Territory and Construction Engineering
Ph.D. Dissertation
Energy Exchange in a Network of
Microgrids
Li Bai
October 2019
Supervisors: Prof. Marco Raugi
Prof. Emanuele Crisostomi Prof. Mauro Tucci
Li Bai, 2019.
Supervisors:
Prof. Marco Raugi University of Pisa, Dept. of Energy, Systems, Territory, and Construction Engineering Prof. Emanuele Crisostomi University of Pisa, Dept. of Energy, Systems,
Territory, and Construction Engineering Prof. Mauro Tucci University of Pisa, Dept. of Energy, Systems,
Territory, and Construction Engineering
Department of Energy, Systems, Territory, and Construction Engineering University of Pisa, Largo Lucio Lazzarino, 1
56122 Pisa, Italy
Typeset in LATEX Pisa, Italy 2019
“There is only one true heroism in the world: to see the world as it is, and to love it.”
UNIVERSITY OF PISA
Abstract
Department of Energy, Systems, Territory and Construction Engineering
Doctor of Philosophy
Energy Exchange in a Network of Microgrids
This PhD thesis consists of 3 parts, all related the problem of energy management in a network of microgrids. In the first part we discuss peer-to-peer (P2P) energy trading among a network of microgrids. Firstly, we adapt a preference-based mechanism into the energy exchange model of a network of microgrids in the frame-work of P2P energy trading in the distribution system. We design an alternative direction method of multiplier (ADMM) based dis-tributed algorithm to realize such P2P energy transactions, consid-ering the coupling constraints of virtual energy trading and physi-cal constraints on the connecting edges between any two microgrids within the network. The preference mechanism applied directly on the energy trading indirectly gives rise to a customized power flow within the network of microgrids by adjusting the preference val-ues, for example to create several clusters of microgrids where each cluster ends up operating in an islanding mode. Two classical dis-tribution networks are thoroughly examined: the first one is used to analyze the effect of the preference mechanism on the customized power flows; the second one is used to evaluate the convergence per-formance of the proposed distributed algorithm considering different time and size scales.
In the second part we discuss the forecast of renewable energy, especially wind power forecasts, required by each local agent when solving its local optimization problem in the designed distributed framework of P2P energy trading. Wind power forecasts are inves-tigated in two different scales where the first scale is for a single wind farm while the second scale is a region where multiple wind farms are included. Such scales are comparable to a network of microgrids in the distribution network when the distribution system operator (DSO) is considered as well. We first discuss the wind power forecast for a single wind farm using machine learning algorithms including shallow neural networks and deep neural networks of convolutional neural networks. A comprehensive comparison is carried out to eval-uate all methods with four different datasets of four wind farms. We also discuss the distributed reconciliation of wind power forecasts in a two-layer hierarchy where the top layer corresponds to the system operator of a region and the bottom layer includes all the individual wind farms within the region. An ADMM-based algorithm is pro-posed to achieve the aggregation consistency that the aggregated forecast at the top layer is equal to the sum of all the individual forecasts at the bottom layer.
Finally, in the last part of the thesis we discuss possible commu-nication solutions involved in the practical implementation of P2P energy trading. Communication techniques play an important role in such an implementation. Each agent collects local data from re-mote terminal units and communicates information to the DSO or to other agents, which may require a hybrid communication solu-tion including many different communicasolu-tion techniques in different segments. We only focus on one candidate communication solution, which is power line communication. We propose an impulsive noise mitigation method to cope with one of the challenging obstacles in the application of power line communication and evaluate it in ideal and frequency selective fading channels.
All the parts and proposed methods are evaluated and analyzed using mathematical tools and extensive simulations.
Acknowledgements
This thesis is the culmination of 3 years of being a PhD student. I spent most of my time in Pisa, a tiny and silent place that is pleasant for doing research. I would like to thank all the people I have met in this period as they have made contributions to this thesis in different ways.
First, I thank Prof. Tao Zheng from Xi’an Jiaotong University, who is the most important teacher coming into my life. He not only helped me with my research during my three years for master’s degree as a supervisor, but also helped me to be a better person as one of my beloved friends. He encouraged me to study abroad to see the world outside. That is why I came to the University of Pisa. I thank Prof. Emanuele Crisostomi from the University of Pisa, who fully supported me on my research work. He arranged me to visit Prof. Pierre Pinson for 3 months in the Autumn of 2018, which helped me to broaden my horizons. He also suggested me attending different workshops in Paris (France), Lucca (Italy) and Naples (Italy), which provided me with more creative ideas for my research. Moreover, he funded me to attend the general meeting in Atlanta, and this trip helped me to meet more professional scholars in the power system field. All those experiences enriched my PhD research life and left me precious memories to cherish in my whole life.
I thank Prof. Pierre Pinson from the Technical University of Denmark (DTU), who is my idol in the academic circle. He hosted me for a 3-month stay, during which he organized a talk with me on the research topic every week, instructed me how to make a better presentation and guided me how to write a scientific paper. After 3 months, I was deeply influenced by his way of working and his virtues, and I feel so proud that I worked together with him for such a period.
I thank all my the other professors and researchers from the University of Pisa, including Prof. Marco Raugi, Mauro Tucci and Dimitri Thomopulos. It has been a great pleasure to work with all of them.
Besides research life, I also met many lovely people in 3 years abroad. I thank all the guys in the office for creating happy at-mosphere in everyday life. I thank all the guys I met in DTU for helping me fitting into the local life. I thank all the people I met on the dancing courses for always being so kind to me.
Last but not least, I thank my dear family. I thank my beloved parents for perfectly supporting me to study abroad although they are being overwhelmed by the heavy life burden of raising four chil-dren. I thank my dear sisters and brother, Jing, Hui and Lu, for always being good listeners to me for everything. I want to speak loudly that I love you all.
Publications
Journal Articles
• L. Bai, D. Thomopulos, E. Crisostomi, Preference-based En-ergy Exchange in a Network of Microgrids, Applied enEn-ergy, 2019 (Under Review)
• L. Bai, E. Crisostomi, M. Raugi, M. Tucci, Wind Turbine Power Curve Estimation Based on Earth Mover Distance ai-cial Neural Network, IET Renewable Power Generation, 2019 (Accepted)
• L. Bai, P. Pinson, Distributed Reconciliation in Day-Ahead Wind Power Forecasting, Energies, 2019, vol. 12, pp. 1112. • L. Bai, M. Tucci and M. Raugi, Impulsive Noise Mitigation
With Interleaving Based on MUSIC in Power Line Communi-cation, IEEE Transactions on Smart Grid, 2019, vol. 10, no. 4, pp. 3575-3584.
• L. Bai, M. Tucci, S. Barmada, M. Raugi, T. Zheng, Impulsive Noise Characterization in Narrowband Power Line Communi-cation, Energies, 2018, vol. 11, pp. 863.
Conference Papers
• L. Bai, E. Crisostomi, Distribution Loss Allocation in Peer-to-Peer Energy Trading in a Network of Microgrids, IEEE PES General Meeting, 2020. (Accepted)
• L. Bai, E. Crisostomi, M. Raugi, M. Tucci, Wind Power Fore-cast Using Wind ForeFore-casts at Different Altitudes in Convo-lutional Neural Networks, IEEE PES General Meeting, 2019, Atlanta.
• L. Bai, M. Tucci, S. Barmada, M. Raugi, T. Zheng, Impul-sive Noise Characterization in Narrowband Power Line Com-munication (Abstract), Progress In Electromagnetics Research Symposium (PIERS), 2019, Roma.
• L. Bai, D. Thomopulos, E. Crisostomi, G. Pannocchia, Dis-tributed Model Predictive Control for Energy Management in a Network of Microgrids (Abstract), 15th IFAC Symposium on Large Scale Systems (IFAC LSS), 2019, Delft.
• M. Tucci, M. Raugi, L. Bai, S. Barmada and T. Zheng, Anal-ysis of noise in in-home channels for narrowband power line communications, 2017 IEEE International Conference on En-vironment and Electrical Engineering and 2017 IEEE Indus-trial and Commercial Power Systems Europe (IEEE EEEIC / I&CPS Europe), Milan, 2017, pp. 1-6.
Abstract vi
Acknowledgements ix
Publications xi
1 Introduction 1
1.1 Changes in Distribution Networks . . . 1
1.2 RESs Policies . . . 3
1.3 Markets: From Centralized to Peer-to-peer Markets . 7 1.3.1 Motivation of Decentralized Prosumer Markets 7 1.3.2 Development of Peer-to-peer Markets . . . . 8
1.3.3 Design of Peer-to-peer Markets . . . 10
1.3.4 Existing Peer-to-peer Projects . . . 12
1.3.5 Peer-to-peer Markets for a Network of Micro-grids . . . 13
1.4 Motivations and Contributions . . . 14
1.4.1 Motivation on Peer-to-peer Energy Trading . 15 1.4.2 Motivation on Forecasting . . . 17
1.4.3 Motivation on Communication Techniques . . 18
1.4.4 Contributions and Thesis Structure . . . 18
Contributions . . . 19
Thesis Structure . . . 19
2 Problem Setup 21 2.1 Model of a Network of Microgrids . . . 21
2.2 Model of a Microgrid . . . 23
2.2.1 Fuel Generator Model . . . 23
2.2.2 Energy Storage System Model . . . 24
2.2.3 Load and RESs Models . . . 24
2.2.4 Distribution Line Models . . . 25
Linearized DistFlow Model . . . . 26
Relaxed DistFlow Model . . . . 26
2.3 Optimization Objectives . . . 28
2.3.1 Preference Mechanism . . . 28
2.3.3 Coupling Constraints . . . 32
2.4 Full Optimization Problem . . . 33
2.4.1 Full Optimization Problem Under LD model 34 2.4.2 Full Optimization Problem Under RD model 37 3 Optimization Problem and Methodology 39 3.1 Full Optimization Problem Description . . . 39
3.2 General Form of ADMM . . . 40
3.3 ADMM-based Distributed Algorithm . . . 41
4 Simulation Results 45 4.1 IEEE 33-bus Distribution System . . . 45
4.1.1 Description of Case Study . . . 45
4.1.2 Effect of Preference Matrix . . . 46
4.1.3 Case Studies . . . 48
Case 1: Equal Preferences . . . 50
Case 2: 2 Clusters . . . 54
Case 3: 3 Clusters . . . 55
4.1.4 Summary of Case Studies . . . 58
4.2 IEEE 123-bus Distribution System . . . 58
4.2.1 Description of Case Study . . . 58
4.2.2 Case Studies . . . 61
Selection of Step Size Parameters . . . 63
Effect of Network Size . . . 63
Effect of Time Horizon . . . 64
4.2.3 Summary of Case Studies . . . 64
5 Wind Power Forecasting 67 5.1 Wind Power Forecast Using Deep Learning Methods 68 5.1.1 Introduction . . . 68 5.1.2 Models of CNNs . . . 70 Filter Shape . . . 71 Altitude . . . 72 Input Matrix . . . 72 Activation Function . . . 73 5.1.3 Case Studies . . . 73 Description of Datasets . . . 73 Benchmarks . . . 74 Results Analysis . . . 76 5.1.4 Summary . . . 79
5.2.1 Hierarchical Time-series and Forecast Recon-ciliation . . . 80 The Forecast Reconciliation Problem . . . 80 Generalized Least Squares Reconciliation . . 81 Trace Minimization Reconciliation . . . 82 5.2.2 Proposed Distributed Reconciliation Methods 84
Game Theoretical Optimal (GTOP) Reconcil-iation . . . 85 Constrained GTOP Solved by ADMM . . . . 86 Online Estimate of Individual Variance . . . 89 Boundary Constraint . . . 90 5.2.3 Application and Case Studies . . . 91 Framework and Verification . . . 91 Reconciliation on the Simulated Dataset . . . 93 Reconciliation on the NREL Dataset . . . 96 Reconciliation on the Sardinia Dataset . . . . 97 5.2.4 Summary . . . 100
6 Power Line Communications 101
6.1 State-of-the-Art and Motivation . . . 102 6.2 System Model . . . 105 6.3 Interleaving . . . 107 6.3.1 Special Structure of Pseudo-Noise Sequence . 107 6.3.2 Interleaving with Pseudo-Noise Sequence . . 108 Case I . . . 109 Case II . . . 109 6.4 Noise Mitigation Based on MUSIC . . . 111
6.4.1 Covariance Matrix Eigenvalue Decomposition (ED) . . . 112 6.4.2 Frequency Estimation . . . 114 6.4.3 IN Estimation Based on Least Squares . . . . 114 6.5 Sparse Bayesian Learning with MUSIC . . . 115 6.6 Simulation . . . 117 6.6.1 System Configuration . . . 117 6.6.2 Simulation with Ideal Channel Model . . . . 118 6.6.3 Simulation with Frequency Selective Fading
Channel Model . . . 120 6.7 Summary . . . 123
7 Conclusions 125
7.1 Summary . . . 125 7.1.1 Peer-to-peer Energy Trading in a Network of
Microgrids . . . 126 7.1.2 Wind Power Forecasting . . . 127 7.1.3 Impulsive Noise Mitigation in Power Line
Com-munication Systems . . . 127 7.2 Open Questions and Proposed Future Works . . . . 128
7.2.1 Peer-to-peer Energy Trading in a Network of Microgrids . . . 128 7.2.2 Wind Power Forecasting . . . 129 7.2.3 Impulsive Noise Mitigation in Power Line
Com-munication Systems . . . 129
1.1 Modern renewable energy consumption (from 2009 to 2016); data source (Global renewable energy
con-sumption) . . . . 2
1.2 Newly installed solar PV capacity in China (from 2013 to 2017); data source (China renewable energy outlook 2018 ) . . . . 3
1.3 Annual installed DER Power Additions by DER Tech-nology in USA (from 2015 to 2024) . . . 4
1.4 P2P market structure. . . 9
1.5 Distributed/Decentralized optimization techniques . 11 1.6 Overview of the energy trading concept . . . 14
2.1 A network of microgrids in distribution system. . . . 22
2.2 Schematic view of a network of microgrids in distri-bution system. . . 22
2.3 DistFlow model . . . . 25
2.4 Splitting bus 1 . . . 28
2.5 Coupling virtual constraints . . . 33
2.6 Coupling physical constraints . . . 33
3.1 Communication system design . . . 44
3.2 Information exchange between MG n and the DSO . 44 4.1 Power flows on the edges in the grid-connected mode in Case 1 . . . 49
4.2 Power flows on the edges in the islanding mode in Case 1 with the LD model . . . 50
4.2 Power flows on the edges in the islanding mode in Case 1 with the LD model . . . 51
4.3 Power flows on the edges in the islanding mode in case 1 with the RD model . . . 52
4.3 Power flows on the edges in the islanding mode in case 1 with the RD model . . . 53
4.4 Power flows on the edges in the grid-connected mode in Case 2 . . . 55
4.5 Power flows on the edges in the islanding mode in Case 2 . . . 56
4.5 Power flows on the edges in the islanding mode in
Case 2 . . . 57
4.6 Power flows on the edges in the grid-connected mode in Case 3 . . . 58
4.7 Power flows on the edges in the islanding mode in Case 3 . . . 59
4.7 Power flows on the edges in the islanding mode in Case 3 . . . 60
4.8 The modified IEEE 123-bus distribution system . . . 61
4.9 The selection of step sizes of ρ1 and ρ2 . . . 64
4.10 Convergence performance of varying scales of a net-work of MGs . . . 65
4.11 Convergence performance of varying prediction time horizon Ts . . . 65
5.1 The structure of CNN1 . . . 71
5.2 The structure of CNN2 . . . 72
5.3 A 2-level hierarchy for wind farms and related wind power forecasts in a portfolio or region of interest . . 81
5.4 Iterations of convergence versus ρ of ADMM and fast ADMM (regarding three datasets) . . . 92
5.5 Boxplots of NRMSEs on the simulated dataset: (a) NRMSE of Node “AGG”, (b) NRMSE at bottom level 94 5.6 Boxplots of NMAEs on the simulated dataset: (a) NMAE of Node “AGG”, (b) NMAE at bottom level 95 5.7 IRMSEs on the NREL dataset: (a) IRMSE of Node “AGG”, (b) IRMSE at bottom level . . . 98
5.8 IRMSEs on the Sardinia dataset: (a) IRMSE of Node “AGG”, (b) IRMSE at bottom level . . . 99
6.1 OFDM PLC system diagram. . . 105
6.2 A hidden Markov BG model with two terms. . . 107
6.3 Interleaved IN in case I. . . 110
6.4 Interleaved IN in case II. . . 111
6.5 PDF of IN sequence correct detection versus threshold 118 6.6 BER comparison of INR=20dB with ideal channel . 119 6.7 BER comparison of INR=30dB with ideal channel . 119 6.8 BER comparison of INR=40dB with ideal channel . 120 6.9 Amplitude response of channel transfer function . . . 121
6.10 BER comparison of INR=20dB with frequency selec-tive fading channel . . . 122
6.11 BER comparison of INR=30dB with frequency selec-tive fading channel . . . 122 6.12 BER comparison of INR=40dB with frequency
4.1 DER capacity . . . 46
4.2 The preference matrices . . . 47
4.3 Comparison of objective values ($) obtained in a dis-tributed and centralized manner in the grid-connected modes for three cases . . . 54
4.4 The connecting buses of distributed units of each mi-crogrid . . . 62
5.1 Abbreviations of all the methods . . . 75
5.2 SVR parameter setting . . . 75
5.3 NN parameter setting . . . 76
5.4 NMSEs comparison versus activation functions . . . 77
5.5 Comparison of wind farm 1 . . . 78
5.6 Comparison of wind farm 2 . . . 78
5.7 Comparison of wind farm 3 . . . 79
5.8 Comparison of wind farm 4 . . . 79
5.9 Covariance matrix Wt+h|t estimators . . . 83
5.10 Dataset features . . . 91
5.11 Wind and power generation parameter configuration 94 5.12 NRMSE (%) of NREL dataset . . . 97
5.13 NMAE (%) of NREL dataset . . . 97
5.14 SNMSE (%) of NREL dataset . . . 97
5.15 NRMSE (%) of Sardinia dataset . . . 99
5.16 NMAE (%) of Sardinia dataset . . . 99
DER Distributed Energy Resource
ADMM Alternating Direction Method of Multipliers P2P Peer-to-Peer
MG MicroGrid
DSO Distribution System Operator RES Renewable Energy Resources ESS Energy Storage System GHG GreenHouse Gas CO2 Carbon Dioxide PV PhotoVoltaic
DSR Demand Side Response FIT Feed-In Tariffs
TGC Tradable Green Certificates NEM Net Energy Metering NEB Net Energy Billing
PPA Power Purchase Agreement IPP Independent Power Producer SEG Smart Export Guarantee ISO Independent System Operator TOU Time-Of-Use
ICT Information Communication Technology FPP Federated Power Plant
VPP Virtual Power Plant
PLC Power Line Communication TSO Transmission System Operator AMI Advanced Metering Infrastructure IN Impulsive Noise
FG Fuel Generator LD Linearized Distflow RD Relaxed Distflow
ANN Artificial Neural Network CNN Convolutional Neural Network
RBFNN Radial Basis Function based Neural Network SGMNN Sigmoid Neural Network
RBFSVR Radial Basis Function based Support Vector Regression PolySVR Polynomial kernel based Support Vector Regression
NWP Numerical Weather Prediction NMAE Normalized Mean Absolute Error NRMSE Normalized Root Mean Square Error IRMSE Improvement of Root Mean Square Error OLS Ordinary Least Square
WLS Weighted Least Square MUSIC Multiple Signal Classification CS Compressive Sensing
SBL Sparse Bayesian Learning MCA Middleton Class A FTT Fast Fourier Transform
OFDM Orthogonal Frequency Division Multiplexing MMSE Minimum Mean Square Error
Ω Set collecting all MGs
ωn Set collecting all trading MG partners of MG n
Bn Set collecting all buses in MG n
Tn Set collecting all distribution lines in MG n
Gn Set collecting all diesel generators (DGs) in MG n Sn Set collecting all ESSs in MG n
E Set collecting all connecting edges within a netwok of microgrids (i, j) Distribution line connecting bus i and j
pG,kg Power generated by the kg-th diesel generator kg ∈ Gn
pchar,ke Power charged by the ke-th ESS, ke∈ Sn
pdisc,ke Power discharged by the ke-th ESS, ke∈ Sn
pnet,k Net load at the k-th bus k ∈ Bn
Pi0 Outlet active power at bus i Q0i Outlet reactive power at bus i Pi Active power of load at bus i
Qi Reactive power of load at bus i
Vi Voltage at bus i
pnm Active power trade between MG n and m
qnm Reactive power trade between MG n and m
pnDSO Active power trade between MG n and DSO
qnDSO Reactive power trade between MG n and DSO
p+nm Active power trade bought by MG n from MG m p−nm Active power trade sold by MG n to MG m
q+nm Reactive power trade bought by MG n from MG m q−nm Reactive power trade sold by MG n to MG m p+nDSO Active power trade bought by MG n from DSO p−nDSO Active power trade sold by MG n to DSO
q+nDSO Reactive power trade bought by MG n from DSO q−nDSO Reactive power trade sold by MG n to DSO R(i,j) Resistance of line (i, j)
X(i,j) Reactance of line (i, j)
λnm Preference of energy trade with MG m for MG n
λnn Local preference for MG n
λnDSO Preference of energy trade with DSO for MG n
κ Scaling parameter for preference terms cDSO Electricity price for energy trade with DSO
closs Electricity price for power loss on the distribution lines
Ts Time horizon for P2P energy trading
Introduction
Abstract: This chapter briefly discusses the ongoing changes in power systems, focusing mostly on the distribution systems, and in electric-ity trading markets, which mainly motivate our research project.
1.1
Changes in Distribution Networks
The Paris agreement, for the first time, brought all nations into a common cause to undertake ambitious efforts to combat climate change and adapt to its effects1. Greenhouse gases (GHGs) such as carbon dioxide (CO2) emissions are the main culprit of the climate change, and their largest source (42%) is in the power sector, creat-ing the demand for energy system decarbonization2. In the context of power systems, decarbonization, specifically, can be achieved by replacing the traditional fuel fossil generation with more renewable energy sources (RESs) generation, such as solar energy, wind energy, bio-mass energy and geothermal energy.
To perform the duties regulated by the Paris agreement, every country has to make its own efforts to reach its own GHG reduc-tion target. As a result, the total renewable energy consumpreduc-tion is increasing steadily, especially solar photovoltaics (PVs), as shown in Fig.1.1. Such renewable energy can be integrated into the power grid by connecting to transmission systems in a larger scale and in
1
https://unfccc.int/process-and-meetings/the-paris-agreement/ the-paris-agreement
2
a centralized manner or onto distribution systems in a smaller scale and in a distributed manner. In this thesis, we discuss the renew-able energy penetrated into the distribution systems, the so-called “behind-the-meter” generation (Staff, 2018).
Figure 1.1: Modern renewable energy
con-sumption (from 2009 to 2016); data source (Global renewable energy consumption)
The Paris agreement calls for each party to make policies and regulations to incentivize end users’ investments on renewable en-ergy installments, which in turn shifts the originally centralized power generation system to a decentralized system with fluctuat-ing energy generations from rooftop PV panels and micro-turbines. For example, Germany’s move toward distributed energy resources is the result of a long-term sustainability strategy of the government, even predating the liberalization of the electricity market in the late 1990s (Burger and Weinmann, 2014). Another example is China, which is also shifting towards distributed wind and solar energy (China renewable energy outlook 2018 ). As shown in Fig.1.2, the newly installed solar PV capacity has been increasing dramatically in recent years.
In distribution systems, a more generalized term of distributed energy resources (DERs) is introduced to describe the potential con-tributions which can be made by end users at the level of distribution systems. DERs include demand side response (DSR) (dispatchable change in usage, either on the demand- or supply-side of energy
Figure 1.2: Newly installed solar PV
capac-ity in China (from 2013 to 2017); data source (China renewable energy outlook 2018 )
markets), energy efficiency (passive reduction in usage), and onsite energy storage systems (ESSs) and generation, ranging from backup diesel to gas-fired micro-turbines, to rooftop solar, to combined heat and power, to electric vehicles (Staff, 2018). For example, DER de-ployments are projected to surpass 40 GW in 2019 in the United States3, as shown in Fig.1.3.
In summary, with the increasing installed capacity of RESs in distribution systems, radial networks are becoming mesh networks, where power flows in a bi-directional way. Consequently, consumers are becoming producers as well, called as “prosumers”, causing a big role change in the electricity trading.
1.2
RESs Policies
With the increasing capacity of RESs in distribution systems, the currently used regulations and policies for energy trading between the grid and the consumers are briefly discussed in the following.
3
https://www.navigantresearch.com/news-and-views/
take-control-of-your-future-part-ii-the-power-of-customer-choice-and -changing-demands
Figure 1.3: Annual installed DER Power
Ad-ditions by DER Technology in USA (from 2015 to 2024)
Several widely used policies and mechanisms were proposed to stim-ulate the renewable energy producers and consumers, including:
• Feed-in Tariffs (FIT). A FIT system obliges electric utilities to purchase electricity generated from RESs on a fixed period contract at a fixed price. The fixed period and price are deter-mined in different ways depending on the countries and local governments. The prices are set either as fixed (above market price) or as bonus tariffs adding to the present market price. The bonus tariffs are different based on each technology gen-eration cost. Such a FIT system has been widely applied in Europe since 1990 (Ringel, 2006). It is price-based instead of quantity-based, which helps to achieve economic efficiency rather than ecological effectiveness. As a matter of fact, it might not guarantee that certain a quantity of RES genera-tion is efficiently injected or consumed in the power grid. • Tradable Green Certificates (TGC). TGC is a new version of
the tradable quotas imposed by the governments to make sure that a fixed number of RESs generated electricity is consumed. Under a tradable quotas model, different entities (producers, consumers or distributors) are obliged to hold a certain share of their overall electricity sales or consumption in RESs in a certain period. In comparison, a green certificate model separates physical electricity consumption from the need to fulfill the quota obligation. The production of green electricity
is measured and certified by an independent certificate issuing authority (Ringel, 2006).
• Net Energy Metering (NEM). The previous two models or systems are mainly used for large-scale RESs injected into transmission or distribution systems. To facilitate small-scale RESs injection into distribution systems, NEM entitles the consumers double roles of producer and consumer by using a bi-directional meter to dynamically observe the power flow direction. Prosumers consume energy from the main grid if the locally generated power is not enough to support their own consumption, while they can sell extra power to the main grid if the generated power exceeds their consumption (Bedi, Singh, and Singh, 2016).
• Net Energy Billing (NEB). NEB was proposed as an alterna-tive policy for NEM, which was voted by Arizona Corporation Commission (ACC) to replace NEM in December 2016, where new customer-generators will be credited at an avoided cost rate for energy exported to the grid, while purchasing at the retail price. Differently, NEM allows that any excess electric-ity that is generated can be banked (think “rollover minutes”) to the customer’s account for future consumption. Regarding the metering system, NEM requires one bi-directional meter, which can run forward and backward, measuring imported mi-nus exported energy in kWh. On the other hand, in NEB two meters are required so that the exported and imported energy are measured separately, as they have different prices (Dufo-López and Bernal-Agustín, 2015).
• Power Purchase Agreement (PPA). A PPA is a legal contract between an electricity generator (provider) and a power pur-chaser (buyer, typically a utility or large power buyer/trader). Contractual terms may last anywhere between 5 and 20 years, during which the power purchaser buys energy, and sometimes also capacity and/or ancillary services, from the electricity generator. Such agreements play a key role in the financing of independently owned (i.e., not owned by a utility) electricity generating assets. The seller under the PPA is typically an in-dependent power producer (IPP)4. It is not widely used but a 4
potential mechanism for injecting renewable energy on users’ side such as the regulations defined in (EMI, 2012).
All the regulations and policies are applied in different regions and they are changing with the development of renewable energy tech-nologies and specific energy situations of each country. For example, a new policy named Smart Export Guarantee (SEG), will come into force from 1 January 2020 in Scotland, substituting FIT5. Unlike the FIT, there will be variation in the rates that customers are given based on how valuable the electricity is in relation to demand at certain times. For example, at peak times when demand is high, customers will be paid more for their electricity than at times when demand is low6. Besides, a number of changes to both market cir-cumstances and policy priorities have resulted in numerous policy innovations, including the emergence of policy hybrids in both de-veloped and developing countries (Healey et al., 2015).
Driven by the GHG reduction target, the newly installed power capacity of distributed RESs in distribution systems has been in-creasing dramatically in recent years. However, policy and decision makers are facing the electricity market design issues as the share of distributed RESs generation rises. From the aspect of the regula-tions, the currently applied regulations are determined administra-tively based on a long history of relying on a hierarchical and top-down approach to power system operation and market organization (Pinson et al., 2017). Things evolve fast, as for the French exam-ple where a law passed early 2017 to support self-consumption of RESs as a community7. The existing regulations are too restrictive to embrace new types of consumers or producers. From the aspect of social nature, most consumers have a tendency to stick to their historical power providers when electricity markets are deregulated. As a result, prosumers fail to play an increasingly important role even though the share of distributed RESs in the power grid keeps increasing. Therefore, revolutionary changes of electricity market are expected to healthily motivate the upcoming prosumers in the distribution systems. 5 https://www.energysavingtrust.org.uk/renewable-energy/ electricity/solar-panels/smart-export-guarantee-and-feed-tariffs 6 https://green-mole.co.uk/2019/09/11/fit-vs-seg-and-grid-tied-solar/ 7 https://www.legifrance.gouv.fr/eli/loi/2017/2/24/2017-227/jo/
1.3
Markets: From Centralized to
Peer-to-peer Markets
1.3.1 Motivation of Decentralized Prosumer Markets
Due to the limitation of consumer-level information communication and control, a traditional wholesale electricity market is designed in a pool form, where producers and consumers meet through a cen-tralized mechanism which determines an equilibrium price and a dispatch (Pinson et al., 2017). All the private information such as cost function curves (load/generator) and forecasts are submitted to an independent system operator (ISO), and then the ISO achieves a common goal of economic dispatch. On the one hand, each market participant has no data privacy and no autonomy of its own objec-tive. On the other hand, each market participant cannot respond effectively to the electricity market since the real consumers of elec-tricity products cannot negotiate mutually and directly with their producer as a regular trade in an economic sector. DSR has been proposed to give customers more insight and therefore more control to join in the electricity market by committing to reducing or shift-ing their energy consumption when electricity demand from the grid threatens to exceed supply8. For example, time-of-use (TOU) price is one of the important DSR methods (Yu and Yu, 2006). However, DSR is all about intelligent energy use, rather than energy use and generation altogether. In this regard, it can only affect prosumers in a partial way.
In the current existing policies and regulations of RESs men-tioned in Section 1.2, a possible market participant with DERs is not given any flexibility to negotiate the price or the quantity of RESs with its unique trade partner, the grid. All the possible elements of a regular trade are pre-determined administratively. In fact, with the increasing amount of DERs in the distribution system, DERs can achieve more targets if they are managed well, such as 1) provide reliability during outages resulting from weather events; 2) man-age energy expenditures; 3) meet customer desires to reduce their environmental footprint and/or support new evolving technologies (Covino, Levitt, and Sotkiewicz, 2016). With the increasing share
8
https://www.edfenergy.com/large-business/energy-solutions/ demand-side-response-dsr
of DERs in the distribution system, a new electricity market is re-quired to suit and mirror the nature of decentralized production and consumption.
Therefore, a more liberalized and decentralized electricity mar-ket is essential to optimize DERs in distribution systems. In prac-tice, a decentralized electricity market can only be built depending on a decentralized design of power system operation, particularly, with both information communication and decision-making author-ity. One of such a design was firstly proposed to implement a free electricity trade market model of coordinated multilateral trades by Wu and Varaiya in (Wu and Varaiya, 1999). Wu and Varaiya ar-gued that security considerations override economic considerations in the traditional power system operation. Specifically, when over-load occurs on a transmission line, generation is curtailed with little consideration to economy. Therefore, Wu and Varaiya proposed a new and decentralized operating paradigm to build a free market for participating suppliers and consumers (Wu and Varaiya, 1999).
1.3.2 Development of Peer-to-peer Markets
The proposed multilateral trade by Wu and Varaiya is a generaliza-tion of bilateral trades, while peer-to-peer (P2P) trading is a specific model of bilateral trades. A P2P market, characterized by the lack of a supervisory agent, consists of a simultaneous negotiation over the price and energy of multi-bilateral trades along a predefined trading scheme (Sorin, Bobo, and Pinson, 2019; Moret and Pinson, 2018; Baroche, Moret, and Pinson, 2019; Zhou, Wu, and Long, 2018; Tushar et al., 2019). In recent years, P2P trading has come into the public eye again, driven by the advanced information and commu-nication technologies (ICTs) and increased DERs. The new role of prosumers allows them to provide diverse services to the grid or to other prosumers in the market and multiple functions for the power grid. The P2P structure displayed in Fig.1.4 is an information-and-communication graph on the market level without considering physical connections. As presented in Fig.1.4, each agent can choose its own trade partners driven by its own objectives. Any trade peer can communicate with each other for negotiating the quantity and the price of each trade.
In the following, we review the development of P2P markets for prosumers in recent years. Parag and Sovacool first discussed three
Figure 1.4: P2P market structure.
kinds of potential prosumer market models: P2P prosumer els, prosumer-to-interconnected or “island” mode microgrid mod-els and organized prosumer groups modmod-els (Parag and Sovacool, 2016). Subsequently, many authors proposed different prosumer market models based on similar P2P concepts. Sousa et al. de-scribed all P2P market structures and classified them as full P2P markets, community-based markets and hybrid P2P markets (Sousa et al., 2019). A peer can be viewed as an entity with whom it is pos-sible to make an energy transaction. As for the first model of P2P markets, Sorin et al. proposed a full P2P market on a multi-bilateral economic dispatch (Sorin, Bobo, and Pinson, 2019). Morstyn et al. implemented a full P2P market for real-time and forward markets (Morstyn, Teytelboym, and Mcculloch, 2019). A community-based market can be viewed as a P2P market where each peer is a commu-nity and the market within each commucommu-nity is built in a pool form. Moret et al. formulated a community-based market in a collabora-tive manner (Moret and Pinson, 2018). Morstyn et al. proposed a P2P market for multi-class energy management where each pro-sumer is modeled with an ESS, RES (solar PV or wind turbine) and load (Morstyn and McCulloch, 2018). The last form of a hybrid P2P market is a combination of the first two models, ending up with different levels for trading energy. In the upper level, all the entities trade in a P2P manner where each entity can be a commu-nity or a DER; in the bottom level, specifically within a commucommu-nity, trades occur in a pool form. Compared with the first two models,
the hybrid P2P model shows a higher compatibility and scalability than any other form of a trade peer.
Besides the basic spirit of P2P trading, more specific principles or methods are considered to fulfill specific objectives in various ap-plications. In P2P markets, prosumers tend to be profit-driven and do not change their energy behaviors altruistically for the benefit of the grid with no or negative financial gain for themselves (Han, Morstyn, and McCulloch, 2019). Therefore, game theory has been identified as a possible methodology to reflect prosumers’ behavior and decisions. Both non-cooperative game theory and cooperative game theory have been adopted in a wide range of studies concerning P2P electricity trading. Zhang et al. designed a P2P energy trad-ing platform ustrad-ing non-cooperative game theory within a microgrid, where the selling and buying prices with suppliers and penalties for trading peers are calculated based on the existing methods that are currently used in the Great Britain electricity wholesale mar-ket (Zhang et al., 2018). Paudel et al. proposed a non-cooperative game-theoretic model (a Stackelberg game) for P2P energy trading among the prosumers within a community (Paudel et al., 2019). Tushar et al. designed a cooperative game-theoretic P2P energy trading scheme by considering a motivation psychology framework with the specific pricing schemes (Tushar et al., 2019). Alam et al. proposed a near-optimal algorithm to coordinate P2P energy trading among smart homes with a DSR system, ensuring Pareto optimality for unfair cost distribution. Interestingly, the prices for the microgrid energy trading are the same for all the houses, indi-cating a unique price is assumed for the P2P trades among smart homes (Alam, St-Hilaire, and Kunz, 2019).
1.3.3 Design of Peer-to-peer Markets
Basically, the design of a P2P market entails distributed (or de-centralized) optimization techniques for reaching consensus under information and communication infrastructures. The original over-all objectives can be decomposed based on augmented Lagrangian relaxations or Karush-Kuhn-Tucker (KKT) conditions. Regarding those 2 decomposition methods, different algorithms can be ap-plied to reach consensus or convergence (Amini et al., 2018). Six popular methods include analytical target cascading (ATC) (Las-siter, Wiecek, and Andrighetti, 2005), classical ADMM (Boyd et
Figure 1.5: Distributed/Decentralized
opti-mization techniques
al., 2011), proximal message passing (Kraning et al., 2014), auxil-iary problem principle (APP) (Cohen, 1978), optimality condition decomposition (Nogales, Prieto, and Conejo, 2003) and consensus and innovations (C+I) (Kar and Hug, 2012), as presented in Fig.1.5. Several scholars developed their own algorithms and approaches. Moret et al. formulated a decentralized P2P based market using a relaxed consensus+ innovation (RCI) to achieve consensus between trade peers (Moret et al., 2018). They further proposed a distributed P2P based market through power consensus multi-bilateral eco-nomic dispatch (PCMBED) (Baroche et al., 2019) for the iterative negotiation convergence (Moret et al., 2018). In addition, Morstyn et al. designed a market whose objective is to find a scalable price-adjustment process which ensures that agents reach an agreement on a set of contracts which constitute a stable outcome (Morstyn, Teytelboym, and Mcculloch, 2019), based on the bilateral contracts in trading networks proposed in (Hatfield et al., 2013). Baroche et al. discussed that a pool market can be reformulated in a decen-tralized manner where the original overall objective is decomposed according to the KKT conditions if the objective function and the power constraints are separable among agents (Baroche, Moret, and Pinson, 2019).
Overall, the virtual trading platform of P2P trading can be set up through blockchain which ensures accuracy, trace-ability, privacy and security (Vangulick, Cornélusse, and Ernst, 2018). However, we do not intend to discuss the ICT platforms here, while the readers can find more details in reference (Jogunola et al., 2017) .
1.3.4 Existing Peer-to-peer Projects
Many research and development (R&D) projects of P2P markets have been launched since 2010s because of the advanced improve-ment of ICTs (Sousa et al., 2019). They are mainly carried out in two aspects: 1) working on the market design and business models for P2P markets; 2) implementing local control and ICT platforms for prosumers and microgrids (Zhang et al., 2017).
In the local control and ICT level, P2P-SmartTest project in-vestigated and demonstrated a smart electricity distribution system integrated with advanced ICT, regional markets and innovative busi-ness models9. The EMPOWER project proposed innovative busi-ness models and built an ICT platform to manage system operation and information exchange (Bullich-Massagué et al., 2017).
On the market design, Enerchain was the first blockchain-based distributed trading infrastructure that enables over-the-counter (OTC) energy trading in power and gas products such as standardised spot and forward contracts. The Enerchain infrastructure can be used for wholesale products, and the launch of Enerchain in local markets is about to run a dedicated test10. NRGcoin aimed to develop a vir-tual currency (Mihaylov et al., 2014) based on blockchain and smart contracts for small prosumers trading in P2P markets. “EnerPort” was an Irish government- and industry-funded collaborative project where blockchain technology is being used to develop a P2P (P2P) energy trading model to support energy trading between microgrids (Verma et al., 2018).
As discussed in (Sousa et al., 2019), in recent years, many star-tups have emerged from R&D projects to address P2P trading, es-pecially starting from the business in the distribution system. Their services can be roughly grouped into 2 classes: 1) P2P exchange of energy surplus where consumers can exchange the energy surplus with their neighbors to maximize the total social welfare within
9
https://www.p2psmartest-h2020.eu/
the community, such as SonnenCommunity11, HivePower12, Pow-erledger13; 2) Energy provision/matching, where prosumers can di-rectly choose local energy generation such as Vanderbron14,
Elec-tron15, Enervalis16, Prosume17.
1.3.5 Peer-to-peer Markets for a Network of
Micro-grids
As DERs may be geographically dispersed and typically highly pen-etrated into distribution systems, it may be hard to impose a P2P architecture on the whole distribution system (Almasalma, Engels, and Deconinck, 2017). To deal with this, it is generally agreed that breaking the complete grid down into smaller microgrids, containing only a limited amount of DERs can be a more convenient solution. Such microgrids may operate according to a P2P market.
In a P2P market, a peer is an agent, which can be a single pro-sumer (or conpro-sumer) (Zhang et al., 2018), a building (Cui, Wang, and Xiao, 2019) or a community. As described in Section 1.3.2, three prosumer markets are introduced, namely, a full P2P market, a community-based market and a hybrid P2P market. As a mat-ter of fact, community based market is a transformed P2P market where a community is a peer. A community is based on members that share common interests and goals, for example, a group of members that are willing to share renewable energy even if they are geographically distant. By contrast, a microgrid is comprised of var-ious distributed generators, energy storage devices, and controllable and uncontrollable loads that are geographically close. It can oper-ate either interconnected or isoloper-ated from the large power-grid, and can be collectively treated by the grid as a controllable load or gen-erator (Venkatraman and Khaitan, 2015). Therefore, a microgrid can be viewed as a local community. Additionally, Morstyn et al. proposed to incentivize prosumers to form federated power plants (FPPs) or virtual power plants (VPPs) using P2P energy trading, because coordinating local DERs to reduce upstream generation and transmission capacity requirements provides a significant value by
11https://sonnengroup.com/ 12 https://www.hivepower.tech/ 13https://www.powerledger.io/ 14 https://vandebron.nl/ 15https://www.electron.org.uk/ 16 https://www.enervalis.com/ 17 https://prosume.io/
increasing network efficiency, reducing pollution and increasing en-ergy security (Morstyn et al., 2018). In some scenarios, VPPs can be used interchangeably with microgrids (Asmus, 2010). In this regard, we discuss P2P energy trading using a microgrid as a peer.
1.4
Motivations and Contributions
An overview of energy trading components is displayed in Fig.1.6, which is adapted from the overviews shown in the two references (Jogunola et al., 2017; Bayram et al., 2014). Three areas are widely discussed in energy trading, including objectives, enabling technolo-gies and modeling, and required frameworks.
Figure 1.6: Overview of the energy trading
concept
Considering the components of energy trading, our work in this thesis is developed including three parts: the first part is P2P en-ergy trading in a network of microgrids in distribution systems; the second part is RES forecasting, specifically wind power forecasting; the third part focuses on communication techniques, specifically on power line communication (PLC).
According to Fig.1.6, the first part of our work is related to the objectives of improved system efficiency, cost optimization and economics, and the distributed framework; the second part is related to the renewable generation model; the third part is related to the communication infrastructures.
1.4.1 Motivation on Peer-to-peer Energy Trading
A P2P market among a network of microgrids provides multiple ser-vices in energy transactions for the participating agents (microgrid or distribution system operator (DSO)) and gives possibilities to op-erate such a distribution system in different manners according to specific targets in the distribution system. In general, a preference mechanism is to add extra terms in the objective function for the sake of achieving diverse goals. Similarly, a preference mechanism introduced in the P2P markets helps to achieve different goals in different scenarios. For example, an electrical distance based prefer-ence mechanism for grid-related cost allocations in the P2P market framework encourages each agent to trade with those who are elec-trically closely to them (as it is more likely to be elecelec-trically closely if geographically close) (Baroche et al., 2019). The preferences given by the DSO are used to cover the grid-related costs, such as conges-tion cost, maintenance cost, and power loss on the lines (Baroche et al., 2019). Another preference mechanism (Morstyn and McCulloch, 2018) was proposed to customize the market participants’ demands accounting for the prosumers’ individual preferences for the source (or destination) of the energy they consume (or produce). In this case, the preferences are given by the prosumers. In a nutshell, a preference mechanism based P2P market can diversify the objec-tives when the preference terms are set by different agents in the market.
In the fulfillment of a P2P market, conflicts exist between build-ing an efficient market and findbuild-ing a feasible solution for power flow in the physical network. Each trade can be achieved on the In-ternet or any trade platform, as if at a “virtual layer”, while the energy dispatch must take physical constraints into account, at a “network layer” or “physical layer”, including the voltage limits of buses, the ramping limits of DERs, and the transmission capacity of distribution lines. If power trades determined in a virtual layer can not be executed in a physical layer, for example because physical constraints are violated, the power trades have to be re-negotiated or the scheduling has to be re-dispatched. The conflict between finding an efficient solution on a virtual layer and finding a feasible solution on a physical layer can be settled in different ways. Con-ventionally, if the primary scheduling faces a re-dispatching issue, the system operator (DSO or TSO) will directly enforce physical
constraints by adjusting the generation or consumption of the sup-plier or consumer, taking little consideration of economic benefits. Wu and Varaiya argued that any restructuring model, no matter how noble its goal, if its proper functioning relies on external en-forcement, rather than internal economic incentives, is in danger of being derailed in practice (Wu and Varaiya, 1999). Wu and Varaiya introduced brokers to offer possible services to the supplier and con-sumers driven by economic benefits to find an efficient and feasible solution when the system operator pulls a “re-dispatching alarm”. In the designed market (Wu and Varaiya, 1999), extra multilateral contracts can be signed to exploit extra marginal costs to increase all the economic benefits for all players (at least better than no further contracts). In addition, as discussed in the exogenous cost allocation (Baroche et al., 2019), the preference mechanism presided by the system operator updates all market participants to renegoti-ate the bidding orders prices by adjusting the unit price regarding grid-related cost.
Considering such a conflict, we adapt a preference mechanism into P2P energy trading model in Chapter 2, and include all the physical constraints into the optimization problem in order to find a feasible solution as well as achieving P2P energy trading among a network of microgrids in the distribution system. Instead of focusing on the social benefits of P2P trading, we focus more on the feasibility on the power flow in the physical networks.
As power grids are complex physical systems, where different, and sometimes contrasting, aspects come into play, different opti-mal power flows can be obtained by tuning a few appropriate pa-rameters, like the preference values. In particular, by appropriately tuning the parameters of a preference matrix, it is possible to re-cover optimal power flows according to a P2P fully-connected power grid (which actually would not be convenient from the perspective of the power grid), grid-connected optimal power flows, or in prin-ciple any topology of optimal power flows of interest. Our proposed solution may be referred as a dynamic management of a network of microgrids.
In this thesis, the optimal energy management problem is solved in a network of microgrids in a distributed way with a preference mechanism. The presence of this preference mechanism has two valuable benefits: 1) It may be used by single microgrids to de-cide with whom they would prefer to trade energy in a customized
way (e.g., to prioritize energy generated by microgrids based on re-newable sources, or to buy energy from neighboring microgrids); 2) Preferences may be imposed by the DSO to choose the direction of energy flows and possibly create clusters of microgrids that work in the islanding mode. It is known that this solution may be convenient for stability proposed from the perspective of the DSO (Ferraro et al., 2018; Hooshmand and Rabiee, 2019). In doing so, the algorithm of alternating direction method of multipliers (ADMM) is applied to solve the optimization problem in a distributed way, which is a convenient strategy, as it allows microgrids not to reveal possibly private information (e.g., amount of locally generated or consumed power). On the other hand, this poses challenging aspects in terms of the solution of mathematical optimal power flow problem, mainly due to the presence of tightly coupled equality constraints, as we shall see in more detail in Chapter 3.
1.4.2 Motivation on Forecasting
In P2P energy trading, each microgrid is assumed to have autonomy to achieve customized objectives and preserve data privacy. Each microgrid optimizes its own objective based on its knowledge of re-newable energy generations and loads. In the P2P energy trading, multiple time steps are usually considered. For example, in the day-ahead market, 24 hours are usually considered. Therefore, each microgrid has to make forecasts of its local renewable power genera-tion and loads. In this thesis, we focus on wind power forecasting in two aspects. First, wind power forecasting is studied by using deep learning algorithms and other machine learning algorithms for a sin-gle wind farm. Then we further investigate wind power forecasting for multiple wind farms located within the same region. For such a geographical distribution and physical connection, a two-layer hier-archy is obtained, where the bottom layer includes all the individual wind farms and the top layer is the system operator. We propose a distributed optimal reconciliation method to satisfy the aggregation consistency that the forecast of the sum is equal to the sum of all the individual forecasts, based on the locally wind power forecasts from each wind farm and that of the system operator. The local wind power forecasts are made by using machine learning algorithms.
It can be found that such a two-layer hierarchy can be similarly built for a network of microgrids and the DSO, where the bottom layer contains all the individual microgrids and the top layer is the
DSO. Therefore, the proposed wind power forecasting algorithm for a single agent (a wind farm) can be applied for each microgrid, and accordingly the proposed distributed reconciliation can be applied for the newly built hierarchy of a network of microgrids and the DSO.
1.4.3 Motivation on Communication Techniques
P2P energy trading cannot be achieved without the support of ICTs. A four-layer system architecture of P2P energy trading was pro-posed, among which the ICT layer consisted of communication de-vices, protocols, applications and information flow (Zhang et al., 2018). Communication devices and protocols can be diverse depend-ing on the specific data transmission and the available and conve-nient communication infrastructure. Communication technologies in a microgrid include wireless and wired communication technolo-gies, such as power line communication (PLC), optical fiber, digi-tal subscriber line (DSL), wireless personal area network (WPAN), Wi-Fi, worldwide interoperability for microwave access (WiMAX), 3rd/4th/5th generation (3G/4G/5G) (Jogunola et al., 2017). Among all of them, PLC uses the already existing electric power cables for data transmission, thus enabling data and power transmission on the same medium. With such an exceptional advantage, PLC is adopted in power systems for specific applications. For example, narrowband PLC has been widely used in the advanced metering infrastructure (AMI) to enable two-way communication between utilities and cus-tomers. Broadband PLC has been commonly used in home area network applications such as home automation. Therefore, PLC is considered as a candidate communication solution among the ICTs for accomplishing P2P energy trading. In this thesis, we investigate impulsive noise (IN) mitigation in PLC systems, as IN interference is a main factor of the degradation of data transmission performance.
1.4.4 Contributions and Thesis Structure
In this thesis, we study different components (as shown in Fig.1.6) involved in the energy trading framework including the three follow-ing parts. The first part is a preference-based P2P energy tradfollow-ing model in a network of microgrids in distribution systems in Chapters 2-4; the second part is RES forecasting, specifically wind power fore-casting in Chapter 5; the third part is communication techniques, specifically on PLC in Chapter 6.
Contributions
The main contributions of this thesis are as follows.
• In the first part of a P2P energy trading model, we adapt a preference mechanism into the paradigm of P2P energy trad-ing model for a network of microgrids, and thus succeedtrad-ing finding a feasible solution for the power flow and an efficient solution for P2P energy trading. In addition, we develop an ADMM-based distributed algorithm to solve the established optimization problem of P2P energy trading, and then evalu-ate it by taking into account different factors.
• In the second part of wind power forecasting, we investigate wind power forecasting for a single wind farm by using deep learning algorithms to fully exploit the information hidden in the large datasets. We further develop a distributed reconcili-ation of wind power forecasts for multiple wind farms that are located within the same region.
• In the last part of PLC, we propose an impulsive noise miti-gation method to improve the quality of data transmission in power line systems.
Thesis Structure
In detail, the structure of this thesis is as follows.
• Chapter 2 describes the models of a network of microgrids and the model of a single microgrid including ESSs, fuel-based generators, RESs (wind or solar PV), distribution lines and objective functions.
• Chapter 3 formulates an optimization problem in the frame-work of P2P trading among a netframe-work of microgrids and de-velops an ADMM-based distributed algorithm to solve it. • Chapter 4 presents two case studies on the IEEE 33-bus and
IEEE 123-bus distribution systems. The effects of the pref-erence mechanism on the resulting power flow are analyzed in the first system, and the convergence performance of the established algorithm is evaluated in the second system.
• Chapter 5 focuses on renewable energy forecasting, specifically for wind power forecasting. Wind power forecasting for day-ahead electricity markets is examined at a single wind farm scale and at a hierarchical scale of multiple wind farms. • Chapter 6 investigates a possible communication solution of
PLC that may be used for P2P energy trading realization. An IN mitigation method is proposed to deal with one of the obstacles of PLC systems.
• Chapter 7 summarizes the contributions of this thesis and out-lines some open problems that have been identified during the work of this thesis.
Problem Setup
Abstract: In this chapter, we describe our model of a network of microgrids, getting down to the level of a single microgrid. Then we discuss possible objective functions for an individual microgrid and for a network of multiple microgrids. This chapter is based on paper (Bai, Thomopulos, and Crisostomi, 2019)
2.1
Model of a Network of Microgrids
With the increasing penetration of multi-owned DERs in the distri-bution system, a distridistri-bution system is driven to be broken down into a network of microgrids for efficient energy management and control (Almasalma, Engels, and Deconinck, 2017). Such a network of microgrids can be operated in grid-connected mode or islanding mode, similar to a single microgrid. As a microgrid is generally defined as a localized cluster of DERs, a distribution system is gen-erally decomposed based on geographical distances. In our applica-tions, we mainly focus on small-scale distribution networks behind one feeder after the transformer in the low voltage level, which in-dicates that there is only one connection point to the main grid for such a network of microgrids.
For example, athe IEEE 33-bus system is shown in Fig.2.1 to illustrate a network of microgrids we focus on, which is also used in Chapter 4. As shown in Fig.2.1, the IEEE 33-bus system is decomposed into a network of 8 microgrids, and it is connected to the main grid via bus 0. The network can be further subtracted
as a schematic graph displayed in Fig.2.2, illustrating the physical connections between any two microgrids in the network.
Figure 2.1: A network of microgrids in
dis-tribution system.
Figure 2.2: Schematic view of a network of
microgrids in distribution system.
As mentioned in Section 1.4, in the spirit of P2P trading, an individual microgrid is given considerable decision and operation autonomy. Additionally, we assume that each microgrid also has an access to the information of the physical units and infrastructure (such as transmission lines), which is required to achieve distributed control and management. This implies that, say, MG 0 in Fig.2.1,
is an independent entity that has both the freedom to decide P2P trades with any other microgrids and the ability to control and man-age all the DERs within itself.
Mathematically, set Ω, of size NΩ, denotes a collection of the
microgrids within a network of microgrids. For any MG n ∈ Ω, ωn represents the set of its trading microgrid partners. Besides, Gn de-notes the set of fuel generators, Sndenotes the set of energy storage
systems, Tn denotes the set of distribution lines and Bn denotes the set of all buses. Additionally, Ts denotes the time horizon for the optimization problem in the following.
2.2
Model of a Microgrid
A microgrid is typically made up of RESs, fuel-based energy sources, ESSs and loads (Hatziargyriou, 2014). In our microgrid model, RESs (e.g., PV and wind energy) are considered as prioritized non-dispatchable units to maximize their usage and reduce environmen-tal footprint. Fuel generators (FGs) are dispatchable, and they are used to ensure stability and offer more flexibility of operational modes to a microgrid. ESSs enhance reliability and resilience of a microgrid and contribute to balance supply and demand within the grid (Banshwar et al., 2019).
2.2.1 Fuel Generator Model
A conventional model is considered for FGs in our microgrid. For every generic MG n, the set of all the FGs within MG n is denoted as Gn. The generation cost for the kg-th FG, kg ∈ Gn for every
generic MG n at time t is expressed as a quadratic function of the active output with constraints (Zhao et al., 2018; Feng et al., 2018),
fG,kg(p t G,kg) = 1 2a 2 kgp t2 G,kg + bkgp t G,kg + ckg, (2.1a) pG,k g ≤ p t G,kg ≤ pG,kg (2.1b) qG,k g ≤ q t G,kg ≤ qG,kg (2.1c) − γG,kg ≤ p t+1 G,kg − p t G,kg ≤ γG,kg, ∀ kg ∈ Gn, t = 1, . . . , Ts. (2.1d) where ptG,k g and q t
G,kg are the generated active and reactive power
and p
G,kg, and pG,kg are the lower and upper bounds of the active
power output. Similarly, q
G,kg, and qG,kg are the counterparts for
the reactive power. Finally, γG,kg represents the ramp rate bound,
and the ramping constraint is in difference form as shown in (2.1d). If only one time step is considered, namely Ts = 1, then (2.1d) is reduced in a similar form as (2.1b). In our model, the time resolution is assumed to be one hour.
2.2.2 Energy Storage System Model
ESSs are modeled by a first-order discrete time model, accounting for the energy losses in the charging and discharging process (Xing, Xie, and Meng, 2019; Razzanelli et al., 2019). For every generic MG n, Sn denotes the set of all its ESSs. The model of the ke-th ESS,
ke∈ Sn for every generic MG n is expressed as
xt+1k
e = x
t
ke + βchar,kep
t
char,ke− βdisc,keptdisc,ke (2.2a)
xke ≤ xtk
e ≤ xke (2.2b)
0 ≤ ptchar,ke ≤ pchar,ke (2.2c)
0 ≤ ptdisc,ke ≤ pdisc,ke, ∀ ke ∈ Sn (2.2d)
where xtk
e denotes the state of charge of MG n at time t, βchar,ke and
βdisc,ke denote the charging and discharging efficiency. In addition,
the state of charge xtk
e, the charging power p
t
char,ke and
discharg-ing power ptdisc,ke are bounded as shown in (2.2b)-(2.2d). If only one time step is considered for the time horizon Ts, then the state of charge in (2.2b) is directly determined by the charging and dis-charging power as the initial state of the battery is given.
2.2.3 Load and RESs Models
Microgrids prioritize energy generated from renewable sources to fully satisfy energy demand, so RESs including wind energy and solar energy are considered as non-dispatchable1. Besides, loads within the microgrid are viewed as non-dispatchable as well, as load flexibility or demand response is not considered in our model. The loads and RES generation can be forecast locally ahead of time, taking into account local information such as numerical weather predictions and local consumer behaviors. Regarding the local load 1Non-dispatchable indicates that RESs are fully accepted by the network
and generation injected into each bus within each microgrid, we consider a single variable of relevance ptnet,k as the net load at time t, referring to the local load offset by RESs generation connecting at the same bus k in MG n.
2.2.4 Distribution Line Models
Figure 2.3: DistFlow model
As most of the distribution networks are radially designed, Dis-tFlow model described in (Baran and Wu, 1989) is a popular choice for distribution lines. The DistFlow model can be described for a distribution line (i, j) ∈ Tn connecting two neighboring buses i and
j, i, j ∈ Bn, as shown in Fig.2.3, as Pi0t− Pj0t− R(i,j)P 0t2 i + Q 0t2 i Vit2 − P t j = 0, (2.3a) Q0it− Q0jt− X(i,j)P 0t2 i + Q 0t2 i Vit2 − Q t j = 0, (2.3b)
Vit2− 2(R(i,j)Pi0t+ X(i,j)Q0it) + (R2(i,j)+ X(i,j)2 )P 0t2 i + Q 0t2 i Vt2 i − Vjt2= 0, (2.3c) Pit= PiDt− PiGt, Qti = QDti − QGti (2.3d) Pjt= PjDt− PjGt, Qtj = QDtj − QGtj , (2.3e) P0 ≤ Pi0t, Pj0t≤ P 0 , Q0 ≤ Q0it, Q0jt≤ Q 0 (2.3f) V ≤ Vit, Vjt≤ V , ∀ (i, j) ∈ Tn, i, j ∈ Bn (2.3g)
where Pi0t, Q0it and Vit represent the active outlet power, reactive outlet power and the voltage of the sending bus i, and Pj0t, Q0jt and Vjt are the counterparts for the receiving end bus j at time t. All these variables are bounded as in (2.3f). In addition, Pjt, Qtj
