Optimisation and Management of Virtual Power Plants Energy Mix Trading Model

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

scholarly journals Decentralized Prosumer-Centric P2P Electricity Market Coordination with Grid Security

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4665
Author(s):  
Duarte Kazacos Winter ◽  
Rahul Khatri ◽  
Michael Schmidt
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Power Flow ◽  
Power Plants ◽  
Optimal Power Flow ◽  
Free Market ◽  
Economic Value ◽  
New Paradigm ◽  
Grid Security ◽  
Virtual Power Plants

The increasing number of prosumers and the accompanying greater use of decentralised energy resources (DERs) bring new opportunities and challenges for the traditional electricity systems and the electricity markets. Microgrids, virtual power plants (VPPs), peer-to-peer (P2P) trading and federated power plants (FPPs) propose different schemes for prosumer coordination and have the potential of becoming the new paradigm of electricity market and power system operation. This paper proposes a P2P trading scheme for energy communities that negotiates power flows between participating prosumers with insufficient renewable power supply and prosumers with surplus supply in such a way that the community welfare is maximized while avoiding critical grid conditions. For this purpose, the proposed scheme is based on an Optimal Power Flow (OPF) problem with a Multi-Bilateral Economic Dispatch (MBED) formulation as an objective function. The solution is realized in a fully decentralized manner on the basis of the Relaxed Consensus + Innovations (RCI) algorithm. Network security is ensured by a tariff-based system organized by a network agent that makes use of product differentiation capabilities of the RCI algorithm. It is found that the proposed mechanism accurately finds and prevents hazardous network operations, such as over-voltage in grid buses, while successfully providing economic value to prosumers’ renewable generation within the scope of a P2P, free market.

scholarly journals Stochastic Mixed-Integer Programming (SMIP)-Based Distributed Energy Resource Allocation Method for Virtual Power Plants

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 67
Author(s):  
Rakkyung Ko ◽  
Sung-Kwan Joo
Keyword(s):  
Renewable Energy ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Energy Resource ◽  
Mixed Integer ◽  
Virtual Power ◽  
Distributed Energy ◽  
Distributed Energy Resource ◽  
Stochastic Mixed Integer Programming ◽  
Virtual Power Plants

Virtual power plants (VPPs) have been widely researched to handle the unpredictability and variable nature of renewable energy sources. The distributed energy resources are aggregated to form into a virtual power plant and operate as a single generator from the perspective of a system operator. Power system operators often utilize the incentives to operate virtual power plants in desired ways. To maximize the revenue of virtual power plant operators, including its incentives, an optimal portfolio needs to be identified, because each renewable energy source has a different generation pattern. This study proposes a stochastic mixed-integer programming based distributed energy resource allocation method. The proposed method attempts to maximize the revenue of VPP operators considering market incentives. Furthermore, the uncertainty in the generation pattern of renewable energy sources is considered by the stochastic approach. Numerical results show the effectiveness of the proposed method.

scholarly journals Mixed Integer Quadratic Programming Based Scheduling Methods for Day-Ahead Bidding and Intra-Day Operation of Virtual Power Plant

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1410 ◽  
Author(s):  
Rakkyung Ko ◽  
Daeyoung Kang ◽  
Sung-Kwan Joo
Keyword(s):  
Quadratic Programming ◽  
Power Systems ◽  
Power Plants ◽  
Power Grids ◽  
Mixed Integer ◽  
Virtual Power ◽  
Integer Quadratic Programming ◽  
Virtual Power Plants ◽  
Mixed Integer Quadratic Programming ◽  
The Impact

As distributed energy resources (DERs) proliferate power systems, power grids face new challenges stemming from the variability and uncertainty of DERs. To address these problems, virtual power plants (VPPs) are established to aggregate DERs and manage them as single dispatchable and reliable resources. VPPs can participate in the day-ahead (DA) market and therefore require a bidding method that maximizes profits. It is also important to minimize the variability of VPP output during intra-day (ID) operations. This paper presents mixed integer quadratic programming-based scheduling methods for both DA market bidding and ID operation of VPPs, thus serving as a complete scheme for bidding-operation scheduling. Hourly bids are determined based on VPP revenue in the DA market bidding step, and the schedule of DERs is revised in the ID operation to minimize the impact of forecasting errors and maximize the incentives, thus reducing the variability and uncertainty of VPP output. The simulation results verify the effectiveness of the proposed methods through a comparison of daily revenue.

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.

Practice of Virtual Power Plants Participating in Electricity Markets in China

2021 ◽  
Author(s):  
Yaxuan Han ◽  
Heping Jia ◽  
Dunnan Liu ◽  
Xuanyuan Wang ◽  
Zhen Liu ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Power Plants ◽  
Virtual Power ◽  
Virtual Power Plants

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.

Sign in / Sign up

Export Citation Format

Share Document