7. Conclusions
in the microgrids integrated ADN. Moreover, the incorporation of the proposed rule-based MPC reduces the energy loss due to active power loss in distribution networks, as is evident from the simulation results obtained by comparing the proposed approach with an existing MPC-based approach. Besides, in terms of the computational burden, the computation time of the proposed controller is found to be less for one sampling period in solving optimization problem, when compared with an existing MPC approach. Furthermore, control performance is evaluated using SSVE performance index.
In Chapter 3, a dual-stage model predictive based voltage control is proposed that optimally co- ordinates the reference voltage of OLTC in the first stage, and PV inverters’ active and reactive power set points, and reference voltage of DSTATCOM in the second stage to maintain net- work voltages within the operating limits. This dual-timescale coordinated algorithm effectively reduces power loss as well as OLTC tap operations. The two functionalities of active distribu- tion management system, i.e., demand response and conservation voltage reduction are further explored in the voltage control methodology to enhance energy efficiency of the distribution net- works. Simulation results depict that the integration of these two functions in the MPC-based VVC helps in reduction of energy loss, peak demand, energy consumption and controllable re- source utilization. The CVR operation with PV inverters’ reactive power capability yields better results in terms of higher reduction in energy consumptions (0.7%), system losses (5.8%), and deeper voltage reduction within ANSI standard in comparison with only CVR (absence of PV inverter).
In Chapter 4, a dual-stage coordinated control approach has been presented for voltage regu- lation and congestion management of ADN in the presence of PV generators and EVCS. The proposed scheme operates on RBMPC to optimally manage the settings of the regulating devices, i.e., OLTC, DSTATCOM, PV generators, and EV inverters that have different temporal charac- teristics. Here, the voltage and branch current magnitudes are the outputs as well as the states of MPC. Three cases are defined to study the effects of locations of EVCS in the distribution networks. In Case C, although the charging demand is more than the other two cases with two EVCS, the controller performs better than Case B. This is due to the increased availability of reactive power support from on-board chargers of EVs. The number of tap movements and line congestion in the proposed approach are considerably less than the other compared approaches due to the inclusion of timescale decomposition of volt/var devices and branch current constraint
7.1 Summary
with proper coordination among the control elements, could fulfill the desired objectives. Each control element has a role to play in the voltage regulation scheme. While the DSTATCOM helps to limit the maximum voltage magnitudes, the reactive power support from PV inverters aids in minimizing voltage error and energy loss. Moreover, the computation time for a partic- ular sampling instant is evaluated to be few milliseconds, which makes the proposed approach compatible for practical use.
The Chapter 5 proposes a three-stage MPC-based centralized coordinated approach to schedule charging of EV and volt/var devices. The approach aims at maintaining bus voltage magnitudes and state-of-charge of EV battery within desired limits with minimal usage of control resources and cost of electricity consumption. The first stage determines the optimal operating points of traditional discrete control devices on an hourly basis. The second stage dispatches the optimal set-points of power electronics interfaced fast devices [PV and EV inverters] every one minute.
The third stage schedules charging of EV half-hourly with respect to the real-time electricity price. The control approach ensures that EVs attain the desired state-of-charge (SoC) at the time of their departure from the charging station without violating the voltage limits. The EV charging behaviors have been taken into consideration while modeling EVCS. The reactive power support from EVCS has also been utilized to regulate the voltages. Moreover, the lo- calized volt/var curve integrated into the DG units is adjusted according to the reactive power set-points obtained from the MPC-based centralized controller. From the simulation results, it can be observed that the bus voltage magnitudes of the distribution networks are regulated within allowable voltage ranges and the SoCs of the EVs reach the desired values at the time of their departure for all the operating conditions. However, the energy loss and resource utilization in Type I charging (uncoordinated charging) are more compared to Type II charging. With coor- dinated charging, energy loss decreases by 20.16% compared to uncoordinated charging method.
It is observed that energy consumption due to EV charging during high price charging hours is 40.36% less for Type II charging compared to Type I charging. In Scenario II, due to heavy loading and cloudy condition, more voltage dips are observed in the voltage magnitudes. How- ever, the proposed approach shows that the voltage could be brought back to the desirable value with active and reactive power injections of EVCS. Moreover, it is observed that, with increase in the network dimension and control variables, the computation times increase proportionately.
The Chapter 6 proposes a three-stage, two-level receding horizon control-based volt/var opti- mization for the optimal power dispatch of EV and solar inverters in ADN. The approach aims
7. Conclusions
at coordinating different voltage regulating devices depending on their slow (first stage) or fast (second stage) responses to maintain the nodal voltages magnitudes and scheduling the charg- ing of electric vehicles (third stage) in the first level of operation. EV aggregators, being the interface between DNO and EV users, is an independent entity that also seeks its own sustain- able benefits from the coordinated optimal scheduling and regulation services. In the proposed control algorithm, attempts have been taken to maximize the benefit of EVA while performing ancillary services through grid-to-vehicle and vehicle-to-grid infrastructure. Demand response is also used in the third stage of operation. The model has been formulated as a mixed-integer non- linear programming problem and implemented in general algebraic modeling system software.
Furthermore, the reactive power of the fast converters are dispatched through the local Q(V) characteristics in the second control level. Moreover, the voltage regulation objective in all the stages benefits DNO technically. The proposed method ensures that the EV users’ satisfaction of desired SoC at departure time is attained at all the buses. Simulations have been performed with slow and fast charging schemes. It has been observed that the maximum voltage dip in fast charging scheme is 9.17% more compared to slow charging scheme with same charging infras- tructure capacity. Moreover, profit gained from charging and regulation services is almost 50%
more in fast charging scheme than slow charging scheme for both the aggregators. Furthermore, it is observed that the proposed (Type II charging) method is better than uncoordinated (Type I charging) method in terms of technical (reduced energy loss, voltage deviations) and economical (more profit) considerations.
The simulation study is conducted for different cases in MATLAB software. The CONOPT/CPLEX solver of General Algebraic Modelling System /IBM ILOG community edition software is used as the solution tool. The validation of the proposed control approach is done in 33-bus as well as 38-bus distribution networks. The summary of this thesis are as follows:
(i) A rule-based MPC approach has been formulated for coordinating OLTC and PV inverters which can minimize energy losses and can maintain the node voltages, as well. The proposed approach acts as a corrective controller that brings the voltage magnitudes within their desired limits in ADN integrated with and without microgrids.
(ii) An MPC scheme based on dual-time scale coordinated algorithm has been developed, that co-