Informe Focus Group

studies [19]. In a grid-connected system, an induction generator (IG) can get reactive power from the grid system, such as, static var compensator (SVC), capacitor banks, synchronous condenser, and static synchronous compensator (STATCOM) system, whereas, the reactive power demand of an IMG need to be supplied from an arrangement, such as built-in capac- itor bank with WPS or synchronous condenser or converter system. Many loads of a large remote community are inductive in nature and the power factor of a typical residential load varies around 0.92 while the same for the commercial/industrial loads ranges from 0.8 to 1.0. The high REP IMGs are expected to operate keeping the DGS idle for a substantial amount of time. In such a situation, the reactive power demand need to be supplied from a converter system. The mismatch in generation and consumption of the reactive power in an IMG can cause serious problems such as large voltage fluctuations at generator terminals [20]. Refer- ence [21] proposes a centralized energy management system (EMS) for an isolated microgrid considering both real and reactive powers. Presently, due to integrating RERs in the existing DGS-based off-grid power systems, the reactive power demands of IG are supplied by a dedi- cated DGS that runs at no-load operation [1], [22]. Thus, the DGS can provide reactive power in a low REP IMG. If the sizes of component in a large REP IMG are insufficient for supplying reactive power, large voltage fluctuations in the IMGs are imminentness.

1.4

Control Strategies and Optimal Sizing of IMGs

The investigations on existing (i) operating strategies, and (ii) approaches of optimization for the IMGs are required at the outset of optimal sizing design. The already carried out studies and the remaining scopes on the topics are discussed below.

1.4.1

Control Strategies/Operating Policies for the IMGs

The economic performance of an IMG heavily relies on operating policies, i.e., the coordi- nated controls among the DERs of an IMG. The control strategies that pertain the energy flows among the components in an IMG is known as dispatch/energy menegement/power manage-

12 Chapter 1. Introduction

ment strategy (PMS). An IMG that is comprised of DGS, BESS, and RERs offers many modes of operation. As such, Figure 1.3 signifies the energy-flow options of a high REP-based IMG. Figure 1.3 shows a dump load that is required in a high REP-based IMG in order to absorb the access generation, though few researchers [23] have proposed dynamic control schemes to regulate output power of a WPS during high wind and suggested to eliminate the use of dump load in the IMGs. The DGS of an IMG ensures reliability of power supply, as well as main- tains the voltage and frequency. For an IMG, the operating strategies decide the sequences of start/stop and charge/discharge for the DGS and BESS, respectively. The PMS of an IMG also maintains a balance among costs, reliability, amount of dump energy, and REP.

However, the grid-connected microgrids integrated with BESS usually do not employ DGS and thus, the PMS of a grid-connected microgrid is straightforward and simple. Figure 1.4 represents a PMS for a grid-connected microgrid integrated with a BESS. Moreover, the PMS of an IMG that contains either DGS or BESS, along with RERs, does not generally provide many modes of operation. Therefore, the PMS of an IMG consisting of the components of Figures 1.3 is not as simple as grid-connected microgrid due to various possible modes of op- eration. The formulation of a PMS includes constraints associated with operational limits of the generating units, power balance, energy balance of energy storage systems, and spinning reserve. In numerous studies [21, 24–31], the PMS concepts, economic benefits, energy sav-

renewable energy

(e.g., wind and PV)

thermal energy (e.g.

diesel generator)

energy storage (e.g.,

battery bank)

load

energy

dump load

energy

ings by the PMSs, and optimization of the parameters of a PMS are focused. Considering a wind-PV-diesel-battery system, a few PMSs have been proposed and the effects of those PMSs

1.4. Control Strategies and Optimal Sizing of IMGs 13

start

no

Determine desired load power, calculate renewable power (Pre),

battery DOD load power,PL>=

Pre

SOC<SOCmax

Power the load and supply the surplus power to

grid

Power the load and charge the

battery yes PL=Pre Power the load from renewable no yes SOC<= SOCmin no yes Power the load from battery and renewable yes Power the load from renewable and grid no

end

Figure 1.4: Grid-connected mode supervisory control

on LCC have been analyzed in [24]. The proposed four main PMSs of [24] are (i) load fol- lowing strategy, (ii)SOC setpoint dispatch strategy (iii)full power dispatch strategy, and (iv) frugal discharge dispatch strategythat are explained in general, i.e., without considering the constraint of component sizes. Developing a typical PMS for a PV-diesel-battery system, ref- erence [25] has optimized the PMS by means of the DGS starting and stopping set points. The state of charge (SOC) setpoint of the battery bank has been optimized using genetic algorithm (GA) in [26] upon analyzing and combining a few dispatch strategies of [24]. Reference [27] has reiterated that the performance of an IMG significantly relies on supervisory control and the reference has proposed a scheme accordingly for a PV-wind-battery system. Without taking the optimal sizes into account, a PMS for a stand-alone PV-wind-fuel energy system has been analyzed in [28]. Based on the combination of components, reference [29] has proposed op- timal sizing method of a high REP wind-PV-battery system for three operating modes, which are the (i) RER and BESS, (ii) RER and DGS, and (iii) RER, DGS, and BESS. A central-

14 Chapter 1. Introduction

ized energy management system (EMS) for an optimal dispatch of energy has been introduced in [21] where the reactive power effect is adopted in the EMS design. The optimization of the operating states for a PMS of an IMG has been proposed in [30] based on battery life-cycle characteristics. Using predictions and controllable load, reference [32] has proposed a load management strategy for a wind-diesel-battery system so that the DGS use can be minimized. Reference [33] utilizes artificial neural network for the operation and control of PV-diesel- battery system under known insolation and load demand. Reference [34] proposes energy flow and dynamic power flow model for an autonomous wind-diesel system to investigate the daily and monthly performance of the system under various wind and load regime. Various power management strategies are proposed in [35] for a stand-alone hybrid power system integrated with hydrogen energy storage. The strategies describe the scheme of fuel cell based hydrogen energy storage operations at above or below residual power. The “source following” and “grid- following” dynamic control approaches are proposed in reference [36] in order to exchange the powers among the sources, and to manage energy for a grid integrated system. Using the com- bination of DGS and/or BESS with RERs, reference [31] has presented a sequential simulation technique for evaluating four operating modes of a stand-alone power system.

The aforementioned studies of PMS for the IMGs have either used both the DGS and BESS or any one of the DGS and BESS in their systems. The system consisting any one of BESS and DGS does not require to investigate many dispatch strategies and thus the related studies with the said configuration have not analyzed many PMS either. The above discussion further reveal that the studies with both BESS and DGS have mostly optimized the SOC set point strategy. In some of the studies, the comparisons of the PMSs have been performed based on diesel fuel savings only, i.e., not by adopting in any optimization scheme. The reliability evaluation aspects for IMGs by the use of the PMSs are avoided in the aforementioned studies. The most studies have not accounted in the reactive power effect in the PMSs. Above all, none of the studies have structurally modeled the PMSs, especially, in the context of incorporating any constraints of the components.

1.4. Control Strategies and Optimal Sizing of IMGs 15

1.4.2

Optimal Sizing of IMGs

The general optimization problems, techniques to solve them, and various optimization studies for the IMGs are discussed below.