SHORT-TERM EFFECTS OF THE CLOSURE OF COAL-FIRED POWER PLANTS ON THE SPANISH ELECTRICITY MARKET

2021 ◽  
Vol 10 (1) ◽  
pp. [11 p.]-[11 p.]
Author(s):  
JUAN ANTONIO BALLESTEROS GALLARDO ◽  
FERNANDO NUÑEZ
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Supply And Demand ◽  
Real Data ◽  
Comparative Statics ◽  
Wholesale Market ◽  
Power Stations ◽  
Demand Curves ◽  
Hourly Data ◽  
Ecological Transition

ABSTRACT: This article analyses the effect of the closure of the Spanish coal-fired power stations on the price and quantity of energy sold in the daily electricity market. The comparative statics analysis is based on the hourly data of energy offer and demand bids published by the OMIE (Operator of the Iberian Energy Market) and made by the participants in the daily electricity market during the year 2018. Our analysis does not require any simulation of the market supply and demand curves, since they are obtained by aggregation using real data from the wholesale market. The main conclusion of our analysis is that, after the closure of the coal-fired stations, the hourly price of energy would increase 12,06% on average while the average amount of energy would decrease 2,57%. Keywords: Price of electricity; coal plants; ecological transition; supply and demand surpluses; renewable energy.

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 Efficiency and cost modelling of thermal power plants

2010 ◽  
Vol 14 (3) ◽  
pp. 821-834 ◽  
Author(s):  
Péter Bihari ◽  
Gyula Gróf ◽  
Iván Gács
Keyword(s):  
Power Generation ◽  
Electricity Market ◽  
Marginal Cost ◽  
Power Plants ◽  
Free Market ◽  
Power Station ◽  
Efficiency Function ◽  
Ic Engine ◽  
Power Stations ◽  
The Cost

The proper characterization of energy suppliers is one of the most important components in the modelling of the supply/demand relations of the electricity market. Power generation capacity i. e. power plants constitute the supply side of the relation in the electricity market. The supply of power stations develops as the power stations attempt to achieve the greatest profit possible with the given prices and other limitations. The cost of operation and the cost of load increment are thus the most important characteristics of their behaviour on the market. In most electricity market models, however, it is not taken into account that the efficiency of a power station also depends on the level of the load, on the type and age of the power plant, and on environmental considerations. The trade in electricity on the free market cannot rely on models where these essential parameters are omitted. Such an incomplete model could lead to a situation where a particular power station would be run either only at its full capacity or else be entirely deactivated depending on the prices prevailing on the free market. The reality is rather that the marginal cost of power generation might also be described by a function using the efficiency function. The derived marginal cost function gives the supply curve of the power station. The load level dependent efficiency function can be used not only for market modelling, but also for determining the pollutant and CO2 emissions of the power station, as well as shedding light on the conditions for successfully entering the market. Based on the measurement data our paper presents mathematical models that might be used for the determination of the load dependent efficiency functions of coal, oil, or gas fuelled power stations (steam turbine, gas turbine, combined cycle) and IC engine based combined heat and power stations. These efficiency functions could also contribute to modelling market conditions and determining the environmental impact of power stations.

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.

Reliability Index of Wind-Solar Hybrid Power Plants using the Expected Energy Not Supplied Method

2019 ◽  
Vol 8 (4) ◽  
pp. 9449-9456
Keyword(s):  
Power Plant ◽  
Power Systems ◽  
Electricity Market ◽  
Reliability Index ◽  
Power Plants ◽  
Initial Conditions ◽  
Hybrid Plant ◽  
Total Power ◽  
Hybrid Power ◽  
Hybrid Power Plant

This paper proposes the reliability index of wind-solar hybrid power plants using the expected energy not supplied method. The location of this research is wind-solar hybrid power plants Pantai Baru, Bantul, Special Region of Yogyakarta, Indonesia. The method to determine the reliability of the power plant is the expected energy not supplied (EENS) method. This analysis used hybrid plant operational data in 2018. The results of the analysis have been done on the Pantai Baru hybrid power plant about reliability for electric power systems with EENS. The results of this study can be concluded that based on the load duration curve, loads have a load more than the operating kW of the system that is 99 kW. In contrast, the total power contained in the Pantai Baru hybrid power plant is 90 kW. This fact makes the system forced to release the load. The reliability index of the power system in the initial conditions, it produces an EENS value in 2018, resulting in a total value of 2,512% or 449 kW. The EENS value still does not meet the standards set by the National Electricity Market (NEM), which is <0.002% per year. Based on this data, it can be said that the reliability of the New Coast hybrid power generation system in 2018 is in the unreliable category.

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 Decentralized Prosumer-Centric P2P Electricity Market Coordination with Grid Security

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4665
Author(s):  
Duarte Kazacos Winter ◽  
Rahul Khatri ◽  
Michael Schmidt
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Power Flow ◽  
Power Plants ◽  
Optimal Power Flow ◽  
Free Market ◽  
Economic Value ◽  
New Paradigm ◽  
Grid Security ◽  
Virtual Power Plants

The increasing number of prosumers and the accompanying greater use of decentralised energy resources (DERs) bring new opportunities and challenges for the traditional electricity systems and the electricity markets. Microgrids, virtual power plants (VPPs), peer-to-peer (P2P) trading and federated power plants (FPPs) propose different schemes for prosumer coordination and have the potential of becoming the new paradigm of electricity market and power system operation. This paper proposes a P2P trading scheme for energy communities that negotiates power flows between participating prosumers with insufficient renewable power supply and prosumers with surplus supply in such a way that the community welfare is maximized while avoiding critical grid conditions. For this purpose, the proposed scheme is based on an Optimal Power Flow (OPF) problem with a Multi-Bilateral Economic Dispatch (MBED) formulation as an objective function. The solution is realized in a fully decentralized manner on the basis of the Relaxed Consensus + Innovations (RCI) algorithm. Network security is ensured by a tariff-based system organized by a network agent that makes use of product differentiation capabilities of the RCI algorithm. It is found that the proposed mechanism accurately finds and prevents hazardous network operations, such as over-voltage in grid buses, while successfully providing economic value to prosumers’ renewable generation within the scope of a P2P, free market.

scholarly journals Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3747
Author(s):  
Ricardo Faia ◽  
Tiago Pinto ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Portfolio Optimization ◽  
Electricity Markets ◽  
Electricity Market ◽  
Retail Market ◽  
Wholesale Market ◽  
Generation Capacity ◽  
Wholesale Electricity Market ◽  
Portfolio Optimization Model ◽  
Photovoltaic Generators

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

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