Smart Grid Simulation Diversification of Demand Side Power Management

Smart Grid Simulation Diversification of Demand Side Power Management

Suraj Kumar Khatkar1,

1, 2, 3 Electrical Engineering, 1National Institute of Technology, Nagaland,

Ms. Kousar Jahan2, Aayush Agrawal3

2, 3 Dr. K N Modi University, Newai Tonk Rajasthan

Abstract- Demand Side Management (DSM) is the process of managing the consumption of energy, generally to optimize available and planned generation resources. DSM plays an important role in improving energy efficiency, system reliability and security. DSM reduces costs for both customers and utilities and mitigates environmental damage. Smart grid has been proposed as a way to increase grid robustness and reduce consumption peaks and at the same time decrease electricity costs for the end users. The aim is to develop simulation models for smart grids focusing on smart houses. Smart industries which are used to test if it is possible to reduce consumption peaks and at the same time increase grid stability. This paper proposes and develops a flexible simulation framework to study the behaviors and impacts of smart grid enabled renewable energy source at demand side, introduce the effects of demand side management and analyzes the impact of renewable energy on demand response.

Keywords- Smart grid, Renewable Energy (RE), Distributed Generation (DG), Demand Response (DR), Demand Side Management (DSM).

1. INTRODUCTION

   IN the modern smart grid, the diversity of loads and the demands for highly efficient consumption, as well as the use of renewable energy (solar, wind, biomass energy, etc.) generation and grid connection technology through the power electronics interfaces, have brought great challenges to governing power quality. Compared with the traditional power system, the micro grid or distributed power plant, which integrates a variety of energy inputs, multiple load characteristics, and varied energy conversion technologies, is a nonlinear and complex system with inter-coupling of chemical energy, thermodynamics and electrodynamics. Meanwhile, due to the limitation of natural resources, the distributed generations (DGs) appear the features of intermittency, complexity, diversity and instability. Accordingly, some new problems with novel features occur in maintaining power quality. Therefore, power quality control theory and technology will play an important role in ensuring the stable and secure operation of the power grid when micro grids or distributed power plants are connected to it. In the Indian national program for long and medium-term scientific and technological development (2010-2030), it is specifically mentioned that the analysis, detection, and control technology for power quality should be a priority objective in the energy field, and it is highly recommended to promote to renewable source and to control the power quality actively.

   Electric power systems constitute the fundamental infrastructure of modern society. Often continental in scale,

   electric power grids and distribution networks reach virtually every home, office, factory, and institution in developed countries and have made remarkable. The connection of distributed resources, primarily small generators at the moment, is growing rapidly. The extent of interconnect, like the number of sources, controls, and loads, has grown with time. In terms of the sheer number of nodes, as well as the variety of sources, controls, and loads, electric power grids are among the most complex networks ever made.

2. GENERAL FEATURES OF SMART GRID

   The term smart grid refers to the use of computer, communication, sensing, and control technology which operates in parallel with an electric power grid for the purpose of enhancing the reliability of electric power delivery, minimizing the cost of electric energy to consumers, and facilitating the interconnection of new generating sources

   To the grid, a smart grid may be defined in a number of ways. In simple words, smart grid is an electric grid that employs monitoring, control, communication, and self-healing capabilities to efficiently deliver reliable, economic, and sustainable electricity services. Smart grid has different aspects and can be characterized as follows:-

   • Self-healing from power disturbance events.

   • Enabling active participation by consumers in de- response.

   • Operating resiliently against physical and cyber- attacks.

   • Providing power quality for 21st century needs.

   • Accommodating all generation and storage options.

   • Enabling new products, services, and markets.

   • Optimizing assets and operating efficiently.

     End-to-end electric power system (from fuel source, to generation, transmission, distribution, and end use) of the future will:

   • Allow secure and real-time 2-way power and information flows

   • Enable integration of intermittent renewable energy sources and help decarbonizes power system

   • Enable effective demand management, customer choice, secure and efficient operation of the grid

   • Enable the secure collection and communication of detailed data regarding energy usage to help reduce demand and increase efficiency.

3. DEMAND SIDE POWER MANAGEMENT Smart Grid demand side power management describe

   following attributes-

   • Deployment and integration of demand response, demand side resources and energy efficiency resources.

   • Deployment and integration of distributed resources and generation, including renewable resources.

   • Deployment of smart technologies for metering, communications concerning grid operations/status and distribution automation.

   • Adoption of Demand Side Management (DSM) techniques like volt/VAR control, voltage reduction, etc.

   • Deployment and integration of advanced electricity storage and peak-shaving technologies, including plug-in electric and hybrid electric vehicles, etc.

   1. Demand Response

      Demand response (DR) refers to mechanisms to manage the demand from customers in response to supply conditions, for example, having electricity customers reduce their consumption at critical times or in response to market prices. There has been a recent upsurge in interest and activity in demand response, primarily due to the tight supply conditions in certain regions of the country that have created a need for resources that can be quickly deployed. Demand response can broadly be of two types, incentive-based demand response and time-based rates. Incentive based demand response includes direct load control, interruptible/curtails able rates, demand bidding/buyback programs, emergency demand response programs, capacity market programs, and ancillary services market programs.

   2. Demand Side Power Management

      Demand-side power management is the planning, implementation and monitoring of those utility activities designed to influence customer use of electricity in ways that will produce desired changes in the utilitys load shape, i.e., changes in the time pattern and magnitude of a utilitys load. Utility programs falling under the umbrella of demand-side management include load management, new uses, strategic conservation, electrification, customer generation and adjustments in market share. Demand-side management is even more encompassing than the above definition implies because it includes the management of all forms of energy at the demand side, not just electricity. In addition, groups other than just electric utilities (including natural gas suppliers, gvernment organizations, nonprofit groups, and private parties) implement demand-side management programs. In general, demand-side management embraces the following critical aspects of energy planning:

      • Demand-side management will influence customer use. Any program intended to influence the customers use of energy is considered demand-side management.

      • Demand-side management must achieve selected objectives. To constitute a desired load shape change, the program must further the achievement of selected objectives.

      • Demand-side management will be evaluated against no demand-side management alternatives. The concept also requires that selected demand-side management programs further these objectives to at least as great an extent as no demand-side management alternatives, such as generating units, purchased power or supply- side storage devices. In other words, it requires that demand-side management alternatives be compared to supply-side alternatives. It is at this stage of evaluation that demand-side management becomes part of the integrated resource planning process.

      • Demand-side management value is influenced by load shape. Finally, this definition of demand- side management focuses upon the load shape. This implies an evaluation process that examines the value of programs according to how they influence costs and benefits throughout the day, week, month, and year.

   3. Integration of Renewable Energy Source

      In case of smart grid, Renewable Energy has been one of the rising trends in the field of Energy engineering. Renewable energy system has its source over the renewable resources such as solar, wind, tidal, wave, biomass etc. Power extraction through tidal and wave energy is bounded due to less availability; lack of technological Improvements these components, incorporating the applicable criteria that Follow. Distributed RE sources may potentially delay upgrade of transmission and distribution infrastructures with increasing demand. Therefore, RE resources at the residential level should be utilized to get the most benefits out of residential demand response (DR) programs. Integration of Renewable source at distribution to reduced power demand and improves grid stability and reliability and improves voltage profile, active power and reactive power and also improve voltage regulation and grid efficiency.

4. DISTRIBUTION END POWER MANAGEMENT Demand side centric flexible simulation framework

   proposed a simulation framework that is open to integration of different components under the smart grid infrastructure, and puts more emphasis on the residential demand side of the story.

   Fig.1 Distribution End with Distributed Generation at load side

   This framework aims at providing an easy interface for implementing different appliance coordination algorithms and performing simulation tests with minimal effort.

   Fig.2 Distribution End with PVs, DG Integration to Grid

   Distributed Generation added on load side

   • Solar Energy (photovoltaic & thermal)

   • Wind Energy

   • Bio-energy and Fuels

   • Fuel Cells and UPS Power

   1. Industry (b) Residential Houses Fig.2 Load Neighborhood Model

5. SIMULATION RESULTS

   Simulation-

   Power flow simulations return voltage and current phasor. These quantities are computed in the frequency domain using complex numbers, and by specifying the fixed frequency of interest (e.g., 50/60 Hz). Phasors, however, only contain information about their magnitude (root-mean square, RMS) and phase angle.

   Time Step-

   The simulation time step (or interval) defines how often the simulation's solutions are computed. Since it was determined that load flow simulations were appropriate for this work, the time step was matched to the time step of the recorded data provided by given data (t=1 min=60 s).

   Run Time-

   Load flow simulations execute faster than transient ones for the same circuit topology. This stems from the total number of solutions required to complete a simulation. For example, a 24 Hour load flow solution requires 24×3600/ 60

   =1,440 solutions. A 24 hour transient solution requires 24×3600 50×106 =1.728×109 solutions.

   Simulation Time Span-

   The simulation time span defines the stop time of the simulation. Since load flow simulations execute rapidly, this stop time can be set to 24 hours or 7 days or Monthly or Yearly. On the other hand, in the case of transient simulations, setting these stop values to anything more than seconds produces undesirable simulation wait times.

   Load data:

   Smart grid power system incorporating residential homes, residential industry with electric appliances from the demand side and renewable energy generation mix representing the distribution side. Solar power was added to the distribution end, industry and homes.

   1. Solar House data (b) Smart Houses data

      1. Smart Industry data (d) Heavy industry data Fig3. Load data

         RE was added to the distribution end, industrys, homes and it changes demand Response and peak demand. Demand response in principle can enhance the overall system operation. Positive outcomes are especially associated with the increased level of penetration of variable renewable.Integration of Renewable source at distribution end to reduced power demand and improves grid stability and reliability with much reliable and secure the grid sudden change in load. Demand Side Management (DSM) is the process of managing the consumption of energy, generally to optimize available and planned generation resources. DSM plays an important role in improving energy efficiency, system reliability and security. DSM reduces costs for both customers and utilities and mitigates environmental damage. It help to improves voltage profile, active power and reactive power and also improve voltage regulation and grid efficiency-

         Fig.4 Power availability on Smart Industrys and Smart Houses

         • DSM for improving Voltage and current-

      During operation the primary or intermediate energy sources have to be controlled to adjust voltage and current to match the operation of loads connected in the grid. In worst case, the load should be disconnected by the controller.

      Fig.5 Voltage, current and Power Measurement House1 and House2

      1. Demand Side Power management conditions Case1

         When House power < [Solar + wind + fuel cell + UPS power] Then, Supply power to Grid = House power – [Solar + wind + fuel cell + UPS power], Grid act as load.

         Case2

         When House power > [Solar + wind + fuel cell + UPS power] Then, Grid supply power = House power – [Solar + wind + fuel cell + UPS power], Grid act as source.

         Case3

         When House power = [Solar + wind + fuel cell + UPS power] Then, Grid supply Power = 0, Optimum power management.

         • DSM for improving Supply and Demand Balance- This is more of an optimization function during grid connection operation. However in the islanded mode, available storage should be used. In addition, the 8 controller should incorporate demand response events during this phase of operation to ensure reliability and security of the whole or part of the system.

         • DSM for improving Power Quality-

           Power quality should be maintained for grid in islanded operation with sufficient supply of reactive energy to shrink voltage sags. The energy storage devices should react rapidly to frequency and voltage change by charging/discharging. The control strategies for balancing the dynamic demand in a grid connected hybrid system have been proposed. The aim of this paper is to enhance the greatest use of solar and wind power by maintaining uninterrupted power to the customers. DSM control strategies the power in the grid is made reliable with improved quality. Modeling and simulations were done in MATLAB/Simulink. The simulations were presente to evaluate the dynamic performance of the hybrid system under proposed demand balancing methodology.

           Case4

           When [Solar + wind + fuel cell + UPS Power] =0. Then, Grid supply Power = House Power. This condition is called Peak Demand.

6. CONCLUSION

There is significant potential to increase the functionality of typical demand-side management measures, typical demand response strategies, and typical

1. DSM Response To System as-

   • DSM for improving system reliability-

     The earliest and most commonly practiced DSM focuses on system reliability. A customer, often a large industrial facility, agrees to reduce load to guarantee system reliability under peak demand conditions or other emergency system events, and is paid an incentive for doing so. Since they are designed for emergency use, these DSM programs are infrequently called upon.

   • DSM for improving system efficiency-

     More recently the focus of DSM has been increasingly placed on system efficiency. Many DSM programs have begun to focus on non-crisis peak shaving flattening load curves to improve the efficiency of long-term power system capacity use, since the generation, transmission and distribution capacity of a power system is sized to meet the expected peak demand.

   • DSM for improving system flexibility-

This emerging application of DSM is very important for supporting RE integration, as mentioned in System reliability Objective Frequency of calling Participants Direction Demand response, power management, System efficiency and System Flexibility.

implementation of building-level distributed energy resources by combining them in a cohesive, networked package that fully utilizes smart energy-efficient endues devices, advanced whole-building control systems. It can be concluded that with the advancements being made in the area of renewable energy and distributed power generation, Smart grid has a emending and critical role in the future of efficient power generation and distribution. Renewable energy system is an innovative option for electricity generation, especially the PV system as it is a clean energy resource. However, to achieve a goal, a lots of issues need to be solved or addressed. These issues are basically related to the design and size of the system, the suitable and effective model which can cover the technical and financial aspects of PV smart grid to supply electricity, and the equalized electricity price for integrating PV in a smart grid system. Analyzes the introduction effect of demand side management such as improvement rate of supply and demand balance and increasing quantity of renewable energy, Easy setting change of the grid and demand side condition by the adoption of module architecture and effective simulation is possible.

REFERENCES

1. R. Brown, Impact of smart grid on distribution system design, in Proc. IEEE Power Energy Soc. Gen. Meeting, 2008.

2. International Energy Agency: World Energy Outlook 2011 © OECD/IEA 2011.

3. J. R. Aguero, P. Chongfuangprinya, S. Shao, L. Xu, et al., "Integration of plug-in electric vehicles and distributed energy resources on power distribution systems," in IEEE Electric Vehicle Conference, 2012.

4. A. Kwasinski, A. Toliyat, and F. M. Uriarte, "Effects of high penetration levels of residential photovoltaic generation: Observations from field data," in International Conference on Renewable Energy Research and Applications (ICRERA), Nagasaki, Japan, Nov. 11-14, 2012.

5. L. Bird, M. Milligan, and D. Lew¸ Integrating Variable Renewable Energy: Challenges and Solutions, Technical Report NREL/TP- 6A20-60451 September 2013.

6. R. K Singh, S.K Choudhary, and Paul,R.Kumar, study of Indian Power Sector for Planned Capacity, ÄKGEC International Journal of Technology, vol. 4,No.2,pp.34-38,2013

7. Anthony Papavasiliou, and Shmuel S. Oren, Large scale Integration of deferrable demand and renewable energy source, IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 1,

   JANUARY 2014

8. S.J.Ben Christopher, Dynamic demand balancing using demand side management technique in a grid connected hybrid system, International Journal of Renewable Energy Research, Vol. 4, No.4, 2014.

9. Fabian M. Uriarte and Robert. E. Hebner, Residential Smart Grids: Before and After the Appearance of PVs and EVs, IEEE International Conference on Smart Grid Communications 2014.

10. Yogita Ashtekar, and Gopi Dhole, Effect of demand side management on present Indian power sector scenario, IJIRSET Transl. J. Magn. Japan, Vol. 4, Issue 2, February 2015

11. Sushmita Banerjee and Abhisek Meshram, Integration of renewable energy sources in smart grid,IJIRSET Transl. Vol. 3, Issue 3, March 2015.

12. Bethany Speer and Mackay Miller, Roll of smart grid in Integrating Renewable Energy, Technical Report NREL/TP- 6A20-63919 May 2015.

AUTHOR PROFILE

Suraj Kumar Khatkar is presently working as BSNL JE NE-I, India. He did M. Tech in Power System Engineering in NIT Nagaland. His area of interests in the field of Smart Grid Integration, Demand Response and Demand Side Power managements, modeling controller for nonlinear real

dynamic system, Renewable Energy source, Power system load Economic dispatch and Unit Commitment, LFC, Load flow Study and Fault Protection in Power system

Ms. Kousar Jahan is presently pursuing M. Tech in Power System Engineering in Dr. K N Modi University, Newai, Tonk Rajasthan. Her area of interests in the field of load forecasting, Demand Response and Demand Side Power managements, Renewable Energy

source, Power system load Economic dispatch and Unit Commitment, LFC, Load flow Study and Fault Protection in Power system.

Aayush Agrawal is presently pursuing M. Tech in Power System Engineering in Dr. K N Modi University, Newai, Tonk Rajasthan. His area of interests in the field of Demand Response and Demand Side Power managements, Renewable Energy

source, Power system load Economic dispatch and Unit Commitment, LFC, Load flow.