scholarly journals Multistage Stochastic Programming for VPP Trading in Continuous Intraday Electricity Markets

2021 ◽  
Author(s):  
Priyanka Shinde ◽  
Iasonas Kouveliotis-Lysikatos ◽  
Mikael Amelin
Keyword(s):  
Stochastic Programming ◽  
Electricity Markets ◽  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Programming Approach ◽  
Multistage Stochastic Programming ◽  
Hydro Power ◽  
Dual Dynamic Programming

<div>The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intraday markets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing (IOC) and the Partial Order Clearing (POC). We tackle the proposed problem with a modified version of Stochastic Dual Dynamic Programming (SDDP) algorithm. The functionality of our model is demonstrated by performing illustrative and large scale case studies and comparing the performance with a benchmark model.</div>

scholarly journals Multistage Stochastic Programming for VPP Trading in Continuous Intraday Electricity Markets

2021 ◽  
Author(s):  
Priyanka Shinde ◽  
Iasonas Kouveliotis-Lysikatos ◽  
Mikael Amelin
Keyword(s):  
Stochastic Programming ◽  
Electricity Markets ◽  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Programming Approach ◽  
Multistage Stochastic Programming ◽  
Hydro Power ◽  
Dual Dynamic Programming

<div>The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intraday markets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing (IOC) and the Partial Order Clearing (POC). We tackle the proposed problem with a modified version of Stochastic Dual Dynamic Programming (SDDP) algorithm. The functionality of our model is demonstrated by performing illustrative and large scale case studies and comparing the performance with a benchmark model.</div>

scholarly journals Spatial-Economic Potential Analysis of Wind Power Plants in Germany

Wind ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 77-89
Author(s):  
David Hennecke ◽  
Carsten Croonenbroeck
Keyword(s):  
Power Plant ◽  
Wind Power ◽  
Electricity Market ◽  
Regional Planning ◽  
Power Plants ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Levelized Cost Of Electricity ◽  
Potential Analysis ◽  
Federal States

Before a new wind farm can be built, politics and regional planning must approve of the respective area as a suitable site. For this purpose, large-scale potential computations were carried out to identify suitable areas. The calculation of wind power plant potential usually focuses on capturing the highest energy potential. In Germany, due to an energy production reimbursement factor defined in the Renewable Energy Sources Act (“Erneuerbare-Energien-Gesetz”, EEG) in 2017, the influence of energy quantities on the power plant potential varies, economically and spatially. Therefore, in addition to the calculation of energy potentials, it was also necessary to perform a potential analysis in terms of economic efficiency. This allows, on the one hand, an economic review of the areas tendered by the regional planning and, on the other hand, a spatial-economic analysis that expands the parameters in the search for new areas. In this work, (a) potentials with regard to the levelized cost of electricity (LCOE) were calculated by the example of the electricity market in Germany, which were then (b) spatially and statistically processed on the level of the federal states.

scholarly journals Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1918
Author(s):  
Guoqiang Sun ◽  
Weihang Qian ◽  
Wenjin Huang ◽  
Zheng Xu ◽  
Zhongxing Fu ◽  
...  
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Market Price ◽  
Master Problem ◽  
Programming Approach ◽  
Identification Algorithm ◽  
Virtual Power ◽  
Virtual Power Plants ◽  
Identification Approach

The present study establishes a stochastic adaptive robust dispatch model for virtual power plants (VPPs) to address the risks associated with uncertainties in electricity market prices and photovoltaic (PV) power outputs. The model consists of distributed components, such as the central air-conditioning system (CACS) and PV power plant, aggregated by the VPP. The uncertainty in the electricity market price is addressed using a stochastic programming approach, and the uncertainty in PV output is addressed using an adaptive robust approach. The model is decomposed into a master problem and a sub-problem using the binding scenario identification approach. The binding scenario subset is identified in the sub-problem, which greatly reduces the number of iterations required for solving the model, and thereby increases the computational efficiency. Finally, the validity of the VPP model and the solution algorithm is verified using a simulated case study. The simulation results demonstrate that the operating profit of a VPP with a CACS and other aggregated units can be increased effectively by participating in multiple market transactions. In addition, the results demonstrate that the binding scenario identification algorithm is accurate, and its computation time increases slowly with increasing scenario set size, so the approach is adaptable to large-scale scenarios.

Nash Equilibrium Strategies in Day-Ahead Electricity Markets: Optimum Management of Hydro-Thermal Systems

2004 ◽  
Author(s):  
Cristian Carraretto ◽  
Andrea Zigante
Keyword(s):  
Nash Equilibrium ◽  
Electricity Markets ◽  
Power Plants ◽  
Large Scale ◽  
Thermal Power ◽  
Water Storage ◽  
Market Model ◽  
Hydro Power ◽  
Hydro Power Plants ◽  
Nash Equilibrium Strategies

Robust optimization procedures for power plants production planning are the keypoint to profitably compete in the electricity deregulated market. The authors recently presented a market model useful to optimize the management of a group of thermal power plants belonging to a competitive producer. This model determines the Nash equilibrium among a set of competitors. In this paper, this model is extended to manage groups of thermal and hydro power plants together. Hydro plants part-load operation and technical constraints (feasible working range, feasible water storage range, water storage variation in time as a function of natural and artificial inflows, etc.) are thoroughly included in the market model. The model is applied to a large-scale power system for different market conditions and typical days of the year. Various combinations of power plants and number of producers are investigated. In particular, their effect on power plants management, market equilibrium and prices are discussed.

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.

Optimal Power Flow in Distribution Networks Under N – 1 Disruptions: A Multistage Stochastic Programming Approach

2021 ◽  
Author(s):  
Haoxiang Yang ◽  
Harsha Nagarajan
Keyword(s):  
Stochastic Programming ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Distribution Networks ◽  
Decomposition Algorithm ◽  
Scale Model ◽  
Programming Approach ◽  
Multistage Stochastic Programming ◽  
Optimal Power ◽  
Dual Dynamic Programming

Contingency research to find optimal operations and postcontingency recovery plans in distribution networks has gained major attention in recent years. To this end, we consider a multiperiod optimal power flow problem in distribution networks, subject to the N – 1 contingency in which a line or distributed energy resource fails. The contingency can be modeled as a stochastic disruption, an event with random magnitude and timing. Assuming a specific recovery time, we formulate a multistage stochastic convex program and develop a decomposition algorithm based on stochastic dual dynamic programming. Realistic modeling features, such as linearized AC power flow physics, engineering limits, and battery devices with realistic efficiency curves, are incorporated. We present extensive computational tests to show the efficiency of our decomposition algorithm and out-of-samplex performance of our solution compared with its deterministic counterpart. Operational insights on battery utilization, component hardening, and length of recovery phase are obtained by performing analyses from stochastic disruption-aware solutions. Summary of Contribution: Stochastic disruptions are random in time and can significantly alter the operating status of a distribution power network. Most of the previous research focuses on the magnitude aspect with a fixed set of time points in which randomness is observed. Our paper provides a novel multistage stochastic programming model for stochastic disruptions, considering both the uncertainty in timing and magnitude. We propose a computationally efficient cutting-plane method to solve this large-scale model and prove the theoretical convergence of such a decomposition algorithm. We present computational results to substantiate and demonstrate the theoretical convergence and provide operational insights into how making infrastructure investments can hedge against stochastic disruptions via sensitivity analyses.

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 Electricity Markets during the Liberalization: The Case of a European Union Country

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4317
Author(s):  
Štefan Bojnec ◽  
Alan Križaj
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Primary Energy ◽  
Lessons Learned ◽  
Policy Implications ◽  
Daily Maximum ◽  
Energy Prices ◽  
Electricity Prices ◽  
Electricity Power

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

scholarly journals Sizing and Management of Energy Storage Systems in Large-Scale Power Plants Using Price Control and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3296
Author(s):  
Carlos García-Santacruz ◽  
Luis Galván ◽  
Juan M. Carrasco ◽  
Eduardo Galván
Keyword(s):  
Energy Storage ◽  
Power Plants ◽  
Large Scale ◽  
Storage Systems ◽  
Renewable Energy Sources ◽  
Ancillary Services ◽  
Energy Sources ◽  
Energy Storage Systems ◽  
Electric System ◽  
Fundamental Part

Energy storage systems are expected to play a fundamental part in the integration of increasing renewable energy sources into the electric system. They are already used in power plants for different purposes, such as absorbing the effect of intermittent energy sources or providing ancillary services. For this reason, it is imperative to research managing and sizing methods that make power plants with storage viable and profitable projects. In this paper, a managing method is presented, where particle swarm optimisation is used to reach maximum profits. This method is compared to expert systems, proving that the former achieves better results, while respecting similar rules. The paper further presents a sizing method which uses the previous one to make the power plant as profitable as possible. Finally, both methods are tested through simulations to show their potential.

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