2. Model Predictive Control-Based Optimal Voltage Regulation of Active Distribution Networks with OLTC and Reactive Power Capability of PV Inverters
0 5 10 15 20
1 1.1 1.2
Voltage (pu)
Voltage Profile of Bus-16
Without microgrids With microgrids
0 5 10 15 20
0 0.01 0.02
Active Power (pu)
Active Power Profile of Bus-16
0 5 10 15 20
-0.02 0 0.02
Reactive Power (pu)
Reactive Power Profile of Bus-16
13 13.2 13.4 13.6 13.8 14 14.2 14.4 14.6 14.8 15
1 1.2
13 13.2 13.4 13.6 13.8 14 14.2 14.4 14.6 14.8 15
0 0.01 0.02
13 13.2 13.4 13.6 13.8 14 14.2 14.4 14.6 14.8 15
Time (hour) -0.02
0 0.02
(a)
(b)
(c)
Figure 2.10: Plots of: (a) voltage profile at bus-16 (b) active power profile at bus-16 (c) reactive power profile at bus-16 for 38-bus distribution networks.
2.5 Conclusion
loads. As soon as the voltages are within feasible range, minimization of power loss is considered as the main objective of MPC. Simulation results show that, in addition to regulating the voltages within prescribed limits, the rule-based MPC described in this chapter is successful in significantly reducing energy loss. The energy curtailment, as well as active power distribution losses throughout the day, are computed and compared. The energy loss is moderately higher in the case of microgrids integrated ADN, as the microgrids inject power to the buses during most of the time of the day. Furthermore, it is observed that energy loss gets drastically reduced with the incorporation of RBMPC when compared to an existing MPC approach. Besides, the proposed controller is also compared with an advanced MPC approach in terms of computational burden for one sampling period in solving optimization problem. Furthermore, control performance is evaluated using steady state voltage error performance index.
2. Model Predictive Control-Based Optimal Voltage Regulation of Active Distribution Networks with OLTC and Reactive Power Capability of PV Inverters
3
Model Predictive Control-Based Coordinated Voltage Control in Active Distribution Networks Incorporating CVR and DR
Contents
3.1 Introduction . . . . 51 3.2 System description . . . . 54 3.3 Voltage control model for ADN . . . . 56 3.4 Problem formulation . . . . 59 3.5 Results and analysis . . . . 61 3.6 Conclusion . . . . 70
3. Model Predictive Control-Based Coordinated Voltage Control in Active Distribution Networks Incorporating CVR and DR
3.1 Introduction
In the last chapter, a rule-based voltage control methodology has been developed that coordinates the actions of on-load tap changer and PV units to maintain the voltages within defined limits. This chapter proposes a model predictive based voltage control that optimally coordinates the reference voltage of distribution static synchronous compensator, OLTC, and PV inverters’ active and reactive powers set points to maintain network voltages within the operating limits. To manage the different voltage regulation devices with different temporal characteristics, two-timescale based coordinated algorithm has been developed. Moreover, the two functionalities of active distribution management system (ADMS): demand response (DR) and conservation voltage reduction (CVR) are explored in this voltage control methodology to enhance energy efficiency of the distribution networks. The proposed methodology is implemented in 33-bus and 38-bus distribution networks to verify its effectiveness for different cases. Furthermore, simulation results demonstrate the benefits of CVR and DR on the proposed methodology.
3.1 Introduction
The paradigm shift from passive to active distribution networks has been possible with the massive penetration of distributed energy resources (DER), such as, energy storage systems, solar photovoltaics (PV), wind turbine generators, etc. [58]. The rising number of distributed generations (DG) could impact the operation and control of distribution networks in a negative manner. One of the serious issues is the effective coordination of various DGs with conventionally used voltage regulation devices such as, shunt capacitors, on-load tap changers (OLTC), etc. for voltage control purposes [47].
Volt/var control (VVC) is an important aspect of the active distribution management systems (ADMS) [59]. The VVC aims to maintain the voltages within the operating limits set by American National Standard Institute (ANSI) C84.1 and confine to the interconnection reliability standards.
The VVC in active distribution networks (ADN) is usually performed either in decentralized [8,26,60]
or centralized manner [6,14,21]. Both offline [6,8,60,61] and online [6,14,21,26] VVC methods are available in literature. Recently model predictive controller (MPC) has been used widely as an online voltage control [6,14,21,26]. MPC principles can be applied either in single-timescale [6,14] or multi- timescale [21]. Ref. [6] proposes a centralized MPC to regulate voltages in ADN by coordinating DG units and OLTC in single-timescale. Authors in [21] have developed a voltage control algorithm for distribution networks, coordinating both slow and fast VVC devices.
3. Model Predictive Control-Based Coordinated Voltage Control in Active Distribution Networks Incorporating CVR and DR
The smart inverters interfaced to DG units with their reactive power absorption and generation capabilities can participate in voltage regulation. These devices act as fast controllable resources and have been explored in [10,14,21,21,62]. They either follow commands obtained from control algorithm or any autonomous volt/var curve. In ref. [14], an MPC-based voltage control methodology is presented that coordinates energy storage systems, OLTC, and DG units in preventive and corrective modes of operation. Ref. [62] proposes a centralized MPC to correct the locally obtained reactive power set-point of DG units. All the aforementioned works have considered reactive power capabilities of DG units in their voltage regulation approaches. However, these smart inverters interfacing DG units are slightly oversized to enable their participation in voltage regulation, even with 100% active power production. The usage of custom power devices in VVC is witnessed in recent literature. A voltage control strategy has been developed in [43] utilizing OLTC and distribution static synchronous compensator (DSTATCOM) in an ADN. Although custom power devices have been used with other voltage regulation devices to fulfill different objectives for distribution network operator (DNO) in several literature, very few of them have explored the reactive power capabilities of DSTATCOM in an optimal coordinated voltage control scheme. Moreover, most of the aforesaid works have not explored other features, such as, reduction of energy loss, energy consumption, peak demand or peak power loss.
Conservation voltage reduction (CVR) is another aspect of volt/var optimization (VVO) strategy where the supply voltage to consumers is lowered but within the ANSI standard to reduce the en- ergy consumption of consumers [43,63,64]. A considerable amount of studies has been done on this technique. Several authors have studied VVO deployed with CVR and other objectives in presence of DER. Reference [43] has described the model predictive based multi-timescale coordinated operation of CVR in ADN with electric vehicle (EV) integration. Authors in [63] have increased the energy savings from CVR in PVs integrated ADN by coordinating smart inverters with traditional voltage regulating devices. The trade-off between energy conservation and loss minimization with CVR has been studied in [64].
Similarly, demand response (DR) is another technique to improve energy efficiency. DR implies shifting the load behavior from a particular time instant to another to gain economic benefits, or cur- tailment of certain loads with the coordination of the customers’ choices [30,61,65–67]. DR techniques
3.1 Introduction
a DR control algorithm considering energy pricing limits for a residential PV storage system. In [65], DR is implemented by reducing demand for a specified time duration in a microgrid. A model based predictive control has been discussed in [65] to schedule flexible resources (heating systems and energy storage systems) in presence of solar photovoltaics.
This chapter presents a model predictive based voltage control scheme, that coordinates OLTC, DSTATCOM and reactive power capability of PV inverters to maintain bus voltages. Curtailment of PV power is used as an emergency control action. This work is an extension of Chapter 2, where the preliminary works on coordinated VVC have been described. In this work, unlike Chapter 2, the VVC is allowed to operate in double-timescale. Further, the CVR and DR functionalities are integrated into the VVC separately by incorporating DSTATCOM, voltage dependent loads and flexible loads, respectively into the network and the effects of DR and CVR techniques on VVC have been analyzed.
Moreover, instead of considering all the PV units as controllable resources, PV units at specific buses are considered to be controllable. This is done to reduce computation burden and the necessity of oversized PV inverters for voltage regulation purposes.
In recent survey, CVR and DR both are integrated simultaneously to fulfill different objectives.
In [29], CVR and DR have been used to minimize energy consumption cost in a day-ahead market.
In [68], authors have considered a two-stage optimization structure to determine the size and location of soft open point (SOP) and battery energy storage system (BESS) considering both CVR and DR schemes. While Ref. [29] discusses the economic aspects, i.e., determination of the optimal pricing scheme for every individual customer to participate in the DR, Ref. [68] formulates a planning problem.
However, the online coordinated voltage control structure is missing in [29] and [68].
Unlike previous works on VVC, CVR and DR have been deployed in this work in coordination with the VVC. The VVO objectives are formulated in an MPC-based coordinated framework. Moreover, the proposed scheme considers timescale decomposition of VVC devices. Table3.1points out the dif- ferences in the approach described in this work with other works from previous literature. Considering the above cited works, the major contributions of this work are:
(i) To develop a two-timescale MPC-based coordinated VVC strategy to enhance energy efficiency along with CVR and DR techniques;
(ii) To propose a load-shifting based DR technique in the MPC-based coordinated voltage control framework.
3. Model Predictive Control-Based Coordinated Voltage Control in Active Distribution Networks Incorporating CVR and DR
Table 3.1: Literature survey.
Attributes Timescale Reactive Reactive CVR DR Energy
Considera- Decompos- power sup- power sup- Loss
tion ition port from port from Analysis
DSTATCOM PV
Chapter 1 × × X × × X
Ref. [21] X × X × × ×
Ref. [43] X × X X × X
Ref. [67] × × × × X ×
Ref. [29] × × × X X ×
Ref. [68] × × X X X X
Present Work X X X X X X
The remainder of this work is organized as follows: Section 3.2 describes the system investigated in this chapter. The modeling of voltage dependent loads and flexible loads are also discussed in Section 3.2. Section 3.3 discusses the voltage control model incorporating CVR and DR objectives.
The voltage control problem is formulated in Section 3.4. The simulation results and discussions are presented in Section 3.5. Section 3.6 presents the conclusions of this work.