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Optimal Storage Capacity within an Autonomous Micro Grid with a high Penetration of Renewable Energy Sources P. Lombardi, T. Sokolnikova, K.V. Suslov Member IEEE, Z. Styczynski Senior Member, IEEE,
Abstract--Some Renewable Energy Sources (RES), such as wind and solar, produce power intermittently according to the weather conditions rather than to the power demanded. Energy Storage Systems (ESS) may be used to mitigate the intermittent generation from RES and to increase the quality of power supply. This study aims to find the relationship between the generation from RES and the needed amount of ESS. An autonomous microgrid has been analyzed in which a part of electricity demanded during one year is generated by Wind Turbines and Photovoltaic plants (PV), while the remaining part is produced by fuel based generators. An intelligent Energy Management System (EMS) optimally schedules the fuel based generators according to the load demanded, the weather conditions and to the electricity generation costs. The optimal storage needed to balance the system for different scenarios were investigated and results of this investigation are shown and discussed in this paper. Index Terms--Autonomous microgrid, energy storage systems, mixed integer linear programming, renewable energy sources, smart grid.
I. INTRODUCTION
R
ENEWABLE Energy Sources (RES) are candidates to be the backbone of the future power systems. However some RES, such as wind and solar, generate power not when it is demanded but in an intermittent and variable way. This makes it difficult to integrate the power generated from these RES into the electric network. In order to solve this problem smarter power structures (Smart Grids) are going to be built [1]. Smart Grids are mainly based on Information and Communication Technologies (ICT) and on Energy Storage Systems (ESS). Among the Smart Grids, different structures like micro grids or Virtual Power Plants (VPP) are going to be developed. One of the main characteristics of these structures is the possibility to operate autonomously [3], [10]. In an autonomous power system with a high penetration of RES the problem coming from the intermittent generation has to be P. Lombardi is with the Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg Germany (e-mail:
[email protected]). T. Sokolnikova and K.V. Suslov are with the Department of Power Supply and Electrical Engineering of Irkutsk State Technical University (ISTU), 664074 Irkutsk Russia (e-mail:
[email protected] ). Z. Styczysnki is with Otto-von-Guericke-University Magdeburg, 39106 Magdeburg Germany (e-mail:
[email protected]).
locally solved. Generally Demand Side Management programs (DSM) as well as ESS are the most commonly used solutions. However the contribution of DSM is marginal and it has a low influence on the optimal energy storage capacity [4]. This study aims to search for the relation between the needed storage capacity and the amount of energy generated by wind and solar within an autonomous microgrid. The microgrid is composed by four fuel based generators, a small wind farm and a PV plant. An intelligent Energy Management System (EMS) optimally controls the fuel based generators and the ESS with the aim to minimize the fuel costs. Eleven main scenarios were analyzed. In the first and last scenario the amount of electricity generated by wind and PV is zero and 100%, respectively. Moreover, sensitivity analyses were carried out to find the influence between the type of RES based technology that was used and the energy storage capacity. II. AUTONOMOUS MICRO GRID: MODELING AND PROBLEM FORMULATION
A. Micro grid description and modeling of the installed power Microgrids are defined as a group of generators, energy storage systems and loads which, in part, can be also controlled [5]. The generators may use RES such as wind, solar and biogas, or clean fossil sources such as natural gas. Generally the generators that burn gases produce both electricity as well as thermal energy. The combination of both types of generation has the advantage to increase the overall efficiency by recovering of the energy of the exhausted gases. Energy storage systems are mainly used for power quality and energy storage applications. Flywheels and supercapacitors are generally used for power quality applications. Batteries are mostly devoted for energy management application even if some high temperature battery technologies, such as NaS, can be used both for energy management as well as for power quality purposes [6]. With regard to the loads, some of them, like heating, air conditioning or wash machines, can offer to possibility to be controlled using load management programs which may decide to shift the load or to curtail them during particular conditions [7] . In order to control all the generators, energy storage systems and load information need to be evaluated and sent to a central Energy Management System. For micro grids as well as for the entire smart grid concept ICT are the backbone on which the system depends.
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In this study an autonomous micro grid was analyzed. The micro grid is composed of four conventional generators, a wind farm and a PV plant (Fig.1). An EMS optimally controls the production of the four conventional generators according to their generation costs, to the weather conditions and to the State of Charge (SOC) of the storage system. Besides the control of the generators the EMS also controls the loads. If the generators are not able to cover all of the demand, the EMS curtails a part of the load. Normalized curves were used to model both the generation profiles of the wind farm and PV plant as well as to model the load profile. These curves were measured in a wind farm, a PV plant and in a district all situated in Germany. The values of the full load hours for the used profiles are shown in Table I. TABLE I : FULL LOAD HOURS FOR THE USED PROFILES
Full load hour
Wind farm [hours] 1580
PV plant [hours] 912
Load [hours] 3723
TABLE II: INSTALLED POWER IN EACH SCENARIO Scenario
Gen.1 [MW]
Gen.2 [MW]
Gen.3 [MW]
Gen.4 [MW]
Wind [MW]
PV [MW]
I (0%)
0.25
0.25
0.25
0.25
0
0
II (10%) III (20%) IV (30%) V (40%) VI (50%) VII (60%) VIII(70%) IX (80%) X (90%) XI (100%)
0.225 0.2 0.175 0.15 0.125 0.1 0.075 0.05 0.025 0
0.225 0.2 0.175 0.15 0.125 0.1 0.075 0.05 0.025 0
0.225 0.2 0.175 0.15 0.125 0.1 0.075 0.05 0.025 0
0.225 0.2 0.175 0.15 0.125 0.1 0.075 0.05 0.025 0
0.18 0.23 0.35 0.47 0.59 0.7 0.82 0.94 1.06 1.18
0.2 0.4 0.61 0.81 1.02 1.22 1.43 1.63 1.84 2.04
The installed power of the energy storage system was calculated as the difference between the sum of the installed RES power and the minimal demanded power (1). For the considered generation profile of wind and PV the installed power coincides also with the maximum generated power. The application of the storage is mainly dedicated to the energy management rather than to the grid support. In fact, it has to compensate the intermittent generation of the RES based plants. No specific ESS were considered, however an overall efficiency of 81% (ηch=ηdisch=0.9) was assumed, which is typical of some battery technologies. (1) _ _ B. Optimal scheduling of generators
Fig.1 Scheme of the analyzed micro grid
The micro grid has a maximal and a minimal power demand of 1 MW and 0.136 MW, respectively, while the yearly electricity demanded is 3723 MWh. Eleven scenarios were simulated in order to check the relationship between the generated electricity from RES and the needed storage capacity. In the first scenario the energy demanded in one year is totally generated by the four conventional generators, while in the last scenario it is completely generated by the wind farm and the PV plant. In the first scenario no ESS was considered. In the other nine scenarios a part of the demanded energy is produced using RES while the rest is generated using the four conventional generators. The energy produced by RES is equally divided between the wind farm and the PV plant. Table II describes the installed power for each technology in each scenario.
As mentioned above, the EMS optimally controls the conventional generators for minimizing the fuel costs. A quadratic cost function was used which relates the fuel cost to the generated power (2). Table III shows the data of the generators. ·
·
(2)
TABLE III: DATA OF GENERATORS Generator
1 2 3 4 *See Table II
Pmax [MW]
* * * *
Pmin [MW]
0 0 0 0
Fuel cost coefficients
a [€/MW2h] 0.01 0.023 0.026 0.024
b [€/MWh] 30 42 32 97
Start up costs [€] c [€/h] 109 97 109 100
28 28 28 28
Since the mixed integer linear programming algorithm was chosen to optimally schedule the generators, the cost functions were linearized. Therefore, instead of a quadratic function, piecewise linear functions were calculated. The objective function as well as the constrains of the optimization problem
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are shown in (3) and (4), respectively.
RES the needed storage capacity increases too (Fig.2). Fig.2 also shows that if the RES share is lower than 10%, the total (3) annual costs linearly increase with increasing energy storage capacity.
,
/
·
(4)
·
Where Ci are the generation costs, ui is a binary variable [0 or 1], (1 if the generator is set up) Pi is the produced power from the generator i, t is the time step (one hour), Load is the demanded load for the hour t, Pwind is the power generated during the hour t by the wind farm, Ppv is the power generated during the hour t by the PV, Pch/disch is the power charged and discharged to/from the ESS during the hour t. Such a power is positive when the ESS is charged and negative when it is discharged. C. Optimal storage capacity One of the main aims of this study is to find the optimal storage capacity for the analyzed micro grid. In order to find it a second optimization problem was set. The problem consists in minimizing the total annual cost of the micro grid. The annual costs are defined as the sum of the fuel costs (Costfuel), the discounted investment costs of the ESS (CostinvBatt), the costs for switching off the load if all the generators are not able to cover it (Costswitchoff) and the costs for not producing RES power if the EES is full (CostsurplusRES) (5).
Fig.2 Total annual costs related to the RES share and to the storage capacity
In this study, investing in ESS became profitable only if more than 10% of the annual electricity is produced by RES. The reason mainly depends on the assumed VOLL. If this value is increased, the profitability to have a storage system, even when the share of RES is lower than 10%, increases too. If the micro grid is fully fed through RES, a storage system with an installed power of 3 MW and a capacity of 7.6 hours results as the optimal choice (Fig.3).
(5) The investment costs for the ESS were assumed to be 2500 €/kWh. A life time of 10 years and a discount factor of 10% were considered. The costs due to the switch off of the loads were estimated using the Value of Lost Load (VOLL). This value depends on the kind of load that should be switched off. For industrial and commercial loads it ranges between 10000 and 40000 € per MWh [9],[2]. In this study 10000 € per MWh was assumed as VOLL. Finally, the authors assumed that the costs for not producing from RES may be estimated as 200 € per MWh. III. RESULTS Eleven scenarios considered, as shown in Table II above. In these scenarios the energy generated by the RES is equally divided between the wind farm and the PV plant. The total annual costs in each scenario were evaluated. The analysis shows that by increasing the amount of energy generated by
Fig.3 Optimal storage capacity for different RES generation
The sensitivity analysis showed how the technology used to generate electricity influences the storage capacity. If the micro grid was fully fed using wind turbines, a storage capacity of 57 MWh is required. However, if the micro grid was fed only through PV plants, the storage capacity decreased to nearly 17 MWh, as shown in Fig.4.
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Technical Reports: [6] [7]
[8] [9]
EPRI-DOE,”Handbook of energy storage s for Transmission and Distribution applications”, 2003. G Tsikalakis, J. Oyarzabal, J.A. N. D. Hatziargyriou, A. Dimeas, A. G. Pecas Lopes, G. Kariniotakis, “Manag gement of microgrids in market environment”, 2009 9, Available: http://www.microgrids.eu/micro2000/prresentations/39.pdf Styczynski Z., Lombardi P at allii . “Ellectric Energy Storage Systems”. Electra 255, April 2011”, CIGRE Paris ISBN: 978- 2- 85873- 147-3 V of the Customer Reliability CRA International “Assessment of the Value (VRC)”, August 2002.
Dissertations: [10] P. Lombardi, “Multi criteria optimizaation of an autonomous virtual power plant”. Ph.D. dissertation, Res electricae Magdeburgenses, Bd. 38, ISBN 9783940961556 394096155
VII. BIOGRAPPHIES
Fig.4 Sensitivity analysis
IV. CONCLUSIONS The influence between the energy geneerated using RES based technologies and the optimal storaage capacity was analyzed within an autonomous micro griid structure. The analysis shows that the optimal storage capacity mainly depends on three factors: RES, • Amount of energy generated by R • Type of RES technology , • Value of Lost Load. A low VOLL justifies the use of energyy storage systems only if the share of RES is higher than 30%,, while due to the generation profile of the PV plant such techhnology requires a higher storage capacity. In further research the autonomous sysstem with a grid connection will be investigated for miniimizing the cost function. V. ACKNOWLEDGMENT The authors gratefully acknowledge the finnancial support of the Russian Federation in the scope of the G Grant 220 and the contributions of Miss Xiubei Ge for her worrk on the original version of this document. VI. REFERENCES Periodicals: [1] [2]
S. Mossoud Amin, “Toward a smart grid: powerr delivery for the 21st century”, Power and Energy Magazine, IEEE., 20005 K. K. Kariuki, R. N. Allan, “Applications of Custoomers Outage Costs in System Planning, Design and Operation” IIEEE Proceeding – Generation, Transmission and Distribution 143, 3005-312, 1996
Papers from Conference Proceedings (Publisshed): [3] [4]
[5]
P. Lombardi, P. Vasquez, Z. Styczynski, “Optimissed autonomous power system” in Proc. 2009 Cigre IEEE PES Joint Syymposium Calgary, 29 July 2009. P. Lombardi, M. Stötzer, Z. Styczynski, A. Orths, “Multi-criteria optimization of an energy storage system within a Virtual Power Plant architecture” in Proc. 2011 IEEE Power Engineeering Society General Meeting Conf.,24-29 July 2011 Detroit. C. Marnay, O.C. Bailey. “The CERTS Microgriids and the future of microgrids”, Berkeley, California 2004.
Pio Lombardi studied d mechanical engineering at the Politecnico di Bari, Itally. He graduated in 2006 at the same university with th he degree M.Sc. He joined the Chair of Electric Power Networks N and Renewable Energy Sources at the Otto-von-Guericke University Magdeburg, Germany as a research engineer in 2006. At the same h PhD. In 2011 he joined the university he received his Process and Plant Engineeering of Fraunhofer Institute for Factory Operation and Automation A IFF. His primary field of interest includes modeling, simulation and optimization of Smart Grids. He oup. is a member of the Baikal project research gro Tatyana V. Sokolnikova a graduated in 1985 with M.Sc. from the Irkutsk State Technical T University (ISTU) in Hydrogeology. Between 1985 1 and 2005 she was a leading planning engineer in the Planning Institute Irkutsk. In 2008, she completed her master's degree in Smart Grid nd is now working in the scope of technology at the ISTU an the Bajkal Projekt on her Ph.D. Her research interests are a optimization of autonomous related to the planning and Smart Grids, taking into account the role of energy storages. Konstantin V. Suslov is an Associate professor at the electric power supply department of Irkutsk State Technical University. Hee graduated from the Irkutsk State Technical University wiith the specialty “electric drive and industrial automatiions”. He has a candidate of science degree in techn nics. His research interests are related to computer engineering and automation, equipment in automated information-measuring accounting systems of power consumption. He is a member of the Baikal project research group. Zbigniew A. Styczynsk ki (SM ‘01) received his PhD in EE at the Technical Univ versity of Wroclaw. He worked at the Technical University y of Stuttgart, Germany and 1999 he became Chair of Electric Power Networks and Renewable Energy Sourrces of the Faculty of Electrical Engineering and Informaation Technology at the Otto-vonGuericke University, Maagdeburg, Germany. Since 2006 he has also been the president p of the Centre of the Renewable Energy Sax xonia- Anhalt, Germany. His special field of interest includes modelling g and simulation of the electric power networks systems, renewable, and op ptimization problems. He is the author of more than 150 scientific papers, a senior member of IEEE PES, a member of CIGRE SC C6, VDE ETG and IBN and a fellow of the Conrad Adenauer Foundation. In 2011 he won th he Super Grant of the Russian Federation together with the Irkutsk Statee Technical University (Project Baikal) and is leading a research group at IST TU in the scope of Smart Grid.
