive Control of a Battery Storage for Improved Microgrid Resilience

et al. 2021

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This paper aims to introduce a predictive weather-based control policy for the microgrid energy management to improve the resilience of the microgrid. This policy relies on the application of machine learning models for the prediction of microgrid load demand and solar production and supply interruption in the upstream distribution network. The predictions serve as an input to multiobjective chance constraint optimization that balances the microgrid resilience and economic objectives based on the probability of a supply interruption. The interruption predictions are made with a decision-tree-based model that can predict an upcoming interruption in the distribution network with 78% of the maximum accuracy. The case study microgrid consisting of several customers, solar photovoltaic generation, and battery storage is applied to cluster areas located in Finland. Overall, the developed control policy shows an improvement in the daily resilience of the microgrid in regard to an interruption in the main grid when compared with economic dispatch only.

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Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties

Cited by 32 publications

References 47 publications

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

Validating the Real-Time Performance of Distributed Energy Resources Participating on Primary Frequency Reserves

Weather-Driven Predictive Control of a Battery Storage for Improved Microgrid Resilience

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