scholarly journals Investment Incentives in Competitive Electricity Markets

2018 ◽  
Vol 8 (10) ◽  
pp. 1978 ◽  
Author(s):  
Jaber Valinejad ◽  
Taghi Barforoshi ◽  
Mousa Marzband ◽  
Edris Pouresmaeil ◽  
Radu Godina ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Planning Horizon ◽  
Optimization Method ◽  
Design Criterion ◽  
Mixed Integer ◽  
Investment Incentives ◽  
Mixed Integer Linear Program ◽  
Interconnected Power System ◽  
The Impact

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

scholarly journals Operational Planning and Bidding for District Heating Systems with Uncertain Renewable Energy Production

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3310 ◽  
Author(s):  
Ignacio Blanco ◽  
Daniela Guericke ◽  
Anders Andersen ◽  
Henrik Madsen
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Electricity Markets ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
District Heating ◽  
Operational Planning ◽  
Solution Approach ◽  
The Impact

In countries with an extended use of district heating (DH), the integrated operation of DH and power systems can increase the flexibility of the power system, achieving a higher integration of renewable energy sources (RES). DH operators can not only provide flexibility to the power system by acting on the electricity market, but also profit from the situation to lower the overall system cost. However, the operational planning and bidding includes several uncertain components at the time of planning: electricity prices as well as heat and power production from RES. In this publication, we propose a planning method based on stochastic programming that supports DH operators by scheduling the production and creating bids for the day-ahead and balancing electricity markets. We apply our solution approach to a real case study in Denmark and perform an extensive analysis of the production and trading behavior of the DH system. The analysis provides insights on system costs, how DH system can provide regulating power, and the impact of RES on the planning.

scholarly journals Considerations Regarding the Negative Prices on the Electricity Market

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 26
Author(s):  
Pavel Atănăsoae ◽  
Radu Dumitru Pentiuc ◽  
Eugen Hopulele
Keyword(s):  
Renewable Energy ◽  
Electricity Markets ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Electricity Prices ◽  
Market Areas ◽  
The Impact ◽  
Market Coupling

Increasing of intermittent production from renewable energy sources significantly affects the distribution of electricity prices. In this paper, we analyze the impact of renewable energy sources on the formation of electricity prices on the Day-Ahead Market (DAM). The case of the 4M Market Coupling Project is analyzed: Czech-Slovak-Hungarian-Romanian market areas. As a result of the coupling of electricity markets and the increasing share of renewable energy sources, different situations have been identified in which prices are very volatile.

Measuring the Benefits of Greater Spatial Granularity in Short-Term Pricing in Wholesale Electricity Markets

2011 ◽  
Vol 101 (3) ◽  
pp. 247-252 ◽  
Author(s):  
Frank A Wolak
Keyword(s):  
Natural Gas ◽  
Electricity Markets ◽  
Electricity Market ◽  
Short Term ◽  
Locational Marginal Pricing ◽  
Marginal Pricing ◽  
Before And After ◽  
Natural Gas Prices ◽  
The Impact ◽  
Generation Unit

Hourly generation unit-level output levels, detailed information on the technological characteristics of generation units, and daily delivered natural gas prices to all generation units for the California wholesale electricity market before and after the implementation of locational marginal pricing are used to measure the impact of introducing greater spatial granularity in short-term energy pricing. The average hourly number of generation unit starts increases, but both the total hourly energy consumed and total hourly operating costs for all natural gas-fired generation units fall by more than 2 percent after the implementation of locational marginal pricing.

Analysis of Smart Grid Affecting Electricity Market in China Based on Smart Materials

2011 ◽  
Vol 63-64 ◽  
pp. 493-496
Author(s):  
Hong Ze Li ◽  
Sen Guo ◽  
Bao Wang
Keyword(s):  
Smart Grid ◽  
Power Quality ◽  
Electricity Market ◽  
Smart Materials ◽  
Green Energy ◽  
Market Reform ◽  
Demand Side ◽  
Low Carbon ◽  
Interconnected Power System ◽  
The Impact

China’s electricity market reform has been on almost 10 years, and the proposition of constructing "Strong Smart Grid” in China has aroused widespread concerns, and also made an impact on electricity market. To assess the impact on electricity market, considering the current situation of China's electricity market, based on smart materials, analyze the risks of electricity market from constructing smart grid in terms of low-carbon power accessing grid, wide-area interconnected power system and large-capacity batch of green energy accessing grid, and also propose some requirements for electricity market from the perspective of power quality, demand side management and trading platform. Developing smart grid will promote China’s electricity market reform.

Increasing the Flexibility of Combined Heat and Power Plants With Heat Pumps and Thermal Energy Storage

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Eike Mollenhauer ◽  
Andreas Christidis ◽  
George Tsatsaronis
Keyword(s):  
Thermal Energy ◽  
Electricity Market ◽  
Power Plants ◽  
Combined Heat And Power ◽  
Heat Pumps ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Steam Turbines ◽  
Mixed Integer Linear Program ◽  
District Heating System

Combined heat and power (CHP) plants are efficient regarding fuel, costs, and emissions compared to the separate generation of heat and electricity. Sinking revenues from sales of electricity due to sinking market prices endanger the economically viable operation of the plants. The integration of heat pumps (HP) and thermal energy storages (TESs) represents an option to increase the flexibility of CHP plants so that electricity can be produced only when the market conditions are favorable. The investigated district heating system is located in Germany, where the electricity market is influenced by a high share of renewable energies. The price-based unit-commitment and dispatch problem is modeled as a mixed integer linear program (MILP) with a temporal resolution of 1 h and a planning horizon of 1 yr. This paper presents the optimal operation of a TES unit and a HP in combination with CHP plants as well as synergies or competitions between them. Coal and gas-fired CHP plants with back pressure or extraction condensing steam turbines (STs) are considered, and their results are compared to each other.

Multiperiod Highway Improvement and Construction Scheduling: Model Development and Application

1998 ◽  
Vol 1617 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Wael Eldessouki ◽  
Nagui Rouphail ◽  
Madalena Beja ◽  
S. Ranji Ranjithan
Keyword(s):  
Planning Process ◽  
Model Development ◽  
Mathematical Optimization ◽  
Planning Horizon ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Traffic Demand ◽  
Primary Model

A methodology is presented that emulates the transportation improvement planning process using mathematical optimization techniques. The scheduling problem is formulated as a mixed integer linear program (MILP) and can be considered as a multiperiod network design problem. The three primary model components are discussed: ( a) the input module in which the network, traffic demand, and pool of potential projects are identified over the planning horizon; ( b) the benefits estimation module using network travel time as the benefit criterion; and ( c) the schedule builder, an MILP that attempts to maximize the total benefits subject to annual resources and project precedence constraints. The proposed method is applied in a case-study context to the Lisbon metropolitan region’s network, a portion of Portugal’s highway network, and the results are discussed.

scholarly journals Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 777 ◽  
Author(s):  
Ping Che ◽  
Yanyan Zhang ◽  
Jin Lang
Keyword(s):  
Electricity Market ◽  
Emission Intensity ◽  
Tax Policy ◽  
Programming Model ◽  
Carbon Tax ◽  
Electric Power Industry ◽  
Decomposition Algorithm ◽  
Mixed Integer ◽  
Tax Rate ◽  
The Impact

We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase of generation emission intensity. By introducing a step function to express the variable tax rate, we formulate the generation self-scheduling problem under the proposed carbon-tax policy as a mixed integer nonlinear programming model. The objective function is to maximize total generation profits, which are determined by generation revenue and the levied carbon tax over the scheduling horizon. To solve the problem, a decomposition algorithm is developed where the variable tax rate is transformed into a pure integer linear formulation and the resulting problem is decomposed into multiple generation self-scheduling problems with a constant tax rate and emission-intensity constraints. Numerical results demonstrate that the proposed decomposition algorithm can solve the considered problem in a reasonable time and indicate that the proposed carbon-tax policy can enhance the incentive for generation companies to invest in low-carbon generation capacity.

scholarly journals The capacitated plant location problem with customer and supplier matching and interval demands uncertainty

2014 ◽  
Vol 5 ◽  
pp. A03 ◽  
Author(s):  
Kaveh Mehdi ◽  
Maziar Salahi ◽  
Ali Jamalian
Keyword(s):  
Location Problem ◽  
Product Distribution ◽  
Linear Program ◽  
Interval Uncertainty ◽  
Plant Location ◽  
Integer Linear Program ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Cost Distribution ◽  
The Impact

The capacitated plant location problem with customer and supplier matching can be modeled as a mixed integer linear program, where the product distribution from plants to customers and the material supply from suppliers to plants are considered together. In order to save allocation cost, distribution trip and a supply trip is merged into one triangular trip. Moreover, vehicles from plants visit a customer and a supplier for each trip. In this paper, we assume interval uncertainties in the demands of costumers. We show that the robust counterpart of the original model with interval uncertainty is equivalent to a larger mixed integer linear program. Finally, the original and robust models are compared on several randomly generated examples showing the impact of uncertainty.

scholarly journals Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques

2021 ◽  
Author(s):  
Harmanjot Singh Sandhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Electricity Markets ◽  
Electricity Market ◽  
Numerical Experiments ◽  
Status Report ◽  
Electricity Prices ◽  
Forecasting Accuracy ◽  
Using Data ◽  
The Impact

Various machine learning-based methods and techniques are developed for forecasting day-ahead electricity prices and spikes in deregulated electricity markets. The wholesale electricity market in the Province of Ontario, Canada, which is one of the most volatile electricity markets in the world, is utilized as the case market to test and apply the methods developed. Factors affecting electricity prices and spikes are identified by using literature review, correlation tests, and data mining techniques. Forecasted prices can be utilized by market participants in deregulated electricity markets, including generators, consumers, and market operators. A novel methodology is developed to forecast day-ahead electricity prices and spikes. Prices are predicted by a neural network called the base model first and the forecasted prices are classified into the normal and spike prices using a threshold calculated from the previous year’s prices. The base model is trained using information from similar days and similar price days for a selected number of training days. The spike prices are re-forecasted by another neural network. Three spike forecasting neural networks are created to test the impact of input features. The overall forecasting is obtained by combining the results from the base model and a spike forecaster. Extensive numerical experiments are carried out using data from the Ontario electricity market, showing significant improvements in the forecasting accuracy in terms of various error measures. The performance of the methodology developed is further enhanced by improving the base model and one of the spike forecasters. The base model is improved by using multi-set canonical correlation analysis (MCCA), a popular technique used in data fusion, to select the optimal numbers of training days, similar days, and similar price days and by numerical experiments to determine the optimal number of neurons in the hidden layer. The spike forecaster is enhanced by having additional inputs including the predicted supply cushion, mined from information publicly available from the Ontario electricity market’s day-ahead System Status Report. The enhanced models are employed to conduct numerical experiments using data from the Ontario electricity market, which demonstrate significant improvements for forecasting accuracy.

scholarly journals Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques

2021 ◽  
Author(s):  
Harmanjot Singh Sandhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Electricity Markets ◽  
Electricity Market ◽  
Numerical Experiments ◽  
Status Report ◽  
Electricity Prices ◽  
Forecasting Accuracy ◽  
Using Data ◽  
The Impact

Various machine learning-based methods and techniques are developed for forecasting day-ahead electricity prices and spikes in deregulated electricity markets. The wholesale electricity market in the Province of Ontario, Canada, which is one of the most volatile electricity markets in the world, is utilized as the case market to test and apply the methods developed. Factors affecting electricity prices and spikes are identified by using literature review, correlation tests, and data mining techniques. Forecasted prices can be utilized by market participants in deregulated electricity markets, including generators, consumers, and market operators. A novel methodology is developed to forecast day-ahead electricity prices and spikes. Prices are predicted by a neural network called the base model first and the forecasted prices are classified into the normal and spike prices using a threshold calculated from the previous year’s prices. The base model is trained using information from similar days and similar price days for a selected number of training days. The spike prices are re-forecasted by another neural network. Three spike forecasting neural networks are created to test the impact of input features. The overall forecasting is obtained by combining the results from the base model and a spike forecaster. Extensive numerical experiments are carried out using data from the Ontario electricity market, showing significant improvements in the forecasting accuracy in terms of various error measures. The performance of the methodology developed is further enhanced by improving the base model and one of the spike forecasters. The base model is improved by using multi-set canonical correlation analysis (MCCA), a popular technique used in data fusion, to select the optimal numbers of training days, similar days, and similar price days and by numerical experiments to determine the optimal number of neurons in the hidden layer. The spike forecaster is enhanced by having additional inputs including the predicted supply cushion, mined from information publicly available from the Ontario electricity market’s day-ahead System Status Report. The enhanced models are employed to conduct numerical experiments using data from the Ontario electricity market, which demonstrate significant improvements for forecasting accuracy.

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