Renewable Energy Self-Consumption versus ...

INTERNATIONAL JOURNAL OF ADVANCED RENEWABLE ENERGY RESEARCH Rosemarie Velik, Vol. 3, Issue. 1, 2014

Renewable Energy Self-Consumption versus Financial Gain Maximization Strategies in Grid-Connected Residential Buildings in a Variable Grid Price Scenario Rosemarie Velik CTR Carinthian Tech Research, Villach, Austria  Abstract—The currently ongoing change of residential buildings from passive energy consumers to active prosumers via the integration of PV (photovoltaics) and storage systems and the putting in place of variable grid prices require the development, implementation, and evaluation of novel energy management concepts and strategies. In this article, we investigate and compare (1) a “PV selfconsumption maximization” strategy with (2) a “financial gain maximization” strategy in terms of local PV energy consumption/supply and obtainable financial gain. Analyses are based on real-world household load data, solar irradiance data, and computationally simulated grid prices. Results are analyzed for different storage sizes for both a summer and a winter period. Results show that the achievable PV energy self-consumption/supply is significantly higher for the “PV self-consumption maximization” strategy and reaches a “plateau” at a storage size that equals the average amount of daily PV-production and load consumption. On the contrary, the obtained financial gain over the two investigated seasons is significantly higher when employing the “financial gain maximization” strategy and reaches a plateau at a storage size equaling 50% of the average amount of daily PV-production and load consumption. Index Terms— Photovoltaics, battery storage, variable grid prices, energy trading, building energy management, storage sizing, renewable energy, residential sector, seasonal differences, self-consumption maximization, financial gain maximization

I. INTRODUCTION Public funding models for photovoltaics (PV) energy, having so far promoted a direct feed-in of energy into the grid, are about to change. A more recent funding approach is to provide incentives for the local Manuscript Received October 25, 2013; Revised November 25, 2013; Accepted November 30, 2013.

consumption of self-produced energy. However, in longterm, photovoltaics will have to get along without public funding [1, 2]. Thus, novel, sustainable energy management strategies are needed for the use of PV. These strategies should incorporate and build on novel developments in the energy sector like the employment of storage technologies and the putting in place of variable grid prices [3]. First attempts into this direction have already been taken by different research groups. [4] describe a fuzzy-logic based energy management strategy for a commercial building in a supermarket application using photovoltaic and storage systems as well as load shedding. [5] examine the potential for dispatchable, peak shaving PV arrays using storage and conclude that this use of the system provides much more value than PV systems built to maximize energy output only. [6] propose a probabilistic approach for the energy and operation management of renewable microgrids including renewable energy producer and storage devices considering uncertainties in load demand, market prices, and renewable energy production. [7] focus on building-integrated microgrid design and implementation applied to a building integrated photovoltaic system with energy storage and smart grid communication. They aim at finding an energy management strategy to reduce grid peak consumption, avoid undesirable grid power injection, and make full use of local PV production. For this purpose, grid time-of-use tariffs, grid access limits, storage capacity, load and PV power shedding are considered. [8] investigate energy management strategies including photovoltaic energy, battery storage and neighbourhood energy exchange to maximize local photovoltaics energy consumption in grid-connected residential neighbourhoods. In this article, we discuss, analyze, and compare two promising novel strategies for the management of


renewable energy resources in PV-supplied, storageaugmented residential buildings that are connected to an electricity grid featuring variable grid prices. The first strategy is called “PV self-consumption maximization”. Its objective is to maximize the selfconsumption and supply of produced PV energy within the building. The second strategy is called “financial gain maximization”. Its objective is to achieve a maximal financial gain via the trading of energy with the grid, basically by using, storing, and purchasing energy when the grid price is low and selling it when the grid price is high. Experiments are carried out for different storage sizes and different seasons of the year (summer/winter) using real-world measurement data from six Austrian test households. II. MATERIALS AND METHODS This chapter describes the materials and methods used for our study. Section A explains the system topology for each of our grid-connected, storageaugmented PV-supplied test households. Section B contains a description of the data acquisition processes and the employed data preprocessing methods. Section C summarized model specifications for our system simulations. Finally, Section D illustrates the investigated energy management strategies.

B. Data Acquisition and Preprocessing For our analyses, grid price curves, load profiles, and PV production profiles had to be acquired. The investigation period should cover four consecutive weeks in summer (July) and four consecutive weeks in winter (January). 1) Grid price data

For obtaining a grid price curve, a simple gird price simulator was programmed. This grid price simulator randomly varied the grid price over the day between the values 1 unit/kWh, 2 units/kWh, and 3 units/kWh (see Table I and Figure 2) while assuring that the grid price was in average always 2 units/kWh over a day. The gird price was valid “bidirectionally” for energy purchase from the grid as well as for energy selling to the grid TABLE I.

GRID PRICE CATHEGORIES Grid price Low Middle High

1 unit/kWh 2 units/kWh 3 units/kWh

A. System Topology

Figure 2. Example for the change in grid price over a day

Figure 1. Schematic overview of system topology and building blocks of each test household

Figure 1 shows the system topology of each of the 6 test households [9, 10]. The main components of the system are (1) the household loads, (2) the PV system (including an inverter), (3) the battery storage (also including an inverter), and (4) the grid connection. Figure 1 additionally depicts the possible directions of energy exchange between the components.

2) Load data For obtaining household load profiles, the ADRESCONCEPT dataset [11] was used. This dataset consists of active and reactive power measurements of 30 Austrian households carried out over a summer and a winter week. For this article, data about the total power of the first 6 households of the dataset were used and reproduces 4 times in a row to obtain data for a period of always four weeks in winter and in summer. To be able to compare results, the ADRES load curves of the households were normalized to achieve an average


consumption of 16kWh per day and household when averaging over the weeks in winter and summer:

∑

∑

x … number of data entries for the recorded summer week and winter week, respectively (7 days · 24 entries/day = 168 data entries)

Figure 3 gives representative examples for the normalized load profiles of the 6 households over a day for both winter and summer while Figure 4 shows the averaged load consumption of the six households. 3) PV data For obtaining PV production curves, the S@atel-Light database [12] was used by extracting solar irradiance data of the city Graz for a 4-week period in January 2000 and 4-weeks period in July 2000. Data resolution was one hour. The irradiance data corresponded to a surface oriented to the south with a ground reflectivity factor of 0.15 and a tilt angle of 30°. For obtaining a PV production curve from the irradiance data, data were normalized to yield an average production rate of 16kWh per day when averaging over the recorded four winter and four summer weeks: PPVj

IS ∑yi IS

tel ight

interi

tel ightj ∑yi IS tel ight Summer

k

y … number of data entries for the four recorded winter weeks and the four recorded summer weeks, respectively (28 days · 24 entries/day = 672 data entries)

Figure 3. Examples for load curves of the six test households on a summer day and a winter day

Figure 5. Examples for PV production curves on a summer and a winter day

Figure 5 illustrates examples of normalized PV production profiles for a winter and a summer day as used for each household (assuming that each house has the same roof orientation, tilt angle, and number of installed PV modules, which is often the case in newly built neighborhoods constructed by larger building enterprises). Figure 4. Examples for load curves for a summer day and a winter day averaged over the six test households


TABLE II.

AVERAGE AMOUNT OF PV PRODUCTION AND LOAD CONSUMPTION PER HOUSEHOLD AND DAY DURING A SUMMER MONTH AND A WINTER MONTH

PV [kWh]

Winter

13.1

Summer

18.9

Season Average

16.0

Load Consumption Averaged over 6 Households [kWh]

Load Consumption for each of the 6 Individual Households [kWh] (numbered from 1 to 6) 1

2

3

4

5

6

17.6

16.9

18.8

16.6

16.9

17.0

19.3

14.4

15.1

13.2

15.4

15.1

15.0

12.7

16.0

16.0

16.0

16.0

16.0

16.0

16.0

Via the normalization of the load consumption and PV production curves, it was achieved that the amount of produced energy over the eight recorded weeks (winter and summer) equaled the amount of energy consumed during this period by each individual household (see Table II). C. System Modelling In Figure 1, the system topology of each household was illustrated. To keep system modeling simple, efficiency losses from individual components like PV modules, solar inverters, batteries, and battery inverters were neglected. This allowed for a straightforward analysis of results independent of the performance of any particular technology available today. This approach was selected because technologies employed in the field of renewable energy are currently changing and progressing rapidly. Emulating a particular technology would only yield results of short term significance. Therefore, we considered it as more important to analyze the theoretically possible upper limits of PV energy self-consumption/supply and achievable financial gain. For investigating the effect of the storage size on the amount of achievable PV energy selfconsumption/supply and financial gain, simulations were carried out for battery storage sizes of 0kWh, 4kWh, 8kWh, 16kWh, 24kWh, and 32kWh. These values corresponded to the actually usable storage capacities of the system; not to the nominal capacities indicated by storage producers. D. Energy Management Strategies For our system topology depicted in Figure 1, the energy management strategies described below were implemented in Visual Studio C++ accessing the data

from our PV, load, and grid price curves stored in txtfiles. 1) PV Self-Consumption Maximization Figure 6 illustrated the applied energy management strategy to achieve PV self-consumption/supply maximization. For this purpose, in a first instance, available PV energy is always used to directly supply loads. If afterwards surplus PV energy is remaining, the storage is loaded. Only if the storage is full, PV energy is fed into the grid. On the other hand, if loads cannot be fully supplied by available PV energy, it is attempted to supply them from the storage. Only if the storage is empty, loads are supplied with grid energy.

Figure 6. Applied maximization

strategy

for

PV

self-consumption/supply

2) Financial Gain Maximization The figures 7 to 9 illustrate the energy management strategies for financial gain maximization. As can be seen from these figures, the employed strategy depends on the value of the grid price (low, middle, high). Basically, the strategy employed is to store surplus PV energy when the grid price is low, to sell surplus PV energy when the grid price is middle, and to sell surplus PV energy and stored energy when it is high.


maximization” and “financial gain maximization” for photovoltaic-supplied households and to investigate the effect of the season of the year (summer/winter) and storage size on the achievable energy selfconsumption/supply and the obtainable financial gain. 1) PV Energy Self-Consumption and Supply Table III and Figure 10 summarize the achieved average amount of PV energy self-consumption per household and day for different storage capacities for the analyzed winter and summer periods. Furthermore, the mean value of both seasons is calculated. Figure 7. Applied strategy for financial gain maximization when grid price is low

TABLE III. AVERAGE AMOUNT OF PV SELF-CONSUMPTION PER HOUSEHOLD AND DAY IN KWH FOR THE SUMMER SEANSON, THE WINTER SEASON, AND AS SEASON AVERAGE

Strategy

SelfConsumption Maximization

Figure 8. Applied strategy for financial gain maximization when grid price is medium

Financial Gain Maximization

Storage Size [kWh]

Average PV Energy SelfConsumption per Household and Day [kWh] Season Summer Winter Average

0

6,9

5,3

6,1

4

10,6

8,3

9,5

8

13,0

10,3

11,6

16

14,1

11,6

12,8

24

14,2

11,6

12,9

32

14,2

11,6

12,9

0

6,4

4,9

5,7

4

6,8

5,3

6,1

8

6,8

5,3

6,1

16

6,8

5,3

6,1

24

6,8

5,3

6,1

32

6,8

5,3

6,1

Figure 9. Applied strategy for financial gain maximization when grid price is high

III. RESULTS AND DISCUSSION The objective of this article is to compare to energy management strategies “PV self-consumption

Figure 10. Average PV energy self-consumption in kWh per household and day depending on storage size, season of the year, and employed energy management strategy


To convert these absolute kWh-values into %-values of PV energy self-consumption and PV energy selfsupply, the following formulas were used: [

[ ]

]

[

]

[

[ ]

]

[

] [

[ ]

]

[

[ [

[ ] [ [

[ ]

] ] ] ] [ [

[ ]

]

] ]

TABLE IV. AVERAGE AMOUNT OF PV SELF-CONSUMPTION PER HOUSEHOLD AND DAY IN % FOR THE SUMMER SEANSON, THE WINTER SEASON, AND AS SEASON AVERAGE

Strategy

Storage Size [kWh] 0



Average PV Energy SelfConsumption per Household and Day [%] Season Summer Winter Average 36%

40%

The tables IV and V as well as the figures 11 and 12 show the results of these calculations. The figures shows that with the “PV self-consumption maximization” strategy, for both summer and winter, the achievable PV energy self-consumption and supply increase with storage size until they reach a “plateau” at 16kWh, which equals the value of average daily PV production and load consumption of the households. While PV energy self-consumption is higher in winter (up to 89%), PV energy self-supply is higher in summer (up to 99%). TABLE V. AVERAGE AMOUNT OF PV SELF-SUPPLY PER HOUSEHOLD AND DAY IN % FOR THE SUMMER SEANSON, THE WINTER SEASON, AND AS SEASON AVERAGE

Strategy


Storage Size [kWh]

Average PV Energy Self-Supply per Household and Day [%] Season Summer Winter Average

0

48%

30%

38%

4

74%

47%

59%

8

90%

59%

73%

16

98%

66%

80%

73%

24

99%

66%

81%

80%

32

99%

66%

81%

38%

4

56%

63%

59%

8

69%

79%

16

75%

88%

24

75%

89%

81%

0

44%

28%

35%

32

75%

89%

81%

4

47%

30%

38%

0

34%

38%

35%

8

47%

30%

38%

4

36%

40%

38%

16

47%

30%

38%

8

36%

40%

38%

24

47%

30%

38%

16

36%

40%

38%

32

47%

30%

38%

24

36%

40%

38%

32

36%

40%

38%


Figure 12. Average PV energy self-supply in % per household and day depending on storage size, season of the year, and employed energy management strategy Figure 11. Average PV energy self-consumption in % per household and day depending on storage size, season of the year, and employed energy management strategy

hen employing the “financial gain maximization” strategy, the achieved PV energy self-consumption and


supply was much lower than with the “self-consumption maximization” strategy and results increased only marginally with storage size. Differences in results between summer and winter season were less pronounced. 2) Financial Gain The average achieved financial gain per day of each household was calculated according to the following formulas for the summer season and the winter season, respectively. [

[ ]

[

in winter. Considering the season average, achieved financial trading gains were significantly higher for the “gain maximization” strategy than for the “PV selfconsumption maximization” strategy. An exception was the case where no storage was used. Here, achieved financial gains were only 1% for both energy management strategies. For the “gain maximization” strategy, achieved gains increased with storage size and reached a plateau at a storage size of 8kWh.

]

]

∑ ∑

n … number of data entries for the four recorded winter weeks and the four recorded summer weeks, respectively (28 days · 24 entries/day = 672 data entries)

TABLE VI. AVERAGE ACHIEVED FINANCIAL GAIN PER HOUSEHOLD AND DAY IN % FOR THE SUMMER SEANSON, THE WINTER SEASON, AND AS SEASON AVERAGE

Strategy



Storage Size [kWh]

Average Financial Gain per Household and Day [%] Season Summer Winter Average

0

30%

-27%

1%

4

36%

-23%

6%

8

35%

-24%

6%

16

33%

-27%

3%

24

31%

-30%

1%

32

29%

-32%

-1%

0

28%

-25%

1%

4

43%

-18%

13%

8

47%

-12%

18%

16

47%

-12%

18%

24

47%

-12%

18%

32

47%

-12%

18%

Table VI and Figure 13 show the results of these calculations. As can be observed from Figure 13, independent of the storage size and employed energy management strategy, the financial trading gain of households was always positive in summer and negative

Figure 13. Average achieved financial trading gain % per household and day depending on storage size, season of the year, and employed energy management strategy

IV. CONCLUSION This article analyzed and compared a “PV selfconsumption maximization” energy management strategy with a “financial gain maximization” energy management strategy in terms of local PV energy consumption/supply and obtainable financial gain. Analyses were based on real-world household load data, solar irradiance data, and computationally simulated grid prices and were performed for different storage sizes for both a summer and a winter period. Results showed that the achievable PV energy selfconsumption supply was significantly higher for the “PV self-consumption maximization” strategy and reached a “plateau” at a storage size that equaled the average amount of daily PV-production and load consumption. On the contrary, the obtained financial gain over the two investigated seasons was significantly higher when employing the “financial gain maximization” strategy and reached a “plateau” at a storage size equaling 50% of the average amount of daily PV-production and load consumption. The obtained results clearly show that the employed energy management strategy is crucial for optimal operation of renewable energy systems in


buildings. What strategy to employ heavily depends on the set objectives and goals of the system operator.

[5]

ACKNOWLEDGMENT The work reported in this article has been co-funded by the European Commission within the INTERREG Program for supporting small and medium sized companies (SME) in Italy and Carinthia (Austria) and the Austrian Research Promotion Agency (FFG) within the program Fit4Set (project Vision Step I) and the COMET KProject IPOT. The data for household load curves used in this article were generated in the research project “ADRES-CONCEPT” EZ-IF: Development of concepts for ADRES – Autonomous Decentralized Regenerative Energy Systems, project no. 815 674). This project was funded by the Austrian Climate and Energy Fund and performed under the program “ENERGIE DER ZUKUNFT”. The data for PV production curves were acquired from the S@tel-Light database.

[6]

[7]

[8]

REFERENCES [1] Velik, R., Cognitive architectures as building energy management system for future renewable energy scenarios – A work in progress report, IJSEI International Journal on Science and Engineering Investigations, vol. 2, no. 17, pp. 68-72, 2013. [2] Velik, R. Zucker, G. Dietrich, D., Towards automation 2.0: A neuro-cognitive model for environment recognition, decision-making, and action execution. EURASIP Journal on Embedded Systems, Special Issue on Networked Embedded Systems for Energy Management and Buildings, vol. 2011, 11 pages, 2011. [3] Velik, R., The influence of battery storage size on photovoltaics energy self-consumption for gridconnected residential buildings, IJARER International Journal of Advanced Renewable Energy Research, vol. 2, no. 6, 2013. [4] Zhang, H., Davigny, A., Colas, F., Poste, Y. Robyns, B., Fuzzy logic based energy management strategy for commercial buildings integrating photovoltaic and Rosemarie Velik holds a B.Sc. degree in Electrical Engineering and Information Technology, a M.Sc. degree in Automation Technology, and a PhD in Technical Sciences, all obtained at the Vienna University of Technology. Her employment record includes an assistant professor position at the Institute of Computer Technology of the Vienna University of Technology, a position as senior researcher and project manager at Tecnalia Research & Innovation (Spain), and a position as key researcher and project manager at CTR Carinthian Tech Research (Austria). She holds more than 60 scientific publications in the fields of “smart energy”, automation, artificial intelligence, and biomedical engineering.

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