Spatio-Temporal Distributed Solar Irradiance and Temperature Forecasting

Pathiravasam, Chirath; Arunagirinathan, Paranietharan; Jayawardene, Iroshani; Venayagamoorthy, G.K.; Wang, Yongqiang

doi:10.1109/ijcnn48605.2020.9206936

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Solar power has a similar correlation as it is proportional to solar irradiance. Therefore, predictions based on spatial and temporal inputs improve prediction accuracy [23]. In this study, cellular computational networks [24] are used to predict solar power.…”

Section: A Ccn Based Solar Power Predictionsmentioning

confidence: 99%

“…In this study, cellular computational networks [24] are used to predict solar power. More details on the predictors are found in [23]. The basic architecture of the predictors are as follows:…”

Section: A Ccn Based Solar Power Predictionsmentioning

confidence: 99%

“…Situational intelligence (SI) of solar power generation can reduce demand uncertainty. In [14], prediction of solar irradiance of multiple locations is formulated using cellular computational networks (CCNs) as a spatio-temporal network. The distributed nature and the capacity sizes of the generation make it not cost-effective.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Demand Response Management for a Virtually Connected Community With Solar Power

Pathiravasam

Venayagamorthy

2022

IEEE Access

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With the high proliferation of solar power, curtailments and higher capacity reserves are required for a reliable power system operation. However, system operators can tap into demand flexibility to maintain reliability affordably. Temperature-controlled loads (TCL) have higher flexibility in demand response due to their frequency of operation, power rating, and tolerance in the desired operating region. In this study, a TCL demand flexibility quantification is presented using temperature measurements and consumer preferences, and predictions of TCL demand flexibility and solar power generation are used to improve demand response (DR) reliability. The utility can leverage predictions to issue DR requests considering resource adequacy and operational costs. Consumers are formed as a virtually connected community, and an aggregator facilitates the utility in providing situational intelligence and distributing DR requests among consumers. A distributed DR management framework is proposed based on demand flexibility to (a) simplify the optimization and (b) improve optimality. Typical results show power consumption reduction during peak reduction and emergency DR requests and power consumption with low variability during capacity firming requests compared to individual thermostat controls. Two indices to measure DR reliability and consumer comfort are defined, and results are presented for different DR requests. INDEX TERMS demand flexibility, distributed demand response, DR reliability, solar PV power, temperature-controlled loads, virtually connected community.

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Section: A Ccn Based Solar Power Predictionsmentioning

confidence: 99%

Section: A Ccn Based Solar Power Predictionsmentioning

confidence: 99%

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Distributed Demand Response Management for a Virtually Connected Community With Solar Power

Pathiravasam

Venayagamorthy

2022

IEEE Access

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“…A time-series simulation that uses both the system model and expected operational data serves as an input to the optimizer. The operational data, the data set of independent load flow variables (loads and generation) for the optimized time window T, could be a near-future forecast [30], a sliding window average forecast [31], or a persistent forecast [32]. A near-future forecast is better than a sliding window average forecast, which is better than a persistent forecast.…”

Section: Cell Optimizationmentioning

confidence: 99%

Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

Dharmawardena

Venayagamoorthy

2022

Energies

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Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prioritized and two methods namely, graph and impact-based methods, are studied. The impact-based method requires extra initial computational efforts but thereafter provides better computational throughput than the graph-based method. The DOF is illustrated on a modified standard distribution test case with several DERs. The results from the test case demonstrate that the DOF based volt-var optimization results in consistently better performance than the state-of-the-art volt-var control.

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Spatiotemporal Solar Irradiance and Temperature Data Predictive Estimation

Pathiravasam

Venayagamoorthy

2022

Intelligent Data Mining and Analysis in Power and Energy Systems

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Spatio-Temporal Distributed Solar Irradiance and Temperature Forecasting

Cited by 7 publications

References 15 publications

Distributed Demand Response Management for a Virtually Connected Community With Solar Power

Distributed Demand Response Management for a Virtually Connected Community With Solar Power

Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

Spatiotemporal Solar Irradiance and Temperature Data Predictive Estimation

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