BEST MUTUAL DEMAND RESPONSE AND LOAD PROFILE IN SMART GRIDS

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1 BEST MUTUAL DEMAND RESPONSE AND LOAD PROFILE IN SMART GRIDS HASSAN ABNIKI 1, ALI AKBAR MOTIE BIRJANDI 2, SAEED NATEGHI JAHROMI 2 Key words: Demand response model, Smart grid, Real time pricing, Time of use programs. Smart grids are integrated communications and power system infrastructures to improve the efficiency, reliability and safety of power deliveries and its usages. In this paper, time of use (TOU) programs in smart grids is studied. In this regard, real time pricing method (RTP) has been developed using multi objective load models. Also, the effects of smart grids on demand curve have been simulated by different scenarios. Also, elasticity criterion in this method is optimized, and after that the best elasticity value is achieved. Also, Iran s national grid has been selected to analyze the effects of real time pricing in smart grid environments on load curve. Meanwhile, the results have been compared with TOU programs for better presentation of proposed method and finally excellent performance of the presented technique will be shown. 1. INTRODUCTION Any increase in demand s elasticity will effectively improve the result of time-based demand response (DR) programs, leading to better control on the market price and load profile [1]. One of the primary advantages of this new situation is improvement in reliability and well informed customers able of actively controlling their demand. As an example from a utility company, Xcel energy states that by reducing customer minutes out through fault switching, automatic outage notifications and proactive asset replacement, Xcel energy expects to improve by 10 percent [2]. In order to answer some fundamental questions, use of a model is necessary (model presented in [3]). An essential feature of the real time market is shown in [4]. It is assumed that the entire demand is not constant but adaptive, however this assumption does not always hold true since it is a simplifying assumption. In first approach, combination of adaptive and non-adaptive demands is the most effective way to achieve the desired answer. But the point is, distributed algorithms as suggested cannot directly observe the backlogged demand [5]; in contrast, they only see the effective instantaneous 1 University of Tehran, Department of Electrical Engineering, Tehran, Iran; Phone: , hnabniki@ut.ac.ir 2 Shahid Rajaee Teacher Training University, Department of Electrical Engineering,Tehran, Iran Rev. Roum. Sci. Techn. Électrotechn. et Énerg., 58, 4, p , Bucarest, 2013

2 368 Hassan Abniki, Ali Akbar Motie Birjandi, Saeed Nateghi Jahromi 2 demand; at any point in time where the supply cannot match the effective demand, the backlogged demand changes. The aim of this paper is to optimize the load profile reformation using an adaptive technique in smart grids. In this paper, Iranian National Grid is used for simulation and analyzing and after that several scenarios are tested offline by the time based introduced programs. The results show excellent performance of the proposed technique. If market forces be allowed to the maximum extent value, success or failure of smart grid models is easier for analysis. In this regards, markets create some valuable signals which are very useful; the first one is relative value of different energy services and the second one is the costs of limitations on the amounts of the products. Also, investors are able to use market-based price signals to make investment decisions and then to harvest a share of the market improvements via innovative offerings; so the consumer benefits as well as the investor. Allowing market forces to dictate the development and timing of smart grid models which cause interested consumers to anticipate benefits from such models. Also it will provide risk on such investors rather than a customer of a public utility generally. 2. MODELING DEMAND RESPONSE For analyzing demand respond (DR) on electricity demand and power market, having a model is necessary. So, according to equation (1); two types of elasticity can be defined as will be described later. Technically, self-elasticity, E aa, means the changes in demand in a time interval with respect to changes in price of the same time interval (which is negative) [6]. Da E aa = ρ 0. (1) a Also, cross elasticity, Eab means the changes in demand in a time interval with respect to changes in price of a different time interval that it has positive value. Da E ab = ρ 0, (2) b where: Da demand changes in period a, ρa price changes in period a, D b demand changes in period b, ρb price changes in period b. Here each time interval is supposed to be one hour. After that following definitions are supposed [6]: d( = customer demand in i th hour [MWh], ρ( = spot electricity price in i th hour [$/MWh], B(d() = customer s income in i th hour [$].

3 3 Best mutual demand response and load profile in smart grids 369 It is assumed that customer s demand is set in initial value of d0( i ), which varies to d( after executing DR programs, so: 0 [ ] di () = d() i di () MWh. (3) Customer s benefits defined as term S($), so at i th hour, it will be as following term: Sdi ( ( )) = Bdi ( ( )) di ( )ρ( $. (4) So, in order to be realistic and maximize customer s income, from point of S costumer view, the term should be equal to zero, therefore we have equations d( (5) and (6): S B( d( ) = ρ( = 0, (5) d( d( B( d( ) = ρ(. (6) d( Supposing the quadratic benefit function, it can be written as following: di () d0() i Bdi ( ()) = B0() i +ρ0() i[ di () d0() i] 1+, (7) 2 E( id ) 0( where: B0( i ) benefit during nominal demand ( d0( i )), ρ 0 ( i ) nominal electricity price when demand is nominal. Also, by supposing the equations (7) and (8), equation (9) will be achieved. [ ] di () d0() i ρ( = ρ 0( E( id ) 0(, (8) di () d0() i ρ( ρ 0( = ρ 0(. (9) 2 E( id ) 0( So, customer s demand will be same as the equation (10): E()[ρ() i i ρ 0()] i di () = d0() i 1+. (10) ρ() 0 i The cross elasticity between i th and j th intervals is defined as equation (11):

4 370 Hassan Abniki, Ali Akbar Motie Birjandi, Saeed Nateghi Jahromi 4 { { ρ0( d ( E( i, j) =., (11) d ( ρ( 0 Ei (, j) 0 if i= j self elasticity Ei (, j) 0 if i j cross elasticity. d( If is supposed to be constant, the demand response to price variations ρ( j) could be written as equation (12): 24 d0() i di () = d0() i+ Eij (, ). [ρ( j) ρ 0( j)], i= 1 24 i= 1 ρ() 0 j. (12) Equation (12) shows the customer s consumption to reach the maximum benefit in a 24 hours interval whereas it is the final model [7]. 3. PROPOSED TECHNIQUE, ADAPTIVE ELASTICITY The core of the proposed technique is based on the adaptive elasticity. As mentioned before, the elasticity is one of the important factors which affects the demand response for the load profile reformation. In this section, the best DR is investigated for the best load reformation. So, at first, the influence of using smart grid on time-based-rate DR programs has been evaluated and after that the best DR is founded during the minimum load profile reformation. Another paper in this field is analyzed in the previous paper of this author [8]. The technique used here is based on defining peak, off-peak and low periods depending on how high the electricity demand and relatively its price is. Elasticity for normal TOU program is shown in Table 1. As it is obvious, hours with higher demand are peak periods and the ones with lower electricity demands must be low periods. These periods have been considered in this paper as shown in Table 2. In real time pricing (RTP) program, every two different time periods, whereas each period is an hour in this paper, would have their own cross elasticity, whereas every time period would have a different self-elasticity. Also, hours of a day have been divided to 3 categories, peak, off-peak and low periods. But for modeling RTP program, these time intervals change depending on the load and energy price at that hour and is not be assumed to be fixed during a day. Smart grid s characteristics which defined in previous sections will help customers to respond to changes in electricity price and vary their energy consumption during the day more effectively. In this situation, even smallest customers would have the ability to participate in power market and to adjust their consumption with electricity price to reach the highest welfare. Enabled by smart grid infrastructures, each customer would be able to

5 5 Best mutual demand response and load profile in smart grids 371 install his own electric plant and appear as a producer who sells power to the grid at times of extra production [3]. Also, it has been assumed that in smart grid, customer s participation increases for 10% and reaches a portion of 40%. Elasticity between different times intervals have been increased comparing to the normal TOU programs. The increase is 10% for self-elasticity and 100% for cross elasticity is performed in this study. So smart grid s characteristics can be added to DR model as an increase in self and cross elasticity of demand between different time periods. Table 3 demonstrates the elasticity considered for smart grid environment. This means, customers are able to buy less energy from grid at times of high price, by shifting loads, using their own DG plant or even through discharging batteries of their hybrid vehicle charged during the last night. Table 1 Elasticity for normal TOU program Peak Off-Peak Low Peak Off-Peak Low Table 2 Adaptive periods used for simulating RTP program in smart grid environment Peak period Demand is between 70% and 100% of its range Off-peak period Demand is between 30% and 70% of its range Low period Demand is between 0% and 30% of its range Table 3 Self and cross elasticity for RTP program in smart grid Peak Off-Peak Low Peak Off-peak Low But the plan is started now; the aim of this paper is to find the optimal ρ( j ) in order to introduce the best d( j ) In this regard, first put the ρ( j ) = kd( j) + k) because the goal is to have minimum load prolife reformation in the equation (12), also k is a constant. Moreover, extended simulation shows that using the item deij 0 (, ) Ei (, j) + /2 is better rather than using only the item E( i, j ). So the ρ() 0 j equation (12) is defines as a function of d (. After that using a derivative, the best

6 372 Hassan Abniki, Ali Akbar Motie Birjandi, Saeed Nateghi Jahromi 6 di () is found. This calculation is performed in equation (13), and the result is shown in later section. 24 deij 0 (, ) d0() i di () = d0() i+ Eij (, ) + /2. [ kd( j) + k ρ 0( j) ], i= 1 ρ() 0 j ρ() 0 j d0() i ( ρ() 0 i k) d0() i ρ 0( j) = 0 Ei (, j) =. ρ() 0 j d0() i d0 + E(, i j) 1+ ρ() 0 j With substitute this optimal elasticity, E( i, j ), the optimal demand response for load profile reformation is achieved. Meanwhile, the results show excellent out performance of this adaptive technique. (13) x 10 4 Load Curve Rials/ MW MW MW Best Smart Grid TOU Hour Fig. 1 Comparison between load curve using TOU and best load curve using smart grid. The important note is that any great increase in cross elasticity effectively shift loads, also for example, the influence of DG *1 plants and distributed storages, lies inside this increase. In a real and actual market of electricity, any load variation leads to changes in market price; so for analyzing smart grid effects on load and price curve, it must be noted that in RTP program unlike TOU programs in which only price affects demand, demand would affect price curve too during one day. It is based on this fact that consumers have the ability to change their load in response to price, and this happens through interactions between demand and supply side in real time. 1* Distributed generation

7 7 Best mutual demand response and load profile in smart grids x Energy Best Smart Grid RTP Rials/ MWh MW Hour Fig. 2 Best energy price using smart grid and RTP program. After deriving load curve through elasticity model, a new price curve is obtained through supply curve. If this new price curve is used to derive a new demand curve using elasticity model again, the demand considering smart grid s effect has been obtained which shows the interactions between demand and supply side [6]. Fig. 1 shows the initial load curve, the load curve during TOU program and the load curve during RTP program enabled by smart grid after 4 iterations. Finally it can be found that load profile deeply depends on the DR model. But the exact challenge is what is the best DR model to achieve the minimum load profile reformation. In this paper, we define a new criterion based only on the load curve and after that but deriving the load item, the best demand response is achieved. This means this DR model is the best model that if consumer allegiance from it, the best load profile reformation is achieved and this is very desirable. This program has the ability to be adapted with other program during a smart grid program. Also, it can be seen that using smart grid, a better customer s participation would be achieved and load would respond to price changes more effectively. The price curve would also become smoother and energy would have a cheaper price during the day also in contrast with initial price curve, electricity price doesn t reach its limit ( Rial/MWh, or 9 $/MWh). Price curve using smart grid can be seen in Fig. 1. Load curve using TOU and best load curve using smart grid which is compared in this figure. Also, in Fig. 2, the best energy price using smart grid and the proposed technique based on the elasticity is presented. In this paper, time based demand response programs have been evaluated and the problems which are avoiding these programs to reach their best load profile reformation are identified. In fact, the vice versa process is applied in this state. In order to bring the best load profile reformation, the best demand response is presented using an adaptive technique which is based on the new elasticity based

8 374 Hassan Abniki, Ali Akbar Motie Birjandi, Saeed Nateghi Jahromi 8 criterion. As a solution, smart grid has been introduced and the best DR model is achieved upgraded to best load profile reformation in smart grid environment. This technique is analyzed under different conditions resulted by Iran National Grid during different seasons and time durations. By applying this technique in electrical regional company, the best load profile reformations always are resulted. 4. CONCLUSION In this paper, the effects of smart grids on demand curve have been analyzed by different scenarios. Also real time pricing method (RTP) is developed using multi objective load models. Also, elasticity criterion is optimized, and after that the best elasticity criterion is provided. In fact, by calculating optimal elasticity, the best optimal demand response for load profile reformation is achieved. Moreover, the results have been compared with TOU programs and show excellent performance of the presented technique. In fact using best demand response or its model, best load profile reformation will be inevitable according to the proposed method. Received on September 12, 2012 REFERENCES 1. *** NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 1.0, Office of the National Coordinator for Smart Grid Interoperability NIST Special Publication *** Xcel Energy Smart Grid City Benefits Hypothesis Summary, Available online at: " 3. G. Wang, A. Kowli, M. Negrete-Pincetic, E. Shafieepoorfard, S. Meyn, U. Shanbhag, A control theorist's perspective on dynamic competitive equilibrium in electricity markets, Proc. 18 th World Congress of the International Federation of Automatic Control, IFAC, K. Cho, S.P. Meyn, Efficiency and marginal cost pricing in dynamic competitive markets with friction, 46 th IEEE Conference on decision and Control, Dec. 2007, pp S. Keshav, C. Rosenberg, How internet concepts and technologies can help green and smarten the electrical grid, Selected as one of the best papers in the ACM SIGCOMM Green Networking Workshop 2010, Republished in ACM SIGCOMM CCR, January P. Khajavi, H. Monsef, H. Abniki, Load profile reformation through demand response programs using smart grid, Modern Power Electric System Conference, Poland, 2010, pp D. S. Kirschen, G. Strbac, P. Cumperayot, D. Mendes, Factoring the elasticity of demand in electricity prices, IEEE Transaction on power systems, 15, 2, pp , H. Abniki, Saeed Nateghi, Optimal demand response program for the best load profile reformation based on adaptive technique in smart grids, Environment and Electrical Engineering (EEEIC), Italy, May 2012, pp. 1 6.