Identifying hybrid heating systems in the residential sector from smart meter data

In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole

Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties

Scheduling of prosumer flexibility is challenging in finding an optimal allocation of energy resources for heterogeneous prosumer goals under various forecast uncertainties and operation constraints. This study addresses this challenge by introducing a bottom-up framework for cooperative flexibility scheduling that relies on a decentralized network of scheduling agents to perform a coordinated decision-making and select a subset of households’ net load schedules that fulfills the techno-socio-economic prosumer objectives in the resource operation modes and ensures the reliability of the grid. The resource flexibility in terms of alternative operation schedules is mathematically modeled with multiobjective optimization that attains economic, environmental, and energy self-sufficiency prosumer goals with respect to their relative importance. The coordination is achieved with a privacy-preserving collective learning algorithm that aims to reduce the aggregated peak demand of the households considering prosumers’ willingness to cooperate and accept a less preferred resource schedule. By utilizing the framework and real-world data, the novel case study is demonstrated for prosumers equipped with solar battery systems in a community microgrid. The findings show that the flexibility scheduling with an optimal prosumer cooperation level decreases the global costs of collective peak shaving by 83% while increasing the local prosumer costs by 28% in comparison with noncooperative scheduling. However, the forecast uncertainty in net load and parameters of the frequency containment reserve causes imbalances in the planned schedules. It is suggested that the imbalances can be decreased if the flexibility modeling takes into account variable specific levels of forecast uncertainty

POWER QUALITY FACTORS IN EFFICIENCY BENCHMARKING

Abstrac

Identifying hybrid heating systems in the residential sector from smart meter data

Abstract In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole

Avaliação dos custos operacionais eficientes das empresas de transmissão do setor elétrico Brasileiro: uma proposta de adaptação do modelo dea adotado pela ANEEL

No setor elétrico brasileiro, as companhias de transmissão são remuneradas pela disponibilidade da capacidade de seus ativos, independentemente da quantidade de energia elétrica transmitida. Para induzir a operação eficiente das transmissoras, a ANEEL deve revisar periodicamente as receitas permitidas das transmissoras, considerando custos operacionais eficientes. Recentemente, a ANEEL publicou uma resolução em que descreve a metodologia utilizada no cálculo dos custos operacionais eficientes das transmissoras, a qual inclui um modelo de análise envoltória de dados (DEA). Neste trabalho propomos uma adaptação deste modelo DEA e apresentamos uma análise de sensibilidade dos resultados obtidos pelos dois modelos