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Market based embedded Real Time Operation for Distributed Resources and Flexibility
Systems and Control · Submitted 24 Feb 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We find that the Real Time operation does not substantially decrease or increase the total cost the aggregator faces in the RT market, but this is probably due to parameters that need further tuning and data, that need better processing.
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The abstract, sentence by sentence
We build upon previous work out of UC Berkeley's energy, controls, and applications laboratory (eCal) that developed a model for price prediction of the energy day-ahead market (DAM) and a stochastic load scheduling for distributed energy resources (DER) with DAM based objective\cite{Travacca}. In similar fashion to the work of Travacca et al., in this project we take the standpoint of a DER aggregator pooling a large number of electricity consumers - each of which have an electric vehicle and solar PV panels - to bid their pooled energy resources into the electricity markets. The primary contribution of this project is the optimization of an aggregated load schedule for participation in the California Independent System Operator (CAISO) real time (15-minute) electricity market. The goal of the aggregator is to optimally manage its pool of resources, particularly the flexible resources, in order to minimize its cost in the real time market. We achieves this through the use of a model predictive control scheme. A critical difference between the prior work in \cite{Travacca} is that the structure of the optimization problem is drastically different. Based upon our review of the current and public literature, no similar approaches exist. The main objective of this project were building methods. Nevertheless, to illustrate a simulation with 100 prosumers was realized. The results should therefore be taken with a grain of salt. We find that the Real Time operation does not substantially decrease or increase the total cost the aggregator faces in the RT market, but this is probably due to parameters that need further tuning and data, that need better processing.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We build upon previous work out of UC Berkeley's energy, controls, and applications laboratory (eCal) that developed a model for price prediction of the energy day-ahead market (DAM) and a stochastic load scheduling for distributed energy resources (DER) with DAM based objective\cite{Travacca}. | background 1.00 | 0.09 | 0.02 | asserted 1.00 |
| s1 | In similar fashion to the work of Travacca et al., in this project we take the standpoint of a DER aggregator pooling a large number of electricity consumers - each of which have an electric vehicle and solar PV panels - to bid their pooled energy resources into the electricity markets. | background 0.50 | 0.61 | 0.03 | asserted 0.99 |
| s2 | The primary contribution of this project is the optimization of an aggregated load schedule for participation in the California Independent System Operator (CAISO) real time (15-minute) electricity market. | contribution 1.00 | 0.80 | 0.08 | asserted 1.00 |
| s3 | The goal of the aggregator is to optimally manage its pool of resources, particularly the flexible resources, in order to minimize its cost in the real time market. | contribution 0.69 | 0.32 | 0.06 | asserted 0.99 |
| s4 | We achieves this through the use of a model predictive control scheme. | method 0.99 | 0.79 | 0.03 | asserted 1.00 |
| s5 | A critical difference between the prior work in \cite{Travacca} is that the structure of the optimization problem is drastically different. | contribution 0.63 | 0.33 | 0.04 | asserted 1.00 |
| s6 | Based upon our review of the current and public literature, no similar approaches exist. | gap 0.85 | 0.46 | 0.04 | asserted 0.99 |
| s7 | The main objective of this project were building methods. | contribution 0.96 | 0.60 | 0.04 | asserted 0.95 |
| s8 | Nevertheless, to illustrate a simulation with 100 prosumers was realized. | limitation 0.63 | 0.91 | 0.03 | asserted 1.00 |
| s9 | The results should therefore be taken with a grain of salt. | limitation 1.00 | 0.32 | 0.03 | hedged possibility 0.50 |
| s10 | We find that the Real Time operation does not substantially decrease or increase the total cost the aggregator faces in the RT market, but this is probably due to parameters that need further tuning and data, that need better processing. | result 0.78 | 0.94 | 0.71 | hedged possibility 0.57 |
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The paper
Original abstract
We build upon previous work out of UC Berkeley's energy, controls, and applications laboratory (eCal) that developed a model for price prediction of the energy day-ahead market (DAM) and a stochastic load scheduling for distributed energy resources (DER) with DAM based objective\cite{Travacca}. In similar fashion to the work of Travacca et al., in this project we take the standpoint of a DER aggregator pooling a large number of electricity consumers - each of which have an electric vehicle and solar PV panels - to bid their pooled energy resources into the electricity markets. The primary contribution of this project is the optimization of an aggregated load schedule for participation in the California Independent System Operator (CAISO) real time (15-minute) electricity market. The goal of the aggregator is to optimally manage its pool of resources, particularly the flexible resources, in order to minimize its cost in the real time market. We achieves this through the use of a model predictive control scheme. A critical difference between the prior work in \cite{Travacca} is that the structure of the optimization problem is drastically different. Based upon our review of the current and public literature, no similar approaches exist. The main objective of this project were building methods. Nevertheless, to illustrate a simulation with 100 prosumers was realized. The results should therefore be taken with a grain of salt. We find that the Real Time operation does not substantially decrease or increase the total cost the aggregator faces in the RT market, but this is probably due to parameters that need further tuning and data, that need better processing.
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