Bidding Strategies for Virtual Power Plants in the Iberian Electricity Market

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.

Download Full-text

Clustering time series applied to energy markets

Energy Informatics ◽

10.1186/s42162-019-0076-0 ◽

2019 ◽

Vol 2 (S1) ◽

Author(s):

Cornelia Krome ◽

Jan Höft ◽

Volker Sander

Keyword(s):

Time Series ◽

Power Plant ◽

Electricity Market ◽

Power Plants ◽

Large Scale ◽

Energy Systems ◽

Weather Data ◽

Virtual Power ◽

Virtual Power Plant ◽

Virtual Power Plants

Abstract In Germany and many other countries the energy market has been subject to significant changes. Instead of only a few large-scale producers that serve aggregated consumers, a shift towards regenerative energy sources is taking place. Energy systems are increasingly being made more flexible by decentralised producers and storage facilities, i.e. many consumers are also producers. The aggregation of producers form another type of power plants: a virtual power plant. On the basis of aggregated production and consumption, virtual power plants try to make decisions under the conditions of the electricity market or the grid condition. They are influenced by many different aspects. These include the current feed-in, weather data, or the demands of the consumers. Clearly, a virtual power plant is focusing on developing strategies to influence and optimise these factors. To accomplish this, many data sets can and should be analysed in order to interpret and create forecasts for energy systems. Time series based analytics are therefore of particular interest for virtual power plants. Classifying the different time series according to generators, consumers or customer types simplifies processes. In this way, scalable solutions for forecasts can be found. However, one has to first find the according clusters efficiently. This paper presents a method for determining clusters of time series. Models are adapted and model-based clustered using ARIMA parameters and an individual quality measure. In this way, the analysis of generic time series can be simplified and additional statements can be made with the help of graphical evaluations. To facilitate large scale virtual power plants, the presented clustering workflow is prepared to be applied on big data capable platforms, e.g. time series stored in Apache Cassandra, analysed through an Apache Spark execution framework. The procedure is shown here using the example of the Day-Ahead prices of the electricity market for 2018.

Download Full-text

Generic virtual power plants: management of distributed energy resources under liberalized electricity market

8th International Conference on Advances in Power System Control, Operation and Management (APSCOM 2009) ◽

10.1049/cp.2009.1764 ◽

2009 ◽

Cited By ~ 20

Author(s):

S. You ◽

C. Traeholt ◽

B. Poulsen

Keyword(s):

Electricity Market ◽

Power Plants ◽

Energy Resources ◽

Distributed Energy Resources ◽

Virtual Power ◽

Distributed Energy ◽

Virtual Power Plants

Download Full-text

Bidding Strategy of Virtual Power Plant with Energy Storage Power Station and Photovoltaic and Wind Power

Journal of Engineering ◽

10.1155/2018/6139086 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Zhongfu Tan ◽

Qingkun Tan ◽

Yuwei Wang

Keyword(s):

Energy Storage ◽

Power Plant ◽

Wind Power ◽

Electricity Market ◽

Power Plants ◽

Bidding Strategy ◽

Virtual Power ◽

Power Station ◽

Virtual Power Plant ◽

Virtual Power Plants

For the virtual power plants containing energy storage power stations and photovoltaic and wind power, the output of PV and wind power is uncertain and virtual power plants must consider this uncertainty when they participate in the auction in the electricity market. In this context, this paper studies the bidding strategy of the virtual power plant with photovoltaic and wind power. Assuming that the upper and lower limits of the combined output of photovoltaic and wind power are stochastically variable, the fluctuation range of the day-ahead energy market and capacity price is stochastically variable. If the capacity of the storage station is large enough to stabilize the fluctuation of the output of the wind and photovoltaic power, virtual power plants can participate in the electricity market bidding. This paper constructs a robust optimization model of virtual power plant bidding strategy in the electricity market, which considers the cost of charge and discharge of energy storage power station and transmission congestion. The model proposed in this paper is solved by CPLEX; the example results show that the model is reasonable and the method is valid.

Download Full-text

Optimisation and Management of Virtual Power Plants Energy Mix Trading Model

International Journal of Renewable Energy Development ◽

10.14710/ijred.2022.39295 ◽

2021 ◽

Vol 11 (1) ◽

pp. 83-94

Author(s):

Zahid Ullah ◽

Nayyar Hussain Mirjat ◽

Muhammad Baseer

Keyword(s):

Electricity Markets ◽

Electricity Market ◽

Distribution System ◽

Power Plants ◽

Programming Model ◽

Mixed Integer ◽

Virtual Power ◽

Study Approach ◽

Virtual Power Plants ◽

Trading Model

. In this study, a robust optimisation method (ROM) is proposed with aim to achieve optimal scheduling of virtual power plants (VPPs) in the day-ahead electricity markets where electricity prices are highly uncertain. Our VPP is a collection of various distributed energy resources (DERs), flexible loads, and energy storage systems that are coordinated and operated as a single entity. In this study, an offer and bid-based energy trading mechanism is proposed where participating members in the VPP setting can sell or buy to/from the day-ahead electricity market to maximise social welfare (SW). SW is defined as the maximisation of end-users benefits and minimisation of energy costs. The optimisation problem is solved as a mixed-integer linear programming model taking the informed decisions at various levels of uncertainty of the market prices. The benefits of the proposed approach are consistency in solution accuracy and traceability due to less computational burden and this would be beneficial for the VPP operators. The robustness of the proposed mathematical model and method is confirmed in a case study approach using a distribution system with 18-buses. Simulation results illustrate that in the highest robustness scenario, profit is reduced marginally, however, the VPP showed robustness towards the day-ahead market (DAM) price uncertainty

Download Full-text

Systematic Categorization of Optimization Strategies for Virtual Power Plants

Energies ◽

10.3390/en13236251 ◽

2020 ◽

Vol 13 (23) ◽

pp. 6251

Author(s):

Amit Kumer Podder ◽

Sayemul Islam ◽

Nallapaneni Manoj Kumar ◽

Aneesh A. Chand ◽

Pulivarthi Nageswara Rao ◽

...

Keyword(s):

Energy Management ◽

Electricity Market ◽

Power Distribution ◽

Power Plants ◽

Optimal Operation ◽

Optimization Techniques ◽

Virtual Power ◽

Distributed Energy ◽

Intelligent Technique ◽

Virtual Power Plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

Download Full-text

A Remedial Strategic Scheduling Model for Load Serving Entities Considering the Interaction between Grid-Level Energy Storage and Virtual Power Plants

Energies ◽

10.3390/en11092420 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2420 ◽

Cited By ~ 2

Author(s):

Haiteng Han ◽

Hantao Cui ◽

Shan Gao ◽

Qingxin Shi ◽

Anjie Fan ◽

...

Keyword(s):

Renewable Energy ◽

Energy Storage ◽

Power System ◽

Electricity Market ◽

Power Plants ◽

Level Energy ◽

Renewable Energy Resources ◽

Virtual Power ◽

Virtual Power Plants ◽

Grid Level

More renewable energy resources have been connected to the grid with the promotion of global energy strategies, which presents new opportunities for the current electricity market. However, the growing integration of renewable energy also brings more challenges, such as power system reliability and the participants’ marketable behavior. Thus, how to coordinate integrated renewable resources in the electricity market environment has gained increasing interest. In this paper, a bilevel bidding model for load serving entities (LSEs) considering grid-level energy storage (ES) and virtual power plant (VPP) is established in the day-ahead (DA) market. Then, the model is extended by considering contingencies in the intraday (ID) market. Also, according to the extended bidding model, a remedial strategic rescheduling approach for LSE’s daily profit is proposed. It provides a quantitative assessment of LSE’s loss reduction based on contingency forecasting, which can be applied to the power system dispatch to help LSEs deal with coming contingencies. Simulation results verify the correctness and effectiveness of the proposed method.

Download Full-text