Virtual power plant pricing strategy in storage and energy markets considering the uncertainty in the market price

Number of pages: 107 File Format: word File Code: 32152
Year: 2013 University Degree: Master's degree Category: Electrical Engineering
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  • Summary of Virtual power plant pricing strategy in storage and energy markets considering the uncertainty in the market price

    Dissertation

    Master's degree

    Field: Power Electricity

    Abstract:

    In this thesis, a new framework for planning and developing a strategy for the participation of virtual power plants in the markets Energy and storage are provided. According to the definition, a virtual power plant is a set of small-scale production units with load and covered network managed by a certain entity.

    Distributed production technologies that are of interest in this thesis are gas production units and simultaneous production of electricity and heat and electrochemical storage. Electricity prices of the wholesale market, retail sale, required reserve and time period are among the specific parameters.

    Parameters that include uncertainty are: wholesale price of energy and forecast in consumption demand.

    Non-equilibrium model is used for mathematical modeling of the participation planning problem. In order to solve the optimization problem, the genetic algorithm has been used.

    Uncertainties governing the wholesale price and forecasting consumption needs in the area covered by the virtual power plant have been taken into account, and logarithmic and normal probability distribution functions have been used to model them, respectively, and Monte Carlo simulation has been used to realize uncertain parameters.

    Simulation results in this thesis It shows that the presented framework is a powerful and suitable tool for formulating a strategy for proposing the production of a virtual power plant to the market and its interaction with consumers that has load shedding capability. The process of increasing efficiency and competition in other industries. The need to move in this direction is undeniable for various reasons such as capital-intensiveness, economic inefficiency of the traditional structure, and its exclusivity. While presenting one example of this restructuring (the role of the virtual power plant in the operation of the power system), the present research will examine the results of its presence in the electricity market. In this chapter, the general goals of the plan and the contents of the future chapters will be introduced.

    1-2 Electricity market

    The electricity industry as an infrastructure industry has undergone fundamental changes in the last two decades, from which it has undergone various titles such as restructuring, deregulation and so on.  .  .   It is remembered. In the new structure, unlike the old traditional structure in which the owner of the production, transmission and distribution system is the same and operated under the ownership of the unit, the production, transmission and distribution systems are separated from each other and are managed independently. 

    The low efficiency of the traditional power system in providing electrical energy caused the restructuring of the electricity industry to be proposed, just like the aviation and telecommunication industries. Competition and free access to the transmission system are two fundamental issues in the restructuring of the electricity industry. Privatization and change of existing structures is aimed at creating more competition and free and non-discriminatory access of different producers to the transmission system. In this situation, the management and design of each of these departments is done according to the prevailing environment, requirements and communication with other institutions, and the power distribution system as the last power supply chain can be in different forms. Together with the covered load and network managed by a certain entity, it is called a virtual power plant, which can have an active presence in the wholesale energy and rotating storage market.This idea for decentralized power systems, which consists of scattered production sources, provides the possibility of optimal exploitation of a set of scattered production sources with high efficiency, as well as the possibility of their presence in the electricity market. Exploitation of scattered production resources has caused the centralized structure of power system management to decentralize energy management centers.

    1-4 overview of chapters

    This power plant includes various components and types that will be discussed in the second chapter. Also, different models can be proposed for the presence of virtual power plant in the electricity market. These models include: Hunt and Shuttle, Nash equilibrium, non-equilibrium, equilibrium point estimation, Bertrand, Cournot model and Stackelberg model. The owners of the virtual power plant participate in the electricity market according to their chosen and proposed model and present their proposals in a certain period of time. All the mentioned models have been examined in many different researches, and in the second chapter, these models, their application, equations and how to participate in the electricity market will be discussed. The non-equilibrium model is used to design a virtual power plant proposal strategy, in which the constraints related to the supplied load sources, supply and demand balance constraints, as well as security constraints are considered as network constraints. In the presented model, the virtual power plant can be a participant of the electricity market with a dual role, including producer and consumer in the direction of power exchange with the upstream network. In addition, the virtual power plant can provide the rotating storage service.

    In the third chapter, the modeling of the optimization strategy of the virtual power plant's participation in the electricity market will be discussed. In this section, the modeling method, the solution algorithm, the objective function and the constraints in the problem will be explained. In this research, two networks have been used as virtual power plants. The first network includes a combination of scattered production units, electrochemical storage and final consumers, and the second network includes the first network along with two simultaneous production units, a heat generating unit and a traditional production unit. At first, the relationships in the problem for the first network and then the relationships for the second network are stated.

    One ??of the important points in research whose goals are optimization is the proper use of existing algorithms. Knowing, understanding and how to use the appropriate algorithm for the desired problem is vital in optimization problems. Maybe an algorithm is suitable for one optimization problem and not for another problem. This issue depends on important issues such as the dimensions of the problem, the constraints used, and the execution time.

    So far, many methods have been presented by algorithm designers to search for data. Methods called quick search and binary search are some of the simplest algorithms, but these algorithms may not be efficient when faced with a large volume of data. In the meantime, there is a method that solves big problems simply, and that is "genetic algorithm". The algorithm used in this research is the genetic algorithm, which is based on the transmission of hereditary characteristics by genes. This algorithm includes various operators that are discussed in the third chapter.

    Among the important points and new ideas of this research, we can mention the application of uncertainties. In few articles and researches, the problem of uncertainty and its effect on the parameters of the index and the objective function have been discussed in detail. Therefore, by studying and examining the existing shortcomings and the need to apply them, firstly, the uncertainty in the energy price and then the uncertainty in the load forecast are pointed out. One of the important points of this research is considering the uncertainty in load forecasting in addition to the energy price. In the following, uncertainty modeling is discussed using logarithmic and normal density functions. Due to the application of random and possible variables, Monte Carlo simulation has been used in order to influence these variables on the strategy of the virtual power plant to participate in the electricity market.

  • Contents & References of Virtual power plant pricing strategy in storage and energy markets considering the uncertainty in the market price

    List:

    Title

    Chapter One: Introduction.  1

    1-1 introduction .. 2

    1-2 electricity market .. 2

    1-3 concept of virtual power plant (VPP).  2

    1-4 overview of the contents of the chapters.  3

    Chapter Two: Introduction of the virtual power plant and an overview of the research done.  6

    2-1 Introduction .. 7

    2-2 Concept of Virtual Power Plant (VPP).  7

    2-2-1 Scattered production. 8

    2-2-2 Advantages of using DG. 8

    2-2-3 classification of DG types. 9

    2-2-4 types of VPP. 11

    2-2-5 VPP components. 12

    2-2-6 VPP control strategy. 13

    2-3 virtual power plants in the electricity market. 14

    2-3-1 New market transaction at distribution level. 14

    2-4 VPP participation in the electricity market. 15

    2-5 optimal VPP pricing strategy in the wholesale market. 16

    2-5-1 economic model for offering production in the electricity market using the Nash-SFE equilibrium strategy. 17

    2-5-2- Economic model for offering proposals in the electricity market based on the non-equilibrium model. 18

    2-5-3 SCPBUC strategies for VPP. 19

    2-6 The pricing strategy of the price receiving power plant under price uncertainty. 19

    2-7 Summary. 20

    Chapter three: Modeling the optimal strategy problem of virtual power plant participation in the electricity market and introducing the solution method.  21

    3-1 Introduction .. 22

    3-2 Knowing the dimensions of the problem. 22

    3-3 objective function and constraints in the problem (considering CHP units). 29

    3-3-1 objective function of the problem of economic distribution of simultaneous production of electricity and heat. 30

    3-4 Modeling uncertainty in demand and price and introducing problem solving algorithm. 32

    3-5 genetic algorithm operators. 34

    3-6 Summary. 36

    Chapter four: simulation and analysis of results. 37

    4-1 Introduction... 38

    4-2 Introduction of the first network (virtual power station 1). 38

    4-3 Simulation and analysis of results for virtual power plant 1 in the energy market. 39

    4-3-1 Base state (in the absence of uncertainties). 39

    4-3-2 The first scenario. 42

    4-3-3 second to fifth scenarios (in the presence of price uncertainty). 42

    4-3-4 scenarios six to nine (in the presence of price and demand uncertainty).  46

    4-3-5 simulation results of the ratio of standard deviation to the mean of Monte Carlo stopping.  49

    4-4 simulation and analysis of results for virtual power plant 1 in the reserve market. 50

    4-4-1 First scenario (in the absence of uncertainties). 50

    4-4-2 scenarios two to five (in the presence of uncertainty in energy prices). 51

    4-4-3 scenarios six to nine (in the presence of uncertainty in energy prices and load forecasting). 53

    4-4-4 simulation results of the ratio of standard deviation to the mean of Monte Carlo stopping. 53

    4-5 second case study (virtual power plant 2). 54

    4-6 Simulation and analysis of results for virtual power plant 2 considering the energy market. 55

    4-6-1 Base state (in the absence of uncertainties). 55

    4-6-2 scenarios two to five (in the presence of uncertainty in energy prices). 57

    4-6-3 Sixth to ninth scenarios in the presence of uncertainty in price and demand. 58

    4-6-4 Simulation results of the ratio of standard deviation to the mean of Monte Carlo stopping. 59

    4-7 Simulation and analysis of the results of the new network in the reservation market. 60

    4-7-1 Base state (without considering uncertainty). 60

    4-7-2 second to fifth scenarios (in the presence of energy price uncertainty). 61

    4-7-3 scenarios six to nine (in the presence of uncertainty in energy prices and load forecasting). 62

    4-7-4 simulation results of the ratio of standard deviation to the mean of Monte Carlo stopping. 63

    4-8 Summary. 63

    Chapter five: Conclusion and suggestions. 64

    Conclusion.. 65

    Suggestions.. 66

    Appendix. 67

    Appendix 1) The list of figures related to network simulation in the fourth chapter. 68

    Appendix 2) Sources and References

    Source:

    ]1 [Hossein Nizamabadi, Prasto Nizamabadi, Mehrdad Setaish Nazar and Georg Gharepetian "Optimal Pricing of Virtual Power Plants Using Nash Equilibrium-SFE Strategy" PSC Conference 2011, Tehran,. 68

    Appendix 2) Sources and References

    Source:

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    ]3 [Sara Khazai-Pol, Taghi Barforoshi and Majid Shahabi" Development of a strategy for the production of a virtual power plant in the energy market considering uncertainty in demand and market price" PSC Conference 2013, Kermanshah, Iran.

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    ]6 [Mehnoosh Shojiei, Habib Rajabi Mashhadi and Mahnaz Arwane, "Identification of unknown system parameters" Chaos for Synchronization by Genetic Algorithm" 8th Intelligent Systems Conference, Mashhad, Iran, 2016.

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Virtual power plant pricing strategy in storage and energy markets considering the uncertainty in the market price