SCANERGY: a Scalable and Modular System for Energy Trading

Between Prosumers

M. Mihaylov∗1, S. Jurado1, N. Avellana1, I. Razo-Zapata2, K. Van Moffaert2, A. Canadas2,L. Arco2, I. Grau2 and A. Nowe2

1Sensing & Control Systems, Barcelona, Spain2Vrije Universiteit Brussel, Brussels, Belgium

Author’s copy

Abstract

We present an interactive demo that illustrates theperformance of our multi-agent-based system fortrading ’green’ energy. The system implements theNRG-X-Change concept and uses real-life data forenergy consumption. NRG-X-Change encouragesprosumers to locally trade their excess of energy whilepayments are carried out using NRGcoins, which isa novel decentralized digital currency. We illustratethe performance of a real and typical neighbourhoodin Belgium with 62 residential houses, 56 of them areconsumers while the remaining 6 are prosumers. Par-ticipants can interact with the demo by playing withenergy consumption and production, and analyzingin real-time the behaviour of the energy market andin turn the price for NRGcoins.

1 Introduction

If we are to meet the environmental targets and thelong-term goals set out by the European Commission(EC) Energy Roadmap 2050 [5], cities must offsettheir dependence on fossil and nuclear fuels by rely-ing on renewable energies. The domestic penetrationof small-scale renewable resources enables consumersto become producers of green energy and empowers

∗mmihaylo@vub.ac.be

dwellings to collectively reduce their carbon footprintby trading locally produced renewable energy.

Rewarding green energy trade incentivises the ex-change of locally produced energy and allows con-sumers, who cannot afford the purchase of renew-able generators, to buy green energy at affordableprices. Selling overproduced energy to local con-sumers reduces transmission losses and offsets thedemand for grey energy supplied by the grid. As pre-viously described in [4], although there are numerousapproaches for trading of locally produced energy, nowidely adopted solution exists that aligns the goals ofindividual agents with the goals of the entire smartgrid. In Belgium, for example, the current mech-anism does not encourage producers to trade theirexcess production.

2 NRG-X-Change

In [4] we have proposed the NRG-X-Change concept,which attempts to address the observed flaw usinga novel digital currency called NRGcoin [3]. Lo-cally produced renewable energy is continuously fedinto the grid, and is withdrawn by consumers. Thebilling is performed in real-time by the substation,where the feed-in tariff takes into account total sup-ply and demand in the neighbourhood, rather thanthe individual’s supply and demand, as in the Belgian

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scheme. This tariff incentivises households to bal-ance supply and demand, as well as lower productionand consumption peaks. The innovative aspect ofNRG-X-Change is that all payments are carried outin NRGcoin, instead of fiat money. This new decen-tralised digital currency shares characteristics withBitcoin and has numerous advantages in the smartgrid, as outlined in [4]. Independently from injectionand withdrawal of energy, NRGcoins are traded onan open currency exchange market for their mone-tary equivalent.

We use the adaptive attitude (AA) bidding strat-egy [2], which relies on short-term and long-term at-titudes for adapting to market changes. Whereas ashort-term attitude encourages the agent to be ea-ger for more profit (i.e. selling/buying at high/lowprices), a long-term attitude encourages the agentto be eager for more transactions (i.e. submittinglow/high asks/bids). In this way, based on marketevents (transactions, high bids/asks), AA continu-ously updates an agent’s eagerness to sell or buyNRGcoins. The NRG-X-Change concept has beenthoroughly tested on a smart grid multi-agent sim-ulator using Repast Symphony. To further test thisinnovative concept, we now deploy NRG-X-Changeon hardware and use real data of energy consump-tion in a typical neighbourhood.

3 System Components

The aim of our demonstration is threefold: (i) to raiseawareness about the challenges and impact of possi-ble smart grid scenarios; (ii) to highlight the impactof NRG-X-Change on green energy trade; and (iii)to demonstrate the weather influence on monetaryincentives and market behaviour.

3.1 User Interaction

Our demo offers three types of interaction. 1) Changeconsumption: Users can change the ’virtual’ outdoortemperature, which influences the consumption of allhouses. For example, decreasing the outdoor temper-ature will drive agents to increase their consumptionproportionally, relative to their real-world consump-

Figure 1: Scanergy Demo setup

tion. This simulation of weather conditions allowsusers to observe the effect of chance on the overallenergy balance and on the incentives of agents forexchanging renewable energy. 2) Reduce production:Users can hold in the air cardboard clouds that castshadows over the solar panels of prosumers. 3) In-crease production: Users can install extra panels toobserve the influence of big producers on the real-time energy price and on the energy market. Sim-ilarly, equipping a consumer with solar panels in-creases the percentage of prosumers and thereforechanges the energy balance in the neighbourhood.

3.2 Physical Setting

Within the demo we use real-life data, provided byEandis — a Belgian Distribute System Operator(DSO). All houses in a given neighbourhood are con-nected via the low-voltage grid to one substation ofthe DSO. The dataset is comprised of aggregated 15-minute intervals of electricity consumption and pro-duction of 2928 homes from 44 substations in Bel-gium. In this demo we use a typical substation, whichcontains 6 prosumers and 56 consumers (cf. Fig-ure 1). To find a typical substation, we applied a twolevel clustering that considers global features such astotal trimester consumption, and local features suchas daily consumption [1]. The measurements that weused in this demonstration are collected between 1stof March 2014 and 31st of May 2014.

The substation and the six prosumers are rep-resented by software agents running on individual

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Raspberry Pi boards. Prosumers produce energyin real-time using mini solar panels. A dimmablespotlight projector is automated to simulate theday/night cycle using a z-Wave controller. All 56 con-sumer agents are running in individual threads on twoRaspberry Pi boards. Energy consumption of bothprosumers and consumers is simulated using the realdata. Boards are connected to the Internet, whichallows agents to submit orders for buying and sellingNRGcoins to an online market hosted and running inAzure Cloud. Orders are matched in real-time usingcontinuous double auction, as employed by the NewYork Stock Exchange. Thus, our demo represents amulti-agent system that autonomously exchanges re-newable energy using the NRG-X-Change incentivemechanism. Since the currency market is hosted inthe Cloud, it allows agents to place bids from any-where in the world. Thus, multiple neighbourhoodsrunning in different parts of the world can seamlesslyinteract in the same scenario and demonstrate thescalability of our concept. All software agents aredeveloped in Java, while the exchange market is de-veloped in C# using Azure Service Bus for synchro-nizing actions. All components communicate usingthe RESTful Microservice architecture.

Prosumers have LCD screens showing in real-time:(i) individual energy production and consumptionin kWh; (ii) NRGcoin balance of prosumer; (iii)NRGcoin transactions and payments; and (iv) mar-ket orders for buying and selling the currency. Like-wise, substation’s screens show aggregated informa-tion about: (a) total energy production and con-sumption of all houses; (b) NRGcoin balance of thesubstation; (c) all unmatched buy and sell ordersin the currency market; (d) the evolution of theNRGcoin price.

While in reality measurements are taken every 15minutes, we speed up our demonstration by a factorof 300 to arrive at 3-second time slots. Thus, chartsare updated every 3 seconds, allowing users to ob-serve in less than 5 minutes the behaviour of a realneighbourhood in a whole day. Users are also ableto pause the execution of the entire demonstration(including the day/night cycle) in order to analysethe plots. This functionality is available via the userinterface in the substation’s LCD display.

Acknowledgments

This project has received funding from the EuropeanUnion’s Seventh Programme for research, techno-logical development and demonstration under grantagreement number 324321, project SCANERGY.

References

[1] L. Arco, G. Casas, and A. Nowe. Two-level clus-tering methodology for smart metering data usingglobal and local patterns. In Fifth InternationalWorkshop on Knowledge Discovery, KnowledgeManagement and Decision Making (EUREKAWorkshop 2015), 2015.

[2] H. Ma and H.-F. Leung. An adaptive attitudebidding strategy for agents in continuous doubleauctions. Electronic Commerce Research and Ap-plications, 6(4):383 – 398, 2007. Intelligent agentsin e-services.

[3] M. Mihaylov, S. Jurado, N. Avellana,K. Van Moffaert, I. Magrans de Abril, andA. Nowe. NRGcoin: Virtual Currency forTrading of Renewable Energy in Smart Grids. InProceedings of the 11th International Conferenceon the European Energy Market, 2014.

[4] M. Mihaylov, S. Jurado, K. Van Moffaert,N. Avellana, and A. Nowe. NRG-X-Change: aNovel Mechanism for Trading of Renewable En-ergy in Smart Grids. In Proceedings of the 3rdInternational Conference on Smart Grids andGreen IT Systems, pages 101–106, Apr. 2014.

[5] T. E. E. The commission to the European Parlia-ment, the Council, S. committee, and the com-mittee of the regions. Energy roadmap 2050[com/2011/885]. Technical report.

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