scholarly journals Conceptualization of Vehicle-to-Grid Contract Types and Their Formalization in Agent-Based Models

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Esther H. Park Lee ◽  
Zofia Lukszo ◽  
Paulien Herder
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Supply And Demand ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Vehicle To Grid ◽  
Fuel Cell Electric Vehicles ◽  
Different Types ◽  
Wholesale Electricity Market ◽  
And Control

Fuel cell electric vehicles (FCEVs) have the potential to be used as flexible power plants in future energy systems. To integrate FCEVs through vehicle-to-grid (V2G), agreements are needed between the FCEV owners and the actor that coordinates V2G on behalf of them, usually considered the aggregator. In this paper, we argue that, depending on the purpose of providing V2G and the goal of the system or the aggregator, different types of contracts are needed, not currently considered in the literature. We propose price-based, volume-based, and control-based contracts. Using agent-based modeling and simulation we show how price-based contracts can be applied for selling V2G in the wholesale electricity market and how volume-based contracts can be used for balancing the local energy supply and demand in a microgrid. The models can provide a base to explore strategies in the market and to improve performance in a system highly dependent on V2G.

scholarly journals Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3860
Author(s):  
Priyanka Shinde ◽  
Ioannis Boukas ◽  
David Radu ◽  
Miguel Manuel de Manuel de Villena ◽  
Mikael Amelin
Keyword(s):  
Renewable Energy ◽  
Electricity Market ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Agent Based Modeling ◽  
Market Liquidity ◽  
Energy Sources ◽  
Modeling Framework ◽  
Agent Based

In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.

scholarly journals Household electricity demand, the intrinsic flexibility index and UK wholesale electricity market prices

2020 ◽  
Author(s):  
Jacopo Torriti
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Electricity Demand ◽  
Market Prices ◽  
Vast Number ◽  
Spot Prices ◽  
Time Use Survey ◽  
Wholesale Electricity Market ◽  
The Uk ◽  
Flexibility Index

AbstractDuring peak electricity demand periods, prices in wholesale markets can be up to nine times higher than during off-peak periods. This is because if a vast number of users is consuming electricity at the same time, power plants with higher greenhouse gas emissions and higher system costs are typically activated. In the UK, the residential sector is responsible for about one third of overall electricity demand and up to 60% of peak demand. This paper presents an analysis of the 2014–2015 Office for National Statistics National Time Use Survey with a view to derive an intrinsic flexibility index based on timing of residential electricity demand. It analyses how the intrinsic flexibility varies compared with wholesale electricity market prices. Findings show that spot prices and intrinsic flexibility to shift activities vary harmoniously throughout the day. Reflections are also drawn on the application of this research to work on demand side flexibility.

scholarly journals Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
J. M. Torres ◽  
R. M. Aguilar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Electricity Market ◽  
Power Plants ◽  
Wind Farm ◽  
Polynomial Regression ◽  
Supply And Demand ◽  
Feedforward Neural Networks ◽  
Activation Function ◽  
Renewable Energies

Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12-hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.

scholarly journals Investigations of DDDAS for command and control of UAV swarms with agent-based modeling

2013 ◽  
Author(s):  
Ryan McCune ◽  
Rachael Purta ◽  
Mikolaj Dobski ◽  
Artur Jaworski ◽  
Greg Madey ◽  
...  
Keyword(s):  
Command And Control ◽  
Agent Based Modeling ◽  
Agent Based ◽  
And Control

Synthesis of the Algorithm for Control of the Thermal Power Plant Generating Equipment Based on System Dynamics Models

2021 ◽  
Vol 22 (1) ◽  
pp. 20-27
Author(s):  
I. N. Fomin ◽  
T. E. Shulga ◽  
V. A. Ivaschenko
Keyword(s):  
System Dynamics ◽  
Economic System ◽  
Electricity Market ◽  
Power Plants ◽  
Thermal Power ◽  
Thermal Power Plants ◽  
Mathematical Apparatus ◽  
Wholesale Electricity Market ◽  
Generating Equipment ◽  
Technical And Economic Indicators

The article discusses an original solution for designing an algorithm for selecting the most optimal technical and economic indicators for the operation of generating equipment of thermal power plants, taking into account the requirements of the wholesale electricity market, the day-ahead market and the balancing market. To design an algorithm for controlling generating equipment, the activity of a generating company in the wholesale electricity market was considered in terms of system dynamics. The proposed solution made it possible to select and interpret the state variables of the model, build flow diagrams describing the functioning of a technical-economic system, and visualize cause-and-effect relationships in the form of structured functional dependencies. In this work according to the norms of industry legislation and previously conducted scientific research the most important parameters were identified that form the flows of a dynamic technical and economic system, which are optimization criteria in fact. On the basis of this data, a stream stratification of the production processes of generating companies was carried out and a complex of mathematical models of system dynamics was developed to determine and plan the financial efficiency of the operation of thermal power plants and generating companies. The mathematical apparatus and the algorithm of its functioning are developed on the basis of the digraph of cause-and-effect relationships between the investigated technical and economic indicators. On the basis of the graph of interrelationships of system variables, a system of nonlinear differential equations has been built, which makes it possible to determine planned performance indicators when various technical and economic conditions change. The novelty of the proposed approach is the use of new model solutions based on the mathematical apparatus of system dynamics to represent the proposed model in simulation systems, in industry ERP and MES systems, for the development of DDS.

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