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.

scholarly journals Clustering time series applied to energy markets

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.

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 Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3208 ◽  
Author(s):  
Xiangyu Li ◽  
Dongmei Zhao ◽  
Baicang Guo
Keyword(s):  
Distribution System ◽  
Gas Turbines ◽  
Power Plants ◽  
Large Scale ◽  
Distribution Network ◽  
Virtual Power ◽  
Distributed Energy ◽  
Active Distribution Network ◽  
Active Distribution System ◽  
Virtual Power Plants

In order to build an active distribution system with multi virtual power plants (VPP), a decentralized two-stage stochastic dispatching model based on synchronous alternating direction multiplier method (SADMM) was proposed in this paper. Through the integration of distributed energy and large-scale electric vehicles (EV) in the distribution network by VPP group, coordinative complementarity, and global optimization were realized. On the premise of energy autonomy management of active distribution network (AND) and VPP, after ensuring the privacy of stakeholders, the power of tie-line was taken as decoupling variable based on SADMM. Furthermore, without the participation of central coordinators, the optimization models of VPPs and distribution networks were decoupled to achieve fully decentralized optimization. Aiming at minimizing their own operating costs, the VPPs aggregate distributed energy and large-scale EVs within their jurisdiction to interact with the upper distribution network. On the premise of keeping operation safe, the upper distribution network formulated the energy interaction plan with each VPP, and then, the global energy optimization management of the entire distribution system and the decentralized autonomy of each VPP were achieved. In order to improve the stochastic uncertainty of distributed renewable energy output, a two-stage stochastic optimization method including pre-scheduling stage and rescheduling stage was adopted. The pre-scheduling stage was used to arrange charging and discharging plans of EV agents and output plans of micro gas turbines. The rescheduling stage was used to adjust the spare resources of micro gas turbines to deal with the uncertainty of distributed wind and light. An example of active distribution system with multi-VPPs was constructed by using the improved IEEE 33-bus system, then the validity of the model was verified.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
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.

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>

Bidding Strategies for Virtual Power Plants in the Iberian Electricity Market

2021 ◽  
Author(s):  
Matthew Gough ◽  
Sergio F. Santos ◽  
Jose Oliveira ◽  
Jessica Chaves ◽  
Rui Castro ◽  
...  
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Virtual Power ◽  
Bidding Strategies ◽  
Virtual Power Plants
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