Evaluating Distribution System Operators: Automated Demand Response and Distributed Energy Resources in the Flexibility4Chile Project

Guerrero, Juan Ignacio; Personal, Enrique; García, Sebastián; Parejo, Antonio; Rossi, Mansueto; García, Antonio López; Sanchez, Ricardo Perez; León, Carlos

doi:10.1109/mpe.2020.3000688

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…The proposed methodology was applied to the Savona Campus of the University of Genoa, Italy, where there is a smart polygeneration microgrid (SPM), an innovative facility used to provide electricity and heat to the campus [24]. The SPM constitutes a good example of the penetration of DERs [67] and a complete living lab for smart grid technology.…”

Section: Description Of the Case Studymentioning

confidence: 99%

Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

et al. 2021

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Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.

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Section: Description Of the Case Studymentioning

confidence: 99%

Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

et al. 2021

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“…The maximum number of requests is also established in the agreement. Other approaches propose the organization of flexibility and ancillary services under a specific market where TSOs and DSOs could buy those services that they need from prosumers and aggregators [ 26 ].…”

Section: State Of the Artmentioning

confidence: 99%

OpenADR and Agreement Audit Architecture for a Complete Cycle of a Flexibility Solution

Parejo

García

Personal

et al. 2021

Sensors

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Nowadays, the presence of renewable generation systems and mobile loads (i.e., electric vehicle) spread throughout the distribution network is increasing. The problem is that this type of system introduces an added difficulty since they present a strong dependence on the meteorology and the mobility needs of the users. This problem forces the distribution system operators to seek tools that make it possible to balance the relationship between consumption and generation. In this sense, automated demand response systems are an appropriate solution that allow the operator to request specific reductions in customers’ consumption, offering a discount to the customer and avoiding network congestion. This paper analyzes the implementation and architecture of a demand response solution based on OpenADR standard and its possible integration with a building management system through a use case. As will be analyzed, a key part of the architecture is the measurement system based on smart meters acting as sensors. This is the base of the auditing system which makes it possible to verify compliance with the consumption reduction agreements. Additionally, this study is completed with a parallel auditing system which makes it possible to verify compliance with the consumption reduction agreements. All of the proposed demand response cycle is implemented as a proof of concept in a classroom in the Escuela Politécnica Superior at the University of Seville, which makes it possible to identify the advantages of this architecture in the ambit of connection between distribution network and buildings.

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“…Specifically, distribution system operators (DSOs) are looking for increasing what they call the "observability" and "controllability" of their networks following the smart grid paradigm [2], obtaining a deeper knowledge about their state. This is required due to the high variability of distributed generation (DG) and the requirements of the upcoming growth of electric vehicles (EVs), which may cause congestion in the network during generation and consumption peaks [3].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Forecasting Framework Oriented to Distribution Networks and Microgrids

Parejo,

García,

Personal

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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In electrical distribution networks an adequate management is key for supporting the deployment of renewable generation sources and microgrids while extracting their maximum potential. Among the existing optimization approaches, stochastic and probabilistic methods are experiencing a growth in their use. However, one of the problems when applying these approaches is the complexity of creating and evaluating the quality of the required stochastic forecasts compared to deterministic forecasts. To mitigate this difficulty, this paper proposes a probabilistic forecasting framework that integrates model creation, their evaluation, and the selection of the best model for predicting. Additionally, two novel methods are proposed for creating scenario sets, and a new metric is defined for evaluating and selecting which model to use. The proposed framework is applied in a case study over a dataset of ten secondary distribution substations from a real distribution network located in Manzanilla (Spain), showing the effect of the selection criteria over the forecasting quality.Note to Practitioners-This article was motivated by the challenge of probabilistic forecasting inclusion in automatic management systems applied to power distribution networks and microgrids. Modern stochastic management optimization methods are fed with probabilistic forecasts, which offer richer information than classic deterministic forecasting. Therefore, the management systems should be able to automatically train a certain number of forecasting models (e.g., machine learning models), evaluate and compare them, and apply the best ones for obtaining the forecasts to feed the management optimization system. Considering the variety of models, techniques, probabilistic forecast types, and evaluation metrics, it can be unclear how to perform this process. For these reasons, this article proposes a probabilistic forecasting framework that integrates methods for the construction of diverse types of predictions (quantiles, intervals, and scenario sets), their evaluation, and the selection of the best model for performing each required prediction for feeding optimization systems. This framework could help to facilitate the implantation of modern stochastic optimization management systems for distribution networks and microgrids, as it simplifies the forecasting process.

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Evaluating Distribution System Operators: Automated Demand Response and Distributed Energy Resources in the Flexibility4Chile Project

Cited by 6 publications

References 2 publications

Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

OpenADR and Agreement Audit Architecture for a Complete Cycle of a Flexibility Solution

Probabilistic Forecasting Framework Oriented to Distribution Networks and Microgrids

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