USING SOLAR ENERGY FOR REDUCE CONSUMER COST IN SMART GRID USING GAME THEORY

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1 ISSN: (Print) ISSN: (Online) USING SOLAR ENERGY FOR REDUCE CONSUMER COST IN SMART GRID USING GAME THEORY VAHID AMIR a1, MOHAMMAD RASOUL RAHMATIAN b AND SEYED MOHAMMAD SHARIATMADAR c a Kashan Branch, Islamic Azad University, Kashan, Iran b Jasb Branch, Islamic Azad University, Jasb, Iran c Assistant Professor of Naragh Branch, Islamic Azad University, Naragh, Iran ABSTRACT In this paper, a management method is pursued to reduce consumer costs in a power grid; for this purpose, a new idea is used, the idea of using renewable energy besides fossil fuels. The proposed power grid has some distributed user, so that their cost of consumption electricity and also the absorption of energy from a solar cell and its storage in a battery are varied at any time of the day. Absorbing solar energy absorbed by solar cells, consumers save electricity and use it at the time of the peak power. It should be noted that the game theory algorithm and competitive game is used in this scheme. The results of the simulation indicate that scheme is effective to reduce cost and also it can be seen that automatically reducing costs leads to a reduction in peak load in peak consumption hours. KEYWORDS: Solar cell - smart grid - game theory - reduced costs Due to paying a serious attention to environmental issues and having a cleaner and less polluting environment, renewable energies have been extensively used. Using demand side management (DSM) and a combination of renewable energy and fossil fuels, it is possible to reduce environmental pollution and also to decrease consumer costs. To response customer demands, grid capacity must be measured both at peak times and less consuming times and then designed a plan to meet these needs. To achieve such programs it is necessary to use smart metering equipment in the network, which is costly and time consuming.to resolve this problem,a smart grid is suggested which using a specific process swill reduce consumer costs and this will automatically lead to a reduction in peak load. Using its solar energy, each consumer canst ore cleaner energy and use it at the time of peak power consumption. In this paper, the game theory and non-cooperative games are used in the framework of the absorption model to do a theoretical analysis on future smart grids. LITERATURE REVIEW DSM application using electricity storage is intended to improve future smart grids. Basically, electricity storage is considered as one of the main concerns of energy providers using chemicals and large batteries [2]. The emergence of new types of batteries with a large capacity and low cost, energy storage as become an important part of the future smart grids [3].In addition, by the deployment of a smart meter device, storage management and using battery power are allowed [4]. The present study indicate that in the framework of game theory to model electricity storage by consumers in the small-scale systems, each consumer pursuing an optimum strategy to pay the lowest cost [5,6]. For this purpose, (i.e. paying less cost) consumers need to exchange information with each other. However, it is not possible to establish such general communication and public consumers are only able to communicate with an energy provider. In this model, the relationship between energy provider and consumers are price signals [8]. By providing a smart pricing strategy, energy providers create incentives for users, and to pay less, expect them to behave in ways that reduce not only their costs but also consumption peak times [7]. For investigating different methods, a battery is used for energy storage. So consumers receive power requirements from the network and store it in their battery. However, the main problem of this method is that if consumers attempt to charge the battery at the same time, network will be faced with a severe voltage drop [12]. System Model A smart power system consists of an energy provider (Energy Supplier) and10 loads (consumers or 1 Corresponding author

2 user)that each consumer is equipped with solar cells and a battery for power storage, as shown in Figure1. 1 a 1 (5) Ifa <0, the consumer is using electricity saved by using a solar cell, if a >0then the consumer is charging battery by the absorbed solar energy, and if a =0then the solar cell does not absorb energy. Depending on the time slots for each level of the battery which is charged or discharged an equation will be obtained as follows: 0 b +a ri Bi, t T (6) That r is the maximum capacity of the battery Figure 1: An overview of the network studied Time is divided in to equal parts [12], which simply T = 24, i.e. 24hours (a day). Other numbers could be used in a similar method For each useri 10,an equation is defined as follows: xi=x,,x, x (1) 0 x x, t o (2) O is thetime operator of the time of each slide for the subscribers. Thus, the energy balance equation is as follows: x = E (3) The placement of b for each level of the battery capacity B for the subscriber i, which the operator a can be related to the charge and discharge of the battery for each consumer i in addition, the vector relation of each solar cell battery for the subscriber i is as follows: a a,,a,,a (4) The secure limits for the charge and discharge of each cell are as follows: being charged or discharged. Other constraints can be expressed as follows: a =0 (7) x +a ri 0 (8) Therefore, the energy consumption for the consumer i is planned as follows: Fi ={xi,ai (2),(3),(4),(5),(6),(7),(8)} (9) It should be noted that the amount of energy absorbed by the solar cell in the early hours of day is assumed as minimum value (0), and gradually increases until late in the day again reach its minimum value. In addition to the solar energy absorbed by each consumer, the consumer should purchase electricity in accordance with its requirement, which this goal is specified by the following equation: l =x +a ri (10) l =l l l (11) Objective Function In this part, it is mainly attempted to formulate an optimization formula and to minimize the cost of the power system network based on the studies conducted in other books and papers [1]. {x,a }best=argmaxui (12) 2.1 The Pricing Model

3 The necessary cost to provide energy in a timeslot in the time interval t is shown with thatis equal to the sum of the base priceφ and the cost of time vacuum when not using energy (ϵ ). =φ +ϵ (13) In this article, the average cost is based on pricing scheme. Specifically, the base price for each time interval depends on the number of users and peak time and customer cost is calculated as follows: φ = (14) C(q )=αq (15) U ({x,a },{x,a })= {(α+μ) (x +a r ) μq }(x +a r } (20) Algorithm In this section, how to implement and achieve the objective function is investigated. To implement the game theory algorithm, a non-cooperative game was used, which this game is played between two players (consumer and energy producer). Each of the players attempting to win or to gain profits as follows: energy producer aims to gain more profit from selling electricity to the users, and consumers attempting to make a profit or non-payment of cost for the incorrect use of power. To win this game, subscribers must manage their use of energy. The flowchart of the proposed scheme is as follows: Q = (16) ϵ =μq Q,μ>0 (17) By replacing the equations(14), (15) and(17) in equation(13), the equation(18) is obtained which indicates energy function in base price. λ =αq +μq Q =(α+μ)q μq (18) According to the energy equation (18), an objective function can be determined that can reduce total cost. U (x,a )= (x +a r ) (19) From equation (18), one can see that if average energy demandq is greater than the average total energy demand at any time, the price would be negative, and vice versa. The objective function mainly aims to maximize the value (-U). These quotations require users to use a correctly compiled program and encourage them to reduce their costs and ultimately lead to a reduction in costs for consumers. Finally,using the equations(13), (18) and(19), it is possible to obtain the following equation: The simulation results In this section, the simulation results for 10 supposed subscribers are checked.figure 2 shows a 24- hour planning for subscribers, which is designed based on reference [11]. From Figure 2 it can be easily understood when consumers are equipped with solar cells, they have reduced their energy consumption; this reduction in energy consumption reduced cost along with load peak. From Figure 3 and 4, it could be seen that,using demandside management preceding installing the solar cell,users'

4 costs are reduced. Also, when users are equipped with a solar cell costs have declined further. CONCLUSION In the present paper, a theoretical framework for the design of a new pricing model is described, and also a distributed algorithm to achieve the Nash equilibrium min non-cooperative games is proposed to minimize consumers' consumption costs. Similar to other demandside managements, such algorithm also needs interaction between the consumer and energy producer for pricing.as the simulation results show this algorithm can reduce energy costs and peak load during peak hours. REFERENCES Figure 2: 24-hour consumption energy planning A. Mohsenian-Rad, V. Wong, J. Jatskevich, R. Schober and A. Leon-Garcia: Autonomous demand-side management based on game theoretic energy consumption scheduling for the future smart grid, IEEE, 1(3): (Dec. 2010) M. Korpaas: Operation and sizing of energy storage for wind power plants in a market system, International Journal Electrical Power Energy Systems, 25(8): (Oct. 2003) U. D. Energy, "Grid 2030"-A National Vision for Electricity's Second 100 Years, "U.S. Department Energy, Tech. Rep., Jul [Online]. Available: eventcalendar/files/ grid pdf Figure 3: Subscribers' daily cost P. Vytelingum, T. D. Voice, S. D. Ramchurn, A. Rogers and N. R. Jennings: Based micro-storage management for the smart grid, International Foundation for Autonomous Agents and MuItiagent Systems, 1(10):39-46 ( 2010) B. Daryanian, and R. Bohn, R. Tabors: Optimal demand-side response to electricity spot prices for storage-type customers, TEEE Transactions on Power Systems, 4(3): (Aug. 1989). L. Exarchakos, M. Leach and G. Exarchakos: Modelling electricity storage systems management under the influence of demand-side management programmes, Tnt. I. Energy Res, 33(1):62-76 (Jan. 2009). Figure 4: Total subscribers' cost C. Chen, S. Kishore and L.Snyder: An innovative rtpbased residential power scheduling scheme for smart grids, in Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference, pp (May. 2011)

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