IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09-11, 2014, Jaipur India

Generation Scheduling at PCC in Grid Connected Microgrid

Nandkishor Gupta Department of Electrical Engineering

Malaviya National Institute of Technology Jaipur, India

nkguptaI987@gmail.com

Abstract- A microgrid is a self-sustainable grid which can be

operated in two modes, i.e. Grid coneected mode and grid

isolated mode. In grid connected mode microgrid can be

connected to grid at Point of Common Coupling (PCC). This

paper considers grid connected microgrid for generation

scheduling. This paper analyzes the Generation scheduling at

PCC in grid connected mode of microgrid. Here microgrid is

having renewable generators (i.e. Wind and PV) and

dispatchable units, and generation scheduling problem at PCC is

formed by robust approach and solved by using GAMS (General

Algebraic Modeling System). Practical case study considering

excess renewable generation and excess demand are discussed.

Keywords- Microgrid; Generation Scheduling; Point of Comman Coupling (PCC); Robust Optimization

I. NOMENCLATURE

A. Sets or Indices j Index of conventional dispatchable units running

from 1 to Nj

Index of time period running from 1 to Nt B. Variables

TC Total cost [$]

Uj,t Status of unit j at time t

PGout,t

Pshed,t

Pcurt,t

Ushed,1

Ucurt,t

Power generated by unit j at time t [kW] Power imported from the upstream grid at time t [kW]

Power exported to the upstream grid at time t [kW]

Load shedding at time t [kW]

Power curtailment at time t [kW]

Load shedding decision variable at time t

Power curtailment decision variable at time t

Reserve provided by unit j at time t [kW] SRgridt Reserve provided by the upstream grid at time

t [kW] SRallt A vailable spinning reserve in the system at time

[978-1-4799-4040-0/14/$31.00 ©2014 IEEE]

t [kW] C. Parameters

Nj

Nt

4r

4 grid

4DSM

pmin J

pmax J

pmax grid

Number of dispatchable units

N umber of time periods

Reserve price [$/ kWh]

Upstream grid power price [$ / kWh] Load shedding and power curtailment [$/ kWh]

price

Forecasted system's demand at time t [kW]

Forecasted wind power generation at time t [kW]

Forecasted solar power generation at time t [kW] System's spinning reserve requirement at time t [kW]

Minimum output power of unit j [kW]

Maximum output power of unit j [kW]

Capacity of the line linking the upstream grid and the MG [kW]

II. INTRODUCTION

Integration of distributed generators in the electric grid poses a number of challenges to the system operators. One of the suggested method is to locate a group of generators and loads next to each other such that they are seen by the rest of the electric grid as a single generator or load [1]. This is referred as a Microgrid. Several types of generators are usually considered in a microgrid including dispatchable generators, such as micro-turbines and combined heat and power generators, and non-dispatchable generators, such as wind turbines and Photovoltaic (PV) systems. In addition to that, energy storage systems can also be integrated in microgrids. Microgrids are connected to the upstream grid at a single point called the Point of Common Coupling (PCC). Microgrids can operate in isolated mode, where the microgrid is disconnected from the distribution network and depends only on its local generation, or in grid-connected mode, where the upstream grid can participate in supplying the microgrid's demand.

IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09-11, 2014, Jaipur India

In order to maximize the benefits of the resources available in a microgrid, an optimal scheduling of the power generation is required. Generation scheduling problem is an optimization problem that consists of two sub-problems: Unit Commitment (UC), and Economic Dispatch (ED). The unit commitment problem provides the on/off status of the dispatchable generation units over a daily or weekly time horizon. On the other hand, the economic dispatch problem fmds the optimal output power for the units committed by the unit commitment problem over shorter time horizons: i.e., hourly or in real time. Both problems search for an optimal solution that satisfies the generators' and network's constraints while meeting the demand and the reserve requirement. In large power systems, unit commitment problem deals with large generation plants of hundreds to thousands of megawatts. While in microgrids, generation capacities are in the range of tens of kilowatts to few megawatts. This reduction in the size affects the operation parameters of the generators leading to more flexible and frequent on/off switching actions [2].

Uncertainties associated with renewable generators must be taken into consideration when scheduling the power generation in microgrids in order to achieve reliable solutions. Hence, reformulating the scheduling problem and developing new models is a necessity to produce efficient and robust commitment schedules. The scheduling of generators in a microgrid has several differences from the case of a large power system. The size of the dispatchable units in a microgrid is much smaller than large power systems. The reduction in the size reflects an easier switching operation which results in a more flexible scheduling problem [2]. Since the concept of the microgrid is still relatively new, standards on microgrid integration and operation are still under development [7]. Different policies were presented in the literature to solve the generation scheduling problem. However, the majority of the previous studies considered one of the following two policies: minimizing the expenses, or maximizing the profit [3], [9], [lO]. Scheduling the power generation in a microgrid can significantly benefit its performance and maximize the resources utilization. A planned scheduling model for the economic dispatch problem in a combined heat and power (CHP) based microgrid is discussed in [11]. Optimal locations, sizes, and types of distributed energy resources (DERs) were first selected considering minimwn power losses as the objective function. Economic power sharing between a mix of DERs was then performed using differential evolution while satisfying all the constraints. However, the work presented in [11] did not consider any type of renewable resources and studied the system only during the grid-connected mode. Furthermore, the applied optimization technique, i.e. differential evolution, is a meta-heuristic technique that cannot guarantee an optimal solution. Multi-agent system for the real time operation of a microgrid including generation scheduling and demand side management is discussed in [lO]. Generation scheduling was performed using a two-stage process that included day-ahead and real time scheduling. A real time digital simulator (RTDS) was used to model the operation of a microgrid in real time. RTDS provided the feedback needed to perform the real time scheduling. Computational intelligence techniques such as Genetic Algorithm (GA) were applied in the decision making modules. However, in spite of their near

optimal performance, these techniques cannot guarantee an optimal solution. So there is a need to develop a technique that gives optimal solution for generation scheduling problems in grid connected microgrid under uncertainties in generation. For modeling of uncertainties the lower and upper bound at 95% confidence interval are forcasted on the basis of historical data. Here uncertainties in wind generation is only considered while demand and PV generation is not considered uncertain.

This paper discusses the Generation scheduling at PCC in grid connected microgrid. Here microgrid is considered having renewable generators (i.e wind generator and PV generator) and dispatchable units and generation scheduling problem at PCC is formed by using MILP (Mixed Integer Linear Programming) by proposed robust approach and then problem is solved by using GAMS (General Algebraic Modeling System). Two Study cases of Microgrid i.e. excess renewable generation at PCC and excess demand at PCC are considered and results are discussed and compared with deterministic approach of scheduling.

III. PROBLEM FORMULA nON

This section presents the microgrid modeling in grid connected mode and problem formulation. Microgrid is modeled by using deterministic approach and robust approach. Here two renewable generators (i.e. solar PV and wind generators) are considered for microgrid modeling.

A. Basic Formulation In grid connected mode, the upstream grid is connected to

the microgrid and power exchange is allowed. The upstream grid can participate in providing power and spinning reserve to the microgrid [3]. The day-ahead unit commitment problem should minimize the total expenses of operating the microgrid in grid-connected mode for a scheduling horizon of 24 hours. The total expenses consist of the local generators' operating cost, the cost of the power imported from the upstream grid, and the cost of providing the spinning reserve. The operation of a microgrid in grid-connected mode can be summarized in the following three cases:

a) Normal operation: In this case, the demand can be easily supplied by the local (renewable) generators. The power available from the renewable generators is only used to supply the demand. The upstream grid is connected but neither supplying nor receiving any power. Spinning reserve requirement is provided by the upstream grid.

b) Excess demand: In this case, the demand exceeds the capacity of the renewable generators. Therefore, the upstream grid supplies the excess demand. The spinning reserve is provided by the remaining capacity of the upstream grid.

c) Excess renewable generation: In this case, the entire demand can be supplied by the renewable generators and any excess power is exported to the upstream grid. The spinning reserve is provided by the upstream grid.

In grid-connected mode, the basic objective function is formulated in deterministic approach as:

IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09-11, 2014, Jaipur India

min Te = �[� (C; (Pi,l ) + SUi,I ) + SRallf * Ar + PGin,1 * Agrid ] (1)

Subject to

a.) System power balance

j I Pj,t + Wt + P Vt + PCIn,t - PCout,t = Dt (2) j=1

b.) Dispatchable units output limit and spinning reserve

Pmin < p. < pmax (3) J - J,t - J

SRU't=( U'tXpmax ) _pt (4) J, J, J J,

c.) Upstream grid power limits and spinning reserve

0< D < pmax - rCin,t - grid

0< D < pmax - l-Cout,t - grid

SRgridt = P;:'?d - PCin,t

d.) Total available spinning reserve

j SRallt = I SRu j,t + SRgridt

j=i

(5)

(6)

(7)

(8)

Where, C j (Pjt ) and SU jt are the linearized fuel cost function and the start-up cost function of unitj.

B. Proposed Formulation In this problem wind is considered as uncertain parameter

and for modeling uncertainties in wind power here scenarios are not generated. Here fust Wind power is forcasted on the basis of historical data than its lower and upper limits are generated at 95% confidence interval. Than robust optimization formulation is used as follows: [N, j N, N, ] min z = ��( Aj (PjI )+SUjI)+ �(SRall, * A.,.)+ �(PGlnJ * Agrid)

Subject to

t,P+PV +P. -P.. +[t,W +z�+ t,q] = D

Z +q � Wy 'ifi* 0, nE N

y � 0 'if n

(3)-(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

In equation (10), r; is the degree of robustness which controls the robustness of problem, while variable Zi and qm are dual variables used to take into account the known bounds of wind power while Yn is an auxiliary variable used to obtain the corresponding linear expressions.

C. Proposed Algorithm The following algorithm is used to build the hourly wind power offering: Step 1: Set Wr =Wtmax (t = l... .... Nt), and r;=Nt (the no. of

hours i.e. 24) to take into account all possible wind deviations.

A

Step 2: Set W k = Gk (wmax -wmm) (t = l... .... NJ ' where I,m ( I

Gk is a factor that takes increasing values in [0,1] and k represents the iteration counter. Note that parameter Gk enables building a sequence of nested

subintervals of the form -[Wtmax ,Wtmax -Wk]. Note l.ill

that the minus sign affecting this interval is needed due to the minimization nature of problem.

Step 3: Robust problem is solved to obtain the hourly production of wind power at iteration k.

Step 4: Repeat iteratively (indexed by k), steps 2,3 above covering the whole range of factor Gk . The increment step is (j > 0 .

Step 5: Build the hourly offering curve using the obtained results. Each iteration k provides wind power per time period.

IV. RESULTS AND DISCUSSION

A MG that has four wind turbines of 335 KW each, and one PV system and 10 dispatch able units is considered in this study. The capacity of the wind turbine is equal to 1.34 MW. The used turbine model and its power curve are detailed in manufacturer database [15]. The historical wind speed data used in this study taken form publically available database at Illinois Institute of Rural Affair, USA. [16]. The PV system capacity is 200kW, where the system parameters, the insolation profile, and the temperature profile are obtained from [13]. The capacity of the line linking the upstream grid and the MG is assumed to be equal to 1000kW. A 24 hour demand profile is created as fig. 1. To simulate the cases of excess demand and excess renewable generation. One third of the demand is assumed to be critical.

IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09-11, 2014, Jaipur India

2500

2000

1500

10001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hours

Fig. 1. Hourly demand profile

350 �--���----�-----r�======���� A A·A·.,..", •• -•••. Upper limit

300

� ""' S 200

0-'5 o � 150 o '"

100

50

\ f \:&. t --e- Forecasted

\! \ /\ l 1 \ : irA ;..

--,,6, ..... Lower lim it

V .-IIt i A-./i." \A..A-A

10 15 20 Time (h)

Fig. 2. Hourly wind power

25

The parameters of dispatchable units are considered as following table:

TABLE I. DISPATCHABLE UNIT PARAMETERS

Dispatchable Pmax Pmin Cost Parameters unit (KW) (KW) a b c

I 600 100 5 4 0.001

2 600 100 5 6 0.002

3 400 100 20 8 0.0025

4 400 100 20 10 0.0025

5 300 50 30 10 0.002

6 300 100 30 12 0.002

7 200 100 40 14 0.0015

8 200 50 40 16 0.0015

9 100 50 55 15 0.0012

10 100 50 55 17 0.0012

The models presented in Section II were modeled using the General Algebraic Modeling System (GAMS), and solved using ePLEX solver [17]. The time horizon of both models is 24 hours. The price of the grid power is set at 100$/kWh. The spinning reserve requirement in the grid-connected mode is equal to 10% of the entire load. However, the wind power

uncertainty is modeled as shown in Fig. 2. This fig. shows the forecasted wind power, and lower limit and upper limit of wind power at 95% confidence interval for 24 hours.

TABLE II. COMAPARITIVE ANALYSIS

Approach Optimal Cost

Deterministic $3898179

Robust $ 3531891.219

In both cases I.e. determinIstic mode and Robust mode generation Scheduling problem is solved and optimal cost for 24 hour microgrid scheduling is obtained are shown in Table II. From the table it is observed that in robust approach the generation scheduling cost is lesser than the deterministic approach because in robust approach the uncertain wind power is forcasted by using 95% confidence interval in its lower & upper bounds. Here only uncertainty in wind power is considered further uncertainty in PV Generation and demand profile may also be considered and more accurate and comparable results are obtained.

V. CONCLUSION

This paper discusses the Generation scheduling at pee in grid connected microgrid. Here microgrid is considered having renewable generators (i.e wind generator and PV generator) and dispatchable units and generation scheduling problem at pee is formed by using MILP (Mix Integer Linear Programming) by proposed robust approach and then problem is solved by using GAMS (General Algebraic Modeling System). Two Study cases of Microgrid i.e. excess renewable generation at pee and excess demand at pee are considered and results are discussed and compared with deterministic approach of scheduling. In proposed approach wind power is considered as uncertain parameter and is modeled in its lower and upper bounds in 95% confidence interval. However uncertainty in PV generation and load profile is also present. this may be considered in future work.

REFERENCES

[11 A. K. Basu, S. Chowdhury, and S. P. Chowdhury, "Impact of Strategic Deployment of CHP-Based DERs on Microgrid Reliability," Power Delivery, IEEE Transactions on, vo1.25, no.3, pp.1697-1705, July 2010.

[2] C. A. Hernandez-Aramburo, T. C. Green, and N. Mugniot, "Fuel consumption minimization of a microgrid," Industry Applications, IEEE Transactions on, vo1.41, no.3, pp. 673- 681, May-June 2005.

[3] S. X. Chen, H. B. Gooi, and M. Q. Wang, "Sizing of Energy Storage for Microgrids," Smart Grid, IEEE Transactions on, vol.3, no.l, pp.142-151, March 2012.

[4] F. Katiraei, R. Iravani, N. Hatziargyriou, and A. Dimeas, "Microgrids management," Power and Energy Magazine, IEEE, vol.6, no.3, pp.54-65, May-June 2008.

[5] I. S. C. C. 21, IEEE Standard for Interconnecting Distributed Resources With Electric Power Systems. IEEE 15471, 2005.

[6] F. Blaabjerg, R. Teodorescu, M. Liserre and A. Timbus, \Overview of Control and Grid Synchronization for Distributed Power Generation Systems," IEEE Trans. on Ind. Electron., vol. 53, pp. 1398{1409, Oct. 2006.

[7] Eltawil, M.,Zhao,Z.,2010.Grid-connected photovoltaic power systems: technical and potential problems-a review. Renewable and Sustainable Energy Reviews 14, pp. 112-129.

IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09-11, 2014, Jaipur India

[8] Fiji Electricity Authority, 2010. Fiji's renewable energy power development plan, Hasmukh Patel, Chief Executive Officer. Presentation to the Fiji National University (FNU) Symposium on Renewable Energy Technologies, Suva, Fiji, October 2010.

[9] A. Y. Saber, and G. K. Venayagamoorthy, "Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles," Systems Journal, iEEE, vol.6, no.l, pp.103-109, March 2012

[10] M. Q. Wang, and H. B. Gooi, "Spinning Reserve Estimation in Microgrids," Power Systems, iEEE Transactions on, vo1.26, no.3, pp.1l64-1174, Aug. 2011.

[II] R. Doherty, and M. O'Malley, "A new approach to quantify reserve demand in systems with significant installed wind capacity," Power Systems, iEEE Transactions on, vo1.20, no.2, pp. 587- 595, May 2005.

[12] M. Carrion, and 1. M. Arroyo, "A computationally efficient mixed integer linear formulation for the thermal unit commitment problem," Power Systems, iEEE Transactions on, vo1.21, no.3, pp.1371-1378, Aug. 2006.

[13] T. Logenthiran, and D. Srinivasan, "Short term generation scheduling of a Microgrid," TENCON 2009 - 2009 IEEE Region 10 Conference, pp.l-6,23-26 Jan. 2009.

[14] Y. M. Atwa, E. F. EI-Saadany, M. M. A. Salama, and R. Seethapathy, "Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization," Power Systems, IEEE Transactions on, vo1.25, no.l, pp.360-370, Feb. 2010.

[15] ENERCON Wind Turbines, Wellesweiler, Germany. [Online]. Available: http://www.enercon.de/en-en/Windenergieanlagen.htm

[16] Illinois Institute of Rural Affair, USA. [Online]. Available: http://www.illinoiswind.org/index.asp

[17] General Algebraic Modeling System, GAMS, 2013. [Online]. Available: http://www.gams.com/