Anvari-Moghaddam, Amjad

Diaz, Enrique Rodriguez

Guerrero, Josep M.

Quintero, Juan Carlos Vasquez

Enrique Rodriguez-Diaz

Amjad Anvari-Moghaddam

Juan C. Vasquez

Josep M. Guerrero

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Multi-level energy management and optimal control of a residential DC microgrid

Extensive exploitation of renewable energies together with the increased role of low-voltage DC (LVDC) micro-sources in the generation mix of the future electricity networks, have become the driving force behind the DC microgrid applications. In this paper, an optimal dispatch model of a residential DC microgrid (R-DCMG) with different distributed generations (DGs) and loads is proposed and implemented as an optimal hierarchical control strategy. A system-level optimizer is designed to calculate the optimal operating points of the controllable energy sources (CESs) when needed, while lower-level controllers are utilized to enforce the CESs to follow optimal set-points

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    Aalborg UniversitetMulti-Level Energy Management and Optimal Control of a Residential DC MicrogridDiaz, Enrique Rodriguez; Anvari-Moghaddam, Amjad; Quintero, Juan Carlos Vasquez;Guerrero, Josep M.Published in:IEEEPublication date:2017Link to publication from Aalborg UniversityCitation for published version (APA):Diaz, E. R., Anvari-Moghaddam, A., Quintero, J. C. V., & Guerrero, J. M. (2017). Multi-Level EnergyManagement and Optimal Control of a Residential DC Microgrid. In IEEE: International Conference onConsumer Electronics (ICCE)General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: May 01, 2017Abstract—Extensive exploitation of renewable energies together with the increased role of low-voltage DC (LVDC) micro-sources in the generation mix of the future electricity networks, have become the driving force behind the DC microgrid applications. In this paper, an optimal dispatch model of a residential DC microgrid (R-DCMG) with different distributed generations (DGs) and loads is proposed and implemented as an optimal hierarchical control strategy. A system-level optimizer is designed to calculate the optimal operating points of the controllable energy sources (CESs) when needed, while lower-level controllers are utilized to enforce the CESs to follow optimal set-points. I. INTRODUCTION With the technological advancements in power electronics together with the increasing penetration of DC loads and sources especially in residential sector, DC microgrids (DCMG) have attracted more attention and become the subject area for many ongoing research works ‎[1]-‎[4]. A number of initiatives have been paid a particular attention to demonstrating the advantages of DCMG over the existing AC networks and some others have been focused on DC networks topologies and inverter interfaced technologies ‎[5]-‎[6]. However there exist several challenges in control and operation of residential DCMGs (R-DCMGs) where conventional energy sources and loads are being replaced by efficient power electronic-based DC micro-sources (such as photovoltaic systems (PV) and fuel-cells (FC)) and advanced consumer electronics ‎[7]. In this paper, an optimal dispatch model of a R-DCMG with   different means of distributed generation (DG) (such as small-scale wind turbines (WTs), PVs, FC-based micro-combined heat and power generator (FC-μCHP) and energy storage options) is proposed and implemented as a hierarchical control strategy. In a strategic control layer, an optimizer is designed and operated to track the system behavior in real-time and to update the operating points of the controllable energy sources (CESs) during the study period, while in the operation layer, device-level controllers are designed and utilized to enforce the CESs to follow optimal set-points coming from the system optimizer. The mentioned optimal operation and dispatch model is formulated as a mixed-objective optimization problem with different constraints stemming from the real technical‎ and‎ operational‎ limitations‎ of‎ the‎ system’s‎components and is solved using General Algebraic Modeling System (GAMS) as the optimization engine.  II. DC MICROGRID MODELING AND OPTIMAL CONTROL A. System Architecture Fig. 1 illustrates a grid-tied R-DCMG used as a benchmark in this paper. The system includes a cluster of residential buildings as loads and different DGs in forms of non-dispatchable (renewable sources such as PVs and WTs) and dispatchable (such as FC-mCHP and batteries) as energy sources. As shown in the figure, all dispatchable units are operated in power control mode (PCM) to follow a power reference received from the system optimizer, while the upstream power grid is operated in voltage control mode to 1 Multi-Level‎Energy‎Management‎and‎Optimal‎Control‎of‎a‎Residential‎DC‎Microgrid‎ Enrique Rodriguez-Diaz, Amjad Anvari-Moghaddam, Juan C. Vasquez, and Josep M. Guerrero  Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark {erd,aam,juq,joz}@et.aau.dk    Fig. 1. System architecture and control scheme of the examined grid-tied residential DC microgrid.   GSolar IrradiationWind SpeedMain GridLoadsLoadsESSESSFC-µCHPPV WT  Y12 Y23 Y34Node 1(PCC)Node 2 Node 3 Node 4MPPT ControlLoadsµCHP ProfilePDG iPLoad i PRES iPRES iDroop CurveViPESS iDroop Control for ESSiLoadsMPPT ControlPRES 4PRES 2PLoad 2 PLoad 3 PLoad 4 PESS 2PESS 4Optimization AlgorithmViSOCiPLoad i PRES iPDG i Vi *PDG i *Droop CurveAdaptative Droop Control for ESS iPDG 3Solar IrradiationWind SpeedMPPT ControlLoadsPLoad i PRES iPRES iMPPT ControlViPESS i *Conventional Control SchemeOptimized Control Scheme  keep the PCC voltage constant and provide the required system stability. B.  Optimal Control Methodology To optimally manage and control the behavior of the described R-DCMG, a coordinated operation between the system-level optimizer and device-level controllers is needed. More importantly, a well-defined action plan for the strategic control layer, where the system optimizer exists, is required.  Conventionally, proportional power sharing strategy based on droop control has been widely adopted for DG management. However, this power management strategy is not an optimized solution in terms of operation economies. In this work, optimal control of DG units is proposed from a cost-effective viewpoint. Regarding this, a mixed-objective function with related constraints, as stated in (1), is considered as the proposed optimization model. For each time interval which triggers the decision point, the system optimizer collects the required operating data (e.g., nodal voltages Vi, DG outputs PDG,k, and loading levels Lj) and solves the mentioned optimization model to find the optimal power dispatch of controllable DG units (P*DG,k={ P*G,k , P*ESS,k }).    1 21: ( ) ( ), ( ),..., ( ). . ( ) 0 ; 1,...,( ) 0 ; 1,...,mm k kkijMin F x f x f x f x f xs t g x i qh x j p    (1) where F(x) is the vector of objectives (which is defined as the weighted sum of total operating cost and power loss of the R-DCMG in this paper) and ψk is the weighting coefficient reflecting the significance of the corresponding objective from the system operator’s‎ prospective. gi(x) and hj(x) denote the inequality and equality constraints for the examined system. III. RESULTS AND DISCUSSION The proposed multi-level energy management and optimal control strategy has been tested through computer case studies and Hardware in the Loop simulations (HIL) and its performance has been demonstrated against a conventional droop control method. The results are depicted in Fig. 2 in terms of output powers of DGs and bus voltages. As can be seen in the upper subfigure, the proposed energy manager dispatches the controllable units in a cost-efficient manner, and accordingly reduces the global operational costs of the R-DCMG over the studied period, by taking into account the real-time electricity prices (RTP), degradation cost of the energy storage units, power losses over the distribution lines, and the fuel consumption cost of the FC-μCHP. In comparison with the conventional control strategy, although the‎ average‎ deviation‎ of‎ the‎ nodes’‎ voltages‎ is‎increased by use of the proposed strategy, it is still within the allowed range. It is noteworthy that there is a trade-off between the system operating cost and voltage deviations in nodes which can be further studied through a modified multi-objective optimization process.   Fig. 2. Upper subfigure: Active power generation of the dispatchable units without optimal control (dashed) and with optimal control (solid). Lower subfigure: Voltage level at the nodes without optimal control (dashed) and with optimal control (solid). IV. CONCLUSIONS An optimal energy management and power sharing model for a grid-tied residential DC microgrid based on the principles of hierarchical control was presented and verified through computer simulations and experimental studies. The proposed optimal dispatch model not only reduced the running cost of the system throughout the network, but also satisfied system’s‎constraints (e.g. allowed voltage deviation, DG units’ power and energy capacities, etc.) under any working conditions. It also reduced the power losses in the network by making a better power balance at each node (i.e., through optimal dispatch of DGs to supply their local loads), thus reducing the power flow between the nodes. REFERENCE  [1] T. Morstyn, B. Hredzak, G. D. Demetriades and V. G. Agelidis, "Unified Distributed Control for DC Microgrid Operating Modes," IEEE Trans. Power Systems, vol. 31, no. 1, pp. 802-812, Jan. 2016. [2] E. Rodriguez-Diaz, J. C. Vasquez and J. M. Guerrero, "Intelligent DC Homes in Future Sustainable Energy Systems: When efficiency and intelligence work together," IEEE Consumer Electron. Mag., vol. 5, no. 1, pp. 74-80, Jan. 2016. [3] F. Nejabatkhah and Y. W. Li, "Overview of Power Management Strategies of Hybrid AC/DC Microgrid," IEEE Trans. Power Electronics, vol. 30, no. 12, pp. 7072-7089, Dec. 2015. [4] N.‎L.‎Diaz,‎T.‎Dragičević,‎J.‎C.‎Vasquez‎and‎J.‎M.‎Guerrero,‎"Intelligent‎Distributed Generation and Storage Units for DC Microgrids—A New Concept on Cooperative Control Without Communications Beyond Droop Control," IEEE Trans. Smart Grid, vol. 5, no. 5, pp. 2476-2485, Sept. 2014. [5] S. Moayedi and A. Davoudi, "Distributed Tertiary Control of DC Microgrid Clusters," IEEE Trans. Power Electronics, vol. 31, no. 2, pp. 1717-1733, Feb. 2016. [6] A. Anvari-Moghaddam, T. Dragicevic, J. C. Vasquez and J. M. Guerrero, "Optimal utilization of microgrids supplemented with battery energy storage systems in grid support applications," IEEE 1st Int. Conf. DC Microgrids (ICDCM), 2015, Atlanta, GA, 2015, pp. 57-61. [7] A. Anvari-Moghaddam, J. C. Vasquez and J. M. Guerrero, "Load shifting control and management of domestic microgeneration systems for improved energy efficiency and comfort," 41st Conf. IEEE Ind. Electron. Society, IECON 2015 -, Yokohama, 2015, pp. 96-101.  

Multi-Level Energy Management and Optimal Control of a Residential DC Microgrid

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Multi-Level Energy Management and Optimal Control of a Residential DC Microgrid

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