scholarly journals Industrial consumers’ electricity market participation options: a case study of an industrial cooling process in Denmark

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Nicolas Fatras ◽  
Zheng Ma ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Industrial Process ◽  
Operating Conditions ◽  
Comparative Approach ◽  
Market Participation ◽  
Cooling Process ◽  
Process Flexibility

AbstractIn a deregulated market context, industrial consumers often have multiple market participation options available to bid their flexible consumption in electricity markets and thereby reduce their electricity bill. Yet most participation strategies for demand response are developed in a fixed and predefined set of submarkets. Meanwhile, little literature has compared multiple market options for market participants. Therefore, this paper proposes a comparative approach between available market options to evaluate savings from different market participation options. More specifically, this study implements an optimisation program in Python to investigate the impacts of changes in an industrial process’ flexibility on savings with different market participation options. The optimisation program is tested with a case study of an industrial cooling process in three Danish submarkets (day-ahead, intraday, and regulating power markets). The market participation options are formed by different combinations of these three submarkets, and the type and amount of process flexibility are varied by changing time and load constraints in the optimisation program. The results show that bidding in market options with multiple submarkets yields higher savings than single-market bidding, but that increases in available flexibility impact savings in each market option differently. Increased flexibility will only bring additional savings if it allows to take further advantage of price variations in a market option. Additionally, increases in savings with flexibility depend on the considered type of flexibility. These changes in relative savings between market options at each flexibility level imply that the optimal market option is not a static choice for a process with variable operating conditions. The optimal market option for an industrial consumer depends not only on market price signals, but also on the type and amount of available flexibility.

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.

scholarly journals Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7473
Author(s):  
Hakan Acaroğlu ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Energy ◽  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Load Forecasting ◽  
Structural Development ◽  
Power Prediction ◽  
Comprehensive Review ◽  
Forecasting Methods ◽  
Area Of Concern

Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.

scholarly journals Electricity Market Price Assesment Utilizing Hybrid PSO – ANN Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 12867-12870
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Competitive Bidding ◽  
Computational Time ◽  
Price Forecasting ◽  
Prediction Errors ◽  
Key Factors ◽  
Bidding Strategies ◽  
Forecasting Errors

Prediction of cost is the most imperative task and the reason for settling on choices in competitive bidding strategies. Reliability, Robustness and optimal benefits for the market players are the fundamental concerns which can be accomplished by a point value anticipating module constitute of diminutive prediction errors, reduced complexity and lesser computational time. Thus in this work, a coordinated methodology dependent on Artificial Neural Networks (ANN) prepared with Particle Swarm Optimization (PSO) is proposed for momentary market clearing costs anticipating in pool based electricity markets. The proposed methodology overcomes the difficulties like trapping towards local minima and moderate convergence as in existing techniques. The work was speculated on territory Spain electricity markets and the outcomes obtained are compared with hybrid models presented in the previous literature. The response shows decline in forecasting errors that are recognized in price forecasting. The total research may help the ISO in finding the key factors that are fit for expectation with low errors.

scholarly journals Electricity saving in households due to the market liberalization and change in the consumer behaviour

Energetika ◽  
2016 ◽  
Vol 61 (3-4) ◽  
Author(s):  
Gatis Bažbauers ◽  
Uldis Bariss ◽  
Lelde Timma ◽  
Dace Lauka ◽  
Andra Blumberga ◽  
...  
Keyword(s):  
System Dynamics ◽  
Electricity Markets ◽  
Electricity Market ◽  
Electricity Consumption ◽  
Market Liberalization ◽  
Long Term Effects ◽  
Factors Affecting ◽  
Development Scenarios ◽  
Electricity Saving

In case of opening the electricity market, various factors interact with each other. Although research has been done on various factors affecting the liberal electricity market, little attention has been paid to studying the dynamic relations between the actors involved in the liberal electricity market and projections on electricity consumption in households. The main aim of the research is to explore both short- and long-term effects on the electricity consumption at liberal market conditions by modelling various development scenarios. The electricity market in operation in Latvia was used as the case study. For the simulation of electricity market liberalization, system dynamics has been chosen. This method can determine electricity savings in case of electricity market opening, because system dynamics allows conducting simulation of complex systems and analysing the obtained data to forecast probability of the development of several scenarios. Obtained results show that cumulative electricity savings in households could reach 560 GWh by the end of 2020 due to the opening of the electricity market, implementation of energy saving measures and other reasons. In case of scenario analysis using the change of consumption behaviour, it was obtained that the cumulative electricity saving could be almost twice as big if the majority of households were guided by the environmental concerns. Although the system dynamics model was based on the Latvian case study, its general application to other countries and electricity markets is also possible.

scholarly journals A Comparative Study of Pricing Mechanisms to Reduce Side-Payments in the Electricity Market: A Case Study for South Korea

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3395
Author(s):  
Hansol Shin ◽  
Tae Hyun Kim ◽  
Kyuhyeong Kwag ◽  
Wook Kim
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Market Price ◽  
Production Costs ◽  
Market Participants ◽  
Locational Marginal Pricing ◽  
Side Payments ◽  
Sales Company ◽  
Convex Costs ◽  
The Cost

Under marginal-cost pricing, some generators cannot recover their production costs at the market price due to non-convexities in the electricity market. For this reason, most electricity markets pay side-payments to generators whose costs are not sufficiently recovered, but side-payments present the problem of deteriorating transparency in the market. Recently, convex hull pricing and extended locational marginal pricing have been reviewed or gradually introduced to reduce side-payments. Another method is to include non-convex costs in the market price, which is applied in the Korean electricity market. Although it is not generally considered in the electricity market, the Vickrey auction method is also one of the pricing mechanisms that can reduce side-payments. The main purpose of this study is to analyze the financial impact of these alternative pricing mechanisms on market participants through rigorous simulation. We applied the alternative pricing schemes to the Korean electricity market, and the impacts are analyzed by comparing the cost aspect of an electricity sales company and the profit aspect of generation companies. As a result of the simulation study, each pricing mechanism not only differed in the degree to which side-payments are reduced but also has different effects on the type of generators.

scholarly journals Assessing Socioeconomic Impacts of Integrating Distributed Energy Resources in Electricity Markets through Input-Output Models

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4486
Author(s):  
Carmen Ramos Carvajal ◽  
Ana Salomé García-Muñiz ◽  
Blanca Moreno Cuartas
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Market Price ◽  
Total Output ◽  
Input Output ◽  
Economic Sectors ◽  
Socioeconomic Impacts ◽  
Virtual Power Plants

In competitive electricity markets, the growth of electricity generated by renewable sources will reduce the market price of electricity assuming marginal cost pricing. However, small renewable distributed generation (RDG) alone cannot modify the formation of electricity prices. By aggregating small RDG units into a Virtual Power Plants (as a single unit market) they are capable of dealing at the wholesale electricity market analogous to large-scale producer following in changes in wholesale prices. This paper investigates the socioeconomic impacts of different type of RDG technologies on Spanish economic sectors and households. To this end, we applied an input-output price model to detail the activities more sensitive to changes in electricity price due to RDG technologies deployment and the associated modifications in income and total output associated with the households’ consumption variation. Detailed Spanish electricity generation disaggregation of the latest available Spanish Input-Output table, which refers to 2015, was considered. It was found that the integration of RDG units in the electricity market project a better situation for the economy and Spanish households. This paper’s scope and information can be used to benefit decision-making with respect to electricity pricing policies.

scholarly journals Multi-Objective Optimization of Steel Off-Gas in Cogeneration Using the ε-Constraint Method: A Combined Coke Oven and Converter Gas Case Study

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2741
Author(s):  
Sergio García García ◽  
Vicente Rodríguez Montequín ◽  
Marina Díaz Piloñeta ◽  
Susana Torno Lougedo
Keyword(s):  
Co2 Emissions ◽  
Electricity Market ◽  
Optimization Problems ◽  
Coke Oven ◽  
Operating Conditions ◽  
Mixed Integer ◽  
Inflection Points ◽  
Decision Making Processes ◽  
Constraint Method

Increasingly demanding environmental regulations are forcing companies to reduce their impacts caused by their activity while defending the economic viability of their manufacturing processes, especially energy and carbon-intensive ones. Therefore, these challenges must be addressed by posing optimization problems that involve several objectives simultaneously, corresponding to different conditions, and often conflicting between. In this study, the residual gases of an integral steel factory were evaluated and modeled with the goal of developing an optimization problem considering two opposing objectives: CO2 emissions and profit. The problem was first approached in a mono-objective manner, optimizing profit through Mixed Integer Linear Programming (MILP), and then was extended to a bi-objective problem solved by means of the ε-constraint method, to find the Pareto front relating profit and CO2 emissions. The results show that multiobjective optimization is a very valuable resource for plant managers’ decision-making processes. The model makes it possible to identify inflection points from which the level of emissions would increase disproportionately. It gives priority to the consumption of less polluting fuels. The model also makes it possible to make the most of temporary buffers such as the gas holders, adapting to the hourly price of the electricity market. By applying this method, CO2 emissions decrease by more than 3%, and profit amounts up to 14.8% compared to a regular case under normal operating conditions. The sensitivity analysis of the CO2 price and CO2 constraints is also performed.

scholarly journals Automation Pyramid as Constructor for a Complete Digital Twin, Case Study: A Didactic Manufacturing System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4656
Author(s):  
Edwin Mauricio Martinez ◽  
Pedro Ponce ◽  
Israel Macias ◽  
Arturo Molina
Keyword(s):  
Industry 4.0 ◽  
Manufacturing System ◽  
Industrial Process ◽  
Operating Conditions ◽  
Shop Floor ◽  
Virtual Model ◽  
Digital Twin ◽  
Disruptive Technologies ◽  
Pyramid Model

Nowadays, the concept of Industry 4.0 aims to improve factories’ competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.

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