(373ai) A Blockchain Model for Residential Distributed Energy Resources Networks | AIChE

(373ai) A Blockchain Model for Residential Distributed Energy Resources Networks


Klymenko, O. - Presenter, University of Surrey
Mechleri, E., University of Surrey
Shafiei, Z., University of Surrey
Dorneanu, B., University of Surrey
Arellano-Garcia, H., Brandenburgische Technische Universität Cottbus-Senftenberg
The energy production landscape is reshaped by distributed energy resources (DERs) – photovoltaic (PV) panels, combined heat and power (CHP), wind turbines (WT), fuel cells or battery storage systems, to name just a few [1]. Microgrids, collections of units or DERs that are locally controlled, close to the consumption point and cooperating with each other and the centralised grid [2], allow for the reduction in energy losses compared to traditional generation due to the close proximity to end users. Due to its volatility, the integration of this non-controllable generation poses severe challenges to the current energy system and ensuring a reliable balance of energy becomes an increasingly demanding task [3]. The optimal design and scheduling of the DERs and subsequent microgrid is of high importance in order to increase the reliability and determine their effectiveness in reducing losses, emissions and costs compared to conventional generation so that they may be implemented at faster rates to reduce global emissions and fossil fuel usage. In distributed energy systems, individual users typically have flexible tariffs while they also have the capability not only to use, but also to store and trade electric power. Direct transactions schemes can save money for end users, generate revenues for producers, reduce transmission losses and promote the use of renewable energy [4]. But it must be a robust, efficient and low-cost trading system to handle the rapid changes of information and value in the system.

The blockchain technology can fulfil these requirements by enabling the implementation of optimal energy management strategies through distributed databases. Since its introduction as the underlying technology of Bitcoin, the blockchain technology has emerged from its use as a verification mechanism for cryptocurrencies and heads to a broader field of applications. Blockchain-based systems are basically a combination of a distributed ledger, a decentralised consensus mechanism, and cryptographic security measures [5]. More precisely, it allows the resolution of conflicts and dismantles information asymmetries by providing transparent and valid records of past transactions that cannot be altered retrospectively [6]. With the help of specific algorithms and applications, multiple operations can be performed automatically on the blockchain, using this information together with information from the Internet or the real world (e.g. on whether, energy pricing, etc.). Furthermore, smart contracts can be implemented between the nodes of the microgrid.

This paper introduces a model for the implementation of a blockchain and smart contracts into the scheduling of a residential DER network. The blockchain is implemented in terms of energy rather than voltages [7], to allow for the decentralised operation of the microgrid without a centralised microgrid aggregator. Thus, the model will minimise only the operational cost. Furthermore, the DER network model is improved by the addition of more detailed transmission losses and costs within the microgrid and between the microgrid and the national grid. The resulted energy flows are stored and information on the availability/demand are exchanged between the network nodes. To appropriately compensate the DER operators in the microgrid for their services and to charge the consumers for withdrawals, nodal clearing prices are determined and implemented through smart contracts. The resulting MILP model minimises the overall investment and operating costs of the system.


[1]. Munsing, E., Mather, B., Moura, S., 2017, Blockchains for decentralised optimisation of energy resources in microgrid networks, Proceedings of the IEEE Conference on Control Technology and Applications, 2164, 2171

[2]. Wouters, C., Fraga, E.S., James, A.M., 2015, An energy integrated, multi-grid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study, Energy 85, 30-44

[3]. Mengelkamp, E., Garttner, J., Rock, K., Kessler, S., Orsini, L., Weinhardt, C., 2017, Designing microgrid energy markets. A case study: The Brooklyn microgrid, Applied Energy 210, 870-880

[4]. Noor, S., Yang, W., Guo, M., Van Dam, K.H., Wang, X., 2018, Energy demand side management within micro-grid networks enhanced by blockchain, Applied Energy 228, 1385-1398

[5]. Mihaylov, M., Jurado, S., Avellana, N., Van Moffaert, K., De Abril, I.M., Nowe, A., 2014, Nrgcoin: Virtual currency for trading of renewable energy in smart grids, Proceedings of the 11th Conference on the European Energy Market, IEEE, 1-6.

[6]. Mengelkamp, E., Notheisen, B., Beer, C., Dauer, D., Weinhardt, C., 2018, A blockchain-based smart grid: towards sustainable local energy markets, Comput Sci Res Dev 33, 207-214