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Journal Article•DOI•

A Microgrid Energy Management System Based on the Rolling Horizon Strategy

Rodrigo Palma-Behnke1, Carlos Benavides1, F. Lanas1, Bernardo Severino1, Lorenzo Reyes1, Jacqueline Llanos1, Doris Saez1 •
University of Chile1
22 Jan 2013-IEEE Transactions on Smart Grid (IEEE)-Vol. 4, Iss: 2, pp 996-1006
TL;DR: The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system and the results show the economic sense of the proposal.
Abstract: A novel energy management system (EMS) based on a rolling horizon (RH) strategy for a renewable-based microgrid is proposed. For each decision step, a mixed integer optimization problem based on forecasting models is solved. The EMS provides online set points for each generation unit and signals for consumers based on a demand-side management (DSM) mechanism. The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system. A coherent forecast information scheme and an economic comparison framework between the RH and the standard unit commitment (UC) are proposed. Solar and wind energy forecasting are based on phenomenological models with updated data. A neural network for two-day-ahead electric consumption forecasting is also designed. The system is tested using real data sets from an existent microgrid in Chile (ESUSCON). The results based on different operation conditions show the economic sense of the proposal. A full practical implementation of the system for ESUSCON is envisioned.

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HAL Id: hal-01803112
https://hal.archives-ouvertes.fr/hal-01803112
Submitted on 12 Jun 2018
HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entic research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diusion de documents
scientiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
Operating reserve in microgrids: an approach to deal
with uncertainty
Fausto Calderón-Obaldía, Amjad M Anvari-Moghaddam, Josep M Guerrero,
Jordi Badosa, Anne Migan-Dubois, Vincent Bourdin
To cite this version:
Fausto Calderón-Obaldía, Amjad M Anvari-Moghaddam, Josep M Guerrero, Jordi Badosa, Anne
Migan-Dubois, et al.. Operating reserve in microgrids: an approach to deal with uncertainty. In-
ternational Conference on Smart Energy Systems and Technologies, Sep 2018, Barcelona, Spain.
�10.1109/TSG.2012.2231440�. �hal-01803112�
Operating reserve in microgrids: an approach to deal
with uncertainty
Fausto Calderón-Obaldía
GeePs-LMD-LIMSI
Université Paris VI (UPMC)
Paris, France
fcalderon@lmd.polytechnique.fr
Jordi Badosa
SIRTA
Laboratoire de Météorologie Dynamique
Paris, France
Jordi.badosa@lmd.polytechnique.fr
Amjad Anvari-Moghaddam
Department of Energy Technology
Aalborg University
Aalborg, Denmark
aam@et.aau.dk
Anne Migan-Dubois
Phemadic
GeePs-UPMC
Paris, France
anne.migan-
dubois@geeps.centralesupelec.fr
Josep M. Guerrero
Department of Energy Technology
Aalborg University
Aalborg, Denmark
joz@et.aau.dk
Vincent Bourdin
CTEMO
LIMSI
Paris, France
vincent.bourdin@limsi.fr
Summary— Uncertainty is an unavoidable issue when working with
renewable generation and consumption forecasts; and it represents
one of the big challenges to overcome in distributed generation
schemes. Renewable microgrids are subject to this problem that poses
different challenges for their management in terms of power quality,
stability, planning and scheduling, among others[1]. Many of the
current state-of-the-art energy management systems (EMS) deal with
this issue from a predictive perspective. In other words, they perform
the planning and scheduling based on forecasts, before the actual net-
demand (ND) unveils[2][3][4]. This leads to unavoidable deviations
between optimal and real scheduling. In the proposal presented in this
work, the forecast-uncertainty problem is tackled in-deferred, once
the uncertainty has revealed itself, so it can be taken into account,
with certainty, in the optimization and scheduling process performed
by the EMS. This objective is achieved by bringing down to
microgrid-scale the concept of operating reserve (OR), known in
utility-scale power systems, in a sort of closed-loop corrective control
variation. The proposed approach allows including the errors between
forecasts and real data, in an optimal way within the energy
management loop, so that optimal performance can be achieved. In
this work, two main objectives are sought: finding out the optimal
time interval at which the corrections (optimization and scheduling)
should be made; and study the performance and stability of the
system when a real-time sizing of the operating reserve is held, based
on estimations of forecasts uncertainty.
Fig. 1. Microgrid emulation module for experimental tests (Microgrids
group, Aalborg University)
The core (and novelty) of the presented proposal is the inclusion of an
extra energy storage (operating reserve), which is not ‘seen’ by the
EMS. This element allows all the dispatchable resources in the
microgrid to be optimally scheduled. This is achieved thanks to the
compensation role assumed by this new element when net-demand
deviations (from forecasted values) occur. Given the expected
reduced-size of this unit (compared to the main energy storage), its
technology can be chosen so that its cycling life is high enough as to
consider its marginal cost negligible (i.e. supercapacitors/flywheels
[5]). Deviations in ND are reflected as changes on the state-of-charge
(SoC) of the OR which are included in the optimization process of the
EMS every timestep t. The required capacity of this OR unit is also
recalculated every timestep t, based on uncertainty estimations for the
next timestep. When uncertainty is such that the total OR capacity is
not required for compensation purposes, part of the OR capacity can
be assigned to the system to be used as part of the main energy
storage, increasing its capacity factor and providing the microgrid
with a temporary extra-storage capacity. Net-demand uncertainty
estimations are obtained using an analog ensemble method, which is
also presented in this work.
An emulated microgrid consisting on photovoltaic production,
household-type consumption, battery energy storage and a bi-
directional grid connection is used to perform the tests. Preliminary
experiments are performed in the microgrids laboratory of the Energy
Technology Department of Aalborg University (Denmark) using a
simple EMS as reference. The optimization performed by the EMS is
intended to minimize the cost of energy bought from the main grid
under a variable electricity-price scenario in Denmark. The system is
emulated using fully programmable bi-directional 2.2kW inverters
linked to a common bus controlled via dSpace/Matlab (Fig.1). Its
output is the optimal power profile for the battery and grid
connection, as seen in Fig.2.
Fig. 2. EMS example of optimal power profiles for battery and grid
The results, under unlimited OR capacity, showed improved
performance for two different ND scenarios (summer and winter
weeks) with respect to the base case system (Table I).
TABLE I. WEEKLY OPERATION COST
Weekly operation
cost(€)
Reference case
Proposed approach
Winter week
0.77
0.52 (+32.5%)
Summer week
-0.8
-1.05 (+19.3%)
As seen in Fig.3, the SoC of the OR showed stability for a timestep of
one hour, for both net-demand scenarios (winter and summer weeks).
Ongoing tests are held to validate stability for other timesteps as well
as to find out the optimal timestep t (at which optimization and
scheduling is performed), under the criteria of performance (operation
cost), SoC stability and capital cost. The aforementioned aspects,
along with real-time OR sizing tests (aiming to evaluate capital costs
and economic feasibility), are the core of the present work.
Assumptions, conditions, limitations and future perspectives for
further research on this proposal are also discussed to ponder its
validity and usefulness in real-case microgrid systems, as a way to
soften the negative effects of uncertainty with the aim to make
renewable microgrids and distributed systems a more affordable and
reliable source of clean energy.
REFERENCES
[1] Dalia Eltigani, Syafrudin Masri, “Challenges of integrating renewable
energy sources to smart grids: A review,” In Renewable and Sustainable
Energy Reviews, vol. 52, pp. 770-780, 2015. (ISSN 1364-0321)
[2] Lexuan Meng, Eleonora Riva Sanseverino, Adriana Luna, Tomislav
Dragicevic, Juan C. Vasquez, Josep M. Guerrero, “Microgrid
supervisory controllers and energy management systems: A literature
review,” In Renewable and Sustainable Energy Reviews, vol. 60, pp.
1263-1273, 2016. (ISSN 1364-0321)
[3] Wencong Su, Jianhui Wang, “Energy Management Systems in
Microgrid Operations,” In The Electricity Journal, vol. 25, pp. 45-60,
2012. (ISSN 1040-6190)
[4] R. Palma-Behnke et al., "A Microgrid Energy Management System
Based on the Rolling Horizon Strategy," in IEEE Transactions on Smart
Grid, vol. 4, no. 2, pp. 996-1006, June 2013.
(doi: 10.1109/TSG.2012.2231440)
[5] Xing Luo, Jihong Wang, Mark Dooner, Jonathan Clarke, “Overview of
current development in electrical energy storage technologies and the
application potential in power system operation,” In Applied Energy,
vol. 137, pp. 511-536, 2015. (ISSN 0306-2619)
Fig. 3. Delta SoC for OR given PV production mismatch
Citations
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Journal Article•DOI•
Trends in Microgrid Control

[...]

Daniel E. Olivares1, Ali Mehrizi-Sani2, Amir H. Etemadi3, Claudio A. Canizares1, Reza Iravani3, Mehrdad Kazerani1, Amir H. Hajimiragha, Oriol Gomis-Bellmunt, Maryam Saeedifard4, Rodrigo Palma-Behnke5, Guillermo Jimenez-Estevez5, Nikos Hatziargyriou6 •
University of Waterloo1, Washington State University2, University of Toronto3, Purdue University4, University of Chile5, National Technical University of Athens6
20 May 2014-IEEE Transactions on Smart Grid
TL;DR: The major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems).
Abstract: The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

2,358 citations

Cites background from "A Microgrid Energy Management Syste..."

  • ...R. Palma-Behnke and G. A. Jiménez-Estévez are with the Center of Energy, Faculty of Mathematical and Physical Sciences, School of Engineering, University of Chile (CMM, ISCI, DIE), Santiago 412-3, Chile (e-mail: rodpalma@cec.uchile.cl; gjimenez@ing.uchile.cl)....

    [...]

  • ...These projects include those of Bella Coola [11] and Hydro-Quebec in Canada, CERTS in the United States, Microgrids andMoreMicrogrids in Europe, Huatacondo in Chile [12], and New Energy and Industrial Technology Development Organization (NEDO) in Japan [7]....

    [...]

  • ...A key issue that impacts the secondary and tertiary control in the case of isolated microgrids is the community involvement [12], [25]....

    [...]

Journal Article•DOI•
State of the Art in Research on Microgrids: A Review

[...]

Sina Parhizi1, Hossein Lotfi1, Amin Khodaei1, Shay Bahramirad•
University of Denver1
10 Jun 2015-IEEE Access
TL;DR: This paper presents a review of issues concerning microgrid issues and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, micro grid operation and control, micro grids clusters, and protection and communications issues.
Abstract: The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.

875 citations

Journal Article•DOI•
A Model Predictive Control Approach to Microgrid Operation Optimization

[...]

Alessandra Parisio1, Evangelos Rikos, Luigi Glielmo2•
Royal Institute of Technology1, University of Sannio2
09 Jan 2014-IEEE Transactions on Control Systems and Technology
TL;DR: A model predictive control approach is applied to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints and the experimental results show the feasibility and the effectiveness of the proposed approach.
Abstract: Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.

673 citations

Cites background from "A Microgrid Energy Management Syste..."

  • ...In [32], an energy management system based on a rolling horizon strategy is proposed for an islanded microgrid comprising photovoltaic (PV) panels, two wind turbines, a diesel generator, and an energy storage system....

    [...]

Journal Article•DOI•
A Centralized Energy Management System for Isolated Microgrids

[...]

Daniel E. Olivares1, Claudio A. Canizares1, Mehrdad Kazerani1•
University of Waterloo1
25 Apr 2014-IEEE Transactions on Smart Grid
TL;DR: Using the model predictive control technique, the optimal operation of the microgrid is determined using an extended horizon of evaluation and recourse, which allows a proper dispatch of the energy storage units.
Abstract: This paper presents the mathematical formulation of the microgrid's energy management problem and its implementation in a centralized Energy Management System (EMS) for isolated microgrids Using the model predictive control technique, the optimal operation of the microgrid is determined using an extended horizon of evaluation and recourse, which allows a proper dispatch of the energy storage units The energy management problem is decomposed into Unit Commitment (UC) and Optimal Power Flow (OPF) problems in order to avoid a mixed-integer non-linear formulation The microgrid is modeled as a three-phase unbalanced system with presence of both dispatchable and non-dispatchable distributed generation The proposed EMS is tested in an isolated microgrid based on a CIGRE medium-voltage benchmark system Results justify the need for detailed three-phase models of the microgrid in order to properly account for voltage limits and procure reactive power support

537 citations

Cites background from "A Microgrid Energy Management Syste..."

  • ...This relaxation considers (22)–(36) and (38), together with a piece-wise linear approximation of (37) [9]; depending on the size of diesel generators, a suitable linear approximation can be obtained using 1 or 2 linear segments, specially when considering the reduced operating range used to avoid carbon build-up [23]....

    [...]

  • ...Residential load, wind power and solar power forecasts are obtained from real data from a real forecasting systems used in a rural microgrid in Huatacondo, Chile [9]....

    [...]

Journal Article•DOI•
Coordinated Energy Management of Networked Microgrids in Distribution Systems

[...]

Zhaoyu Wang1, Bokan Chen2, Jianhui Wang3, Miroslav M. Begovic1, Chen Chen3 •
Georgia Institute of Technology1, Iowa State University2, Argonne National Laboratory3
01 Jan 2015-IEEE Transactions on Smart Grid
TL;DR: A novel control strategy for coordinated operation of networked microgrids (MGs) in a distribution system considered as a stochastic bi-level problem with the DNO in the upper level and MGs in the lower level to achieve the equilibrium among all entities.
Abstract: This paper proposes a novel control strategy for coordinated operation of networked microgrids (MGs) in a distribution system. The distribution network operator (DNO) and each MG are considered as distinct entities with individual objectives to minimize the operation costs. It is assumed that both the dispatchable and nondispatchable distributed generators (DGs) exist in the networked MGs. In order to achieve the equilibrium among all entities and take into account the uncertainties of DG outputs, we formulate the problem as a stochastic bi-level problem with the DNO in the upper level and MGs in the lower level. Each level consists of two stages. The first stage is to determine base generation setpoints based on the load and nondispatchable DG output forecasts and the second stage is to adjust the generation outputs based on the realized scenarios. A scenario reduction method is applied to enhance a tradeoff between the accuracy of the solution and the computational burden. Case studies of a distribution system with multiple MGs of different types demonstrate the effectiveness of the proposed methodology. The centralized control, deterministic formulation, and stochastic formulation are also compared.

495 citations

Cites methods from "A Microgrid Energy Management Syste..."

  • ...[6] presented a novel EMS based on a rolling horizon algorithm for a RES-based MG....

    [...]

References
More filters
DOI•
A Description of the Advanced Research WRF Version 3

[...]

C. Skamarock, B. Klemp, Jimy Dudhia, O. Gill, Dale Barker, G. Duda, Xiang-Yu Huang, Wei Wang, G. Powers 
01 Jan 2008
TL;DR: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication.
Abstract: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication. Reports in this series are issued by the NCAR Scientific Divisions ; copies may be obtained on request from the Publications Office of NCAR. Designation symbols for the series include: Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

9,022 citations

"A Microgrid Energy Management Syste..." refers background in this paper

  • ...2) Wind Speed: For forecasting the wind speed, this work considers a global forecast system (GFS) for the bounded conditions of a weather research and forecast (WRF) model [20], [21]....

    [...]

Book•
Grid Converters for Photovoltaic and Wind Power Systems

[...]

Remus Teodorescu, Marco Liserre1, Pedro Rodriguez•
Polytechnic University of Bari1
21 Feb 2011
TL;DR: In this article, the authors present an overview of the Grid Converter and its application in photovoltaic (PV) power converters, including the following: 1.1 Introduction. 2.3 Inverter Structures Derived from H-Bridge Topology. 3.4 Power Quality. 4.5 Adaptive Filtering.
Abstract: About the Authors. Preface. Acknowledgements. 1 Introduction. 1.1 Wind Power Development. 1.2 Photovoltaic Power Development. 1.3 The Grid Converter The Key Element in Grid Integration of WT and PV Systems. 2 Photovoltaic Inverter Structures. 2.1 Introduction. 2.2 Inverter Structures Derived from H-Bridge Topology. 2.3 Inverter Structures Derived from NPC Topology. 2.4 Typical PV Inverter Structures. 2.5 Three-Phase PV Inverters. 2.6 Control Structures. 2.7 Conclusions and Future Trends. 3 Grid Requirements for PV. 3.1 Introduction. 3.2 International Regulations. 3.3 Response to Abnormal Grid Conditions. 3.4 Power Quality. 3.5 Anti-islanding Requirements. 3.6 Summary. 4 Grid Synchronization in Single-Phase Power Converters. 4.1 Introduction. 4.2 Grid Synchronization Techniques for Single-Phase Systems. 4.3 Phase Detection Based on In-Quadrature Signals. 4.4 Some PLLs Based on In-Quadrature Signal Generation. 4.5 Some PLLs Based on Adaptive Filtering. 4.6 The SOGI Frequency-Locked Loop. 4.7 Summary. 5 Islanding Detection. 5.1 Introduction. 5.2 Nondetection Zone. 5.3 Overview of Islanding Detection Methods. 5.4 Passive Islanding Detection Methods. 5.5 Active Islanding Detection Methods. 5.6 Summary. 6 Grid Converter Structures forWind Turbine Systems. 6.1 Introduction. 6.2 WTS Power Configurations. 6.3 Grid Power Converter Topologies. 6.4 WTS Control. 6.5 Summary. 7 Grid Requirements for WT Systems. 7.1 Introduction. 7.2 Grid Code Evolution. 7.3 Frequency and Voltage Deviation under Normal Operation. 7.4 Active Power Control in Normal Operation. 7.5 Reactive Power Control in Normal Operation. 7.6 Behaviour under Grid Disturbances. 7.7 Discussion of Harmonization of Grid Codes. 7.8 Future Trends. 7.9 Summary. 8 Grid Synchronization in Three-Phase Power Converters. 8.1 Introduction. 8.2 The Three-Phase Voltage Vector under Grid Faults. 8.3 The Synchronous Reference Frame PLL under Unbalanced and Distorted Grid Conditions. 8.4 The Decoupled Double Synchronous Reference Frame PLL (DDSRF-PLL). 8.5 The Double Second-Order Generalized Integrator FLL (DSOGI-FLL). 8.6 Summary. 9 Grid Converter Control for WTS. 9.1 Introduction. 9.2 Model of the Converter. 9.3 AC Voltage and DC Voltage Control. 9.4 Voltage Oriented Control and Direct Power Control. 9.5 Stand-alone, Micro-grid, Droop Control and Grid Supporting. 9.6 Summary. 10 Control of Grid Converters under Grid Faults. 10.1 Introduction. 10.2 Overview of Control Techniques for Grid-Connected Converters under Unbalanced Grid Voltage Conditions. 10.3 Control Structures for Unbalanced Current Injection. 10.4 Power Control under Unbalanced Grid Conditions. 10.5 Flexible Power Control with Current Limitation. 10.6 Summary. 11 Grid Filter Design. 11.1 Introduction. 11.2 Filter Topologies. 11.3 Design Considerations. 11.4 Practical Examples of LCL Filters and Grid Interactions. 11.5 Resonance Problem and Damping Solutions. 11.6 Nonlinear Behaviour of the Filter. 11.7 Summary. 12 Grid Current Control. 12.1 Introduction. 12.2 Current Harmonic Requirements. 12.3 Linear Current Control with Separated Modulation. 12.4 Modulation Techniques. 12.5 Operating Limits of the Current-Controlled Converter. 12.6 Practical Example. 12.7 Summary. Appendix A Space Vector Transformations of Three-Phase Systems. A.1 Introduction. A.2 Symmetrical Components in the Frequency Domain. A.3 Symmetrical Components in the Time Domain. A.4 Components 0 on the Stationary Reference Frame. A.5 Components dq0 on the Synchronous Reference Frame. Appendix B Instantaneous Power Theories. B.1 Introduction. B.2 Origin of Power Definitions at the Time Domain for Single-Phase Systems. B.3 Origin of Active Currents in Multiphase Systems. B.4 Instantaneous Calculation of Power Currents in Multiphase Systems. B.5 The p-q Theory. B.6 Generalization of the p-q Theory to Arbitrary Multiphase Systems. B.7 The Modified p-q Theory. B.8 Generalized Instantaneous Reactive Power Theory for Three-Phase Power Systems. B.9 Summary. Appendix C Resonant Controller. C.1 Introduction. C.2 Internal Model Principle. C.3 Equivalence of the PI Controller in the dq Frame and the P+Resonant Controller in the Frame. Index.

2,509 citations

"A Microgrid Energy Management Syste..." refers methods in this paper

  • ...When this is not possible, the wind power maybe calculated based on the wind speed and the wind turbine power coefficient obtained from the basic model expression [18]....

    [...]

Journal Article•DOI•
Centralized Control for Optimizing Microgrids Operation

[...]

A.G. Tsikalakis1, Nikos Hatziargyriou1•
National and Kapodistrian University of Athens1
15 Feb 2008-IEEE Transactions on Energy Conversion
TL;DR: The controller aims to optimize the operation of the microgrid during interconnected operation, i.e., maximize its value by optimizing the production of the local DGs and power exchanges with the main distribution grid.
Abstract: Microgrids are low-voltage (LV) distribution networks comprising various distributed generators (DGs), storage devices, and controllable loads that can operate either interconnected or isolated from the main distribution grid as a controlled entity. This paper describes the operation of a central controller for microgrids. The controller aims to optimize the operation of the microgrid during interconnected operation, i.e., maximize its value by optimizing the production of the local DGs and power exchanges with the main distribution grid. Two market policies are assumed including demand-side bidding options for controllable loads. The developed optimization algorithms are applied on a typical LV study case network operating under various market policies and assuming realistic spot market prices and DG bids reflecting realistic operational costs. The effects on the microgrid and the distribution network operation are presented and discussed.

932 citations

Book•
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook

[...]

Magnus Nrgaard, Ole Ravn, Niels Kjølstad Poulsen, Lars Kai Hansen
02 Feb 2000
TL;DR: In this paper, the authors present an approach for detecting models and controllers from data using a multilayer perceptron (MLP) model and a linear model of the control system.
Abstract: 1. Introduction.- 1.1 Background.- 1.1.1 Inferring Models and Controllers from Data.- 1.1.2 Why Use Neural Networks?.- 1.2 Introduction to Multilayer Perceptron Networks.- 1.2.1 The Neuron.- 1.2.2 The Multilayer Perceptron.- 1.2.3 Choice of Neural Network Architecture.- 1.2.4 Models of Dynamic Systems.- 1.2.5 Recurrent Networks.- 1.2.6 Other Neural Network Architectures.- 2. System Identification with Neural Networks.- 2.1 Introduction to System Identification.- 2.1.1 The Procedure.- 2.2 Model Structure Selection.- 2.2.1 Some Linear Model Structures.- 2.2.2 Nonlinear Model Structures Based on Neural Networks.- 2.2.3 A Few Remarks on Stability.- 2.2.4 Terminology.- 2.2.5 Selecting the Lag Space.- 2.2.6 Section Summary.- 2.3 Experiment.- 2.3.1 When is a Linear Model Insufficient?.- 2.3.2 Issues in Experiment Design.- 2.3.3 Preparing the Data for Modelling.- 2.3.4 Section Summary.- 2.4 Determination of the Weights.- 2.4.1 The Prediction Error Method.- 2.4.2 Regularization and the Concept of Generalization.- 2.4.3 Remarks on Implementation.- 2.4.4 Section Summary.- 2.5 Validation.- 2.5.1 Looking for Correlations.- 2.5.2 Estimation of the Average Generalization Error.- 2.5.3 Visualization of the Predictions.- 2.5.4 Section Summary.- 2.6 Going Backwards in the Procedure.- 2.6.1 Training the Network Again.- 2.6.2 Finding the Optimal Network Architecture.- 2.6.3 Redoing the Experiment.- 2.6.4 Section Summary.- 2.7 Recapitulation of System Identification.- 3. Control with Neural Networks.- 3.1 Introduction to Neural-Network-based Control.- 3.1.1 The Benchmark System.- 3.2 Direct Inverse Control.- 3.2.1 General Training.- 3.2.2 Direct Inverse Control of the Benchmark System.- 3.2.3 Specialized Training.- 3.2.4 Specialized Training and Direct Inverse Control of the Benchmark System.- 3.2.5 Section Summary.- 3.3 Internal Model Control (IMC).- 3.3.1 Internal Model Control with Neural Networks.- 3.3.2 Section Summary.- 3.4 Feedback Linearization.- 3.4.1 The Basic Principle of Feedback Linearization.- 3.4.2 Feedback Linearization Using Neural Network Models..- 3.4.3 Feedback Linearization of the Benchmark System.- 3.4.4 Section Summary.- 3.5 Feedforward Control.- 3.5.1 Feedforward for Optimizing an Existing Control System.- 3.5.2 Feedforward Control of the Benchmark System.- 3.5.3 Section Summary.- 3.6 Optimal Control.- 3.6.1 Training of an Optimal Controller.- 3.6.2 Optimal Control of the Benchmark System.- 3.6.3 Section Summary.- 3.7 Controllers Based on Instantaneous Linearization.- 3.7.1 Instantaneous Linearization.- 3.7.2 Applying Instantaneous Linearization to Control.- 3.7.3 Approximate Pole Placement Design.- 3.7.4 Pole Placement Control of the Benchmark System.- 3.7.5 Approximate Minimum Variance Design.- 3.7.6 Section Summary.- 3.8 Predictive Control.- 3.8.1 Nonlinear Predictive Control (NPC).- 3.8.2 NPC Applied to the Benchmark System.- 3.8.3 Approximate Predictive Control (APC).- 3.8.4 APC applied to the Benchmark System.- 3.8.5 Extensions to the Predictive Controller.- 3.8.6 Section Summary.- 3.9 Recapitulation of Control Design Methods.- 4. Case Studies.- 4.1 The Sunspot Benchmark.- 4.1.1 Modelling with a Fully Connected Network.- 4.1.2 Pruning of the Network Architecture.- 4.1.3 Section Summary.- 4.2 Modelling of a Hydraulic Actuator.- 4.2.1 Estimation of a Linear Model.- 4.2.2 Neural Network Modelling of the Actuator.- 4.2.3 Section Summary.- 4.3 Pneumatic Servomechanism.- 4.3.1 Identification of the Pneumatic Servomechanism.- 4.3.2 Nonlinear Predictive Control of the Servo.- 4.3.3 Approximate Predictive Control of the Servo.- 4.3.4 Section Summary.- 4.4 Control of Water Level in a Conic Tank.- 4.4.1 Linear Analysis and Control.- 4.4.2 Direct Inverse Control of the Water Level.- 4.4.3 Section Summary.- References.

923 citations

Book•DOI•
Neural Networks for Modelling and Control of Dynamic Systems

[...]

M. Norgaard, Ole Ravn, Niels Kjølstad Poulsen, Lars Kai Hansen
01 Jan 2000
TL;DR: This chapter discusses Neural-Network-based Control, a method for automating the design and execution of nonlinear control systems, and its application to Predictive Control.
Abstract: 1. Introduction.- 1.1 Background.- 1.1.1 Inferring Models and Controllers from Data.- 1.1.2 Why Use Neural Networks?.- 1.2 Introduction to Multilayer Perceptron Networks.- 1.2.1 The Neuron.- 1.2.2 The Multilayer Perceptron.- 1.2.3 Choice of Neural Network Architecture.- 1.2.4 Models of Dynamic Systems.- 1.2.5 Recurrent Networks.- 1.2.6 Other Neural Network Architectures.- 2. System Identification with Neural Networks.- 2.1 Introduction to System Identification.- 2.1.1 The Procedure.- 2.2 Model Structure Selection.- 2.2.1 Some Linear Model Structures.- 2.2.2 Nonlinear Model Structures Based on Neural Networks.- 2.2.3 A Few Remarks on Stability.- 2.2.4 Terminology.- 2.2.5 Selecting the Lag Space.- 2.2.6 Section Summary.- 2.3 Experiment.- 2.3.1 When is a Linear Model Insufficient?.- 2.3.2 Issues in Experiment Design.- 2.3.3 Preparing the Data for Modelling.- 2.3.4 Section Summary.- 2.4 Determination of the Weights.- 2.4.1 The Prediction Error Method.- 2.4.2 Regularization and the Concept of Generalization.- 2.4.3 Remarks on Implementation.- 2.4.4 Section Summary.- 2.5 Validation.- 2.5.1 Looking for Correlations.- 2.5.2 Estimation of the Average Generalization Error.- 2.5.3 Visualization of the Predictions.- 2.5.4 Section Summary.- 2.6 Going Backwards in the Procedure.- 2.6.1 Training the Network Again.- 2.6.2 Finding the Optimal Network Architecture.- 2.6.3 Redoing the Experiment.- 2.6.4 Section Summary.- 2.7 Recapitulation of System Identification.- 3. Control with Neural Networks.- 3.1 Introduction to Neural-Network-based Control.- 3.1.1 The Benchmark System.- 3.2 Direct Inverse Control.- 3.2.1 General Training.- 3.2.2 Direct Inverse Control of the Benchmark System.- 3.2.3 Specialized Training.- 3.2.4 Specialized Training and Direct Inverse Control of the Benchmark System.- 3.2.5 Section Summary.- 3.3 Internal Model Control (IMC).- 3.3.1 Internal Model Control with Neural Networks.- 3.3.2 Section Summary.- 3.4 Feedback Linearization.- 3.4.1 The Basic Principle of Feedback Linearization.- 3.4.2 Feedback Linearization Using Neural Network Models..- 3.4.3 Feedback Linearization of the Benchmark System.- 3.4.4 Section Summary.- 3.5 Feedforward Control.- 3.5.1 Feedforward for Optimizing an Existing Control System.- 3.5.2 Feedforward Control of the Benchmark System.- 3.5.3 Section Summary.- 3.6 Optimal Control.- 3.6.1 Training of an Optimal Controller.- 3.6.2 Optimal Control of the Benchmark System.- 3.6.3 Section Summary.- 3.7 Controllers Based on Instantaneous Linearization.- 3.7.1 Instantaneous Linearization.- 3.7.2 Applying Instantaneous Linearization to Control.- 3.7.3 Approximate Pole Placement Design.- 3.7.4 Pole Placement Control of the Benchmark System.- 3.7.5 Approximate Minimum Variance Design.- 3.7.6 Section Summary.- 3.8 Predictive Control.- 3.8.1 Nonlinear Predictive Control (NPC).- 3.8.2 NPC Applied to the Benchmark System.- 3.8.3 Approximate Predictive Control (APC).- 3.8.4 APC applied to the Benchmark System.- 3.8.5 Extensions to the Predictive Controller.- 3.8.6 Section Summary.- 3.9 Recapitulation of Control Design Methods.- 4. Case Studies.- 4.1 The Sunspot Benchmark.- 4.1.1 Modelling with a Fully Connected Network.- 4.1.2 Pruning of the Network Architecture.- 4.1.3 Section Summary.- 4.2 Modelling of a Hydraulic Actuator.- 4.2.1 Estimation of a Linear Model.- 4.2.2 Neural Network Modelling of the Actuator.- 4.2.3 Section Summary.- 4.3 Pneumatic Servomechanism.- 4.3.1 Identification of the Pneumatic Servomechanism.- 4.3.2 Nonlinear Predictive Control of the Servo.- 4.3.3 Approximate Predictive Control of the Servo.- 4.3.4 Section Summary.- 4.4 Control of Water Level in a Conic Tank.- 4.4.1 Linear Analysis and Control.- 4.4.2 Direct Inverse Control of the Water Level.- 4.4.3 Section Summary.- References.

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