The increasing growth of non-dispatchable renewable energy sources (RES) such as solar and wind and the upcoming plug-in electric vehicles (PEVs) makes the secure power dispatch a challenging optimisation. In this paper, a coordinated power dispatch strategy in microgrids with a battery management system is proposed. The power dispatch strategy dynamically fits the power production to the power demand by appropriately dispatching the controllable distributed energy resources (DER). A strategy is presented where an active battery scheduling system includes the load and generation profiles of the microgrid and the electricity prices of the spot market in order to maximise the microgrid revenue

Deckmyn, Christof

Moradzadeh, Mohammad

Vandevelde, Lieven

Vandoorn, Tine L.

University of Huddersfield Repository

Dynamic Optimisation For Power Dispatch In Microgrids

Vandoorn, Tine

Ghent University Academic Bibliography

Dynamic Optimisation for Power Dispatch inMicrogridsChristof Deckmyn, Tine L. Vandoorn, Mohammad Moradzadehand Lieven VandeveldeElectrical Energy Laboratory (EELAB), Department of Electrical Energy, Systems and Automation (EESA),Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium,e-mail: Christof.Deckmyn@UGent.beAbstract—The increasing growth of non-dispatchable renew-able energy sources (RES) such as solar and wind and theupcoming plug-in electric vehicles (PEVs) makes the securepower dispatch a challenging optimisation. In this paper, acoordinated power dispatch strategy in microgrids with a batterymanagement system is proposed. The power dispatch strategydynamically fits the power production to the power demandby appropriately dispatching the controllable distributed energyresources (DER). A strategy is presented where an active batteryscheduling system includes the load and generation profiles ofthe microgrid and the electricity prices of the spot market inorder to maximise the microgrid revenue.Index Terms—Microgrid, power dispatch, multi-objective op-timisation, energy management.I. INTRODUCTIONDistributed generation (DG) and storage become part of themodern energy structure. Unfortunately, conventional energygrids are not designed for the bidirectional power flows in thedistribution networks. Moreover, the aging electrical distribu-tion networks are not actively managed, i.e., they are conceivedas passive facilities of the transmission network in which thecontrol and stability is achieved. The challenge of ensuring areliable and sustainable electricity supply is driving the needfor technology improvements in electricity networks. Besides,the enormous efforts in the pursuit of low-carbon economyhave led to a huge boom in the use of intermittent renewableenergy sources (RES). The increasing growth of distributedelectricity production and the unpredictability of electricityconsumption could lead to grid congestion problems. As thesetechniques grow in popularity, so do the challenges. Controlstrategies for the coordinated power dispatch of distributedenergy resources (DER) gain importance [1]. The microgridconcept, with a more reliable and more sustainable framework,is a promising approach and forms a solution to this challenge.A microgrid is an active distribution network defined asan integrated power delivery system. It consists of a low-voltage (LV) network including DG units, storage devicesand (controllable) loads and behaves as a single controllableentity. A microgrid can provide both power and heat andmay be operated to achieve several objectives, such as marketparticipation, reducing the electricity cost through flatteningof the load profile, and CO2 reduction by maximising the useof RES. The priority goals of a microgrid are to provide acoordinated integration of DG in the electric power system,to improve the reliability, and to allow a more efficientuse of energy [2]. Due to the dynamics of RES, differentgenerator power dispatch levels and external grid character-istics, a microgrid can be characterised by specific operationsand limitations [3]. Taking into account these criteria, themicrogrid needs a flexible energy management system (EMS)[4], [5]. In this paper, a coordinated power dispatch strategy inmicrogrids with a battery management system is proposed. Forthe development of the dynamic optimisation for power dis-patch in microgrids, we have further improved the microgridenvironomic scheduling approach we have developed in ourearlier work [6]. The power dispatch strategy aims to fulfillthe time-varying energy demand at lowest cost and lowestemission, and presents a battery scheduling systems whichtakes into account the load and generation profiles and theelectricity prices on the spot market. This paper is organisedas follows: Section II describes the microgrid power dispatchin which an existing environomic dispatch strategy is describedbriefly, followed by an introduction of a microgrid batterymanagement system. Section III describes the formulation ofthe optimisation problem. The implementation of the algorithmis presented in Section IV.II. MICROGRID POWER DISPATCHA. Environomic power dispatchIn [6] we developed an environomic microgrid schedulingapproach where the focus was on the dynamic response of themicrogrid in terms of dispatching the power among differentDER in an economically and environmentally sound way. Dueto the intermittency of the RES involved and the multiple ob-jectives that needed to be satisfied, the power dispatch systemcalculates the generator setpoints quickly and continuously.Operational constraints such as generator limits and powerthe balance were satisfied. The microgrid concept, used inthis work, is presented in Fig. 1. It consists of an industrialpark including fuel-based thermal generators (micro turbine,diesel generator and fuel cell), renewable energy productionby wind turbines and photovoltaics, a battery device, and twomanufacturing companies with a varying demand profile. Toimprove the matching performance of the power generationand consumption in the microgrid, the power dispatch appli-cation dynamically fits the power production to the powerdemand by optimally dispatching the controllable DER andthe storage device. Additionally, the microgrid features astorage unit. Stored energy was controlled to balance thepower generation of renewable sources, to cover the overallmicrogrid demand and to optimise the overall power exchangebetween utility grid and microgrid. Simulations were carriedout with real measurement data of Ghent University and wereperformed over one day with a sampling time of 15 minutes.The microgrid demand and the renewable energy productionis presented in Fig. 2. In order to meet the microgrid demand,the micro sources were dispatched according to the minimumoperating costs and minimum emissions. The power dispatchprofile, which is presented in Fig. 3, follows the shape ofthe microgrid demand profile. Between 5 p.m. and midnight,the surplus of the energy produced by RES (presented inFig. 2), while feeding the requiring loads, was stored. Withinthis period, the microgrid demand is covered by RES andthe micro sources are dispatched to zero. Stored power wasused during periods when no sun and no wind are available,and was used instead of using power from the utility grid, asshown in Fig. 4. The proposed scheduling approach reducesthe microgrid operating costs and emissions, and reduces thepower exchange with the utility grid.B. BatteryIn the application described above, the storage unit par-ticipates passively in the optimisation strategy. It will onlyFig. 1. Microgrid conceptFig. 2. Microgrid demand and renewable energy productionbe charged with the surplus of renewables and dischargedto minimise the use of the utility grid. The charging anddischarging process of the storage device does not considerthe electricity price of the spot market.The main contribution of this paper is the improved powerdispatch strategy for microgrids in a emission-constrainedcompetitive environment. The optimisation method finds theoptimal trade-off between maximising profit (or equivalentlyminimising the microgrid operating cost) by managing themicro sources without excessive emissions [7]. Additionallyan active battery scheduling system considers the load andFig. 3. Microgrid environomic power dispatchFig. 4. Battery dischargegeneration profiles and the electricity prices in order to max-imise the microgrid revenue [8].A similar microgrid model, as presented in Fig. 1, will beused for the dynamic optimisation strategy. The microgridoperates in grid-connected mode, and can sell and purchaseelectricity to and from the utility grid. Local energy is pro-duced by DG units and RES, and energy can be stored usingthe storage device. Two manufacturing companies provide avarying energy demand profile and are considered as non-controllable loads. The microgrid coordinated power dispatchstrategy minimises the overall microgrid operating cost andthe CO2-emissions while satisfying operational constraints.The battery management system will further reduce the overallmicrogrid operating cost by importing power during the cheaphours and exporting power during more expensive hours.III. FORMULATION OF THE OPTIMISATION PROBLEMA. Mathematical battery modelAn efficient storage scheduling strategy for a microgridbattery system takes in to account the load and generationprofiles, and electricity price for every 15 minutes. By optimalscheduling the charging and discharging process of the batterysystem, the microgrid operating cost can be further reduced[9], [10]. The optimisation of the storage device can bedescribed asminb(k)N∑k=1Egrid(k).C(k) (1)Where k is a single unit of time (here 15 minutes willbe considered). N is the total number of repetitions of the15-minute intervals (here, 96 time steps will be consideredwhich form a 24-hour simulation). C(k) is the market priceof electricity in e/kWh for k. Egrid is the exchanged energywith the utility grid, where negative values stands for energyexport to the utility grid. Local production by RES and thedispatchable generators, as well as the microgrid demandand the charged and discharged energy from the battery areincorporated in Egrid.Egrid(k) = Egen(k)− Edemand(k) + b(k) (2)where, b(k) is the charged (+) and discharged (-) energyof the microgrid battery in one time period. b(k) forms thedecision variable of optimisation problem which is boundedby charge and discharge rates and the lower and upper boundsof the energy level stored in the battery.Ebattery(k + 1) = Ebattery(k) + b(k) (3)Elb < E(k) < Eub (4)IV. ALGORITM IMPLEMENTATIONConsidering the objective function bounded by several con-straints, which have a linear decision variable b(k), the opti-misation strategy can be defined as a linear programming (LP)optimisation problem. Linear programming involves the min-imisation of the objective function, described in (1), boundedby linear equality and inequality constraints, described in (3)and (4). The linear programming formulation of the mathe-matical battery model has the advantage of being easy to besolved by the Matlab optimisation toolbox. In several practicalapplications, such as operational optimisation or profit max-imisation, the linear programming method reformulates theproblem which makes it easy to implement. To determine theeffectiveness of the proposed battery scheduling system, thisoptimisation method will be implemented using the MatLablinprog function and integrated in the existing environomicdispatch model. This creates a multi-level optimisation, wheredifferent models of a system will be used. The first level willcover the microgrid demand at the lowest cost and lowestemission, bounded by its constraints. Subsequently, a secondlevel controls the battery management system, which aims toreduce the microgrid operational cost, taking into account theprice of electricity.V. CONCLUSIONSimulation results of the microgrid power dispatch systemconfirms the effectiveness of the environomic scheduling sys-tem with reduced microgrid operating costs and emissions.Still, there is room for improvement by integrating an activebattery management system and participation in the electricitymarket. This paper analysed how a battery unit can activelyparticipate in the dispatch strategy of a micro grid. A strat-egy is described where an active battery scheduling systemincludes the load and generation profiles of the microgrid andthe electricity prices of the spot market in order to maximisethe microgrid revenue. Simulations are in progress and resultswill be published in the another paper.ACKNOWLEDGMENTThe authors would like to acknowledge all partners of theACE (Answers to the Carbon Economy) project for theirfinancial support.REFERENCES[1] M. Abido, “Multiobjective evolutionary algorithms for electric powerdispatch problem,” Evolutionary Computation, IEEE Transactions on,vol. 10, no. 3, pp. 315–329, June 2006.[2] T. L. Vandoorn and L. Vandevelde, “Contribution of microgrids in thedevelopment of the smart grid,” CRC Press, 2014.[3] F. Katiraei, R. Iravani, N. Hatziargyriou, and A. Dimeas, “Microgridsmanagement,” Power and Energy Magazine, IEEE, vol. 6, no. 3, pp.54–65, May 2008.[4] C. Deckmyn, T. Vandoorn, L. Vandevelde, J. Desmet Lemcko,G. Van Eetvelde, and J. Timmerman, “Energy management and dynamicoptimisation of eco-industrial parks,” in Power Engineering Conference(UPEC), 2013 48th International Universities’, Sept 2013, pp. 1–5.[5] A. Chaouachi, R. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjectiveintelligent energy management for a microgrid,” Industrial Electronics,IEEE Transactions on, vol. 60, no. 4, pp. 1688–1699, April 2013.[6] C. Deckmyn, M. Vandoorn, T.L. Moradzadehand, and L. Vandevelde,“Dynamic optimisation for environomic power dispatch in microgrids,”in Conference Process Integration, Modelling and Optimisation forEnergy Saving and Pollution Reduction (PRES), Submitted’, 2014.[7] J. P. S. Catalao, S. J. P. S. Mariano, V. M. F. Mendes, and L. A. F. M.Ferreira, “Profit-based unit commitment with emission limitations: Amultiobjective approach,” in Power Tech, 2007 IEEE Lausanne, July2007, pp. 1417–1422.[8] A. Parisio and L. Glielmo, “Energy efficient microgrid managementusing model predictive control,” in Decision and Control and EuropeanControl Conference (CDC-ECC), 2011 50th IEEE Conference on, Dec2011, pp. 5449–5454.[9] P. Mahat, J. Escribano Jimenez, E. Rodriguez Moldes, S. Haug,I. Szczesny, K. Pollestad, and L. Totu, “A micro-grid battery storagemanagement,” in Power and Energy Society General Meeting (PES),2013 IEEE, July 2013, pp. 1–5.[10] Z. Miao, L. Xu, V. Disfani, and L. Fan, “An soc-based battery manage-ment system for microgrids,” Smart Grid, IEEE Transactions on, vol. 5,no. 2, pp. 966–973, March 2014.

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Dynamic optimisation for power dispatch in microgrids

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