Bacha, Seddik

Hably, Ahmad

Hajar, Khaled

Obeid, Ziad

Rafhi, Ahmad

Hal - Université Grenoble Alpes

Optimal centralized control application on microgridsKhaled Hajar, Ahmad Hably, Seddik Bacha, Ahmad Rafhi, Ziad ObeidTo cite this version:Khaled Hajar, Ahmad Hably, Seddik Bacha, Ahmad Rafhi, Ziad Obeid. Optimal centralizedcontrol application on microgrids. International Conference on Renewable Energies for Devel-oping countries (REDEC 2016), Jul 2016, Zouk Mosbeh, Lebanon. <hal-01323250>HAL Id: hal-01323250https://hal.archives-ouvertes.fr/hal-01323250Submitted on 30 May 2016HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestine´e au de´poˆt et a` la diffusion de documentsscientifiques de niveau recherche, publie´s ou non,e´manant des e´tablissements d’enseignement et derecherche franc¸ais ou e´trangers, des laboratoirespublics ou prive´s.Optimal centralized control application onmicrogridsKhaled Hajar ∗+, Ahmad Hably ∗, Seddik Bacha ∗, Ahmad Rafhi , Ziad Obeid ∗Univ. Grenoble Alpes, GIPSA-LabF-38000 Grenoble, France.khaled.hajar@gipsa-lab.grenoble-inp.fr Universite de Technologie et de Sciences Appliquees Libano-Francaise - Lebanon.Abstract—In this paper, an optimization algorithm hasbeen applied on a group of interconnected microgrids withthe main grid. The objective is to maximize the benefitsfor all the elements of the grid. Results have shown theperformance of the proposed algorithm.I. INTRODUCTIONA microgrid (MG) is a smart small-scale power supplynetwork that is designed to provide power for a smallcommunity enabling local power generation for localloads. It contains various small power generating sourceswhich can be renewable energy sources (RES) anddistributed generation (DG), that make it highly flexibleand efficient, with energy storage systems (ESS) likebatteries, hydraulic storage, electrical vehicles, etc. Thesize of a MG may range from housing estate to municipalregions.Micro grid applications have different objectives andcharacteristics but they share some common technicalproblems due to the use of RES. Theses RES have anintermittent, random, non programmable nature, whichmakes it difficult to provide a continuous power supplyto isolated loads and to assure energy dispatch of RESin distribution grids.MG operates in a connected manner, but also in anisolated one in case of faults or maintenance [1]. How-ever, it is also expected to provide sufficient generationcapacity, controls and operational strategies to supply atleast a portion of the load, after being disconnected fromthe distribution system and remains operational as anautonomous (islanded) entity [2].Implementation of MG provides many advantages bothfor the user and for the distribution system operators(DSO). For the user’s application, and when the MG isconnected to the grid, it can improve network quality,reduce emissions and reduce the cost to be incurred. Forthe DSO, implementation of MGs and their distributedgeneration systems can reduce the power flow on trans-mission and distribution lines, so losses are reduced aswell as costs for additional power [3]. In addition, theextra power produced by the MG can be sold to the DSOand vice-versa, which is considered as a benefit for bothparties.The research field of control and optimization a networkof MGs has recently received much attention. A decen-tralized optimal state feedback of multi-power systemhas been proposed in [4]. In [5], a distributed ModelPredective Control (MPC) has been proposed where con-trollers are supposed to operate separately, their designis based on a global model of the overall system. In [6],a decentralized linear quadratic Gaussian control (LQG)approach has been investigated. In [7], a mixed integerlinear approach on modeling and optimization of micro-grids has been proposed. In [8], the proposed controlleroptimizes the operation of the MG during interconnectedoperation. In [9], a model predictive control (MPC) forthe optimal power exchanges in a network of microgrids(MGs) has been presented.In this present paper, we apply a centralized controlof the power flow among a network of MGs and withDSO inspired from [9]. This solution takes into accountthe economic profit of all the actors of the Grid (DSOand all the MGs). The paper is organized as follows. InSection II, MG elements are presented. MG modeling aswell as optimization problem are developed in SectionIII. The results of the application of the methodologyare presented and commented in Section IV. Section Vprovides some conclusions and perspectives.II. MICROGRID ELEMENTSIn the literature, several models for MG have beenproposed. We have inspired a model of MG from themodeling procedure proposed by [10]. Each MG in-tegrates several units: local loads, wind power, photo-voltaic (PV), distributed generation, and energy storagesystems (ESS). Wind power and PV are affected by thevariation of wind speed and solar irradiance respectively.ESS has two status: charging and discharging, dependingon the loads and the production. A control unit (CU)is used in order to assure the local consumption, tomaximize the power stored in ESS according to theapproach proposed in our previous work [11]. The CUoptimizes the control of each MG and reveals its state toa Global Management Controller (GMC). It shows if theMicrogrid	1	PV	and	Wind	farm	 Energy	Storage	System	 Distributed	Genera;on	 Loads	Microgrid	M	DSO	Microgrid	2	o/i	o/i	o/i	o/i	Control	Unit	(CU)	Global	Management	Controller	(GMC)	Microgrid	3	o/i	Fig. 1: Microgird composition.MG has excess of power or vise versa. For each MG thegoal can be achieved by controlling the charge/dischargeof ESS, starting or stopping DG, and receiving/sendingpower from/to neighboring MGs. The GMC is dedicatedto manage the power in the grid, the relation betweenMGs as well as with the DSO with a predicted control.It takes also the decision wherever the excess power is tobe sent to other MGs or to DSO. The different elementsof MG are presented in Fig.1. This figure also showsus the units of producing or consuming energy. Alsoa microgrid can be connected or disconnect from DSOor/and can be connected to other MGs.To summarize, a MG integrates:• ESS improving the quality, and reliability of supply.• Loads representing consumers demand.• Control Unit (CU) transferring the data to GMC.• RES providing clean sources.• DG improving the reliability of supply.• DSO providing/purchasing power for/from the MG.III. MICROGRID MODELING AND OPTIMIZATIONA. ModelingFigure 2 shows the interaction among MGs on onehand and between DSO and MGs on the other hand.The power flow is as the following:• PS,D: Power sold by the DSO (kW).• PS,M : Power sold by the MG (kW).• PB,D: Power bought by the DSO (kW).• PB,M : Power bought by the MG (kW).• Pbal: Power balance.Microgrid	1		 PS,D 1  PS,M(1-2) D	S	O	2	 M	……	Pbal  PB,D 1 PB,M(1-2) PB,M(2-1) PS,M(2-1) PS,M(2…M) PB,M(2…M) PB,M(M…2) PS,M(M…2) PS,M(1-M) PB,M(M-1) PB,M(M-1) PS,M(1-M)  PS,D 2   PB,D 2 D	S	O	 PS,D M   PB,D M Fig. 2: Power flow in the grid (MG+DSO).The stored energy equation in each MG:x(i, t+ k) = x(i, t+ k − 1) + (1)δchar,iPchar(i, t+ k)− δdis,iPdis(i, t+ k)where• x: Stored energy (kWh)• δchar,i: Charging efficiency of the ESS in the i-thMG.• δdis,i: Discharging efficiency of the ESS in the i-thMG.• Pcha: Charged energy (kWh).• Pdis: Discharged energy (kWh).B. Optimization FormulationA nonlinear centralized optimization is used to formu-late the energy management in MG [8]. Our objective isto optimize the grid in way that the power generation,power balance, ESS, energy exchange between MGs aresatisfied under certain constraints that will be presentedlater. The cost function Cf to be maximized is given bythe following equation:Cf =Nc∑k=1m∑i=1[µD(k)PS,D(i, t+ k)TS,D(i, t+ k) (2)−µˆD(k)PB,D(i, t+ k)TB,D(i, t+ k)]+Nc∑k=1M∑i=1m∑j,j 6=i[µM (k)PS,M (i, j, t+ k)TS,M (j, t+ k)−µˆM (k)PB,M (i, j, t+ k)TB,M (j, t+ k)]with• k : Prediction step.• t : Control step.• Nc : Control horizon.• i, j : MG index.• m : MGs number.• µD(k) : Scale factor depending on the benefit ofselling power to DSO.• µM (k) : Scale factor depending on the benefit ofselling power to the MGs.• µˆD(k) : Scale factor depending on the benefit ofbuying power from DSO.• µˆM (k) : Scale factor depending on the benefit ofbuying power from the MGs.• TS,D : Expected cost value of selling energy by theDSO (e/kWh).• TS,M : Expected cost value of selling energy by theMG (e/kWh).• TB,D : Expected cost value of buying energy by theDSO (e/kWh).• TB,M : Expected cost value of buying energy bythe MG (e/kWh).As described in Fig. 2, the first term in the objectivefunction is related to the energy sold/bought to/fromDSO and the second term is related to the energysold/bought to/from neighboring MGs. The constraintsfor this optimization problem are given in the followingsection.C. ConstraintsA MG cannot buy and sell energy at the same time.Selling can be done when the MG has an excess of powerotherwise MG can buy power when its production islower than its consumption:• If ∆PMG,j(t+ k) < 0 then PS,D(i, t+ k) = 0 andPS,M (j, i, t+ k) = 0• If ∆PMG,j(t+ k) > 0 then PB,D(i, t+ k) = 0 andPB,M (j, i, t+ k) = 0with ∆PMG,j(t+k) =∑(PEP,j(t+k)−PEL,j(t+k)),Where• ∆PMG,j : Power balance of j-th MG at (t+k) instant.• PEP,j : Power of the expected production of j-th MGat (t+ k) instant.• PEL,j : Power of the expected load of j-th MG at(t+ k) instant.An upper and a lower bound (PS,D,min, PS,D,max)constrained the predicted power sold to the DSO in eachMG:PS,D,min ≤ PS,D(i, t+ k) ≤ PS,D,maxAlso the predicted power bought from the DSO is alsoconstrained by an upper and lower bound (PB,D,min,PB,D,max)PB,D,min ≤ PB,D(i, t+ k) ≤ PB,D,maxAn upper and a lower bound (xi,min, xi,max) constrainedthe stored energy in each ESS. In the i-th MG:xi,min ≤ x(i, t+ k) ≤ xi,maxThe charging power must be lower than certain limitedpower value Pcharmax,i:0 ≤ Pchar(i, t+ k) ≤ Pcharmax,iAlso the discharging power must be lower than certainlimited power value Pdismax,i:0 ≤ Pdis(i, t+ k) ≤ Pdismax,iThe power sold by the i-th MG is equal the power boughtby the j-th MG:PB,M (i, j, t+ k) = PS,M (j, i, t+ k)IV. SIMULATION RESULTSThe above described optimization problem has beenimplemented in Matlab. We have used a modified versionof the study case presented in [8]. The simulation willtake in consideration five interconnected MGs. When thesupply in a MG is less the demand, the solution is todischarge the ESS or purchase power from DSO or manyneighbors MG. Each MG generates power and feeds agroup of consumer in an interval time (t, t+ 1). Also, itis connected to all neighboring MGs.As for the tarif vector of selling and buying energy,(TB,M , TS,M , TS,D, TB,D) are represented for each MGin a random value between 0.07e/kWh and 0.2e/kWh atday time, and 0.075e/kWh from midnight to 6 am (Fig.3). In Fig. 4, we show the predicted value of Pbal foreach MG. The value is calculated based on the load andproduction curves of [8] where we made some randomcalculation to create more MGs.The ESS can go from 25% to 90% of power storage.The power of ESS in each MG is different than theother. The sold and bought power from/to DSO is limitedto 200 kWh for each MG. The optimization algorithmsearches to use the surplus power available in each MG,under constraints, and to be exchanged. If the constraintsare not satisfied, the DSO interferes while respecting itsconstraints.The optimal results shown in Fig.5 gives us an overviewabout the interaction of MG1 with the other MGs. Asresult of selling and buying, MG1 sells 578kWh in totalespecially 235kWh to MG2 and 167kWh to MG3 andfinaly to MG4 176kWh. In addition, MG1 buys 235kWhfrom MG2, 158 kWh MG4, and 224kWh MG5. So intotal, MG1 buys 617kWh. Fig.6 shows us the optimalTime(h)	Cents/kWh	Cents/kWh	Cents/kWh	Cents/kWh	Cents/kWh	0,05	0,1	0,15	0,2	1	 4	 7	 10	 13	 16	 19	 22	T	B,M	TS,M	T	S,D	T	B,D	MG1	0,05	0,1	0,15	0,2	0,25	1	 4	 7	 10	 13	 16	 19	 22	T	B,M	TS,M	T	S,D	T	B,D	MG2	0,05	0,1	0,15	0,2	0,25	1	 4	 7	 10	 13	 16	 19	 22	T	B,M	TS,M	T	S,D	T	B,D	MG3	0,05	0,1	0,15	0,2	0,25	1	 4	 7	 10	 13	 16	 19	 22	T	B,M	TS,M	T	S,D	T	B,D	MG4	0,05	0,1	0,15	0,2	0,25	1	 4	 7	 10	 13	 16	 19	 22	T	B,M	TS,M	T	S,D	T	B,D	MG5	Fig. 3: The tarif vector.Fig. 4: MGs power balance.Fig. 5: Control results for MG1.energy exchanges of MG2. The total energy bought is871 kWh, the main bought energy is from MG3 with526kWh, the rest of bought energy comes from MG1 andMG5 with 235kWh and 110 kWh respectively. OptimalFig. 6: Control results for MG2.energy strategy for MG3 is shown Fig.7. It is obviousthat MG3 buys energy more than selling. In total, it buys1109 Kwh from other MGs where the big amount comesfrom MG2 with 526kWh, from MG1 167Kwh, 323Kwhfrom MG4, and 93kWh from MG5. It sells a smallamount of 365 KWh in total, distributed between MG2and MG4 with 195kWh and 170kWh respectively. ForFig. 7: Control results for MG3.MG4 (Fig.8), the sold energy represents 616 Kwh whereMG1 buys 158 Kwh, MG3 buys 323kWh and MG5 buys134kWh. Total bought energy is purchased from MG1with 175kWh, MG2 170 kWh and MG5 218 kWh. MG5Fig. 8: Control results for MG4.represents the maximum benefit of sold/bought energyto other MGs with a total 740/243 kWh (Fig.9). Fig.10Fig. 9: Control results for MG5.gives us the interaction between MGs and DSO in termsof power exchange. Finally, Fig.12 reveals the chargeFig. 10: Power exchange with DSO.and discharge of ESS in each MGFig. 11: Total energy exchange (kWh) between MGs andDSO.Fig. 12: Charge and discharge of ESS.V. CONCLUSIONThe paper presents a centralized optimization for theinteraction between a network of MGs and DSO. Resultshas shown the performance of the proposed algorithm.Economically speaking, both MGs and DSO have gainedby applying this algorithm. A decentralized version ofthis algorithm is under study.VI. ACKNOWLEDGEMENTThis work has been a joint collaboration by the projectPARADISE which is sponsored by the National ResearchAgency (ANR).REFERENCES[1] H. Al-Nasseri, M. Redfern, and R. O’Gorman, “Protectingmicro-grid systems containing solid-state converter generation,”in Future Power Systems, 2005 International Conference on.IEEE, 2005, pp. 5–pp.[2] J. Shah, B. F. Wollenberg, and N. 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Optimal centralized control application on microgrids

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Optimal centralized control application on microgrids

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