Automated Demand Response for Residential Consumers

Transcription

1 Automated Demand Response for Residential Consumers Vaibhav Bhosale, Prasad Hadawale, Akash Borole. Department of Electrical Engineering, Sardar Patel College of Engineering, Mumbai, India. Nandkishor Kinhekar, Member, IEEE. Department of Electrical Engineering, Sardar Patel College of Engineering, Mumbai, India. Abstract Demand response activities to mitigate peak and eliminate its drawback are carried out only at large industries or commercial complex. The residential sector is overlooked due to the unavailability of the required architecture and remains unexploited thus possessing great potential. The advent of smart grid technology has bolstered up the importance of residential sector in Demand response. Taking advantage of this fact our paper aims at providing an economical hardware solution to equip residential sector with the architecture required to capitalize on its untouched potential. Keywords Demand Response, Grid State, Curtailable loads, Shiftable loads, Residential, Voltage Control,Dimming. I. INTRODUCTION Demand response refers to a protocol followed during peak period to reduce the stress on the generators and auxiliary equipment s by ensuring efficient use of energy to maintain the demand under control. Demand Response is used to modify the load curve to prevent any peak in the curve by controlling the load connected to the system. Thus the main objective of DR is to maintain a flat load curve. The Energy management is classified into two broad categories as follows: 1. Supply or Source side management (SSM): It refers to the activities carried out at the supply side to reduce the stress on the system during the peak period. The activities are carried out at the generation, transmission and distribution level. Some of the activities undertaken are as follows: Power import contracts Clean coal technologies Renewable energy penetration Modernization of existing power plant and substation to improve efficiency Static power factor correction It can be seen that all above are static activities and do not respond dynamically as per the status of the grid. The basic objective of these activities is to meet the current peak demand using the existing resources eliminating the need of installing a new power plant. 2. Demand or Load side management (DSM): Supply side management as seen is static and thus provides low flexibility and reliability. Demand side management refers to activities carried out on the consumer side to control overall demand. These activities are carried out as the need arises and thus are dynamic in nature providing greater flexibility. In this, the consumer load is controlled to control the demand. The advantages of implementing DSM are; Reduction in the import of power during Peak. Eliminating the need for installation of a new power station to meet peak demand. Indirectly also reduce pollution by preventing the construction of a new power plant Demand response currently is been carried out on a small scale generally with industries. Residential consumers are neglected due to non-availability of the required hardware and software architecture. In India, the residential consumption accounts for 23% of the total electricity generation [1]. It is said that when consumers are made aware of their consumption, the consumption reduces by 7% [2]. Thus, implementation of demand response in the Residential sector proves to be a viable solution. The advent of the smart grid provides a thrust to the idea of implementing DSM, but we still require a complete architecture to implement demand side management successfully. The load curve of each consumer is unique and thus a generalized solution cannot be used for all the consumers. Managing such a complex network manually is not possible and would lead to substantial errors. Besides this implementing this at consumer premises require the consumer to be educated and made aware of its operation which would delay the program and acceptability among the consumers. This necessitates the process to be automated [3]. Making the process automated would relieve the consumers from thinking about their loads and keeping track of their consumption and the utility signals thereby increasing the acceptability among the consumers. This would accelerate the implementation of this DR technology. For successful implementation of DSM, the residential sector should be retrofitted with modern equipment. That has the capabilities to communicate at real time with the utility, monitor the consumption accurately and /14/$ IEEE

2 process the data to control the load accordingly. To operate the system under safe condition by eliminating peak [4]. 3. Related Works: The paper by Jongkwan Seo et al. "Automated Residential Demand Response Based on Advanced Metering Infrastructure Network [5]" address the same field but differ from our paper on the following issues. Our paper focuses on a more economical solution with optimum performance of the system to ensure its acceptability in developing countries like India. Their paper performs DR action based on DR level signalled by Utility. While in our paper we determine the level based on Utility signal and the cluster consumption to ensure optimum performance. Their paper proposes to use smart meter in every consumer house which is replaced by an Individual Control unit in our model to reduce cost. Thus our papers aims at providing a more economic and consumer oriented solution to ensure its acceptability. II. SYSTEM MODEL As a prerequisite, our system requires awareness about Automated Demand Response (ADR) and direct load control, also decision from customer regarding fraction of their load to be included in ADR activities. Once consumer is willing to participate, then we can proceed to implementing system hardware. The basic components required for implementing the demand response technology in the Residential sector is as follows [6] and also shown in Fig. 1; Central metering unit (CMU) Individual Load control Unit (ICU) Smart switches (Voltage controller, Relay Switch) A. Central Metering Unit (CMU) The central metering unit is the heart and brain of the entire system. The major tasks performed by the CMU are: Calculating Real time consumption of a consumer Analysing the Utility signals Processing the data and determining the control action Communicating with the ICU The Major components of CMU are: 1. Microcontroller - ATmega 328P/ 2560 (Arduino Software). 2. Current Measurement unit 3. Voltage measurement unit Working: The CMU consist of a micro-controller (ATmega 328P/2560) that collects the data from the current transformer and potential transformer and then processes it to generate a control signal. We are using Open energy meter [6] based algorithm for energy calculations. This control signal is then transmitted to the ICU using I2C communication. The data from the current and potential transformer is processed by a signal conditioning unit before it is given to the CMU. The signal conditioning unit is used to convert the raw data from the CT and PT in a range that is compatible with the CMU i.e. within a limit of 0 to 5 volts. We are using a Non-invasive CT- 013 current transformer that has 1:2000 turns ratio and has a maximum current capacity of 100 A, with a burden resistance of 33 ohms. To measure the voltage we are using a 230/9 volt AC step-down transformer, using the signal conditioning unit the output is converted into 0 to 5 volts range. Besides this, the CMU also monitors the control signal received from the utility continuously which determines the state of the grid i.e. Off-Peak or ON-Peak and performs the required operation as per the specified Demand response algorithm. B. Individual Control Unit (ICU) The function of ICU is to control the individual appliances in the consumer house. In order to control the appliances the ICU uses various components such as; Voltage control unit Relay Board The ICU is actually an ATmega based Microcontroller that controls the user loads. The ICU communicates with the CMU using I2C communication to receive information regarding the operation to be performed. It acts as a SLAVE to the CMU as it has to perform the operation specified by the CMU. The CMU after analysing the data transmits the STATE of the system to the ICU, The ICU then performs the predefined operation on the load as per the value of the STATE received from CMU. C. Voltage Dimmer This is one of the terminal devices which actually implements the command of curtailing the part of the load. This device is Fig. 1: System Model

3 suitable for some of the residential loads such as Incandescent lamps & Fans. Circuit Explanation: It basically uses triac & its firing circuit to control the waveform going to the load. The firing circuit consists of the optocoupler to detect the zero crossing of the voltage waveform and microcontroller on it gives the required delay after which the triac will be fired. So in all, the load is operated at reduced RMS voltage and in turn reduced power. Thus, the load is partially cut according considering comfort level of the consumer [7]. D. Smart Switch This is another terminal device which completely curtails the load by shedding it or by shifting it. This uses relay circuit having a relay in NC position and it trips when a control signal is received. It operates in two modes: Shifting Mode: The load is cut-off for a fixed duration during the peak period and switched ON automatically. Shedding Mode: The Load is cut-off as long as system peak prevails. III. SYSTEM ARCHITECTURE The architecture determines the way in which the units are interconnected to each other and communicate [8] [9]. The central metering unit (CMU) as shown in Fig.1 is located at the pole mounted distribution transformer which supplies power to the group of a consumer (Buildings). It is connected in series between the Distribution transformer and the load as shown in Fig. 2. The CMU has a communication link with the utility or substation and helps in remotely monitoring the consumer demand under grid peak conditions and hence provides demand response capabilities to the utility. Thus, CMU is the basic requirement of Demand response system. The CMU is just the processing unit that monitors all the system parameter (Voltage, Current, P.F, Grid status etc.) and determines the action to be performed after analysis, the actual task of performing this commands into physical action is done by the Individual control unit (ICU). The ICU is located in the house of every individual who agrees to participate in the Demand response activity. The ICU functions as a slave and executes the command given by the CMU. The ICU performs various operations like: Voltage control Load Shifting & Load Curtailing Besides this the ICU also gives an indication to the consumer about the Grid status and provides the consumer the power to override the actions and exit the Demand response session, thus giving complete control to the consumer. IV. DEMAND RESPONSE ALGORITHM The system operation is governed by a Demand response algorithm [10] [11] which is as shown in Fig. 3. The value of kw_set is determined based on the consumer load and value of LT and UT is determined based on the value Fig. 2: SLD for Entire System Architecture Fig. 2: SLD for Entire System Architecture of kw_set to prevent oscillations in the control system and lock-down, due to continuous toggling of the state when control action is performed as per Table 1. TABLE 1: CONTROL STRATEGY FOR DIFFERENT GRID STATE [7] State Condition Control 0 OFF PEAK Store Electricity 1 kw < kw_lt Do nothing 2 kw_lt < kw < kw_set SET Dimming 3 kw_ut > kw > kw_set SET Cutoff 4 kw > kw_ut Cutoff and Dimming.

4 Fig. 3: Flowchart for Program logic Fig. 4: Prototyped Setup of Actual System

5 V. FEASIBILITY A. Survey From the survey or feedback taken from residential consumers, it is observed that: 100% of users want 24x7 electricity supply. 85% of users wants to minimize their electricity bills. 71% of users are willing to cut off their load and minimize their consumption. B. Prototype Design For calculation purpose, we consider a residential consumer in an urban city consisting of all the commonly used appliances as shown Fig.4. Let us assume a Residential Consumer with the appliances installed as shown in Table 2. TABLE 2: CATEGORIZING IN-HOUSE APPLIANCES Uncontrollable Controllable Rating (in Watts) Rating (in Watts) Mixer 600 Shift-able Tube light 40 Air Conditioner 1000 CFL 20 Washing Machine 500 Water Filter 50 Iron 1000 Oven 1500 Water Heater 3000 Refrigerator 400 Air Cooler 500 Desktop PC 150 Induction cooktop 2000 TV 100 Vacuum cleaner 700 Router 15 Toaster 800 Total 2875 Total 9500 Curtail-able Tube light 40 Incandescent Bulb 100 CFL 20 Dish washer 1500 Dimmable Total 1660 Incandescent Bulb 100 All types of Fans 50 Total 150 From it can be seen that the load can be categorized as below; Total Load: 15 kw Shiftable: 10 kw Curtailable: 1.5 kw Dimmable: 150 W Thus, we have a total controllable load of Approximate 11 kw out of the total 15 kw of the consumer Load. Thus, Residential Demand Response Posses great potential which still remains untapped [2]. We tried to replicate a residential consumer house with a scaled-down prototype of a total connected load of 1.5 kw (Scaling factor of 10). We used Incandescent lamps of 200 and 100 Watt ratings for the prototype to make up 1500 Watt of a load. The Load used in the prototype can be divided as: Total Load: 1.5 kw Shiftable: 1.0 kw Curtailable: 150 W Dimmable: 50 W Thus, the prototype has high resemblance with the actual consumer load and thus the results can be scaled and stand true for an actual consumer as well. Under Peak condition, Curtailing and dimming action relieve a load of 200 Watts. In our prototype, we perform only Curtailing and dimming action. Thus, the load can also be further reduced if shifting action is also performed. The threshold limits are set as Upper Threshold (UT) = 700 W; SET = 500 W; Lower Threshold (LT) = 300 W. C. Cost Analysis For CMU, the total module cost come out to be around Rs.1500 and it is to be paid by a group of consumers (like society). For ICU: TABLE 3: COST OF ICU UNIT Component Cost (Rs) Micro-controller (ATmega 328P) 150 Voltage Controller (BT136D,4N25,MOC3021) 100 Relay Board 100 Auxiliaries (Wires, Batteries, Buzzer etc) 150 PCB Printing Charges 100 Total 600 D. Results The experiment conducted using the scaled prototype gave the results as shown in Table 4. TABLE 4: PROTOTYPED RESULTS Grid Load State Load Connected Status Reduced Off-Peak 0 (Don t Care: No control) 0 1 < 300 W 0 Peak 2 >300 but < 500 W 50 W 3 >500 but < 700 W 100 W 4 > 700 W 150 W Thus, It can be seen that if the state is 3 then a load of 100W is curtailed thus if we assume that that the peak period duration is 1 hr then 0.1 kwh are saved in a single day on scaling it is equivalent to 1 kwh(unit) saved per day. If the consumer participates for only 20 days out of the 30 days in a month on an average the units saved in 1 month are 20 units. Currently, we are using block rate tariff thus the cost of a unit varies depending on the consumption. Thus, we have assumed an average rate of 5 Rs/Unit uniform throughout. Thus, savings in the monthly bill of the consumer is 100 Rs. From Table 3, the payback period comes out to be 600/100=6 months. The results obtained from the prototype illustrate our model to be an economical and viable solution for implementing DR activities in the still untapped residential sector.

6 VI. CONCLUSION The Prototyped Automated Demand Response system can lead to the conclusion that use of Demand Response techniques ensures efficient use of energy & resources as consumers become aware of the grid situation. It is beneficial for both consumers as well as utility. This technique indirectly can also act as a MoU between two ends of the grid. The system will serve as a backbone for the renewable energy integration system in future for consumers. Also the system can be extended with Automatic Power factor correction capability during the period when low PF equipment such as water pump is switched ON using CMU and a Capacitor bank. Since DR is automated in this system, it becomes more user-friendly and will be used widely once consumers get used to it. REFERENCES [1] Ministry Of Statistics And Programme Implementation Government Of India, "Energy Statistics 2016", Chapter 6, [2] F. Hemmes, E. Papyrakis, P. Beukering, Waste Not, Want Not: How Utilities Can Help Consumers Save Energy The Journal of Sustainable Development Vol. 7, Iss. 1, Pp.1 16, [3] A. H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, and R. Schober, "Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid," in Innovative Smart Grid Technologies (ISGT), IEEE, 2010, pp [4] N. Kinhekar, N. P. Padhy, F. Li and H. O. Gupta, Utility Oriented Demand Side Management Using Smart AC and Micro DC Grid Cooperative, IEEE Transactions on Power Systems, vol. 31, no. 2, pp , March [5] Jongkwan Seo et al. "Automated Residential Demand Response Based on Advanced Metering Infrastructure Network", International Journal of Distributed Sensor Networks February 2016 vol. 12 no. 2. [6] Open Energy Monitor blocks. [7] Y. Chen, R. P. Liu, C. Wang, M. de Groot, and Z. Zeng, "Consumer operational comfort level based power demand management in the smart grid," in Innovative Smart Grid Technologies (ISGT Europe), IEEE PES, 2012, pp [8] G. T. Costanzo, G. Zhu, M. F. Anjos, and G. Savard, "A System architecture for autonomous demand side load management in smart buildings," IEEE Trans. Smart Grid, vol. 3, no. 4, pp , [9] T. H. Chang, M. Alizadeh, and A. Scaglione, "Real-Time power balancing via decentralized coordinated home energy scheduling," IEEE Trans. Smart Grid, vol. 4, no. 3, pp , [10] N. Kinhekar, N. P. Padhy and H. O. Gupta, Multiobjective Demand Side Management Solutions for Utilities with Peak Demand Deficit, International Journal of Electrical Power and Energy Systems, vol. 55, pp , October [11] M. Pipattanasomporn, M. Kuzlu, and S. Rahman, "An algorithm for intelligent home energy management and demand response analysis," IEEE Trans. Smart Grid, vol. 3, no. 4, pp , 2012.