OPTIMISING THE SELF-CONSUMPTION OF SOLAR POWERED SMART MICRO GRIDS

A. Mahran, A. Minde, M. Noebels, K. Peter and J. Glatz-Reichenbach International Solar Energy Research Center - ISC - Konstanz, Rudolf-Diesel-Str. 15, D-78467 Konstanz, Germany

Phone: +49-7531-3618 351, e-mail: joachim.glatz-reichenbach@isc-konstanz.de

Smart (sum) meter Single device meters Raspberry Pi

Remotely switchable sockets and plugs Smart battery charger

Smart grid components

Device

Device

Device management

Task management

Multi-agent system External services

Graphical User Interface (GUI)

Household Internet Neighbourhood

Monitoring and controlling

The Graphical User Interface (GUI) is the link to the users. The mediator (in this case a Raspberry Pi) holds the connection to the internet for interactive exchange with integrated users, external feeds and the backup CoSSMic cloud server. Device management, task management and the MAS are running on the Raspberry Pi.

The CoSSMic (http://cossmic.eu) ICT architecture

Due to the continuous rise of retail electricity price and the falling remuneration of photovoltaic (PV) electricity feed-in tariffs (FIT), consumers who own PV installations are encouraged to increase their self-consumption. Furthermore, the intermittency of renewable energy resources (RES) hinders the exclusive reliance on RES as a sustainable energy resource. Additionally, batteries still reveal several technical and economic disadvantages when used to increase self-consumption. This research presents an approach to model a smart neighbourhood by using a multi-agent system (MAS). The model’s consensus is to increase the self-consumption as well as PV-based self-generation of an overall smart grid community. Furthermore, updated PV yield forecasts in addition to recorded load consumption profiles are used as input to the MAS to invoke demand-side management and urge participants to exchange their excess energy. In order to minimise the misfit between forecasted and measured PV yields, a recursive optimisation algorithm is applied to improve future estimations, based on past prediction errors. Results show that the self-consumption of single-users as well as the entire community – without using batteries – can exceed 60% with a potential to achieve full grid independency when supported by electrical storage. Moreover, community consumption can utilise all the electricity generated by PV, which leads to less grid congestion by feed-ins from renewable energy sources.

priority Item

1st self-consumption

2nd neighbourhood consumption

3rd local storage and electric vehicles

4th grid feed-in

priority Item

1st own PV system

2nd neighbouring PV systems

3rd local storage units

4th Grid

Allocation of offered PV power in a CoSSMic neighbourhood follows a prioritised order for feed-in tariffs lower than grid purchase tariffs.

Purchase of energy in order to execute a task in a CoSSMic neighbourhood follows a prioritised order for feed-in tariffs lower than grid purchase tariffs.

Grid feed-in

Shared with community

Self-consumption

User ID (Installed PV )

G1 (60kWp)

C1 (12.4kWp)

N1 (6.9kWp)

R1 (10kWp)

R2 (0kWp)

R3 (10.2kWp)

Overall (99.5kWp)

A: Self-cons. [%] – 44 1 18 0 36 2.6

A: Self-gen. [%] 0 85 100 24 – 36 25.7

B: Self-cons. [%] – 62 8 38 0 51 10

B: Self-gen. [%] 100 100 100 100 – 100 100

C: Self-cons. [%] – 80 2.8 67 0 82 5.8

C: Self-gen. [%] 0 48 100 10 – 13 15

D: Self-cons. [%] – 82 36 74 72 83 38

D: Self-gen. [%] 100 100 100 100 – 100 100

E: Self-cons. [%] - 83 59 75 76 83 60

E: Self-gen. [%] 100 100 100 100 - 100 65

Self-consumption and self-generation values of the model participants in different cases: A: Before energy sharing on a cloudy day B: After energy sharing using MAS on a cloudy day. C: Before energy sharing on a sunny day. D: After energy sharing using MAS on a sunny day E: After energy sharing using MAS on a sunny day + an additional G2 with 140kWp

Comparison between the PV production (right axis) and load demand (left axis) in kW on a cloudy day (Oct 14th) for an industrial prosumer “N1”, located in Konstanz. Notice that load demand is more significant when compared to the PV generation.

Demand and PV yield of a prosumer

Operation of a smart community using multi-agent system

Variation of the values of self-consumption (red) and self-generation (blue) of community versus the additional PV generator’s self-generation (cyan) while varying the installed PV capacity. A trade off between increasing community’s self-consumption and self-generation occurs and thence is decided upon based on system needs and financial constraints.

Smart neighbourhood model comprising consumers (R2), generators (G1), prosumers (R1, R3, C1, N1) and public grid.

Operation flow diagram of the multi-agent system representing the smart community.

E-Car

Integration of a recursive PV yield prediction

Weather service (DWD) computes COSMO-EU forecast

CoSSMic cloud server provides irradiation and temperature data

for neighbourhoods

Household calculates PV yield prediction

Measured (black) and simulated power as calculated with forecasted weather data from DWD at 00.00h, 06.00h and 12.00h of a day of August 2015.

An open-source photovoltaic yield prediction, relying only on easy to access data and hardware was developed. Consequential, a system without further required knowledge to install or unnecessary cost can be utilized for further improvements or projects. The improvement of the prediction performance by simple optimization efforts adverts high potential and could be further investigated.

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Resulting prediction error distribution of 122 consecutive days from 15th Oct 2015 to 31st Mar 2016, left side without and right side by taken into account the algorithm to gain recursively optimised hourly efficiency values.

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Conclusion Improved forecast of PV yield by recursive optimisation based on self-learning algorithm Increased PV self-consumption of up to 60% reached by MAS implementation without

electrical storage Increased PV self-generation of up to 65% with less feed-in leads to less public grid

congestion Improved customer profitability through increased share of self-consumed PV electricity Affordable capital investment for ICT components as compared to new PV systems

and/or batteries

References (1) http://cossmic.eu (2) A. Amato, B. Di Martino, M. Scialdone, S. Venticinque, S. Hallsteinsen, S. Jiang, A Distributed System for

Smart Energy Negotiation, Springer International Publishing, ISBN 978-3-319-11691-4, pp. 422-434, 2014 (3) S. Jiang, S. Venticinque, G. Horn, S. Hallsteinsen, M. Noebels, A distributed agent-based system for

coordinating smart solar-powerd microgrids,accepted at: IEEE Technically Sponsored SAI Computing Conference 2016, 13-15 July 2016 | London, UK

(4) M. Noebels, J. Glatz-Reichenbach, A. Mahran, A. Minde, K. Peter, Developing and investigating a smart solar powered energy system for increased PV self-consumption, Proceedings 31st EUPVSEC, p.2676-9, ISBN: 3-936338-39-6, 2015

(5) L. Tasquier, M. Scialdone, R. Aversa, S. Venticinque (2014). Agent based negotiation of decentralized energy production. In: Proceedings of 8th International Symposium on Intelligent Distributed Computing (IDC-2014). STUDIES IN COMPUTATIONAL INTELLIGENCE, p. 59-67

Acknowledgements This work is financially supported by the EU Project CoSSMic under FP7 Grant agreement no. 608806

- Self-consumption: percentage of load demand covered from the local PV generation unit(s). - Self-generation: share of the total PV yield utilised by local load demand (i.e. not fed into the grid).

Results