A Fuzzy Logic based Power Management System for an integrated AC ...

2017 Moratuwa Engineering Research Conference (MERCon)

A Fuzzy Logic based Power Management System for an integrated AC-DC Hybrid Microgrid Model H.W.D. Hettiarachchi, K.T.M.U. Hemapala and A.G.B.P Jayasekara Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka

(FLC), Artificial Neural Networks (ANN), optimization algorithms like Generic algorithm (GA) and Particle Swarm Optimization (PSO)[2]. Fuzzy Logic (FL) has stepped out of these providing an enhanced and efficient control mechanism providing much simple way of handling [3]. A proper power management and control is essential in order to ensure the power quality and the reliability of the system which includes the functions such as active and reactive power flow control, islanding detection, grid synchronization and system recovery. As it is well observed in the literature that the frequency control techniques are well implemented to show their effectiveness to the MG, this paper mainly concentrates on developing sequential operating methodology in DC bus bar control at various scenarios and voltage control scheme of the AC busbar. The remaining paper is organized as follows: Section II summarizes the relevant control hierarchy and topologies for each DC and AC busbar. Section III presents proposed control architecture. Section IV depicts the results based on the integrated simulation model considering few critical cases of operation and finally section V summarizes the appropriate conclusions.

Abstract—Recent development in Fuzzy logic control algorithm has led to the advancement of the existing smart grid technologies where it requires powerful control algorithm in an uncertain environment due to high penetration of the Renewable Energy Sources(RES). Due to the adaptive and human-like control decisions taken by Fuzzy Inference System (FIS), this would be much suitable to tolerate the uncertainties caused in multi-scenario operation of Micro Grid(MG). This paper presents an enhanced control methodology for a DC busbar in AC-DC hybrid MG with an agent based automated distribution system. A laboratory scaled MG which contains a few Dispersed Generators(DGs), a storage devices and a distribution system based on Multi Agent System(MAS) along with the protection system, advanced smart metering system and reliable communication methodology has been developed as a fully automated operating system in both grid connected and islanded modes for the purpose of governing better modes of control. This improvement in control needs to be simulated in the MATLAB/SIMULINK environment to ensure its capability to restore the disturbed situation prior to the hardware implementation. Keywords— DC bus bar; Microgrid; Multi Agent System, Fuzzy Logic Control; Fuzzy Inference System

I. INTRODUCTION

II. CONTROL OF THE MICROGRID

MG is a cluster of DGs usually linked through power electronic interfaces like converters and inverters to the utility grid. Conventional power plants are centralized and the longdistance power flow over the transmission lines leads to high degree of energy losses hence decreases the efficiency of the overall power system. Here the alternative is proposed as MG where the close connection with the renewable energy replacement where the whole world is enthusiastic on adaptation. Renewable Micro-Sources (MCs) like PV and wind generation add considerable uncertainty which has to be addressed through the compensation methods like storage devices and fuel cells while back up diesel generators provide added redundancy to the system [1]. In such scenario, sharing loads between the sources become much problematic where it needs to be supported by an extensive control mechanism to provide remedial actions with the collaboration of power electronic interfaces. The review prior to the study shows that the overall control aspects can be categorized in to several main criteria namely Battery Energy Storage System(BESS) control module, Load Frequency Control (LFC) module and Voltage control scheme. Recent studies show that all those control schemes are aided with adaptive controllers of Fuzzy Logic Control

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The grid comprises of two separate control modes, grid connected and isolated mode where the requirement of control topologies needs to be considered separately. As it is mentioned above, the control can be separately considered for the AC and DC bus bar for the simplification, under several agents linked to those, based on the architecture of the MG as shown as in Fig. 1. As voltage and frequency are critical parameters of operation, these require high attention of control under the main control agent of the whole MG namely MicroGrid Operation and Control Center (MGOCC). In comparison to that, critical parameters of DC busbar are DC voltage and the battery state of charge (SOC) level which require lesser hierarchical control than pervious which is done by the DC agent and BESS agent respectively. In this paper, study is limited to the development of the controller for the DC bus bar performance optimization. Once the MGOCC is identified that the Grid is operating in particular state then the required local controller for each parameter is selected by the MGOCC itself and the communication topology facilitates this signal to be sent to the local controller. Overall AC-DC hybrid MG with all the power electronic control interfaces linked to each other is depicted in Fig.2.

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• To keep the SOC in a secured range which is prementioned by the manufacturer to avoid the deep discharge and over charging. • To ensure the power balance: - In isolation mode, as the system is compensated by the Inverter by charging • Peak shaving can be done by discharging in the peak time where charging is possible in off peak. • To enhance the battery life by reducing number of cycles of charging. . Fig. 1. Overview of hybrid micro grid control

A.

DC bus bar control

When considering the DC busbar separately, two attributes emerge as main control units for the integrated control. Primarily, DC bus bar needs to maintain a voltage in a predetermined range to serve its DC loads. This may be a considerable issue when it comes to a long radial distribution line where voltage drop is considerable. Moreover, certain sensitive loads without a voltage compensator would malfunction and the voltage stability might deteriorate further as the bus voltages become seriously diminished[4]. Secondly, Battery SOC level control should be done in order to achieve following key objectives,

For the purpose of DC bus bar voltage control, a lot of technologies have been used regarding controlling the DCDC converter at the solar input to the DC bus[5,6]. Furthermore, if the distribution line is long ,then enough distribution of DC sources is required. Overall control activities of DC voltage control can be described as below. I. If the solar generated voltage is less than the minimum required voltage of DC-DC converter to match with the dc busbar voltage, then solar input is cutoff. II. If the battery SOC level is too low hence battery voltage is dropped to a level less than lower limit of dc bus bar voltage III. If the AC bus bar is operating in one of the utility grid available states, then voltage set up point is set by the bidirectional converter. IV. If AC bus bar is operating in a critical state (In an inverter unavailable), then voltage controlling is done by the either BESS or PV agent or both collectively based on their output power.

Fig. 2. Overview of the common configuration of MG

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For the simplicity of the design, voltage drop has been neglected even though for a long distribution line this is considerable. In consideration of constraints mentioned above, state transition diagram can be developed for the DC bus bar considering status of PV (Ok/Low), status of BESS (healthy/unhealthy) and status of inverter (unavailable/available). Once after the state is recognized by the state diagnosing unit, voltage controller is selected based on logic algorithm. Priority levels of control can be described as below. If the inverter is available, then voltage control is possible through that inverter. Next, if the Battery and PV panels are healthy in operation, then the decision has to be made that which is going to be the voltage controller based on their output power. If power delivered by the battery is larger than the PV output power at this instant priority is given to the battery. In case of battery SOC drain beyond its lower limit then the voltage control of the battery is transferred to the local control of the PV which is a DC-DC converter where simple PI controller will take care of this[7]. 1) Problem formulation , < < , , > − , min , = , × (1) As explained in (1) , voltage control can have three masters according to the scenario it is operating in. Different types of architecture are used for each case. Voltage control through the BESS will be discussed later in this paper where it is much related to the battery SOC. 1. Voltage control through PV Control is quite simple as the DC-DC converter’s duty is varied by the PV agent’s PI controller which is shown in the Fig.3. 2. Voltage control through Inverter Inverter voltage control can be summarized as Fig.4 where AC gird voltage and inductor current are considered for the Linear Quadratic Integral control DC voltage and DC voltage reference are considered to generate the sine wave which is extensively described in[8].

B.

Battery SOC level control For the purpose of Battery SOC level control to achieve aforementioned objectives, there should be a state mechanism approach to react to the multi scenario operation. BESS is the main agent for this case and the main inputs are battery SOC level, grid and inverter availability and load level. 1) Problem formulation Ultimate objective of this section would be mathematically explained as in (2), < < (2) Certain constraints should be considered the objective shown in (3), < < , (3) , Two 12V,135Ah Lithium ion batteries were used for laboratory test bed and same modelled in Simulink using Li ion battery model of Simulink library. Healthy battery SOC level is considered as between 20% to 80% according to manufacturer’s specifications. Maintaining the battery SOC level can be done with a comprehensive control strategy of charging current under the charging current constraint. Firstly, based on voltage and current data acquired, SOC computation module calculates the current SOC of the battery which would be an input to the next module called Battery Mode Identification System where the other inputs like load level from the MGOCC /DC agent and grid/inverter availability from the MGOCC are fetched show in Fig.5. Hence with the help of the state mechanism approach operating state is governed to choose the closed loop control system. As number of factors and scenarios have to be considered firstly the problem is categorized in to three cases to meet above constraints where Battery SOC level is too low, too high and healthy based on the predetermined SOC level mentioned by the Battery manufacturer to avoid deep discharge and overcharge. If battery level is too low or too high, charging or discharging would be done considering the load level at that time. In the healthy operating state, it is difficult to understand the charging or discharging rate of the battery with the SOC, Solar input and dynamic load changing environment. Hence it is required to set up a control algorithm with the aid of the FL. Microgrid MGCC Grid availability/inverter availability

Battery Energy Storage System SOC Computation Module

Fig. 3. Block diagram of voltage control through PV

Voltage Power measurements

Battery Mode Identifications system

Firing signals Converter

Fig. 5. Control system blocks of BESS Fig. 4. Diagram for voltage control using an inverter

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Closed loop control

Battery

3) Fuzzy Output for charging controller Charging current: - This is also a triangular membership function defined as below. 0-0.1C low 0.05-0.25C medium 0.2-0.3C high

III. DEVELOPMENT OF THE DUAL FUZZY CONTROL Some of the Fuzzy charging units are discovered in the literature and most of them adopted Fuzzy controllers only for charging of the battery[9]. When it comes to the discharging, if the sources are readily available and battery is considerably charged, then necessary control action should be taken to discharge the battery for the purpose of peak shaving as mentioned under the objectives of BESS control[10-12]. This would be supported by a Fuzzy discharge controller as the decision taking should be based on the load level and current SOC level. Hence dual Fuzzy architecture is developed to charge/discharge the batteries after carefully studying the critical factors of operation. For the Fuzzy charging control, solar input and SOC level are considered as viable factors and fuzzy input and output sets are categorized as below[13].

4) Fuzzy rules for charging Charging Fuzzy rules are adopted as same as in[14]. B. Fuzzy Discharging Controller 1) Discharging controller designed considering not to result in quick discharging rate hence each input is considered in 5states. SOC level is considered as