- Methodology
- Open Access

# Coordination of battery energy storage and power-to-gas in distribution systems

- Teodor Ognyanov Trifonov
^{1}Email author

**2**:38

https://doi.org/10.1186/s41601-017-0072-y

© The Author(s) 2017

**Received:**30 May 2017**Accepted:**13 October 2017**Published:**17 November 2017

## Abstract

Concerning the rapid development and deployment of Renewable Energy Systems (RES) and Energy Storage System (ESS) including Power-to-Gas (PtG) technology can significantly improve the friendliness of the integration of renewable energy. The purpose of this paper is to develop a coordination strategy between a battery energy storage and a PtG system. A simulation case is created with an electrical and a natural gas grid as well as steady-state models of RES and PtG. Charging strategies are developed accordingly for the ESS as well as production strategies for the PtG system. The size of the ESS is then observed with regards to the RES and PtG systems. As a result, it is found that surplus energy from RES can be stored and then used to support the electrical grid and the natural gas grid. It is also concluded that the capacity of the ESS can be affected, given a proper charging and production strategy, which needs to be tailored to each system. As shown in the paper, due to an improper charging strategy in the first quarter of a month, the ESSPC size has increased from its optimal size of 314 MWh to roughly 576 MWh. It can also be seen that given a proper charging strategy, this capacity can be less than 200 MWh.

## Keywords

- Electrical energy storage
- Power-to-gas
- Coordination
- Integrated energy systems

## 1 Introduction

In the past few decades, fossil-fuel energy meets most of the energy needs of the world [1]. However, with the high number of environmental problems and the depletion of fossil fuels, renewable energy will be a major electricity source for the power grid and the penetration of renewable energy such as wind and solar power will keep increasing.

According to the German Advisory Council on Global Change, the time to phase out the fossil-fuel-based energy is closer than ever. The turning point should occur around the year 2030 [2], and by then much work needs to be done to make different RES operate more efficiently. This can happen through new technologies, finding specific optimal geographical locations for placing RES or even creating new RES.

Denmark has set an ambitious goal where 100% of the energy supply must come from RES. So in the next years, it is expected that more and more of the energy supply of the country will be provided by RES, mainly from wind farms (both onshore and offshore). According to relative planning, 50% of electrical demand should be covered by wind power by 2020 and coal, and oil energy should be phased out from the Danish power system by 2030. By the year 2035 both electricity and heat demand should be met by RES and by the year 2050 100% of energy supply including electrical, heat, industry and transport should be covered by RES [3].

This paper focuses on the 2035 milestone, and in that case, RES will need to cover more than one energy sector thus needing to increase its installed capacity. ESS can be paired together with these RES to store part of the excess energy. Also, a PtG system, which transforms water into a synthetic natural gas (SNG) through the use of electricity, can use the stored electrical energy in the ESS, that is produced by the RES, and use it to help cover the gas needs of consumers [4–6].

This paper aims to contribute to the study of interconnected energy systems and focuses on the coordination between RES and PtG. Through the information presented here, the author has created a program in Matlab called ESSPC (Electrical Storage System and Power to Gas Coordinator), through which a study can be carried out for a test grid for a period. Information can be obtained on different ESS capacities, the optimal number of RES to be installed, how much the community can save by adding an ESS and when the ESS is coordinated with a PtG, how much natural gas can be produced and how much that will affect the ESS size. As the RES simulation steps are not shown in this work, refer to materials [7–10], for more information on how the RES are modeled.

The remainder of the paper is organized as follows: Section 2 presents the model that is used in this article, as well as an explanation of how the PtG system is modeled. This section also provides information on how the production strategy for the PtG is formed. Section 3 presents different sizing scenarios for the ESS. Section 4 presents the results from these scenarios. Section 5 is dedicated to conclusions that are formed from this paper.

## 2 Methods and Modelling

In order to help the local consumers of the two cities, with SNG produced from surplus energy from RES, peak regulation is chosen to be performed for each city. This service is selected because GPGs are fast-acting and also the PtG system is not able to produce enough SNG to take care of the base load.

The peak regulation is performed with the help of a load prediction sequence, in an attempt to minimise the gas demand as much as possible. The two load prediction algorithms used are called Kalman Filter and Least Error Squares (LES). Depending on the type of load presented, one of the two algorithms is chosen and the electrical load for the next 24 h is predicted. Afterwards, if the predicted load is less than the actual load, the GPGs are activated and supply the remaining power that is needed.

### 2.1 Power-to-gas model

In this section, the steps of calculating how much energy the PtG system can withdraw from the ESS are shown.

_{2}is turned into 1 mol of SNG. The energy relationship between electric power and SNG is given by the following equation:

Where G_{D,PtG} is Gas demand from the PtG system; E_{D,PTG} is the Energy demand from the PtG system at the moment “(m)”. The energy unit is turned into [MWh] through multiplying ∆H by 2.77778e-72.77778*e*
^{−7}.

_{P,PtG}is the natural gas generation rate of the PtG, m

^{3}/h; E

_{D,PtG}is the consumed electrical power in the process; C

_{ PtG }is a constant value which can be calculated by:

Energy efficiency of the PtG is marked by ηPtG. The energy density of natural gas is presented via its Lower Heating Value (LHV), MJ/ m3.

### 2.2 Gas grid flow calculations

To analyze the steady-state flow in the gas grid, the nodal balance equations of the system are presented firstly. Then, to solve the nonlinear model, Newton–Raphson method is adopted which is a popular method in the gas and power system analysis [12].

_{k-l}is calculated

*Z*

_{ k − l }.

Thus, a load flow sequence for the natural gas grid is created.

## 3 Optimal sizing of the ESS in coordination with the PtG

This paper presents three methods for sizing the storage system. The first method sizes the storage system depending on the exported energy from the local grid (overproduction from the RES) and the imported energy from other interconnected power systems. The second method is where the import/export of the grid is monitored, as well as the price of electricity. The third method is created based on analysis of the first two approaches, where the best one is chosen, and coordination between the ESS and the PtG is performed. This is done so that a clear distinction can be made, whether adding a PtG to a system with ESS can further on improve the size of the storage system,

The network presented in Fig. 1, contains RES, wind turbines and photovoltaics. ESSPC needs to be supplied with electrical consumption data, grid parameters and weather data (wind speed and humidity) for some days. Through the weather data, different turbine and photovoltaic manufacturers can be reviewed for whether or not their production fits the area, both in production and whether the weather conditions are optimal for that technology.

_{G,G}is the power generated in the grid [MW], P

_{Gn,WT}and P

_{Gn,PV}, refer to the power produced by the wind turbines and the photovoltaics under the effect of the weather. To size the ESS, the number of RES installed in the grid needs to be varied. This is set through “x” and “y” in Eq. 12. Obtaining the optimal number of RES can be found by testing different combinations of RES and thus finding the most optimal ESS size.

Equation 13 presents a case where two wind turbine technologies are tested and one photovoltaic technology. Each wind turbine and photovoltaic technology have a minimal and maximal number of installed units in the network, set by the user. Through varying ×1, ×2 and y, the power generation in the network for each combination for each period is found. After simulation of all possible results, the smallest ESS size is found and traced back to its RES combination.

### 3.1 Sizing storage system with regards to the import/export

P_{R,G} is the extra grid power, the difference between the generated and consumed power in the grid at data point “m”. R_{PER} is user-set and refers to what percentage of the residual power value must charge/discharge the ESS. When the residual power value is positive, the excess energy is transferred to other interconnected networks. A percentage (Rper) of that power is used for charging the storage system. When the residual energy in the grid is negative, a percentage (Rper) of the necessary electric power is supplied from the ESS. This way the ESS can support the local network, where the RES are installed. The power that is charged/discharged in the ESS is given by P_{IO,SS}.

_{CAP,SS}has reached for the entire dataset.

### 3.2 Sizing storage system with regards to the import/export and the price of electrical power

_{G,G}) by the RES is greater than the total consumption (P

_{C,G)}that is needed in the test grid at that point in time. In such an event, after accounting for the pre-set residual percentage, the ESS is charged P

_{C,SS}. Because for MATPOWER, the ESS acts like a generator and a motor at the same time, in the event of charging the ESS, the generation of the ESS (P

_{G,SS}) must be set to zero.

_{PRI,G}) is greater than the set power price limit (P

_{PR,G}), it is determined that it is too expensive to import power, and as such the ESS supplies all the necessary power to the consumers.

In the event, where the price of electricity is low enough, the ESS does not perform any action.

To find the size of the ESS, Eqs. 15 and 16 are used. Here P_{I/O,SS} is either P_{C,SS} or P_{G,SS}, depending on the action that the ESS must perform at that moment.

This method allows the researcher to make a more educated decision whether the RES are suitable for the current area based on information about how costly it is going to be to supply the test grid when the RES is not producing. It also allows for sizing the ESS, depending on the cost of importing electricity into the test grid, when the RES is not working.

## 4 Numerical simulation

The following section presents the results for three different cases-sizing ESS capacity.

### 4.1 Model information

The electricity demand data is obtained from [14] and is given in hourly intervals for one month. The RES production is achieved via weather data, obtained at a given moment and location. The wind power generation is determined by the air density and wind speed. Such data is obtained from [15] in the form of hourly intervals. The hourly solar irradiance and the temperature are obtained from [16] for the Photovoltaics. Moreover, from [17] the hourly electrical price has been obtained.

To create the steady-state model, information about the gas consumption of the two GPGs is required. That is obtained the method explained in section 2.

### 4.2 Sizing storage system capacity with surplus power

### 4.3 Sizing storage system capacity with surplus power and price method

The next charging method that includes the price of electricity is executed. The number of RES stays the same as the number of RES as before. Here, it can be seen that the ESS is constantly charging for the whole period. The reason for this behavior is the significant number of RES that is present. Because the number of RES is greater than what this method needs, the ESS almost constantly charges throughout the whole period and at the end reaches a size of around 1800 MWh. It can also be seen that the charging cycles have not increased in number, compared to Fig. 3, but some have increased in power. However, the biggest change, which also affects the ESS, is the lack of discharging cycles in the middle of the given period. This is due to the change of charging strategy, and those areas where it is noticed a lack of discharging events correspond to the low price of electricity, deducted from 3.2. With that information in mind, it is deduced that the PtG must be paired together with an ESS that uses the Surplus Power and Price method.

Sizing ESS without PtG (portion)

Total Price Paid for Imported Power Whole Period [$] | ||

No Storage | Surplus Power Method | Surplus Power and Price Method |

4,14E + 05 | 3,43E + 05 | 3,77E + 05 |

Total Size of the Storage System [MWh] | ||

No Storage | Surplus Power Method | Surplus Power and Price Method |

0 | 331,4224 | 1,83E + 03 |

Highest Charge Rate [MW/interval] | ||

No Storage | Surplus Power Method | Surplus Power and Price Method |

0 | 30,995 | 30,995 |

Highest Discharge Rate [MW/interval] | ||

No Storage | Surplus Power Method | Surplus Power and Price Method |

0 | 10,7622 | 10,7622 |

Sizing ESS with PtG

Total Price Paid for Imported Power Whole Period [$] | |

No Storage | Surplus Power and Price Method |

4,03E + 05 | 4,33E + 05 |

Total Size of the Storage System with PtG [MWh] | |

No Storage | Surplus Power and Price Method |

0 | 5,76E + 02 |

Gas Produced entire period [m^3] | |

No Storage | Surplus Power and Price Method |

0 | 7,03E + 04 |

Highest Charge Rate [MW/interval] | |

No Storage | Surplus Power and Price Method |

0 | 1,94E + 01 |

Highest Discharge Rate [MW/interval] | |

No Storage | Surplus Power and Price Method |

0 | 1,32E + 01 |

This information is proof that for the currently chosen Grids, Consumers, and RES, the combination of ESS and PtG will not bring better results in lowering the ESS capacity. It can be deduced that the PtG has the potential of lowering the ESS, given slightly different starting conditions, like changing the demand profiles, the prediction algorithms and introducing various more RESs.

## 5 Conclusions

Coordination between ESS and PtG systems will play a major role in the future power system. Due to the large number of RES that are installed and will continue to be installed, a method for utilizing the excess renewable energy is needed. It is found out that it is possible to store surplus energy from RES and store it as electrochemical energy, which afterward can be turned into methane. With the proper charging strategy, tailored to the given area, the size of the ESS can be optimised to be as small as possible.

Furthermore, by setting the gas demand strategies with load prediction algorithms, it was found that using the PtG system under these initial conditions will increase the ESS size. The ESS size in coordination with a PtG system can potentially be lowered by studying various grid layouts, improvement of the gas demand strategies or even implementation of a gas storage facility in the grid.

## Declarations

### Competing interests

The author declares that he/she has no competing interests.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

## References

- D. G. Rachel Carnegie, David Nderitu, Paul V. Preckel, “utility scale energy storage systems,” state utility forecasting Group 2013.Google Scholar
- ISPRE, “Research and Development on Renewable Energies ” ISPRE2011.Google Scholar
- (1/9). Available: https://ens.dk/sites/ens.dk/files/contents/material/file/the_danish_energy_model.pdf.
- B. V. L. Mathiesen, Henrik; Hansen, Kenneth; Skov, Iva Ridjan; Djørup, Søren Roth;, S. S. Nielsen, Peter; Thellufsen, Jakob Zinck; Grundahl, Lars; Lund, Rasmus, and D. W. C. Søgaard; Drysdale, David; Østergaard, Poul Alberg, “IDA’s Energy Vision 2050: A Smart Energy System strategy for 100% renewable Denmark,” Aalborg University 2015.Google Scholar
- L. Bird, M. Milligan, and D. Lew, “Integrating variable renewable energy: Challenges and solutions,” National Renewable Energy Laboratory, Technical ReportSeptember 2013 2013.Google Scholar
- Mathiesen, B. V., Lund, H., Connolly, D., Wenzel, H., Østergaard, P. A., Möller, B., et al. (2015). Smart energy systems for coherent 100% renewable energy and transport solutions.
*Applied Energy, vol. 145*, 139–154, 5/1.View ArticleGoogle Scholar - Richardson, R. D., & McNerney, G. M. (1993). Wind energy systems.
*Proc IEEE, 81*, 378–389.View ArticleGoogle Scholar - S. B. Armando Bellini, Vincenzo Iacovone, Cristina Cornaro, “simplified model of a photovoltaic module,” 2009.Google Scholar
- WXWGDA. (2004). Capel, “A novel modeling method for photovoltaic cells,” Presented at the power electronics specialists conference. PESC 04. 2004 IEEE 35th annual 2004.Google Scholar
- K. Provence, “calculating PV Array voltage,” ed, 2013.Google Scholar
- Zeng, Q., Fang, J., Li, J., & Chen, Z. Steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion. Applied Energy.Google Scholar
- P. S. J. H. Lukas Grond, Systems analyses power to gas, KEMA Nederland B.V, Technology Review20/06 2013.Google Scholar
- Zimmerman, R. D., Murillo-Sanchez, C. E., & Thomas, R. J. (2011). MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education.
*IEEE Trans Power Syst, 26*, 12–19.View ArticleGoogle Scholar - “Distribution Zone Substation Information,” Ausgrid, Ed., ed: Ausgrid, 2016.Google Scholar
- “Modern-Era Retrospective Analysis for Research and Applications (MERRA), version 2,” ed. gmao.gsfc.nasa.gov: National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center, 2016.
- “CAMS Radiation Service v2.7 all-sky irradiation (derived from satellite data).”, ed: MINES ParisTech (France), 2016.Google Scholar
- “National Electricity Market Price,” AEMO, Ed., ed: AEMO, 2016.Google Scholar