Typification of the demand-generation relationship of Colombian electricity market and forecast of demand at an hourly-daily level based on consumption patterns

Suárez-Riveros, Lilian; Santiango, Jejen-Salinas; Patarroyo-Godoy, Laura M.; Dante, Conti

doi:10.1109/icics52457.2021.9464588

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The forecast readings generated by the forecasting service adopt a typical load approach, using historical meter readings from the applicable utility and regularly enhanced with most recent real data supplied by the meter data providers. This means that until actual meter readings are received, all meters have a generic baseline profile, also known as typified profile [57], for different time periods which can be individualised per meter and time period but can also be defined equally for similar participant types (e.g., all consumption meters from participants with similar features can have the same baseline load profiles and all generation meters with similar characteristics). Forecasts for BESS SOC and energy rate can be conducted using a comparable approach, or alternatively, they can be computed taking into consideration both generation and load factors.…”

Section: Forecasting Servicementioning

confidence: 99%

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

Ali,

Azim,

Ojha

et al. 2024

IEEE Access

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Peer-to-peer (P2P) trading in a local energy market (LEM) offers various participants the opportunity to negotiate and strike energy deals among themselves using a distributed ledger technology called blockchain. In this paper, a new local model is presented using a layer-2 scalability solution for secondgeneration (Gen2) blockchain technology to enable P2P trading among four types of participants: consumers, prosumers with solar photovoltaic (PV) systems, prosumers with solar PV systems and battery energy storage systems (BESSs), and electric vehicles (EVs). The proposed LEM trading platform involves several critical steps, including the creation of typical forecasting profiles for load consumption, solar generation, and battery state-of-charge (SOC) through a forecasting solution. Next, the LEM participants place their pricing bids using a trading agent service, and the trading engine collects the profiles data and bid prices, which performs matchmaking in a forward-facing market. The output of the trading engine consists of dispatch signals for prices and energy values that are sent to each participant to execute actual trading. Furthermore, the trading engines store the accepted and past bidding data and energy values of P2P trades for each participant in blockchain technology, which can be retrieved and displayed on the LEM user interface screens of participants and administrators using their blockchain addresses at any time during the trading process. This study focuses on simulating proposed LEM models, incorporating functional limitations and market rules. These rules aim to reduce energy costs, enhance margins for utilities and retailers, and mitigate grid congestion through BESSs, resulting in reduced operational and capital expenditure. LEM outcomes are analysed and compared with a Business-as-usual (BAU) model. Participants' energy trading behaviour, costrevenue dynamics, grid impact, and blockchain implementation costs are explored. The study highlights LEM benefits in terms of reduced CO2 emissions by 984 kg CO2, increased self-sufficiency by 2.2%, and improved financial benefits of all participants by 21.6%. The use of modern blockchain technology guarantees secure data storage and rapid, cost-effective energy trading, thereby making the proposed LEM platform a viable solution in the distribution market.INDEX TERMS Blockchain technology, forecasting service, LEM, P2P energy trading, smart contracts.

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Section: Forecasting Servicementioning

confidence: 99%

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

Ali,

Azim,

Ojha

et al. 2024

IEEE Access

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An intelligence energy consumption model based on BP neural network in mobile edge computing

Zhou

et al. 2022

Journal of Parallel and Distributed Computing

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Flow Prediction for Hydropower Generation using LMST Neural Networks

Achig,

Gonzales,

Hidalgo

2023

J. Phys.: Conf. Ser.

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The objective of this investigation is to predict the necessary flow to satisfy the demand in order to guarantee the generation of energy in a hydropower, for which the artificial neural network LMST (Long Short-Term Memory) was used. In this context, in this article, historical data from the years 2010 to 2019 of flow and power of the plant was used, which were provided by it, in the first instance the data was purified to group them and graph the heat diagram with which the variables of interest were determined, then the training was carried out with which the network was structured with 10 neurons, 2 hidden layers, 2 freezing layers and 1 output layer. In addition, the RMSprop optimizer was selected with 10 numbers of delays with which an absolute error of 5.12% was obtained, finally tests were made comparing the results obtained in the training with data from one day after those that were used in the training and testing database. It is worth mentioning that by predicting the flow for the production of electrical energy, it contributes to the National Development Plan “Toda una Vida” of Ecuador, which is why it focuses on optimizing the use of water resources for required production levels.

show abstract

Typification of the demand-generation relationship of Colombian electricity market and forecast of demand at an hourly-daily level based on consumption patterns

Cited by 3 publications

References 8 publications

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

Integrating Forecasting Service and Gen2 Blockchain Into a Local Energy Trading Platform to Promote Sustainability Goals

An intelligence energy consumption model based on BP neural network in mobile edge computing

Flow Prediction for Hydropower Generation using LMST Neural Networks

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